From 8c75af6e741c8a01e76b07c36ca474554f922f8d Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Tue, 13 Dec 2022 10:23:01 +0900 Subject: [PATCH 01/17] translated into korean --- chapter5/colab/Association.ipynb | 109 +++++++++++++------------ chapter5/colab/BPR.ipynb | 62 +++++++------- chapter5/colab/FM.ipynb | 74 ++++++++--------- chapter5/colab/IMF.ipynb | 66 +++++++-------- chapter5/colab/Item2vec.ipynb | 72 ++++++++-------- chapter5/colab/LDA_collaboration.ipynb | 66 +++++++-------- chapter5/colab/LDA_content.ipynb | 96 ++++++++++++---------- chapter5/colab/NMF.ipynb | 60 +++++++------- chapter5/colab/Popularity.ipynb | 68 +++++++-------- chapter5/colab/RF.ipynb | 76 ++++++++--------- chapter5/colab/Random.ipynb | 64 +++++++-------- chapter5/colab/SVD.ipynb | 60 +++++++------- chapter5/colab/UMCF.ipynb | 95 ++++++++++----------- chapter5/colab/Word2vec.ipynb | 80 +++++++++--------- chapter5/colab/data_download.ipynb | 50 ++++++------ chapter5/notebook/Association.ipynb | 27 +++--- chapter5/notebook/BPR.ipynb | 12 +-- chapter5/src/association.py | 26 +++--- chapter5/src/base_recommender.py | 6 +- chapter5/src/bpr.py | 14 ++-- chapter7/metrics.ipynb | 10 +-- 21 files changed, 605 insertions(+), 588 deletions(-) diff --git a/chapter5/colab/Association.ipynb b/chapter5/colab/Association.ipynb index 83b24d2..9f3f361 100644 --- a/chapter5/colab/Association.ipynb +++ b/chapter5/colab/Association.ipynb @@ -13,23 +13,32 @@ "id": "c7dc31f1", "metadata": {}, "source": [ - "# アソシエーション分析" + "# 어소시에이션 분석" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1b316a16", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/bin/bash: wget: command not found\n", + "unzip: cannot find or open ../data/ml-10m.zip, ../data/ml-10m.zip.zip or ../data/ml-10m.zip.ZIP.\n" + ] + } + ], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -49,50 +58,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -334,10 +343,10 @@ } ], "source": [ - "# ユーザー×映画の行列形式に変更\n", + "# 사용자 x 영화 행렬 형식으로 변환한다\n", "user_movie_matrix = movielens_train.pivot(index='user_id', columns='movie_id', values='rating')\n", "\n", - "# ライブラリ使用のために、4以上の評価値は1, 4未満の評価値と欠損値は0にする\n", + "# 라이브러리를 사용하기 위해 4 이상의 평갓값은 1, 4 미만의 평갓값과 결손값은 0으로 한다\n", "user_movie_matrix[user_movie_matrix < 4] = 0\n", "user_movie_matrix[user_movie_matrix.isnull()] = 0\n", "user_movie_matrix[user_movie_matrix >= 4] = 1\n", @@ -352,7 +361,7 @@ "metadata": {}, "outputs": [], "source": [ - "# アソシエーションルールのライブラリのインストール(インストールされていなければ、コメントアウトを外して、実行してください)\n", + "# 어소시에이션 규칙 라이브러리 설치(설치되어 있지 않다면 주석을 해제하고 실행합니다)\n", "# !pip install mlxtend" ] }, @@ -434,7 +443,7 @@ "source": [ "from mlxtend.frequent_patterns import apriori\n", "\n", - "# 支持度が高い映画の表示\n", + "# 지지도가 높은 영화를 표시\n", "freq_movies = apriori(\n", " user_movie_matrix, min_support=0.1, use_colnames=True)\n", "freq_movies.sort_values('support', ascending=False).head()" @@ -499,7 +508,7 @@ } ], "source": [ - "# movie_id=593のタイトルの確認(羊たちの沈黙)\n", + "# movie_id=593의 제목 확인(양들의 침묵)\n", "movies[movies.movie_id == 593]" ] }, @@ -587,7 +596,7 @@ "source": [ "from mlxtend.frequent_patterns import association_rules\n", "\n", - "# アソシエーションルールの計算（リフト値の高い順に表示）\n", + "# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n", "rules = association_rules(freq_movies, metric='lift', min_threshold=1)\n", "rules.sort_values('lift', ascending=False).head()[['antecedents', 'consequents', 'lift']]" ] @@ -664,7 +673,7 @@ } ], "source": [ - "# movie_id=4993, 5952のタイトルの確認(ロード・オブ・ザ・リング)\n", + "# movie_id=4993, 5952의 제목 확인(반지의 제왕)\n", "movies[movies.movie_id.isin([4993, 5952])]" ] }, @@ -675,7 +684,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用データで評価値が4以上のものだけ取得する。\n", + "# 학습용 데이터 평갓값이 4 이상인 것만 얻는다.\n", "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]" ] }, @@ -797,7 +806,7 @@ } ], "source": [ - "# user_id=2のユーザーが4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 4 이상의 평가를 남긴 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1013,13 +1022,13 @@ } ], "source": [ - "# user_id=2のユーザーが4以上の評価を付けた映画一覧\n", + "# user_id=2의 사용자가 4 이상의 평가를 남긴 영화 목록\n", "user2_data = movielens_train_high_rating[movielens_train_high_rating.user_id==2]\n", "\n", - "# ユーザーが直近評価した５つの映画を取得\n", + "# 사용자가 최근 평가한 4개의 영화 얻기\n", "input_data = user2_data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", "\n", - "# それらの映画が、条件部に含まれるアソシエーションルールを抽出\n", + "# 그 영화들이 조건부로 포함된 어오시에이션 규칙을 추출\n", "matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n", "rules[matched_flags]" ] @@ -1053,14 +1062,14 @@ "source": [ "from collections import defaultdict, Counter\n", "\n", - "# アソシエーションルールの帰結部の映画をリストに格納する\n", - "# 同じ映画が複数回帰結部に出現することがある\n", + "# 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장한다\n", + "# 같은 영화가 여러 차례 귀결부에 나타날 수 있다\n", "\n", "consequent_movies = []\n", "for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows(): # lift値でソートして、上位１０個のルールだけを使うようにするなどの工夫も可能です\n", " consequent_movies.extend(row[\"consequents\"])\n", " \n", - "# 帰結部での登場頻度をカウント\n", + "# 귀결부에서의 출현 빈도 카운트\n", "counter = Counter(consequent_movies)\n", "counter.most_common(10)" ] @@ -1127,8 +1136,8 @@ } ], "source": [ - "# movie_id=1196が92回、帰結部に登場しているので、user_id=2には、movie_id=1196(Star Wars: Episode V )がおすすめ候補となる\n", - "# （user_id=2の学習データでは、Star Warsのエピーソード4,6を高く評価している）\n", + "# movie_id=1196가 92번 귀결부에 출현하므로, user_id=2에는 movie_id=1196(Star Wars: Episode V)가 추천 후보가 된다\n", + "# (user_id=2의 학습 데이터에서는 Star Wars 에피소드 4, 6의 평가가 높다)\n", "movies[movies.movie_id == 1196]" ] }, @@ -1139,7 +1148,7 @@ "metadata": {}, "outputs": [], "source": [ - "# おすすめの仕方には、lift値が高いものを抽出するやり方もあり、いくつか方法を試してみて、自社のデータに適したやり方を選択ください" + "# 추천 방법에는 lift 값이 높은 것을 추출하는 방법 등이 있다. 몇 가지 방법을 시도해 보고 자사의 데이터에 맞는 방법을 선택한다." ] }, { @@ -1623,30 +1632,30 @@ } ], "source": [ - "# アソシエーションルールを使って、各ユーザーにまだ評価していない映画を１０本推薦する\n", + "# 어소시에이션 규칙을 사용해 각 사용자가 아직 평가하지 않은 영화 10편을 추천한다\n", "pred_user2items = defaultdict(list)\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "\n", "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # ユーザーが直近評価した５つの映画を取得\n", + " # 사용자가 최근 5편의 영화를 얻는다\n", " input_data = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", - " # それらの映画が条件部に１本でも含まれているアソシエーションルールを抽出\n", + " # 그 영화들이 조건부에 1편이라도 포함되어 있는 어소시에이션 규칙을 추출한다\n", " matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n", "\n", - " # アソシエーションルールの帰結部の映画をリストに格納し、登場頻度順に並び替え、ユーザーがまだに評価していないければ、推薦リストに追加する\n", + " # 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장하고, 출편 빈도 순으로 배열하고, 사용자가 아직 평가하지 않았다면 추천 리스트에 추가한다\n", " consequent_movies = []\n", " for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows():\n", " consequent_movies.extend(row[\"consequents\"])\n", - " # 登場頻度をカウント\n", + " # 출현 빈도를 센다\n", " counter = Counter(consequent_movies)\n", " for movie_id, movie_cnt in counter.most_common():\n", " if movie_id not in user_evaluated_movies[user_id]:\n", " pred_user2items[user_id].append(movie_id)\n", - " # 推薦リストが10本になったら終了する\n", + " # 추천 리스트가 10편이 되면 종료한다\n", " if len(pred_user2items[user_id]) == 10:\n", " break\n", "\n", - "# 各ユーザーに対するレコメンドリスト\n", + "# 각 사용자에 대한 추천 리스트\n", "pred_user2items" ] }, @@ -1768,7 +1777,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1854,7 +1863,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(1196, 593, 1198)\n", + "# user_id=2에 대한 추천(1196, 593, 1198)\n", "movies[movies.movie_id.isin([1196, 593, 1198])]" ] }, @@ -1867,7 +1876,7 @@ "source": [ "# apriori(user_movie_matrix, min_support=0.1, use_colnames=True)\n", "# association_rules(freq_movies, metric='lift', min_threshold=1)\n", - "# min_supportとmin_thresholdが重要なパラメーターとなるので、変化させてお試しください" + "# min_support와 min_threshold가 중요한 파라미터가 되므로 바꾸어 가면서 시험해봅니다." ] }, { @@ -1895,7 +1904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/BPR.ipynb b/chapter5/colab/BPR.ipynb index 6dfea17..f3e7258 100644 --- a/chapter5/colab/BPR.ipynb +++ b/chapter5/colab/BPR.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,11 +105,11 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 10\n", - "# 評価数の閾値\n", + "# 평가 수의 임곗값\n", "minimum_num_rating = 0\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 50" ] }, @@ -120,14 +120,14 @@ "metadata": {}, "outputs": [], "source": [ - "# 行列分解用に行列を作成する\n", + "# 행렬 분석용으로 행렬을 작성한다\n", "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", ")\n", "\n", "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", "\n", - "# 行列のインデックスと映画/ユーザーを対応させる辞書を作成\n", + "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", @@ -142,7 +142,7 @@ "outputs": [], "source": [ "from scipy.sparse import lil_matrix\n", - "# スパース行列を初期化して、各セルに値を入れていく\n", + "# 희소 행렬을 초기화하고 각 셀에 값을 넣는다\n", "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", "for i, row in movielens_train_high_rating.iterrows():\n", " user_index = user_id2index[row[\"user_id\"]]\n", @@ -167,7 +167,7 @@ "metadata": {}, "outputs": [], "source": [ - "# colab上で、implicitライブラリの実行時にエラーが出る場合は、「ランタイム→ランタイムのタイプを変更」でハードウェア アクセラレータとしてGPUを選択してください" + "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" ] }, { @@ -179,7 +179,7 @@ "source": [ "import implicit\n", "\n", - "# モデルの初期化\n", + "# 모델 초기화\n", "model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs)" ] }, @@ -205,7 +205,7 @@ } ], "source": [ - "# 学習\n", + "# 학습\n", "model.fit(movielens_matrix)" ] }, @@ -1438,7 +1438,7 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 推薦\n", + "# 추천\n", "recommendations = model.recommend_all(movielens_matrix.T)\n", "pred_user2items = defaultdict(list)\n", "for user_id, user_index in user_id2index.items():\n", @@ -1567,7 +1567,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1653,7 +1653,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(593, 356, 296)\n", + "# user_id=2에 대한 추천(593, 356, 296)\n", "movies[movies.movie_id.isin([593, 356, 296])]" ] } @@ -1674,7 +1674,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/FM.ipynb b/chapter5/colab/FM.ipynb index 4fc2b59..312befd 100644 --- a/chapter5/colab/FM.ipynb +++ b/chapter5/colab/FM.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,15 +105,15 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 10\n", - "# 評価数の閾値\n", + "# 평가 수의 임곗값\n", "minimum_num_rating = 200\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 50\n", - "# 学習率\n", + "# 학습률\n", "lr = 0.01\n", - "# 補助情報の利用\n", + "# 초기 정보 사용\n", "use_side_information = False" ] }, @@ -124,12 +124,12 @@ "metadata": {}, "outputs": [], "source": [ - "# 評価値がminimum_num_rating件以上ある映画に絞る\n", + "# 평갓값이 minimum_num_rating건 이상인 영화를 필터링한다\n", "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", ")\n", "\n", - "# ユーザーが評価した映画\n", + "# 사용자가 평가한 영화\n", "user_evaluated_movies = (\n", " filtered_movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", ")\n" @@ -145,7 +145,7 @@ "import numpy as np\n", "from sklearn.feature_extraction import DictVectorizer\n", "\n", - "# FM用にデータの整形\n", + "# FM용으로 데이터를 정형한다\n", "train_data_for_fm = []\n", "y = []\n", "for i, row in filtered_movielens_train.iterrows():\n", @@ -179,7 +179,7 @@ "metadata": {}, "outputs": [], "source": [ - "# colabでxlearnを動かすための設定\n", + "# colab에서 xlearn을 동작시키기 위한 설정\n", "# https://github.com/aksnzhy/xlearn/issues/74#issuecomment-580701773\n", "import os\n", "os.environ['USER'] = 'test'" @@ -194,7 +194,7 @@ "source": [ "import xlearn as xl\n", "\n", - "# FMモデルの初期化\n", + "# FM 모델 초기화\n", "fm_model = xl.FMModel(task=\"reg\", metric=\"rmse\", lr=lr, opt=\"sgd\", k=factors, epoch=n_epochs)" ] }, @@ -295,7 +295,7 @@ } ], "source": [ - "# FMモデルの学習\n", + "# FM 모델 학습\n", "fm_model.fit(X, y, is_lock_free=False)" ] }, @@ -306,7 +306,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 行列のインデックスと映画/ユーザーを対応させる辞書を作成\n", + "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", "unique_user_ids = sorted(filtered_movielens_train.user_id.unique())\n", "unique_movie_ids = sorted(filtered_movielens_train.movie_id.unique())\n", "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", @@ -320,7 +320,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 予測用のデータの準備\n", + "# 예측용 데이터를 준비한다\n", "test_data_for_fm = []\n", "for user_id in unique_user_ids:\n", " for movie_id in unique_movie_ids:\n", @@ -375,7 +375,7 @@ } ], "source": [ - "# 予測する\n", + "# 예측한다\n", "y_pred = fm_model.predict(X_test)\n", "pred_matrix = y_pred.reshape(len(unique_user_ids), len(unique_movie_ids))" ] @@ -618,7 +618,7 @@ } ], "source": [ - "# 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする\n", + "# 학습용에 나오지 않은 사용자나 영화의 예측 평가는 평균 평갓값으로 한다\n", "average_score = movielens_train.rating.mean()\n", "movie_rating_predict = movielens_test.copy()\n", "pred_results = []\n", @@ -1642,7 +1642,7 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 各ユーザーに対するレコメンドリストの作成\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", "\n", "pred_user2items = defaultdict(list)\n", "\n", @@ -1942,7 +1942,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -2023,7 +2023,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(318, 50, 527)\n", + "# user_id=2에 대한 추천(318, 50, 527)\n", "movies[movies.movie_id.isin([318, 50, 527])]" ] }, @@ -2052,7 +2052,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/IMF.ipynb b/chapter5/colab/IMF.ipynb index 50d3cf7..6d9d857 100644 --- a/chapter5/colab/IMF.ipynb +++ b/chapter5/colab/IMF.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,11 +105,11 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 10\n", - "# 評価数の閾値\n", + "# 평가 수의 임곗값\n", "minimum_num_rating = 0\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 50\n", "# alpha\n", "alpha = 1.0" @@ -122,14 +122,14 @@ "metadata": {}, "outputs": [], "source": [ - "# 行列分解用に行列を作成する\n", + "# 행력 분석용으로 행렬을 작성한다\n", "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", ")\n", "\n", "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", "\n", - "# 行列のインデックスと映画/ユーザーを対応させる辞書を作成\n", + "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", @@ -144,12 +144,12 @@ "outputs": [], "source": [ "from scipy.sparse import lil_matrix\n", - "# スパース行列を初期化して、各セルに値を入れていく\n", + "# 희소 행렬을 초기화하고, 각 셀에 값을 넣는다\n", "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", "for i, row in movielens_train_high_rating.iterrows():\n", " user_index = user_id2index[row[\"user_id\"]]\n", " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " movielens_matrix[movie_index, user_index] = 1.0 * alpha # 今回、映画の評価値を0/1で２値化していますが、クリック数などのデータのときは、log(click数)などの変形も効果的です" + " movielens_matrix[movie_index, user_index] = 1.0 * alpha # 이후, 영화의 평갓값을 0/1로 이진화한다. 클릭 수 등의 데이터인 경우에는 log(click 수) 등으로 변형하는 것도 효과적이다." ] }, { @@ -169,7 +169,7 @@ "metadata": {}, "outputs": [], "source": [ - "# colab上で、implicitライブラリの実行時にエラーが出る場合は、「ランタイム→ランタイムのタイプを変更」でハードウェア アクセラレータとしてGPUを選択してください" + "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" ] }, { @@ -189,7 +189,7 @@ "source": [ "import implicit\n", "\n", - "# モデルの初期化\n", + "# 모델 초기화\n", "model = implicit.als.AlternatingLeastSquares(\n", " factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1\n", ")" @@ -217,7 +217,7 @@ } ], "source": [ - "# 学習\n", + "# 학습\n", "model.fit(movielens_matrix)" ] }, @@ -1288,7 +1288,7 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 推薦\n", + "# 추천\n", "recommendations = model.recommend_all(movielens_matrix.T)\n", "pred_user2items = defaultdict(list)\n", "for user_id, user_index in user_id2index.items():\n", @@ -1417,7 +1417,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1505,7 +1505,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(1196, 1, 589)\n", + "# user_id=2에 대한 추천(1196, 1, 589)\n", "movies[movies.movie_id.isin([1196, 1, 589])]" ] }, @@ -1516,7 +1516,7 @@ "metadata": {}, "outputs": [], "source": [ - "# IMFでは、factorsやalphaの設定が予測精度に重要なため、値を変えてお試しください" + "# IMF에서는 factors나 alpha 설정이 예측 정밀도에 중요하므로, 값을 바꾸어가며 시도해봅니다." ] } ], @@ -1536,7 +1536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/Item2vec.ipynb b/chapter5/colab/Item2vec.ipynb index 0696651..97c8237 100644 --- a/chapter5/colab/Item2vec.ipynb +++ b/chapter5/colab/Item2vec.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,17 +105,17 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 100\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 30\n", - "# windowサイズ\n", + "# window 크기\n", "window = 100\n", - "# スキップグラム\n", + "# 스킵 그램\n", "use_skip_gram = 1\n", - "# 階層的ソフトマックス\n", + "# 계측적 소프트맥스\n", "use_hierarchial_softmax = 0\n", - "# 使用する単語の出現回数のしきい値\n", + "# 사용할 단어의 출현 횟수의 임곗값\n", "min_count = 5" ] }, @@ -126,12 +126,12 @@ "metadata": {}, "outputs": [], "source": [ - "# item2vecのインプットに使うデータの生成\n", + "# item2vec의 입력으로 사용할 데이터를 생성한다\n", "item2vec_data = []\n", "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # 評価された順に並び替える\n", - " # item2vecではwindowというパラメータがあり、itemの評価された順番も重要な要素となる\n", + " # 평가된 순서대로 나열한다\n", + " # item2vec에서는 window라는 파라미터가 있으며, item이 평가된 순서도 중요한 요소이다\n", " item2vec_data.append(data.sort_values(\"timestamp\")[\"movie_id\"].tolist())\n" ] }, @@ -163,7 +163,7 @@ "source": [ "import gensim\n", "\n", - "# item2vecの学習\n", + "# item2vec 학습\n", "model = gensim.models.word2vec.Word2Vec(\n", " item2vec_data,\n", " vector_size=factors,\n", @@ -199,8 +199,8 @@ } ], "source": [ - "# スターウォーズ・エピーソード５(movie_id=1196）を入力したときの類似映画\n", - "# スターウォーズのエピーソード4,6が上位にきているので、学習されていることが分かる\n", + "# 스타워즈/에피소드 5(movie_id=1196）를 입력했을 때의 유사 영화\n", + "# 스타워즈/에피소드 4, 6이 상위에 나타나므로 학습되었음을 알 수 있다\n", "for movie_id, score in model.wv.most_similar(1196):\n", " title = movies[movies.movie_id == movie_id].title.tolist()[0]\n", " print(f'movie_id={movie_id}, title={title}, score={score}')" @@ -1220,7 +1220,7 @@ } ], "source": [ - "# 各ユーザーにレコメンドリストの作成\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", "\n", "pred_user2items = dict()\n", "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", @@ -1229,7 +1229,7 @@ " if item_id in model.wv.key_to_index:\n", " input_data.append(item_id)\n", " if len(input_data) == 0:\n", - " # おすすめ計算できない場合は空配列\n", + " # 추천 계산할 수 없는 경우에는 빈 배열\n", " pred_user2items[user_id] = []\n", " continue\n", " recommended_items = model.wv.most_similar(input_data, topn=10)\n", @@ -1356,7 +1356,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1442,7 +1442,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(480, 1196, 589)\n", + "# user_id=2에 대한 추천(480, 1196, 589)\n", "movies[movies.movie_id.isin([480, 1196, 589])]" ] }, @@ -1453,7 +1453,7 @@ "metadata": {}, "outputs": [], "source": [ - "# item2vecでは、因子数,エポック数,windowサイズ,使用する単語の出現回数のしきい値のどれもが重要なので、グリッドサーチしながら、最適な値を決めてください" + "# item2vec에서는 인자 수, 에폭 수, windows 크기, 사용하는 단어의 출현 횟수의 임곗값 모두가 중요하므루, 그리드 서치를 통해 최적의 값을 결정합니다." ] } ], @@ -1473,7 +1473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/LDA_collaboration.ipynb b/chapter5/colab/LDA_collaboration.ipynb index 3e4fbf6..5bd9e85 100644 --- a/chapter5/colab/LDA_collaboration.ipynb +++ b/chapter5/colab/LDA_collaboration.ipynb @@ -13,7 +13,7 @@ "id": "942a9897", "metadata": {}, "source": [ - "# Latent Dirichlet Allocation (LDA)の行動データへの適用" + "# Latent Dirichlet Allocation (LDA)를 행동 데이터에 적용" ] }, { @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,9 +105,9 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 50\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 30" ] }, @@ -139,7 +139,7 @@ "source": [ "from gensim.corpora.dictionary import Dictionary\n", "\n", - "# LDAのインプットに使うデータの作成\n", + "# LDA 입력으로 사용할 데이터를 작성한다\n", "lda_data = []\n", "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", @@ -180,10 +180,10 @@ } ], "source": [ - "# 各ユーザーの所属トピックが格納される\n", + "# 각 사용자의 소속 토픽이 저장된다\n", "lda_topics = lda_model[common_corpus]\n", "\n", - "# 例: とあるユーザーの所属トピック\n", + "# 예: 어떤 사용자의 소속 토픽\n", "lda_topics[0]" ] }, @@ -211,7 +211,7 @@ } ], "source": [ - "# topic0の映画一覧\n", + "# topic0인 영화 목록\n", "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", " movie_id = int(common_dictionary.id2token[token_id])\n", " title = movies[movies.movie_id == movie_id].title.tolist()[0]\n", @@ -285,7 +285,7 @@ } ], "source": [ - "# スターウォーズ・エピーソード５(movie_id=1196）のトピック(各トピックの所属確率)\n", + "# 스타워즈/에피소드 5(movie_id=1196)의 토픽(각 토픽에 소속될 확률)\n", "lda_model[common_dictionary.doc2bow([\"1196\"])]" ] }, @@ -1585,17 +1585,17 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 各ユーザーにレコメンドリストの作成\n", - "# そのユーザーの一番所属確率が高いトピックを取得して、そのトピック内で確率の高いアイテムから格納していく\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", + "# 각 사용자의 소속 확률이 가장 높은 토픽을 얻고, 해당 토픽 안에서 확률이 높은 아이템을 저장해 나간다\n", "\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "\n", "pred_user2items = defaultdict(list)\n", "for i, (user_id, data) in enumerate(movielens_train_high_rating.groupby(\"user_id\")):\n", " evaluated_movie_ids = user_evaluated_movies[user_id]\n", - " # ユーザーの所属確率が一番高いトピックを取得\n", + " # 사용자의 소속 확률이 가장 높은 토픽을 얻는다\n", " user_topic = sorted(lda_topics[i], key=lambda x: -x[1])[0][0]\n", - " # そのトピック内で、確率の高いアイテムを取得\n", + " # 해당 토픽 안에서 확률이 높은 아이템을 얻는다\n", " topic_movies = lda_model.get_topic_terms(user_topic, topn=len(movies))\n", "\n", " for token_id, score in topic_movies:\n", @@ -1726,7 +1726,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1812,7 +1812,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(1198, 1196, 1240)\n", + "# user_id=2에 대한 추천(1198, 1196, 1240)\n", "movies[movies.movie_id.isin([1198, 1196, 1240])]" ] } @@ -1833,7 +1833,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/LDA_content.ipynb b/chapter5/colab/LDA_content.ipynb index 0bbae9e..4ed495f 100644 --- a/chapter5/colab/LDA_content.ipynb +++ b/chapter5/colab/LDA_content.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,9 +105,9 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 50\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 30" ] }, @@ -119,13 +119,13 @@ "outputs": [], "source": [ "movie_content = movies.copy()\n", - "# tagが付与されていない映画もあるが、genreはすべての映画に付与されている\n", - "# tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく\n", - "# tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする\n", + "# tag가 부여되어 있지 않은 영화가 있지만, genre는 모든 영화에 부여되어 있다\n", + "# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 하여 비슷한 영화를 찾아서 추천한다\n", + "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", "movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n", "movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: list(map(str, x)))\n", "\n", - "# タグとジャンルデータを使って、ldaを学習する\n", + "# 태그와 장르 데이터를 사용해 lda를 학습한다\n", "tag_genre_data = movie_content.tag_genre.tolist()" ] }, @@ -157,7 +157,7 @@ "source": [ "from gensim.corpora.dictionary import Dictionary\n", "\n", - "# LDAのインプットに使うデータの作成\n", + "# LDA의 입력으로 사용할 데이터를 작성한다\n", "common_dictionary = Dictionary(tag_genre_data)\n", "common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n" ] @@ -171,7 +171,7 @@ "source": [ "import gensim\n", "\n", - "# LDAの学習\n", + "# LDA 학습\n", "lda_model = gensim.models.LdaModel(\n", " common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n", ")" @@ -201,7 +201,7 @@ } ], "source": [ - "# topic0の単語一覧\n", + "# topic0인 단어 목록\n", "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", " word = common_dictionary.id2token[token_id]\n", " print(f'word={word}, score={score}')" @@ -274,7 +274,7 @@ } ], "source": [ - "# サムライという単語のトピック(各トピックの所属確率)\n", + "# samurai라는 단어의 토픽(각 토픽에 소속될 확률)\n", "lda_model[common_dictionary.doc2bow(['samurai'])]" ] }, @@ -492,15 +492,15 @@ } ], "source": [ - "# 各映画のトピックを格納\n", + "# 각 영화의 토픽을 저장한다\n", "lda_topics = lda_model[common_corpus]\n", "\n", - "# 各映画に最も確率の高いトピックを１つ取り出し格納していく\n", + "# 각 영화에 가장 확률이 높은 토픽을 1개 추출해서 저장한다\n", "movie_topics = []\n", "movie_topic_scores = []\n", "for movie_index, lda_topic in enumerate(lda_topics):\n", " sorted_topic = sorted(lda_topics[movie_index], key=lambda x: -x[1])\n", - " # 最も確率の高いトピック\n", + " # 가장 확률이 높은 토픽\n", " movie_topic, topic_score = sorted_topic[0]\n", " movie_topics.append(movie_topic)\n", " movie_topic_scores.append(topic_score)\n", @@ -3714,9 +3714,9 @@ "from collections import defaultdict\n", "from collections import Counter\n", "\n", - "# 各ユーザーのレコメンドリストを作成していく\n", - "# ユーザーが高く評価した映画が、どのトピックに所属していることが多いかをカウントする\n", - "# 一番多いトピックをユーザーの好きなトピックとみなして、そのトピックの映画をおすすめする\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", + "# 사용자가 높게 평가한 영화가, 어떤 토픽에 많이 소속되어 있는지 카운트한다\n", + "# 가장 많은 토픽을 사용자가 좋아하는 토픽으로 간주하고, 해당 토픽의 영화를 추천한다\n", "\n", "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", @@ -3724,18 +3724,18 @@ "movie_id2index = dict(zip(movie_content.movie_id.tolist(), range(len(movie_content))))\n", "pred_user2items = defaultdict(list)\n", "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # ユーザーが高く評価した映画\n", + " # 사용자가 높이 평가한 영화\n", " evaluated_movie_ids = user_evaluated_movies[user_id]\n", - " # 直近閲覧した映画を取得\n", + " # 최근 열람한 영화를 얻는다\n", " movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-10:]\n", "\n", " movie_indexes = [movie_id2index[id] for id in movie_ids]\n", " \n", - " # 直近閲覧した映画のトピックを取得して、出現回数をカウントする\n", + " # 최근 열람한 영화의 토픽을 얻고, 풀현 횟수를 카운트한다\n", " topic_counter = Counter([movie_topics[i] for i in movie_indexes])\n", - " # 一番出現回数が多かったトピックを取得\n", + " # 가장 출현 횟수가 많았던 토픽을 얻는다\n", " frequent_topic = topic_counter.most_common(1)[0][0]\n", - " # そのトピックの映画の中でもスコアが高いものをおすすめする\n", + " # 해당 토픽의 영화 중에서도 점수가 높은 것을 추천한다\n", " topic_movies = (\n", " movie_content[movie_content.topic == frequent_topic]\n", " .sort_values(\"topic_score\", ascending=False)\n", @@ -3868,7 +3868,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -3954,19 +3954,27 @@ } ], "source": [ - "# user_id=2に対するおすすめ(2115, 76, 2006)\n", + "# user_id=2에 대한 추천(2115, 76, 2006)\n", "movies[movies.movie_id.isin([2115, 76, 2006])]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "129f2877", "metadata": {}, "outputs": [], "source": [ - "# 今回は、各ユーザーの評価履歴から１つのトピックをピックアップしましたが、トピックの確率値を利用して、重み付けしながら、アイテムを抽出することも可能です" + "# 여기에서는 각 사용자의 평가 이력으로부터 1개의 토픽을 픽업했지만, 토픽의 확률값을 사용해서 가중치를 주면서 아이템을 추출할 수도 있습니다." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a30f15c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -3985,7 +3993,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/NMF.ipynb b/chapter5/colab/NMF.ipynb index 9d1cb34..cd11431 100644 --- a/chapter5/colab/NMF.ipynb +++ b/chapter5/colab/NMF.ipynb @@ -13,7 +13,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# 非負値行列分解" + "# 비부값 행렬 분석" ] }, { @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -100,14 +100,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "8e6879e3", "metadata": {}, "outputs": [], "source": [ - "# 欠損値の穴埋め方法\n", + "# 결손값을 채우는 방법\n", "fillna_with_zero = True\n", - "# 因子数\n", + "# 인자 수\n", "factors = 5" ] }, @@ -135,7 +135,7 @@ } ], "source": [ - "# 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする\n", + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", @@ -176,7 +176,7 @@ "source": [ "from sklearn.decomposition import NMF\n", "\n", - "# NMFの実行\n", + "# NMF 실행\n", "nmf = NMF(n_components=factors)\n", "nmf.fit(matrix)\n" ] @@ -281,7 +281,7 @@ ], "source": [ "import numpy as np\n", - "# 予測評価値行列\n", + "# 예측 평갓값 행렬\n", "pred_matrix = np.dot(P, Q)\n", "pred_matrix" ] @@ -293,7 +293,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする\n", + "# 학습용에 나타나지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다\n", "average_score = movielens_train.rating.mean()\n", "movie_rating_predict = movielens_test.copy()\n", "pred_results = []\n", @@ -1437,7 +1437,7 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から予測値が高い順にする\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", "pred_user2items = defaultdict(list)\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "for user_id in movielens_train.user_id.unique():\n", @@ -1737,7 +1737,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2의 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -1844,7 +1844,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(2959, 4993, 5952)\n", + "# user_id=2에 대한 추천(2959, 4993, 5952)\n", "movies[movies.movie_id.isin([2959, 4993, 5952])]" ] }, @@ -1873,7 +1873,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/Popularity.ipynb b/chapter5/colab/Popularity.ipynb index c5146c0..82b7723 100644 --- a/chapter5/colab/Popularity.ipynb +++ b/chapter5/colab/Popularity.ipynb @@ -13,10 +13,10 @@ "id": "4ffa2166", "metadata": {}, "source": [ - "# 人気度順推薦\n", - "## 人気度の定義\n", - "* 今回は評価値が高いものを人気が髙い映画とする\n", - "* 人気度の定義は「クリック数が多いもの」「購入が多いもの」「評価値が髙いもの」など複数あり、自社サービスに最も適した定義を利用\n" + "# 인기도 순 추천\n", + "## 인기도의 정의\n", + "* 여기에서는 평갓값이 높은 것을 인기가 높은 영화로 간주합니다\n", + "* 인기도는 '클릭 수가 많은 것', '구매가 많은 것', '평갓값이 높은 것' 등 다양하게 정의할 수 있으므로 자사 서비스에 맞춰 적합한 정의를 사용합니다.\n" ] }, { @@ -26,11 +26,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -50,50 +50,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -108,7 +108,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 評価数の閾値\n", + "# 평가 수의 임곗값\n", "minimum_num_rating = 200\n" ] }, @@ -213,8 +213,8 @@ "source": [ "import numpy as np\n", "\n", - "# 評価値の平均が髙い映画の確認\n", - "# 評価数が１件の映画が多数上位にきている\n", + "# 평갓값의 평균이 높은 영화를 확인한다\n", + "# 평가 수가 1건인 영화가 상위에 여럿 나타난다\n", "movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", "movie_stats.sort_values(by=('rating', 'mean'), ascending=False).head()" ] @@ -318,8 +318,8 @@ } ], "source": [ - "# しきい値を導入して、評価数が少ない映画を取り除く\n", - "# ショーシャンクの空にや七人の侍などの見慣れた映画が上位にきている\n", + "# 임곗값을 도입해 평가 수가 적은 영화를 제거한다\n", + "# 쇼생크 탈출이나 7인의 사무라이 등 익숙한 영화가 상위에 나타난다\n", "movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", "atleast_flg = movie_stats['rating']['size'] >= 100\n", "movies_sorted_by_rating = movie_stats[atleast_flg].sort_values(by=('rating', 'mean'), ascending=False)\n", @@ -334,9 +334,9 @@ "outputs": [], "source": [ "import numpy as np \n", - "# 各アイテムごとの平均の評価値を計算し、その平均評価値を予測値として利用する\n", + "# 각 아이템별로 평균 평갓값을 계싼하고, 해당 평균 평갓값을 예측값으로 사용한다\n", "movie_rating_average = movielens_train.groupby(\"movie_id\").agg({\"rating\": np.mean})\n", - "# テストデータに予測値を格納する。テストデータのみに存在するアイテムの予測評価値は０とする\n", + "# 테스트 데이터에 예측값을 저장한다. 테스트 데이터에만 존재하는 아이템의 예측 평갓값은 0으로 한다.\n", "movie_rating_predict = movielens_test.merge(\n", " movie_rating_average, on=\"movie_id\", how=\"left\", suffixes=(\"_test\", \"_pred\")\n", ").fillna(0)" @@ -1371,8 +1371,8 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から評価値が高いもの10作品とする\n", - "# ただし、評価件数が少ないとノイズが大きいため、minimum_num_rating件以上評価がある映画に絞る\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 평갓값이 높은 10작품으로 한다\n", + "# 단, 평가 건수가 작으면 노이즈가 크므로 minimum_num_rating건 이상 평가가 있는 영화로 필터링한다\n", "pred_user2items = defaultdict(list)\n", "user_watched_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "movie_stats = movielens_train.groupby(\"movie_id\").agg({\"rating\": [np.size, np.mean]})\n", @@ -1673,7 +1673,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -1775,7 +1775,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(318, 50, 527)\n", + "# user_id=2에 대한 추천(318, 50, 527)\n", "movies[movies.movie_id.isin([318, 50, 527])]" ] }, @@ -1804,7 +1804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/RF.ipynb b/chapter5/colab/RF.ipynb index 725f441..06f8fda 100644 --- a/chapter5/colab/RF.ipynb +++ b/chapter5/colab/RF.ipynb @@ -13,7 +13,7 @@ "id": "4935d845", "metadata": {}, "source": [ - "# RandomForest(回帰モデル)" + "# RandomForest(회귀 모델)" ] }, { @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -494,7 +494,7 @@ } ], "source": [ - "# 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする\n", + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다\n", "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", @@ -508,14 +508,14 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習に用いる学習用データ中のユーザーと映画の組を取得する\n", + "# 학습에 사용하는 학습용 데이터 안의 사용자와 영화의 조합을 얻는다\n", "train_keys = movielens_train[[\"user_id\", \"movie_id\"]]\n", - "# 学習用データ中の評価値を学習の正解データとして取得する\n", + "# 학습용 데이터 안의 평갓값을 학습의 정답 데이터로 얻는다\n", "train_y = movielens_train.rating.values\n", "\n", - "# 評価値を予測したいテスト用データ中のユーザーと映画の組を取得する\n", + "# 평갓값을 예측할 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다\n", "test_keys = movielens_test[[\"user_id\", \"movie_id\"]]\n", - "# ランキング形式の推薦リスト作成のために学習用データに存在するすべてのユーザーとすべての映画の組み合わせを取得する\n", + "# 순위 형식의 추천 리스트 작성을 위해 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 얻는다\n", "train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[[\"user_id\", \"movie_id\"]]\n" ] }, @@ -526,7 +526,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 特徴量を作成する\n", + "# 특징량을 작성한다\n", "train_x = train_keys.copy()\n", "test_x = test_keys.copy()\n", "train_all_x = train_all_keys.copy()" @@ -539,8 +539,8 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用データに存在するユーザーごとの評価値の最小値、最大値、平均値\n", - "# 及び、映画ごとの評価値の最小値、最大値、平均値を特徴量として追加\n", + "# 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값\n", + "# 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량으로 추가한다\n", "aggregators = [\"min\", \"max\", \"mean\"]\n", "user_features = movielens_train.groupby(\"user_id\").rating.agg(aggregators).to_dict()\n", "movie_features = movielens_train.groupby(\"movie_id\").rating.agg(aggregators).to_dict()\n", @@ -551,7 +551,7 @@ " train_x[f\"m_{agg}\"] = train_x[\"movie_id\"].map(movie_features[agg])\n", " test_x[f\"m_{agg}\"] = test_x[\"movie_id\"].map(movie_features[agg])\n", " train_all_x[f\"m_{agg}\"] = train_all_x[\"movie_id\"].map(movie_features[agg])\n", - "# テスト用データにしか存在しないユーザーや映画の特徴量を、学習用データ全体の平均評価値で埋める\n", + "# 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다\n", "average_rating = train_y.mean()\n", "test_x.fillna(average_rating, inplace=True)" ] @@ -565,7 +565,7 @@ "source": [ "import itertools\n", "\n", - "# 映画が特定の genre であるかどうかを表す特徴量を追加\n", + "# 영화가 특정한 genre인지 나타내는 특징량을 추가한다\n", "movie_genres = movies[[\"movie_id\", \"genre\"]]\n", "genres = set(list(itertools.chain(*movie_genres.genre)))\n", "for genre in genres:\n", @@ -583,7 +583,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 特徴量としては使わない情報を削除\n", + "# 특징량으로 사용하지 않는 정보는 삭제한다\n", "train_x = train_x.drop(columns=[\"user_id\", \"movie_id\"])\n", "test_x = test_x.drop(columns=[\"user_id\", \"movie_id\"])\n", "train_all_x = train_all_x.drop(columns=[\"user_id\", \"movie_id\"])" @@ -609,7 +609,7 @@ "source": [ "from sklearn.ensemble import RandomForestRegressor as RFR\n", "\n", - "# Random Forest を用いた学習\n", + "# Random Forest를 사용한 학습\n", "reg = RFR(n_jobs=-1, random_state=0)\n", "reg.fit(train_x.values, train_y)\n" ] @@ -621,7 +621,7 @@ "metadata": {}, "outputs": [], "source": [ - "# テスト用データ内のユーザーと映画の組に対して評価値を予測する\n", + "# 테스트용 데이터 안의 사용와 영화의 조합에 대해 평갓값을 예측한다\n", "test_pred = reg.predict(test_x.values)\n", "\n", "movie_rating_predict = test_keys.copy()\n", @@ -4466,15 +4466,15 @@ "from collections import defaultdict\n", "import numpy as np\n", "\n", - "# 学習用データに存在するすべてのユーザーとすべての映画の組み合わせに対して評価値を予測する\n", + "# 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합에 대해 평갓값을 예측한다\n", "train_all_pred = reg.predict(train_all_x.values)\n", "\n", "pred_train_all = train_all_keys.copy()\n", "pred_train_all[\"rating_pred\"] = train_all_pred\n", "pred_matrix = pred_train_all.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating_pred\")\n", "\n", - "# ユーザーが学習用データ内で評価していない映画の中から\n", - "# 予測評価値が高い順に10件の映画をランキング形式の推薦リストとする\n", + "# 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서\n", + "# 예측 평갓값이 높은 순으로 10편의 영화를 순위 형식으로 추천 리스트로 만든다\n", "pred_user2items = defaultdict(list)\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "for user_id in movielens_train.user_id.unique():\n", @@ -4771,7 +4771,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -4878,7 +4878,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(4210, 4961, 4105)\n", + "# user_id=2에 대한 추천(4210, 4961, 4105)\n", "movies[movies.movie_id.isin([4210, 4961, 4105])]" ] }, @@ -4907,7 +4907,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/Random.ipynb b/chapter5/colab/Random.ipynb index 5744960..5719d24 100644 --- a/chapter5/colab/Random.ipynb +++ b/chapter5/colab/Random.ipynb @@ -13,8 +13,8 @@ "id": "b8e120c7", "metadata": {}, "source": [ - "# ランダム推薦\n", - "0.5から5の一様乱数を予測評価値とする" + "# 무작위 추천\n", + "0.5부터 5사이의 균등 난수를 예측 평갓값으로 한다" ] }, { @@ -24,11 +24,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -48,50 +48,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -106,7 +106,7 @@ "metadata": {}, "outputs": [], "source": [ - "# ユーザーIDとアイテムIDに対して、０始まりのインデックスを割り振る\n", + "# 사용자 ID와 아이템 ID에 대해 0부터 시작하는 인덱스를 할당한다\n", "unique_user_ids = sorted(movielens_train.user_id.unique())\n", "unique_movie_ids = sorted(movielens_train.movie_id.unique())\n", "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", @@ -121,7 +121,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "# ユーザー×アイテムの行列で、各セルの予測評価値は0.5〜5.0の一様乱数とする\n", + "# 사용자 x 아이템 행결에서 각 셀의 예측 평갓값은 0.5~5.0의 균등 난수로 한다\n", "pred_matrix = np.random.uniform(0.5, 5.0, (len(unique_user_ids), len(unique_movie_ids)))\n" ] }, @@ -132,16 +132,16 @@ "metadata": {}, "outputs": [], "source": [ - "# rmse評価用に、テストデータに出てくるユーザーとアイテムの予測評価値を格納する\n", + "# rmse 평가용으로 테스트 데이터에 나타나는 사용자와 아이템의 예측 평갓값을 저장한다.\n", "movie_rating_predict = movielens_test.copy()\n", "pred_results = []\n", "for i, row in movielens_test.iterrows():\n", " user_id = row[\"user_id\"]\n", - " # テストデータのアイテムIDが学習用に登場していない場合も乱数を格納する\n", + " # 테스트 데이터의 아이템 ID가 학습용에 나타나지 않는 경우에도 난수를 저장한다\n", " if row[\"movie_id\"] not in movie_id2index:\n", " pred_results.append(np.random.uniform(0.5, 5.0))\n", " continue\n", - " # テストデータに現れるユーザーIDとアイテムIDのインデックスを取得し、評価値行列の値を取得する\n", + " # 테스트 데이터에 나타난 사용자 ID와 아이템 ID의 인덱스를 얻어, 평갓값 행렬의 값을 얻는다\n", " user_index = user_id2index[row[\"user_id\"]]\n", " movie_index = movie_id2index[row[\"movie_id\"]]\n", " pred_score = pred_matrix[user_index, movie_index]\n", @@ -4733,11 +4733,11 @@ "source": [ "from collections import defaultdict\n", "\n", - "# ランキング評価用のデータ作成\n", - "# 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中からランダムに10作品とする\n", - "# キーはユーザーIDで、バリューはおすすめのアイテムIDのリスト\n", + "# 순위 평가용 데이터를 작성한다\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 10작품을 무작위로 선택한다\n", + "# 키는 사용자 ID, 값은 추천 아이템 ID 리스트\n", "pred_user2items = defaultdict(list)\n", - "# ユーザーがすでに評価した映画を取得する\n", + "# 사용자가 이미 평가한 영화를 얻는다\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "for user_id in unique_user_ids:\n", " user_index = user_id2index[user_id]\n", @@ -5034,7 +5034,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -5115,7 +5115,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(5656, 714, 4560)\n", + "# user_id=2에 대한 추천(5656, 714, 4560)\n", "movies[movies.movie_id.isin([5656, 714, 4560])]" ] }, @@ -5144,7 +5144,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/SVD.ipynb b/chapter5/colab/SVD.ipynb index 5e3a71c..4035a2b 100644 --- a/chapter5/colab/SVD.ipynb +++ b/chapter5/colab/SVD.ipynb @@ -13,7 +13,7 @@ "id": "05fd5c37", "metadata": {}, "source": [ - "# 特異値分解(SVD)" + "# 특이값 분석(SVD)" ] }, { @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,9 +105,9 @@ "metadata": {}, "outputs": [], "source": [ - "# 欠損値の穴埋め方法\n", + "# 결손값을 채우는 방법\n", "fillna_with_zero = True\n", - "# 因子数\n", + "# 인자 수\n", "factors = 5" ] }, @@ -118,7 +118,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする\n", + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", @@ -143,7 +143,7 @@ } ], "source": [ - "# スパース情報\n", + "# 희소 정보\n", "user_num = len(user_movie_matrix.index)\n", "item_num = len(user_movie_matrix.columns)\n", "non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n", @@ -164,7 +164,7 @@ "from scipy.sparse.linalg import svds\n", "import numpy as np\n", "\n", - "# 因子数kで特異値分解を行う\n", + "# 인자 수 k로 특이점 분석을 수행한다\n", "P, S, Qt = svds(matrix, k=factors)" ] }, @@ -183,7 +183,7 @@ } ], "source": [ - "# 予測評価値行列\n", + "# 예측 평갓값 행렬\n", "pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n", "\n", "print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")" @@ -196,7 +196,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする\n", + "# 학습용에 나타나지 않은 사용자와 영화 예측 평갓값은 평균 평갓값으로 한다\n", "average_score = movielens_train.rating.mean()\n", "movie_rating_predict = movielens_test.copy()\n", "pred_results = []\n", @@ -1556,7 +1556,7 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から予測値が高い順にする\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", "pred_user2items = defaultdict(list)\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "for user_id in movielens_train.user_id.unique():\n", @@ -1857,7 +1857,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -1964,7 +1964,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(2959, 4993, 5952)\n", + "# user_id=2에 대한 추천(2959, 4993, 5952)\n", "movies[movies.movie_id.isin([2959, 2571, 4993])]" ] }, @@ -1993,7 +1993,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/UMCF.ipynb b/chapter5/colab/UMCF.ipynb index dd2f086..4732c61 100644 --- a/chapter5/colab/UMCF.ipynb +++ b/chapter5/colab/UMCF.ipynb @@ -13,7 +13,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# ユーザー間型メモリベース法協調フィルタリング" + "# 사용자-사용자 메모리 기반 협조 필터링" ] }, { @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -106,7 +106,7 @@ "outputs": [], "source": [ "import numpy as np\n", - "# ピアソンの相関係数\n", + "# 피어슨 상관 계수\n", "def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float:\n", " u_diff = u - np.mean(u)\n", " v_diff = v - np.mean(v)\n", @@ -126,12 +126,12 @@ "source": [ "from collections import defaultdict\n", "\n", - "# 評価値をユーザー×映画の行列に変換\n", + "# 평갓값을 사용자 x 화면의 행렬로 변환한다\n", "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", "\n", - "# 予測対象のユーザーと映画の組\n", + "# 예측 대상 사용자와 영화의 조합\n", "movie_rating_predict = movielens_test.copy()\n", "pred_user2items = defaultdict(list)" ] @@ -143,50 +143,50 @@ "metadata": {}, "outputs": [], "source": [ - "# 愚直に類似度を計算（こちらの計算はとても重いです。そのため、途中でbreakを入れています。速度向上のために、ライブラリを使った実装も次のセルで紹介します。）\n", + "# 우직하게 유사도를 계산한다(이 계산은 매우 무겁습니다. 그렇기 떄문에 도중에 break를 넣었습니다. 속도 향상을 위해 라이브러리르 사용한 구현도 다음 셀에서 소개합니다)\n", "\n", - "# 予測対象のユーザーID\n", + "# 예측 대상 사용자 ID\n", "test_users = movie_rating_predict.user_id.unique()\n", "\n", - "# 予測対象のユーザー(ユーザー1）に注目する\n", + "# 예측 대상 사용자(사용자 1)에 주목한다\n", "for user1_id in test_users:\n", " similar_users = []\n", " similarities = []\n", " avgs = []\n", "\n", - " # ユーザ−１と評価値行列中のその他のユーザー（ユーザー２）との類似度を算出する\n", + " # 사용자 1과 평갓값 행렬 안의 기타 사용자(사용자 2)와의 유사도를 산출한다\n", " for user2_id in user_movie_matrix.index:\n", " if user1_id == user2_id:\n", " continue\n", "\n", - " # ユーザー１とユーザー２の評価値ベクトル\n", + " # 사용자 1과 사용자 2의 평갓값 벡터\n", " u_1 = user_movie_matrix.loc[user1_id, :].to_numpy()\n", " u_2 = user_movie_matrix.loc[user2_id, :].to_numpy()\n", "\n", - " # `u_1` と `u_2` から、ともに欠損値でない要素のみ抜き出したベクトルを取得\n", + " # `u_1`과 `u_2`로부터, 동시에 결손값이 없는 요소만 추출한 벡터를 얻는다\n", " common_items = ~np.isnan(u_1) & ~np.isnan(u_2)\n", "\n", - " # 共通して評価したアイテムがない場合はスキップ\n", + " # 공통으로 평가한 아이템이 없으면 스킵\n", " if not common_items.any():\n", " continue\n", "\n", " u_1, u_2 = u_1[common_items], u_2[common_items]\n", "\n", - " # ピアソンの相関係数を使ってユーザー１とユーザー２の類似度を算出\n", + " # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출\n", " rho_12 = peason_coefficient(u_1, u_2)\n", "\n", - " # ユーザー1との類似度が0より大きい場合、ユーザー2を類似ユーザーとみなす\n", + " # 사용자 1과의 유사도가 0 보다 큰 경우, 사용자 2를 유사 사용자로 간주한다\n", " if rho_12 > 0:\n", " similar_users.append(user2_id)\n", " similarities.append(rho_12)\n", " avgs.append(np.mean(u_2))\n", "\n", - " # ユーザー１の平均評価値\n", + " # 사용자 1의 평균 평갓값\n", " avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy())\n", "\n", - " # 予測対象の映画のID\n", + " # 예측 대상 영화의 ID\n", " test_movies = movie_rating_predict[movie_rating_predict[\"user_id\"] == user1_id].movie_id.values\n", - " # 予測できない映画への評価値はユーザー１の平均評価値とする\n", + " # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다\n", " movie_rating_predict.loc[(movie_rating_predict[\"user_id\"] == user1_id), \"rating_pred\"] = avg_1\n", "\n", " if similar_users:\n", @@ -195,7 +195,7 @@ " r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy()\n", " rating_exists = ~np.isnan(r_xy)\n", "\n", - " # 類似ユーザーが対象となる映画への評価値を持っていない場合はスキップ\n", + " # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않은 경우는 스킵한다\n", " if not rating_exists.any():\n", " continue\n", "\n", @@ -204,13 +204,13 @@ " avg_x = np.array(avgs)[rating_exists]\n", " r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum()\n", "\n", - " # 予測評価値を格納\n", + " # 예측 평갓값을 저장\n", " movie_rating_predict.loc[\n", " (movie_rating_predict[\"user_id\"] == user1_id)\n", " & (movie_rating_predict[\"movie_id\"] == movie_id),\n", " \"rating_pred\",\n", " ] = r_hat_1y\n", - " break # 計算が重いため、forループの一回目で終わるようにしています。各変数にどんな値が入っているかを確認してもらえれば、アルゴリズムの理解が深まります" + " break # 계산이 무거우므로, for 루프는 1번만 수행한 뒤 종료합니다. 각 변수에 어떤 값이 들어있는지 확인하면 알고리즘을 더 깊이 이해할 수 있습니다." ] }, { @@ -4075,31 +4075,32 @@ } ], "source": [ - "# Surpriseを利用した実装\n", + "# surprise를 사용한 구현\n", "\n", "from surprise import KNNWithMeans, Reader\n", "from surprise import Dataset as SurpriseDataset\n", - "# Surprise用にデータを加工\n", + "# surprise용으로 데이터를 가공한다\n", "reader = Reader(rating_scale=(0.5, 5))\n", "data_train = SurpriseDataset.load_from_df(\n", " movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n", ").build_full_trainset()\n", "\n", - "sim_options = {\"name\": \"pearson\", \"user_based\": True} # 類似度を計算する方法を指定する # False にするとアイテムベースとなる\n", + "sim_options = {\"name\": \"pearson\", \"user_based\": True} # 유사도를 계산하는 방법을 정의한다 # False로 하면 아이템 기반이 된다\n", "knn = KNNWithMeans(k=30, min_k=1, sim_options=sim_options)\n", "knn.fit(data_train)\n", "\n", - "# 学習データセットで評価値のないユーザーとアイテムの組み合わせを準備\n", + "# 학습 데이터셋에서 평갓값이 없는 사용자와 아이템의 조합을 준비한다\n", "data_test = data_train.build_anti_testset(None)\n", "predictions = knn.test(data_test)\n", "\n", "def get_top_n(predictions, n=10):\n", - " # 各ユーザーごとに、予測されたアイテムを格納する\n", + " # 각 사용자별로 예측된 아이템을 저장한다\n", " top_n = defaultdict(list)\n", " for uid, iid, true_r, est, _ in predictions:\n", " top_n[uid].append((iid, est))\n", "\n", - " # ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する\n", + " ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する\n", + " # 사용자별로 아이템을 예측 평갓값 순으로 나열하고, 상위 n개를 저장한다\n", " for uid, user_ratings in top_n.items():\n", " user_ratings.sort(key=lambda x: x[1], reverse=True)\n", " top_n[uid] = [d[0] for d in user_ratings[:n]]\n", @@ -4113,7 +4114,7 @@ "for _, row in movielens_test.iterrows():\n", " user_id = row[\"user_id\"]\n", " movie_id = row[\"movie_id\"]\n", - " # 学習データに存在せずテストデータにしか存在しないユーザーや映画についての予測評価値は、全体の平均評価値とする\n", + " # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자와 영화에 대한 예측 평갓값은 전체의 평균 평갓값으로 한다\n", " if user_id not in user_id2index or movie_id not in movie_id2index:\n", " pred_results.append(average_score)\n", " continue\n", @@ -4407,7 +4408,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", "movielens_train[movielens_train.user_id==2]" ] }, @@ -4514,7 +4515,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(1198, 1196, 1097)\n", + "# user_id=2에 대한 추천(1198, 1196, 1097)\n", "movies[movies.movie_id.isin([3473, 4135, 4850])]" ] }, @@ -4543,7 +4544,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/Word2vec.ipynb b/chapter5/colab/Word2vec.ipynb index 81d4945..c33b451 100644 --- a/chapter5/colab/Word2vec.ipynb +++ b/chapter5/colab/Word2vec.ipynb @@ -23,11 +23,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", "\n", - "# データのダウンロードと解凍\n", + "# 데이터 다운로드와 압축 풀기\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -47,50 +47,50 @@ } ], "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", "import pandas as pd\n", "\n", - "# movieIDとタイトル名のみ使用\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "\n", "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "\n", - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "\n", "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "\n", "\n", - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "\n", "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -105,17 +105,17 @@ "metadata": {}, "outputs": [], "source": [ - "# 因子数\n", + "# 인자 수\n", "factors = 100\n", - "# エポック数\n", + "# 에폭 수\n", "n_epochs = 30\n", - "# windowサイズ\n", + "# window 크기\n", "window = 100\n", - "# スキップグラム\n", + "# 스킵 그램\n", "use_skip_gram = 1\n", - "# 階層的ソフトマックス\n", + "# 계층적 소프트맥스\n", "use_hierarchial_softmax = 0\n", - "# 使用する単語の出現回数のしきい値\n", + "# 사용할 단어의 출현 횟수의 임곗값\n", "min_count = 5" ] }, @@ -127,9 +127,9 @@ "outputs": [], "source": [ "movie_content = movies.copy()\n", - "# tagが付与されていない映画もあるが、genreはすべての映画に付与されている\n", - "# tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく\n", - "# tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする\n", + "# tag가 부여되지 않은 영화는 있지만, genre는 모든 영화에 부여되어 있다\n", + "# tag와 genre를 결합한 것을 영화의 콘텐츠 정보로 해서 비슷한 영화를 찾아 추천한다\n", + "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", "movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n", "movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: set(map(str, x)))" ] @@ -162,7 +162,7 @@ "source": [ "import gensim\n", "\n", - "# タグとジャンルデータを使って、word2vecを学習する\n", + "# 태그와 장르 데이터를 사용해 word2vec을 학습한다\n", "tag_genre_data = movie_content.tag_genre.tolist()\n", "model = gensim.models.word2vec.Word2Vec(\n", " tag_genre_data,\n", @@ -202,7 +202,7 @@ } ], "source": [ - "# animeタグに似ているタグを確認\n", + "# anime 태크와 비슷한 태그를 확인한다\n", "model.wv.most_similar('anime')" ] }, @@ -213,8 +213,8 @@ "metadata": {}, "outputs": [], "source": [ - "# 各映画のベクトルを計算する\n", - "# 各映画に付与されているタグ・ジャンルのベクトルの平均を映画のベクトルとする\n", + "# 각 영화의 벡터를 계산한다\n", + "# 각 영화에 부여되어 있는 태그/장르 벡터의 평균을 영화의 벡터로 한다\n", "movie_vectors = []\n", "tag_genre_in_model = set(model.wv.key_to_index.keys())\n", "\n", @@ -222,10 +222,10 @@ "ids = []\n", "\n", "for i, tag_genre in enumerate(tag_genre_data):\n", - " # word2vecのモデルで使用可能なタグ・ジャンルに絞る\n", + " # word2vec 모델에서 사용할 수 있는 태그/장르로 필터링한다\n", " input_tag_genre = set(tag_genre) & tag_genre_in_model\n", " if len(input_tag_genre) == 0:\n", - " # word2vecに基づいてベクトル計算できない映画にはランダムのベクトルを付与\n", + " # word2vec에 기반해 벡터 계산할 수 없는 영화에는 무작위 벡터를 부여한다\n", " vector = np.random.randn(model.vector_size)\n", " else:\n", " vector = model.wv[input_tag_genre].mean(axis=0)\n", @@ -243,10 +243,10 @@ "source": [ "import numpy as np\n", "\n", - "# 後続の類似度計算がしやすいように、numpyの配列で保持しておく\n", + "# 후속 유사도 계산을 쉽게 할 수 있도록 numpy 배열로 저장한다\n", "movie_vectors = np.array(movie_vectors)\n", "\n", - "# 正規化したベクトル\n", + "# 정규화한 벡터\n", "sum_vec = np.sqrt(np.sum(movie_vectors ** 2, axis=1))\n", "movie_norm_vectors = movie_vectors / sum_vec.reshape((-1, 1))\n" ] @@ -258,7 +258,7 @@ "metadata": {}, "outputs": [], "source": [ - "# まだ評価していない映画で、類似度が高いアイテムを返す関数\n", + "# 아직 평가하지 않은 영화에서 유사도가 높은 아이템을 반환하는 함수\n", "def find_similar_items(vec, evaluated_movie_ids, topn=10):\n", " score_vec = np.dot(movie_norm_vectors, vec)\n", " similar_indexes = np.argsort(-score_vec)\n", @@ -1285,7 +1285,7 @@ } ], "source": [ - "# ユーザーが評価値４以上をつけた映画\n", + "# 사용자가 평갓값 4 이상을 부여한 영화\n", "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", "\n", @@ -1420,7 +1420,7 @@ } ], "source": [ - "# user_id=2のユーザーが学習データで、4以上の評価を付けた映画一覧\n", + "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" ] }, @@ -1506,7 +1506,7 @@ } ], "source": [ - "# user_id=2に対するおすすめ(1198, 1196, 1097)\n", + "# user_id=2에 대한 추천(1198, 1196, 1097)\n", "movies[movies.movie_id.isin([1198, 1196, 1097])]" ] }, @@ -1535,7 +1535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/colab/data_download.ipynb b/chapter5/colab/data_download.ipynb index 25338b4..80476ae 100644 --- a/chapter5/colab/data_download.ipynb +++ b/chapter5/colab/data_download.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Movielensのデータのダウンロード" + "# Movielens 데이터 다운로드" ] }, { @@ -46,7 +46,7 @@ } ], "source": [ - "# MovieLensのデータセットをdataディレクトリにダウンロードして展開\n", + "# MovieLens 데이터셋을 data 디렉터리에 다운로드 한 뒤 압축을 푼다\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -55,7 +55,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Movielensのデータの確認" + "# Movielens 데이터 확인" ] }, { @@ -155,12 +155,12 @@ } ], "source": [ - "# 映画の情報の読み込み(10681作品)\n", - "# movieIDとタイトル名のみ使用\n", + "# 영화 정보 로딩(10681작품)\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "movies.head()" ] @@ -252,11 +252,11 @@ } ], "source": [ - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 바꾼다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "user_tagged_movies.head()" @@ -278,9 +278,9 @@ } ], "source": [ - "print(f'タグ種類={len(user_tagged_movies.tag.unique())}')\n", - "print(f'タグレコード数={len(user_tagged_movies)}')\n", - "print(f'タグが付いている映画数={len(user_tagged_movies.movie_id.unique())}')" + "print(f'태그 종류={len(user_tagged_movies.tag.unique())}')\n", + "print(f'태그 레코드 수={len(user_tagged_movies)}')\n", + "print(f'태그가 붙어있는 영화 수={len(user_tagged_movies.movie_id.unique())}')" ] }, { @@ -384,10 +384,10 @@ } ], "source": [ - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 태그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", "movies.head()" @@ -480,7 +480,7 @@ } ], "source": [ - "# 評価値データの読み込み\n", + "# 평갓값 데이터만 로딩한다\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "ratings.head()\n" @@ -573,7 +573,7 @@ } ], "source": [ - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "ratings.head()" @@ -691,7 +691,7 @@ } ], "source": [ - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "movielens.head()" ] @@ -778,7 +778,7 @@ ], "source": [ "import numpy as np\n", - "# ユーザ情報\n", + "# 사용자 정보\n", "movielens.groupby('user_id').agg({'movie_id': len}).agg({'movie_id':[min, max, np.mean, len]})" ] }, @@ -846,7 +846,7 @@ } ], "source": [ - "# 映画情報\n", + "# 영화 정보\n", "movielens.groupby('movie_id').agg({'user_id': len}).agg({'user_id':[min, max, np.mean, len]})" ] }, @@ -864,7 +864,7 @@ } ], "source": [ - "print(f'評価値数={len(movielens)}')" + "print(f'평갓값 수={len(movielens)}')" ] }, { @@ -1005,7 +1005,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# データの分割" + "# 데이터 분할" ] }, { @@ -1014,10 +1014,10 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -1049,7 +1049,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Association.ipynb b/chapter5/notebook/Association.ipynb index e11979f..bacf611 100644 --- a/chapter5/notebook/Association.ipynb +++ b/chapter5/notebook/Association.ipynb @@ -5,7 +5,7 @@ "id": "c7dc31f1", "metadata": {}, "source": [ - "# アソシエーション分析" + "# 어소시에이션 분석" ] }, { @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -268,10 +268,10 @@ } ], "source": [ - "# ユーザー×映画の行列形式に変更\n", - "user_movie_matrix = movielens.train.pivot(index='user_id', columns='movie_id', values='rating')\n", + "# 사용자 x 영화 행렬 형식으로 변환한다\n", + "user_movie_matrix = movielens_train.pivot(index='user_id', columns='movie_id', values='rating')\n", "\n", - "# ライブラリ使用のために、4以上の評価値は1, 4未満の評価値と欠損値は0にする\n", + "# 라이브러리를 사용하기 위해 4 이상의 평갓값은 1, 4 미만의 평갓값과 결손값은 0으로 한다\n", "user_movie_matrix[user_movie_matrix < 4] = 0\n", "user_movie_matrix[user_movie_matrix.isnull()] = 0\n", "user_movie_matrix[user_movie_matrix >= 4] = 1\n", @@ -357,7 +357,7 @@ "source": [ "from mlxtend.frequent_patterns import apriori\n", "\n", - "# 支持度が高い映画の表示\n", + "# 지지도가 높은 영화를 표시\n", "freq_movies = apriori(\n", " user_movie_matrix, min_support=0.1, use_colnames=True)\n", "freq_movies.sort_values('support', ascending=False).head()" @@ -422,7 +422,7 @@ } ], "source": [ - "# movie_id=593のタイトルの確認\n", + "# movie_id=593의 제목 확인(양들의 침묵)\n", "movielens.item_content[movielens.item_content.movie_id == 593]" ] }, @@ -509,8 +509,7 @@ ], "source": [ "from mlxtend.frequent_patterns import association_rules\n", - "\n", - "# アソシエーションルールの計算（リフト値の高い順に表示）\n", + "# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n", "rules = association_rules(freq_movies, metric='lift', min_threshold=1)\n", "rules.sort_values('lift', ascending=False).head()[['antecedents', 'consequents', 'lift']]" ] @@ -522,7 +521,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Associationレコメンド\n", + "# 어소시에이션 추천\n", "from src.association import AssociationRecommender\n", "recommender = AssociationRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -543,7 +542,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -571,7 +570,7 @@ } ], "source": [ - "# min_supportと精度の関係\n", + "# min_support와 정밀도의 관계\n", "for min_support in [0.06, 0.07, 0.08, 0.09, 0.1, 0.11]:\n", " recommend_result = recommender.recommend(movielens, min_support=min_support)\n", " metrics = metric_calculator.calc(\n", @@ -605,7 +604,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/BPR.ipynb b/chapter5/notebook/BPR.ipynb index a6ea8fe..fda56c8 100644 --- a/chapter5/notebook/BPR.ipynb +++ b/chapter5/notebook/BPR.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -70,7 +70,7 @@ } ], "source": [ - "# BPRレコメンド\n", + "# BPR 추천\n", "from src.bpr import BPRRecommender\n", "recommender = BPRRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -91,7 +91,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -212,7 +212,7 @@ } ], "source": [ - "# 因子数と精度の関係\n", + "# 인자 수와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", " recommend_result = recommender.recommend(movielens, factors=factors)\n", " metrics = metric_calculator.calc(\n", @@ -246,7 +246,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/src/association.py b/chapter5/src/association.py index 0e65a50..e1d4c53 100644 --- a/chapter5/src/association.py +++ b/chapter5/src/association.py @@ -10,50 +10,50 @@ class AssociationRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 評価数の閾値 + # 평갓값의 임곗값 min_support = kwargs.get("min_support", 0.1) min_threshold = kwargs.get("min_threshold", 1) - # ユーザー×映画の行列形式に変更 + # 사용자 x 영화 행렬 형식으로 변경 user_movie_matrix = dataset.train.pivot(index="user_id", columns="movie_id", values="rating") - # ライブラリ使用のために、4以上の評価値は1, 4未満の評価値と欠損値は0にする + # 라이브러리 사용을 위해 4 이상의 평갓값은 1, 4 미만의 평갓값은 0으로 한다 user_movie_matrix[user_movie_matrix < 4] = 0 user_movie_matrix[user_movie_matrix.isnull()] = 0 user_movie_matrix[user_movie_matrix >= 4] = 1 - # 支持度が高い映画 + # 지지도가 높은 영화 freq_movies = apriori(user_movie_matrix, min_support=min_support, use_colnames=True) - # アソシエーションルールの計算（リフト値の高い順に表示） + # 어소시에이션 규칙 계산(리프트값이 높은 순으로 표시) rules = association_rules(freq_movies, metric="lift", min_threshold=min_threshold) - # アソシエーションルールを使って、各ユーザーにまだ評価していない映画を１０本推薦する + # 어소시에이션 규칙을 사용해, 각 사용자가 아직 평가하지 않은 영화를 10개 추천한다 pred_user2items = defaultdict(list) user_evaluated_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() - # 学習用データで評価値が4以上のものだけ取得する。 + # 학습용 데이터에서 평갓값이 4 이상인 것만 얻는다 movielens_train_high_rating = dataset.train[dataset.train.rating >= 4] for user_id, data in movielens_train_high_rating.groupby("user_id"): - # ユーザーが直近評価した５つの映画を取得 + # 사용자가 직전에 평가한 5개의 영화를 얻는다 input_data = data.sort_values("timestamp")["movie_id"].tolist()[-5:] - # それらの映画が条件部に１本でも含まれているアソシエーションルールを抽出 + # 그 영화들이 조건부에 하나라도 포함되는 어소시에이션 규칙을 검출한다 matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1 - # アソシエーションルールの帰結部の映画をリストに格納し、登場頻度順に並び替え、ユーザーがまだに評価していないければ、推薦リストに追加する + # 어소시에이션 규칙의 귀결부의 영화를 리스트에 저장하고, 등록 빈도 수로 정렬해 사용자가 아직 평가하지 않았다면, 추천 목록에 추가한다 consequent_movies = [] for i, row in rules[matched_flags].sort_values("lift", ascending=False).iterrows(): consequent_movies.extend(row["consequents"]) - # 登場頻度をカウント + # 등록 빈도 세기 counter = Counter(consequent_movies) for movie_id, movie_cnt in counter.most_common(): if movie_id not in user_evaluated_movies[user_id]: pred_user2items[user_id].append(movie_id) - # 推薦リストが10本になったら終了する + # 추천 리스트가 10이 되면 종료한다 if len(pred_user2items[user_id]) == 10: break - # アソシエーションルールでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # 어소시에이션 규칙에서는 평갓값을 예측하지 않으므로, rmse 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/base_recommender.py b/chapter5/src/base_recommender.py index 19c22fb..f0943f0 100644 --- a/chapter5/src/base_recommender.py +++ b/chapter5/src/base_recommender.py @@ -10,11 +10,11 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: pass def run_sample(self) -> None: - # Movielensのデータを取得 + # Movielens 데이터 취득 movielens = DataLoader(num_users=1000, num_test_items=5, data_path="../data/ml-10M100K/").load() - # 推薦計算 + # 추천 계산 recommend_result = self.recommend(movielens) - # 推薦結果の評価 + # 추천 결과 평가 metrics = MetricCalculator().calc( movielens.test.rating.tolist(), recommend_result.rating.tolist(), diff --git a/chapter5/src/bpr.py b/chapter5/src/bpr.py index 252fed2..20b5256 100644 --- a/chapter5/src/bpr.py +++ b/chapter5/src/bpr.py @@ -10,14 +10,14 @@ class BPRRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 10) - # 評価数の閾値 + # 평갓값의 임곗값 minimum_num_rating = kwargs.get("minimum_num_rating", 0) - # エポック数 + # 에폭 스 n_epochs = kwargs.get("n_epochs", 50) - # 行列分解用に行列を作成する + # 행력 분해용 행렬을 작성한다 filtered_movielens_train = dataset.train.groupby("movie_id").filter( lambda x: len(x["movie_id"]) >= minimum_num_rating ) @@ -38,10 +38,10 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: # initialize a model model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs) - # 学習 + # 학습 model.fit(movielens_matrix) - # 推薦 + # 추천 recommendations = model.recommend_all(movielens_matrix.T) pred_user2items = defaultdict(list) for user_id, user_index in user_id2index.items(): @@ -49,7 +49,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: for movie_index in movie_indexes: movie_id = unique_movie_ids[movie_index] pred_user2items[user_id].append(movie_id) - # BPRでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # BPR에서는 평갓값을 예측하지 않으므로, rmse 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter7/metrics.ipynb b/chapter7/metrics.ipynb index 462adb8..4e63d58 100644 --- a/chapter7/metrics.ipynb +++ b/chapter7/metrics.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 予測誤差指標" + "# 예측 오차 지표" ] }, { @@ -89,7 +89,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 集合の評価指標" + "# 집합 평가 지표" ] }, { @@ -173,7 +173,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ランキング評価指標" + "# 순위 평가 지표" ] }, { @@ -279,7 +279,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -293,7 +293,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.8" } }, "nbformat": 4, From b63ce3767610e004646e6db4cec849db8690d44e Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Sun, 18 Dec 2022 10:38:05 +0900 Subject: [PATCH 02/17] translated ipynb --- chapter5/notebook/FM.ipynb | 12 ++-- chapter5/notebook/IMF.ipynb | 14 ++--- chapter5/notebook/Item2vec.ipynb | 10 ++-- chapter5/notebook/LDA_collaboration.ipynb | 10 ++-- chapter5/notebook/LDA_content.ipynb | 20 +++---- chapter5/notebook/MF.ipynb | 14 ++--- chapter5/notebook/NMF.ipynb | 14 ++--- chapter5/notebook/Popularity.ipynb | 26 ++++---- chapter5/notebook/RF.ipynb | 10 ++-- chapter5/notebook/Random.ipynb | 14 ++--- chapter5/notebook/SVD.ipynb | 26 ++++---- chapter5/notebook/Template.ipynb | 10 ++-- chapter5/notebook/UMCF.ipynb | 12 ++-- chapter5/notebook/Word2vec.ipynb | 20 +++---- chapter5/notebook/data_download.ipynb | 73 +++++++++-------------- 15 files changed, 134 insertions(+), 151 deletions(-) diff --git a/chapter5/notebook/FM.ipynb b/chapter5/notebook/FM.ipynb index 69a60c0..0cc104c 100644 --- a/chapter5/notebook/FM.ipynb +++ b/chapter5/notebook/FM.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부포 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -158,7 +158,7 @@ } ], "source": [ - "# FMレコメンド\n", + "# FM 추천\n", "from src.fm import FMRecommender\n", "recommender = FMRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -179,7 +179,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -312,7 +312,7 @@ } ], "source": [ - "# 補助情報の利用\n", + "# 보충 정보 사용\n", "recommend_result = recommender.recommend(movielens, use_side_information=True)\n", "metrics = metric_calculator.calc(\n", "movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -353,7 +353,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/IMF.ipynb b/chapter5/notebook/IMF.ipynb index 81ae6c0..bd57840 100644 --- a/chapter5/notebook/IMF.ipynb +++ b/chapter5/notebook/IMF.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -77,7 +77,7 @@ } ], "source": [ - "# Implicit Matrix Factorizationレコメンド\n", + "# Implicit Matrix Factorization 추천\n", "from src.imf import IMFRecommender\n", "recommender = IMFRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -98,7 +98,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -219,7 +219,7 @@ } ], "source": [ - "# 因子数と精度の関係\n", + "# 인자 수와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", " recommend_result = recommender.recommend(movielens, factors=factors)\n", " metrics = metric_calculator.calc(\n", @@ -376,7 +376,7 @@ } ], "source": [ - "# alphaと精度の関係\n", + "# alpha와 정밀도의 관계\n", "for alpha in [0.5, 1.0, 2.0, 5.0]:\n", " recommend_result = recommender.recommend(movielens, alpha=alpha)\n", " metrics = metric_calculator.calc(\n", @@ -410,7 +410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Item2vec.ipynb b/chapter5/notebook/Item2vec.ipynb index b6b3f35..1b2394c 100644 --- a/chapter5/notebook/Item2vec.ipynb +++ b/chapter5/notebook/Item2vec.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -50,7 +50,7 @@ } ], "source": [ - "# Item2vecContentレコメンド\n", + "# Item2vecContent 추천\n", "from src.item2vec import Item2vecRecommender\n", "recommender = Item2vecRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -71,7 +71,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -96,7 +96,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/LDA_collaboration.ipynb b/chapter5/notebook/LDA_collaboration.ipynb index 7639c46..eaa480b 100644 --- a/chapter5/notebook/LDA_collaboration.ipynb +++ b/chapter5/notebook/LDA_collaboration.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -320,7 +320,7 @@ } ], "source": [ - "# LDACollaborationレコメンド\n", + "# LDACollaboration 추천\n", "from src.lda_collaboration import LDACollaborationRecommender\n", "recommender = LDACollaborationRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -341,7 +341,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -374,7 +374,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/LDA_content.ipynb b/chapter5/notebook/LDA_content.ipynb index 6664eab..2d9af6c 100644 --- a/chapter5/notebook/LDA_content.ipynb +++ b/chapter5/notebook/LDA_content.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -1730,20 +1730,20 @@ "from gensim.corpora.dictionary import Dictionary\n", "\n", "movie_content = movielens.item_content.copy()\n", - "# tagが付与されていない映画もあるが、genreはすべての映画に付与されている\n", - "# tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく\n", - "# tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする\n", + "# tag가 부여되어 있지 않은 영화도 있지만, genre는 모든 영화에 부여되어 있다\n", + "# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 해서 비슷한 영화를 찾아 추천한다\n", + "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", "movie_content['tag_genre'] = movie_content['tag'].fillna(\"\").apply(list) + movie_content['genre'].apply(list)\n", "movie_content['tag_genre'] = movie_content['tag_genre'].apply(lambda x:list(map(str, x)))\n", "\n", - "# タグとジャンルデータを使って、LDAを学習する\n", + "# 태그와 장르 데이터를 사용해 LDA를 학습한다\n", "tag_genre_data = movie_content.tag_genre.tolist()\n", "\n", "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n", "common_dictionary = Dictionary(tag_genre_data)\n", "common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n", "\n", - "# LDAの学習\n", + "# LDA 학습\n", "lda_model = gensim.models.LdaModel(common_corpus, id2word=common_dictionary, num_topics=50, passes=30)\n", "lda_topics = lda_model[common_corpus]\n", "\n", @@ -3447,7 +3447,7 @@ } ], "source": [ - "# LDAContentレコメンド\n", + "# LDAContent추천\n", "from src.lda_content import LDAContentRecommender\n", "recommender = LDAContentRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -3468,7 +3468,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -3501,7 +3501,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/MF.ipynb b/chapter5/notebook/MF.ipynb index 7c8a3eb..d408ae1 100644 --- a/chapter5/notebook/MF.ipynb +++ b/chapter5/notebook/MF.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -41,7 +41,7 @@ "metadata": {}, "outputs": [], "source": [ - "# MFレコメンド\n", + "# MF 추천\n", "from src.mf import MFRecommender\n", "recommender = MFRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -62,7 +62,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -88,7 +88,7 @@ } ], "source": [ - "# 評価数のしきい値と精度の関係\n", + "# 평가 수의 임곗값과 정밀도의 관계\n", "for minimum_num_rating in [0, 10, 100, 300]:\n", " recommend_result = recommender.recommend(movielens, minimum_num_rating=minimum_num_rating)\n", " metrics = metric_calculator.calc(\n", @@ -114,7 +114,7 @@ } ], "source": [ - "# 因子数kと精度の関係\n", + "# 인자 수 k와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", " recommend_result = recommender.recommend(movielens, factors=factors)\n", " metrics = metric_calculator.calc(\n", @@ -148,7 +148,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/NMF.ipynb b/chapter5/notebook/NMF.ipynb index 69f2d7e..0b4962c 100644 --- a/chapter5/notebook/NMF.ipynb +++ b/chapter5/notebook/NMF.ipynb @@ -5,7 +5,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# 非負値行列分解" + "# 비부값 행렬 분석" ] }, { @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -58,7 +58,7 @@ } ], "source": [ - "# NMFレコメンド\n", + "# NMF 추천\n", "from src.nmf import NMFRecommender\n", "recommender = NMFRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -79,7 +79,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -118,7 +118,7 @@ } ], "source": [ - "# 欠損値のを平均値で穴埋め\n", + "# 결손값을 평균값으로 채운다\n", "recommend_result = recommender.recommend(movielens, fillna_with_zero=False)\n", "metrics = metric_calculator.calc(\n", "movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -151,7 +151,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Popularity.ipynb b/chapter5/notebook/Popularity.ipynb index 10f5665..5812150 100644 --- a/chapter5/notebook/Popularity.ipynb +++ b/chapter5/notebook/Popularity.ipynb @@ -5,10 +5,10 @@ "id": "4ffa2166", "metadata": {}, "source": [ - "# 人気度順推薦\n", - "## 人気度の定義\n", - "* 今回は評価値が高いものを人気が髙い映画とする\n", - "* 人気度の定義は「クリック数が多いもの」「購入が多いもの」「評価値が髙いもの」など複数あり、自社サービスに最も適した定義を利用\n" + "# 인기도순 추천\n", + "## 인기도의 정의\n", + "* 여기에서는 평갓값이 높은 것을 인기도가 높은 영화로 간주한다\n", + "* 인기도의 정의는 '클릭 수가 많은 것', '구입이 많은 것', '평갓값이 높은 것' 등 다양하므로, 자사의 서비스에 적합한 정의를 사용한다\n" ] }, { @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -136,9 +136,9 @@ } ], "source": [ - "\n", "import numpy as np\n", - "# 評価値が髙い映画の確認\n", + "\n", + "# 평갓값이 높은 영화 확인\n", "movie_stats = movielens.train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", "movie_stats.sort_values(by=('rating', 'mean'), ascending=False).head()" ] @@ -242,7 +242,7 @@ } ], "source": [ - "# しきい値を導入\n", + "# 임곗값을 도입\n", "movie_stats = movielens.train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", "atleast_flg = movie_stats['rating']['size'] >= 100\n", "movies_sorted_by_rating = movie_stats[atleast_flg].sort_values(by=('rating', 'mean'), ascending=False)\n", @@ -256,7 +256,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 人気度推薦\n", + "# 인기도 추천\n", "from src.popularity import PopularityRecommender\n", "recommender = PopularityRecommender()\n", "recommend_result = recommender.recommend(movielens, minimum_num_rating=100)" @@ -277,7 +277,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -301,7 +301,7 @@ } ], "source": [ - "# しきい値を変更したときの挙動\n", + "# 임곗값을 변경했을 때의 동작\n", "for minimum_num_rating in [1, 200]:\n", " recommend_result = recommender.recommend(movielens, minimum_num_rating=minimum_num_rating)\n", " metrics = metric_calculator.calc(\n", @@ -327,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/RF.ipynb b/chapter5/notebook/RF.ipynb index fae3559..c812dfe 100644 --- a/chapter5/notebook/RF.ipynb +++ b/chapter5/notebook/RF.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -57,7 +57,7 @@ } ], "source": [ - "# Random Forest を用いた回帰レコメンド\n", + "# Random Forest를 사용한 회귀 추천\n", "from src.rf import RFRecommender\n", "recommender = RFRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -78,7 +78,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -111,7 +111,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Random.ipynb b/chapter5/notebook/Random.ipynb index f4532e3..4487c74 100644 --- a/chapter5/notebook/Random.ipynb +++ b/chapter5/notebook/Random.ipynb @@ -5,8 +5,8 @@ "id": "b8e120c7", "metadata": {}, "source": [ - "# ランダム推薦\n", - "0.5から5の一様乱数を予測評価値とする" + "# 무작위 추천\n", + "0.5~5의 균등 난수를 예측 평갓값으로 한다" ] }, { @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -30,7 +30,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 렌즈 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -42,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "# ランダムレコメンド\n", + "# 무작위 추천\n", "from src.random import RandomRecommender\n", "recommender = RandomRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -63,7 +63,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -96,7 +96,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/SVD.ipynb b/chapter5/notebook/SVD.ipynb index d99bf38..9227231 100644 --- a/chapter5/notebook/SVD.ipynb +++ b/chapter5/notebook/SVD.ipynb @@ -5,7 +5,7 @@ "id": "05fd5c37", "metadata": {}, "source": [ - "# 特異値分解(SVD)" + "# 특이값 분해SVD)" ] }, { @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -449,13 +449,13 @@ } ], "source": [ - "# スパース情報\n", + "# 희소 정보\n", "user_num = len(user_movie_matrix.index)\n", "item_num = len(user_movie_matrix.columns)\n", "non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n", "non_null_ratio = non_null_num / (user_num*item_num)\n", "\n", - "print(f'ユーザー数={user_num}, アイテム数={item_num}, 密度={non_null_ratio:.2f}')" + "print(f'사용자 수={user_num}, 아이템 수={item_num}, 정밀도={non_null_ratio:.2f}')" ] }, { @@ -875,17 +875,17 @@ "import scipy\n", "import numpy as np\n", "\n", - "# 評価値をユーザー×映画の行列に変換。欠損値は、平均値で穴埋めする\n", + "# 평갓값을 사용자 x 영화의 행렬로 변환. 결손값은 평균값으로 채운다\n", "user_movie_matrix = movielens.train.pivot(index='user_id', columns='movie_id', values='rating')\n", "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", "matrix = user_movie_matrix.fillna(movielens.train.rating.mean()).to_numpy()\n", "\n", "\n", - "# 因子数kで特異値分解を行う\n", + "# 인자 수 x 특이값 분해를 수행한다\n", "P, S, Qt = scipy.sparse.linalg.svds(matrix, k=5)\n", "\n", - "# 予測評価値行列\n", + "# 예측 평갓값 행렬\n", "pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n", "\n", "print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")" @@ -898,7 +898,7 @@ "metadata": {}, "outputs": [], "source": [ - "# SVDレコメンド\n", + "# SVD 추천\n", "from src.svd import SVDRecommender\n", "recommender = SVDRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -919,7 +919,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -942,7 +942,7 @@ } ], "source": [ - "# 欠損値のを平均値で穴埋め\n", + "# 결손값을 평균값으로 채운다\n", "recommend_result = recommender.recommend(movielens, fillna_with_zero=False)\n", "metrics = metric_calculator.calc(\n", "movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -967,7 +967,7 @@ } ], "source": [ - "# 因子数kと精度の関係\n", + "# 인자 수와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", " recommend_result = recommender.recommend(movielens, factors=factors, fillna_with_zero=False)\n", " metrics = metric_calculator.calc(\n", @@ -1001,7 +1001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Template.ipynb b/chapter5/notebook/Template.ipynb index 7293bfb..f9842c6 100644 --- a/chapter5/notebook/Template.ipynb +++ b/chapter5/notebook/Template.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -21,7 +21,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -33,7 +33,7 @@ "metadata": {}, "outputs": [], "source": [ - "# ランダムレコメンド\n", + "# 무작위 추천\n", "from src.random import RandomRecommender\n", "recommender = RandomRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -46,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -71,7 +71,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/UMCF.ipynb b/chapter5/notebook/UMCF.ipynb index 816db09..bb41031 100644 --- a/chapter5/notebook/UMCF.ipynb +++ b/chapter5/notebook/UMCF.ipynb @@ -5,7 +5,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# ユーザー間型メモリベース法協調フィルタリング" + "# 사용자-사용자 메모리 기반 방법 협조 필터링" ] }, { @@ -35,7 +35,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -49,7 +49,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='./data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -93,7 +93,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -144,7 +144,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -177,7 +177,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/Word2vec.ipynb b/chapter5/notebook/Word2vec.ipynb index 4e8ddf4..e182dc2 100644 --- a/chapter5/notebook/Word2vec.ipynb +++ b/chapter5/notebook/Word2vec.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "# 親のフォルダのパスを追加\n", + "# 부모 폴더의 경로를 추가\n", "import sys; sys.path.insert(0, '..')\n", "\n", "from util.data_loader import DataLoader\n", @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Movielensのデータの読み込み\n", + "# Movielens 데이터 로딩\n", "data_loader = DataLoader(num_users=1000, num_test_items=5, data_path='../data/ml-10M100K/')\n", "movielens = data_loader.load()" ] @@ -55,13 +55,13 @@ "\n", "movie_content = movielens.item_content.copy()\n", "\n", - "# tagが付与されていない映画もあるが、genreはすべての映画に付与されている\n", - "# tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく\n", - "# tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする\n", + "# tag가 부여되어 있지 않은 영화가 있지만. genre는 모든 영화에 부여되어 있다\n", + "# tag와 genre를 결합한 것을 영화의 콘텐츠 정보로 해서 비슷한 영화를 찾아서 추천한다\n", + "# tag가 없는 영화에서는 NaN으로 되어 있으므로, 빈 리스트로 초기화한다\n", "movie_content['tag_genre'] = movie_content['tag'].fillna(\"\").apply(list) + movie_content['genre'].apply(list)\n", "movie_content['tag_genre'] = movie_content['tag_genre'].apply(lambda x:set(map(str, x)))\n", "\n", - "# タグとジャンルデータを使って、word2vecを学習する\n", + "# 태그와 장르 데이터를 사용해서, word2vec를 학습한다\n", "tag_genre_data = movie_content.tag_genre.tolist()\n", "model = gensim.models.word2vec.Word2Vec(tag_genre_data, vector_size=100, window=100, sg=1, hs=0, epochs=50, min_count=5)\n" ] @@ -93,7 +93,7 @@ } ], "source": [ - "# animeタグに似ているタグを確認\n", + "# anime 태그와 비슷한 태그를 확인한다\n", "model.wv.most_similar('anime')" ] }, @@ -104,7 +104,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Word2vecContentレコメンド\n", + "# Word2vecContent 추천\n", "from src.word2vec import Word2vecRecommender\n", "recommender = Word2vecRecommender()\n", "recommend_result = recommender.recommend(movielens)" @@ -125,7 +125,7 @@ } ], "source": [ - "# 評価\n", + "# 평가\n", "metric_calculator = MetricCalculator()\n", "metrics = metric_calculator.calc(\n", " movielens.test.rating.tolist(), recommend_result.rating.tolist(),\n", @@ -166,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/chapter5/notebook/data_download.ipynb b/chapter5/notebook/data_download.ipynb index 1523aa9..e3fe52b 100644 --- a/chapter5/notebook/data_download.ipynb +++ b/chapter5/notebook/data_download.ipynb @@ -4,42 +4,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Movielensのデータのダウンロード" + "# Movielens 데이터 다운로드" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2021-11-30 00:17:51-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", - "files.grouplens.org (files.grouplens.org) をDNSに問いあわせています... 128.101.65.152\n", - "files.grouplens.org (files.grouplens.org)|128.101.65.152|:443 に接続しています... 接続しました。\n", - "HTTP による接続要求を送信しました、応答を待っています... 200 OK\n", - "長さ: 65566137 (63M) [application/zip]\n", - "`../data/ml-10m.zip' に保存中\n", - "\n", - "ml-10m.zip 100%[===================>] 62.53M 778KB/s 時間 72s \n", - "\n", - "2021-11-30 00:19:03 (895 KB/s) - `../data/ml-10m.zip' へ保存完了 [65566137/65566137]\n", - "\n", - "Archive: ../data/ml-10m.zip\n", - " creating: ../data/ml-10M100K/\n", - " inflating: ../data/ml-10M100K/allbut.pl \n", - " inflating: ../data/ml-10M100K/movies.dat \n", - " inflating: ../data/ml-10M100K/ratings.dat \n", - " inflating: ../data/ml-10M100K/README.html \n", - " inflating: ../data/ml-10M100K/split_ratings.sh \n", - " inflating: ../data/ml-10M100K/tags.dat \n" + "/bin/bash: wget: command not found\n", + "unzip: cannot find or open ../data/ml-10m.zip, ../data/ml-10m.zip.zip or ../data/ml-10m.zip.ZIP.\n" ] } ], "source": [ - "# MovieLensのデータセットをdataディレクトリにダウンロードして展開\n", + "# MovieLens의 데이터셋을 data 디렉터리에 다운로드 한 뒤 압축을 푼다\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", "!unzip -n ../data/ml-10m.zip -d ../data/" ] @@ -48,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Movielensのデータの確認" + "# Movielens 데이터 확인" ] }, { @@ -148,12 +131,12 @@ } ], "source": [ - "# 映画の情報の読み込み(10681作品)\n", - "# movieIDとタイトル名のみ使用\n", + "# 영화정보 로딩(10681작품)\n", + "# movieID와 제목만 사용\n", "m_cols = ['movie_id', 'title', 'genre']\n", "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", "\n", - "# genreをlist形式で保持する\n", + "# genre를 list 형식으로 저장한다\n", "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", "movies.head()" ] @@ -245,11 +228,11 @@ } ], "source": [ - "# ユーザが付与した映画のタグ情報の読み込み\n", + "# 사용자 부여한 영화의 태그 정보 로딩\n", "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", "\n", - "# tagを小文字にする\n", + "# tag를 소문자로 한다\n", "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", "\n", "user_tagged_movies.head()" @@ -271,9 +254,9 @@ } ], "source": [ - "print(f'タグ種類={len(user_tagged_movies.tag.unique())}')\n", - "print(f'タグレコード数={len(user_tagged_movies)}')\n", - "print(f'タグが付いている映画数={len(user_tagged_movies.movie_id.unique())}')" + "print(f'태그 종류={len(user_tagged_movies.tag.unique())}')\n", + "print(f'태그 레코드 수={len(user_tagged_movies)}')\n", + "print(f'태그가 붙어있는 영화 수={len(user_tagged_movies.movie_id.unique())}')" ] }, { @@ -377,10 +360,10 @@ } ], "source": [ - "# tagを映画ごとにlist形式で保持する\n", + "# tag를 영화별로 list 형식으로 저장한다\n", "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", "\n", - "# タグ情報を結合する\n", + "# 내그 정보를 결합한다\n", "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", "\n", "movies.head()" @@ -473,7 +456,7 @@ } ], "source": [ - "# 評価値データの読み込み\n", + "# 평갓값 데이터 로딩\n", "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", "ratings.head()\n" @@ -566,7 +549,7 @@ } ], "source": [ - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", + "# 데이터량이 많으므로 사용자 수를 1000명으로 줄여서 시험한다\n", "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", "ratings.head()" @@ -684,7 +667,7 @@ } ], "source": [ - "# 映画のデータと評価のデータを結合する\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", "movielens = ratings.merge(movies, on='movie_id')\n", "movielens.head()" ] @@ -771,7 +754,7 @@ ], "source": [ "import numpy as np\n", - "# ユーザ情報\n", + "# 사용자 정보\n", "movielens.groupby('user_id').agg({'movie_id': len}).agg({'movie_id':[min, max, np.mean, len]})" ] }, @@ -839,7 +822,7 @@ } ], "source": [ - "# 映画情報\n", + "# 영화 정보\n", "movielens.groupby('movie_id').agg({'user_id': len}).agg({'user_id':[min, max, np.mean, len]})" ] }, @@ -857,7 +840,7 @@ } ], "source": [ - "print(f'評価値数={len(movielens)}')" + "print(f'평갓값 수={len(movielens)}')" ] }, { @@ -998,7 +981,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# データの分割" + "# 데이터 분할" ] }, { @@ -1007,10 +990,10 @@ "metadata": {}, "outputs": [], "source": [ - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", + "# 학습용과 테스트용으로 데이터를 분할한다\n", + "# 각 사용자의 직전 5건의 영화를 평가용으로 사용하고, 그 이외는 학습용으로 한다\n", + "# 먼저, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 직전에 부여한 영화부터 순번을 부여한다(1부터 시작)\n", "\n", "movielens['timestamp_rank'] = movielens.groupby(\n", " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", @@ -1042,7 +1025,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.10.8" } }, "nbformat": 4, From cdf1650dba34cf7452b2eb6016f76d3e1778cfe6 Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Sun, 18 Dec 2022 20:12:31 +0900 Subject: [PATCH 03/17] translated .py files(chapter5) --- chapter5/src/bpr.py | 2 +- chapter5/src/fm.py | 16 +++++------ chapter5/src/imf.py | 16 +++++------ chapter5/src/item2vec.py | 20 +++++++------- chapter5/src/lda_collaboration.py | 6 ++--- chapter5/src/lda_content.py | 14 +++++----- chapter5/src/mf.py | 26 +++++++++--------- chapter5/src/nmf.py | 10 +++---- chapter5/src/popularity.py | 10 +++---- chapter5/src/random.py | 18 ++++++------- chapter5/src/rf.py | 31 +++++++++++----------- chapter5/src/svd.py | 12 ++++----- chapter5/src/umcf.py | 44 +++++++++++++++---------------- chapter5/src/word2vec.py | 34 ++++++++++++------------ 14 files changed, 130 insertions(+), 129 deletions(-) diff --git a/chapter5/src/bpr.py b/chapter5/src/bpr.py index 20b5256..0cbb15b 100644 --- a/chapter5/src/bpr.py +++ b/chapter5/src/bpr.py @@ -49,7 +49,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: for movie_index in movie_indexes: movie_id = unique_movie_ids[movie_index] pred_user2items[user_id].append(movie_id) - # BPR에서는 평갓값을 예측하지 않으므로, rmse 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환). + # BPR에서는 평갓값을 예측하기 어려우므로 rmse 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환한다). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/fm.py b/chapter5/src/fm.py index 4c82b65..19030f5 100644 --- a/chapter5/src/fm.py +++ b/chapter5/src/fm.py @@ -10,23 +10,23 @@ class FMRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 10) - # 評価数の閾値 + # 평갓값의 임곗값 minimum_num_rating = kwargs.get("minimum_num_rating", 200) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 50) - # 学習率 + # 학습률 lr = kwargs.get("lr", 0.01) - # 補助情報の利用 + # 보충 정보 사용 use_side_information = kwargs.get("use_side_information", False) - # 評価値がminimum_num_rating件以上ある映画に絞る + # 평갓값이 minimum_num_rating건 이상인 영화로 필터링한다 filtered_movielens_train = dataset.train.groupby("movie_id").filter( lambda x: len(x["movie_id"]) >= minimum_num_rating ) - # ユーザーが評価した映画 + # 사용자가 평가한 영화 user_evaluated_movies = ( filtered_movielens_train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() ) @@ -70,7 +70,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: y_pred = fm_model.predict(X_test) pred_matrix = y_pred.reshape(len(unique_user_ids), len(unique_movie_ids)) - # 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする + # 학습용에 나오지 않은 사용자나 영화의 예측 평갓값은 평균 평간값으로 한다 average_score = dataset.train.rating.mean() movie_rating_predict = dataset.test.copy() pred_results = [] diff --git a/chapter5/src/imf.py b/chapter5/src/imf.py index 8c97e42..610cfa8 100644 --- a/chapter5/src/imf.py +++ b/chapter5/src/imf.py @@ -10,11 +10,11 @@ class IMFRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 10) - # 評価数の閾値 + # 평갓값의 임곗값 minimum_num_rating = kwargs.get("minimum_num_rating", 0) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 50) # alpha alpha = kwargs.get("alpha", 1.0) @@ -23,7 +23,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: lambda x: len(x["movie_id"]) >= minimum_num_rating ) - # 行列分解用に行列を作成する + # 행렬 분석용으로 행렬을 작성한다 movielens_train_high_rating = filtered_movielens_train[dataset.train.rating >= 4] unique_user_ids = sorted(movielens_train_high_rating.user_id.unique()) @@ -37,15 +37,15 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: movie_index = movie_id2index[row["movie_id"]] movielens_matrix[movie_index, user_index] = 1.0 * alpha - # モデルの初期化 + # 모델 초기화 model = implicit.als.AlternatingLeastSquares( factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1 ) - # 学習 + # 학습 model.fit(movielens_matrix) - # 推薦 + # 추천 recommendations = model.recommend_all(movielens_matrix.T) pred_user2items = defaultdict(list) for user_id, user_index in user_id2index.items(): @@ -53,7 +53,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: for movie_index in movie_indexes: movie_id = unique_movie_ids[movie_index] pred_user2items[user_id].append(movie_id) - # IMFでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # IMF에서는 평갓값의 예측이 어려우므로 rmse 평가는 수행하지 않는다(편의상, 테스트 데이터를 그대로 반환한다). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/item2vec.py b/chapter5/src/item2vec.py index 09a2730..14fd72f 100644 --- a/chapter5/src/item2vec.py +++ b/chapter5/src/item2vec.py @@ -9,24 +9,24 @@ class Item2vecRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 100) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 30) - # windowサイズ + # window 크기 window = kwargs.get("window", 100) - # スキップグラム + # 스킵 그램 use_skip_gram = kwargs.get("use_skip_gram", 1) - # 階層的ソフトマックス + # 계층적 소프트맥스 use_hierarchial_softmax = kwargs.get("use_hierarchial_softmax", 0) - # 使用する単語の出現回数のしきい値 + # 사용할 단어의 출현 횟수의 임곗값 min_count = kwargs.get("min_count", 5) item2vec_data = [] movielens_train_high_rating = dataset.train[dataset.train.rating >= 4] for user_id, data in movielens_train_high_rating.groupby("user_id"): - # 評価された順に並び替える - # item2vecではwindowというパラメータがあり、itemの評価された順番も重要な要素となる + # 평가된 순으로 나열한다 + # item2vec에서는 window라는 파라미터가 있으며, item의 평가된 순서도 중요한 요소가 된다 item2vec_data.append(data.sort_values("timestamp")["movie_id"].tolist()) model = gensim.models.word2vec.Word2Vec( @@ -46,13 +46,13 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: if item_id in model.wv.key_to_index: input_data.append(item_id) if len(input_data) == 0: - # おすすめ計算できない場合は空配列 + # 추천 계싼할 수 없는 경우에는 빈 배열 pred_user2items[user_id] = [] continue recommended_items = model.wv.most_similar(input_data, topn=10) pred_user2items[user_id] = [d[0] for d in recommended_items] - # Word2vecでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # Word2vec에서는 평갓값 예측이 어려우므로, rmse는 평가하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환한다). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/lda_collaboration.py b/chapter5/src/lda_collaboration.py index ef20e44..2b34ad8 100644 --- a/chapter5/src/lda_collaboration.py +++ b/chapter5/src/lda_collaboration.py @@ -11,9 +11,9 @@ class LDACollaborationRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 50) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 30) logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO) @@ -46,7 +46,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: if len(pred_user2items[user_id]) == 10: break - # LDAでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # LDA에서는 평갓값 예측이 어려우므로 rmse 평가는 수행하지 않는다(편의상, 테스트 데이터를 그대로 반환한다). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/lda_content.py b/chapter5/src/lda_content.py index df70932..a38a36a 100644 --- a/chapter5/src/lda_content.py +++ b/chapter5/src/lda_content.py @@ -12,19 +12,19 @@ class LDAContentRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 50) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 30) movie_content = dataset.item_content.copy() - # tagが付与されていない映画もあるが、genreはすべての映画に付与されている - # tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく - # tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする + # tag가 부여되저 있지 않은 영화는 있지만, genre는 모든 영화에 부여되어 있다 + # tag와 genre를 결함한 것을 영화의 콘텐츠로 해서 비슷한 영화를 차아서 추천한다 + # tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환해서 처리한다 movie_content["tag_genre"] = movie_content["tag"].fillna("").apply(list) + movie_content["genre"].apply(list) movie_content["tag_genre"] = movie_content["tag_genre"].apply(lambda x: list(map(str, x))) - # タグとジャンルデータを使って、word2vecを学習する + # 태그와 장르 데이터를 사용해 word2vec을 학습한다 tag_genre_data = movie_content.tag_genre.tolist() logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO) @@ -70,7 +70,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: if len(pred_user2items[user_id]) == 10: break - # LDAでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # LDA에서는 평갓값의 예측이 어려우므로 rmse의 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환한다). return RecommendResult(dataset.test.rating, pred_user2items) diff --git a/chapter5/src/mf.py b/chapter5/src/mf.py index aa540c3..e5b29a9 100644 --- a/chapter5/src/mf.py +++ b/chapter5/src/mf.py @@ -11,47 +11,47 @@ class MFRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 5) - # 評価数の閾値 + # 평갓값의 임곗값 minimum_num_rating = kwargs.get("minimum_num_rating", 100) - # バイアス項の使用 + # 바이어스 항 사용 use_biase = kwargs.get("use_biase", False) - # 学習率 + # 학습률 lr_all = kwargs.get("lr_all", 0.005) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 50) - # 評価数がminimum_num_rating件以上ある映画に絞る + # 평갓값이 minimum_num_rating건 이상 있는 연화로 필터링한다 filtered_movielens_train = dataset.train.groupby("movie_id").filter( lambda x: len(x["movie_id"]) >= minimum_num_rating ) - # Surprise用にデータを加工 + # Surprise용으로 데이터를 가공 reader = Reader(rating_scale=(0.5, 5)) data_train = SurpriseDataset.load_from_df( filtered_movielens_train[["user_id", "movie_id", "rating"]], reader ).build_full_trainset() - # Surpriseで行列分解を学習 - # SVDという名前だが、特異値分解ではなく、Matrix Factorizationが実行される + # Surprise로 행렬 분새를 학습 + # SVD라는 이름을 사용하지만, 특이점 분해가 아니라 Matrix Factorization이 실행된다 matrix_factorization = SVD(n_factors=factors, n_epochs=n_epochs, lr_all=lr_all, biased=use_biase) matrix_factorization.fit(data_train) def get_top_n(predictions, n=10): - # 各ユーザーごとに、予測されたアイテムを格納する + # 각 사용자별로 예측된 아이템을 저장한다 top_n = defaultdict(list) for uid, iid, true_r, est, _ in predictions: top_n[uid].append((iid, est)) - # ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する + # 사용자별로 아이템을 예측 평값값순으로 나열하고 상위 n개를 저장한다 for uid, user_ratings in top_n.items(): user_ratings.sort(key=lambda x: x[1], reverse=True) top_n[uid] = [d[0] for d in user_ratings[:n]] return top_n - # 学習データに出てこないユーザーとアイテムの組み合わせを準備 + # 학습 데이터에 나오지 않은 사용자와 아이템의 조합을 준비한다 data_test = data_train.build_anti_testset(None) predictions = matrix_factorization.test(data_test) pred_user2items = get_top_n(predictions, n=10) @@ -61,7 +61,7 @@ def get_top_n(predictions, n=10): ) movie_rating_predict = dataset.test.merge(test_data, on=["user_id", "movie_id"], how="left") - # 予測ができない箇所には、平均値を格納する + # 예측할 수 없는 위치에는 평균값을 저장한다 movie_rating_predict.rating_pred.fillna(filtered_movielens_train.rating.mean(), inplace=True) return RecommendResult(movie_rating_predict.rating_pred, pred_user2items) diff --git a/chapter5/src/nmf.py b/chapter5/src/nmf.py index c5c4c1b..bf2323a 100644 --- a/chapter5/src/nmf.py +++ b/chapter5/src/nmf.py @@ -9,11 +9,11 @@ class NMFRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 欠損値の穴埋め方法 + # 결손값을 채우는 방법 fillna_with_zero = kwargs.get("fillna_with_zero", True) factors = kwargs.get("factors", 5) - # 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする + # 평갓값을 사용자 x 영화의 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다) user_movie_matrix = dataset.train.pivot(index="user_id", columns="movie_id", values="rating") user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index)))) movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns)))) @@ -27,10 +27,10 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: P = nmf.fit_transform(matrix) Q = nmf.components_ - # 予測評価値行列 + # 예측 평갓값 행렬 pred_matrix = np.dot(P, Q) - # 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする + # 학습용에 나오지 않은 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다 average_score = dataset.train.rating.mean() movie_rating_predict = dataset.test.copy() pred_results = [] @@ -45,7 +45,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: pred_results.append(pred_score) movie_rating_predict["rating_pred"] = pred_results - # 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から予測値が高い順にする + # 각 사용자에게 대한 추천 영화는 그 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다 pred_user2items = defaultdict(list) user_evaluated_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() for user_id in dataset.train.user_id.unique(): diff --git a/chapter5/src/popularity.py b/chapter5/src/popularity.py index 603afb8..d282066 100644 --- a/chapter5/src/popularity.py +++ b/chapter5/src/popularity.py @@ -8,18 +8,18 @@ class PopularityRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 評価数の閾値 + # 평갓값의 임곗값 minimum_num_rating = kwargs.get("minimum_num_rating", 200) - # 各アイテムごとの平均の評価値を計算し、その平均評価値を予測値として利用する + # 각 아이템별 평균 평갓값을 계산하고, 그 평균 평갓값을 예측값으로 사용한다 movie_rating_average = dataset.train.groupby("movie_id").agg({"rating": np.mean}) - # テストデータに予測値を格納する。テストデータのみに存在するアイテムの予測評価値は０とする + # 테스트 데이터에 예측값을 저장한다. 테스트 데이터에만 존재하는 아이템의 예측 평갓값은 0으로 한다 movie_rating_predict = dataset.test.merge( movie_rating_average, on="movie_id", how="left", suffixes=("_test", "_pred") ).fillna(0) - # 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から評価値が高いもの10作品とする - # ただし、評価件数が少ないとノイズが大きいため、minimum_num_rating件以上評価がある映画に絞る + # 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 평균값이 높은 10개 작품으로 한다 + # 단, 평가 건수가 적으면 노이즈가 커지므로 minimum_num_rating건 이상 평가가 있는 영화로 한정한다 pred_user2items = defaultdict(list) user_watched_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() movie_stats = dataset.train.groupby("movie_id").agg({"rating": [np.size, np.mean]}) diff --git a/chapter5/src/random.py b/chapter5/src/random.py index 7defa52..dacc3e6 100644 --- a/chapter5/src/random.py +++ b/chapter5/src/random.py @@ -8,36 +8,36 @@ class RandomRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # ユーザーIDとアイテムIDに対して、０始まりのインデックスを割り振る + # 사용자 ID와 아이템 ID에 대해 0부터 시작하는 인덱스를 할당한다 unique_user_ids = sorted(dataset.train.user_id.unique()) unique_movie_ids = sorted(dataset.train.movie_id.unique()) user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids)))) movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids)))) - # ユーザー×アイテムの行列で、各セルの予測評価値は0.5〜5.0の一様乱数とする + # 사용자 x 아이템의 행렬에서 각 셀의 예측 평갓값은 0.5~5.0의 균등 난수로 한다 pred_matrix = np.random.uniform(0.5, 5.0, (len(unique_user_ids), len(unique_movie_ids))) - # rmse評価用に、テストデータに出てくるユーザーとアイテムの予測評価値を格納する + # rmse 평가용으로 테스트 데이터에 나오는 사용자와 아이템의 예측 평갓값을 저장한다 movie_rating_predict = dataset.test.copy() pred_results = [] for i, row in dataset.test.iterrows(): user_id = row["user_id"] - # テストデータのアイテムIDが学習用に登場していない場合も乱数を格納する + # 테스트 데이터의 아이템 ID가 학습용으로 등장하지 않는 경우도 난수를 저장한다 if row["movie_id"] not in movie_id2index: pred_results.append(np.random.uniform(0.5, 5.0)) continue - # テストデータに現れるユーザーIDとアイテムIDのインデックスを取得し、評価値行列の値を取得する + # 테스트 데이터에 나타나는 사용자 ID와 아이템 ID의 인덱스를 얻어, 평갓값 행렬값을 얻는다 user_index = user_id2index[row["user_id"]] movie_index = movie_id2index[row["movie_id"]] pred_score = pred_matrix[user_index, movie_index] pred_results.append(pred_score) movie_rating_predict["rating_pred"] = pred_results - # ランキング評価用のデータ作成 - # 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中からランダムに10作品とする - # キーはユーザーIDで、バリューはおすすめのアイテムIDのリスト + # 순위 평가용 데이터 작성 + # 각 사용자에 대한 추천 영화는, 해당 사용자가 아직 평가하지 않은 영화 중에서 무작위로 10개 작품으로 한다 + # 키는 사용자 ID, 값은 추천 아이템의 ID 리스트 pred_user2items = defaultdict(list) - # ユーザーがすでに評価した映画を取得する + # 사용자가 이미 평가한 영화를 저장한다 user_evaluated_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() for user_id in unique_user_ids: user_index = user_id2index[user_id] diff --git a/chapter5/src/rf.py b/chapter5/src/rf.py index fa2dd23..85a5d37 100644 --- a/chapter5/src/rf.py +++ b/chapter5/src/rf.py @@ -10,28 +10,29 @@ class RFRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする + # 평갓값을 사용자 x 영화의 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다 user_movie_matrix = dataset.train.pivot(index="user_id", columns="movie_id", values="rating") user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index)))) movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns)))) - # 学習に用いる学習用データ中のユーザーと映画の組を取得する + # 학습에 사용하는 학습용 데이터 중 사용자와 영화의 조합을 얻는다 train_keys = dataset.train[["user_id", "movie_id"]] - # 学習用データ中の評価値を学習の正解データとして取得する + # 학습용 데이터 중의 평갓값을 학습의 정답 데이터로서 얻는다 train_y = dataset.train.rating.values - # 評価値を予測したいテスト用データ中のユーザーと映画の組を取得する + # 평갓값을 예측하고자 하는 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다 test_keys = dataset.test[["user_id", "movie_id"]] - # ランキング形式の推薦リスト作成のために学習用データに存在するすべてのユーザーとすべての映画の組み合わせを取得する + # 순위 형식의 추천 리스르 작성을 윟 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 저장한다 train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[["user_id", "movie_id"]] - # 特徴量を作成する + # 특징량을 작성한다 train_x = train_keys.copy() test_x = test_keys.copy() train_all_x = train_all_keys.copy() - # 学習用データに存在するユーザーごとの評価値の最小値、最大値、平均値 + # 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값 # 及び、映画ごとの評価値の最小値、最大値、平均値を特徴量として追加 + # 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량ㅇ로 추가한다 aggregators = ["min", "max", "mean"] user_features = dataset.train.groupby("user_id").rating.agg(aggregators).to_dict() movie_features = dataset.train.groupby("movie_id").rating.agg(aggregators).to_dict() @@ -42,11 +43,11 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: train_x[f"m_{agg}"] = train_x["movie_id"].map(movie_features[agg]) test_x[f"m_{agg}"] = test_x["movie_id"].map(movie_features[agg]) train_all_x[f"m_{agg}"] = train_all_x["movie_id"].map(movie_features[agg]) - # テスト用データにしか存在しないユーザーや映画の特徴量を、学習用データ全体の平均評価値で埋める + # 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다 average_rating = train_y.mean() test_x.fillna(average_rating, inplace=True) - # 映画が特定の genre であるかどうかを表す特徴量を追加 + # 영화가 특정한 genre에 있는지를 나타태는 특징량을 추가 movie_genres = dataset.item_content[["movie_id", "genre"]] genres = set(list(itertools.chain(*movie_genres.genre))) for genre in genres: @@ -56,30 +57,30 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: test_x = test_x.merge(movie_genres, on="movie_id") train_all_x = train_all_x.merge(movie_genres, on="movie_id") - # 特徴量としては使わない情報を削除 + # 특징량으로서는 사용하지 않늦 정보를 삭제 train_x = train_x.drop(columns=["user_id", "movie_id"]) test_x = test_x.drop(columns=["user_id", "movie_id"]) train_all_x = train_all_x.drop(columns=["user_id", "movie_id"]) - # Random Forest を用いた学習 + # Random Forest를 사용한 학습 reg = RFR(n_jobs=-1, random_state=0) reg.fit(train_x.values, train_y) - # テスト用データ内のユーザーと映画の組に対して評価値を予測する + # 테스트용 데이터 안의 사용자와 영화의 조합에 대한 평갓값을 예측한다 test_pred = reg.predict(test_x.values) movie_rating_predict = test_keys.copy() movie_rating_predict["rating_pred"] = test_pred - # 学習用データに存在するすべてのユーザーとすべての映画の組み合わせに対して評価値を予測する + # 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합애 대해 평갓값을 예측한다 train_all_pred = reg.predict(train_all_x.values) pred_train_all = train_all_keys.copy() pred_train_all["rating_pred"] = train_all_pred pred_matrix = pred_train_all.pivot(index="user_id", columns="movie_id", values="rating_pred") - # ユーザーが学習用データ内で評価していない映画の中から - # 予測評価値が高い順に10件の映画をランキング形式の推薦リストとする + # 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서 + # 예측 평갓값이 높은 순으로 10건의 영화를 순위 형식의 추천 리스트로 한다 pred_user2items = defaultdict(list) user_evaluated_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() for user_id in dataset.train.user_id.unique(): diff --git a/chapter5/src/svd.py b/chapter5/src/svd.py index 59ae71c..d7de6d1 100644 --- a/chapter5/src/svd.py +++ b/chapter5/src/svd.py @@ -9,11 +9,11 @@ class SVDRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 欠損値の穴埋め方法 + # 결손값을 채우는 방법 fillna_with_zero = kwargs.get("fillna_with_zero", True) factors = kwargs.get("factors", 5) - # 評価値をユーザー×映画の行列に変換。欠損値は、平均値または０で穴埋めする + # 평갓값을 사용자 x 영화의 행렬로 변환한다. 평갓값 또는 0으로 채운다. user_movie_matrix = dataset.train.pivot(index="user_id", columns="movie_id", values="rating") user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index)))) movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns)))) @@ -22,13 +22,13 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: else: matrix = user_movie_matrix.fillna(dataset.train.rating.mean()).to_numpy() - # 因子数kで特異値分解を行う + # 인자 수 k로 특이값 분해를 수행한다 P, S, Qt = scipy.sparse.linalg.svds(matrix, k=factors) - # 予測評価値行列 + # 예측 평갓값 행렬 pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt) - # 学習用に出てこないユーザーや映画の予測評価値は、平均評価値とする + # 학습용에 나오지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다 average_score = dataset.train.rating.mean() movie_rating_predict = dataset.test.copy() pred_results = [] @@ -43,7 +43,7 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: pred_results.append(pred_score) movie_rating_predict["rating_pred"] = pred_results - # 各ユーザに対するおすすめ映画は、そのユーザがまだ評価していない映画の中から予測値が高い順にする + # 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화중에서 예측값이 높은 순으로 한다 pred_user2items = defaultdict(list) user_evaluated_movies = dataset.train.groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() for user_id in dataset.train.user_id.unique(): diff --git a/chapter5/src/umcf.py b/chapter5/src/umcf.py index a8c7def..8af5499 100644 --- a/chapter5/src/umcf.py +++ b/chapter5/src/umcf.py @@ -12,7 +12,7 @@ class UMCFRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # ピアソンの相関係数 + # 피어슨 상관 계수 def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float: u_diff = u - np.mean(u) v_diff = v - np.mean(v) @@ -24,59 +24,59 @@ def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float: is_naive = kwargs.get("is_naive", False) - # 評価値をユーザー×映画の行列に変換 + # 평갓값을 사용자 x 영화 행렬로 변환한다 user_movie_matrix = dataset.train.pivot(index="user_id", columns="movie_id", values="rating") user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index)))) movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns)))) - # 予測対象のユーザーと映画の組 + # 예측 대상 사용자와 영화 그룹 movie_rating_predict = dataset.test.copy() pred_user2items = defaultdict(list) if is_naive: - # 予測対象のユーザーID + # 예측 대상 사용자 ID test_users = movie_rating_predict.user_id.unique() - # 予測対象のユーザー(ユーザー1）に注目する + # 예측 대상 사요자(사용자 1)에 주목한다 for user1_id in test_users: similar_users = [] similarities = [] avgs = [] - # ユーザ−１と評価値行列中のその他のユーザー（ユーザー２）との類似度を算出する + # 사용자 1과 평갓값 행렬 안의 다른 사용자(사용자 2)와의 유사도를 산출한다 for user2_id in user_movie_matrix.index: if user1_id == user2_id: continue - # ユーザー１とユーザー２の評価値ベクトル + # 사용자 1과 사용자 2의 평갓값 벡터 u_1 = user_movie_matrix.loc[user1_id, :].to_numpy() u_2 = user_movie_matrix.loc[user2_id, :].to_numpy() - # `u_1` と `u_2` から、ともに欠損値でない要素のみ抜き出したベクトルを取得 + # `u_1`과 `u_2` 모두에서 결손값이 없는 요소만 추출한 벡터를 얻는다 common_items = ~np.isnan(u_1) & ~np.isnan(u_2) - # 共通して評価したアイテムがない場合はスキップ + # 공통으로 평가한 아이템이 없는 경우는 스킵한다 if not common_items.any(): continue u_1, u_2 = u_1[common_items], u_2[common_items] - # ピアソンの相関係数を使ってユーザー１とユーザー２の類似度を算出 + # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출한다 rho_12 = peason_coefficient(u_1, u_2) - # ユーザー1との類似度が0より大きい場合、ユーザー2を類似ユーザーとみなす + # 사용자 1과의 유사도가 0보다 큰 경우, 사용자 2를 유사 사용자로 간주한다 if rho_12 > 0: similar_users.append(user2_id) similarities.append(rho_12) avgs.append(np.mean(u_2)) - # ユーザー１の平均評価値 + # 사용자 1의 평균 평갓값 avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy()) - # 予測対象の映画のID + # 예측 대상의 영화 ID test_movies = movie_rating_predict[movie_rating_predict["user_id"] == user1_id].movie_id.values - # 予測できない映画への評価値はユーザー１の平均評価値とする + # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다 movie_rating_predict.loc[(movie_rating_predict["user_id"] == user1_id), "rating_pred"] = avg_1 if similar_users: @@ -85,7 +85,7 @@ def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float: r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy() rating_exists = ~np.isnan(r_xy) - # 類似ユーザーが対象となる映画への評価値を持っていない場合はスキップ + # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않는 경우는 스킵한다 if not rating_exists.any(): continue @@ -94,7 +94,7 @@ def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float: avg_x = np.array(avgs)[rating_exists] r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum() - # 予測評価値を格納 + # 예측 평갓값을 저장한다 movie_rating_predict.loc[ (movie_rating_predict["user_id"] == user1_id) & (movie_rating_predict["movie_id"] == movie_id), @@ -102,27 +102,27 @@ def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float: ] = r_hat_1y else: - # Surprise用にデータを加工 + # Surprise용으로 데이터를 가공한다 reader = Reader(rating_scale=(0.5, 5)) data_train = SurpriseDataset.load_from_df( dataset.train[["user_id", "movie_id", "rating"]], reader ).build_full_trainset() - sim_options = {"name": "pearson", "user_based": True} # 類似度を計算する方法を指定する # False にするとアイテムベースとなる + sim_options = {"name": "pearson", "user_based": True} # 유사도를 계산하는 방법을 지정한다 # False로 하면 아이템 기반이 된다 knn = KNNWithMeans(k=30, min_k=1, sim_options=sim_options) knn.fit(data_train) - # 学習データセットで評価値のないユーザーとアイテムの組み合わせを準備 + # 학습 데이터셋에서 평갓값이 없는 사용자와 아이템의 조합을 준비 data_test = data_train.build_anti_testset(None) predictions = knn.test(data_test) def get_top_n(predictions, n=10): - # 各ユーザーごとに、予測されたアイテムを格納する + # 각 사용자별로 예측된 아이템을 저장한다 top_n = defaultdict(list) for uid, iid, true_r, est, _ in predictions: top_n[uid].append((iid, est)) - # ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する + # 상요자별로 아이템을 예측 평갓값순으로 나열하고 상위 n개를 저장한다 for uid, user_ratings in top_n.items(): user_ratings.sort(key=lambda x: x[1], reverse=True) top_n[uid] = [d[0] for d in user_ratings[:n]] @@ -136,7 +136,7 @@ def get_top_n(predictions, n=10): for _, row in dataset.test.iterrows(): user_id = row["user_id"] movie_id = row["movie_id"] - # 学習データに存在せずテストデータにしか存在しないユーザーや映画についての予測評価値は、全体の平均評価値とする + # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자나 영화에 관한 예측 평갓값는 전체 평균 평갓값으로 한다 if user_id not in user_id2index or movie_id not in movie_id2index: pred_results.append(average_score) continue diff --git a/chapter5/src/word2vec.py b/chapter5/src/word2vec.py index d5b0762..fcc9a48 100644 --- a/chapter5/src/word2vec.py +++ b/chapter5/src/word2vec.py @@ -9,27 +9,27 @@ class Word2vecRecommender(BaseRecommender): def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: - # 因子数 + # 인자 수 factors = kwargs.get("factors", 100) - # エポック数 + # 에폭 수 n_epochs = kwargs.get("n_epochs", 30) - # windowサイズ + # window 크기 window = kwargs.get("window", 100) - # スキップグラム + # 스킵 그램 use_skip_gram = kwargs.get("use_skip_gram", 1) - # 階層的ソフトマックス + # 계층적 소프트맥스 use_hierarchial_softmax = kwargs.get("use_hierarchial_softmax", 0) - # 使用する単語の出現回数のしきい値 + # 사용한 단어의 출현 횟수의 임곗값 min_count = kwargs.get("min_count", 5) movie_content = dataset.item_content.copy() - # tagが付与されていない映画もあるが、genreはすべての映画に付与されている - # tagとgenreを結合したものを映画のコンテンツ情報として似ている映画を探して推薦していく - # tagがない映画に関しては、NaNになっているので、空のリストに変換してから処理をする + # tag가 부여되지 않은 영화는 있지만, genre는 모든 영화에 부여되어 있다 + # tag와 genre를 결합한 것을 영화 콘텐츠 정보로해서 비슷한 영화를 찾아 추천한다 + # tag가 없는 영화의 경우에는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다 movie_content["tag_genre"] = movie_content["tag"].fillna("").apply(list) + movie_content["genre"].apply(list) movie_content["tag_genre"] = movie_content["tag_genre"].apply(lambda x: set(map(str, x))) - # タグとジャンルデータを使って、word2vecを学習する + # 태그와 장르 데이터를 사용해 word2vec을 학습한다 tag_genre_data = movie_content.tag_genre.tolist() model = gensim.models.word2vec.Word2Vec( tag_genre_data, @@ -41,8 +41,8 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: min_count=min_count, ) - # 各映画のベクトルを計算する - # 各映画に付与されているタグ・ジャンルのベクトルの平均を映画のベクトルとする + # 각 영화의 벡터를 계산한다 + # 각 영화에 부여되어 있는 태그/장르의 벡트 평균을 영화 벡터로 한다 movie_vectors = [] tag_genre_in_model = set(model.wv.key_to_index.keys()) @@ -50,10 +50,10 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: ids = [] for i, tag_genre in enumerate(tag_genre_data): - # word2vecのモデルで使用可能なタグ・ジャンルに絞る + # word2vec 모델에서 사용할 수 있는 태그/장르로 한정한다 input_tag_genre = set(tag_genre) & tag_genre_in_model if len(input_tag_genre) == 0: - # word2vecに基づいてベクトル計算できない映画にはランダムのベクトルを付与 + # word2vec에 기반해 벡터 계산할 수 없는 형화에는 무작위 벡터를 부여한다 vector = np.random.randn(model.vector_size) else: vector = model.wv[input_tag_genre].mean(axis=0) @@ -61,10 +61,10 @@ def recommend(self, dataset: Dataset, **kwargs) -> RecommendResult: ids.append(movie_content.iloc[i]["movie_id"]) movie_vectors.append(vector) - # 後続の類似度計算がしやすいように、numpyの配列で保持しておく + # 후속 유사도 계산을 쉽게할 수 있도록 numpy 배열로 저장해 둔다 movie_vectors = np.array(movie_vectors) - # 正規化したベクトル + # 정규화 벡터 sum_vec = np.sqrt(np.sum(movie_vectors ** 2, axis=1)) movie_norm_vectors = movie_vectors / sum_vec.reshape((-1, 1)) @@ -93,7 +93,7 @@ def find_similar_items(vec, evaluated_movie_ids, topn=10): recommended_items = find_similar_items(user_vector, evaluated_movie_ids, topn=10) pred_user2items[user_id] = recommended_items - # Word2vecでは評価値の予測は難しいため、rmseの評価は行わない。（便宜上、テストデータの予測値をそのまま返す） + # Word2vec에서는 평갓값 예측이 어려우므로, rmse의 평가는 수행하지 않는다(편의상, 테스트 데이터의 예측값을 그대로 반환한다) return RecommendResult(dataset.test.rating, pred_user2items) From 55d61c1f5309cf3e81d5f3b771b67ca180c416a6 Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Sun, 18 Dec 2022 20:22:07 +0900 Subject: [PATCH 04/17] updated README.md --- README.md | 43 +++++++++++++++++++++++++++++-------------- 1 file changed, 29 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 3c39a51..5f6d869 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# 推薦システム実践入門 +# 추천 시스템 실전 입문 --- @@ -6,26 +6,41 @@ --- -本リポジトリはオライリー・ジャパン発行書籍『[推薦システム実践入門](https://www.oreilly.co.jp/books/9784873119663/)』のサポートサイトです。 + +이 저장소는 한빛미디어 발행 [추천 시스템 실전 입문]()의 지원 사이트입니다. -## サンプルコード + -本書の目的は、読者の仕事を助けることであり、一般に本書に掲載しているコードは読者のプログラムやドキュメントに使用してかまいません。コードの大部分を転載する場合を除き、我々に許可を求める必要はありません。たとえば、本書のコードの一部を使用するプログラムを作成するために許可は必要ありません。本書のコード例を販売、配布する場合には、許可が必要です。 +## 샘플 코드 -本書や本書のコード例を引用して質問などに答える場合、許可は必要ありません。本書のコード例のかなりの部分を製品マニュアルに転載するような場合には、許可が必要です。 + -出典を明記することを求めたりはしませんが、していただけるとありがたいです。出典には、通常、タイトル、著者、出版社、ISBNを入れてください。たとえば、「風間正弘、飯塚洸二郎、松村優也著『推薦システム実践入門』（オライリー・ジャパン、ISBN978-4-87311-966-3）」のようになります。 +이 책의 목적은 독자의 업무를 돕는 것으로, 일반적으로 이 책에 실린 코드는 독자의 프로그램이나 문서에 사용해도 좋습니다. 코드의 대부분을 전재하는 경우를 제외하고는 특별한 허가를 받을 필요는 없습니다. 예를 들어, 이 책의 코드의 일부를 사용하는 프로그램을 작성하기 위한 허가는 필요하지 않습니다. 이 책의 코드 예를 판매/배포하는 경우에는 허가를 받아야 합니다. -### ファイル構成 + -|フォルダ名 |説明 | -|:-- |:-- | -|chapter5 |5章で使用するコードやデータ | -|chapter7 |7章で使用するコード | +이 책이나 이 책의 코드 예를 인용해서 질문 등에 답하는 경우에는 허가가 필요하지 않습니다. 이 책의 코드 예의 상당한 부분을 제품 매뉴얼에 전재쟈하는 등의 경우에는 허가를 받아야 합니다. -コードやデータの解説は本書籍をご覧ください。 + -## 正誤表 +출처를 명기해야 하는 것은 아니지만, 표기한다면 감사하겠습니다. 출처에는 일반적으로 제목, 저자, 출판사, ISBN을 기재해 주십시오. 예를 들어, 「風間正弘、飯塚洸二郎、松村優也著『推薦システム実践入門』（オライリー・ジャパン、ISBN978-4-87311-966-3）」과 같습니다. -まだありません。誤植など間違いを見つけた方は、japan@oreilly.co.jpまでお知らせください。 + + +### 파일 구성 + +|파일명|설명| +|:---|:---| +|chapter5|5장에서 사용하는 코드와 데이터| +|chapter7|7장에서 사용하는 코드| + + + +코드나 데이터 설명은 책의 본문을 참조하기 바랍니다. + +## 정오표 + + + +아직 없습니다. 오기 등 잘못을 발견했을 때는 japan@oreilly.co.jp로 알려주십시오. From ebf1bc6dd8c4747eb39820eddcee9cdb0588dcc0 Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Tue, 27 Dec 2022 15:07:02 +0900 Subject: [PATCH 05/17] translate google colab notebooks --- chapter5/colab/Association.ipynb | 1913 +-------- chapter5/colab/BPR.ipynb | 1683 +------- chapter5/colab/FM.ipynb | 2061 +--------- chapter5/colab/IMF.ipynb | 1545 +------ chapter5/colab/Item2vec.ipynb | 1482 +------ chapter5/colab/LDA_collaboration.ipynb | 1842 +-------- chapter5/colab/LDA_content.ipynb | 4002 +----------------- chapter5/colab/MF.ipynb | 1608 +------- chapter5/colab/NMF.ipynb | 1882 +-------- chapter5/colab/Popularity.ipynb | 1813 +-------- chapter5/colab/RF.ipynb | 4916 +--------------------- chapter5/colab/Random.ipynb | 5153 +----------------------- chapter5/colab/SVD.ipynb | 2002 +-------- chapter5/colab/UMCF.ipynb | 4553 +-------------------- chapter5/colab/Word2vec.ipynb | 1544 +------ chapter5/colab/data_download.ipynb | 1058 +---- 16 files changed, 16 insertions(+), 39041 deletions(-) diff --git a/chapter5/colab/Association.ipynb b/chapter5/colab/Association.ipynb index 9f3f361..0a2ff61 100644 --- a/chapter5/colab/Association.ipynb +++ b/chapter5/colab/Association.ipynb @@ -1,1912 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d9777c7d", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Association.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "c7dc31f1", - "metadata": {}, - "source": [ - "# 어소시에이션 분석" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1b316a16", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/bin/bash: wget: command not found\n", - "unzip: cannot find or open ../data/ml-10m.zip, ../data/ml-10m.zip.zip or ../data/ml-10m.zip.ZIP.\n" - ] - } - ], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c1b8db75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f29c7aac", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "movie_id 1 2 3 4 5 6 7 8 9 \\\n", - "user_id \n", - "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - "movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n", - "user_id ... \n", - "1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - "movie_id 63992 64716 \n", - "user_id \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "\n", - "[5 rows x 6673 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 사용자 x 영화 행렬 형식으로 변환한다\n", - "user_movie_matrix = movielens_train.pivot(index='user_id', columns='movie_id', values='rating')\n", - "\n", - "# 라이브러리를 사용하기 위해 4 이상의 평갓값은 1, 4 미만의 평갓값과 결손값은 0으로 한다\n", - "user_movie_matrix[user_movie_matrix < 4] = 0\n", - "user_movie_matrix[user_movie_matrix.isnull()] = 0\n", - "user_movie_matrix[user_movie_matrix >= 4] = 1\n", - "\n", - "user_movie_matrix.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3f090eb7", - "metadata": {}, - "outputs": [], - "source": [ - "# 어소시에이션 규칙 라이브러리 설치(설치되어 있지 않다면 주석을 해제하고 실행합니다)\n", - "# !pip install mlxtend" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ac2211c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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supportitemsets
420.415(593)
230.379(318)
210.369(296)
190.361(260)
250.319(356)
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" - ], - "text/plain": [ - " support itemsets\n", - "42 0.415 (593)\n", - "23 0.379 (318)\n", - "21 0.369 (296)\n", - "19 0.361 (260)\n", - "25 0.319 (356)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlxtend.frequent_patterns import apriori\n", - "\n", - "# 지지도가 높은 영화를 표시\n", - "freq_movies = apriori(\n", - " user_movie_matrix, min_support=0.1, use_colnames=True)\n", - "freq_movies.sort_values('support', ascending=False).head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "18be737d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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" - ], - "text/plain": [ - " movie_id title genre \\\n", - "587 593 Silence of the Lambs, The (1991) [Crime, Horror, Thriller] \n", - "\n", - " tag \n", - "587 [based on a book, anthony hopkins, demme, psyc... " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# movie_id=593의 제목 확인(양들의 침묵)\n", - "movies[movies.movie_id == 593]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "470dafd1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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antecedentsconsequentslift
649(4993)(5952)5.459770
648(5952)(4993)5.459770
1462(1196, 1198)(1291, 260)4.669188
1463(1291, 260)(1196, 1198)4.669188
1460(1291, 1196)(260, 1198)4.171359
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" - ], - "text/plain": [ - " antecedents consequents lift\n", - "649 (4993) (5952) 5.459770\n", - "648 (5952) (4993) 5.459770\n", - "1462 (1196, 1198) (1291, 260) 4.669188\n", - "1463 (1291, 260) (1196, 1198) 4.669188\n", - "1460 (1291, 1196) (260, 1198) 4.171359" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlxtend.frequent_patterns import association_rules\n", - "\n", - "# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n", - "rules = association_rules(freq_movies, metric='lift', min_threshold=1)\n", - "rules.sort_values('lift', ascending=False).head()[['antecedents', 'consequents', 'lift']]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "781b86a4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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" - ], - "text/plain": [ - " movie_id title \\\n", - "4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n", - "5852 5952 Lord of the Rings: The Two Towers, The (2002) \n", - "\n", - " genre \\\n", - "4899 [Action, Adventure, Fantasy] \n", - "5852 [Action, Adventure, Fantasy] \n", - "\n", - " tag \n", - "4899 [based on a book, big budget, new zealand, sce... \n", - "5852 [based on a book, big budget, new zealand, sce... " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# movie_id=4993, 5952의 제목 확인(반지의 제왕)\n", - "movies[movies.movie_id.isin([4993, 5952])]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7495518b", - "metadata": {}, - "outputs": [], - "source": [ - "# 학습용 데이터 평갓값이 4 이상인 것만 얻는다.\n", - "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "782e5a9d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 4 이상의 평가를 남긴 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0599276e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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antecedentsconsequentsantecedent supportconsequent supportsupportconfidenceliftleverageconviction
3(110)(1)0.2910.2630.1050.3608251.3719570.0284671.153048
5(260)(1)0.3610.2630.1530.4238231.6114930.0580571.279120
25(1210)(1)0.2730.2630.1160.4249081.6156210.0442011.281535
31(110)(32)0.2910.2550.1040.3573881.4015230.0297951.159332
33(260)(32)0.3610.2550.1370.3795011.4882410.0449451.200647
..............................
1476(1210, 1196)(2571, 260)0.1970.1610.1080.5482233.4051140.0762831.857112
1477(2571, 260)(1210, 1196)0.1610.1970.1080.6708073.4051140.0762832.439302
1479(1196, 260)(1210, 2571)0.2240.1390.1080.4821433.4686540.0768641.662621
1480(1210)(1196, 2571, 260)0.2730.1410.1080.3956042.8057050.0695071.421255
1482(260)(1210, 2571, 1196)0.3610.1240.1080.2991692.4126530.0632361.249945
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" - ], - "text/plain": [ - " antecedents consequents antecedent support \\\n", - "3 (110) (1) 0.291 \n", - "5 (260) (1) 0.361 \n", - "25 (1210) (1) 0.273 \n", - "31 (110) (32) 0.291 \n", - "33 (260) (32) 0.361 \n", - "... ... ... ... \n", - "1476 (1210, 1196) (2571, 260) 0.197 \n", - "1477 (2571, 260) (1210, 1196) 0.161 \n", - "1479 (1196, 260) (1210, 2571) 0.224 \n", - "1480 (1210) (1196, 2571, 260) 0.273 \n", - "1482 (260) (1210, 2571, 1196) 0.361 \n", - "\n", - " consequent support support confidence lift leverage conviction \n", - "3 0.263 0.105 0.360825 1.371957 0.028467 1.153048 \n", - "5 0.263 0.153 0.423823 1.611493 0.058057 1.279120 \n", - "25 0.263 0.116 0.424908 1.615621 0.044201 1.281535 \n", - "31 0.255 0.104 0.357388 1.401523 0.029795 1.159332 \n", - "33 0.255 0.137 0.379501 1.488241 0.044945 1.200647 \n", - "... ... ... ... ... ... ... \n", - "1476 0.161 0.108 0.548223 3.405114 0.076283 1.857112 \n", - "1477 0.197 0.108 0.670807 3.405114 0.076283 2.439302 \n", - "1479 0.139 0.108 0.482143 3.468654 0.076864 1.662621 \n", - "1480 0.141 0.108 0.395604 2.805705 0.069507 1.421255 \n", - "1482 0.124 0.108 0.299169 2.412653 0.063236 1.249945 \n", - "\n", - "[326 rows x 9 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2의 사용자가 4 이상의 평가를 남긴 영화 목록\n", - "user2_data = movielens_train_high_rating[movielens_train_high_rating.user_id==2]\n", - "\n", - "# 사용자가 최근 평가한 4개의 영화 얻기\n", - "input_data = user2_data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", - "\n", - "# 그 영화들이 조건부로 포함된 어오시에이션 규칙을 추출\n", - "matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n", - "rules[matched_flags]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "689b0be8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(1196, 92),\n", - " (593, 41),\n", - " (1198, 34),\n", - " (260, 34),\n", - " (1210, 34),\n", - " (318, 20),\n", - " (296, 19),\n", - " (2571, 18),\n", - " (356, 17),\n", - " (589, 16)]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict, Counter\n", - "\n", - "# 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장한다\n", - "# 같은 영화가 여러 차례 귀결부에 나타날 수 있다\n", - "\n", - "consequent_movies = []\n", - "for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows(): # lift値でソートして、上位１０個のルールだけを使うようにするなどの工夫も可能です\n", - " consequent_movies.extend(row[\"consequents\"])\n", - " \n", - "# 귀결부에서의 출현 빈도 카운트\n", - "counter = Counter(consequent_movies)\n", - "counter.most_common(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e1f61bb6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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" - ], - "text/plain": [ - " movie_id title \\\n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "\n", - " genre \\\n", - "1171 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag \n", - "1171 [lucas, george lucas, george lucas, gfei own i... " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# movie_id=1196가 92번 귀결부에 출현하므로, user_id=2에는 movie_id=1196(Star Wars: Episode V)가 추천 후보가 된다\n", - "# (user_id=2의 학습 데이터에서는 Star Wars 에피소드 4, 6의 평가가 높다)\n", - "movies[movies.movie_id == 1196]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7540d95c", - "metadata": {}, - "outputs": [], - "source": [ - "# 추천 방법에는 lift 값이 높은 것을 추출하는 방법 등이 있다. 몇 가지 방법을 시도해 보고 자사의 데이터에 맞는 방법을 선택한다." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "53918e5b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {2: [1196, 593, 1198, 318, 296, 2571, 356, 589, 1240, 1291],\n", - " 6: [593, 296, 318, 541, 47, 608, 50, 589, 527, 1],\n", - " 9: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n", - " 10: [858, 1196, 260, 318],\n", - " 11: [2858, 50, 296, 593],\n", - " 12: [260],\n", - " 13: [593, 318, 527, 356, 260, 47, 110, 2858, 589, 457],\n", - 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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 어소시에이션 규칙을 사용해 각 사용자가 아직 평가하지 않은 영화 10편을 추천한다\n", - "pred_user2items = defaultdict(list)\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # 사용자가 최근 5편의 영화를 얻는다\n", - " input_data = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", - " # 그 영화들이 조건부에 1편이라도 포함되어 있는 어소시에이션 규칙을 추출한다\n", - " matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n", - "\n", - " # 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장하고, 출편 빈도 순으로 배열하고, 사용자가 아직 평가하지 않았다면 추천 리스트에 추가한다\n", - " consequent_movies = []\n", - " for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows():\n", - " consequent_movies.extend(row[\"consequents\"])\n", - " # 출현 빈도를 센다\n", - " counter = Counter(consequent_movies)\n", - " for movie_id, movie_cnt in counter.most_common():\n", - " if movie_id not in user_evaluated_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " # 추천 리스트가 10편이 되면 종료한다\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "\n", - "# 각 사용자에 대한 추천 리스트\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "7342fdc7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "587 593 Silence of the Lambs, The (1991) \n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n", - "\n", - " genre \\\n", - "587 [Crime, Horror, Thriller] \n", - "1171 [Action, Adventure, Sci-Fi] \n", - "1173 [Action, Adventure] \n", - "\n", - " tag \n", - "587 [based on a book, anthony hopkins, demme, psyc... \n", - "1171 [lucas, george lucas, george lucas, gfei own i... \n", - "1173 [egypt, lucas, seen more than once, dvd collec... " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(1196, 593, 1198)\n", - "movies[movies.movie_id.isin([1196, 593, 1198])]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9b22d1f6", - "metadata": {}, - "outputs": [], - "source": [ - "# apriori(user_movie_matrix, min_support=0.1, use_colnames=True)\n", - "# association_rules(freq_movies, metric='lift', min_threshold=1)\n", - "# min_support와 min_threshold가 중요한 파라미터가 되므로 바꾸어 가면서 시험해봅니다." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64ae2120", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"d9777c7d","metadata":{"id":"d9777c7d"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Association.ipynb)"]},{"cell_type":"markdown","id":"c7dc31f1","metadata":{"id":"c7dc31f1"},"source":["# 어소시에이션 분석"]},{"cell_type":"code","execution_count":1,"id":"1b316a16","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"1b316a16","executionInfo":{"status":"ok","timestamp":1672116308177,"user_tz":-540,"elapsed":3911,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5beace71-80d1-430b-f7a5-ab9f156eb138"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:45:01-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.8MB/s in 1.0s \n","\n","2022-12-27 04:45:02 (63.8 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"c1b8db75","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c1b8db75","executionInfo":{"status":"ok","timestamp":1672116375742,"user_tz":-540,"elapsed":67569,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e9660e0b-1ab9-4f2f-c69b-890b464bfe3b"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"f29c7aac","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":267},"id":"f29c7aac","executionInfo":{"status":"ok","timestamp":1672116375743,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c6b3c719-f6d4-46dd-b765-6a569ec2f3d6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["movie_id 1 2 3 4 5 6 7 8 9 \\\n","user_id \n","1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","\n","movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n","user_id ... \n","1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","5 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","\n","movie_id 63992 64716 \n","user_id \n","1 0.0 0.0 \n","2 0.0 0.0 \n","3 0.0 0.0 \n","4 0.0 0.0 \n","5 0.0 0.0 \n","\n","[5 rows x 6673 columns]"],"text/html":["\n","
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user_id
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5 rows × 6673 columns

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supportitemsets
420.415(593)
230.379(318)
210.369(296)
190.361(260)
250.319(356)
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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\n"," "]},"metadata":{},"execution_count":6}],"source":["# movie_id=593의 제목 확인(양들의 침묵)\n","movies[movies.movie_id == 593]"]},{"cell_type":"code","execution_count":7,"id":"470dafd1","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"470dafd1","executionInfo":{"status":"ok","timestamp":1672116376968,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c5b8e4f6-14dc-4ad5-ba32-e1585aba6ba6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" antecedents consequents lift\n","649 (4993) (5952) 5.459770\n","648 (5952) (4993) 5.459770\n","1462 (1196, 1198) (1291, 260) 4.669188\n","1463 (1291, 260) (1196, 1198) 4.669188\n","1460 (1291, 1196) (260, 1198) 4.171359"],"text/html":["\n","
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antecedentsconsequentslift
649(4993)(5952)5.459770
648(5952)(4993)5.459770
1462(1196, 1198)(1291, 260)4.669188
1463(1291, 260)(1196, 1198)4.669188
1460(1291, 1196)(260, 1198)4.171359
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\n"," "]},"metadata":{},"execution_count":7}],"source":["from mlxtend.frequent_patterns import association_rules\n","\n","# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n","rules = association_rules(freq_movies, metric='lift', min_threshold=1)\n","rules.sort_values('lift', ascending=False).head()[['antecedents', 'consequents', 'lift']]"]},{"cell_type":"code","execution_count":8,"id":"781b86a4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"781b86a4","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"efb286e7-011c-48c5-bca0-fc8d74077602"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","5852 5952 Lord of the Rings: The Two Towers, The (2002) \n","\n"," genre \\\n","4899 [Action, Adventure, Fantasy] \n","5852 [Action, Adventure, Fantasy] \n","\n"," tag \n","4899 [based on a book, big budget, new zealand, sce... \n","5852 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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\n"," "]},"metadata":{},"execution_count":8}],"source":["# movie_id=4993, 5952의 제목 확인(반지의 제왕)\n","movies[movies.movie_id.isin([4993, 5952])]"]},{"cell_type":"code","execution_count":9,"id":"7495518b","metadata":{"id":"7495518b","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용 데이터 평갓값이 4 이상인 것만 얻는다.\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]"]},{"cell_type":"code","execution_count":10,"id":"782e5a9d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"782e5a9d","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c6e2b6b2-6336-45c4-8bb8-df6bf91d99db"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2인 사용자가 4 이상의 평가를 남긴 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":11,"id":"0599276e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"0599276e","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"118190a5-3aa0-40a2-c32c-e80489f5c618"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" antecedents consequents antecedent support \\\n","3 (110) (1) 0.291 \n","5 (260) (1) 0.361 \n","25 (1210) (1) 0.273 \n","31 (110) (32) 0.291 \n","33 (260) (32) 0.361 \n","... ... ... ... \n","1476 (1210, 1196) (2571, 260) 0.197 \n","1477 (2571, 260) (1210, 1196) 0.161 \n","1479 (1196, 260) (1210, 2571) 0.224 \n","1480 (1210) (1196, 2571, 260) 0.273 \n","1482 (260) (1210, 2571, 1196) 0.361 \n","\n"," consequent support support confidence lift leverage conviction \n","3 0.263 0.105 0.360825 1.371957 0.028467 1.153048 \n","5 0.263 0.153 0.423823 1.611493 0.058057 1.279120 \n","25 0.263 0.116 0.424908 1.615621 0.044201 1.281535 \n","31 0.255 0.104 0.357388 1.401523 0.029795 1.159332 \n","33 0.255 0.137 0.379501 1.488241 0.044945 1.200647 \n","... ... ... ... ... ... ... \n","1476 0.161 0.108 0.548223 3.405114 0.076283 1.857112 \n","1477 0.197 0.108 0.670807 3.405114 0.076283 2.439302 \n","1479 0.139 0.108 0.482143 3.468654 0.076864 1.662621 \n","1480 0.141 0.108 0.395604 2.805705 0.069507 1.421255 \n","1482 0.124 0.108 0.299169 2.412653 0.063236 1.249945 \n","\n","[326 rows x 9 columns]"],"text/html":["\n","
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antecedentsconsequentsantecedent supportconsequent supportsupportconfidenceliftleverageconviction
3(110)(1)0.2910.2630.1050.3608251.3719570.0284671.153048
5(260)(1)0.3610.2630.1530.4238231.6114930.0580571.279120
25(1210)(1)0.2730.2630.1160.4249081.6156210.0442011.281535
31(110)(32)0.2910.2550.1040.3573881.4015230.0297951.159332
33(260)(32)0.3610.2550.1370.3795011.4882410.0449451.200647
..............................
1476(1210, 1196)(2571, 260)0.1970.1610.1080.5482233.4051140.0762831.857112
1477(2571, 260)(1210, 1196)0.1610.1970.1080.6708073.4051140.0762832.439302
1479(1196, 260)(1210, 2571)0.2240.1390.1080.4821433.4686540.0768641.662621
1480(1210)(1196, 2571, 260)0.2730.1410.1080.3956042.8057050.0695071.421255
1482(260)(1210, 2571, 1196)0.3610.1240.1080.2991692.4126530.0632361.249945
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326 rows × 9 columns

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\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2의 사용자가 4 이상의 평가를 남긴 영화 목록\n","user2_data = movielens_train_high_rating[movielens_train_high_rating.user_id==2]\n","\n","# 사용자가 최근 평가한 4개의 영화 얻기\n","input_data = user2_data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n","\n","# 그 영화들이 조건부로 포함된 어오시에이션 규칙을 추출\n","matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n","rules[matched_flags]"]},{"cell_type":"code","execution_count":12,"id":"689b0be8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"689b0be8","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e06efaee-cb09-405b-f283-9252ea6a3e63"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(1196, 92),\n"," (593, 41),\n"," (1198, 34),\n"," (260, 34),\n"," (1210, 34),\n"," (318, 20),\n"," (296, 19),\n"," (2571, 18),\n"," (356, 17),\n"," (589, 16)]"]},"metadata":{},"execution_count":12}],"source":["from collections import defaultdict, Counter\n","\n","# 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장한다\n","# 같은 영화가 여러 차례 귀결부에 나타날 수 있다\n","\n","consequent_movies = []\n","for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows(): # lift値でソートして、上位１０個のルールだけを使うようにするなどの工夫も可能です\n"," consequent_movies.extend(row[\"consequents\"])\n"," \n","# 귀결부에서의 출현 빈도 카운트\n","counter = Counter(consequent_movies)\n","counter.most_common(10)"]},{"cell_type":"code","execution_count":13,"id":"e1f61bb6","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"e1f61bb6","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"4137dad7-e0b6-4859-aad3-8abb4ed70219"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","\n"," genre \\\n","1171 [Action, Adventure, Sci-Fi] \n","\n"," tag \n","1171 [lucas, george lucas, george lucas, gfei own i... "],"text/html":["\n","
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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\n","
\n"," "]},"metadata":{},"execution_count":13}],"source":["# movie_id=1196가 92번 귀결부에 출현하므로, user_id=2에는 movie_id=1196(Star Wars: Episode V)가 추천 후보가 된다\n","# (user_id=2의 학습 데이터에서는 Star Wars 에피소드 4, 6의 평가가 높다)\n","movies[movies.movie_id == 1196]"]},{"cell_type":"code","execution_count":14,"id":"7540d95c","metadata":{"id":"7540d95c","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 추천 방법에는 lift 값이 높은 것을 추출하는 방법 등이 있다. 몇 가지 방법을 시도해 보고 자사의 데이터에 맞는 방법을 선택한다."]},{"cell_type":"code","execution_count":15,"id":"53918e5b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53918e5b","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":6233,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"21d86b30-94bb-4d15-efb7-8d68fefe6448"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {2: [1196, 593, 1198, 318, 296, 2571, 356, 589, 1240, 1291],\n"," 6: [593, 296, 318, 541, 47, 608, 50, 589, 527, 1],\n"," 9: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 10: [858, 1196, 260, 318],\n"," 11: [2858, 50, 296, 593],\n"," 12: [260],\n"," 13: [593, 318, 527, 356, 260, 47, 110, 2858, 589, 457],\n"," 17: [1196, 296, 1200, 1240, 541, 2571, 1198, 1210],\n"," 18: [1200, 1197, 50, 858, 1193],\n"," 22: [318, 1196, 260, 457, 608, 2571, 1210, 1240, 1198, 541],\n"," 23: [1196, 1210, 1198, 2571, 318, 1291, 1240, 356, 858, 110],\n"," 24: [1198, 1196, 296, 593, 1221, 1213, 1193, 1214, 541, 2028],\n"," 26: [593, 318, 296, 1196, 260, 50, 356, 527, 1210, 1240],\n"," 27: [296, 593, 50, 318, 541, 858, 2858, 1, 260, 1198],\n"," 33: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 37: [527, 356, 1196, 260, 608, 2858, 457, 858, 480, 1198],\n"," 40: [],\n"," 41: [],\n"," 42: [1196, 260, 1198, 296, 318, 593],\n"," 44: [1196, 260, 1198, 593],\n"," 45: [296, 318, 47, 527, 541, 32],\n"," 46: [593, 1196, 260, 1240, 1210, 296, 318, 2571, 457, 356],\n"," 50: [50, 1196, 260, 1193, 2028, 47, 2858, 1198, 858, 32],\n"," 52: [2858, 296, 593],\n"," 53: [2571, 1196, 318, 296, 593],\n"," 54: [1196, 260, 527, 1240, 1210, 608, 2571, 1198, 32, 1200],\n"," 56: [],\n"," 59: [356, 50, 1196, 527, 260, 110, 589, 1198, 480, 47],\n"," 60: [1196, 1200, 1240, 2571, 1198, 589, 1210],\n"," 61: [1196, 260, 457, 50, 110, 589, 1198, 1210, 150, 480],\n"," 62: [1196, 260, 457, 50, 356, 589, 527, 1198, 110, 1210],\n"," 63: [296, 318, 593, 1196, 260, 2959, 2762, 2028, 2571, 50],\n"," 64: [1210, 1198, 480, 1196, 356, 589, 110, 296, 318, 593],\n"," 71: [593, 318, 50, 356, 527, 457, 110, 1196, 47, 260],\n"," 72: [296, 318, 589, 110, 150, 1198, 1210, 1, 2571, 1196],\n"," 75: [593, 318, 356, 1196, 527, 1210, 260, 457, 50, 590],\n"," 76: [1196, 260, 1240, 1210, 2571, 356, 527, 1036, 1200, 1214],\n"," 77: [260],\n"," 80: [356, 480, 589, 527, 588, 590, 1198, 1196, 260],\n"," 83: [1200, 2571, 589, 593, 296],\n"," 84: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 85: [1196, 260, 1210, 1198, 589, 318, 3578, 4993, 5952, 2959],\n"," 86: [593, 296, 318, 356, 260, 150, 1, 527],\n"," 87: [2571, 318, 296, 593],\n"," 95: [1196, 50, 527, 260, 457, 1210, 47, 2571, 1240, 608],\n"," 99: [2858, 2571, 1196, 593],\n"," 100: [],\n"," 102: [593, 50, 110, 527, 1196, 589, 260, 608, 32, 2571],\n"," 104: [589, 1210, 1200, 296],\n"," 105: [2571, 593, 356, 1240, 589, 296, 1291, 318, 1, 1200],\n"," 106: [364, 480, 457, 110, 593, 296, 318],\n"," 111: [527, 1196, 1210, 260, 589, 50, 1198, 1, 2571, 590],\n"," 113: [2858, 2571, 1196, 318, 296, 593],\n"," 116: [1196, 260, 2571, 1240, 1, 1036, 1200, 1214, 541, 2028],\n"," 119: [1196, 260, 50, 1210, 2571],\n"," 121: [260, 1036, 589],\n"," 129: [2571, 1196, 318, 296, 593],\n"," 132: [50, 1196, 260, 527, 110, 589, 1198, 47, 150, 1210],\n"," 137: [],\n"," 138: [457, 527, 480, 150, 608, 858, 1213, 111, 1097, 590],\n"," 141: [318, 296, 50, 110, 1196, 260, 1193, 2028, 47, 2858],\n"," 142: [296, 318, 1196, 260, 356, 50, 589, 527, 110, 1198],\n"," 145: [318, 1196, 260, 527, 589, 110, 1198, 47, 608, 480],\n"," 148: [2571, 1198, 858, 593],\n"," 149: [],\n"," 150: [593, 318, 296, 110, 1196, 260, 457, 50, 150, 589],\n"," 151: [593, 541, 858, 32, 2858, 1, 1198, 1210],\n"," 152: [296, 32, 527],\n"," 153: [593, 318, 50, 356, 1196, 47, 608, 1210, 2858, 457],\n"," 154: [318],\n"," 155: [],\n"," 161: [1036],\n"," 162: [593, 457, 318, 380, 150],\n"," 164: [50, 110, 1196, 260, 1193, 2028, 2858, 1198, 858, 2571],\n"," 166: [318, 356, 296, 150, 480, 589, 110, 50, 527, 380],\n"," 168: [593, 318, 296, 110, 527, 1196, 1210, 260, 150, 457],\n"," 171: [593, 858],\n"," 172: [593, 318, 1196, 260, 296, 356, 1210, 527, 1240, 2571],\n"," 174: [50, 356, 110, 1196, 47, 260, 457, 589, 1210, 150],\n"," 176: [296, 318, 1196, 260, 356, 50, 589, 527, 110, 1198],\n"," 177: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 178: [296, 50, 590, 480, 2028, 47, 2858, 1198, 858, 32],\n"," 179: [593, 318, 527, 47, 457, 2858, 356],\n"," 180: [2858, 2571, 1196, 318, 296, 593],\n"," 183: [296, 50, 356, 47, 457, 150, 608, 589, 2858, 32],\n"," 184: [593, 318, 356, 527, 1196, 260, 47, 457, 589, 2858],\n"," 188: [318, 1196, 260, 50, 1240, 1210, 2571, 47, 1198, 2028],\n"," 189: [1196, 318, 1210, 50, 356, 527, 2571, 1291, 1240, 480],\n"," 190: [],\n"," 193: [1196, 1214, 2571, 1198, 858, 32, 1210, 608],\n"," 199: [318, 1196, 260, 356, 50, 589, 527, 110, 1198, 480],\n"," 202: [593, 318, 296, 356, 50, 110, 1196, 260, 589, 457],\n"," 204: [1196, 1210, 1198, 593, 296, 2571, 318, 1291, 1240, 589],\n"," 206: [1198, 2858, 858, 110, 589, 260, 356, 318, 296],\n"," 210: [5952],\n"," 211: [318, 1210, 296, 356, 527, 2571, 1036, 590, 480, 588],\n"," 213: [318, 356, 110, 260, 1198, 858, 457, 2959, 1193, 590],\n"," 216: [],\n"," 221: [],\n"," 226: [],\n"," 228: [260],\n"," 229: [593, 296, 318, 541, 47, 50, 589, 2571, 527, 1196],\n"," 230: [2571],\n"," 231: [593, 318, 296, 47, 527, 608, 1196, 260, 110, 457],\n"," 232: [2571],\n"," 234: [593, 318, 296, 356, 110, 1196, 260, 589, 1193, 590],\n"," 235: [1240, 589, 1196],\n"," 236: [541, 858, 32, 2858, 1, 260, 1198, 1196, 1210],\n"," 237: [356, 589, 110, 318, 1, 1270, 541, 2028, 858, 480],\n"," 238: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 239: [260, 1210, 593, 2571, 1291, 1240, 589, 318, 296, 110],\n"," 243: [296, 593, 50, 318, 541, 32, 1, 527],\n"," 245: [2571, 1198, 2858, 1196, 1210, 110, 589, 527, 260, 356],\n"," 246: [260, 1198, 1196, 296, 593, 1221, 1213, 1193, 541, 2028],\n"," 248: [296, 593],\n"," 251: [318],\n"," 252: [1196, 1198, 296, 593, 318, 2571, 858, 110, 589, 356],\n"," 255: [296, 110, 527, 1196, 1210, 260, 457, 480, 589, 1270],\n"," 256: [296, 318, 50, 1196, 260, 457, 589, 527, 110, 1198],\n"," 257: [1196, 593, 1291, 1210, 296, 318, 589, 1197, 1136, 1270],\n"," 260: [2571, 1291, 50, 318],\n"," 261: [1196, 260, 593, 1210, 296, 318, 1198, 589, 541, 47],\n"," 267: [588, 1704, 1213],\n"," 268: [260],\n"," 270: [1196, 1198, 593, 2571, 296, 1240, 589, 1291, 356, 318],\n"," 272: [593, 318, 110, 1196, 260, 457, 1193, 590, 2028, 47],\n"," 273: [1196, 1198, 1214, 296],\n"," 276: [],\n"," 277: [318, 50, 1196, 260, 457, 527, 110, 589, 1198, 1210],\n"," 278: [593, 457, 356, 296, 318, 380, 588, 589, 110, 150],\n"," 281: [1291, 1240, 1198, 2571, 1196, 260],\n"," 287: [593, 318, 296, 527, 1196, 260, 589, 1210, 50, 1198],\n"," 290: [593, 318, 260, 296, 356, 110, 527, 1240, 1210, 150],\n"," 291: [296, 260, 318, 593],\n"," 293: [50, 356, 1196, 260, 527, 110, 589, 1198, 47, 1210],\n"," 296: [110, 1196, 260, 608, 1210, 1198, 1704, 1136, 588],\n"," 300: [457, 593],\n"," 301: [457, 590, 480, 110, 589],\n"," 307: [593, 318, 296, 527, 1291, 858, 457, 150, 50, 1197],\n"," 308: [593, 356, 296, 318, 380, 589, 110, 150, 1198, 1210],\n"," 309: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 311: [593, 318, 296, 110, 527, 1196, 1210, 260, 457, 589],\n"," 313: [318, 593],\n"," 314: [1214, 2571, 32, 50, 608],\n"," 315: [260],\n"," 317: [1196, 260, 50, 296, 593, 1214, 2858, 2571, 1198, 858],\n"," 321: [318, 1196, 260, 1210, 110, 527, 150, 1240, 2571, 1198],\n"," 324: [1196, 260, 1214, 1198, 858, 50, 608, 296, 593],\n"," 328: [593, 296, 1210, 110, 527, 457, 356, 47, 1, 1036],\n"," 329: [50, 296],\n"," 330: [1198, 356, 593],\n"," 331: [593, 296, 356, 110, 527, 1196, 47, 457, 260, 150],\n"," 332: [589, 1198, 1210, 2571, 1196, 527, 260],\n"," 333: [1196, 260, 457, 50, 589, 1198, 110, 1210, 480, 150],\n"," 335: [5952, 2571],\n"," 337: [296, 50, 356, 1196, 260, 457, 527, 110, 589, 1198],\n"," 338: [1196, 260, 50, 527, 589, 1210, 1198, 47, 608, 2571],\n"," 339: [50, 356, 527, 110, 1196, 47, 260, 608, 589, 1210],\n"," 341: [50, 356, 47, 608, 1210, 32, 480, 150, 1089, 111],\n"," 342: [593, 318, 296, 260, 356, 50, 1196, 110, 1198, 589],\n"," 343: [1196, 260, 296, 318, 1210, 1240, 2571, 527, 1198, 1],\n"," 346: [2571, 318, 296, 593],\n"," 349: [260],\n"," 353: [318, 260, 457, 527, 1198, 110, 1210, 480, 150, 608],\n"," 355: [527, 1196, 47, 260, 589, 608, 480, 1210, 1198, 588],\n"," 358: [541, 1198, 858],\n"," 362: [296, 47, 608, 457, 1089, 1617, 541, 2858, 32, 858],\n"," 363: [260, 593, 1210, 318, 1198, 2571, 50, 356, 589, 110],\n"," 364: [527, 1196, 47, 356, 260, 608, 589, 1210, 2571, 541],\n"," 369: [50, 356, 527, 110, 1196, 47, 260, 608, 589, 1210],\n"," 371: [593, 318, 50, 527, 356, 47, 260, 608, 110, 1210],\n"," 372: [260, 593, 1240, 296, 318, 356, 110, 858, 1270, 1214],\n"," 373: [593, 296, 50, 356, 110, 527, 1196, 47, 457, 260],\n"," 375: [480, 589, 457, 296, 593, 318],\n"," 376: [1196, 318, 527, 457, 356, 47, 50, 1, 1036, 380],\n"," 377: [],\n"," 378: [2571, 1198, 593, 1240, 589, 296, 1291, 110, 318, 1],\n"," 379: [1196, 593, 260, 318, 296, 1210, 50, 1198, 589, 2858],\n"," 382: [593, 1210, 260, 318, 608, 589, 1196, 110, 541, 47],\n"," 384: [593, 296, 527, 1196, 1210, 260, 589, 1270, 47, 2028],\n"," 388: [593, 318, 296, 1196, 260, 47, 527, 608, 1210, 110],\n"," 390: [1270, 1214, 1197, 541, 527, 1097, 1036, 47, 32],\n"," 391: [1196, 260, 1240, 1210, 2571, 527, 1036, 1200, 1214, 1291],\n"," 392: [260, 541, 608, 2571, 1, 1210, 1196],\n"," 395: [593, 296, 527, 356, 110, 47, 1196, 457, 589, 2858],\n"," 398: [1210, 2571, 1291, 1240, 589, 318, 296, 858, 1270, 1214],\n"," 400: [1196, 260, 1210, 1198, 296, 318, 480, 356, 3578, 4993],\n"," 403: [1196, 260, 1198, 318, 593],\n"," 404: [593, 1196, 260, 1240, 1210, 2571, 527, 1036, 1200, 1214],\n"," 412: [296, 50, 356, 260, 110, 527, 457, 1210, 47, 1240],\n"," 413: [318, 593],\n"," 415: [593],\n"," 417: [1196, 1210, 1198, 593, 1291, 1240, 589, 296, 858, 356],\n"," 420: [593, 318, 50, 110, 1196, 260, 589, 457, 1193, 2028],\n"," 421: [593, 318, 296, 110, 527, 1196, 260, 1210, 50, 1198],\n"," 422: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 425: [858, 296, 318, 593],\n"," 426: [],\n"," 428: [260, 589, 1210, 2571, 593, 1214, 296, 318],\n"," 437: [110, 32, 858, 1198, 589, 356],\n"," 439: [593, 318, 296, 110, 1196, 1210, 260, 150, 457, 589],\n"," 440: [],\n"," 441: [318, 2571, 1196, 527, 541, 608, 356, 1, 260, 1210],\n"," 443: [296, 356, 50, 1196, 527, 260, 110, 589, 1198, 150],\n"," 444: [593, 318, 110, 527, 260, 150, 2028, 50, 858],\n"," 445: [4993, 2571],\n"," 446: [1196, 260, 1210, 1240, 2571, 110, 527, 1198, 1, 1036],\n"," 447: [1196, 1198, 1291, 1240, 858, 356, 1200, 1214, 1270, 541],\n"," 448: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 449: [1198, 318, 296, 1291, 110, 1240, 858, 457, 1270, 1214],\n"," 453: [593, 318, 50, 527, 356, 1196, 260, 608, 110, 1210],\n"," 455: [1196, 2571, 1198, 593, 356, 1240, 589, 296, 1291, 110],\n"," 458: [2571, 1196, 318, 296, 593],\n"," 459: [318, 457, 50, 1198, 608, 858, 1213, 111, 1617, 588],\n"," 460: [318, 1196, 356, 260, 50, 527, 589, 1198, 1210, 480],\n"," 461: [593, 318, 50, 1196, 541, 1198, 589, 527, 110, 47],\n"," 462: [296, 356, 527, 150, 480, 589, 110, 1196, 858, 380],\n"," 463: [],\n"," 465: [],\n"," 466: [1196, 260, 593, 2571, 1198, 1240, 296, 356, 318, 110],\n"," 467: [2571, 1291, 1198, 1196, 260, 593],\n"," 471: [588],\n"," 472: [],\n"," 474: [1198, 296, 318, 3578, 4993, 5952, 2959, 2762, 2028, 1291],\n"," 475: [2858, 2571, 296, 593],\n"," 479: [541, 47, 589, 260, 1210, 1196],\n"," 480: [1036, 318],\n"," 484: [296, 593, 50, 318, 541, 858, 2858, 1198, 527, 1196],\n"," 485: [110, 1210, 150, 47, 1704, 1036, 590, 2028, 1136, 1270],\n"," 487: [593, 110, 527, 1196, 1210, 260, 150, 457, 480, 589],\n"," 488: [1196, 260, 296, 318, 1210, 356, 1240, 2571, 110, 527],\n"," 489: [296, 593],\n"," 491: [296, 593, 1198, 1193, 1214, 541, 2028, 608, 1210, 50],\n"," 492: [4993, 2571],\n"," 494: [32, 2571, 1196],\n"," 496: [318, 50, 527, 356, 1196, 110, 260, 608, 2858, 32],\n"," 497: [296, 541, 47, 608, 50, 589, 1, 260, 110],\n"," 499: [593, 318, 50, 356, 527, 457, 110, 1196, 47, 589],\n"," 500: [2858, 50],\n"," 501: [318, 296, 589, 50, 527, 590],\n"," 502: [2571, 1196, 318, 296, 593],\n"," 508: [260, 1210, 1198, 593, 2571, 1291, 1240, 589, 318, 296],\n"," 510: [50, 110, 1196, 260, 589, 1193, 2028, 47, 2858, 1198],\n"," 518: [1198, 2858, 858, 110, 356, 296],\n"," 520: [593, 296, 50, 356, 110, 527, 1196, 47, 457, 260],\n"," 521: [296, 527, 47, 110, 1196, 457, 260, 589, 150, 2858],\n"," 525: [1196, 260, 356, 50, 589, 527, 1198, 480, 1210, 47],\n"," 526: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 527: [1196, 260, 593, 1198, 2571, 296, 589, 110, 1240, 318],\n"," 529: [593, 318, 47, 608, 50, 589, 2571, 1, 1196, 110],\n"," 532: [110, 527, 593, 318],\n"," 534: [2571],\n"," 536: [1196, 1198, 2571, 1240, 589, 1291, 356, 318, 110, 858],\n"," 537: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 539: [356, 110, 1196, 260, 457, 608, 2571, 541, 1193, 2028],\n"," 544: [593, 318, 32, 589, 110, 356, 527],\n"," 547: [318, 296, 50, 1196, 260, 1193, 2028, 47, 2858, 1198],\n"," 548: [593, 50, 356, 2571, 110, 589, 527, 608, 47, 1240],\n"," 549: [356, 50, 1196, 527, 260, 110, 589, 1198, 47, 480],\n"," 551: [318, 296, 356, 50, 110, 589, 457, 1193, 47, 2858],\n"," 553: [541, 858, 2858, 1, 260, 1198, 527, 1196, 1210],\n"," 557: [1198, 1196, 296, 593, 1221, 1213, 1193, 1214, 541, 2028],\n"," 559: [1196, 260, 2571, 1198, 593, 356, 1240, 589, 296, 1291],\n"," 563: [1196, 1210, 1198, 593, 296, 2571, 318, 1291, 1240, 589],\n"," 564: [593, 110, 527, 457, 590, 588, 47, 2028, 1, 2571],\n"," 569: [318, 1196, 260, 356, 589, 527, 110, 1198, 150, 480],\n"," 570: [296, 593, 50, 858, 541, 32, 2858, 1, 1198, 1196],\n"," 573: [150, 110, 296],\n"," 574: [356, 318, 110, 150, 1198, 1210, 1, 2571, 1196, 260],\n"," 576: [150, 356, 593],\n"," 579: [1617, 2028, 858, 608],\n"," 584: [296, 593, 318],\n"," 586: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 587: [1198, 2571, 1240, 1291, 50, 858, 1270, 1214, 1200, 541],\n"," 589: [318, 2571],\n"," 590: [],\n"," 593: [1196, 260, 527, 1240, 2571, 1210, 608, 32, 1198, 1036],\n"," 594: [1196, 1198, 593, 2571, 296, 356, 1240, 589, 318, 1291],\n"," 595: [1210, 589, 318, 3578, 4993, 5952, 2959, 1200, 2028, 47],\n"," 596: [593, 296, 50, 110, 47, 457, 150, 589, 1210, 2571],\n"," 600: [1196, 260, 1214, 2571, 589, 593],\n"," 603: [593, 50, 260, 457, 1193, 47, 150, 1198, 32],\n"," 605: [593, 2571, 457, 2858, 858, 2028, 1270, 1704, 1213, 590],\n"," 606: [2858, 527, 356, 593, 318, 296],\n"," 607: [1196, 1210, 2571, 296, 1291, 1240, 589, 356, 1214, 541],\n"," 609: [1196, 260, 608, 2858, 858, 1198, 2571, 1089, 1617, 541],\n"," 613: [260, 1210, 1198, 593, 2571, 1291, 1240, 589, 318, 296],\n"," 614: [593, 318, 589, 296, 356, 1210, 150, 527, 2571, 50],\n"," 616: [1196],\n"," 619: [593, 1196, 296, 318, 2571, 356, 527, 1036, 380, 1291],\n"," 625: [318, 356, 296, 150, 480, 110, 50, 527, 380, 588],\n"," 628: [593, 296, 356, 50, 110, 527, 457, 1196, 47, 589],\n"," 632: [318, 296],\n"," 637: [593, 318, 296, 356, 1196, 527, 1210, 260, 457, 50],\n"," 640: [260, 593, 1200, 1036, 1240, 296],\n"," 641: [260, 1196, 1210, 1240, 2571, 110, 527, 356, 47, 50],\n"," 642: [],\n"," 646: [364, 110, 318],\n"," 649: [593, 318, 50, 527, 1196, 47, 260, 608, 110, 589],\n"," 653: [858, 296, 318, 593],\n"," 654: [1196, 527, 1210, 260, 50, 589, 2028, 47, 1198, 2571],\n"," 655: [110, 527, 593],\n"," 656: [1291, 1240, 1198, 589, 2571, 1196, 260],\n"," 660: [858, 1, 260, 1198, 1196, 1210],\n"," 661: [260],\n"," 662: [1200, 1240, 2571, 1198, 589],\n"," 663: [1291, 1240, 1198, 589, 2571, 1196, 260],\n"," 666: [260, 1210, 593, 1291, 1240, 589, 318, 356, 110, 1270],\n"," 667: [1210, 1036, 1214, 2571, 593, 296],\n"," 668: [593, 318, 50, 1196, 260, 527, 356, 47, 608, 110],\n"," 669: [1196, 260, 527, 1210, 2571, 1240, 50, 1198, 1, 47],\n"," 672: [318],\n"," 675: [50, 527, 110, 1196, 260, 457, 1210, 47, 608, 2858],\n"," 677: [260],\n"," 679: [1198, 2571],\n"," 684: [588],\n"," 685: [1196, 593, 296, 1198, 2571, 318, 356, 50, 1240, 589],\n"," 686: [],\n"," 690: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 696: [2571, 1196, 318, 296, 593],\n"," 699: [593, 296, 527, 356, 47, 110, 1196, 608, 457, 260],\n"," 701: [593, 318, 356, 296, 150, 480, 589, 110, 50, 527],\n"," 704: [],\n"," 705: [260, 1196, 1198, 296, 589, 1210, 318, 593, 2571, 1200],\n"," 707: [318, 1617, 2571, 858, 50, 1198, 608, 1196, 260, 356],\n"," 708: [593, 1196, 1210, 296, 318, 457, 356, 110, 527, 1036],\n"," 709: [593, 318, 1210, 296, 356, 480, 110, 50, 527, 150],\n"," 710: [593],\n"," 711: [593, 318, 50, 527, 356, 1196, 47, 260, 608, 110],\n"," 712: [593, 50, 541, 858, 32, 2858, 1, 260, 1198, 1196],\n"," 715: [593, 50, 318, 32, 2571, 589, 110, 1196, 356, 527],\n"," 720: [50, 110, 1196, 260, 589, 1193, 2028, 2858, 1198, 858],\n"," 723: [593, 296, 1196, 260, 1210, 150, 457, 480, 1198, 858],\n"," 724: [296, 593, 50, 318, 541, 858, 2858, 1198, 527, 1196],\n"," 726: [296, 318, 260, 50, 589, 150, 480, 47, 2571, 608],\n"," 727: [608, 1196, 260, 2571, 589, 588, 1089, 1617, 541, 2858],\n"," 728: [260, 593, 1198, 318, 1291, 356, 110, 527, 1200, 1214],\n"," 729: [480, 589, 457, 296, 593, 318],\n"," 730: [593, 50, 356, 110, 527, 1196, 260, 47, 457, 1210],\n"," 733: [593, 1196, 260, 1240, 1210, 296, 318, 457, 356, 110],\n"," 735: [318, 527, 608, 1196, 260, 541, 589, 2571, 1089, 1617],\n"," 737: [593, 296, 318, 541, 47, 608, 50, 589, 2571, 527],\n"," 739: [296, 318, 1196, 260, 527, 1198, 1210, 150, 1240, 32],\n"," 740: [296, 50, 356, 527, 47, 457, 589, 150, 2858, 32],\n"," 741: [260, 1210, 1198, 3578, 4993, 5952, 2959, 1200, 1214, 1240],\n"," 742: [593, 527],\n"," 743: [593, 457, 318, 589, 110, 150, 1198, 1210, 2571, 1196],\n"," 749: [1210, 260, 1198, 608, 480, 32, 1196, 356, 589, 110],\n"," 750: [296, 1196, 260, 318, 593, 1198, 2762, 1617, 2028, 2571],\n"," 751: [296, 318, 1196, 260, 457, 50, 356, 589, 527, 1198],\n"," 753: [1196, 1210, 296, 2571, 1291, 50, 356, 527, 858, 110],\n"," 758: [318, 356, 296, 1196, 527, 1210, 260, 457, 50, 589],\n"," 759: [593, 296, 318, 47, 356, 608, 50, 527, 1],\n"," 761: [1196, 260, 1240],\n"," 762: [1196, 1210, 1198, 593, 296, 2571, 318, 1291, 1240, 589],\n"," 763: [1196, 1210, 1198, 2571, 1291, 1240, 589, 858, 356, 318],\n"," 766: [593, 296, 527, 150, 457, 589, 480, 858, 590, 588],\n"," 773: [318, 356, 527, 457, 50, 589, 590, 150, 2028, 47],\n"," 776: [296, 318, 2959, 2762, 1617, 2571, 608, 1196, 527],\n"," 777: [593, 296, 318, 541, 47, 2571, 527, 1, 1210, 1196],\n"," 785: [1196, 260, 150, 608, 1210, 2858, 32, 1198, 2571, 1704],\n"," 786: [589, 1200, 1214, 593, 296],\n"," 787: [1196, 260, 593],\n"," 792: [593, 356, 527, 110, 47, 457, 1196, 608, 260, 589],\n"," 793: [1198, 608, 480, 32, 356, 589, 296, 318, 593],\n"," 794: [1196, 260, 1198, 1210, 2571, 356, 593],\n"," 797: [110, 527, 1196, 1210, 260, 480, 590, 1270, 47, 2028],\n"," 799: [50, 1196, 260, 1198, 2571, 1291, 1240, 1193, 2028, 47],\n"," 804: [318, 356, 296, 1196, 527, 260, 457, 50, 589, 590],\n"," 809: [593, 318, 296, 1210, 150, 457, 480, 589, 588, 2028],\n"," 812: [1196, 1198, 2571],\n"," 814: [356, 1196, 1210, 260, 457, 50, 589, 590, 480, 588],\n"," 815: [318],\n"," 816: [1196, 260, 50, 356, 589, 527, 1198, 110, 1210, 480],\n"," 821: [1196, 260, 593],\n"," 822: [593, 1196, 1210, 260, 588, 1270, 47, 2028, 1198, 2571],\n"," 825: [318],\n"," 826: [110, 527, 150, 590, 588, 1198],\n"," 832: [593, 296, 318, 2571, 356, 457, 110, 527, 1198, 1036],\n"," 833: [1196, 1291, 1210, 1036, 110, 50, 457],\n"," 835: [1196, 1214, 2571, 1198, 858, 32, 1210, 608],\n"," 840: [],\n"," 842: [1210, 260, 1198, 608, 480, 32, 1196, 356, 589, 296],\n"," 851: [593],\n"," 852: [1196, 260, 1240, 1210, 2571, 110, 527, 1036, 1200, 1214],\n"," 854: [1196, 260, 1200, 1240, 541, 2571, 1198, 589, 1210],\n"," 855: [541, 858, 32, 2858, 1, 260, 1198, 527, 1196, 1210],\n"," 856: [593, 457, 356, 296, 318, 588, 110, 150, 1198, 1210],\n"," 857: [260, 296, 318, 1210, 1198, 50, 457, 527, 1291, 150],\n"," 859: [318, 2858],\n"," 861: [1196, 260, 1198, 318, 593],\n"," 863: [1196, 260, 457, 541, 858, 1198],\n"," 864: [296, 318, 593, 2571, 1196, 1617, 2028, 858, 50, 1198],\n"," 865: [1198, 1291, 589, 1240, 527, 1270, 1214, 1200, 541, 858],\n"," 873: [593, 296, 318, 541, 47, 608, 50, 527, 1, 260],\n"," 878: [1210, 2571, 1291, 356, 110, 1270, 2028, 2858, 1, 2762],\n"," 879: [296, 318, 1196, 260, 50, 457, 356, 527, 589, 110],\n"," 888: [4993, 2571],\n"," 891: [593],\n"," 901: [1196, 260, 1210, 593, 1198, 1240, 296, 1291, 541, 3578],\n"," 903: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 904: [593, 110, 1196, 260, 150, 588, 1270, 47, 2028, 1198],\n"," 909: [1196, 260, 1210, 593, 589, 296, 318, 3578, 1200, 1214],\n"," 910: [260, 1198, 1196, 1221, 1193, 541, 2028, 608, 2571, 1210],\n"," 916: [2571, 5952, 2858, 296, 593],\n"," 920: [1196, 1210, 1198, 589, 2571, 1291, 296, 318, 1214, 356],\n"," 925: [50, 527, 1196, 47, 457, 589, 150, 608, 1210, 2571],\n"," 927: [593, 318, 296, 110, 527, 1196, 260, 150, 457, 589],\n"," 932: [1291, 1240, 1198, 2571, 1196],\n"," 935: [1210, 593],\n"," 938: [50, 1196, 260, 356, 589, 527, 110, 1198, 480, 1210],\n"," 939: [296, 356, 50, 1196, 527, 110, 589, 1198, 47, 480],\n"," 940: [593, 318, 296, 150, 480, 589, 527, 5952, 380, 588],\n"," 942: [1196, 260, 356, 50, 589, 527, 110, 1198, 150, 480],\n"," 946: [296, 318],\n"," 947: [593, 296, 318, 3578, 4993, 5952, 2959, 2762, 1200, 1214],\n"," 948: [],\n"," 951: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 955: [593, 318, 296, 356, 50, 110, 1196, 260, 589, 457],\n"," 958: [2858, 1196, 296, 593],\n"," 960: [50, 318, 32, 2571, 110, 1196],\n"," 961: [296, 593, 858, 50, 318],\n"," 966: [],\n"," 967: [1196, 527, 260, 858, 541, 608, 50, 589, 1, 1210],\n"," 968: [356, 50, 527, 1196, 260, 589, 47, 1210, 1198, 608],\n"," 970: [1196, 260, 457, 356, 589, 527, 1198, 1210, 480, 47],\n"," 972: [],\n"," 975: [593, 318, 527, 47, 1196, 260, 110, 541, 457, 858],\n"," 976: [593, 541, 47, 608, 50, 589, 2571, 527, 260, 1210],\n"," 980: [1196, 1198, 318, 2571, 589, 1240, 110, 1291, 527, 1],\n"," 982: [1196, 1210, 1198, 593, 2571, 296, 1291, 1240, 589, 356],\n"," 985: [2571],\n"," 988: [],\n"," 989: [593, 318, 356, 47, 110, 1210, 589, 457, 480, 150],\n"," 990: [47, 110, 1196, 260, 608, 2858, 32, 858, 1198, 2571],\n"," 992: [2858, 296],\n"," 994: [593, 296, 50, 356, 110, 527, 1196, 47, 457, 260],\n"," 1001: [593, 318, 296, 527, 1196, 1210, 260, 50, 1198, 1],\n"," 1004: [527],\n"," 1005: [],\n"," 1008: [],\n"," 1009: [1196, 260, 1210, 593, 1198, 589, 296, 3578, 4993, 5952],\n"," 1011: [318, 2571, 47, 527, 110],\n"," 1012: [296, 318, 2959, 2762, 1617, 2028, 2571, 858, 50, 1198],\n"," 1013: [1240, 32, 47, 608, 150, 1036, 1200, 380, 1214, 1291],\n"," 1014: [50, 318, 32, 1, 1198, 1196, 1210],\n"," 1015: [50, 296, 593],\n"," 1016: [593, 541, 47, 50, 589, 2571, 1, 1210, 1196, 110],\n"," 1017: [260, 2571, 1198, 593, 296, 356, 1240, 589, 1291, 110],\n"," 1021: [588],\n"," 1022: [1196, 260, 593],\n"," 1023: [318, 356, 296, 1210, 457, 50, 589, 590, 588, 150],\n"," 1025: [593, 296, 50, 356, 527, 47, 150, 32, 1704, 590],\n"," 1030: [1196, 260, 296, 318],\n"," 1031: [318, 356, 296, 1196, 457, 527, 1210, 260, 589, 50],\n"," 1034: [2571, 1198, 2858, 858, 1196, 1210, 110, 589, 527, 260],\n"," 1037: [318, 527, 608, 1196, 260, 110, 457, 1089, 1617, 541],\n"," 1039: [2858, 32, 2571, 589, 110, 1196],\n"," 1041: [608, 480, 32, 356],\n"," 1044: [1196, 2571, 1198, 593, 356, 1240, 589, 296, 1291, 110],\n"," 1046: [318, 1196, 260, 527, 110, 50, 589, 1198, 1210, 2571],\n"," 1048: [1617, 858, 608, 527, 260, 356],\n"," 1053: [593, 318, 296, 527, 457, 50, 590, 480, 588, 150]})"]},"metadata":{},"execution_count":15}],"source":["# 어소시에이션 규칙을 사용해 각 사용자가 아직 평가하지 않은 영화 10편을 추천한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," # 사용자가 최근 5편의 영화를 얻는다\n"," input_data = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n"," # 그 영화들이 조건부에 1편이라도 포함되어 있는 어소시에이션 규칙을 추출한다\n"," matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n","\n"," # 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장하고, 출편 빈도 순으로 배열하고, 사용자가 아직 평가하지 않았다면 추천 리스트에 추가한다\n"," consequent_movies = []\n"," for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows():\n"," consequent_movies.extend(row[\"consequents\"])\n"," # 출현 빈도를 센다\n"," counter = Counter(consequent_movies)\n"," for movie_id, movie_cnt in counter.most_common():\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," # 추천 리스트가 10편이 되면 종료한다\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","# 각 사용자에 대한 추천 리스트\n","pred_user2items"]},{"cell_type":"code","execution_count":16,"id":"7342fdc7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"7342fdc7","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":15,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c194befc-f9ec-4450-c916-02791f71155a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":16}],"source":["# user_id=2인 사용자 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":17,"id":"3034b39c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"3034b39c","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"7fc872ce-8f9b-41d8-c0bb-a55dfdcf86fa"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","587 593 Silence of the Lambs, The (1991) \n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n","\n"," genre \\\n","587 [Crime, Horror, Thriller] \n","1171 [Action, Adventure, Sci-Fi] \n","1173 [Action, Adventure] \n","\n"," tag \n","587 [based on a book, anthony hopkins, demme, psyc... \n","1171 [lucas, george lucas, george lucas, gfei own i... \n","1173 [egypt, lucas, seen more than once, dvd collec... "],"text/html":["\n","
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
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\n"," "]},"metadata":{},"execution_count":17}],"source":["# user_id=2에 대한 추천(1196, 593, 1198)\n","movies[movies.movie_id.isin([1196, 593, 1198])]"]},{"cell_type":"code","execution_count":18,"id":"9b22d1f6","metadata":{"id":"9b22d1f6","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# apriori(user_movie_matrix, min_support=0.1, use_colnames=True)\n","# association_rules(freq_movies, metric='lift', min_threshold=1)\n","# min_support와 min_threshold가 중요한 파라미터가 되므로 바꾸어 가면서 시험해봅니다."]},{"cell_type":"code","execution_count":18,"id":"64ae2120","metadata":{"id":"64ae2120","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/BPR.ipynb b/chapter5/colab/BPR.ipynb index f3e7258..d673ac6 100644 --- a/chapter5/colab/BPR.ipynb +++ b/chapter5/colab/BPR.ipynb @@ -1,1682 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4b7f67c6", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/BPR.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "1dd49045", - "metadata": {}, - "source": [ - "# Bayesian Personalized Ranking(BPR)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e483d5c3", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9e2d716c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "65728cf5", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 10\n", - "# 평가 수의 임곗값\n", - "minimum_num_rating = 0\n", - "# 에폭 수\n", - "n_epochs = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "959a4009", - "metadata": {}, - "outputs": [], - "source": [ - "# 행렬 분석용으로 행렬을 작성한다\n", - "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", - " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", - ")\n", - "\n", - "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", - "\n", - "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", - "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", - "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", - "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", - "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "072a29db", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.sparse import lil_matrix\n", - "# 희소 행렬을 초기화하고 각 셀에 값을 넣는다\n", - "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", - "for i, row in movielens_train_high_rating.iterrows():\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " movielens_matrix[movie_index, user_index] = 1.0\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6c90bc6", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install implicit==0.4.4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25e71312", - "metadata": {}, - "outputs": [], - "source": [ - "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6c0cf1a4", - "metadata": {}, - "outputs": [], - "source": [ - "import implicit\n", - "\n", - "# 모델 초기화\n", - "model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cee6bdcb", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "70b845e6f3eb4cf0b609c81347d747f4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n", - "" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4457d3f0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
293296Pulp Fiction (1994)[Comedy, Crime, Drama][quotable, samuel l. jackson, quentin tarantin...
352356Forrest Gump (1994)[Comedy, Drama, Romance, War][psychology, tom hanks, seen more than once, l...
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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" - ], - "text/plain": [ - " movie_id title \\\n", - "293 296 Pulp Fiction (1994) \n", - "352 356 Forrest Gump (1994) \n", - "587 593 Silence of the Lambs, The (1991) \n", - "\n", - " genre \\\n", - "293 [Comedy, Crime, Drama] \n", - "352 [Comedy, Drama, Romance, War] \n", - "587 [Crime, Horror, Thriller] \n", - "\n", - " tag \n", - "293 [quotable, samuel l. jackson, quentin tarantin... \n", - "352 [psychology, tom hanks, seen more than once, l... \n", - "587 [based on a book, anthony hopkins, demme, psyc... " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(593, 356, 296)\n", - "movies[movies.movie_id.isin([593, 356, 296])]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"4b7f67c6","metadata":{"id":"4b7f67c6"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/BPR.ipynb)"]},{"cell_type":"markdown","id":"1dd49045","metadata":{"id":"1dd49045"},"source":["# Bayesian Personalized Ranking(BPR)"]},{"cell_type":"code","execution_count":1,"id":"e483d5c3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e483d5c3","executionInfo":{"status":"ok","timestamp":1672116735554,"user_tz":-540,"elapsed":6236,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"21abcbdb-9e9a-4754-b011-d451a8a5c819"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:52:06-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 21.2MB/s in 3.0s \n","\n","2022-12-27 04:52:10 (21.2 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"9e2d716c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9e2d716c","executionInfo":{"status":"ok","timestamp":1672116794339,"user_tz":-540,"elapsed":58791,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"af9f8155-e2a1-407d-830c-5efe4794f0a2"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"65728cf5","metadata":{"id":"65728cf5","executionInfo":{"status":"ok","timestamp":1672116794339,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 10\n","# 평가 수의 임곗값\n","minimum_num_rating = 0\n","# 에폭 수\n","n_epochs = 50"]},{"cell_type":"code","execution_count":4,"id":"959a4009","metadata":{"id":"959a4009","executionInfo":{"status":"ok","timestamp":1672116794861,"user_tz":-540,"elapsed":539,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 행렬 분석용으로 행렬을 작성한다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n","\n","# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n","unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n","unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))"]},{"cell_type":"code","execution_count":5,"id":"072a29db","metadata":{"id":"072a29db","executionInfo":{"status":"ok","timestamp":1672116798146,"user_tz":-540,"elapsed":3287,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse import lil_matrix\n","# 희소 행렬을 초기화하고 각 셀에 값을 넣는다\n","movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n","for i, row in movielens_train_high_rating.iterrows():\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," movielens_matrix[movie_index, user_index] = 1.0\n"]},{"cell_type":"code","execution_count":6,"id":"b6c90bc6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b6c90bc6","executionInfo":{"status":"ok","timestamp":1672116874351,"user_tz":-540,"elapsed":76226,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c94b6b49-5769-465c-ea87-fc27280963d5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting implicit==0.4.4\n"," Downloading implicit-0.4.4.tar.gz (1.1 MB)\n","\u001b[K |████████████████████████████████| 1.1 MB 11.7 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n","Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n","Building wheels for collected packages: implicit\n"," Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825514 sha256=7d630d694700acd323d4158a90d015d03f6613575de9846072360a19481a67e8\n"," Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n","Successfully built implicit\n","Installing collected packages: implicit\n","Successfully installed implicit-0.4.4\n"]}],"source":["!pip install implicit==0.4.4"]},{"cell_type":"code","execution_count":7,"id":"25e71312","metadata":{"id":"25e71312","executionInfo":{"status":"ok","timestamp":1672116874352,"user_tz":-540,"elapsed":7,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다"]},{"cell_type":"code","execution_count":8,"id":"6c0cf1a4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6c0cf1a4","executionInfo":{"status":"ok","timestamp":1672116874906,"user_tz":-540,"elapsed":6,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"bf46043c-1d89-4e29-aa41-b9429e68b845"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:implicit:GPU training requires factor size to be a multiple of 32 - 1. Increasing factors from 10 to 31.\n"]}],"source":["import implicit\n","\n","# 모델 초기화\n","model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs)"]},{"cell_type":"code","execution_count":9,"id":"cee6bdcb","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["0da9107a2135419b80d81eeef564292f","ddb3a89fe8634c0fa69705137e88db1d","b5c338dd0da64c47b4184538c7780b19","2700ce5424694283b893b686f7467a85","9e92efe91d6a41c4b20c220c43f1ae26","f9a7d7da750a411fa3b1c90b31a0197f","e97a61c4370c435ea93ae76ad616b02d","9a4aa4972d13480f8734832345fdea59","a78a46d01434404288168d8402a317d5","ebdc7cd012c14d028350d7a39daa0bc2","0318cc47c7ff4fad82354bce4fedb82f"]},"id":"cee6bdcb","executionInfo":{"status":"ok","timestamp":1672116876849,"user_tz":-540,"elapsed":1947,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e7ac20c7-47a4-4941-e069-6d76794e9dcb"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/50 [00:00\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
293296Pulp Fiction (1994)[Comedy, Crime, Drama][quotable, samuel l. jackson, quentin tarantin...
352356Forrest Gump (1994)[Comedy, Drama, Romance, War][psychology, tom hanks, seen more than once, l...
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(593, 356, 296)\n","movies[movies.movie_id.isin([593, 356, 296])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 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\ No newline at end of file diff --git a/chapter5/colab/FM.ipynb b/chapter5/colab/FM.ipynb index 312befd..1a3e269 100644 --- a/chapter5/colab/FM.ipynb +++ b/chapter5/colab/FM.ipynb @@ -1,2060 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "44c20b2f", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/FM.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "975827da", - "metadata": {}, - "source": [ - "# Factorization Machiens" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81944a53", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c4cf1864", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d05d29df", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 10\n", - "# 평가 수의 임곗값\n", - "minimum_num_rating = 200\n", - "# 에폭 수\n", - "n_epochs = 50\n", - "# 학습률\n", - "lr = 0.01\n", - "# 초기 정보 사용\n", - "use_side_information = False" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "eb06c38c", - "metadata": {}, - "outputs": [], - "source": [ - "# 평갓값이 minimum_num_rating건 이상인 영화를 필터링한다\n", - "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", - " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", - ")\n", - "\n", - "# 사용자가 평가한 영화\n", - "user_evaluated_movies = (\n", - " filtered_movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e863d3f4", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from sklearn.feature_extraction import DictVectorizer\n", - "\n", - "# FM용으로 데이터를 정형한다\n", - "train_data_for_fm = []\n", - "y = []\n", - "for i, row in filtered_movielens_train.iterrows():\n", - " x = {\"user_id\": str(row[\"user_id\"]), \"movie_id\": str(row[\"movie_id\"])}\n", - " if use_side_information:\n", - " x[\"tag\"] = row[\"tag\"]\n", - " x[\"user_rating_avg\"] = np.mean(user_evaluated_movies[row[\"user_id\"]])\n", - " train_data_for_fm.append(x)\n", - " y.append(row[\"rating\"])\n", - "\n", - "y = np.array(y)\n", - "\n", - "vectorizer = DictVectorizer()\n", - "X = vectorizer.fit_transform(train_data_for_fm).toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a88d5ec", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install xlearn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "511e8f9f", - "metadata": {}, - "outputs": [], - "source": [ - "# colab에서 xlearn을 동작시키기 위한 설정\n", - "# https://github.com/aksnzhy/xlearn/issues/74#issuecomment-580701773\n", - "import os\n", - "os.environ['USER'] = 'test'" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e7208e9a", - "metadata": {}, - "outputs": [], - "source": [ - "import xlearn as xl\n", - "\n", - "# FM 모델 초기화\n", - "fm_model = xl.FMModel(task=\"reg\", metric=\"rmse\", lr=lr, opt=\"sgd\", k=factors, epoch=n_epochs)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9b99ec6a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m----------------------------------------------------------------------------------------------\n", - " _\n", - " | |\n", - " __ _| | ___ __ _ _ __ _ __\n", - " \\ \\/ / | / _ \\/ _` | '__| '_ \\ \n", - " > <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable early-stopping.\u001b[0m\n", - "\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable (-x rmse) option.\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mxLearn uses 4 threads for training task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] 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"\u001b[32m[------------] \u001b[0mTime cost for saving model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to save txt model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mTXT Model file: /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpl21ck8hl\n", - "\u001b[32m[------------] \u001b[0mTime cost for saving txt model: 0.01 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Finish training\u001b[0m\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 0.33 (sec)\u001b[0m\n" - ] - } - ], - "source": [ - "# FM 모델 학습\n", - "fm_model.fit(X, y, is_lock_free=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3d4a06ff", - "metadata": {}, - "outputs": [], - "source": [ - "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", - "unique_user_ids = sorted(filtered_movielens_train.user_id.unique())\n", - "unique_movie_ids = sorted(filtered_movielens_train.movie_id.unique())\n", - "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", - "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "3cd06940", - "metadata": {}, - "outputs": [], - "source": [ - "# 예측용 데이터를 준비한다\n", - "test_data_for_fm = []\n", - "for user_id in unique_user_ids:\n", - " for movie_id in unique_movie_ids:\n", - " x = {\"user_id\": str(user_id), \"movie_id\": str(movie_id)}\n", - " if use_side_information:\n", - " tag = dataset.item_content[dataset.item_content.movie_id == movie_id].tag.tolist()[0]\n", - " x[\"tag\"] = tag\n", - " x[\"user_rating_avg\"] = np.mean(user_evaluated_movies[row[\"user_id\"]])\n", - " test_data_for_fm.append(x)\n", - "\n", - "X_test = vectorizer.transform(test_data_for_fm).toarray()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "8ed3df1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m----------------------------------------------------------------------------------------------\n", - " _\n", - " | |\n", - " __ _| | ___ __ _ _ __ _ __\n", - " \\ \\/ / | / _ \\/ _` | '__| '_ \\ \n", - " > <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[32m[------------] \u001b[0mxLearn uses 8 threads for prediction task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Load model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mLoad model from /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpvpvqm4eg\n", - "\u001b[32m[------------] \u001b[0mLoss function: squared\n", - "\u001b[32m[------------] \u001b[0mScore function: fm\n", - "\u001b[32m[------------] \u001b[0mNumber of Feature: 1061\n", - "\u001b[32m[------------] \u001b[0mNumber of K: 10\n", - "\u001b[32m[------------] \u001b[0mTime cost for loading model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] \u001b[0mBinary file (/var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmphoetn82z.bin) NOT found. Convert text file to binary file.\n", - "\u001b[32m[------------] \u001b[0mTime cost for reading problem: 0.07 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to predict ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mThe test loss is: 6.944547\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 0.12 (sec)\u001b[0m\n" - ] - } - ], - "source": [ - "# 예측한다\n", - "y_pred = fm_model.predict(X_test)\n", - "pred_matrix = y_pred.reshape(len(unique_user_ids), len(unique_movie_ids))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f37b05f0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rankrating_pred
011225.0838985046Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...1.03.574791
52171223.0844429650Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...5.03.574791
338921224.0850079961Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...5.03.574791
46591853.0838984807Net, The (1995)[Action, Crime, Thriller][computers, computers, internet, irwin winkler...1.03.574791
47621855.0834874598Net, The (1995)[Action, Crime, Thriller][computers, computers, internet, irwin winkler...5.03.574791
..............................
13265784950974.01013470449Bright Eyes (1934)[Comedy, Drama]NaN2.03.574791
1326609516384.0849628182Jack and Sarah (1995)[Romance][babies, ian mckellen, baby, british]5.03.574791
132717100513845.0897254984Substance of Fire, The (1996)[Drama]NaN1.03.574791
132731934561.0945901052Kids of the Round Table (1995)[Adventure, Children, Fantasy]NaN5.03.574791
1328041018649973.01230668562War of the Worlds (2005)[Action]NaN1.03.574791
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5000 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp title \\\n", - "0 1 122 5.0 838985046 Boomerang (1992) \n", - "5 217 122 3.0 844429650 Boomerang (1992) \n", - "33 892 122 4.0 850079961 Boomerang (1992) \n", - "46 59 185 3.0 838984807 Net, The (1995) \n", - "47 62 185 5.0 834874598 Net, The (1995) \n", - "... ... ... ... ... ... \n", - "132657 849 5097 4.0 1013470449 Bright Eyes (1934) \n", - "132660 951 638 4.0 849628182 Jack and Sarah (1995) \n", - "132717 1005 1384 5.0 897254984 Substance of Fire, The (1996) \n", - "132731 934 56 1.0 945901052 Kids of the Round Table (1995) \n", - "132804 1018 64997 3.0 1230668562 War of the Worlds (2005) \n", - "\n", - " genre \\\n", - "0 [Comedy, Romance] \n", - "5 [Comedy, Romance] \n", - "33 [Comedy, Romance] \n", - "46 [Action, Crime, Thriller] \n", - "47 [Action, Crime, Thriller] \n", - "... ... \n", - "132657 [Comedy, Drama] \n", - "132660 [Romance] \n", - "132717 [Drama] \n", - "132731 [Adventure, Children, Fantasy] \n", - "132804 [Action] \n", - "\n", - " tag timestamp_rank \\\n", - "0 [dating, nudity (topless - brief), can't remem... 1.0 \n", - "5 [dating, nudity (topless - brief), can't remem... 5.0 \n", - "33 [dating, nudity (topless - brief), can't remem... 5.0 \n", - "46 [computers, computers, internet, irwin winkler... 1.0 \n", - "47 [computers, computers, internet, irwin winkler... 5.0 \n", - "... ... ... \n", - "132657 NaN 2.0 \n", - "132660 [babies, ian mckellen, baby, british] 5.0 \n", - "132717 NaN 1.0 \n", - "132731 NaN 5.0 \n", - "132804 NaN 1.0 \n", - "\n", - " rating_pred \n", - "0 3.574791 \n", - "5 3.574791 \n", - "33 3.574791 \n", - "46 3.574791 \n", - "47 3.574791 \n", - "... ... \n", - "132657 3.574791 \n", - "132660 3.574791 \n", - "132717 3.574791 \n", - "132731 3.574791 \n", - "132804 3.574791 \n", - "\n", - "[5000 rows x 9 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 학습용에 나오지 않은 사용자나 영화의 예측 평가는 평균 평갓값으로 한다\n", - "average_score = movielens_train.rating.mean()\n", - "movie_rating_predict = movielens_test.copy()\n", - "pred_results = []\n", - "for i, row in movielens_test.iterrows():\n", - " user_id = row[\"user_id\"]\n", - " if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n", - " pred_results.append(average_score)\n", - " continue\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " pred_score = pred_matrix[user_index, movie_index]\n", - " pred_results.append(pred_score)\n", - "movie_rating_predict[\"rating_pred\"] = pred_results\n", - "movie_rating_predict" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2847fe2c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {1: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 2: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 3: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 4: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 5: [318, 50, 260, 1193, 2959, 2858, 2571, 1136, 1198, 1196],\n", - " 6: [318, 50, 527, 2959, 593, 541, 2858, 1136, 1617, 296],\n", - " 7: [318, 527, 858, 1193, 2959, 2858, 2571, 1136, 1198, 296],\n", - " 8: [318, 858, 1193, 296, 110, 356, 1, 150, 2396, 2716],\n", - " 9: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2571],\n", - " 10: [318, 50, 858, 260, 2959, 2858, 2571, 1136, 1198, 1196],\n", - " 11: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 1136],\n", - " 12: [318, 50, 527, 858, 260, 1193, 593, 541, 2571, 1136],\n", - " 13: [318, 527, 858, 260, 2959, 593, 541, 2858, 2571, 1136],\n", - " 14: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 16: [318, 50, 527, 858, 1193, 2959, 593, 2858, 2571, 1136],\n", - " 17: [318, 527, 2959, 541, 2858, 2571, 1136, 1198, 1196, 1617],\n", - " 18: [50, 858, 1193, 1617, 1197, 1200, 588, 595, 21, 141],\n", - " 19: [2959, 593, 2858, 2571, 1617, 296, 1214, 2762, 2028, 47],\n", - " 22: [318, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 23: [318, 858, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 24: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 26: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 27: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 28: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 30: [50, 858, 2959, 541, 2858, 2571, 1617, 296, 1197, 1214],\n", - " 33: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 34: [1193, 2858, 1617, 1214, 1036, 1291, 2396, 1270, 2716, 1097],\n", - " 35: [318, 50, 527, 858, 260, 1193, 593, 541, 1136, 1198],\n", - " 36: [318, 527, 1193, 541, 1136, 1197, 1214, 47, 110, 1200],\n", - " 37: [527, 858, 260, 1193, 541, 2858, 1198, 1196, 1617, 608],\n", - " 38: [50, 527, 858, 260, 1193, 541, 2858, 1136, 1198, 1196],\n", - " 40: [858, 260, 2959, 541, 1136, 1198, 1197, 1214, 2762, 2028],\n", - " 41: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2571],\n", - " 42: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 43: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 44: [858, 260, 1193, 2959, 593, 541, 2571, 1198, 1196, 1617],\n", - " 45: [318, 527, 1193, 2959, 541, 1136, 296, 1197, 1214, 47],\n", - " 46: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 47: [527, 1193, 1617, 608, 1197, 47, 356, 457, 1265, 1097],\n", - " 50: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 51: [318, 50, 2959, 541, 2571, 1136, 1198, 1196, 1197, 47],\n", - " 52: [318, 527, 858, 260, 2959, 593, 541, 2858, 2571, 1136],\n", - " 53: [318, 858, 260, 1193, 2959, 593, 541, 2571, 1136, 1198],\n", - " 54: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 55: [318, 50, 527, 858, 260, 2959, 593, 541, 2858, 2571],\n", - " 56: [318, 2959, 608, 1197, 2762, 47, 1200, 1240, 457, 150],\n", - " 57: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 58: [858, 1193, 2959, 1136, 1196, 608, 1214, 47, 1200, 3578],\n", - " 59: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 60: [2959, 2858, 2571, 1198, 1196, 1617, 1197, 2762, 2028, 110],\n", - " 61: [50, 858, 260, 2959, 541, 2571, 1136, 1198, 1196, 1197],\n", - " 62: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 63: [318, 50, 260, 2959, 593, 541, 2571, 1136, 1198, 1196],\n", - " 64: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 65: [50, 527, 858, 1193, 2959, 593, 541, 1617, 296, 608],\n", - " 66: [318, 50, 527, 858, 260, 1193, 593, 541, 2571, 1136],\n", - " 67: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 68: [318, 50, 527, 858, 2959, 593, 1136, 1198, 1196, 1197],\n", - " 69: [50, 858, 260, 2959, 593, 541, 2571, 1136, 1198, 1196],\n", - " 70: [50, 260, 2959, 593, 2858, 2571, 1136, 1196, 296, 608],\n", - " 71: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 72: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 73: [527, 1193, 1136, 1617, 1197, 1214, 110, 3578, 1210, 1240],\n", - " 75: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 76: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 77: [318, 50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 78: [50, 593, 47, 3578, 1036, 1291, 457, 1, 364, 733],\n", - " 79: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 80: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 81: [318, 50, 527, 858, 260, 593, 2858, 1136, 1198, 1196],\n", - " 82: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 83: [318, 50, 527, 2959, 593, 2858, 2571, 1617, 296, 2762],\n", - " 84: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 85: [318, 858, 260, 1193, 2959, 541, 1136, 1198, 1196, 1617],\n", - " 86: [318, 50, 527, 260, 1193, 2959, 593, 541, 2858, 1136],\n", - " 87: [318, 50, 527, 858, 260, 1193, 593, 541, 2571, 1136],\n", - " 88: [318, 50, 527, 858, 1193, 593, 541, 1617, 296, 608],\n", - " 89: [527, 858, 260, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 90: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 91: [858, 2959, 593, 1617, 296, 2762, 110, 3578, 457, 150],\n", - " 92: [50, 858, 1193, 2959, 593, 541, 1136, 1617, 296, 1197],\n", - " 94: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 95: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 96: [318, 2959, 47, 356, 150, 1270, 364, 733, 1073, 588],\n", - " 97: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 98: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 99: [50, 260, 2959, 593, 2858, 2571, 1198, 1196, 608, 1197],\n", - " 100: [1193, 2959, 541, 1136, 1617, 1197, 1214, 2762, 1200, 3578],\n", - " 101: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 102: [50, 527, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 103: [318, 858, 1193, 2959, 593, 2858, 2571, 1617, 1197, 1214],\n", - " 104: [50, 527, 2959, 541, 2858, 1136, 296, 608, 2762, 2028],\n", - " 105: [318, 50, 527, 858, 2959, 593, 541, 2858, 2571, 1136],\n", - " 106: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 1136],\n", - " 107: [318, 50, 1193, 2959, 593, 2858, 1136, 1198, 296, 608],\n", - " 110: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 111: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 112: [2959, 2858, 2571, 1617, 2762, 2028, 3578, 356, 2396, 589],\n", - " 113: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 114: [318, 50, 527, 2959, 593, 541, 2858, 2571, 1136, 1198],\n", - " 115: [50, 858, 260, 2959, 593, 541, 2571, 1198, 1196, 1617],\n", - " 116: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 117: [318, 527, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 118: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 119: [50, 527, 260, 1193, 2959, 541, 2858, 2571, 1136, 1196],\n", - " 120: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 121: [260, 2959, 2858, 1214, 2762, 2028, 47, 110, 3578, 1036],\n", - " 122: [541, 1617, 2028, 47, 1, 32, 364, 595, 349, 539],\n", - " 123: [318, 50, 858, 2959, 593, 541, 2571, 1136, 1198, 1196],\n", - " 124: [318, 50, 527, 858, 2959, 593, 2858, 1617, 296, 1197],\n", - " 125: [1617, 1197, 357, 10, 329, 153],\n", - " 126: [357, 21, 329],\n", - " 128: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 129: [318, 50, 527, 858, 1193, 593, 541, 2571, 1136, 1198],\n", - " 130: [318, 260, 2959, 2571, 1196, 1617, 1197, 1214, 2762, 2028],\n", - " 131: [318, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 132: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 134: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 135: [318, 50, 527, 1193, 2959, 2858, 2571, 1617, 608, 2762],\n", - " 136: [50, 527, 260, 2959, 593, 541, 2858, 2571, 1196, 1617],\n", - " 137: [858, 260, 1193, 541, 1136, 1617, 608, 1197, 1214, 110],\n", - " 138: [527, 858, 1193, 608, 1197, 1291, 457, 1, 150, 2396],\n", - " 139: [50, 858, 1193, 1214, 1200, 1291, 457, 150, 2396, 34],\n", - " 140: [858, 260, 2959, 541, 2571, 1136, 1198, 1196, 1617, 296],\n", - " 141: [318, 50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 142: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 143: [50, 541, 1617, 1036, 1240, 589, 349, 253, 165, 587],\n", - " 144: [1193, 1197, 2396, 1073, 590, 595, 349, 539, 587, 39],\n", - " 145: [318, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 148: [318, 527, 858, 1193, 2959, 593, 2858, 2571, 1136, 1198],\n", - " 149: [318, 527, 260, 1193, 593, 2571, 1136, 1197, 2762, 2028],\n", - " 150: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 151: [858, 1193, 2959, 593, 541, 2858, 1198, 1617, 1197, 1214],\n", - " 152: [527, 1193, 2959, 2858, 1136, 296, 608, 1197, 1, 150],\n", - " 153: [318, 50, 1193, 593, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 154: [318, 527, 858, 2858, 1136, 1617, 1197, 2028, 3578, 2396],\n", - " 155: [527, 608, 1197, 1200, 1291, 2396, 364, 733, 588, 590],\n", - " 156: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 157: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 158: [318, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 159: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 160: [318, 50, 527, 1193, 2959, 593, 541, 1136, 1617, 296],\n", - " 161: [858, 1193, 1617, 2028, 1036, 356, 457, 150, 32, 2396],\n", - " 162: [318, 50, 1193, 593, 541, 1617, 608, 1197, 1200, 1036],\n", - " 163: [318, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 164: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 165: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 166: [318, 50, 527, 858, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 167: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 168: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 169: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2571],\n", - " 170: [527, 858, 260, 1193, 593, 541, 1198, 1196, 1617, 608],\n", - " 171: [527, 858, 260, 1193, 593, 541, 2571, 1136, 1198, 1196],\n", - " 172: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 173: [2959, 541, 2858, 2571, 1136, 1617, 1197, 2762, 2028, 110],\n", - " 174: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1196],\n", - " 175: [1291, 34, 1073, 141, 780, 165, 10, 2683, 316, 329],\n", - " 176: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 177: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 178: [50, 858, 2959, 541, 2858, 2571, 1198, 1617, 296, 608],\n", - " 179: [318, 527, 2959, 593, 2858, 1136, 2762, 47, 1291, 356],\n", - " 180: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 182: [260, 541, 1198, 1214, 47, 1200, 1210, 1036, 1240, 1291],\n", - " 183: [50, 858, 2959, 541, 2858, 2571, 1617, 296, 608, 1197],\n", - " 184: [318, 527, 858, 260, 1193, 593, 541, 2858, 2571, 1136],\n", - " 185: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 186: [318, 527, 858, 1193, 2959, 2858, 1136, 1196, 296, 1197],\n", - " 187: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 188: [318, 50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 189: [318, 50, 527, 858, 1193, 2959, 2858, 2571, 1136, 1196],\n", - 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" 895: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 896: [318, 858, 1193, 2959, 2858, 2571, 1198, 1196, 1617, 1197],\n", - " 897: [318, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 898: [318, 50, 527, 858, 1193, 2959, 593, 2858, 1136, 1198],\n", - " 899: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 900: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 901: [50, 527, 260, 1193, 2959, 593, 541, 2858, 1136, 1198],\n", - " 902: [527, 541, 110, 150, 32, 1265, 34, 357, 590, 539],\n", - " 903: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 904: [50, 858, 260, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 905: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 906: [318, 541, 1198, 1214, 47, 110, 1200, 3578, 1036, 1240],\n", - " 907: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 908: [318, 50, 1193, 2959, 593, 2858, 1136, 1617, 296, 608],\n", - " 909: [318, 50, 858, 260, 1193, 593, 541, 1136, 1196, 1617],\n", - " 910: [527, 260, 1193, 541, 2571, 1198, 1196, 608, 1197, 2762],\n", - " 911: [318, 50, 527, 858, 1193, 2959, 593, 541, 1136, 1617],\n", - " 912: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 913: [318, 50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 914: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 915: [318, 527, 858, 1193, 2959, 1136, 1198, 1196, 1617, 296],\n", - " 916: [318, 50, 527, 858, 260, 1193, 593, 541, 2858, 2571],\n", - " 917: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 918: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 919: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 1136],\n", - " 920: [318, 50, 527, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 921: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 922: [50, 527, 858, 260, 1193, 593, 541, 2858, 1198, 1196],\n", - " 923: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 924: [50, 527, 858, 260, 1193, 2959, 593, 541, 2571, 1136],\n", - " 925: [50, 527, 1193, 541, 2858, 2571, 1196, 1617, 608, 2762],\n", - " 927: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 928: [527, 858, 1214, 2028, 110, 1200, 1036, 1291, 2396, 2716],\n", - " 930: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 931: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 932: [318, 50, 527, 858, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 933: [527, 858, 1193, 2858, 1136, 608, 1197, 2762, 110, 3578],\n", - " 934: [318, 50, 527, 858, 1193, 2959, 593, 2858, 2571, 1136],\n", - " 935: [318, 527, 858, 1193, 2959, 593, 541, 2858, 1136, 1617],\n", - " 936: [318, 1197, 457, 1, 1270, 2716, 364, 733, 34, 1097],\n", - " 937: [858, 260, 1193, 593, 2858, 2571, 1136, 1198, 1196, 296],\n", - " 938: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 939: [50, 527, 858, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 940: [318, 527, 858, 1193, 2959, 593, 541, 1136, 1198, 1196],\n", - " 941: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 942: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 943: [318, 50, 527, 858, 260, 2959, 593, 541, 2858, 2571],\n", - " 945: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 946: [318, 527, 1193, 2959, 541, 2858, 1136, 296, 2762, 2028],\n", - " 947: [318, 50, 527, 858, 2959, 593, 541, 2858, 1136, 1617],\n", - " 948: [260, 1193, 2959, 541, 1136, 1196, 1197, 1214, 1200, 3578],\n", - " 949: [50, 1193, 593, 541, 1198, 608, 1214, 47, 1200, 1036],\n", - " 950: [318, 50, 527, 858, 2959, 2571, 1617, 1197, 2762, 2028],\n", - " 951: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 952: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 953: [318, 50, 527, 858, 1193, 2959, 593, 2858, 1198, 1617],\n", - " 954: [318, 50, 527, 858, 1193, 593, 541, 2858, 1136, 1198],\n", - " 955: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 956: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 957: [2762, 3578, 2396, 364, 733, 34, 357, 588, 595, 349],\n", - " 958: [50, 527, 858, 260, 1193, 593, 541, 2858, 1136, 1198],\n", - " 960: [318, 50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 961: [318, 50, 858, 260, 2959, 593, 541, 2571, 1136, 1196],\n", - " 962: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2571],\n", - " 963: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 964: [50, 541, 2858, 1136, 1198, 1617, 1197, 1214, 2762, 1200],\n", - " 965: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 966: [318, 50, 527, 858, 593, 1198, 608, 2762, 2028, 47],\n", - " 967: [50, 527, 858, 260, 541, 1136, 1198, 1196, 1617, 608],\n", - " 968: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 969: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 970: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 971: [527, 1193, 541, 1214, 2762, 47, 1200, 3578, 1036, 1291],\n", - " 972: [50, 260, 541, 1136, 1198, 1196, 1197, 1214, 1200, 1210],\n", - " 973: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 974: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 975: [318, 527, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 976: [50, 527, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 977: [2959, 2762, 110, 3578, 356, 457, 150, 32, 2396, 1270],\n", - " 978: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 979: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 980: [318, 527, 1193, 2571, 1198, 1196, 1617, 1197, 2028, 47],\n", - " 982: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 983: [50, 527, 260, 1193, 593, 541, 1196, 1617, 296, 608],\n", - " 984: [50, 527, 260, 541, 2858, 2571, 1136, 1198, 1617, 1197],\n", - " 985: [318, 50, 527, 858, 1193, 2959, 593, 2858, 2571, 1136],\n", - " 986: [858, 260, 1193, 2959, 2858, 2571, 1136, 1198, 1196, 1617],\n", - " 987: [527, 858, 260, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 988: [527, 110, 2396, 733, 34, 1073, 480, 539, 25, 141],\n", - " 989: [318, 2959, 593, 1197, 1214, 2762, 47, 110, 1200, 1210],\n", - " 990: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 991: [858, 1193, 2959, 593, 541, 2858, 2571, 1617, 296, 608],\n", - " 992: [50, 527, 858, 1193, 2959, 541, 2858, 1136, 1198, 1617],\n", - " 993: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 994: [50, 527, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 995: [50, 527, 858, 260, 2959, 593, 541, 2858, 2571, 1136],\n", - " 996: [50, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 997: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 998: [318, 50, 527, 858, 260, 1193, 593, 541, 2858, 1136],\n", - " 1001: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1002: [318, 50, 527, 1193, 2959, 593, 541, 2858, 2571, 1136],\n", - " 1003: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1004: [527, 541, 1197, 1214, 1200, 3578, 1036, 1291, 2396, 2716],\n", - " 1005: [50, 858, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 1006: [50, 858, 1193, 2959, 593, 541, 2858, 2571, 1136, 1198],\n", - " 1007: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1008: [1136, 2028, 1240, 589, 349, 25, 21, 165, 10, 454],\n", - " 1009: [50, 527, 858, 260, 2959, 593, 541, 1136, 1198, 1196],\n", - " 1011: [318, 527, 858, 2959, 2858, 2571, 1197, 2762, 47, 110],\n", - " 1012: [318, 50, 527, 858, 260, 1193, 2959, 541, 2571, 1136],\n", - " 1013: [1193, 2959, 541, 2858, 1136, 1617, 608, 1197, 1214, 2762],\n", - " 1014: [318, 50, 1193, 2959, 2571, 1136, 1198, 1196, 1197, 1214],\n", - " 1015: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2571],\n", - " 1016: [50, 858, 2959, 593, 541, 2571, 1136, 1198, 1196, 1617],\n", - " 1017: [318, 50, 527, 858, 260, 1193, 2959, 593, 2858, 2571],\n", - " 1018: [50, 527, 858, 1193, 1198, 1200, 3578, 1036, 1240, 1291],\n", - " 1019: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 1020: [318, 50, 260, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 1021: [318, 50, 527, 1193, 541, 1136, 1617, 296, 608, 47],\n", - " 1022: [318, 50, 858, 260, 1193, 2959, 593, 541, 2858, 2571],\n", - " 1023: [318, 50, 858, 1193, 1136, 1617, 296, 608, 1197, 2762],\n", - " 1025: [50, 527, 1193, 2959, 593, 541, 1617, 296, 2762, 47],\n", - " 1026: [318, 50, 527, 858, 2858, 1617, 296, 47, 110, 356],\n", - " 1027: [318, 50, 527, 858, 260, 1193, 593, 541, 2858, 2571],\n", - " 1028: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 1029: [527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 1030: [318, 50, 527, 260, 1193, 2959, 541, 2858, 1196, 1617],\n", - " 1031: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 1032: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1033: [858, 1193, 2959, 541, 2858, 2571, 1617, 608, 1214, 3578],\n", - " 1034: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1035: [50, 527, 260, 1193, 541, 1198, 1196, 1617, 608, 1197],\n", - " 1037: [318, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 1038: [318, 50, 527, 858, 2959, 541, 2858, 2571, 1136, 1198],\n", - " 1039: [1193,\n", - " 2959,\n", - " 2858,\n", - " 2571,\n", - " 1136,\n", - " 1198,\n", - " 1196,\n", - " 1197,\n", - " 1214,\n", - " 2762],\n", - " 1040: [50, 527, 858, 260, 1193, 2959, 541, 2858, 2571, 1136],\n", - " 1041: [1193, 541, 1136, 608, 1197, 1240, 356, 457, 150, 32],\n", - " 1043: [318, 50, 527, 858, 260, 1193, 2959, 593, 541, 2858],\n", - " 1044: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 1045: [608, 1214, 1200, 1240, 2716, 34, 357, 480, 349, 25],\n", - " 1046: [318, 50, 527, 858, 260, 1193, 2959, 541, 2858, 2571],\n", - " 1047: [260, 1193, 593, 1196, 1617, 1214, 1200, 1240, 32, 2716],\n", - " 1048: [527, 858, 260, 1193, 541, 1136, 1617, 608, 1197, 1214],\n", - " 1050: [858, 260, 1193, 2959, 541, 2858, 2571, 1136, 1198, 1196],\n", - " 1051: [318, 50, 527, 858, 1193, 2959, 593, 541, 2858, 2571],\n", - " 1052: [318, 50, 527, 858, 260, 1193, 593, 541, 2858, 2571],\n", - " 1053: [318, 50, 527, 858, 1193, 2959, 593, 1136, 1617, 296]})" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 각 사용자에 대한 추천 리스트를 작성한다\n", - "\n", - "pred_user2items = defaultdict(list)\n", - "\n", - "for user_id in unique_user_ids:\n", - " user_index = user_id2index[user_id]\n", - " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", - " for movie_index in movie_indexes:\n", - " movie_id = unique_movie_ids[movie_index]\n", - " if movie_id not in user_evaluated_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6a343574", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cadf27f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
4950Usual Suspects, The (1995)[Crime, Mystery, Thriller][kevin spacey, ensemble cast, complicated, mus...
315318Shawshank Redemption, The (1994)[Drama][based on a short story, directorial debut, fr...
523527Schindler's List (1993)[Drama, War][speilberg, drama, holocaust, steven spielberg...
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" - ], - "text/plain": [ - " movie_id title genre \\\n", - "49 50 Usual Suspects, The (1995) [Crime, Mystery, Thriller] \n", - "315 318 Shawshank Redemption, The (1994) [Drama] \n", - "523 527 Schindler's List (1993) [Drama, War] \n", - "\n", - " tag \n", - "49 [kevin spacey, ensemble cast, complicated, mus... \n", - "315 [based on a short story, directorial debut, fr... \n", - "523 [speilberg, drama, holocaust, steven spielberg... " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(318, 50, 527)\n", - "movies[movies.movie_id.isin([318, 50, 527])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fe9fc9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"44c20b2f","metadata":{"id":"44c20b2f"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/FM.ipynb)"]},{"cell_type":"markdown","id":"975827da","metadata":{"id":"975827da"},"source":["# Factorization Machiens"]},{"cell_type":"code","execution_count":1,"id":"81944a53","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"81944a53","executionInfo":{"status":"ok","timestamp":1672117143146,"user_tz":-540,"elapsed":4132,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"2dfbde1d-175b-44c8-b39b-47352fe4d09a"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:58:56-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 64.6MB/s in 1.0s \n","\n","2022-12-27 04:58:57 (64.6 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"c4cf1864","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c4cf1864","executionInfo":{"status":"ok","timestamp":1672117206637,"user_tz":-540,"elapsed":63496,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"becfffd4-7200-4bc9-bac4-316e0ec9c6c2"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"d05d29df","metadata":{"id":"d05d29df","executionInfo":{"status":"ok","timestamp":1672117206637,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 10\n","# 평가 수의 임곗값\n","minimum_num_rating = 200\n","# 에폭 수\n","n_epochs = 50\n","# 학습률\n","lr = 0.01\n","# 초기 정보 사용\n","use_side_information = False"]},{"cell_type":"code","execution_count":4,"id":"eb06c38c","metadata":{"id":"eb06c38c","executionInfo":{"status":"ok","timestamp":1672117207077,"user_tz":-540,"elapsed":453,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 평갓값이 minimum_num_rating건 이상인 영화를 필터링한다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","# 사용자가 평가한 영화\n","user_evaluated_movies = (\n"," filtered_movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n",")\n"]},{"cell_type":"code","execution_count":5,"id":"e863d3f4","metadata":{"id":"e863d3f4","executionInfo":{"status":"ok","timestamp":1672117209450,"user_tz":-540,"elapsed":2376,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np\n","from sklearn.feature_extraction import DictVectorizer\n","\n","# FM용으로 데이터를 정형한다\n","train_data_for_fm = []\n","y = []\n","for i, row in filtered_movielens_train.iterrows():\n"," x = {\"user_id\": str(row[\"user_id\"]), \"movie_id\": str(row[\"movie_id\"])}\n"," if use_side_information:\n"," x[\"tag\"] = row[\"tag\"]\n"," x[\"user_rating_avg\"] = np.mean(user_evaluated_movies[row[\"user_id\"]])\n"," train_data_for_fm.append(x)\n"," y.append(row[\"rating\"])\n","\n","y = np.array(y)\n","\n","vectorizer = DictVectorizer()\n","X = vectorizer.fit_transform(train_data_for_fm).toarray()"]},{"cell_type":"code","execution_count":6,"id":"7a88d5ec","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7a88d5ec","executionInfo":{"status":"ok","timestamp":1672117310471,"user_tz":-540,"elapsed":101026,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"28358a13-ad71-4f33-a6da-023ea61345c4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting xlearn\n"," Downloading xlearn-0.40a1.tar.gz (4.9 MB)\n","\u001b[K |████████████████████████████████| 4.9 MB 4.7 MB/s \n","\u001b[?25hBuilding wheels for collected packages: xlearn\n"," Building wheel for xlearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for xlearn: filename=xlearn-0.40a1-py3-none-any.whl size=229985 sha256=1c9655f81f543749a98e73cf23691b792b0191fc6466ca61b978157e473aae4f\n"," Stored in directory: /root/.cache/pip/wheels/0c/0a/da/aaa813e25436bce2a282cf1b3c9162c60a6e966a714e4a0b36\n","Successfully built xlearn\n","Installing collected packages: xlearn\n","Successfully installed xlearn-0.40a1\n"]}],"source":["!pip install xlearn"]},{"cell_type":"code","execution_count":7,"id":"511e8f9f","metadata":{"id":"511e8f9f","executionInfo":{"status":"ok","timestamp":1672117310471,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# colab에서 xlearn을 동작시키기 위한 설정\n","# https://github.com/aksnzhy/xlearn/issues/74#issuecomment-580701773\n","import os\n","os.environ['USER'] = 'test'"]},{"cell_type":"code","execution_count":8,"id":"e7208e9a","metadata":{"id":"e7208e9a","executionInfo":{"status":"ok","timestamp":1672117310471,"user_tz":-540,"elapsed":16,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import xlearn as xl\n","\n","# FM 모델 초기화\n","fm_model = xl.FMModel(task=\"reg\", metric=\"rmse\", lr=lr, opt=\"sgd\", k=factors, epoch=n_epochs)"]},{"cell_type":"code","execution_count":9,"id":"9b99ec6a","metadata":{"id":"9b99ec6a","executionInfo":{"status":"ok","timestamp":1672117311217,"user_tz":-540,"elapsed":761,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# FM 모델 학습\n","fm_model.fit(X, y, is_lock_free=False)"]},{"cell_type":"code","execution_count":10,"id":"3d4a06ff","metadata":{"id":"3d4a06ff","executionInfo":{"status":"ok","timestamp":1672117311218,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n","unique_user_ids = sorted(filtered_movielens_train.user_id.unique())\n","unique_movie_ids = sorted(filtered_movielens_train.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))"]},{"cell_type":"code","execution_count":11,"id":"3cd06940","metadata":{"id":"3cd06940","executionInfo":{"status":"ok","timestamp":1672117311979,"user_tz":-540,"elapsed":765,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 예측용 데이터를 준비한다\n","test_data_for_fm = []\n","for user_id in unique_user_ids:\n"," for movie_id in unique_movie_ids:\n"," x = {\"user_id\": str(user_id), \"movie_id\": str(movie_id)}\n"," if use_side_information:\n"," tag = dataset.item_content[dataset.item_content.movie_id == movie_id].tag.tolist()[0]\n"," x[\"tag\"] = tag\n"," x[\"user_rating_avg\"] = np.mean(user_evaluated_movies[row[\"user_id\"]])\n"," test_data_for_fm.append(x)\n","\n","X_test = vectorizer.transform(test_data_for_fm).toarray()"]},{"cell_type":"code","execution_count":12,"id":"8ed3df1d","metadata":{"id":"8ed3df1d","executionInfo":{"status":"ok","timestamp":1672117313646,"user_tz":-540,"elapsed":1671,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 예측한다\n","y_pred = fm_model.predict(X_test)\n","pred_matrix = y_pred.reshape(len(unique_user_ids), len(unique_movie_ids))"]},{"cell_type":"code","execution_count":13,"id":"f37b05f0","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"f37b05f0","executionInfo":{"status":"ok","timestamp":1672117313997,"user_tz":-540,"elapsed":354,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"bf414e95-35c7-4846-e35d-53efc0f3efa6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp title \\\n","0 1 122 5.0 838985046 Boomerang (1992) \n","5 217 122 3.0 844429650 Boomerang (1992) \n","33 892 122 4.0 850079961 Boomerang (1992) \n","46 59 185 3.0 838984807 Net, The (1995) \n","47 62 185 5.0 834874598 Net, The (1995) \n","... ... ... ... ... ... \n","132657 849 5097 4.0 1013470449 Bright Eyes (1934) \n","132660 951 638 4.0 849628182 Jack and Sarah (1995) \n","132717 1005 1384 5.0 897254984 Substance of Fire, The (1996) \n","132731 934 56 1.0 945901052 Kids of the Round Table (1995) \n","132804 1018 64997 3.0 1230668562 War of the Worlds (2005) \n","\n"," genre \\\n","0 [Comedy, Romance] \n","5 [Comedy, Romance] \n","33 [Comedy, Romance] \n","46 [Action, Crime, Thriller] \n","47 [Action, Crime, Thriller] \n","... ... \n","132657 [Comedy, Drama] \n","132660 [Romance] \n","132717 [Drama] \n","132731 [Adventure, Children, Fantasy] \n","132804 [Action] \n","\n"," tag timestamp_rank \\\n","0 [dating, nudity (topless - brief), can't remem... 1.0 \n","5 [dating, nudity (topless - brief), can't remem... 5.0 \n","33 [dating, nudity (topless - brief), can't remem... 5.0 \n","46 [computers, computers, internet, irwin winkler... 1.0 \n","47 [computers, computers, internet, irwin winkler... 5.0 \n","... ... ... \n","132657 NaN 2.0 \n","132660 [babies, ian mckellen, baby, british] 5.0 \n","132717 NaN 1.0 \n","132731 NaN 5.0 \n","132804 NaN 1.0 \n","\n"," rating_pred \n","0 3.574791 \n","5 3.574791 \n","33 3.574791 \n","46 3.574791 \n","47 3.574791 \n","... ... \n","132657 3.574791 \n","132660 3.574791 \n","132717 3.574791 \n","132731 3.574791 \n","132804 3.574791 \n","\n","[5000 rows x 9 columns]"],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rankrating_pred
011225.0838985046Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...1.03.574791
52171223.0844429650Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...5.03.574791
338921224.0850079961Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...5.03.574791
46591853.0838984807Net, The (1995)[Action, Crime, Thriller][computers, computers, internet, irwin winkler...1.03.574791
47621855.0834874598Net, The (1995)[Action, Crime, Thriller][computers, computers, internet, irwin winkler...5.03.574791
..............................
13265784950974.01013470449Bright Eyes (1934)[Comedy, Drama]NaN2.03.574791
1326609516384.0849628182Jack and Sarah (1995)[Romance][babies, ian mckellen, baby, british]5.03.574791
132717100513845.0897254984Substance of Fire, The (1996)[Drama]NaN1.03.574791
132731934561.0945901052Kids of the Round Table (1995)[Adventure, Children, Fantasy]NaN5.03.574791
1328041018649973.01230668562War of the Worlds (2005)[Action]NaN1.03.574791
\n","

5000 rows × 9 columns

\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":13}],"source":["# 학습용에 나오지 않은 사용자나 영화의 예측 평가는 평균 평갓값으로 한다\n","average_score = movielens_train.rating.mean()\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results\n","movie_rating_predict"]},{"cell_type":"code","execution_count":14,"id":"2847fe2c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2847fe2c","executionInfo":{"status":"ok","timestamp":1672117314449,"user_tz":-540,"elapsed":457,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"ba123efc-95b3-4296-d669-04c74035c0b4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 2: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 3: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 4: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 5: [318, 50, 1193, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 6: [318, 50, 527, 541, 593, 2959, 2858, 1617, 1136, 608],\n"," 7: [318, 527, 858, 1193, 2959, 1198, 2858, 2571, 1136, 2028],\n"," 8: [318, 858, 1193, 296, 110, 356, 150, 1, 2396, 2716],\n"," 9: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 10: [318, 50, 858, 260, 2959, 1198, 2858, 2571, 1196, 1136],\n"," 11: [318, 50, 527, 858, 1193, 541, 593, 2959, 2858, 1136],\n"," 12: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2571],\n"," 13: [318, 527, 858, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 14: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 16: [318, 50, 527, 858, 1193, 593, 2959, 1198, 2858, 2571],\n"," 17: [318, 527, 541, 2959, 1198, 2858, 2571, 1196, 1617, 1136],\n"," 18: [50, 858, 1193, 1617, 1197, 1200, 595, 588, 21, 141],\n"," 19: [593, 2959, 2858, 2571, 1617, 2028, 296, 1214, 2762, 1200],\n"," 22: [318, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 23: [318, 858, 1193, 541, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 24: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 26: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 27: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 28: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 30: [50, 858, 541, 2959, 2858, 2571, 1617, 2028, 1197, 296],\n"," 33: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 34: [1193, 2858, 1617, 1214, 1036, 1291, 2396, 1270, 2716, 1097],\n"," 35: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 1617],\n"," 36: [318, 527, 1193, 541, 1136, 1197, 110, 1214, 1200, 47],\n"," 37: [527, 858, 1193, 541, 260, 1198, 2858, 1196, 1617, 608],\n"," 38: [50, 527, 858, 1193, 541, 260, 1198, 2858, 1196, 1617],\n"," 40: [858, 541, 260, 2959, 1198, 1136, 2028, 1197, 110, 1214],\n"," 41: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 42: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 43: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 44: [858, 1193, 541, 260, 593, 2959, 1198, 2571, 1196, 1617],\n"," 45: [318, 527, 1193, 541, 2959, 1136, 1197, 296, 1214, 47],\n"," 46: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 47: [527, 1193, 1617, 608, 1197, 47, 457, 356, 1265, 1097],\n"," 50: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 51: [318, 50, 541, 2959, 1198, 2571, 1196, 1136, 1197, 110],\n"," 52: [318, 527, 858, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 53: [318, 858, 1193, 541, 260, 593, 2959, 1198, 2571, 1196],\n"," 54: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 55: [318, 50, 527, 858, 541, 260, 593, 2959, 1198, 2858],\n"," 56: [318, 2959, 608, 1197, 2762, 1200, 47, 457, 1240, 150],\n"," 57: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 58: [858, 1193, 2959, 1196, 1136, 608, 1214, 1200, 47, 1036],\n"," 59: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 60: [2959, 1198, 2858, 2571, 1196, 1617, 2028, 1197, 110, 2762],\n"," 61: [50, 858, 541, 260, 2959, 1198, 2571, 1196, 1136, 2028],\n"," 62: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 63: [318, 50, 541, 260, 593, 2959, 1198, 2571, 1196, 1136],\n"," 64: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 65: [50, 527, 858, 1193, 541, 593, 2959, 1617, 608, 2028],\n"," 66: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2571],\n"," 67: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 68: [318, 50, 527, 858, 593, 2959, 1198, 1196, 1136, 2028],\n"," 69: [50, 858, 541, 260, 593, 2959, 1198, 2571, 1196, 1617],\n"," 70: [50, 260, 593, 2959, 2858, 2571, 1196, 1136, 608, 2028],\n"," 71: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 72: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 73: [527, 1193, 1617, 1136, 1197, 110, 1214, 3578, 1210, 1291],\n"," 75: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 76: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 77: [318, 50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 78: [50, 593, 47, 1036, 3578, 457, 1291, 1, 733, 34],\n"," 79: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 80: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 81: [318, 50, 527, 858, 260, 593, 1198, 2858, 1196, 1617],\n"," 82: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 83: [318, 50, 527, 593, 2959, 2858, 2571, 1617, 2028, 296],\n"," 84: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 85: [318, 858, 1193, 541, 260, 2959, 1198, 1196, 1617, 1136],\n"," 86: [318, 50, 527, 1193, 541, 260, 593, 2959, 2858, 1617],\n"," 87: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2571],\n"," 88: [318, 50, 527, 858, 1193, 541, 593, 1617, 608, 2028],\n"," 89: [527, 858, 1193, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 90: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 91: [858, 593, 2959, 1617, 296, 110, 2762, 3578, 457, 150],\n"," 92: [50, 858, 1193, 541, 593, 2959, 1617, 1136, 1197, 296],\n"," 94: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 95: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 96: [318, 2959, 47, 356, 150, 1270, 733, 364, 1073, 588],\n"," 97: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 98: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 99: [50, 260, 593, 2959, 1198, 2858, 2571, 1196, 608, 2028],\n"," 100: [1193, 541, 2959, 1617, 1136, 1197, 1214, 2762, 1200, 1036],\n"," 101: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 102: [50, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 103: [318, 858, 1193, 593, 2959, 2858, 2571, 1617, 2028, 1197],\n"," 104: [50, 527, 541, 2959, 2858, 1136, 608, 2028, 296, 2762],\n"," 105: [318, 50, 527, 858, 541, 593, 2959, 2858, 2571, 1617],\n"," 106: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 107: [318, 50, 1193, 593, 2959, 1198, 2858, 1136, 608, 1197],\n"," 110: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 111: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 112: [2959, 2858, 2571, 1617, 2028, 2762, 3578, 356, 2396, 589],\n"," 113: [318, 50, 527, 858, 1193, 541, 593, 2959, 2858, 2571],\n"," 114: [318, 50, 527, 541, 593, 2959, 1198, 2858, 2571, 1617],\n"," 115: [50, 858, 541, 260, 593, 2959, 1198, 2571, 1196, 1617],\n"," 116: [50, 527, 858, 1193, 541, 260, 2959, 2858, 2571, 1196],\n"," 117: [318, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 118: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 119: [50, 527, 1193, 541, 260, 2959, 2858, 2571, 1196, 1617],\n"," 120: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 121: [260, 2959, 2858, 2028, 110, 1214, 2762, 47, 1036, 3578],\n"," 122: [541, 1617, 2028, 47, 1, 32, 364, 595, 349, 539],\n"," 123: [318, 50, 858, 541, 593, 2959, 1198, 2571, 1196, 1617],\n"," 124: [318, 50, 527, 858, 593, 2959, 2858, 1617, 1197, 296],\n"," 125: [1617, 1197, 357, 10, 329, 153],\n"," 126: [357, 21, 329],\n"," 128: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 129: [318, 50, 527, 858, 1193, 541, 593, 1198, 2571, 1196],\n"," 130: [318, 260, 2959, 2571, 1196, 1617, 2028, 1197, 110, 1214],\n"," 131: [318, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 132: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 134: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 135: [318, 50, 527, 1193, 2959, 2858, 2571, 1617, 608, 2028],\n"," 136: [50, 527, 541, 260, 593, 2959, 2858, 2571, 1196, 1617],\n"," 137: [858, 1193, 541, 260, 1617, 1136, 608, 1197, 110, 1214],\n"," 138: [527, 858, 1193, 608, 1197, 457, 1291, 150, 1, 2396],\n"," 139: [50, 858, 1193, 1214, 1200, 457, 1291, 150, 2396, 34],\n"," 140: [858, 541, 260, 2959, 1198, 2571, 1196, 1617, 1136, 608],\n"," 141: [318, 50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 142: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 143: [50, 541, 1617, 1036, 1240, 589, 349, 165, 253, 587],\n"," 144: [1193, 1197, 2396, 590, 1073, 595, 349, 539, 587, 39],\n"," 145: [318, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 148: [318, 527, 858, 1193, 593, 2959, 1198, 2858, 2571, 1617],\n"," 149: [318, 527, 1193, 260, 593, 2571, 1136, 2028, 1197, 110],\n"," 150: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 151: [858, 1193, 541, 593, 2959, 1198, 2858, 1617, 2028, 1197],\n"," 152: [527, 1193, 2959, 2858, 1136, 608, 1197, 296, 150, 1],\n"," 153: [318, 50, 1193, 541, 593, 1198, 2858, 2571, 1196, 1617],\n"," 154: [318, 527, 858, 2858, 1617, 1136, 2028, 1197, 3578, 2396],\n"," 155: [527, 608, 1197, 1200, 1291, 2396, 733, 364, 590, 595],\n"," 156: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 157: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 158: [318, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 159: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 160: [318, 50, 527, 1193, 541, 593, 2959, 1617, 1136, 608],\n"," 161: [858, 1193, 1617, 2028, 1036, 457, 356, 150, 32, 2396],\n"," 162: [318, 50, 1193, 541, 593, 1617, 608, 1197, 1200, 1036],\n"," 163: [318, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 164: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 165: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 166: [318, 50, 527, 858, 1193, 541, 2959, 1198, 2858, 2571],\n"," 167: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 168: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 169: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 170: [527, 858, 1193, 541, 260, 593, 1198, 1196, 1617, 608],\n"," 171: [527, 858, 1193, 541, 260, 593, 1198, 2571, 1196, 1617],\n"," 172: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 173: [541, 2959, 2858, 2571, 1617, 1136, 2028, 1197, 110, 2762],\n"," 174: [50, 858, 1193, 541, 260, 2959, 2858, 2571, 1196, 1136],\n"," 175: [1291, 34, 1073, 141, 165, 10, 780, 2683, 316, 329],\n"," 176: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 177: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 178: [50, 858, 541, 2959, 1198, 2858, 2571, 1617, 608, 2028],\n"," 179: [318, 527, 593, 2959, 2858, 1136, 2762, 47, 457, 1291],\n"," 180: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 182: [541, 260, 1198, 1214, 1200, 47, 1036, 1210, 457, 1291],\n"," 183: [50, 858, 541, 2959, 2858, 2571, 1617, 608, 1197, 296],\n"," 184: [318, 527, 858, 1193, 541, 260, 593, 1198, 2858, 2571],\n"," 185: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 186: [318, 527, 858, 1193, 2959, 2858, 1196, 1136, 2028, 1197],\n"," 187: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 188: [318, 50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 189: [318, 50, 527, 858, 1193, 2959, 2858, 2571, 1196, 1617],\n"," 190: [318, 50, 527, 1193, 2858, 1617, 608, 47, 457, 733],\n"," 192: [318, 260, 1198, 1196, 2028, 1197, 110, 2762, 1200, 1036],\n"," 193: [858, 1193, 2959, 1198, 2858, 2571, 1196, 1617, 1136, 608],\n"," 194: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 195: [318, 50, 541, 260, 593, 2959, 1198, 2571, 1196, 1136],\n"," 196: [50, 1193, 541, 2858, 1617, 2762, 1200, 47, 3578, 32],\n"," 197: [318, 50, 541, 260, 593, 2959, 1198, 2858, 2571, 1196],\n"," 198: [1198, 457, 1291, 357, 349, 25, 539, 480, 21, 141],\n"," 199: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 200: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 201: [318, 1193, 541, 260, 2959, 1198, 2571, 1196, 1136, 1197],\n"," 202: 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858, 1193, 541, 260, 2959, 1198, 2858],\n"," 218: [50, 858, 1193, 541, 260, 2959, 1198, 2571, 1196, 1617],\n"," 219: [318, 50, 858, 541, 593, 1198, 2571, 1196, 1617, 1136],\n"," 220: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 221: [1193, 541, 2959, 1617, 1136, 608, 1197, 1214, 2762, 1200],\n"," 222: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 223: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 224: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 225: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 226: [527, 1193, 2959, 2858, 150, 1, 2396, 34, 364, 1097],\n"," 227: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 228: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 229: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 230: [318, 858, 1193, 1198, 2571, 2762, 47, 1036, 1210, 457],\n"," 231: [318, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 232: [1193, 2959, 2858, 2571, 1617, 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1198, 2858],\n"," 489: [541, 593, 2959, 1198, 2858, 2571, 1196, 1617, 2028, 1197],\n"," 490: [50, 1193, 541, 260, 593, 2959, 2858, 2571, 1617, 1136],\n"," 491: [50, 1193, 541, 593, 1198, 1617, 1136, 608, 2028, 296],\n"," 493: [593, 1198, 1196, 1136, 1214, 1200, 1240, 589, 1265, 733],\n"," 494: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 495: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 496: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 497: [50, 1193, 541, 260, 2959, 1198, 2858, 1617, 1136, 608],\n"," 498: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 499: [318, 50, 527, 1193, 541, 593, 2959, 2858, 2571, 1196],\n"," 500: [50, 858, 1193, 260, 2959, 1198, 2858, 2571, 1196, 1136],\n"," 501: [318, 50, 527, 858, 541, 2959, 2858, 1617, 296, 2762],\n"," 502: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2571],\n"," 503: [50, 1193, 260, 1196, 1617, 608, 1197, 110, 1214, 1200],\n"," 504: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 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1196, 1617, 1136, 1197, 47],\n"," 555: [541, 1197, 1214, 1200, 1036, 457, 2396, 34, 364, 1097],\n"," 556: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 557: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 558: [1193, 541, 2762, 316, 329],\n"," 559: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 560: [858, 541, 260, 1198, 2571, 1196, 2028, 1197, 110, 1214],\n"," 561: [318, 50, 527, 858, 1193, 1198, 1617, 1136, 608, 1197],\n"," 562: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 563: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 564: [527, 541, 593, 2959, 2858, 2571, 1136, 2028, 1197, 110],\n"," 565: [318, 50, 527, 541, 260, 593, 2959, 2571, 1196, 2028],\n"," 567: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 568: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 569: [318, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 570: [50, 858, 1193, 541, 593, 2959, 1198, 2858, 2571, 1196],\n"," 571: [1193, 541, 593, 2959, 608, 296, 110, 1214, 2762, 457],\n"," 572: [318, 1036, 3578, 150, 2396, 34, 1097, 590, 357, 588],\n"," 573: [858, 1193, 541, 260, 2959, 1198, 1196, 1136, 1197, 296],\n"," 574: [318, 50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 575: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 576: [50, 858, 1193, 541, 260, 593, 2959, 1198, 2571, 1196],\n"," 577: [527, 1193, 541, 260, 2959, 2858, 2571, 1617, 1136, 2028],\n"," 578: [318, 50, 527, 1193, 593, 2959, 1617, 2028, 1197, 2762],\n"," 579: [858, 1193, 1617, 608, 2028, 1197, 110, 1200, 457, 1240],\n"," 581: [50, 858, 1193, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 582: [541, 2959, 2858, 2571, 1136, 1197, 2762, 47, 1036, 3578],\n"," 583: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 584: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 585: [110, 2762, 47, 3578, 733, 597, 253, 292, 288, 231],\n"," 586: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2571],\n"," 587: [50, 858, 541, 2959, 1198, 2858, 2571, 1617, 2028, 1197],\n"," 588: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 589: [318, 2959, 2858, 2571, 1617, 1136, 2028, 1197, 2762, 3578],\n"," 590: [527, 858, 1193, 541, 2959, 2858, 1617, 1136, 1197, 1214],\n"," 591: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2571],\n"," 593: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 594: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 595: [318, 50, 1193, 2959, 1617, 2028, 1200, 47, 3578, 1210],\n"," 596: [50, 541, 593, 2959, 2571, 1197, 296, 110, 1214, 47],\n"," 597: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 598: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 599: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 600: [318, 50, 527, 1193, 541, 260, 593, 1198, 2571, 1196],\n"," 601: [50, 858, 541, 2959, 2858, 2571, 1617, 1136, 608, 2028],\n"," 602: [318, 50, 527, 1193, 541, 2959, 2858, 2571, 1196, 1617],\n"," 603: [50, 1193, 260, 593, 1198, 1617, 1197, 2762, 47, 1036],\n"," 604: [1193, 541, 2959, 1198, 2858, 2571, 1196, 1617, 1136, 2028],\n"," 605: [858, 593, 2959, 2858, 2571, 1617, 2028, 1197, 2762, 1036],\n"," 606: [318, 50, 527, 1193, 541, 593, 2959, 2858, 1617, 1136],\n"," 607: [527, 1193, 541, 2959, 2858, 2571, 1196, 1617, 1136, 2028],\n"," 608: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2858],\n"," 609: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 610: [318, 50, 541, 2959, 2858, 2571, 1196, 608, 1197, 296],\n"," 612: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 613: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 614: [318, 50, 527, 1193, 541, 593, 2959, 2858, 2571, 1617],\n"," 616: [858, 1193, 541, 1196, 1617, 1136, 1197, 1214, 1200, 1240],\n"," 617: [318, 858, 1193, 2959, 2858, 2571, 1617, 608, 2028, 1214],\n"," 618: [858, 1193, 541, 260, 2959, 1198, 1196, 1136, 1197, 296],\n"," 619: [318, 50, 527, 1193, 541, 593, 2959, 2858, 2571, 1196],\n"," 620: [318, 50, 527, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 621: [110, 34, 595, 25, 597, 141, 253, 587, 500, 454],\n"," 622: [50, 527, 858, 1193, 1617, 1136, 608, 1197, 1214, 1200],\n"," 623: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 624: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 625: [318, 50, 527, 858, 1193, 541, 2959, 1198, 2858, 2571],\n"," 627: [527, 1193, 541, 260, 2959, 1198, 1196, 1617, 1136, 1197],\n"," 628: [50, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 629: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 630: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 631: [318, 50, 858, 541, 2959, 2858, 2571, 1196, 1617, 1136],\n"," 632: [318, 527, 858, 1193, 541, 260, 2959, 1198, 1617, 1136],\n"," 633: [2762, 1036, 1291, 1240, 589, 1270, 1097, 357, 595, 25],\n"," 634: [318, 50, 527, 858, 1193, 2959, 2858, 2571, 2028, 296],\n"," 635: [318, 50, 858, 1193, 260, 2959, 1198, 2571, 1196, 1617],\n"," 636: [541, 2959, 2858, 608, 1197, 110, 1214, 1200, 47, 356],\n"," 637: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 638: [858, 1193, 593, 2959, 1198, 2858, 2571, 1196, 1617, 1136],\n"," 639: [50, 527, 858, 1193, 541, 260, 1198, 2858, 2571, 1196],\n"," 640: [50, 527, 1193, 260, 593, 1617, 1136, 608, 1197, 296],\n"," 641: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 642: [527, 1198, 110, 733, 364, 1073, 595, 349, 588, 141],\n"," 643: [2959, 1198, 2858, 2762, 47, 1036, 3578, 1291, 150, 1],\n"," 644: [318, 50, 527, 858, 1193, 2959, 2858, 1617, 1136, 1197],\n"," 645: [50, 858, 1193, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 646: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 647: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 648: [527, 260, 2959, 1198, 2571, 1136, 1197, 296, 110, 1214],\n"," 649: [318, 50, 527, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 650: [527, 593, 2959, 1617, 1136, 2028, 296, 110, 1200, 457],\n"," 651: [318, 50, 593, 2959, 2571, 1196, 2028, 1197, 296, 110],\n"," 652: [318, 50, 858, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 653: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 654: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 655: [50, 527, 593, 2959, 2858, 2571, 1617, 2028, 110, 2762],\n"," 656: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 657: [1136, 1200, 1240, 39, 316, 288, 329],\n"," 658: [318, 50, 527, 858, 1193, 541, 260, 1198, 1196, 1617],\n"," 659: [50, 1193, 2959, 1617, 2028, 110, 3578, 1291, 150, 1],\n"," 660: [858, 260, 1198, 1196, 1617, 1136, 1197, 110, 1214, 1200],\n"," 661: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 662: [527, 2959, 1198, 2571, 1136, 2762, 1200, 47, 1036, 3578],\n"," 663: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 664: [318, 50, 527, 858, 541, 2959, 1198, 2858, 2571, 1196],\n"," 665: [318, 50, 527, 858, 260, 593, 2959, 1198, 2571, 1196],\n"," 666: [318, 50, 527, 260, 593, 1617, 608, 2028, 1197, 110],\n"," 667: [50, 527, 1193, 541, 593, 2959, 2858, 2571, 1136, 1197],\n"," 668: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 669: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 670: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 671: [318, 2959, 2571, 1197, 110, 1200, 47, 1036, 3578, 1210],\n"," 672: [318, 1193, 541, 1136, 1214, 1200, 1036, 457, 1240, 1270],\n"," 674: [318, 50, 527, 858, 2959, 1198, 2858, 1617, 1136, 608],\n"," 675: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 676: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 677: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 678: [2959, 2858, 2571, 1617, 2028, 2762, 47, 3578, 1, 2396],\n"," 679: [318, 50, 527, 1193, 541, 2959, 1198, 2858, 2571, 1617],\n"," 680: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 1617],\n"," 681: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 683: [318, 50, 527, 1193, 541, 593, 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1196],\n"," 914: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 915: [318, 527, 858, 1193, 2959, 1198, 1196, 1617, 1136, 296],\n"," 916: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2858],\n"," 917: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 918: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 919: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 920: [318, 50, 527, 1193, 541, 2959, 1198, 2858, 2571, 1196],\n"," 921: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 922: [50, 527, 858, 1193, 541, 260, 593, 1198, 2858, 1196],\n"," 923: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 924: [50, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2571],\n"," 925: [50, 527, 1193, 541, 2858, 2571, 1196, 1617, 608, 2028],\n"," 927: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 2858],\n"," 928: [527, 858, 2028, 110, 1214, 1200, 1036, 1291, 2396, 2716],\n"," 930: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 931: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 932: [318, 50, 527, 858, 1193, 541, 2959, 1198, 2858, 2571],\n"," 933: [527, 858, 1193, 2858, 1136, 608, 1197, 110, 2762, 3578],\n"," 934: [318, 50, 527, 858, 1193, 593, 2959, 1198, 2858, 2571],\n"," 935: [318, 527, 858, 1193, 541, 593, 2959, 2858, 1617, 1136],\n"," 936: [318, 1197, 457, 1, 1270, 2716, 733, 34, 364, 1097],\n"," 937: [858, 1193, 260, 593, 1198, 2858, 2571, 1196, 1136, 608],\n"," 938: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 939: [50, 527, 858, 1193, 541, 2959, 1198, 2858, 2571, 1196],\n"," 940: [318, 527, 858, 1193, 541, 593, 2959, 1198, 1196, 1617],\n"," 941: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 942: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 943: [318, 50, 527, 858, 541, 260, 593, 2959, 1198, 2858],\n"," 945: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 946: [318, 527, 1193, 541, 2959, 2858, 1136, 2028, 296, 2762],\n"," 947: [318, 50, 527, 858, 541, 593, 2959, 2858, 1617, 1136],\n"," 948: [1193, 541, 260, 2959, 1196, 1136, 1197, 1214, 1200, 1036],\n"," 949: [50, 1193, 541, 593, 1198, 608, 1214, 1200, 47, 1036],\n"," 950: [318, 50, 527, 858, 2959, 2571, 1617, 2028, 1197, 110],\n"," 951: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 952: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 953: [318, 50, 527, 858, 1193, 593, 2959, 1198, 2858, 1617],\n"," 954: [318, 50, 527, 858, 1193, 541, 593, 1198, 2858, 1617],\n"," 955: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 956: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 957: [2762, 3578, 2396, 733, 34, 364, 357, 595, 349, 588],\n"," 958: [50, 527, 858, 1193, 541, 260, 593, 1198, 2858, 1196],\n"," 960: [318, 50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 961: [318, 50, 858, 541, 260, 593, 2959, 2571, 1196, 1136],\n"," 962: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 963: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 964: [50, 541, 1198, 2858, 1617, 1136, 1197, 1214, 2762, 1200],\n"," 965: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 966: [318, 50, 527, 858, 593, 1198, 608, 2028, 2762, 47],\n"," 967: [50, 527, 858, 541, 260, 1198, 1196, 1617, 1136, 608],\n"," 968: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 969: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 970: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 971: [527, 1193, 541, 1214, 2762, 1200, 47, 1036, 3578, 1291],\n"," 972: [50, 541, 260, 1198, 1196, 1136, 1197, 1214, 1200, 1036],\n"," 973: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 974: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 975: [318, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 976: [50, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 977: [2959, 110, 2762, 3578, 457, 356, 150, 32, 2396, 1270],\n"," 978: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 979: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 980: [318, 527, 1193, 1198, 2571, 1196, 1617, 2028, 1197, 110],\n"," 982: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 983: [50, 527, 1193, 541, 260, 593, 1196, 1617, 608, 2028],\n"," 984: [50, 527, 541, 260, 1198, 2858, 2571, 1617, 1136, 1197],\n"," 985: [318, 50, 527, 858, 1193, 593, 2959, 1198, 2858, 2571],\n"," 986: [858, 1193, 260, 2959, 1198, 2858, 2571, 1196, 1617, 1136],\n"," 987: [527, 858, 1193, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 988: [527, 110, 2396, 733, 34, 1073, 25, 539, 480, 597],\n"," 989: [318, 593, 2959, 1197, 110, 1214, 2762, 1200, 47, 1036],\n"," 990: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 991: [858, 1193, 541, 593, 2959, 2858, 2571, 1617, 608, 2028],\n"," 992: [50, 527, 858, 1193, 541, 2959, 1198, 2858, 1617, 1136],\n"," 993: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 994: [50, 527, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 995: [50, 527, 858, 541, 260, 593, 2959, 1198, 2858, 2571],\n"," 996: [50, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 997: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 998: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2858],\n"," 1001: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1002: [318, 50, 527, 1193, 541, 593, 2959, 1198, 2858, 2571],\n"," 1003: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1004: [527, 541, 1197, 1214, 1200, 1036, 3578, 1291, 2396, 2716],\n"," 1005: [50, 858, 1193, 541, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 1006: [50, 858, 1193, 541, 593, 2959, 1198, 2858, 2571, 1196],\n"," 1007: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1008: [1136, 2028, 1240, 589, 349, 25, 21, 165, 10, 454],\n"," 1009: [50, 527, 858, 541, 260, 593, 2959, 1198, 1196, 1617],\n"," 1011: [318, 527, 858, 2959, 2858, 2571, 1197, 110, 2762, 47],\n"," 1012: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2571],\n"," 1013: [1193, 541, 2959, 2858, 1617, 1136, 608, 1197, 1214, 2762],\n"," 1014: [318, 50, 1193, 2959, 1198, 2571, 1196, 1136, 1197, 1214],\n"," 1015: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1016: [50, 858, 541, 593, 2959, 1198, 2571, 1196, 1617, 1136],\n"," 1017: [318, 50, 527, 858, 1193, 260, 593, 2959, 1198, 2858],\n"," 1018: [50, 527, 858, 1193, 1198, 1200, 1036, 3578, 457, 1291],\n"," 1019: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 1020: [318, 50, 541, 260, 2959, 1198, 2858, 2571, 1196, 1136],\n"," 1021: [318, 50, 527, 1193, 541, 1617, 1136, 608, 296, 110],\n"," 1022: [318, 50, 858, 1193, 541, 260, 593, 2959, 1198, 2858],\n"," 1023: [318, 50, 858, 1193, 1617, 1136, 608, 2028, 1197, 296],\n"," 1025: [50, 527, 1193, 541, 593, 2959, 1617, 296, 2762, 47],\n"," 1026: [318, 50, 527, 858, 2858, 1617, 296, 110, 47, 457],\n"," 1027: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2858],\n"," 1028: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 1029: [527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196],\n"," 1030: [318, 50, 527, 1193, 541, 260, 2959, 2858, 1196, 1617],\n"," 1031: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 1032: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1033: [858, 1193, 541, 2959, 2858, 2571, 1617, 608, 1214, 1036],\n"," 1034: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1035: [50, 527, 1193, 541, 260, 1198, 1196, 1617, 608, 1197],\n"," 1037: [318, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 1038: [318, 50, 527, 858, 541, 2959, 1198, 2858, 2571, 1617],\n"," 1039: [1193, 2959, 1198, 2858, 2571, 1196, 1136, 1197, 110, 1214],\n"," 1040: [50, 527, 858, 1193, 541, 260, 2959, 1198, 2858, 2571],\n"," 1041: [1193, 541, 1136, 608, 1197, 457, 356, 1240, 150, 32],\n"," 1043: [318, 50, 527, 858, 1193, 541, 260, 593, 2959, 1198],\n"," 1044: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 1045: [608, 1214, 1200, 1240, 2716, 34, 357, 349, 25, 480],\n"," 1046: [318, 50, 527, 858, 1193, 541, 260, 2959, 1198, 2858],\n"," 1047: [1193, 260, 593, 1196, 1617, 1214, 1200, 1240, 32, 2716],\n"," 1048: [527, 858, 1193, 541, 260, 1617, 1136, 608, 1197, 110],\n"," 1050: [858, 1193, 541, 260, 2959, 1198, 2858, 2571, 1196, 1617],\n"," 1051: [318, 50, 527, 858, 1193, 541, 593, 2959, 1198, 2858],\n"," 1052: [318, 50, 527, 858, 1193, 541, 260, 593, 1198, 2858],\n"," 1053: [318, 50, 527, 858, 1193, 593, 2959, 1617, 1136, 608]})"]},"metadata":{},"execution_count":14}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 리스트를 작성한다\n","\n","pred_user2items = defaultdict(list)\n","\n","for user_id in unique_user_ids:\n"," user_index = user_id2index[user_id]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = unique_movie_ids[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":15,"id":"6a343574","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"6a343574","executionInfo":{"status":"ok","timestamp":1672117314449,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6b0147f5-1cdf-489b-9212-fe876c1dcb1e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":15}],"source":["# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":16,"id":"cadf27f5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"cadf27f5","executionInfo":{"status":"ok","timestamp":1672117314451,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3506e8df-2a9d-47ff-843b-e76531289d73"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title genre \\\n","49 50 Usual Suspects, The (1995) [Crime, Mystery, Thriller] \n","315 318 Shawshank Redemption, The (1994) [Drama] \n","523 527 Schindler's List (1993) [Drama, War] \n","\n"," tag \n","49 [kevin spacey, ensemble cast, complicated, mus... \n","315 [based on a short story, directorial debut, fr... \n","523 [speilberg, drama, holocaust, steven spielberg... "],"text/html":["\n","
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movie_idtitlegenretag
4950Usual Suspects, The (1995)[Crime, Mystery, Thriller][kevin spacey, ensemble cast, complicated, mus...
315318Shawshank Redemption, The (1994)[Drama][based on a short story, directorial debut, fr...
523527Schindler's List (1993)[Drama, War][speilberg, drama, holocaust, steven spielberg...
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\n","
\n"," "]},"metadata":{},"execution_count":16}],"source":["# user_id=2에 대한 추천(318, 50, 527)\n","movies[movies.movie_id.isin([318, 50, 527])]"]},{"cell_type":"code","execution_count":16,"id":"8fe9fc9a","metadata":{"id":"8fe9fc9a","executionInfo":{"status":"ok","timestamp":1672117314451,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/IMF.ipynb b/chapter5/colab/IMF.ipynb index 6d9d857..1744802 100644 --- a/chapter5/colab/IMF.ipynb +++ b/chapter5/colab/IMF.ipynb @@ -1,1544 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "31a1f64c", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/IMF.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "c0dccd9c", - "metadata": {}, - "source": [ - "# Implicit Matrix Factorization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c302ab23", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4f1322e1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "79120322", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 10\n", - "# 평가 수의 임곗값\n", - "minimum_num_rating = 0\n", - "# 에폭 수\n", - "n_epochs = 50\n", - "# alpha\n", - "alpha = 1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "df43e6c5", - "metadata": {}, - "outputs": [], - "source": [ - "# 행력 분석용으로 행렬을 작성한다\n", - "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", - " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", - ")\n", - "\n", - "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", - "\n", - "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", - "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", - "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", - "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", - "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4c2f401f", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.sparse import lil_matrix\n", - "# 희소 행렬을 초기화하고, 각 셀에 값을 넣는다\n", - "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", - "for i, row in movielens_train_high_rating.iterrows():\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " movielens_matrix[movie_index, user_index] = 1.0 * alpha # 이후, 영화의 평갓값을 0/1로 이진화한다. 클릭 수 등의 데이터인 경우에는 log(click 수) 등으로 변형하는 것도 효과적이다." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ee004fe", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install implicit==0.4.4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e76c9ca3", - "metadata": {}, - "outputs": [], - "source": [ - "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c5a6a5bc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n" - ] - } - ], - "source": [ - "import implicit\n", - "\n", - "# 모델 초기화\n", - "model = implicit.als.AlternatingLeastSquares(\n", - " factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e812e181", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9594468ecdf34e6fb090e00d9ca19b15", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n", - "" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "04b38bdb", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "0 1 Toy Story (1995) \n", - "583 589 Terminator 2: Judgment Day (1991) \n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "\n", - " genre \\\n", - "0 [Adventure, Animation, Children, Comedy, Fantasy] \n", - "583 [Action, Sci-Fi] \n", - "1171 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag \n", - "0 [pixar, pixar, pixar, animation, pixar, animat... \n", - "583 [action, sci-fi, dvd, seen more than once, tim... \n", - "1171 [lucas, george lucas, george lucas, gfei own i... " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(1196, 1, 589)\n", - "movies[movies.movie_id.isin([1196, 1, 589])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c7399b0", - "metadata": {}, - "outputs": [], - "source": [ - "# IMF에서는 factors나 alpha 설정이 예측 정밀도에 중요하므로, 값을 바꾸어가며 시도해봅니다." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"31a1f64c","metadata":{"id":"31a1f64c"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/IMF.ipynb)"]},{"cell_type":"markdown","id":"c0dccd9c","metadata":{"id":"c0dccd9c"},"source":["# Implicit Matrix Factorization"]},{"cell_type":"code","execution_count":1,"id":"c302ab23","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c302ab23","executionInfo":{"status":"ok","timestamp":1672118046019,"user_tz":-540,"elapsed":10729,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"99be911a-fa2c-4848-b9fe-de4ba2570907"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:12:10-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 13.6MB/s in 6.1s \n","\n","2022-12-27 05:12:18 (10.3 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"4f1322e1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4f1322e1","executionInfo":{"status":"ok","timestamp":1672118103815,"user_tz":-540,"elapsed":57828,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5aef6143-782d-4602-9b40-0fc6cb3f0475"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"79120322","metadata":{"id":"79120322","executionInfo":{"status":"ok","timestamp":1672118103816,"user_tz":-540,"elapsed":32,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 10\n","# 평가 수의 임곗값\n","minimum_num_rating = 0\n","# 에폭 수\n","n_epochs = 50\n","# alpha\n","alpha = 1.0"]},{"cell_type":"code","execution_count":4,"id":"df43e6c5","metadata":{"id":"df43e6c5","executionInfo":{"status":"ok","timestamp":1672118103817,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 행력 분석용으로 행렬을 작성한다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n","\n","# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n","unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n","unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))"]},{"cell_type":"code","execution_count":5,"id":"4c2f401f","metadata":{"id":"4c2f401f","executionInfo":{"status":"ok","timestamp":1672118107348,"user_tz":-540,"elapsed":3561,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse import lil_matrix\n","# 희소 행렬을 초기화하고, 각 셀에 값을 넣는다\n","movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n","for i, row in movielens_train_high_rating.iterrows():\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," movielens_matrix[movie_index, user_index] = 1.0 * alpha # 이후, 영화의 평갓값을 0/1로 이진화한다. 클릭 수 등의 데이터인 경우에는 log(click 수) 등으로 변형하는 것도 효과적이다."]},{"cell_type":"code","execution_count":6,"id":"7ee004fe","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7ee004fe","executionInfo":{"status":"ok","timestamp":1672118180966,"user_tz":-540,"elapsed":73628,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"7561d4a0-8562-4190-93ec-2966e0cf9e32"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting implicit==0.4.4\n"," Downloading implicit-0.4.4.tar.gz (1.1 MB)\n","\u001b[K |████████████████████████████████| 1.1 MB 30.9 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n","Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n","Building wheels for collected packages: implicit\n"," Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825530 sha256=4f9058d1d351d60c74e7cfac5a34e01c831ecc5683e4cb2c3b0bc2816f422259\n"," Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n","Successfully built implicit\n","Installing collected packages: implicit\n","Successfully installed implicit-0.4.4\n"]}],"source":["!pip install implicit==0.4.4"]},{"cell_type":"code","execution_count":7,"id":"e76c9ca3","metadata":{"id":"e76c9ca3","executionInfo":{"status":"ok","timestamp":1672118180966,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다"]},{"cell_type":"code","execution_count":8,"id":"c5a6a5bc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c5a6a5bc","executionInfo":{"status":"ok","timestamp":1672118181566,"user_tz":-540,"elapsed":607,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"2bf44fbc-c3cb-4966-854d-99bc40b41415"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:implicit:GPU training requires factor size to be a multiple of 32. Increasing factors from 10 to 32.\n","WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n"]}],"source":["import implicit\n","\n","# 모델 초기화\n","model = implicit.als.AlternatingLeastSquares(\n"," factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1\n",")"]},{"cell_type":"code","execution_count":9,"id":"e812e181","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["fb6ab01e37634a9abeec2b179da03960","e1607d7715e04820885029cdf3a74c6f","2fdc799239424e849ffee921e6ee603f","6092009309584cdf9c993516020fd83d","5f77d52aa8c24e999f128edf4bba9ba1","5a37456cfd97490b82dc40007c2e2df1","4a54ee19a6434221b42e0604706c3e98","c2f622744a7b4e9e93eea9d685587d1a","cbb30e2efa2b43189dc50c6c9aebb349","1866fe3ab47444b2b106afb7f42b56ad","2292d6ee2146480c929ca724004f0eb0"]},"id":"e812e181","executionInfo":{"status":"ok","timestamp":1672118185699,"user_tz":-540,"elapsed":4134,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"35f00436-113b-421c-da6c-4b24030ef824"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/50 [00:00\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n","
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\n"," \n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"04b38bdb","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":239},"id":"04b38bdb","executionInfo":{"status":"ok","timestamp":1672118185701,"user_tz":-540,"elapsed":21,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3deca087-a3c9-4705-b213-014e3e68b09a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","583 589 Terminator 2: Judgment Day (1991) \n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","\n"," genre \\\n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","583 [Action, Sci-Fi] \n","1171 [Action, Adventure, Sci-Fi] \n","\n"," tag \n","0 [pixar, pixar, pixar, animation, pixar, animat... \n","583 [action, sci-fi, dvd, seen more than once, tim... \n","1171 [lucas, george lucas, george lucas, gfei own i... "],"text/html":["\n","
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(1196, 1, 589)\n","movies[movies.movie_id.isin([1196, 1, 589])]"]},{"cell_type":"code","execution_count":13,"id":"3c7399b0","metadata":{"id":"3c7399b0","executionInfo":{"status":"ok","timestamp":1672118185702,"user_tz":-540,"elapsed":20,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# IMF에서는 factors나 alpha 설정이 예측 정밀도에 중요하므로, 값을 바꾸어가며 시도해봅니다."]}],"metadata":{"kernelspec":{"display_name":"Python 3 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\ No newline at end of file diff --git a/chapter5/colab/Item2vec.ipynb b/chapter5/colab/Item2vec.ipynb index 97c8237..eab2eb9 100644 --- a/chapter5/colab/Item2vec.ipynb +++ b/chapter5/colab/Item2vec.ipynb @@ -1,1481 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "41d4a649", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Item2vec.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "e9d7e57d", - "metadata": {}, - "source": [ - "# Item2vec" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d70dbb6", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c4f50ee4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3a74e8b5", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 100\n", - "# 에폭 수\n", - "n_epochs = 30\n", - "# window 크기\n", - "window = 100\n", - "# 스킵 그램\n", - "use_skip_gram = 1\n", - "# 계측적 소프트맥스\n", - "use_hierarchial_softmax = 0\n", - "# 사용할 단어의 출현 횟수의 임곗값\n", - "min_count = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e70dbcd2", - "metadata": {}, - "outputs": [], - "source": [ - "# item2vec의 입력으로 사용할 데이터를 생성한다\n", - "item2vec_data = []\n", - "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # 평가된 순서대로 나열한다\n", - " # item2vec에서는 window라는 파라미터가 있으며, item이 평가된 순서도 중요한 요소이다\n", - " item2vec_data.append(data.sort_values(\"timestamp\")[\"movie_id\"].tolist())\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31c6f683", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install gensim==4.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "503fed11", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "import gensim\n", - "\n", - "# item2vec 학습\n", - "model = gensim.models.word2vec.Word2Vec(\n", - " item2vec_data,\n", - " vector_size=factors,\n", - " window=window,\n", - " sg=use_skip_gram,\n", - " hs=use_hierarchial_softmax,\n", - " epochs=n_epochs,\n", - " min_count=min_count,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c68290ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "movie_id=1210, title=Star Wars: Episode VI - Return of the Jedi (1983), score=0.8640398979187012\n", - "movie_id=260, title=Star Wars: Episode IV - A New Hope (a.k.a. Star Wars) (1977), score=0.8317610025405884\n", - "movie_id=1198, title=Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981), score=0.8280032873153687\n", - "movie_id=2571, title=Matrix, The (1999), score=0.8175885677337646\n", - "movie_id=1240, title=Terminator, The (1984), score=0.7891822457313538\n", - "movie_id=1197, title=Princess Bride, The (1987), score=0.7843142747879028\n", - "movie_id=1291, title=Indiana Jones and the Last Crusade (1989), score=0.7821475267410278\n", - "movie_id=1270, title=Back to the Future (1985), score=0.7789417505264282\n", - "movie_id=1200, title=Aliens (1986), score=0.7704472541809082\n", - "movie_id=1214, title=Alien (1979), score=0.7694951891899109\n" - ] - } - ], - "source": [ - "# 스타워즈/에피소드 5(movie_id=1196）를 입력했을 때의 유사 영화\n", - "# 스타워즈/에피소드 4, 6이 상위에 나타나므로 학습되었음을 알 수 있다\n", - "for movie_id, score in model.wv.most_similar(1196):\n", - " title = movies[movies.movie_id == movie_id].title.tolist()[0]\n", - " print(f'movie_id={movie_id}, title={title}, score={score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7c07eb79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{1: [380, 110, 150, 457, 592, 165, 595, 225, 10, 590],\n", - " 2: [480, 1196, 589, 527, 593, 356, 318, 457, 296, 592],\n", - " 3: [593, 1196, 2571, 318, 527, 2028, 1198, 4993, 2959, 3578],\n", - " 4: [380, 356, 377, 457, 318, 593, 10, 539, 349, 527],\n", - " 5: [1193, 1208, 36, 296, 318, 904, 750, 1213, 1288, 908],\n", - " 6: [1240, 593, 1210, 2858, 1291, 1270, 1617, 541, 1036, 1214],\n", - " 7: [750, 1208, 858, 1193, 1267, 1204, 922, 2019, 1247, 1221],\n", - " 8: [1210, 4993, 110, 5952, 296, 2028, 260, 356, 480, 2716],\n", - " 9: [1136, 1089, 1617, 296, 593, 2571, 750, 608, 1196, 1265],\n", - " 10: [608, 1247, 1207, 318, 1230, 1271, 300, 593, 150, 1304],\n", - " 11: [1197, 1036, 589, 1214, 1200, 457, 1265, 356, 110, 919],\n", - " 12: [2571, 1196, 1240, 1580, 1291, 2028, 1270, 1210, 589, 1036],\n", - " 13: [1196, 1270, 2571, 593, 1240, 356, 1198, 260, 1200, 608],\n", - 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" 905: [150, 1, 527, 380, 21, 480, 1079, 1097, 1784, 590],\n", - " 906: [318, 1265, 2762, 111, 1208, 2571, 2329, 1394, 912, 923],\n", - " 907: [356, 480, 47, 377, 50, 588, 364, 161, 165, 539],\n", - " 908: [1197, 1376, 593, 318, 1617, 608, 32, 648, 3527, 750],\n", - " 909: [1196, 3578, 2762, 4226, 1210, 1291, 593, 2028, 589, 7153],\n", - " 910: [1196, 527, 2028, 1193, 2762, 608, 2571, 1198, 1291, 1221],\n", - " 911: [1197, 608, 1304, 1193, 912, 1270, 527, 858, 1617, 904],\n", - " 912: [1240, 2571, 1197, 1580, 1210, 1291, 1198, 1036, 589, 1265],\n", - " 913: [1197, 1207, 1193, 318, 527, 858, 1198, 912, 1234, 608],\n", - " 914: [9, 667, 737, 765, 95, 608, 783, 494, 762, 1],\n", - " 915: [1196, 1198, 1291, 1617, 318, 1304, 1136, 919, 296, 110],\n", - " 916: [2858, 3578, 5952, 2571, 2762, 3996, 4226, 2329, 7153, 296],\n", - " 917: [260, 783, 1393, 527, 508, 34, 1183, 805, 7, 733],\n", - " 918: [653, 1, 805, 494, 9, 783, 5, 141, 7, 6],\n", - " 919: [1036, 589, 1196, 2571, 1198, 1270, 1197, 1374, 1200, 1527],\n", - 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" 999: [1077, 1252, 1214, 750, 1200, 904, 1270, 1196, 908, 924],\n", - " 1001: [380, 590, 589, 377, 318, 593, 539, 10, 349, 296],\n", - " 1002: [733, 95, 248, 648, 609, 719, 765, 158, 694, 1],\n", - " 1003: [2959, 2858, 2716, 3578, 2028, 1196, 1617, 1704, 1580, 1610],\n", - " 1004: [527, 150, 589, 260, 480, 50, 1196, 36, 32, 1197],\n", - " 1005: [364, 590, 508, 11, 34, 47, 21, 32, 1036, 141],\n", - " 1006: [36, 296, 593, 25, 17, 50, 508, 590, 21, 110],\n", - " 1007: [357, 356, 593, 527, 1784, 318, 497, 2858, 608, 296],\n", - " 1008: [1197, 2858, 1617, 2028, 1, 111, 110, 1136, 589, 912],\n", - " 1009: [2028, 1196, 2762, 1291, 1036, 1210, 1198, 3578, 1580, 260],\n", - " 1011: [318, 527, 1197, 1196, 260, 1210, 1198, 1240, 356, 858],\n", - " 1012: [2762, 2571, 2716, 1617, 2028, 1196, 296, 2959, 3578, 2396],\n", - " 1013: [318, 47, 356, 50, 110, 2959, 2858, 2571, 1196, 32],\n", - " 1014: [912, 1193, 1617, 923, 1208, 1230, 1094, 1225, 1250, 318],\n", - " 1015: [750, 908, 912, 1193, 541, 608, 1196, 858, 1267, 923],\n", - " 1016: [593, 858, 50, 1617, 1089, 1196, 1208, 2028, 1221, 2762],\n", - " 1017: [2571, 4886, 5445, 6539, 3578, 1198, 1214, 260, 1291, 4226],\n", - " 1018: [318, 50, 527, 541, 1197, 1193, 1200, 1617, 1198, 260],\n", - " 1019: [62, 32, 832, 9, 6, 5, 1073, 36, 737, 653],\n", - " 1020: [318, 1270, 1196, 34, 110, 260, 1259, 1097, 1079, 919],\n", - " 1021: [1617, 608, 2571, 296, 1193, 541, 1210, 1291, 1240, 912],\n", - " 1022: [1197, 1193, 1207, 1247, 912, 1196, 1304, 858, 260, 1208],\n", - " 1023: [1291, 1036, 589, 1210, 1240, 296, 318, 1270, 356, 2028],\n", - " 1025: [904, 1617, 750, 1193, 1196, 1197, 1234, 1204, 1252, 1208],\n", - " 1026: [110, 527, 318, 356, 2028, 296, 1265, 858, 32, 1580],\n", - " 1027: [4993, 5952, 7153, 2329, 4886, 33166, 2571, 45722, 37731, 33794],\n", - " 1028: [110, 377, 589, 592, 318, 588, 316, 474, 161, 292],\n", - " 1029: [110, 150, 161, 474, 225, 380, 356, 349, 480, 589],\n", - " 1030: [1196, 3578, 1210, 1270, 1197, 1291, 780, 480, 260, 589],\n", - " 1031: [380, 589, 356, 377, 150, 457, 318, 588, 349, 292],\n", - 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" 1050: [150, 380, 34, 592, 377, 588, 608, 539, 36, 6],\n", - " 1051: [141, 780, 36, 11, 1393, 783, 708, 736, 5, 539],\n", - " 1052: [356, 1704, 2571, 2762, 593, 2858, 1210, 1196, 318, 1197],\n", - " 1053: [593, 1136, 2858, 2028, 1617, 2918, 608, 32, 480, 1220]}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 각 사용자에 대한 추천 리스트를 작성한다\n", - "\n", - "pred_user2items = dict()\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " input_data = []\n", - " for item_id in data.sort_values(\"timestamp\")[\"movie_id\"].tolist():\n", - " if item_id in model.wv.key_to_index:\n", - " input_data.append(item_id)\n", - " if len(input_data) == 0:\n", - " # 추천 계산할 수 없는 경우에는 빈 배열\n", - " pred_user2items[user_id] = []\n", - " continue\n", - " recommended_items = model.wv.most_similar(input_data, topn=10)\n", - " pred_user2items[user_id] = [d[0] for d in recommended_items]\n", - "\n", - "pred_user2items" - ] - 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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
476480Jurassic Park (1993)[Action, Adventure, Sci-Fi, Thriller][based on a book, biology, michael crichton, s...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "476 480 Jurassic Park (1993) \n", - "583 589 Terminator 2: Judgment Day (1991) \n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "\n", - " genre \\\n", - "476 [Action, Adventure, Sci-Fi, Thriller] \n", - "583 [Action, Sci-Fi] \n", - "1171 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag \n", - "476 [based on a book, biology, michael crichton, s... \n", - "583 [action, sci-fi, dvd, seen more than once, tim... \n", - "1171 [lucas, george lucas, george lucas, gfei own i... " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(480, 1196, 589)\n", - "movies[movies.movie_id.isin([480, 1196, 589])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8107e735", - "metadata": {}, - "outputs": [], - "source": [ - "# item2vec에서는 인자 수, 에폭 수, windows 크기, 사용하는 단어의 출현 횟수의 임곗값 모두가 중요하므루, 그리드 서치를 통해 최적의 값을 결정합니다." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"41d4a649","metadata":{"id":"41d4a649"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Item2vec.ipynb)"]},{"cell_type":"markdown","id":"e9d7e57d","metadata":{"id":"e9d7e57d"},"source":["# Item2vec"]},{"cell_type":"code","execution_count":1,"id":"5d70dbb6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5d70dbb6","executionInfo":{"status":"ok","timestamp":1672118007200,"user_tz":-540,"elapsed":9080,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"de64103d-3866-4307-867d-d113afdebf1a"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:13:14-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 15.2MB/s in 5.6s \n","\n","2022-12-27 05:13:21 (11.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"c4f50ee4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c4f50ee4","executionInfo":{"status":"ok","timestamp":1672118076735,"user_tz":-540,"elapsed":69538,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b92eaa23-ded4-4964-8554-6d2d30bad418"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"3a74e8b5","metadata":{"id":"3a74e8b5","executionInfo":{"status":"ok","timestamp":1672118076736,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 100\n","# 에폭 수\n","n_epochs = 30\n","# window 크기\n","window = 100\n","# 스킵 그램\n","use_skip_gram = 1\n","# 계측적 소프트맥스\n","use_hierarchial_softmax = 0\n","# 사용할 단어의 출현 횟수의 임곗값\n","min_count = 5"]},{"cell_type":"code","execution_count":4,"id":"e70dbcd2","metadata":{"id":"e70dbcd2","executionInfo":{"status":"ok","timestamp":1672118077324,"user_tz":-540,"elapsed":593,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# item2vec의 입력으로 사용할 데이터를 생성한다\n","item2vec_data = []\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," # 평가된 순서대로 나열한다\n"," # item2vec에서는 window라는 파라미터가 있으며, item이 평가된 순서도 중요한 요소이다\n"," item2vec_data.append(data.sort_values(\"timestamp\")[\"movie_id\"].tolist())\n"]},{"cell_type":"code","execution_count":5,"id":"31c6f683","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"31c6f683","executionInfo":{"status":"ok","timestamp":1672118084326,"user_tz":-540,"elapsed":7006,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"a77de23f-2b79-40c5-9e0f-f27877eb938e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.4 MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":6,"id":"503fed11","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"503fed11","executionInfo":{"status":"ok","timestamp":1672118193280,"user_tz":-540,"elapsed":108966,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"218adfc6-d5e6-45bf-e1c0-290ac2b92e68"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["import gensim\n","\n","# item2vec 학습\n","model = gensim.models.word2vec.Word2Vec(\n"," item2vec_data,\n"," vector_size=factors,\n"," window=window,\n"," sg=use_skip_gram,\n"," hs=use_hierarchial_softmax,\n"," epochs=n_epochs,\n"," min_count=min_count,\n",")"]},{"cell_type":"code","execution_count":7,"id":"c68290ed","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c68290ed","executionInfo":{"status":"ok","timestamp":1672118193280,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"a951f33b-d4c7-4915-bb82-72dd6b17624a"},"outputs":[{"output_type":"stream","name":"stdout","text":["movie_id=260, title=Star Wars: Episode IV - A New Hope (a.k.a. Star Wars) (1977), score=0.8508290648460388\n","movie_id=1210, title=Star Wars: Episode VI - Return of the Jedi (1983), score=0.839510977268219\n","movie_id=1198, title=Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981), score=0.8310489058494568\n","movie_id=2571, title=Matrix, The (1999), score=0.7958545684814453\n","movie_id=1240, title=Terminator, The (1984), score=0.7907897233963013\n","movie_id=541, title=Blade Runner (1982), score=0.7818757891654968\n","movie_id=1197, title=Princess Bride, The (1987), score=0.7788391709327698\n","movie_id=1200, title=Aliens (1986), score=0.7773821949958801\n","movie_id=1214, title=Alien (1979), score=0.7692050933837891\n","movie_id=1291, title=Indiana Jones and the Last Crusade (1989), score=0.7649016976356506\n"]}],"source":["# 스타워즈/에피소드 5(movie_id=1196）를 입력했을 때의 유사 영화\n","# 스타워즈/에피소드 4, 6이 상위에 나타나므로 학습되었음을 알 수 있다\n","for movie_id, score in model.wv.most_similar(1196):\n"," title = movies[movies.movie_id == movie_id].title.tolist()[0]\n"," print(f'movie_id={movie_id}, title={title}, score={score}')"]},{"cell_type":"code","execution_count":8,"id":"7c07eb79","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7c07eb79","executionInfo":{"status":"ok","timestamp":1672118195816,"user_tz":-540,"elapsed":2541,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9fa1c855-057c-4201-cfb7-4bd66c52cec4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{1: [380, 457, 110, 150, 590, 592, 318, 595, 165, 500],\n"," 2: [589, 1196, 593, 356, 480, 1198, 457, 318, 296, 1291],\n"," 3: [2571, 1196, 318, 1198, 527, 2028, 593, 4993, 3578, 5952],\n"," 4: [457, 356, 380, 377, 318, 593, 10, 349, 296, 367],\n"," 5: [1208, 1193, 318, 36, 904, 296, 1213, 750, 908, 1617],\n"," 6: [1240, 1291, 1617, 1210, 541, 1036, 589, 593, 2858, 1214],\n"," 7: [750, 1208, 1267, 910, 858, 1193, 1247, 1221, 1204, 541],\n"," 8: [1210, 4993, 110, 260, 356, 2028, 5952, 318, 296, 3147],\n"," 9: [1136, 750, 1617, 2791, 1089, 296, 1265, 1206, 2571, 50],\n"," 10: [1207, 608, 1247, 318, 1271, 593, 1230, 1185, 300, 858],\n"," 11: [1197, 1036, 457, 589, 1214, 110, 1200, 356, 1265, 541],\n"," 12: [2571, 1196, 1240, 2028, 1291, 1210, 589, 1580, 1198, 1617],\n"," 13: [1196, 1198, 1270, 1240, 2571, 110, 260, 356, 593, 589],\n"," 14: [1198, 7153, 4306, 4886, 4993, 5952, 2571, 8360, 356, 912],\n"," 16: [541, 2571, 1198, 1270, 750, 589, 1291, 1374, 1036, 1197],\n"," 17: [1196, 750, 541, 904, 1208, 1617, 1240, 1221, 912, 1198],\n"," 18: [1210, 50, 858, 1197, 1617, 1610, 1270, 1265, 480, 1221],\n"," 19: [593, 34, 150, 296, 1265, 1097, 608, 590, 858, 440],\n"," 22: [318, 457, 590, 592, 377, 34, 349, 10, 165, 1],\n"," 23: [318, 356, 457, 150, 110, 36, 34, 357, 588, 590],\n"," 24: [736, 6, 95, 653, 1393, 141, 356, 783, 608, 32],\n"," 26: [480, 356, 590, 593, 318, 592, 474, 364, 595, 296],\n"," 27: [260, 1, 6, 648, 1210, 786, 653, 95, 589, 1393],\n"," 28: [2797, 1307, 1220, 1198, 2791, 1394, 1079, 1285, 3039, 2716],\n"," 29: [4447, 4369, 3717, 5459, 2000, 1580, 2001, 1610, 1370, 2353],\n"," 30: [457, 589, 1197, 508, 296, 21, 1036, 587, 36, 474],\n"," 33: [25, 58, 62, 318, 296, 34, 593, 527, 260, 1393],\n"," 34: [356, 1196, 1036, 150, 733, 380, 1291, 2762, 1580, 296],\n"," 35: [1196, 858, 593, 1198, 1214, 296, 1240, 318, 1617, 1089],\n"," 36: [1198, 2571, 110, 318, 1197, 2028, 47, 608, 3578, 527],\n"," 37: [1196, 2571, 1214, 1198, 32, 593, 260, 50, 541, 110],\n"," 38: [110, 3578, 2762, 480, 589, 1196, 4993, 1198, 1291, 50],\n"," 40: [356, 858, 2762, 2028, 47, 110, 32, 1265, 223, 2959],\n"," 41: [1265, 1278, 1097, 1079, 1394, 1234, 1304, 1198, 2791, 1196],\n"," 42: [318, 356, 150, 527, 457, 593, 34, 21, 497, 296],\n"," 43: [356, 357, 380, 780, 586, 150, 377, 237, 733, 248],\n"," 44: [36, 1617, 593, 1213, 1784, 800, 1193, 1394, 111, 1704],\n"," 45: [1196, 318, 296, 2571, 527, 1198, 260, 1214, 1210, 1197],\n"," 46: [1196, 1198, 1291, 1136, 4993, 2571, 858, 5952, 3578, 1197],\n"," 47: [356, 47, 480, 527, 457, 1, 1617, 1197, 1704, 4886],\n"," 48: [53322, 51935, 51091, 51662, 45499, 8644, 6059, 8937, 45722, 3578],\n"," 50: [296, 588, 380, 589, 364, 508, 34, 47, 592, 50],\n"," 51: [1207, 1252, 1267, 1250, 1292, 1288, 1219, 800, 1221, 2858],\n"," 52: [1207, 908, 912, 923, 1247, 858, 750, 1221, 1276, 608],\n"," 53: [2571, 1617, 1196, 2959, 1704, 1265, 356, 3147, 3114, 110],\n"," 54: [527, 588, 377, 47, 364, 592, 349, 595, 474, 316],\n"," 55: [1208, 858, 2858, 1617, 111, 904, 1136, 1207, 923, 912],\n"," 56: [1304, 1276, 1288, 608, 899, 1225, 1267, 1233, 1278, 318],\n"," 57: [736, 64, 662, 609, 467, 62, 762, 141, 608, 107],\n"," 58: [1207, 608, 1196, 1193, 858, 1276, 2858, 1221, 1136, 1213],\n"," 59: [110, 296, 527, 356, 589, 380, 161, 480, 588, 377],\n"," 60: [1207, 912, 904, 1276, 908, 111, 750, 1617, 919, 1136],\n"," 61: [527, 858, 1208, 923, 296, 2858, 1225, 111, 904, 1250],\n"," 62: [457, 110, 590, 480, 377, 356, 318, 589, 165, 592],\n"," 63: 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1204, 50],\n"," 79: [527, 21, 356, 337, 150, 265, 47, 1089, 235, 34],\n"," 80: [593, 356, 150, 589, 527, 47, 590, 588, 592, 34],\n"," 81: [593, 1196, 527, 318, 1617, 858, 1240, 110, 2028, 1198],\n"," 82: [7153, 5952, 6377, 4995, 4226, 8961, 3578, 3996, 1196, 7361],\n"," 83: [858, 1234, 904, 1304, 1196, 1193, 1276, 1288, 1208, 1221],\n"," 84: [25, 608, 733, 780, 736, 508, 6, 95, 1, 527],\n"," 85: [318, 608, 1617, 1213, 356, 858, 111, 2762, 47, 1089],\n"," 86: [260, 1197, 1270, 1387, 1580, 541, 592, 1, 593, 1265],\n"," 87: [2571, 1617, 2706, 1704, 2028, 3114, 2599, 2355, 356, 1265],\n"," 88: [589, 2762, 593, 1291, 380, 480, 1270, 1036, 733, 318],\n"," 89: [593, 356, 457, 590, 589, 527, 377, 349, 47, 592],\n"," 90: [736, 95, 648, 5, 719, 9, 6, 765, 1393, 667],\n"," 91: [1304, 858, 908, 260, 1234, 1276, 1221, 1617, 1240, 608],\n"," 92: [593, 589, 2959, 2858, 4993, 50, 858, 296, 541, 1200],\n"," 93: [51540, 8937, 60069, 51662, 50872, 44199, 47423, 55830, 51935, 54997],\n"," 94: [380, 589, 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2858],\n"," 207: [318, 356, 527, 150, 457, 21, 357, 296, 110, 337],\n"," 208: [457, 356, 377, 589, 480, 292, 593, 733, 474, 780],\n"," 209: [36, 25, 318, 17, 527, 457, 150, 356, 296, 21],\n"," 210: [356, 1196, 260, 527, 457, 1198, 2028, 1265, 1617, 1240],\n"," 211: [1208, 750, 1193, 541, 1207, 1213, 1036, 1291, 1387, 2571],\n"," 212: [2858, 1230, 1247, 1225, 1267, 1276, 1094, 527, 922, 1254],\n"," 213: [589, 3578, 356, 110, 318, 457, 2959, 1291, 1610, 1198],\n"," 214: [593, 318, 1210, 608, 296, 2858, 1580, 589, 2571, 1036],\n"," 215: [527, 1204, 899, 590, 2329, 969, 1201, 265, 1090, 2115],\n"," 216: [356, 1270, 589, 1291, 1617, 457, 1240, 527, 858, 150],\n"," 217: [356, 357, 588, 318, 457, 377, 364, 11, 440, 236],\n"," 218: [608, 1617, 50, 1193, 1089, 1704, 25, 1208, 858, 1094],\n"," 219: [1197, 356, 1265, 608, 1259, 1079, 1278, 593, 17, 539],\n"," 220: [1276, 969, 908, 1234, 904, 1254, 1252, 910, 1207, 1247],\n"," 221: [110, 1291, 1240, 592, 150, 1, 377, 1580, 1197, 1265],\n"," 222: 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2762, 2396, 608, 1213, 2395, 296, 1136, 593],\n"," 239: [110, 593, 457, 260, 1210, 318, 589, 1036, 527, 1197],\n"," 241: [356, 593, 500, 367, 380, 454, 1270, 318, 161, 1],\n"," 242: [648, 36, 786, 608, 356, 1393, 457, 783, 832, 1210],\n"," 243: [1197, 1136, 1265, 2791, 1278, 1270, 1214, 1291, 1617, 1234],\n"," 244: [908, 1207, 923, 1617, 1288, 1221, 1213, 111, 903, 1094],\n"," 245: [1208, 2858, 1193, 1617, 1196, 608, 296, 1213, 1136, 593],\n"," 246: [2571, 2959, 1196, 1136, 1198, 608, 593, 1265, 1089, 2762],\n"," 247: [110, 318, 349, 185, 474, 296, 440, 527, 508, 733],\n"," 248: [2858, 2571, 1196, 1617, 2959, 2762, 2028, 1206, 858, 541],\n"," 249: [2858, 110, 296, 260, 1210, 1270, 858, 5349, 1617, 527],\n"," 250: [527, 593, 356, 590, 457, 296, 508, 161, 337, 282],\n"," 251: [318, 457, 1197, 527, 1617, 2571, 50, 356, 1356, 780],\n"," 252: [923, 858, 1247, 750, 1617, 1208, 1196, 1276, 1304, 913],\n"," 253: [1196, 110, 593, 1265, 2762, 1210, 2028, 260, 318, 296],\n"," 254: [1617, 1198, 1213, 1240, 608, 2028, 1291, 110, 1036, 2762],\n"," 255: [457, 110, 377, 380, 527, 588, 539, 480, 296, 364],\n"," 256: [357, 457, 1079, 527, 318, 1197, 539, 21, 1265, 2797],\n"," 257: [1196, 1291, 589, 2028, 260, 457, 1200, 858, 2762, 1221],\n"," 258: [1198, 1240, 1036, 457, 858, 1617, 1221, 1197, 750, 1270],\n"," 259: [318, 1196, 110, 1198, 1197, 1210, 260, 589, 1136, 858],\n"," 260: [1196, 1198, 318, 110, 457, 1214, 50, 356, 1, 1291],\n"," 261: [1196, 2762, 1617, 2858, 1198, 1210, 260, 2028, 1214, 1270],\n"," 262: [589, 1210, 356, 593, 2028, 733, 1610, 480, 1580, 1270],\n"," 263: [356, 2028, 1617, 1208, 150, 924, 1089, 1580, 7153, 4306],\n"," 264: [318, 110, 150, 527, 47, 590, 380, 364, 592, 377],\n"," 265: [457, 589, 1036, 380, 1370, 2571, 648, 480, 736, 1291],\n"," 266: [1198, 1196, 750, 1240, 858, 912, 608, 1221, 1617, 924],\n"," 267: [750, 1208, 1196, 1240, 858, 1221, 1198, 2858, 912, 1252],\n"," 268: [380, 318, 225, 590, 364, 500, 589, 165, 588, 733],\n"," 269: [736, 733, 648, 653, 260, 95, 1073, 786, 788, 104],\n"," 270: [608, 780, 1, 32, 593, 356, 36, 318, 6, 589],\n"," 271: [1240, 589, 2571, 316, 1198, 1097, 1036, 1291, 733, 110],\n"," 272: [1617, 1193, 1213, 2571, 608, 1089, 111, 593, 318, 1136],\n"," 273: [356, 593, 1291, 1196, 1198, 47, 296, 457, 2762, 2858],\n"," 274: [1198, 912, 969, 908, 858, 1617, 1207, 904, 1291, 2571],\n"," 275: [858, 527, 1213, 356, 1198, 36, 1197, 2571, 1394, 1784],\n"," 276: [1617, 1214, 527, 608, 2028, 858, 260, 1213, 1136, 1200],\n"," 277: [589, 1196, 1291, 1240, 1198, 110, 2571, 1210, 457, 356],\n"," 278: [110, 356, 593, 589, 318, 1196, 1210, 1198, 457, 296],\n"," 279: [110, 1197, 2858, 356, 2028, 541, 1036, 32, 858, 1617],\n"," 280: [356, 318, 150, 2858, 2396, 1784, 593, 110, 36, 296],\n"," 281: [356, 380, 588, 590, 480, 34, 377, 364, 161, 508],\n"," 282: [780, 141, 736, 1393, 25, 6, 648, 95, 356, 457],\n"," 285: [608, 1196, 1136, 2858, 1617, 593, 1198, 858, 1213, 1278],\n"," 286: [110, 2762, 1197, 1617, 858, 318, 1580, 356, 480, 1610],\n"," 287: [593, 318, 589, 480, 150, 296, 380, 527, 590, 588],\n"," 288: [912, 1207, 923, 1254, 910, 1247, 1204, 1267, 969, 750],\n"," 289: [1230, 1208, 1198, 1288, 1196, 260, 2396, 541, 910, 899],\n"," 290: [34, 594, 1197, 1028, 1210, 1198, 110, 1270, 356, 480],\n"," 291: [912, 1208, 1198, 908, 1247, 260, 541, 919, 1221, 904],\n"," 292: [2571, 1617, 1270, 1208, 2028, 1036, 2762, 924, 589, 527],\n"," 293: [457, 110, 380, 480, 356, 589, 296, 377, 364, 10],\n"," 294: [593, 589, 150, 480, 590, 733, 474, 527, 316, 10],\n"," 295: [1259, 34, 318, 457, 1193, 593, 858, 608, 260, 440],\n"," 296: [1196, 1617, 1198, 50, 1291, 5445, 608, 3996, 260, 1200],\n"," 297: [780, 36, 733, 1073, 6, 25, 593, 1196, 318, 356],\n"," 298: [1196, 1198, 1210, 260, 588, 356, 608, 593, 497, 1097],\n"," 300: [1198, 593, 1197, 1097, 1207, 2858, 1291, 50, 1036, 1259],\n"," 301: [1617, 1193, 2028, 1197, 111, 1089, 904, 1207, 1214, 924],\n"," 302: [356, 1198, 593, 457, 1610, 1265, 1270, 1036, 480, 1],\n"," 303: [593, 1198, 1214, 50, 589, 1617, 1240, 1291, 2762, 1208],\n"," 304: [1193, 1207, 1247, 318, 1208, 1704, 1259, 608, 527, 593],\n"," 305: [318, 380, 364, 34, 440, 161, 595, 474, 21, 592],\n"," 306: [1197, 1968, 1265, 1136, 2797, 1079, 1394, 1198, 1278, 1784],\n"," 307: [1291, 1036, 1197, 1527, 32, 2762, 1097, 3578, 593, 1374],\n"," 308: [588, 356, 150, 377, 457, 380, 595, 539, 586, 318],\n"," 309: [780, 733, 1393, 141, 608, 356, 1210, 648, 1356, 783],\n"," 310: [1265, 1193, 1210, 904, 908, 150, 1230, 1208, 2028, 953],\n"," 311: [457, 588, 377, 539, 318, 110, 380, 34, 480, 593],\n"," 312: [908, 903, 750, 923, 1204, 1207, 930, 1254, 910, 1267],\n"," 313: [1196, 593, 2858, 1198, 2762, 318, 527, 1617, 1197, 296],\n"," 314: [1193, 1197, 1208, 1214, 608, 1291, 1213, 2571, 1270, 1200],\n"," 315: [380, 590, 593, 110, 225, 588, 364, 34, 589, 474],\n"," 316: [736, 9, 5, 786, 648, 719, 765, 609, 694, 141],\n"," 317: [1196, 858, 2858, 912, 1198, 2762, 2571, 904, 260, 750],\n"," 318: 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34, 590, 300, 151, 21],\n"," 334: [1270, 1240, 1036, 1220, 1580, 593, 1265, 589, 480, 527],\n"," 335: [913, 1204, 1207, 903, 1208, 1617, 1221, 1267, 1193, 2858],\n"," 336: [1198, 1270, 1197, 1291, 858, 1580, 356, 2028, 2762, 4993],\n"," 337: [858, 2858, 296, 608, 1136, 527, 1193, 1208, 1617, 111],\n"," 338: [318, 527, 50, 1196, 47, 1198, 150, 858, 589, 1213],\n"," 339: [457, 593, 150, 110, 356, 589, 527, 161, 480, 377],\n"," 340: [1, 32, 592, 588, 21, 590, 1270, 1097, 858, 34],\n"," 341: [1221, 1197, 2028, 541, 1610, 608, 1210, 750, 1387, 1265],\n"," 342: [1196, 2571, 589, 1240, 1291, 1198, 1036, 2028, 318, 1617],\n"," 343: [457, 110, 377, 590, 356, 316, 165, 593, 474, 318],\n"," 344: [593, 318, 589, 110, 356, 1036, 527, 150, 1197, 480],\n"," 345: [1265, 1196, 2791, 593, 1079, 2716, 318, 260, 1220, 1259],\n"," 346: [500, 1210, 539, 1196, 1265, 780, 2571, 593, 440, 141],\n"," 347: [1214, 1200, 1210, 1198, 1270, 750, 1291, 589, 2716, 1036],\n"," 348: [1196, 50, 1210, 541, 260, 5445, 1136, 1197, 1206, 1704],\n"," 349: [21, 357, 34, 11, 161, 588, 440, 380, 300, 62],\n"," 350: [36, 527, 508, 497, 34, 150, 300, 17, 457, 515],\n"," 351: [527, 1259, 750, 1136, 1278, 1198, 356, 541, 1252, 235],\n"," 352: [356, 539, 150, 1784, 110, 380, 1704, 587, 318, 457],\n"," 353: [2858, 1198, 1240, 50, 318, 260, 858, 1036, 1617, 1291],\n"," 354: [1196, 2028, 527, 296, 4226, 1291, 589, 1198, 47, 4886],\n"," 355: [356, 527, 589, 380, 47, 480, 588, 161, 592, 377],\n"," 356: [608, 6, 260, 141, 1183, 62, 356, 1, 593, 527],\n"," 357: [2797, 1197, 2918, 356, 3039, 357, 1259, 608, 2716, 1307],\n"," 358: [1036, 1291, 110, 1198, 541, 592, 2028, 457, 380, 47],\n"," 359: [7361, 4993, 5952, 7153, 2571, 1208, 2997, 1196, 1206, 1193],\n"," 360: [25, 58, 608, 265, 357, 62, 1393, 497, 21, 509],\n"," 361: [750, 111, 1208, 608, 1213, 1193, 1304, 858, 1276, 1089],\n"," 362: [1196, 2571, 4226, 2959, 2858, 4993, 858, 296, 1291, 5952],\n"," 363: [4226, 2571, 2858, 3578, 2762, 5445, 4878, 2028, 4995, 6539],\n"," 364: [356, 150, 589, 480, 592, 588, 47, 349, 377, 364],\n"," 365: [318, 588, 539, 377, 380, 589, 440, 47, 161, 595],\n"," 366: [356, 593, 1210, 1270, 480, 1, 318, 588, 527, 296],\n"," 367: [1207, 1276, 1208, 910, 1084, 608, 969, 903, 1254, 1299],\n"," 368: [2858, 593, 1265, 318, 1240, 1270, 2762, 2028, 356, 1208],\n"," 369: [593, 356, 110, 150, 50, 527, 457, 589, 47, 590],\n"," 370: [733, 36, 260, 648, 783, 95, 608, 25, 1393, 719],\n"," 371: [1198, 1136, 1197, 2858, 593, 2571, 858, 1240, 1270, 260],\n"," 372: [1240, 260, 2028, 593, 2858, 110, 318, 1270, 356, 858],\n"," 373: [2396, 1197, 1617, 356, 595, 457, 588, 39, 608, 593],\n"," 375: [1278, 1394, 1197, 1307, 1292, 1079, 1265, 2406, 5060, 1270],\n"," 376: [1196, 1198, 858, 750, 1291, 1036, 924, 318, 1136, 2858],\n"," 377: [1207, 1208, 1094, 1225, 1617, 608, 912, 1230, 593, 1213],\n"," 378: [1198, 1197, 1617, 593, 608, 2028, 318, 1270, 1136, 2858],\n"," 379: [356, 110, 457, 589, 593, 318, 480, 380, 1210, 1196],\n"," 380: [2571, 4993, 3996, 1196, 4306, 2762, 4963, 4226, 2959, 1580],\n"," 381: [592, 236, 282, 337, 736, 497, 141, 367, 1, 265],\n"," 382: [457, 150, 356, 480, 110, 590, 364, 377, 589, 318],\n"," 383: [527, 1198, 2028, 1213, 1136, 356, 2959, 32, 750, 589],\n"," 384: [380, 593, 377, 110, 597, 539, 588, 225, 480, 527],\n"," 385: [36, 21, 1393, 265, 508, 783, 1183, 318, 34, 356],\n"," 386: [2571, 1197, 1259, 541, 1193, 1207, 50, 2716, 919, 750],\n"," 387: [2571, 1196, 3717, 4963, 2683, 3578, 2762, 589, 2959, 3793],\n"," 388: [1196, 589, 318, 2028, 858, 593, 2858, 110, 2959, 296],\n"," 389: [4973, 5445, 6539, 4886, 2762, 4306, 6377, 1196, 47, 6874],\n"," 390: [2858, 1036, 47, 527, 32, 541, 6539, 1270, 1214, 1089],\n"," 391: [457, 588, 110, 34, 527, 539, 597, 508, 357, 500],\n"," 392: [590, 380, 508, 608, 161, 595, 1, 349, 733, 165],\n"," 393: [780, 1, 736, 1210, 494, 95, 141, 608, 36, 356],\n"," 394: [2762, 593, 2571, 527, 356, 1210, 110, 1193, 1136, 1240],\n"," 395: [110, 593, 356, 2571, 1196, 589, 1198, 527, 480, 2858],\n"," 396: [589, 1240, 1270, 356, 110, 593, 2028, 1197, 1214, 318],\n"," 397: [2959, 1617, 50, 1213, 1196, 47, 2997, 608, 1208, 4993],\n"," 398: [318, 364, 508, 480, 595, 589, 380, 21, 296, 592],\n"," 399: [1, 36, 62, 25, 1210, 356, 494, 653, 786, 5],\n"," 400: [318, 1704, 2762, 527, 296, 1196, 1265, 356, 2396, 1213],\n"," 401: [1196, 1136, 1208, 1240, 1193, 1200, 904, 1270, 750, 2858],\n"," 402: [4993, 3578, 4226, 4995, 5952, 2571, 4886, 6539, 4306, 5418],\n"," 403: [1196, 1198, 2762, 593, 50, 2858, 4993, 318, 1291, 3578],\n"," 404: [356, 593, 318, 150, 474, 588, 165, 590, 316, 296],\n"," 405: [733, 1, 6, 608, 648, 736, 36, 1393, 356, 95],\n"," 406: [457, 380, 356, 377, 590, 110, 318, 593, 480, 588],\n"," 407: [1196, 1210, 260, 110, 356, 1617, 1270, 2858, 1200, 318],\n"," 408: [2858, 2918, 1136, 1617, 858, 2791, 50, 1080, 1089, 1208],\n"," 409: [225, 457, 380, 539, 356, 248, 110, 736, 588, 733],\n"," 410: [457, 260, 380, 349, 296, 590, 592, 588, 21, 34],\n"," 411: [260, 1, 36, 25, 6, 17, 736, 32, 783, 141],\n"," 412: [527, 1617, 296, 1193, 2571, 1198, 50, 2762, 1213, 110],\n"," 413: [750, 1208, 1221, 1213, 912, 1617, 1206, 1207, 908, 1252],\n"," 414: [608, 318, 50, 527, 2571, 1089, 2762, 47, 1136, 1213],\n"," 415: [318, 593, 356, 527, 608, 150, 110, 50, 457, 36],\n"," 416: [110, 318, 589, 296, 539, 527, 595, 165, 357, 21],\n"," 417: [1196, 912, 608, 904, 1198, 858, 1197, 1208, 1617, 750],\n"," 418: [593, 356, 527, 150, 457, 508, 590, 357, 589, 380],\n"," 419: [1196, 2858, 1240, 1265, 2028, 924, 1207, 1304, 111, 2762],\n"," 420: [318, 593, 25, 608, 509, 508, 337, 150, 356, 590],\n"," 421: [318, 364, 110, 593, 380, 480, 34, 539, 527, 296],\n"," 422: [780, 736, 786, 648, 6, 1210, 95, 832, 1, 62],\n"," 424: [1207, 1094, 1276, 1197, 1193, 1394, 1288, 1225, 858, 593],\n"," 425: [2762, 2858, 2571, 2959, 1617, 593, 318, 2329, 1196, 296],\n"," 426: [318, 2858, 750, 912, 1206, 1221, 1089, 1136, 924, 904],\n"," 427: [1198, 2028, 1036, 1291, 110, 1196, 296, 858, 47, 2571],\n"," 428: [1210, 480, 1197, 457, 1097, 1270, 589, 780, 260, 329],\n"," 429: [593, 1240, 2571, 1196, 589, 50, 260, 318, 1198, 1210],\n"," 430: [3578, 4993, 3147, 2959, 1036, 589, 47, 5952, 4226, 2762],\n"," 431: [3578, 1291, 1196, 2329, 1198, 1036, 4226, 5445, 2959, 32587],\n"," 432: [34, 357, 161, 539, 608, 47, 440, 377, 225, 589],\n"," 433: [480, 150, 593, 1036, 6, 318, 1240, 316, 34, 588],\n"," 434: [1240, 589, 1214, 1270, 1197, 1200, 593, 318, 32, 480],\n"," 435: [2858, 1089, 1196, 5952, 47, 7153, 1208, 296, 1200, 4993],\n"," 436: [593, 150, 588, 589, 590, 527, 47, 377, 592, 161],\n"," 437: [1617, 1193, 1207, 858, 912, 923, 1276, 1225, 1198, 1304],\n"," 438: [1036, 1291, 608, 2028, 527, 50, 1270, 780, 1610, 592],\n"," 439: [858, 2858, 296, 1208, 1196, 1198, 593, 1213, 318, 1617],\n"," 440: [1196, 2571, 2028, 1036, 589, 1617, 1240, 260, 1089, 1270],\n"," 441: [110, 480, 356, 150, 593, 318, 592, 590, 474, 296],\n"," 443: [527, 608, 296, 1617, 1358, 337, 1704, 1213, 300, 2028],\n"," 444: [260, 1210, 110, 1617, 593, 480, 1200, 858, 318, 912],\n"," 445: [4993, 1196, 4226, 912, 858, 750, 1193, 111, 2858, 1206],\n"," 446: [110, 590, 47, 527, 50, 364, 592, 377, 10, 165],\n"," 447: [2858, 50, 1213, 1265, 1193, 858, 1196, 904, 1208, 235],\n"," 448: [2997, 1196, 2959, 1617, 1136, 2571, 2762, 2396, 858, 2028],\n"," 449: [1617, 110, 2028, 1198, 3147, 318, 2502, 1036, 296, 4226],\n"," 450: [356, 1291, 1265, 296, 1136, 457, 1213, 912, 1704, 1],\n"," 451: [1198, 541, 2959, 1214, 1240, 1200, 3578, 1291, 2762, 260],\n"," 452: [110, 589, 593, 1210, 733, 380, 457, 1196, 150, 588],\n"," 453: [457, 150, 356, 318, 590, 593, 110, 589, 377, 527],\n"," 454: [356, 150, 318, 593, 380, 480, 527, 592, 539, 47],\n"," 455: [780, 1393, 32, 356, 62, 593, 648, 736, 25, 457],\n"," 456: [150, 356, 380, 527, 590, 589, 508, 377, 474, 364],\n"," 458: [2571, 1617, 1196, 2028, 2959, 1198, 858, 1704, 1265, 260],\n"," 459: [3578, 4993, 1240, 1198, 2028, 2762, 2858, 1270, 4226, 50],\n"," 460: [356, 318, 480, 589, 592, 377, 364, 292, 47, 527],\n"," 461: [593, 318, 356, 110, 780, 527, 589, 6, 457, 1210],\n"," 462: [527, 296, 356, 150, 110, 508, 1, 47, 21, 589],\n"," 463: [1291, 2858, 541, 1265, 608, 1208, 1213, 924, 2959, 1387],\n"," 464: [1097, 318, 356, 1196, 858, 1704, 2571, 1198, 110, 2028],\n"," 465: [923, 527, 4226, 2997, 541, 1136, 1196, 2028, 32, 750],\n"," 466: [1196, 1198, 2571, 1291, 260, 1214, 1240, 1036, 1200, 1197],\n"," 467: [1196, 2571, 1210, 1291, 1198, 541, 1527, 1270, 260, 480],\n"," 468: [318, 50, 21, 356, 34, 590, 110, 608, 36, 337],\n"," 469: [1197, 1617, 858, 110, 50, 750, 1580, 2959, 1193, 1208],\n"," 470: [1193, 1213, 923, 593, 2571, 1214, 1196, 527, 904, 1288],\n"," 471: [377, 380, 595, 588, 480, 150, 185, 590, 356, 161],\n"," 472: [377, 110, 380, 508, 592, 597, 440, 594, 161, 733],\n"," 473: [36, 1, 593, 141, 296, 6, 318, 21, 1393, 527],\n"," 474: [589, 1198, 1374, 1580, 1291, 1036, 750, 2916, 1584, 32],\n"," 475: [904, 2858, 1617, 1196, 2571, 2762, 2997, 1193, 1208, 2028],\n"," 476: [858, 2762, 1089, 1193, 1213, 111, 912, 1704, 6874, 1270],\n"," 477: [2858, 1208, 1240, 750, 589, 3578, 110, 1206, 32, 111],\n"," 479: [858, 1221, 904, 923, 1230, 908, 1225, 1276, 912, 1089],\n"," 480: [1270, 1210, 1036, 1208, 318, 589, 1193, 1213, 1089, 527],\n"," 481: [110, 2762, 541, 1197, 527, 1610, 1193, 608, 750, 924],\n"," 482: [3578, 2571, 48516, 51662, 2858, 1291, 1196, 7438, 6874, 50],\n"," 483: [377, 588, 296, 161, 225, 292, 508, 11, 474, 47],\n"," 484: [736, 648, 786, 62, 719, 64, 1210, 36, 104, 667],\n"," 485: [1265, 260, 356, 34, 1197, 1207, 1198, 904, 2028, 110],\n"," 486: [318, 2858, 356, 593, 1198, 858, 2762, 296, 1196, 1193],\n"," 487: [2858, 296, 593, 2959, 1617, 527, 1136, 47, 1196, 1213],\n"," 488: [110, 377, 356, 150, 165, 364, 316, 590, 588, 593],\n"," 489: [593, 527, 1198, 912, 296, 1196, 1617, 1197, 541, 923],\n"," 490: [1197, 260, 1036, 593, 1193, 1136, 1097, 356, 1213, 296],\n"," 491: [3578, 589, 1198, 2028, 593, 296, 4993, 50, 1240, 1036],\n"," 492: [318, 593, 1196, 858, 356, 527, 2858, 296, 1198, 260],\n"," 493: [593, 608, 1617, 1196, 110, 2571, 2028, 1208, 111, 1198],\n"," 494: [356, 150, 6, 527, 474, 590, 377, 349, 588, 480],\n"," 495: [150, 457, 377, 161, 539, 11, 356, 225, 266, 474],\n"," 496: [356, 110, 1036, 1196, 318, 527, 480, 1270, 1198, 1291],\n"," 497: [2858, 1196, 541, 593, 50, 1193, 296, 1136, 750, 1617],\n"," 498: [908, 923, 912, 904, 1230, 1193, 1288, 1136, 1207, 1208],\n"," 499: [593, 318, 36, 527, 1196, 25, 1213, 1193, 1617, 111],\n"," 500: [50, 2858, 1089, 1094, 34, 1193, 1265, 337, 1208, 246],\n"," 501: [34, 318, 1240, 527, 380, 1259, 589, 296, 780, 1610],\n"," 502: [2571, 2959, 1196, 2355, 3578, 1704, 2028, 3147, 2683, 2502],\n"," 503: [1196, 1291, 1214, 1208, 50, 260, 47, 2329, 1210, 1617],\n"," 504: [508, 590, 110, 21, 377, 539, 588, 380, 161, 357],\n"," 505: [457, 480, 150, 593, 377, 733, 296, 597, 527, 500],\n"," 506: [608, 110, 1197, 1240, 1036, 590, 21, 293, 1617, 34],\n"," 507: [356, 589, 593, 318, 380, 780, 480, 377, 527, 1],\n"," 508: [593, 858, 2858, 318, 1198, 2028, 1208, 2571, 1240, 1193],\n"," 509: [1196, 318, 3578, 4993, 110, 1198, 589, 1036, 1210, 260],\n"," 510: [457, 110, 588, 296, 380, 377, 589, 508, 539, 592],\n"," 511: [457, 356, 377, 480, 318, 593, 292, 589, 588, 316],\n"," 512: [1213, 36, 1094, 527, 296, 994, 1193, 593, 25, 778],\n"," 513: [1094, 1213, 1270, 2791, 1225, 235, 969, 1097, 1183, 110],\n"," 515: [904, 923, 1247, 913, 903, 910, 1250, 1193, 899, 1252],\n"," 516: [589, 1036, 1240, 50, 1136, 608, 1193, 1197, 1, 1265],\n"," 517: [593, 356, 457, 589, 590, 527, 165, 225, 480, 474],\n"," 518: [1198, 296, 858, 110, 1213, 2858, 1617, 50, 32, 541],\n"," 520: [858, 593, 296, 1213, 527, 1193, 50, 111, 1208, 2858],\n"," 521: [110, 296, 527, 47, 1198, 589, 2762, 1196, 457, 1291],\n"," 522: [527, 1193, 50, 1196, 1198, 32, 1207, 110, 356, 1617],\n"," 523: [593, 296, 25, 111, 1089, 21, 1196, 1183, 1617, 1198],\n"," 524: [2762, 1196, 1617, 593, 4993, 1198, 527, 1193, 1136, 1291],\n"," 525: [318, 110, 589, 296, 356, 380, 150, 480, 592, 527],\n"," 526: [736, 1393, 356, 593, 62, 318, 141, 1210, 104, 296],\n"," 527: [457, 380, 110, 150, 364, 1265, 589, 318, 593, 780],\n"," 529: [593, 318, 2858, 1196, 50, 1198, 1136, 2571, 858, 110],\n"," 530: [356, 457, 161, 593, 225, 377, 588, 539, 11, 480],\n"," 531: [457, 593, 356, 110, 380, 296, 588, 364, 527, 34],\n"," 532: [2858, 1208, 541, 750, 608, 2959, 1089, 1193, 318, 1214],\n"," 533: [1213, 2028, 111, 1304, 1288, 356, 1097, 1089, 150, 1732],\n"," 534: [7153, 4963, 3578, 5349, 4226, 6539, 356, 2959, 4011, 5445],\n"," 535: [908, 1207, 910, 1221, 1617, 1284, 1193, 926, 1250, 2858],\n"," 536: [356, 318, 590, 527, 110, 21, 440, 380, 364, 36],\n"," 537: [25, 62, 733, 780, 1, 1393, 593, 32, 58, 318],\n"," 539: [356, 318, 457, 110, 296, 480, 595, 380, 592, 34],\n"," 540: [1196, 1240, 2571, 2716, 1210, 2762, 1198, 1197, 593, 1036],\n"," 541: [318, 608, 296, 380, 1265, 1580, 21, 150, 858, 2762],\n"," 542: [923, 750, 1247, 1276, 1208, 1304, 969, 1234, 1230, 608],\n"," 543: [110, 2028, 750, 858, 50, 296, 608, 1136, 1610, 47],\n"," 544: [4226, 2858, 1136, 2571, 858, 4011, 593, 1214, 541, 1206],\n"," 545: [1196, 260, 1197, 1210, 3578, 1207, 912, 904, 592, 1219],\n"," 546: [6539, 45722, 36529, 32587, 4963, 4886, 33166, 5445, 44191, 49272],\n"," 547: [356, 380, 318, 592, 296, 34, 733, 316, 161, 165],\n"," 548: [1197, 356, 1097, 1265, 1270, 593, 110, 527, 1291, 150],\n"," 549: [110, 356, 296, 527, 364, 380, 480, 589, 592, 34],\n"," 550: [1197, 593, 1221, 1252, 1208, 1259, 1307, 1213, 1204, 1219],\n"," 551: [1196, 1197, 1240, 110, 318, 457, 1221, 1210, 296, 1193],\n"," 552: [593, 1617, 527, 858, 1208, 2571, 1732, 1196, 47, 1197],\n"," 553: [356, 150, 590, 588, 316, 377, 527, 10, 364, 780],\n"," 554: [1208, 1193, 296, 923, 1617, 750, 858, 593, 2997, 608],\n"," 555: [2571, 1214, 1197, 1036, 1136, 858, 541, 1200, 1208, 924],\n"," 556: [457, 590, 588, 589, 539, 380, 161, 337, 25, 608],\n"," 557: [6, 36, 608, 733, 1, 780, 95, 25, 832, 9],\n"," 558: [1196, 2571, 2762, 260, 47, 589, 1193, 1240, 1210, 1],\n"," 559: [1196, 260, 1240, 1198, 1291, 1036, 1527, 589, 1200, 1214],\n"," 560: [1213, 527, 608, 858, 111, 1208, 1617, 2571, 1196, 356],\n"," 561: [1198, 480, 318, 2762, 2028, 50, 1270, 592, 4226, 4886],\n"," 562: [480, 508, 296, 21, 316, 440, 236, 248, 454, 141],\n"," 563: [593, 296, 318, 527, 6, 1, 356, 17, 50, 858],\n"," 564: [1197, 1240, 593, 858, 457, 527, 912, 2571, 1278, 1234],\n"," 565: [318, 593, 1196, 527, 2028, 1197, 260, 296, 1210, 589],\n"," 566: [1193, 1208, 36, 1246, 923, 1207, 1183, 318, 111, 527],\n"," 567: [150, 110, 597, 225, 593, 349, 587, 733, 292, 780],\n"," 568: [648, 376, 5, 25, 260, 9, 356, 608, 32, 457],\n"," 569: [318, 356, 150, 110, 380, 589, 527, 480, 161, 377],\n"," 570: [593, 296, 858, 1196, 1198, 1193, 110, 2028, 1617, 1197],\n"," 571: [593, 1036, 1240, 1270, 356, 2858, 2762, 296, 110, 589],\n"," 572: [1193, 318, 2762, 1136, 1036, 3578, 1214, 1617, 50, 589],\n"," 573: [296, 1617, 150, 110, 111, 1193, 2997, 457, 1196, 858],\n"," 574: [356, 110, 318, 150, 733, 349, 590, 592, 539, 780],\n"," 575: [733, 780, 1, 736, 36, 719, 805, 765, 9, 64],\n"," 576: [593, 608, 1617, 1193, 36, 50, 858, 111, 356, 1089],\n"," 577: [527, 1304, 1196, 356, 1193, 1265, 260, 34, 1617, 1197],\n"," 578: [1196, 1210, 1270, 1197, 457, 1374, 858, 1617, 924, 110],\n"," 579: [110, 1240, 2028, 858, 1270, 457, 1617, 1200, 1197, 1610],\n"," 580: [356, 1196, 589, 586, 296, 3578, 318, 593, 364, 50],\n"," 581: [356, 457, 593, 150, 110, 380, 34, 377, 296, 539],\n"," 582: [923, 953, 750, 1278, 1197, 1221, 1213, 1288, 1196, 920],\n"," 583: [2571, 1196, 541, 1580, 1617, 608, 1214, 1200, 1089, 1210],\n"," 584: [150, 110, 165, 588, 364, 318, 590, 161, 593, 597],\n"," 585: [1197, 1208, 1221, 800, 318, 1259, 265, 1204, 1233, 1235],\n"," 586: [1617, 608, 527, 1265, 318, 912, 1193, 296, 858, 1394],\n"," 587: [296, 1198, 2858, 1213, 858, 50, 2959, 47, 1291, 110],\n"," 588: [318, 150, 593, 110, 21, 380, 590, 377, 364, 589],\n"," 589: [593, 318, 608, 111, 21, 110, 1213, 457, 1208, 858],\n"," 590: [110, 356, 1036, 1197, 1617, 1265, 858, 527, 1270, 2858],\n"," 591: [1196, 1580, 1198, 1517, 2762, 2571, 2918, 589, 1291, 110],\n"," 592: [608, 1094, 1617, 750, 904, 1208, 908, 1230, 2858, 800],\n"," 593: [150, 480, 592, 527, 377, 364, 292, 474, 595, 32],\n"," 594: [1, 736, 786, 1393, 6, 32, 608, 480, 494, 356],\n"," 595: [1193, 1259, 919, 1278, 1617, 457, 318, 1210, 912, 904],\n"," 596: [1207, 908, 1304, 1193, 1234, 1276, 858, 1094, 1225, 1394],\n"," 597: [17, 356, 527, 34, 1247, 1617, 1197, 1207, 25, 593],\n"," 598: [1240, 1997, 1200, 541, 1258, 1387, 2288, 1196, 750, 1374],\n"," 599: [908, 904, 910, 899, 946, 1247, 1207, 903, 969, 1304],\n"," 600: [1196, 1198, 2571, 260, 1291, 593, 1036, 589, 1270, 1197],\n"," 601: [590, 592, 780, 733, 161, 225, 300, 736, 367, 1036],\n"," 602: [1196, 912, 1136, 919, 1193, 1207, 1225, 1240, 750, 541],\n"," 603: [593, 265, 150, 1207, 1193, 457, 1094, 357, 17, 590],\n"," 604: [6, 356, 110, 589, 527, 36, 150, 260, 1, 780],\n"," 605: [593, 1213, 110, 1617, 858, 111, 2858, 457, 1198, 1208],\n"," 606: [1196, 1240, 1036, 1291, 593, 1197, 1617, 1210, 1270, 589],\n"," 607: [1196, 296, 2571, 2028, 2858, 1617, 1213, 527, 541, 1136],\n"," 608: [1196, 541, 2571, 1240, 1198, 2858, 32, 1291, 1136, 858],\n"," 609: [150, 593, 377, 592, 165, 292, 539, 474, 597, 508],\n"," 610: [1207, 908, 912, 1208, 608, 1225, 1234, 1221, 953, 1252],\n"," 612: [780, 95, 6, 653, 260, 104, 376, 9, 356, 377],\n"," 613: [1197, 260, 1210, 1198, 1240, 457, 589, 1580, 1265, 1270],\n"," 614: [1291, 1036, 1214, 589, 1210, 1270, 1197, 2571, 593, 541],\n"," 616: [1196, 1240, 260, 1270, 858, 1617, 1200, 364, 1214, 1197],\n"," 617: [318, 364, 590, 296, 592, 595, 608, 161, 1265, 587],\n"," 618: [296, 50, 1213, 36, 356, 25, 457, 1265, 150, 21],\n"," 619: [1196, 1036, 1291, 2571, 1197, 1270, 1221, 1610, 593, 541],\n"," 620: [4226, 4993, 3996, 4027, 3578, 4995, 4963, 5952, 4973, 2959],\n"," 621: [858, 260, 750, 1288, 1208, 1213, 2571, 924, 527, 1278],\n"," 622: [1291, 1036, 3578, 50, 6539, 527, 4226, 1270, 858, 541],\n"," 623: [457, 356, 150, 527, 588, 590, 377, 508, 357, 480],\n"," 624: [2762, 2571, 2716, 1210, 110, 588, 589, 2858, 1580, 1265],\n"," 625: [1198, 1240, 1291, 858, 1214, 110, 2571, 1200, 1221, 2028],\n"," 627: [2959, 1196, 1198, 1617, 47, 1036, 1210, 260, 527, 1291],\n"," 628: [457, 593, 356, 377, 110, 508, 21, 266, 380, 539],\n"," 629: [356, 318, 539, 21, 440, 457, 357, 508, 593, 590],\n"," 630: [648, 786, 6, 62, 9, 376, 141, 380, 260, 79],\n"," 631: [1207, 1208, 912, 858, 908, 1221, 904, 923, 1276, 1252],\n"," 632: [1617, 260, 912, 1197, 858, 919, 1198, 2396, 750, 1193],\n"," 633: [912, 1136, 318, 924, 1206, 1247, 1197, 1288, 1291, 1276],\n"," 634: [1270, 318, 592, 150, 527, 296, 2797, 50, 2571, 364],\n"," 635: [2762, 318, 2028, 1259, 608, 1197, 1704, 1196, 2571, 1265],\n"," 636: [1196, 2858, 593, 1036, 1208, 1214, 1193, 50, 1240, 589],\n"," 637: [589, 2571, 1196, 593, 1240, 318, 457, 1210, 356, 527],\n"," 638: [593, 21, 457, 265, 161, 236, 497, 377, 110, 58],\n"," 639: [3578, 110, 2571, 4226, 1196, 2762, 589, 50, 1198, 2028],\n"," 640: [593, 296, 260, 1240, 1036, 2762, 50, 2329, 527, 1200],\n"," 641: [356, 110, 150, 296, 588, 364, 590, 592, 165, 316],\n"," 642: [527, 356, 1198, 1207, 1208, 1136, 2028, 2571, 1307, 904],\n"," 643: [1198, 1276, 1214, 1230, 541, 1136, 1265, 924, 2858, 1267],\n"," 644: [1580, 480, 1197, 356, 1610, 2762, 858, 2716, 780, 32],\n"," 645: [733, 780, 457, 589, 349, 110, 356, 480, 95, 316],\n"," 646: [356, 364, 318, 110, 380, 480, 150, 593, 457, 589],\n"," 647: [356, 736, 1036, 316, 161, 593, 494, 1210, 292, 10],\n"," 648: [608, 1208, 908, 1221, 750, 527, 1276, 1230, 296, 1288],\n"," 649: [110, 1198, 1197, 593, 318, 1196, 527, 150, 1704, 1],\n"," 650: [1265, 593, 1617, 2791, 1136, 110, 858, 1097, 2028, 527],\n"," 651: [904, 1208, 913, 1304, 1230, 111, 1254, 922, 1234, 1288],\n"," 652: [1247, 1197, 919, 1207, 318, 34, 1230, 364, 1097, 608],\n"," 653: [593, 318, 356, 296, 150, 527, 1196, 110, 1784, 1265],\n"," 654: [457, 380, 296, 589, 480, 527, 47, 377, 592, 588],\n"," 655: [589, 593, 110, 527, 588, 50, 34, 1097, 32, 608],\n"," 656: [457, 588, 589, 590, 500, 21, 296, 527, 292, 780],\n"," 657: [750, 1136, 1206, 2762, 1304, 1265, 1276, 1094, 1394, 1234],\n"," 658: [2858, 318, 2762, 1196, 858, 50, 1213, 1617, 527, 3147],\n"," 659: [296, 1617, 2959, 50, 2028, 608, 1198, 1193, 4226, 750],\n"," 660: [296, 527, 356, 110, 2959, 858, 1196, 589, 2329, 1198],\n"," 661: [150, 318, 377, 349, 592, 588, 165, 539, 474, 364],\n"," 662: [1208, 593, 1207, 527, 318, 1247, 923, 904, 1276, 908],\n"," 663: [1198, 1196, 260, 1240, 1214, 1291, 858, 1136, 1200, 296],\n"," 664: [1196, 1198, 318, 1197, 1240, 2571, 527, 110, 608, 296],\n"," 665: [2858, 750, 1193, 1617, 858, 1136, 904, 1221, 1213, 1196],\n"," 666: [541, 2959, 1206, 2858, 1208, 4226, 750, 1136, 924, 1198],\n"," 667: [1197, 1193, 593, 1210, 1036, 1259, 1617, 2571, 527, 1214],\n"," 668: [1208, 541, 750, 1193, 858, 111, 1221, 2858, 1213, 1196],\n"," 669: [593, 377, 590, 364, 296, 10, 474, 527, 47, 595],\n"," 670: [780, 736, 1, 376, 608, 648, 494, 260, 95, 25],\n"," 671: [1207, 904, 908, 1304, 1276, 913, 910, 1234, 800, 318],\n"," 672: [318, 2858, 589, 47, 1213, 1036, 32, 608, 1265, 1240],\n"," 674: [1210, 1200, 1198, 1214, 589, 1291, 1097, 1527, 1376, 2916],\n"," 675: [110, 589, 457, 380, 47, 50, 588, 377, 592, 780],\n"," 676: [377, 457, 225, 161, 356, 539, 587, 440, 597, 21],\n"," 677: [356, 380, 377, 539, 349, 165, 592, 595, 47, 292],\n"," 678: [1207, 608, 1214, 1265, 904, 111, 50, 1617, 1136, 1],\n"," 679: [589, 1198, 1240, 457, 1036, 1197, 1291, 380, 318, 733],\n"," 680: [1580, 1198, 1036, 780, 110, 1197, 1291, 457, 1, 1610],\n"," 681: [2028, 2571, 1196, 1198, 2762, 1240, 1291, 1580, 593, 1617],\n"," 683: [1240, 2762, 2028, 457, 1610, 4963, 260, 2959, 592, 2706],\n"," 684: [4993, 4306, 4886, 7153, 4963, 5952, 2959, 47, 5349, 318],\n"," 685: [32, 1196, 593, 1198, 780, 733, 356, 318, 1036, 1240],\n"," 686: [1617, 541, 1208, 912, 1213, 1207, 1193, 2571, 1252, 1270],\n"," 688: [1208, 1198, 1617, 593, 318, 1221, 1291, 2028, 1200, 2959],\n"," 689: [380, 589, 377, 364, 527, 592, 47, 165, 10, 34],\n"," 690: [648, 356, 62, 1210, 653, 104, 95, 1393, 380, 32],\n"," 691: [1270, 2762, 858, 592, 1214, 1097, 1200, 377, 1617, 150],\n"," 693: [150, 356, 590, 508, 377, 34, 539, 380, 589, 161],\n"," 694: [608, 527, 318, 1247, 1197, 1230, 1193, 1225, 296, 1196],\n"," 695: [457, 1210, 1196, 296, 589, 608, 1265, 588, 380, 1617],\n"," 696: [2571, 2858, 2959, 1196, 2716, 1210, 1617, 1197, 593, 356],\n"," 697: [],\n"," 698: [1213, 858, 608, 541, 260, 1136, 356, 2997, 1221, 1270],\n"," 699: [2571, 1196, 589, 2028, 1291, 110, 3578, 1036, 1210, 32587],\n"," 700: [296, 356, 110, 590, 50, 21, 480, 508, 592, 364],\n"," 701: [356, 500, 248, 367, 380, 377, 165, 150, 364, 161],\n"," 702: [1198, 1197, 1270, 858, 1291, 1193, 2571, 1617, 1213, 1036],\n"," 703: [2797, 1197, 1079, 1220, 2918, 1234, 2174, 1198, 2406, 1285],\n"," 704: [1291, 4306, 1198, 2706, 2858, 1036, 356, 3147, 1617, 318],\n"," 705: [1617, 1196, 2028, 2762, 1089, 50, 2571, 1208, 858, 593],\n"," 706: [736, 6, 260, 95, 786, 1, 494, 1073, 653, 608],\n"," 707: [318, 356, 608, 265, 337, 300, 25, 34, 509, 515],\n"," 708: [1196, 1198, 1210, 1197, 1617, 1036, 1291, 1374, 1270, 1221],\n"," 709: [1240, 1291, 1036, 1214, 1210, 1270, 541, 593, 110, 1213],\n"," 710: [912, 904, 1136, 1213, 593, 923, 1276, 1208, 1196, 1234],\n"," 711: [356, 45499, 110, 588, 480, 4993, 1196, 50, 4306, 51662],\n"," 712: [593, 508, 34, 21, 36, 457, 300, 515, 110, 539],\n"," 714: [593, 1196, 1198, 2028, 608, 1265, 1193, 260, 1213, 1210],\n"," 715: [318, 593, 356, 150, 110, 457, 589, 527, 50, 590],\n"," 716: [593, 296, 318, 858, 110, 1136, 1394, 356, 1, 1094],\n"," 718: [296, 527, 318, 508, 2858, 356, 150, 590, 300, 34],\n"," 719: [380, 225, 161, 318, 11, 185, 440, 593, 589, 349],\n"," 720: [356, 110, 593, 589, 597, 592, 595, 34, 165, 10],\n"," 721: [377, 95, 356, 457, 597, 150, 141, 480, 225, 6],\n"," 723: [2858, 1196, 2571, 4993, 50, 593, 296, 2028, 858, 1291],\n"," 724: [36, 25, 780, 356, 6, 593, 318, 1210, 296, 141],\n"," 725: [1617, 1196, 2396, 2028, 2395, 2716, 2997, 2700, 2580, 2706],\n"," 726: [260, 1136, 1270, 1193, 1265, 1240, 1220, 318, 1278, 1200],\n"," 727: [457, 588, 589, 480, 508, 377, 34, 1, 592, 161],\n"," 728: [2959, 1291, 1036, 1198, 858, 1214, 2858, 260, 3578, 110],\n"," 729: [539, 236, 282, 377, 457, 356, 440, 590, 587, 597],\n"," 730: [110, 356, 593, 457, 480, 589, 588, 364, 377, 47],\n"," 731: [457, 1198, 380, 34, 608, 1291, 1617, 588, 1, 1278],\n"," 732: [318, 7361, 4973, 6711, 1193, 2858, 527, 4226, 6377, 296],\n"," 733: [2762, 2571, 2959, 1196, 47, 5952, 50, 1240, 2028, 4226],\n"," 734: [318, 110, 150, 527, 588, 589, 480, 380, 592, 1036],\n"," 735: [318, 527, 150, 47, 590, 589, 588, 480, 380, 364],\n"," 736: [2571, 4993, 4226, 2762, 2959, 4963, 3897, 1196, 4027, 2028],\n"," 737: [780, 6, 260, 1, 1527, 1210, 36, 608, 589, 25],\n"," 738: [736, 786, 1, 6, 95, 32, 1073, 9, 589, 480],\n"," 739: [356, 110, 1210, 318, 47, 1036, 349, 480, 780, 2571],\n"," 740: [858, 296, 527, 1197, 1193, 1213, 1240, 356, 1291, 541],\n"," 741: [356, 527, 2028, 110, 1213, 2959, 1198, 260, 1784, 1197],\n"," 742: [593, 858, 4995, 1197, 527, 2502, 1193, 1213, 1214, 1089],\n"," 743: [356, 110, 457, 150, 318, 589, 592, 593, 377, 590],\n"," 744: [527, 608, 1207, 1196, 1617, 260, 1208, 2858, 1221, 1276],\n"," 745: [2028, 356, 457, 1580, 1136, 1208, 4993, 904, 592, 380],\n"," 746: [1196, 858, 750, 904, 2571, 912, 1208, 2028, 1221, 111],\n"," 748: [593, 296, 457, 21, 161, 34, 589, 36, 337, 608],\n"," 749: [733, 780, 736, 786, 480, 1073, 95, 1210, 260, 6],\n"," 750: [4226, 2571, 2762, 2329, 4993, 1196, 7153, 3578, 2028, 4878],\n"," 751: [380, 457, 110, 480, 590, 356, 589, 377, 592, 318],\n"," 752: [318, 593, 356, 110, 457, 380, 364, 592, 527, 480],\n"," 753: [858, 1196, 1208, 1193, 1221, 1617, 1213, 527, 1197, 2571],\n"," 754: [5952, 4995, 4226, 6539, 6016, 5445, 4886, 48516, 6711, 8961],\n"," 755: [318, 508, 265, 356, 337, 34, 590, 457, 110, 357],\n"," 756: [1393, 62, 141, 260, 783, 1357, 265, 508, 527, 21],\n"," 757: [593, 318, 589, 780, 150, 527, 588, 380, 6, 480],\n"," 758: [356, 457, 364, 380, 480, 318, 593, 377, 161, 592],\n"," 759: [1210, 1200, 1214, 593, 356, 1197, 1610, 1580, 858, 318],\n"," 761: [356, 1196, 2571, 110, 2762, 589, 3578, 457, 480, 1270],\n"," 762: [36, 780, 6, 32, 356, 62, 593, 318, 648, 736],\n"," 763: [608, 318, 296, 25, 265, 50, 111, 356, 32, 34],\n"," 764: [296, 36, 1617, 25, 265, 1094, 111, 50, 1358, 858],\n"," 765: [1198, 1196, 260, 1240, 1197, 541, 527, 318, 1278, 1136],\n"," 766: [593, 527, 296, 1291, 589, 1270, 858, 2571, 1036, 1265],\n"," 767: [1240, 1270, 110, 1210, 1200, 2028, 1580, 356, 1136, 541],\n"," 768: [48516, 49272, 48780, 55820, 36529, 8950, 51662, 5903, 27660, 54272],\n"," 769: [508, 34, 150, 457, 440, 539, 11, 111, 161, 222],\n"," 770: [733, 62, 95, 653, 830, 74, 9, 765, 1393, 36],\n"," 771: [1197, 1210, 110, 1580, 2028, 1097, 593, 858, 1265, 592],\n"," 772: [593, 2858, 356, 1196, 318, 1198, 2571, 1617, 296, 2762],\n"," 773: [356, 1198, 1, 593, 32, 318, 2762, 589, 1097, 1784],\n"," 774: [1196, 593, 296, 1198, 2858, 2571, 1221, 527, 1136, 356],\n"," 775: [110, 225, 593, 508, 357, 21, 248, 185, 780, 292],\n"," 776: [1193, 527, 1196, 1221, 318, 1208, 608, 912, 1304, 750],\n"," 777: [1230, 1617, 2289, 1276, 1172, 1265, 750, 1278, 1208, 1304],\n"," 780: [2571, 2858, 2797, 34, 1704, 2791, 1221, 1220, 1, 2918],\n"," 781: [608, 2858, 1394, 593, 296, 50, 2396, 1094, 800, 750],\n"," 782: [1089, 1213, 778, 223, 296, 50, 2858, 1617, 111, 318],\n"," 783: [1198, 2571, 356, 1197, 2858, 1291, 1136, 593, 858, 1210],\n"," 784: [2959, 2762, 356, 589, 1240, 527, 260, 1617, 1193, 1089],\n"," 785: [150, 527, 508, 588, 539, 380, 608, 34, 364, 474],\n"," 786: [589, 110, 356, 380, 593, 1097, 150, 2028, 47, 588],\n"," 787: [593, 318, 150, 1207, 300, 36, 1197, 1193, 34, 265],\n"," 788: [1196, 1240, 296, 1210, 260, 5349, 32587, 541, 1089, 1197],\n"," 789: [296, 34, 597, 480, 364, 589, 21, 11, 161, 440],\n"," 790: [356, 110, 608, 589, 1196, 1207, 588, 1210, 1097, 1278],\n"," 791: [1617, 908, 858, 1208, 1207, 1193, 923, 1247, 750, 1252],\n"," 792: [593, 296, 110, 457, 527, 356, 47, 590, 589, 380],\n"," 793: [1198, 356, 1291, 2028, 2959, 2858, 593, 2762, 589, 3996],\n"," 794: [912, 904, 1196, 750, 541, 1198, 1234, 1617, 1136, 924],\n"," 795: [356, 380, 349, 292, 480, 377, 592, 588, 293, 733],\n"," 796: [780, 36, 25, 32, 1393, 733, 356, 6, 1073, 318],\n"," 797: [593, 527, 296, 34, 1617, 1197, 1198, 50, 110, 1196],\n"," 798: [1291, 858, 2028, 110, 2858, 593, 1617, 1214, 1200, 1208],\n"," 799: [380, 589, 364, 592, 47, 316, 50, 34, 539, 508],\n"," 800: [36, 608, 733, 1393, 25, 780, 1, 593, 1210, 648],\n"," 801: [2858, 1036, 1240, 3578, 318, 2762, 47, 858, 541, 50],\n"," 802: [318, 457, 150, 110, 539, 47, 588, 590, 364, 34],\n"," 803: [1617, 527, 858, 593, 1198, 1278, 1299, 1221, 1197, 750],\n"," 804: [318, 356, 589, 527, 150, 50, 296, 457, 3578, 2762],\n"," 805: [2959, 589, 1036, 1240, 2762, 2028, 4226, 4963, 110, 1580],\n"," 806: [150, 296, 380, 588, 377, 508, 34, 589, 539, 349],\n"," 807: [457, 380, 110, 356, 593, 589, 480, 377, 592, 316],\n"," 808: [2571, 1580, 356, 2762, 1198, 1270, 110, 780, 589, 480],\n"," 809: [2571, 593, 2028, 1198, 318, 858, 296, 1240, 1210, 589],\n"," 810: [150, 318, 357, 588, 457, 593, 364, 34, 377, 508],\n"," 811: [648, 494, 1, 95, 376, 6, 653, 9, 5, 260],\n"," 812: [34, 110, 377, 380, 590, 357, 21, 474, 296, 17],\n"," 813: [1200, 1270, 1210, 1617, 2716, 750, 1265, 1580, 593, 541],\n"," 814: [356, 2797, 1265, 1136, 1197, 1220, 1079, 1198, 1234, 2918],\n"," 815: [1036, 1291, 457, 480, 318, 3578, 1, 1197, 592, 1610],\n"," 816: [457, 377, 161, 380, 590, 110, 318, 356, 480, 316],\n"," 817: [36, 608, 337, 593, 508, 1084, 1225, 25, 1185, 1230],\n"," 818: [858, 1193, 1617, 1208, 1196, 1198, 2858, 296, 1221, 908],\n"," 819: [7153, 4993, 5952, 4226, 2959, 2329, 4776, 4011, 5989, 8874],\n"," 820: [1198, 260, 1617, 593, 858, 32, 1270, 2028, 912, 1036],\n"," 821: [318, 1704, 110, 527, 1961, 2028, 508, 593, 11, 1682],\n"," 822: [593, 34, 36, 357, 588, 589, 608, 592, 47, 595],\n"," 823: [356, 593, 1036, 318, 1270, 377, 590, 527, 34, 50],\n"," 824: [1230, 923, 1193, 1208, 913, 1292, 1288, 1617, 1267, 111],\n"," 825: [2762, 356, 2571, 318, 2858, 527, 296, 2329, 110, 1617],\n"," 826: [1617, 2028, 1198, 1291, 750, 593, 912, 1270, 923, 2762],\n"," 827: [380, 110, 589, 318, 539, 225, 593, 780, 736, 316],\n"," 828: [356, 150, 110, 588, 318, 593, 161, 590, 592, 539],\n"," 829: [608, 1393, 648, 719, 25, 494, 356, 653, 765, 5],\n"," 830: [318, 457, 1270, 1307, 34, 1265, 110, 919, 357, 608],\n"," 831: [440, 150, 588, 457, 34, 593, 318, 1197, 377, 110],\n"," 832: [1200, 1198, 858, 2571, 1208, 1210, 912, 1617, 904, 1221],\n"," 833: [1196, 2762, 1265, 50, 1193, 1210, 32, 1291, 1200, 110],\n"," 834: [593, 356, 296, 1265, 1617, 1136, 1704, 1784, 1198, 1196],\n"," 835: [356, 150, 527, 34, 110, 590, 265, 337, 36, 300],\n"," 836: [36, 780, 6, 25, 736, 494, 648, 9, 786, 1210],\n"," 837: [2997, 1089, 4226, 296, 50, 318, 1617, 1208, 2329, 2571],\n"," 838: [1196, 904, 750, 2858, 608, 1291, 296, 1617, 1252, 1221],\n"," 839: [908, 904, 750, 1207, 1208, 1193, 1252, 1247, 1617, 858],\n"," 840: [904, 1278, 1259, 318, 1213, 1094, 2858, 1270, 969, 34],\n"," 841: [1580, 1198, 1036, 1291, 2916, 2628, 316, 2716, 480, 780],\n"," 842: [364, 356, 480, 380, 110, 457, 318, 590, 589, 593],\n"," 843: [912, 908, 1304, 904, 923, 1234, 1225, 608, 1278, 1292],\n"," 844: [1197, 1214, 1270, 1200, 2571, 2028, 1387, 1617, 1234, 1265],\n"," 846: [110, 380, 50, 47, 349, 161, 588, 34, 21, 364],\n"," 847: [1036, 589, 733, 1240, 2571, 457, 648, 380, 1610, 480],\n"," 848: [318, 593, 527, 21, 50, 457, 150, 356, 265, 36],\n"," 849: [912, 858, 2858, 1198, 2571, 2947, 1240, 1938, 750, 904],\n"," 851: [593, 318, 527, 110, 1, 1240, 589, 1291, 457, 1036],\n"," 852: [110, 150, 590, 592, 527, 165, 316, 10, 50, 595],\n"," 853: [95, 648, 260, 5, 653, 786, 719, 62, 783, 36],\n"," 854: [1193, 1617, 923, 1247, 1213, 908, 904, 1230, 111, 1304],\n"," 855: [527, 2858, 1213, 111, 1704, 1193, 858, 2028, 110, 1089],\n"," 856: [457, 780, 110, 1036, 1240, 1291, 1196, 1198, 733, 1210],\n"," 857: [1291, 1198, 1210, 260, 457, 2762, 780, 1197, 858, 318],\n"," 859: [1240, 1214, 1197, 50, 32, 2762, 47, 318, 2858, 858],\n"," 860: [1641, 2580, 3253, 348, 1188, 1265, 2395, 3044, 1060, 1307],\n"," 861: [541, 1196, 1198, 2571, 1200, 1208, 1240, 1213, 1206, 1291],\n"," 862: [364, 380, 480, 590, 110, 593, 225, 508, 357, 11],\n"," 863: [110, 1196, 457, 260, 1, 1197, 34, 1198, 858, 293],\n"," 864: [1704, 1196, 1617, 2571, 2028, 2959, 1198, 593, 1197, 1968],\n"," 865: [589, 1198, 2762, 2028, 1291, 527, 32, 1036, 480, 1617],\n"," 866: [1193, 541, 1617, 2858, 1214, 1240, 1200, 589, 924, 356],\n"," 867: [1265, 912, 1193, 593, 2858, 1240, 1221, 1307, 1291, 1276],\n"," 868: [608, 296, 50, 356, 2571, 1089, 1196, 1265, 1213, 36],\n"," 869: [2571, 1196, 593, 589, 1617, 296, 1210, 32, 1214, 2858],\n"," 870: [296, 508, 21, 36, 50, 380, 377, 161, 47, 588],\n"," 871: [318, 1230, 1247, 1394, 1304, 1252, 1617, 1198, 235, 1197],\n"," 872: [858, 1617, 750, 1221, 541, 608, 1207, 593, 527, 1196],\n"," 873: [1196, 1240, 260, 1198, 593, 1210, 1617, 527, 1036, 608],\n"," 874: [318, 527, 1210, 296, 1198, 1617, 858, 50, 1291, 260],\n"," 875: [34, 337, 357, 296, 21, 457, 25, 110, 539, 608],\n"," 876: [457, 150, 380, 318, 587, 597, 588, 110, 589, 161],\n"," 878: [908, 1617, 318, 527, 296, 1214, 1206, 1304, 2019, 1089],\n"," 879: [1197, 1136, 1278, 1265, 1394, 858, 2918, 1259, 2797, 1193],\n"," 881: [161, 357, 349, 21, 10, 316, 733, 1, 474, 440],\n"," 882: [356, 733, 380, 457, 6, 150, 318, 648, 589, 377],\n"," 883: [1196, 1198, 2571, 356, 1291, 318, 260, 1197, 1240, 593],\n"," 884: [780, 733, 95, 36, 25, 5, 1, 6, 608, 494],\n"," 885: [1617, 457, 912, 1278, 1234, 1265, 750, 1276, 1291, 2028],\n"," 886: [1617, 1196, 356, 1198, 296, 1197, 260, 1210, 1213, 110],\n"," 887: [2762, 527, 588, 1196, 1393, 1210, 480, 1197, 608, 1198],\n"," 888: [318, 593, 296, 47, 527, 50, 111, 608, 356, 293],\n"," 889: [457, 356, 318, 593, 377, 588, 110, 539, 225, 364],\n"," 890: [1196, 1198, 1210, 457, 260, 1610, 380, 593, 165, 3147],\n"," 891: [1196, 593, 1198, 2028, 1291, 858, 541, 527, 1210, 356],\n"," 892: [589, 1198, 380, 480, 1, 377, 592, 1240, 34, 588],\n"," 893: [2959, 4993, 50, 47, 356, 1291, 593, 296, 4226, 1036],\n"," 894: [50872, 48780, 49530, 52973, 53000, 53972, 56174, 55820, 48774, 58559],\n"," 895: [457, 480, 588, 539, 318, 349, 110, 165, 592, 593],\n"," 896: [318, 593, 1213, 1617, 111, 2858, 1208, 21, 47, 260],\n"," 897: [318, 457, 356, 150, 296, 527, 589, 161, 380, 377],\n"," 898: [1240, 1200, 1270, 1374, 1580, 1198, 480, 316, 1291, 1127],\n"," 899: [356, 236, 440, 539, 318, 588, 380, 500, 248, 225],\n"," 900: [17, 58, 62, 141, 593, 318, 296, 733, 780, 21],\n"," 901: [1196, 1198, 1240, 260, 593, 1210, 110, 2028, 1291, 457],\n"," 902: [110, 1240, 32, 527, 541, 592, 1265, 150, 3147, 380],\n"," 903: [736, 648, 95, 1, 653, 6, 1210, 480, 457, 377],\n"," 904: [593, 608, 150, 25, 265, 509, 21, 110, 50, 508],\n"," 905: [150, 527, 1198, 1784, 380, 1097, 21, 1210, 1, 34],\n"," 906: [318, 1208, 1265, 111, 2762, 1198, 2571, 923, 356, 47],\n"," 907: [356, 480, 47, 377, 588, 50, 364, 161, 165, 34],\n"," 908: [1197, 1376, 593, 750, 32, 1617, 1370, 318, 648, 1],\n"," 909: [1196, 2762, 3578, 4226, 1291, 589, 7153, 1210, 1240, 1036],\n"," 910: [1196, 527, 1193, 2028, 1198, 2571, 2762, 111, 608, 1221],\n"," 911: [1197, 912, 858, 1097, 750, 1617, 1304, 1207, 1270, 1240],\n"," 912: [1198, 1240, 1197, 1580, 1210, 1291, 589, 2571, 1265, 1036],\n"," 913: [1198, 1207, 318, 858, 1196, 1193, 1197, 527, 912, 296],\n"," 914: [765, 667, 9, 737, 608, 95, 494, 1, 648, 783],\n"," 915: [1196, 1198, 1291, 1617, 110, 318, 858, 1136, 527, 1304],\n"," 916: [2858, 2571, 3578, 2762, 5952, 4226, 3996, 1196, 318, 7153],\n"," 917: [1393, 783, 1183, 260, 508, 34, 356, 733, 21, 805],\n"," 918: [494, 653, 5, 1, 9, 805, 141, 6, 783, 1393],\n"," 919: [589, 1036, 1198, 1196, 2571, 1374, 1270, 1527, 1356, 2916],\n"," 920: [1207, 908, 1193, 904, 1208, 1198, 750, 1247, 1136, 1278],\n"," 921: [1198, 1, 919, 1197, 364, 1270, 588, 356, 1097, 260],\n"," 922: [589, 1196, 1210, 1198, 1291, 1036, 593, 150, 364, 47],\n"," 923: [780, 736, 62, 95, 648, 36, 260, 783, 356, 1073],\n"," 924: [357, 265, 527, 497, 11, 457, 36, 508, 300, 21],\n"," 925: [1265, 1270, 1196, 1200, 1208, 1240, 1193, 608, 1259, 2716],\n"," 926: [1136, 2858, 858, 1196, 1617, 1080, 2571, 1206, 1198, 1214],\n"," 927: [589, 110, 1291, 457, 1196, 2571, 380, 593, 260, 1580],\n"," 928: [2571, 593, 318, 858, 2028, 1291, 589, 527, 110, 1197],\n"," 930: [225, 11, 380, 440, 110, 357, 34, 266, 364, 480],\n"," 931: [95, 733, 9, 648, 737, 662, 704, 64, 761, 719],\n"," 932: [356, 110, 780, 318, 380, 150, 588, 527, 595, 296],\n"," 933: [1196, 858, 608, 296, 1617, 527, 2858, 1213, 1240, 1210],\n"," 934: [1196, 1197, 1097, 1198, 1270, 1210, 1214, 1240, 1374, 919],\n"," 935: [110, 780, 1210, 380, 356, 1580, 2762, 377, 1197, 2353],\n"," 936: [318, 2762, 858, 608, 1213, 1208, 1197, 5952, 1270, 1221],\n"," 937: [1196, 1198, 593, 2571, 296, 110, 2858, 260, 2762, 356],\n"," 938: [110, 356, 589, 480, 527, 349, 592, 588, 377, 165],\n"," 939: [356, 110, 589, 32, 480, 296, 733, 380, 527, 377],\n"," 940: [318, 527, 593, 3996, 296, 858, 1196, 1617, 541, 1198],\n"," 941: [1304, 912, 750, 908, 1230, 1394, 1265, 969, 1276, 1196],\n"," 942: [110, 480, 589, 356, 150, 318, 592, 590, 364, 377],\n"," 943: [1214, 1240, 1196, 1200, 1198, 1197, 1208, 1206, 1291, 1221],\n"," 945: [719, 36, 780, 64, 733, 783, 95, 608, 802, 9],\n"," 946: [1200, 1036, 541, 1270, 750, 1580, 2028, 318, 592, 1221],\n"," 947: [1198, 1240, 1214, 1291, 1197, 1200, 1270, 1036, 541, 1097],\n"," 948: [1265, 1196, 260, 1197, 1036, 588, 32, 480, 1210, 1213],\n"," 949: [923, 1208, 904, 1617, 2858, 608, 1193, 1198, 750, 593],\n"," 950: [750, 111, 858, 1213, 1617, 1089, 923, 527, 318, 1221],\n"," 951: [765, 61, 6, 783, 74, 260, 141, 802, 805, 3],\n"," 952: [62, 783, 786, 1, 653, 9, 5, 1393, 719, 141],\n"," 953: [1197, 1198, 1240, 1278, 2174, 1291, 1036, 1097, 1394, 110],\n"," 954: [110, 1240, 1291, 1198, 1036, 1580, 1200, 1270, 1527, 356],\n"," 955: [356, 2762, 318, 2028, 2571, 1198, 1196, 110, 2706, 593],\n"," 956: [150, 457, 380, 480, 161, 364, 349, 508, 34, 10],\n"," 957: [608, 296, 750, 1208, 1036, 50, 904, 1089, 1276, 1234],\n"," 958: [589, 1196, 3578, 593, 110, 2858, 356, 32, 47, 296],\n"," 960: [318, 356, 50, 110, 457, 36, 590, 21, 508, 25],\n"," 961: [858, 1196, 593, 1221, 1207, 608, 318, 2028, 1197, 1208],\n"," 962: [2762, 2571, 1704, 356, 3578, 1196, 2572, 2959, 2028, 2716],\n"," 963: [457, 380, 110, 1196, 1036, 1240, 1198, 356, 593, 480],\n"," 964: [50, 2858, 2571, 2762, 1291, 1198, 858, 32, 1210, 1704],\n"," 965: [527, 110, 356, 25, 457, 1213, 293, 1089, 590, 337],\n"," 966: [1198, 1240, 750, 1291, 50, 589, 858, 593, 1270, 2028],\n"," 967: [296, 4226, 2571, 50, 593, 1617, 527, 2762, 1213, 1136],\n"," 968: [356, 589, 380, 377, 592, 588, 296, 527, 161, 165],\n"," 969: [5, 719, 783, 736, 62, 780, 802, 609, 832, 9],\n"," 970: [356, 457, 589, 296, 380, 47, 161, 165, 480, 527],\n"," 971: [1136, 356, 527, 1214, 1291, 541, 2762, 1, 1193, 904],\n"," 972: [508, 36, 377, 21, 1196, 1197, 300, 50, 1198, 260],\n"," 973: [1193, 912, 919, 923, 1207, 1247, 908, 1208, 1278, 1222],\n"," 974: [1207, 1247, 1221, 1208, 750, 1193, 858, 1250, 1276, 924],\n"," 975: [593, 318, 527, 47, 356, 1213, 589, 110, 150, 36],\n"," 976: [593, 356, 110, 457, 527, 589, 380, 34, 480, 364],\n"," 977: [750, 1304, 912, 1247, 527, 1394, 1197, 1198, 1278, 1234],\n"," 978: [1278, 594, 912, 1304, 17, 1035, 908, 2797, 34, 914],\n"," 979: [318, 593, 50, 1196, 296, 260, 110, 527, 1198, 1213],\n"," 980: [1196, 858, 50, 2571, 318, 1193, 1198, 1089, 1617, 1221],\n"," 982: [1210, 318, 36, 593, 25, 733, 1196, 6, 296, 356],\n"," 983: [1196, 1198, 593, 527, 110, 2762, 1210, 260, 2028, 2858],\n"," 984: [593, 2571, 2858, 1265, 527, 1198, 1197, 150, 1617, 296],\n"," 985: [2571, 4226, 5445, 1196, 6539, 7361, 1198, 33794, 4995, 32587],\n"," 986: [225, 292, 440, 47, 21, 508, 34, 500, 185, 364],\n"," 987: [161, 248, 266, 356, 292, 252, 11, 339, 539, 500],\n"," 988: [1197, 1221, 527, 589, 908, 1291, 1207, 1265, 110, 1225],\n"," 989: [750, 1208, 1265, 1221, 593, 1207, 1230, 1276, 1394, 1197],\n"," 990: [150, 110, 457, 47, 508, 589, 34, 592, 21, 480],\n"," 991: [1198, 1240, 260, 1196, 1197, 1214, 858, 1208, 1221, 1387],\n"," 992: [1197, 1198, 1210, 1270, 1240, 1617, 593, 1580, 1291, 356],\n"," 993: [5349, 4995, 2571, 2959, 6539, 7153, 4034, 2762, 4973, 2329],\n"," 994: [527, 356, 1784, 593, 296, 2858, 1968, 1704, 50, 608],\n"," 995: [608, 2858, 1617, 858, 1193, 1221, 1208, 1732, 1089, 2762],\n"," 996: [161, 590, 225, 597, 587, 34, 296, 380, 21, 440],\n"," 997: [6, 9, 260, 608, 832, 719, 104, 1393, 356, 79],\n"," 998: [318, 1193, 527, 593, 2858, 296, 1265, 1207, 356, 1617],\n"," 999: [750, 1252, 1196, 1077, 904, 1214, 908, 1200, 541, 912],\n"," 1001: [380, 590, 318, 589, 593, 377, 296, 10, 539, 34],\n"," 1002: [733, 95, 248, 719, 648, 609, 158, 587, 376, 765],\n"," 1003: [2959, 2858, 1196, 2716, 2028, 3578, 1617, 1704, 1210, 2355],\n"," 1004: [527, 150, 260, 589, 1198, 50, 1196, 32, 1036, 1210],\n"," 1005: [364, 590, 34, 11, 508, 141, 47, 6, 21, 595],\n"," 1006: [593, 36, 296, 25, 50, 21, 17, 356, 590, 508],\n"," 1007: [356, 527, 357, 318, 593, 1784, 2762, 265, 1617, 2858],\n"," 1008: [1198, 2858, 1617, 1197, 110, 1136, 912, 111, 589, 1208],\n"," 1009: [1196, 2028, 1210, 1291, 1036, 1198, 2762, 260, 110, 1617],\n"," 1011: [1196, 1198, 260, 318, 527, 1197, 1210, 858, 110, 356],\n"," 1012: [2762, 2571, 1196, 2716, 2028, 1617, 318, 2959, 1240, 3578],\n"," 1013: [318, 356, 47, 110, 50, 858, 32, 1196, 2571, 1198],\n"," 1014: [912, 1617, 1193, 923, 1208, 318, 750, 1230, 1276, 1250],\n"," 1015: [750, 908, 912, 541, 1196, 858, 1193, 923, 608, 1198],\n"," 1016: [593, 858, 1617, 50, 1196, 1208, 1089, 2762, 541, 1221],\n"," 1017: [2571, 260, 3578, 6539, 1198, 5445, 1240, 1291, 4226, 1214],\n"," 1018: [541, 318, 50, 527, 260, 1198, 1213, 1208, 1617, 858],\n"," 1019: [6, 5, 62, 494, 1393, 104, 653, 1210, 832, 32],\n"," 1020: [318, 34, 1259, 1198, 1196, 110, 1097, 590, 260, 1270],\n"," 1021: [1617, 2571, 608, 296, 541, 750, 318, 1240, 1210, 1265],\n"," 1022: [1207, 1196, 912, 1198, 1197, 260, 858, 1193, 593, 1247],\n"," 1023: [589, 1036, 1240, 1291, 1210, 318, 2028, 356, 296, 1270],\n"," 1025: [904, 750, 1617, 1196, 1197, 1234, 1204, 1193, 1208, 1252],\n"," 1026: [110, 318, 356, 858, 457, 2028, 527, 1265, 296, 1617],\n"," 1027: [4993, 5952, 7153, 4995, 45499, 51662, 2329, 45722, 4878, 2571],\n"," 1028: [110, 318, 589, 377, 592, 316, 588, 161, 292, 364],\n"," 1029: [110, 161, 356, 150, 380, 349, 589, 474, 225, 480],\n"," 1030: [1196, 1210, 260, 589, 110, 3578, 480, 780, 2028, 318],\n"," 1031: [589, 380, 457, 356, 377, 150, 318, 349, 588, 593],\n"," 1032: [733, 1, 9, 62, 260, 376, 141, 104, 788, 377],\n"," 1033: [1265, 1270, 1210, 1036, 858, 2028, 608, 1259, 1617, 2858],\n"," 1034: [1196, 4993, 4306, 356, 1198, 6539, 110, 593, 318, 2571],\n"," 1035: [4993, 1196, 5952, 7153, 3578, 2028, 4878, 2858, 2329, 1291],\n"," 1037: [356, 318, 457, 588, 364, 380, 527, 110, 11, 590],\n"," 1038: [457, 150, 440, 110, 593, 225, 318, 21, 11, 185],\n"," 1039: [593, 2858, 1196, 356, 1136, 32, 1193, 110, 1265, 2028],\n"," 1040: [356, 457, 539, 367, 110, 588, 500, 480, 589, 377],\n"," 1041: [356, 858, 1240, 32, 541, 1213, 1136, 3147, 1270, 1193],\n"," 1043: [733, 786, 376, 141, 1, 25, 457, 783, 7, 653],\n"," 1044: [6, 1356, 104, 141, 494, 356, 480, 9, 62, 783],\n"," 1045: [2571, 589, 1270, 1197, 4993, 608, 1240, 480, 1265, 5952],\n"," 1046: [110, 589, 318, 377, 588, 296, 364, 592, 349, 10],\n"," 1047: [1196, 593, 5952, 1617, 3578, 32, 608, 356, 4878, 260],\n"," 1048: [1291, 2028, 858, 1240, 3578, 110, 1617, 1089, 1136, 356],\n"," 1050: [150, 380, 34, 588, 592, 377, 6, 364, 349, 36],\n"," 1051: [141, 780, 736, 783, 733, 1393, 5, 36, 11, 708],\n"," 1052: [356, 1210, 1196, 2762, 1704, 589, 2858, 2571, 110, 1097],\n"," 1053: [1136, 2858, 2028, 1617, 593, 858, 50, 32, 2918, 4993]}"]},"metadata":{},"execution_count":8}],"source":["# 각 사용자에 대한 추천 리스트를 작성한다\n","\n","pred_user2items = dict()\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," input_data = []\n"," for item_id in data.sort_values(\"timestamp\")[\"movie_id\"].tolist():\n"," if item_id in model.wv.key_to_index:\n"," input_data.append(item_id)\n"," if len(input_data) == 0:\n"," # 추천 계산할 수 없는 경우에는 빈 배열\n"," pred_user2items[user_id] = []\n"," continue\n"," recommended_items = model.wv.most_similar(input_data, topn=10)\n"," pred_user2items[user_id] = [d[0] for d in recommended_items]\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":9,"id":"a87b9ae8","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"a87b9ae8","executionInfo":{"status":"ok","timestamp":1672118195817,"user_tz":-540,"elapsed":12,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"a2f0748a-068e-4e8c-9795-dacd19f0d82b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
476480Jurassic Park (1993)[Action, Adventure, Sci-Fi, Thriller][based on a book, biology, michael crichton, s...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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"cells": [ - { - "cell_type": "markdown", - "id": "d45967ea", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_collaboration.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "942a9897", - "metadata": {}, - "source": [ - "# Latent Dirichlet Allocation (LDA)를 행동 데이터에 적용" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b8cb8f0b", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6769fb75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f650f9d6", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 50\n", - "# 에폭 수\n", - "n_epochs = 30" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f703bf73", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install gensim==4.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d5348432", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "from gensim.corpora.dictionary import Dictionary\n", - "\n", - "# LDA 입력으로 사용할 데이터를 작성한다\n", - "lda_data = []\n", - "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " lda_data.append(data[\"movie_id\"].apply(str).tolist())\n", - "\n", - "common_dictionary = Dictionary(lda_data)\n", - "common_corpus = [common_dictionary.doc2bow(text) for text in lda_data]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d0f0e231", - "metadata": {}, - "outputs": [], - "source": [ - "import gensim\n", - "lda_model = gensim.models.LdaModel(\n", - " common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "d568841a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 0.058512874), (2, 0.056664612), (20, 0.71739906), (33, 0.1163097)]" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 각 사용자의 소속 토픽이 저장된다\n", - "lda_topics = lda_model[common_corpus]\n", - "\n", - "# 예: 어떤 사용자의 소속 토픽\n", - "lda_topics[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "aa768039", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "movie_id=370, title=Naked Gun 33 1/3: The Final Insult (1994), score=0.05946573242545128\n", - "movie_id=256, title=Junior (1994), score=0.02701723948121071\n", - "movie_id=44, title=Mortal Kombat (1995), score=0.02674136497080326\n", - "movie_id=485, title=Last Action Hero (1993), score=0.022686509415507317\n", - "movie_id=442, title=Demolition Man (1993), score=0.013746446929872036\n", - "movie_id=466, title=Hot Shots! Part Deux (1993), score=0.01224201824516058\n", - "movie_id=7153, title=Lord of the Rings: The Return of the King, The (2003), score=0.01153385080397129\n", - "movie_id=315, title=Specialist, The (1994), score=0.01045612245798111\n", - "movie_id=480, title=Jurassic Park (1993), score=0.009095880202949047\n", - "movie_id=160, title=Congo (1995), score=0.008071123622357845\n" - ] - } - ], - "source": [ - "# topic0인 영화 목록\n", - "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", - " movie_id = int(common_dictionary.id2token[token_id])\n", - " title = movies[movies.movie_id == movie_id].title.tolist()[0]\n", - " print(f'movie_id={movie_id}, title={title}, score={score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "9eb2910f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 0.010000048),\n", - " (1, 0.010000048),\n", - " (2, 0.010000048),\n", - " (3, 0.010000048),\n", - " (4, 0.010000048),\n", - " (5, 0.010000048),\n", - " (6, 0.010000048),\n", - 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" (45, 0.010000048),\n", - " (46, 0.010000048),\n", - " (47, 0.010000048),\n", - " (48, 0.010000048),\n", - " (49, 0.5099976)]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 스타워즈/에피소드 5(movie_id=1196)의 토픽(각 토픽에 소속될 확률)\n", - "lda_model[common_dictionary.doc2bow([\"1196\"])]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "8de0ac42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {1: [457, 150, 593, 318, 380, 110, 296, 590, 349, 161],\n", - " 2: [1198, 1196, 1240, 1291, 1200, 1197, 1270, 593, 1193, 1234],\n", - " 3: [7151, 337, 1704, 3181, 2771, 1641, 2336, 3157, 1242, 2762],\n", - " 4: [457, 593, 356, 318, 296, 10, 47, 50, 527, 454],\n", - " 5: [1193, 2858, 1617, 1208, 904, 296, 1252, 1213, 50, 1233],\n", - " 6: [1210, 1580, 1036, 480, 589, 1240, 1270, 3793, 780, 1610],\n", - " 7: [1193, 2858, 527, 1208, 858, 296, 1213, 111, 1233, 1247],\n", - " 8: [1610, 110, 356, 3753, 1, 2706, 2716, 1097, 2529, 1356],\n", - 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" 869: [1617, 2858, 1237, 924, 1234, 7064, 1252, 858, 750, 9018],\n", - " 870: [36, 50, 17, 608, 497, 515, 508, 58, 272, 307],\n", - " 871: [2858, 1617, 1233, 800, 2396, 994, 1212, 910, 2289, 1284],\n", - " 872: [608, 2858, 527, 1617, 912, 904, 858, 296, 1213, 111],\n", - " 873: [1196, 3578, 260, 1210, 1036, 1198, 1240, 1270, 3793, 780],\n", - " 874: [4226, 318, 50, 1089, 32, 4878, 1214, 1206, 858, 293],\n", - " 875: [50, 25, 608, 337, 246, 293, 58, 342, 32, 111],\n", - " 876: [150, 110, 588, 296, 349, 161, 595, 364, 292, 10],\n", - " 878: [2858, 1617, 1233, 2396, 994, 1212, 910, 2289, 1276, 1148],\n", - " 879: [919, 1259, 1197, 1097, 1641, 318, 1304, 1278, 2797, 1265],\n", - " 881: [349, 1, 434, 733, 21, 357, 300, 780, 368, 163],\n", - " 882: [733, 608, 1073, 36, 832, 17, 802, 494, 6, 805],\n", - " 883: [2959, 296, 5952, 4226, 7153, 2571, 4993, 2858, 47, 318],\n", - " 884: [260, 780, 733, 608, 36, 832, 802, 494, 6, 25],\n", - " 885: [1234, 1278, 5060, 357, 1394, 1617, 2804, 1292, 1271, 2324],\n", - " 886: [919, 1259, 1197, 1304, 1278, 1207, 5060, 2791, 1, 953],\n", - " 887: [919, 2396, 608, 1234, 1968, 1259, 1198, 1197, 1097, 1304],\n", - " 888: [1208, 3996, 7153, 1221, 1213, 260, 5060, 4993, 2924, 2858],\n", - " 889: [457, 593, 356, 318, 480, 589, 377, 364, 500, 527],\n", - " 890: [1196, 1198, 1270, 1610, 2916, 1214, 4993, 5349, 4306, 541],\n", - " 891: [6874, 1089, 2329, 593, 7438, 7361, 1206, 1136, 2502, 858],\n", - " 892: [509, 265, 17, 337, 357, 497, 515, 58, 272, 307],\n", - " 893: [2959, 296, 4226, 7153, 4993, 2858, 47, 6874, 50, 32],\n", - " 894: [296, 2028, 2916, 1196, 2620, 2571, 1210, 733, 593, 1101],\n", - " 895: [457, 593, 318, 110, 480, 349, 165, 292, 47, 34],\n", - " 896: [1210, 2028, 1094, 663, 1586, 1407, 1923, 1704, 804, 2571],\n", - " 897: [457, 150, 356, 318, 480, 588, 349, 589, 377, 161],\n", - " 898: [3578, 1036, 480, 1198, 1240, 3793, 1610, 110, 1291, 2028],\n", - " 899: [457, 593, 356, 318, 110, 480, 588, 349, 589, 377],\n", - " 900: [708, 58, 1042, 3, 628, 79, 1405, 135, 762, 140],\n", - " 901: [2959, 296, 5952, 4226, 7153, 4993, 2858, 47, 6874, 50],\n", - " 902: [32, 4878, 2997, 4973, 1206, 2502, 293, 541, 6377, 3949],\n", - " 903: [608, 1073, 36, 832, 17, 6, 25, 805, 1210, 1393],\n", - " 904: [50, 25, 265, 17, 593, 150, 497, 515, 508, 293],\n", - " 905: [919, 2396, 1234, 1097, 1304, 1278, 858, 1207, 1358, 5060],\n", - " 906: [919, 1234, 1198, 1097, 1641, 318, 1304, 2797, 1265, 2716],\n", - " 907: [588, 161, 595, 292, 34, 316, 587, 1, 434, 339],\n", - " 908: [3578, 3793, 2762, 3527, 4993, 5349, 4306, 3996, 3753, 1],\n", - " 909: [1196, 3578, 260, 1210, 1580, 1036, 480, 589, 1270, 1610],\n", - " 910: [2571, 32, 4878, 7361, 589, 1196, 2997, 2762, 4973, 1206],\n", - " 911: [2080, 920, 1270, 3061, 1333, 1247, 1022, 955, 899, 902],\n", - " 912: [2571, 3578, 260, 1210, 1580, 1036, 1198, 589, 1240, 3793],\n", - " 913: [2396, 608, 1234, 1968, 1197, 1097, 1641, 318, 1304, 1278],\n", - " 914: [34271,\n", - " 2023,\n", - " 6281,\n", - " 42725,\n", - " 43936,\n", - " 6887,\n", - " 48304,\n", - " 8117,\n", - " 32029,\n", - " 5373],\n", - " 915: [919, 1198, 1641, 318, 1304, 1704, 858, 1207, 1293, 1961],\n", - " 916: [5952, 1208, 3996, 7153, 1221, 1213, 260, 1258, 5060, 2924],\n", - " 917: [260, 32, 802, 494, 6, 805, 1210, 1393, 786, 1356],\n", - " 918: [608, 32, 141, 36, 17, 6, 25, 805, 112, 58],\n", - " 919: [1196, 3578, 1036, 1198, 589, 1270, 3793, 1214, 2762, 4993],\n", - " 920: [5952, 1208, 3996, 509, 7153, 1213, 1258, 4993, 2924, 2858],\n", - " 921: [919, 4333, 5955, 1198, 1022, 1010, 2396, 588, 2078, 3053],\n", - " 922: [5812, 5878, 4055, 2712, 1446, 1673, 6003, 3871, 4903, 4612],\n", - " 923: [260, 62, 32, 736, 648, 36, 832, 17, 802, 25],\n", - " 924: [288, 4446, 1554, 327, 5400, 3949, 1924, 2571, 4166, 3728],\n", - " 925: [2396, 608, 1259, 1097, 1641, 2797, 2716, 1207, 2028, 357],\n", - " 926: [5952, 3996, 509, 1221, 1213, 260, 5060, 4993, 2924, 2858],\n", - " 927: [260, 1196, 1240, 858, 1291, 1200, 1270, 593, 1193, 1234],\n", - " 928: [7153, 6874, 1214, 1206, 2502, 858, 2028, 1200, 1258, 32587],\n", - " 930: [440, 261, 1084, 442, 277, 34, 262, 1035, 222, 1097],\n", - " 931: [260, 1, 608, 62, 141, 648, 36, 832, 17, 802],\n", - " 932: [150, 356, 318, 110, 588, 296, 590, 349, 161, 165],\n", - " 933: [2396, 608, 1234, 1197, 1641, 1304, 2797, 1265, 858, 1207],\n", - " 934: [1247, 318, 1961, 1949, 3105, 2067, 27820, 147, 3952, 62],\n", - " 935: [370, 256, 44, 485, 442, 7153, 315, 279, 4993, 95],\n", - " 936: [318, 1258, 1213, 750, 1274, 5618, 924, 2716, 1732, 1653],\n", - " 937: [296, 4226, 2571, 4993, 2858, 6874, 1089, 32, 593, 7438],\n", - " 938: [356, 110, 480, 588, 589, 377, 165, 595, 364, 500],\n", - " 939: [62, 17, 802, 494, 25, 805, 1210, 1393, 786, 1356],\n", - " 940: [1196, 3578, 1210, 1580, 1036, 480, 1198, 589, 780, 1610],\n", - " 941: [1333, 1939, 8366, 1215, 1214, 292, 1321, 1358, 1219, 2118],\n", - " 942: [150, 356, 110, 480, 590, 589, 377, 161, 364, 500],\n", - " 943: [1961, 1196, 457, 858, 356, 1610, 743, 260, 1220, 2396],\n", - " 945: [780, 608, 648, 36, 832, 6, 805, 1210, 95, 708],\n", - " 946: [3578, 1580, 1036, 1270, 3793, 780, 2028, 2762, 3527, 4993],\n", - " 947: [1028, 1947, 2080, 1282, 1380, 914, 920, 3061, 1333, 594],\n", - " 948: [919, 1234, 1259, 1197, 1097, 1304, 1278, 2797, 2716, 1207],\n", - " 949: [818, 663, 100, 1120, 4246, 1293, 43, 469, 3553, 1411],\n", - " 950: [1028, 919, 1035, 1947, 2080, 1282, 1380, 914, 920, 1270],\n", - " 951: [260, 802, 25, 805, 1210, 1393, 1356, 647, 58, 14],\n", - " 952: [1, 608, 32, 141, 36, 6, 25, 1393, 786, 1356],\n", - " 953: [3466, 1240, 1320, 111, 2240, 1247, 923, 8128, 750, 1376],\n", - " 954: [1036, 1198, 1240, 1270, 1610, 110, 1291, 1527, 4993, 1676],\n", - " 955: [207, 783, 724, 520, 784, 74, 88, 24, 276, 516],\n", - " 956: [316, 1, 733, 11, 508, 6, 780, 736, 368, 141],\n", - " 957: [2396, 1234, 1641, 2797, 1704, 5060, 357, 1293, 2918, 2791],\n", - " 958: [1777, 2581, 4025, 1197, 3081, 1027, 1136, 2605, 1393, 3988],\n", - " 960: [36, 318, 509, 50, 25, 265, 17, 337, 246, 515],\n", - " 961: [608, 912, 1208, 904, 858, 296, 1252, 111, 541, 50],\n", - " 962: [1584, 2571, 1777, 2581, 4025, 780, 1197, 3081, 318, 1027],\n", - " 963: [2542, 3581, 103, 467, 1466, 180, 836, 32, 1049, 3147],\n", - " 964: [5952, 7153, 4993, 2858, 6874, 50, 32, 4878, 7438, 7361],\n", - " 965: [527, 509, 25, 17, 356, 337, 357, 497, 515, 508],\n", - " 966: [47, 318, 6874, 50, 2329, 593, 7361, 4306, 2762, 3578],\n", - " 967: [1196, 3578, 260, 1210, 1580, 1036, 480, 1198, 589, 1240],\n", - " 968: [356, 380, 588, 589, 377, 161, 595, 364, 500, 10],\n", - " 969: [260, 608, 648, 832, 802, 494, 6, 805, 1210, 95],\n", - " 970: [457, 356, 480, 588, 349, 589, 377, 161, 165, 595],\n", - " 971: [1234, 1259, 1304, 1207, 1358, 5060, 1293, 1961, 1307, 953],\n", - " 972: [919, 1234, 1198, 1197, 1304, 1278, 2797, 2716, 5060, 1079],\n", - " 973: [1409,\n", - " 1407,\n", - " 34271,\n", - " 2023,\n", - " 6281,\n", - " 1050,\n", - " 849,\n", - " 1049,\n", - " 1447,\n", - " 42725],\n", - " 974: [207, 158, 783, 724, 520, 784, 74, 88, 24, 276],\n", - " 975: [527, 36, 318, 509, 25, 265, 17, 356, 593, 337],\n", - " 976: [457, 593, 356, 110, 480, 589, 377, 364, 292, 500],\n", - " 977: [1233, 2396, 1219, 778, 994, 1212, 1284, 1394, 2599, 2997],\n", - " 978: [1035, 2080, 1210, 1282, 1196, 1380, 914, 920, 1270, 1333],\n", - " 979: [1259, 1220, 427, 2143, 1197, 1213, 1372, 1278, 507, 1376],\n", - " 980: [5952, 2571, 47, 318, 6874, 32, 2329, 589, 1196, 2997],\n", - " 982: [733, 62, 36, 832, 17, 802, 494, 6, 805, 1210],\n", - " 983: [1252, 2583, 3091, 3504, 4427, 6170, 3812, 2826, 3196, 2644],\n", - " 984: [1234, 1968, 1198, 1197, 1097, 1641, 1304, 1278, 1358, 5060],\n", - " 985: [48516,\n", - " 49272,\n", - " 33794,\n", - " 48774,\n", - " 59315,\n", - " 51662,\n", - " 6539,\n", - " 4963,\n", - " 1291,\n", - " 48780],\n", - " 986: [595, 364, 292, 10, 47, 34, 1, 733, 21, 329],\n", - " 987: [457, 593, 356, 110, 480, 588, 349, 589, 377, 364],\n", - " 988: [527, 1230, 1221, 1207, 908, 2396, 1179, 1212, 910, 1276],\n", - " 989: [1208, 111, 923, 593, 1247, 1230, 1267, 1221, 1207, 903],\n", - " 990: [110, 349, 161, 595, 292, 500, 47, 34, 316, 1],\n", - " 991: [608, 1193, 2858, 1617, 1208, 904, 858, 296, 1252, 1213],\n", - " 992: [2396, 608, 1234, 1259, 1198, 1197, 1641, 1304, 1278, 1704],\n", - " 993: [2571, 260, 1580, 1036, 1198, 589, 1240, 1270, 780, 1610],\n", - " 994: [48516,\n", - " 49272,\n", - " 54286,\n", - " 7153,\n", - " 8961,\n", - " 33794,\n", - " 5952,\n", - " 40815,\n", - " 8368,\n", - " 5349],\n", - " 995: [1090, 1732, 2502, 7360, 741, 293, 111, 2542, 1196, 296],\n", - " 996: [480, 588, 589, 595, 364, 592, 316, 50, 587, 1],\n", - " 997: [260, 608, 832, 802, 805, 1210, 1393, 1356, 7, 104],\n", - " 998: [919, 2396, 1234, 1968, 1198, 1197, 1641, 318, 1304, 1278],\n", - " 999: [1203, 164, 3005, 3654, 2762, 910, 1912, 4031, 2986, 2858],\n", - " 1001: [593, 318, 380, 296, 590, 349, 165, 292, 47, 34],\n", - " 1002: [207, 158, 783, 724, 784, 74, 88, 24, 276, 516],\n", - " 1003: [105, 425, 1207, 1354, 261, 3263, 605, 2443, 1465, 1624],\n", - " 1004: [161, 527, 474, 339, 586, 21, 225, 11, 288, 508],\n", - " 1005: [590, 34, 316, 50, 253, 32, 11, 231, 288, 508],\n", - " 1006: [296, 36, 50, 25, 17, 356, 593, 300, 357, 246],\n", - " 1007: [608, 1193, 2858, 527, 1617, 912, 1208, 904, 858, 296],\n", - " 1008: [1234,\n", - " 1641,\n", - " 1304,\n", - " 1278,\n", - " 2797,\n", - " 2028,\n", - " 1358,\n", - " 5060,\n", - " 1079,\n", - " 1784],\n", - " 1009: [1584,\n", - " 1777,\n", - " 2581,\n", - " 1197,\n", - " 3081,\n", - " 1027,\n", - " 1136,\n", - " 2605,\n", - " 1393,\n", - " 3988],\n", - " 1011: [2396, 1234, 1259, 1197, 318, 2797, 1704, 2716, 858, 1358],\n", - " 1012: [5952, 1208, 3996, 509, 7153, 1221, 1213, 260, 1258, 5060],\n", - " 1013: [1208, 1213, 1258, 5060, 2924, 2858, 11, 2997, 1584, 3793],\n", - " 1014: [1193, 912, 1208, 1213, 111, 50, 923, 1230, 1221, 903],\n", - " 1015: [608, 1193, 527, 912, 1208, 858, 296, 1213, 111, 541],\n", - " 1016: [1617, 912, 1208, 904, 858, 1252, 541, 50, 1233, 593],\n", - " 1017: [2959, 296, 4226, 2571, 2858, 47, 318, 6874, 50, 1089],\n", - " 1018: [4446,\n", - " 1554,\n", - " 5400,\n", - " 2501,\n", - " 4166,\n", - " 3728,\n", - " 3203,\n", - " 3998,\n", - " 1243,\n", - " 1610],\n", - " 1019: [62, 1073, 32, 36, 832, 17, 802, 494, 6, 25],\n", - " 1020: [919, 2396, 1259, 1198, 1097, 1641, 318, 2797, 1704, 2716],\n", - " 1021: [608, 1234, 1968, 1097, 1641, 318, 1278, 2797, 1265, 1704],\n", - " 1022: [260, 1198, 1196, 1240, 858, 1291, 1197, 1270, 593, 1193],\n", - " 1023: [1210, 1580, 1036, 589, 1240, 1270, 3793, 780, 1610, 2916],\n", - " 1025: [1234, 1968, 1259, 1097, 2797, 1704, 2716, 1358, 357, 1079],\n", - " 1026: [1234, 1259, 1641, 318, 1304, 2797, 1265, 858, 1207, 1358],\n", - " 1027: [7153,\n", - " 8961,\n", - " 33794,\n", - " 5952,\n", - " 4993,\n", - " 40815,\n", - " 8368,\n", - " 59315,\n", - " 31658,\n", - " 4306],\n", - " 1028: [318, 110, 588, 589, 377, 595, 364, 500, 47, 34],\n", - " 1029: [356, 110, 480, 349, 589, 377, 161, 500, 527, 587],\n", - " 1030: [3396,\n", - " 4333,\n", - " 1097,\n", - " 1022,\n", - " 1010,\n", - " 2014,\n", - " 3114,\n", - " 2396,\n", - " 2081,\n", - " 1028],\n", - " 1031: [457, 150, 356, 318, 380, 588, 296, 349, 589, 377],\n", - " 1032: [260, 1, 733, 608, 62, 32, 141, 36, 832, 17],\n", - " 1033: [2396, 608, 1234, 1259, 1641, 1278, 1265, 1704, 858, 5060],\n", - " 1034: [2542, 3581, 103, 467, 1466, 180, 836, 32, 1049, 442],\n", - " 1035: [50, 2329, 4878, 7361, 1214, 1196, 2997, 4973, 1206, 2502],\n", - " 1037: [457, 318, 380, 110, 588, 590, 349, 589, 161, 165],\n", - " 1038: [74, 276, 61, 653, 748, 1485, 491, 3, 788, 694],\n", - " 1039: [1183, 1200, 1094, 1214, 663, 1586, 1, 1220, 32, 1222],\n", - " 1040: [1028, 919, 1035, 1947, 2080, 1282, 1196, 1380, 914, 920],\n", - " 1041: [32, 4306, 1206, 1136, 2502, 293, 541, 6377, 1080, 1258],\n", - " 1043: [260, 733, 608, 1073, 832, 802, 805, 1210, 1393, 786],\n", - " 1044: [608, 62, 141, 36, 832, 17, 494, 6, 805, 1356],\n", - " 1045: [5952, 1214, 4973, 3996, 1200, 1080, 3949, 6711, 778, 2542],\n", - " 1046: [318, 110, 588, 589, 377, 161, 364, 292, 500, 10],\n", - " 1047: [5377,\n", - " 5418,\n", - " 8665,\n", - " 1961,\n", - " 1193,\n", - " 2023,\n", - " 4148,\n", - " 593,\n", - " 1704,\n", - " 30707],\n", - " 1048: [1089, 4878, 7361, 1214, 4973, 1136, 3578, 3996, 858, 541],\n", - " 1050: [34, 1, 733, 11, 780, 266, 236, 39, 282, 1036],\n", - " 1051: [608, 32, 36, 832, 494, 6, 25, 805, 1210, 95],\n", - " 1052: [2571, 1196, 260, 1210, 1580, 1036, 480, 1198, 589, 1240],\n", - " 1053: [480, 780, 2028, 4993, 1676, 2628, 2000, 3996, 457, 1917]})" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 각 사용자에 대한 추천 리스트를 작성한다\n", - "# 각 사용자의 소속 확률이 가장 높은 토픽을 얻고, 해당 토픽 안에서 확률이 높은 아이템을 저장해 나간다\n", - "\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "\n", - "pred_user2items = defaultdict(list)\n", - "for i, (user_id, data) in enumerate(movielens_train_high_rating.groupby(\"user_id\")):\n", - " evaluated_movie_ids = user_evaluated_movies[user_id]\n", - " # 사용자의 소속 확률이 가장 높은 토픽을 얻는다\n", - " user_topic = sorted(lda_topics[i], key=lambda x: -x[1])[0][0]\n", - " # 해당 토픽 안에서 확률이 높은 아이템을 얻는다\n", - " topic_movies = lda_model.get_topic_terms(user_topic, topn=len(movies))\n", - "\n", - " for token_id, score in topic_movies:\n", - " movie_id = int(common_dictionary.id2token[token_id])\n", - " if movie_id not in evaluated_movie_ids:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "1701bd43", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "16eddf46", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
12121240Terminator, The (1984)[Action, Sci-Fi, Thriller][arnold schwarzenegger, sci-fi, time travel, d...
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" - ], - "text/plain": [ - " movie_id title \\\n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n", - "1212 1240 Terminator, The (1984) \n", - "\n", - " genre \\\n", - "1171 [Action, Adventure, Sci-Fi] \n", - "1173 [Action, Adventure] \n", - "1212 [Action, Sci-Fi, Thriller] \n", - "\n", - " tag \n", - "1171 [lucas, george lucas, george lucas, gfei own i... \n", - "1173 [egypt, lucas, seen more than once, dvd collec... \n", - "1212 [arnold schwarzenegger, sci-fi, time travel, d... " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(1198, 1196, 1240)\n", - "movies[movies.movie_id.isin([1198, 1196, 1240])]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"d45967ea","metadata":{"id":"d45967ea"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_collaboration.ipynb)"]},{"cell_type":"markdown","id":"942a9897","metadata":{"id":"942a9897"},"source":["# Latent Dirichlet Allocation (LDA)를 행동 데이터에 적용"]},{"cell_type":"code","execution_count":1,"id":"b8cb8f0b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b8cb8f0b","executionInfo":{"status":"ok","timestamp":1672118267114,"user_tz":-540,"elapsed":4818,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"ca848f2f-0377-4da9-e770-5b4f7e92d5b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:15:58-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.6MB/s in 1.0s \n","\n","2022-12-27 05:15:59 (63.6 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"6769fb75","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6769fb75","executionInfo":{"status":"ok","timestamp":1672118333071,"user_tz":-540,"elapsed":65965,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5ab1c72d-1ab3-46bb-ce4e-9463a2c6e6a2"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"f650f9d6","metadata":{"id":"f650f9d6","executionInfo":{"status":"ok","timestamp":1672118333072,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 50\n","# 에폭 수\n","n_epochs = 30"]},{"cell_type":"code","execution_count":4,"id":"f703bf73","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f703bf73","executionInfo":{"status":"ok","timestamp":1672118340381,"user_tz":-540,"elapsed":7337,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"47c9ad1a-7786-4285-ecbb-b86ca5cf30de"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.6 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":5,"id":"d5348432","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d5348432","executionInfo":{"status":"ok","timestamp":1672118342033,"user_tz":-540,"elapsed":1656,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3c8d0dc9-8126-4750-d870-08019b81e513"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["from gensim.corpora.dictionary import Dictionary\n","\n","# LDA 입력으로 사용할 데이터를 작성한다\n","lda_data = []\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," lda_data.append(data[\"movie_id\"].apply(str).tolist())\n","\n","common_dictionary = Dictionary(lda_data)\n","common_corpus = [common_dictionary.doc2bow(text) for text in lda_data]\n"]},{"cell_type":"code","execution_count":6,"id":"d0f0e231","metadata":{"id":"d0f0e231","executionInfo":{"status":"ok","timestamp":1672118435326,"user_tz":-540,"elapsed":93308,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import gensim\n","lda_model = gensim.models.LdaModel(\n"," common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n",")"]},{"cell_type":"code","execution_count":7,"id":"d568841a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d568841a","executionInfo":{"status":"ok","timestamp":1672118435327,"user_tz":-540,"elapsed":41,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"cfc9c430-1bea-4f52-d193-73e15a1ac543"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(2, 0.13143347), (42, 0.81523174)]"]},"metadata":{},"execution_count":7}],"source":["# 각 사용자의 소속 토픽이 저장된다\n","lda_topics = lda_model[common_corpus]\n","\n","# 예: 어떤 사용자의 소속 토픽\n","lda_topics[0]"]},{"cell_type":"code","execution_count":8,"id":"aa768039","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aa768039","executionInfo":{"status":"ok","timestamp":1672118435327,"user_tz":-540,"elapsed":33,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"00099179-91eb-4fd9-80b6-33b281b38e6c"},"outputs":[{"output_type":"stream","name":"stdout","text":["movie_id=2858, title=American Beauty (1999), score=0.014902905561029911\n","movie_id=4022, title=Cast Away (2000), score=0.013141673058271408\n","movie_id=356, title=Forrest Gump (1994), score=0.009344562888145447\n","movie_id=1625, title=Game, The (1997), score=0.009039949625730515\n","movie_id=3408, title=Erin Brockovich (2000), score=0.008023954927921295\n","movie_id=1610, title=Hunt for Red October, The (1990), score=0.007280151825398207\n","movie_id=2762, title=Sixth Sense, The (1999), score=0.007244058884680271\n","movie_id=1833, title=Mercury Rising (1998), score=0.007027590647339821\n","movie_id=50, title=Usual Suspects, The (1995), score=0.00636146729812026\n","movie_id=2706, title=American Pie (1999), score=0.006130032241344452\n"]}],"source":["# topic0인 영화 목록\n","for token_id, score in lda_model.get_topic_terms(0, topn=10):\n"," movie_id = int(common_dictionary.id2token[token_id])\n"," title = movies[movies.movie_id == movie_id].title.tolist()[0]\n"," print(f'movie_id={movie_id}, title={title}, score={score}')"]},{"cell_type":"code","execution_count":9,"id":"9eb2910f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9eb2910f","executionInfo":{"status":"ok","timestamp":1672118435328,"user_tz":-540,"elapsed":29,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5ecbcb33-daaa-422c-a155-abdcc16844d3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, 0.010000083),\n"," (1, 0.010000083),\n"," (2, 0.010000083),\n"," (3, 0.010000083),\n"," (4, 0.010000083),\n"," (5, 0.010000083),\n"," (6, 0.010000083),\n"," (7, 0.010000083),\n"," (8, 0.010000083),\n"," (9, 0.010000083),\n"," (10, 0.010000083),\n"," (11, 0.010000083),\n"," (12, 0.010000083),\n"," (13, 0.010000083),\n"," (14, 0.010000083),\n"," (15, 0.010000083),\n"," (16, 0.010000083),\n"," (17, 0.010000083),\n"," (18, 0.010000083),\n"," (19, 0.010000083),\n"," (20, 0.010000083),\n"," (21, 0.010000083),\n"," (22, 0.010000083),\n"," (23, 0.010000083),\n"," (24, 0.010000083),\n"," (25, 0.010000083),\n"," (26, 0.010000083),\n"," (27, 0.010000083),\n"," (28, 0.010000083),\n"," (29, 0.010000083),\n"," (30, 0.010000083),\n"," (31, 0.010000083),\n"," (32, 0.010000083),\n"," (33, 0.010000083),\n"," (34, 0.010000083),\n"," (35, 0.010000083),\n"," (36, 0.010000083),\n"," (37, 0.010000083),\n"," (38, 0.010000083),\n"," (39, 0.010000083),\n"," (40, 0.010000083),\n"," (41, 0.50999594),\n"," (42, 0.010000083),\n"," (43, 0.010000083),\n"," (44, 0.010000083),\n"," (45, 0.010000083),\n"," (46, 0.010000083),\n"," (47, 0.010000083),\n"," (48, 0.010000083),\n"," (49, 0.010000083)]"]},"metadata":{},"execution_count":9}],"source":["# 스타워즈/에피소드 5(movie_id=1196)의 토픽(각 토픽에 소속될 확률)\n","lda_model[common_dictionary.doc2bow([\"1196\"])]"]},{"cell_type":"code","execution_count":10,"id":"8de0ac42","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8de0ac42","executionInfo":{"status":"ok","timestamp":1672118441267,"user_tz":-540,"elapsed":5964,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"325da666-9b8b-43b7-ce14-dc81474631fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [457, 380, 593, 150, 349, 590, 318, 165, 110, 296],\n"," 2: [457, 380, 593, 150, 480, 349, 589, 318, 165, 588],\n"," 3: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 356, 2762, 2273],\n"," 4: [457, 593, 318, 296, 356, 10, 47, 50, 1, 474],\n"," 5: [1193, 904, 1252, 750, 1208, 908, 913, 1136, 260, 1304],\n"," 6: [1240, 1210, 589, 1200, 1214, 541, 593, 1374, 1356, 1617],\n"," 7: [1193, 858, 750, 1208, 527, 111, 1247, 1221, 1136, 1206],\n"," 8: [3481, 4034, 4973, 1258, 2692, 7361, 2502, 5902, 296, 4262],\n"," 9: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 10: [1465, 3082, 1610, 474, 2804, 4709, 3469, 2197, 1792, 2989],\n"," 11: [1307, 1197, 1079, 1234, 1278, 1220, 2797, 1968, 1259, 2791],\n"," 12: [720, 1265, 852, 2891, 3254, 4448, 4002, 608, 4223, 4963],\n"," 13: [2571, 4963, 3147, 5349, 6539, 356, 2762, 2273, 6377, 4701],\n"," 14: [2571, 4993, 4963, 3147, 3578, 1580, 356, 2762, 2273, 4701],\n"," 16: [589, 2571, 1198, 2028, 593, 1374, 1356, 1617, 1291, 858],\n"," 17: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 18: [5952, 4993, 7153, 4226, 50, 858, 2329, 1200, 6874, 4886],\n"," 19: [1028, 2797, 2406, 1968, 2791, 2100, 3039, 1784, 1663, 2918],\n"," 22: [457, 590, 318, 10, 434, 1, 474, 204, 442, 733],\n"," 23: [597, 150, 356, 587, 34, 508, 357, 11, 440, 590],\n"," 24: [141, 36, 95, 25, 653, 1393, 58, 52, 14, 783],\n"," 26: [593, 480, 318, 296, 356, 595, 364, 253, 231, 50],\n"," 27: [1, 260, 62, 141, 832, 36, 25, 6, 17, 104],\n"," 28: [1307, 1079, 1234, 1220, 1028, 2797, 1259, 2791, 1380, 914],\n"," 29: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 30: [587, 508, 457, 350, 236, 474, 497, 39, 349, 21],\n"," 33: [260, 733, 494, 832, 25, 802, 104, 805, 653, 1210],\n"," 34: [2572, 3081, 2716, 2396, 1586, 2336, 2858, 1580, 2353, 2541],\n"," 35: [5952, 296, 7153, 3578, 318, 356, 593, 260, 1036, 1198],\n"," 36: [2901, 3005, 2320, 1620, 2997, 3708, 1608, 1258, 1298, 4661],\n"," 37: [1196, 2858, 2762, 356, 260, 1198, 858, 2329, 527, 1270],\n"," 38: [6539, 2273, 4701, 3793, 4886, 780, 4306, 3977, 2028, 3052],\n"," 40: [904, 541, 858, 1252, 923, 912, 750, 1208, 903, 908],\n"," 41: [1234, 1278, 1097, 2791, 1136, 1394, 1663, 1297, 1304, 342],\n"," 42: [150, 356, 587, 34, 508, 593, 339, 11, 440, 457],\n"," 43: [150, 508, 593, 357, 457, 527, 377, 590, 110, 364],\n"," 44: [110, 1968, 2718, 5563, 6281, 5400, 4447, 5418, 2114, 2607],\n"," 45: [5952, 2959, 296, 4993, 7153, 3578, 318, 1210, 4226, 4306],\n"," 46: [2571, 5952, 2959, 296, 4993, 1196, 3578, 318, 1210, 4226],\n"," 47: [356, 47, 527, 1704, 480, 1222, 4886, 1089, 1213, 6377],\n"," 48: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 50: [349, 161, 316, 292, 434, 329, 47, 50, 1, 474],\n"," 51: [3468, 3983, 1084, 694, 4234, 3456, 762, 1250, 1964, 2677],\n"," 52: [541, 858, 923, 912, 608, 750, 903, 908, 527, 111],\n"," 53: [1097, 1193, 356, 919, 3545, 2971, 32, 5504, 1968, 8917],\n"," 54: [595, 153, 253, 1, 527, 32, 204, 442, 225, 208],\n"," 55: [904, 541, 858, 1252, 923, 912, 608, 750, 1208, 903],\n"," 56: [5072, 2905, 1554, 34542, 4144, 4422, 7044, 4085, 1132, 3552],\n"," 57: [466, 543, 520, 2717, 2735, 1982, 5254, 2953, 2002, 2402],\n"," 58: [608, 858, 1094, 2194, 3104, 2289, 457, 1954, 1674, 1299],\n"," 59: [380, 480, 589, 110, 588, 161, 356, 10, 377, 292],\n"," 60: [904, 912, 750, 903, 908, 111, 913, 1207, 1206, 924],\n"," 61: [1307, 2997, 3148, 2804, 2918, 1259, 778, 1704, 1220, 2028],\n"," 62: [457, 480, 589, 110, 356, 377, 364, 47, 50, 1],\n"," 63: [541, 1252, 923, 608, 750, 1208, 903, 908, 111, 913],\n"," 64: [62, 141, 648, 95, 25, 17, 104, 1210, 112, 1356],\n"," 65: [1358, 2028, 2324, 608, 858, 1617, 593, 1094, 2762, 2194],\n"," 66: [55820, 1234, 858, 5147, 923, 4873, 44195, 33880, 111, 4973],\n"," 67: [150, 480, 589, 356, 377, 1, 500, 474, 527, 32],\n"," 68: [858, 912, 750, 527, 111, 1221, 1136, 1304, 910, 1213],\n"," 69: [904, 541, 858, 1252, 923, 608, 750, 1208, 903, 111],\n"," 70: [923, 608, 1208, 903, 111, 1247, 1136, 260, 1206, 1219],\n"," 71: [457, 593, 480, 589, 590, 318, 110, 588, 161, 356],\n"," 72: [150, 589, 318, 110, 296, 161, 316, 377, 592, 364],\n"," 73: [1358, 1704, 590, 2324, 1961, 1617, 1242, 2289, 2243, 36],\n"," 74: [371, 318, 544, 68, 514, 270, 71, 1302, 248, 440],\n"," 75: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 76: [588, 356, 364, 253, 50, 500, 527, 454, 32, 288],\n"," 77: [150, 318, 110, 161, 329, 50, 500, 474, 32, 34],\n"," 78: [4993, 2329, 5952, 5349, 293, 4886, 4878, 4011, 47, 8636],\n"," 79: [527, 111, 353, 112, 232, 307, 290, 6, 306, 16],\n"," 80: [480, 589, 590, 588, 356, 595, 10, 377, 592, 292],\n"," 81: [1307, 1197, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097],\n"," 82: [2959, 1196, 7153, 3578, 318, 1210, 2858, 2762, 356, 2028],\n"," 83: [904, 1252, 1208, 903, 527, 111, 1247, 1206, 1267, 1230],\n"," 84: [1073, 832, 6, 802, 104, 805, 653, 1210, 112, 1356],\n"," 85: [2901, 1732, 1573, 2976, 1617, 2959, 1552, 1917, 3753, 3005],\n"," 86: [260, 541, 32, 593, 1617, 924, 1199, 296, 608, 50],\n"," 87: [2706, 1777, 2485, 2125, 2571, 2394, 1586, 2355, 780, 2424],\n"," 88: [3147, 4246, 4896, 3753, 4306, 4995, 6378, 1704, 2028, 2763],\n"," 89: [457, 593, 480, 349, 589, 590, 356, 595, 316, 10],\n"," 90: [95, 25, 17, 1210, 112, 1356, 1393, 58, 7, 52],\n"," 91: [858, 1252, 908, 111, 1221, 1304, 1225, 910, 1213, 593],\n"," 92: [2959, 296, 4306, 593, 50, 858, 2329, 1214, 1200, 3996],\n"," 93: [5464, 610, 2987, 1089, 5679, 7445, 2140, 1968, 1172, 7254],\n"," 94: [597, 34, 357, 339, 11, 364, 17, 282, 236, 497],\n"," 95: [457, 364, 47, 50, 474, 527, 34, 204, 442, 225],\n"," 96: [1704, 2324, 318, 1094, 2289, 2243, 36, 1641, 1299, 2355],\n"," 97: [446, 317, 500, 2141, 2046, 3409, 92, 708, 919, 2803],\n"," 98: [733, 62, 736, 141, 1073, 32, 494, 832, 95, 17],\n"," 99: [904, 1252, 923, 912, 608, 1208, 903, 908, 913, 1247],\n"," 100: [2959, 4993, 7153, 3578, 4226, 2762, 4306, 1036, 2329, 1214],\n"," 101: [10, 527, 288, 442, 21, 733, 586, 786, 420, 6],\n"," 102: [380, 593, 349, 589, 590, 165, 110, 595, 377, 364],\n"," 103: [1193, 904, 858, 1252, 912, 903, 908, 913, 1221, 1207],\n"," 104: [1358, 1704, 2028, 590, 608, 2396, 1094, 2762, 2194, 3104],\n"," 105: [1240, 589, 1200, 2571, 541, 2028, 593, 1374, 1356, 1617],\n"," 106: [2997, 2683, 858, 1252, 1230, 1221, 1213, 2810, 52, 2706],\n"," 107: [1198, 1214, 593, 1291, 1610, 1036, 2916, 296, 608, 457],\n"," 108: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 2858, 919, 1617],\n"," 110: [150, 349, 165, 110, 588, 296, 161, 595, 316, 10],\n"," 111: [589, 590, 595, 377, 364, 329, 47, 231, 50, 1],\n"," 112: [1148, 1617, 2858, 1952, 1284, 2396, 2395, 720, 1953, 3435],\n"," 113: [2572, 2683, 2706, 3081, 1210, 1777, 2125, 3578, 2571, 2394],\n"," 114: [3949, 55820, 1213, 1222, 7361, 5147, 6874, 2858, 2959, 923],\n"," 115: [2571, 5952, 2959, 4993, 1196, 7153, 1210, 4226, 2028, 4306],\n"," 116: [508, 457, 527, 17, 474, 497, 39, 266, 261, 509],\n"," 117: [1307, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097, 1259],\n"," 118: [588, 595, 364, 1, 500, 34, 339, 733, 367, 586],\n"," 119: [50, 36, 527, 32, 608, 246, 300, 112, 232, 150],\n"," 120: [733, 608, 141, 32, 648, 494, 832, 36, 25, 6],\n"," 121: [260, 1206, 1225, 1212, 1234, 1148, 2019, 1276, 1214, 1254],\n"," 122: [1028, 914, 2100, 342, 1580, 1282, 2407, 1777, 902, 2081],\n"," 123: [370, 318, 1380, 1207, 3053, 188, 2599, 1203, 432, 2081],\n"," 124: [4226, 2959, 3481, 2858, 2542, 2997, 4034, 4973, 2329, 1258],\n"," 125: [2329, 7361, 4878, 778, 8636, 4262, 1748, 2395, 8874, 8464],\n"," 126: [1080, 4973, 4878, 7361, 750, 778, 3949, 1206, 1274, 5618],\n"," 127: [539, 597, 150, 356, 587, 34, 508, 593, 357, 339],\n"," 128: [508, 440, 377, 364, 236, 474, 588, 454, 62, 39],\n"," 129: [1625, 3408, 1610, 1833, 50, 1249, 593, 3101, 1552, 479],\n"," 130: [904, 923, 912, 750, 903, 908, 111, 913, 1247, 1221],\n"," 131: [318, 32, 608, 293, 111, 555, 337, 300, 232, 307],\n"," 132: [150, 480, 589, 110, 588, 161, 10, 377, 292, 364],\n"," 134: [260, 733, 608, 494, 832, 36, 25, 17, 802, 104],\n"," 135: [2571, 2028, 1617, 1610, 2916, 1199, 608, 50, 1676, 318],\n"," 136: [541, 750, 903, 908, 527, 111, 913, 1247, 1221, 260],\n"," 137: [3147, 6539, 356, 2273, 6377, 4246, 2353, 4896, 4886, 5299],\n"," 138: [1291, 858, 527, 3996, 1270, 480, 4886, 1, 1213, 1221],\n"," 139: [4226, 4027, 6711, 2542, 2997, 4993, 4034, 4973, 5952, 1732],\n"," 140: [2571, 5952, 2959, 296, 4993, 1196, 3578, 1210, 2762, 356],\n"," 141: [539, 597, 150, 587, 34, 508, 339, 11, 440, 110],\n"," 142: [296, 50, 318, 223, 527, 293, 47, 246, 555, 337],\n"," 143: [4027, 6711, 2692, 7361, 293, 8636, 6502, 5902, 541, 50],\n"," 144: [2502, 4886, 4979, 3897, 923, 1921, 3911, 1246, 2761, 1784],\n"," 145: [380, 480, 349, 589, 318, 165, 110, 161, 595, 316],\n"," 148: [2571, 1198, 2028, 593, 1356, 1617, 924, 1291, 858, 1610],\n"," 149: [4226, 3481, 4027, 6711, 2542, 4034, 4973, 2329, 1258, 2692],\n"," 150: [296, 50, 318, 36, 25, 223, 32, 593, 608, 293],\n"," 151: [356, 1271, 5418, 2797, 1307, 1721, 2858, 4995, 1246, 3083],\n"," 152: [1704, 590, 2324, 608, 1242, 1094, 3104, 2289, 2243, 1954],\n"," 153: [2571, 5952, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 154: [5952, 4993, 7153, 3578, 318, 4226, 2858, 2028, 4306, 858],\n"," 155: [7153, 4226, 1291, 527, 1200, 6874, 6539, 1222, 4886, 6377],\n"," 156: [1, 260, 733, 494, 832, 36, 95, 17, 802, 805],\n"," 157: [608, 62, 141, 25, 6, 17, 1210, 112, 1356, 58],\n"," 158: [539, 597, 150, 356, 587, 508, 357, 339, 11, 527],\n"," 159: [2140, 2959, 7254, 2143, 1006, 1354, 1967, 3020, 2161, 2683],\n"," 160: [466, 104, 543, 88, 520, 2717, 743, 1982, 724, 1562],\n"," 161: [356, 2028, 4306, 1036, 858, 2329, 1704, 480, 32, 1222],\n"," 162: [318, 4226, 593, 1036, 50, 1291, 2329, 1200, 1704, 1270],\n"," 163: [318, 608, 293, 555, 337, 353, 300, 112, 232, 307],\n"," 164: [110, 161, 364, 329, 253, 50, 1, 34, 185, 204],\n"," 165: [1584,\n"," 431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 1213,\n"," 49530,\n"," 306,\n"," 31696],\n"," 166: [589, 2571, 1198, 541, 32, 2028, 1374, 1356, 1617, 1291],\n"," 167: [260, 733, 1073, 494, 832, 17, 802, 805, 1210, 1356],\n"," 168: [19, 2028, 1784, 1004, 274, 1092, 575, 1610, 2403, 3578],\n"," 169: [1625, 3408, 1610, 2762, 1833, 50, 1249, 593, 3101, 1552],\n"," 170: [5952, 4993, 1196, 7153, 3578, 1210, 2028, 593, 589, 260],\n"," 171: [2571, 5952, 4993, 1196, 7153, 3578, 1210, 4226, 4306, 593],\n"," 172: [380, 593, 349, 590, 318, 296, 161, 356, 595, 316],\n"," 173: [110, 17, 282, 497, 39, 266, 261, 509, 224, 367],\n"," 174: [342, 261, 372, 537, 531, 147, 427, 481, 45, 452],\n"," 175: [908, 1247, 1207, 1304, 1267, 1230, 910, 1148, 1276, 1254],\n"," 176: [480, 349, 589, 318, 110, 588, 296, 356, 595, 10],\n"," 177: [260, 733, 832, 17, 802, 104, 805, 653, 1210, 1356],\n"," 178: [1265, 2567, 1215, 1060, 1343, 2571, 368, 2001, 1500, 2000],\n"," 179: [4226, 2959, 3481, 6711, 2858, 2997, 4034, 4973, 2329, 5952],\n"," 180: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210],\n"," 182: [457, 1674, 260, 1610, 1584, 1198, 1210, 2871, 1036, 1200],\n"," 183: [2324, 608, 1961, 858, 1617, 1242, 2762, 2194, 3104, 2289],\n"," 184: [1466, 1673, 25, 1120, 290, 1912, 1041, 3125, 2718, 5563],\n"," 185: [1230, 912, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358],\n"," 186: [1193, 858, 923, 912, 750, 1208, 908, 527, 913, 1247],\n"," 187: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 188: [318, 165, 588, 161, 595, 10, 377, 292, 364, 47],\n"," 189: [141, 1073, 648, 832, 95, 25, 802, 104, 653, 1210],\n"," 190: [4226, 3481, 4027, 6711, 2858, 2542, 2997, 4034, 4973, 2329],\n"," 191: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 318],\n"," 192: [1234, 5147, 4873, 33880, 1080, 555, 16, 1, 2585, 55805],\n"," 193: [364, 17, 62, 595, 141, 261, 509, 277, 224, 105],\n"," 194: [733, 608, 494, 36, 6, 805, 653, 112, 3, 58],\n"," 195: [1683, 694, 762, 40583, 3730, 1964, 3006, 3363, 2973, 667],\n"," 196: [2863, 2804, 2160, 1256, 346, 2363, 2186, 2273, 861, 1278],\n"," 197: [2683,\n"," 2268,\n"," 648,\n"," 1213,\n"," 2810,\n"," 3000,\n"," 2598,\n"," 8157,\n"," 56286,\n"," 55814],\n"," 198: [3481, 2542, 2997, 4034, 4973, 4011, 778, 4848, 1219, 5618],\n"," 199: [480, 349, 589, 318, 110, 161, 356, 10, 364, 47],\n"," 200: [1, 608, 494, 832, 95, 25, 6, 805, 653, 1210],\n"," 201: [1961, 318, 1242, 2762, 2194, 3104, 2289, 2243, 1954, 1962],\n"," 202: [3578, 2959, 2858, 2707, 589, 2571, 4993, 3157, 1176, 4022],\n"," 203: [2355, 2567, 1968, 2571, 2001, 2396, 2406, 2000, 2706, 1367],\n"," 204: [1073, 832, 36, 802, 104, 805, 653, 1210, 112, 1356],\n"," 205: [904, 923, 750, 903, 527, 1247, 1207, 1219, 1304, 1267],\n"," 206: [1358, 1704, 590, 2324, 1961, 858, 318, 2396, 1242, 1094],\n"," 207: [356, 587, 357, 339, 11, 457, 110, 17, 350, 236],\n"," 208: [457, 593, 480, 589, 590, 356, 595, 377, 292, 364],\n"," 209: [539, 597, 587, 357, 339, 11, 440, 527, 590, 110],\n"," 210: [5952, 2959, 1196, 356, 2028, 260, 1036, 1198, 47, 50],\n"," 211: [1193, 541, 923, 750, 1208, 527, 913, 1247, 1136, 1207],\n"," 212: [1203,\n"," 4406,\n"," 5072,\n"," 7116,\n"," 3310,\n"," 1253,\n"," 39292,\n"," 2726,\n"," 7132,\n"," 3730],\n"," 213: [4993, 3578, 5349, 1580, 6539, 356, 6377, 4701, 3793, 4246],\n"," 214: [1234, 1278, 919, 1394, 1663, 1304, 1246, 2109, 1035, 1081],\n"," 215: [6711, 2329, 6502, 5669, 5377, 4308, 1246, 3471, 3421, 5679],\n"," 216: [3147, 3578, 6539, 356, 6377, 4701, 4025, 2353, 3753, 4995],\n"," 217: [356, 34, 508, 357, 527, 377, 110, 364, 318, 500],\n"," 218: [1265, 852, 3624, 3254, 4448, 3578, 4002, 608, 3745, 4223],\n"," 219: [1079, 1220, 2797, 2406, 1968, 1259, 2791, 1285, 1136, 1101],\n"," 220: [904, 912, 1219, 913, 1193, 928, 903, 1198, 920, 1207],\n"," 221: [2959, 4993, 7153, 3578, 4226, 2762, 4306, 1291, 2329, 1214],\n"," 222: [1210, 97, 2858, 714, 2571, 1094, 1147, 1304, 924, 1090],\n"," 223: [324, 534, 2359, 2858, 2336, 1183, 1252, 2997, 532, 1564],\n"," 224: [260, 733, 832, 802, 805, 653, 1210, 1356, 1393, 58],\n"," 225: [912, 1219, 1193, 928, 1198, 920, 1207, 1258, 4784, 953],\n"," 226: [2959, 6711, 2858, 2997, 4034, 4973, 1258, 1732, 2692, 7361],\n"," 227: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 228: [590, 588, 296, 161, 592, 47, 153, 50, 1, 474],\n"," 229: [62, 494, 832, 36, 95, 6, 17, 802, 104, 653],\n"," 230: [1193, 904, 858, 903, 908, 111, 1207, 1206, 1225, 924],\n"," 231: [6985, 7234, 7072, 3742, 1209, 1260, 3307, 1348, 8125, 6666],\n"," 232: [2571, 2028, 1374, 1356, 1617, 924, 1610, 2916, 1199, 1197],\n"," 234: [5952, 296, 4993, 1196, 7153, 3578, 318, 1210, 2762, 356],\n"," 235: [5952, 1196, 1210, 2762, 356, 2028, 4306, 589, 110, 2329],\n"," 236: [316, 434, 329, 153, 253, 1, 32, 34, 733, 587],\n"," 237: [589, 541, 32, 2028, 1356, 924, 858, 1610, 1036, 2916],\n"," 238: [410, 193, 324, 534, 2359, 1183, 1252, 532, 1564, 3112],\n"," 239: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 2762, 2273],\n"," 241: [4027, 2054, 318, 2858, 1019, 1380, 2761, 1193, 1207, 912],\n"," 242: [608, 1073, 832, 36, 1210, 1393, 3, 58, 7, 52],\n"," 243: [1721, 3148, 1617, 1961, 778, 1704, 1183, 4350, 15, 62],\n"," 244: [923, 903, 908, 111, 1221, 1207, 260, 1206, 1219, 1213],\n"," 245: [3949,\n"," 55820,\n"," 1213,\n"," 1222,\n"," 7361,\n"," 1234,\n"," 5147,\n"," 6874,\n"," 2858,\n"," 2959],\n"," 246: [4226, 2959, 4027, 6711, 2542, 2997, 4993, 4034, 4973, 5952],\n"," 247: [349, 318, 110, 50, 1, 474, 527, 32, 185, 204],\n"," 248: [3578, 2959, 2858, 527, 2707, 589, 2571, 4993, 3157, 1176],\n"," 249: [3481, 2858, 2997, 4973, 1732, 2692, 3996, 3052, 778, 8636],\n"," 250: [296, 50, 36, 25, 223, 527, 32, 593, 608, 293],\n"," 251: [2571, 2028, 1617, 924, 1610, 2916, 1199, 50, 1197, 1676],\n"," 252: [541, 858, 1252, 923, 750, 1208, 903, 111, 913, 1247],\n"," 253: [5952, 2959, 296, 4993, 1196, 7153, 318, 1210, 4226, 2762],\n"," 254: [4993, 7153, 4226, 1036, 1198, 110, 1291, 2329, 1704, 1270],\n"," 255: [457, 380, 480, 349, 589, 165, 110, 296, 595, 316],\n"," 256: [1197, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097, 1968],\n"," 257: [5952, 2959, 296, 4993, 1196, 7153, 318, 1210, 4226, 2858],\n"," 258: [1358, 1704, 2324, 858, 318, 593, 2396, 1242, 1094, 2762],\n"," 259: [3481, 4027, 2542, 4034, 1732, 1089, 293, 4011, 223, 8636],\n"," 260: [2571, 2028, 1374, 1617, 1291, 1610, 2916, 1199, 50, 1197],\n"," 261: [527, 356, 2762, 1080, 919, 2028, 2858, 3534, 5504, 1968],\n"," 262: [2863, 2948, 2947, 1136, 2160, 2951, 1997, 1256, 5060, 346],\n"," 263: [7153, 356, 2028, 4306, 6874, 1222, 1089, 4011, 1527, 1580],\n"," 264: [150, 349, 590, 318, 161, 292, 364, 434, 329, 253],\n"," 265: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 266: [97, 714, 1147, 924, 1090, 4002, 1238, 1235, 1288, 2064],\n"," 267: [3949, 1213, 4873, 44195, 33880, 3089, 56367, 555, 16, 4878],\n"," 268: [34, 508, 357, 11, 440, 17, 282, 236, 588, 497],\n"," 269: [260, 733, 608, 62, 32, 494, 832, 36, 6, 17],\n"," 270: [1, 608, 62, 736, 832, 36, 95, 6, 802, 104],\n"," 271: [1200, 2571, 1198, 2028, 593, 1356, 1617, 1291, 858, 1610],\n"," 272: [6711, 2542, 4973, 1732, 2692, 1206, 32, 1089, 2571, 3052],\n"," 273: [2959, 296, 1196, 4226, 2762, 356, 4306, 1198, 47, 1291],\n"," 274: [1968, 1285, 1101, 1297, 2396, 342, 1198, 440, 1291, 1777],\n"," 275: [2324, 858, 1242, 2194, 3104, 2243, 36, 260, 527, 1271],\n"," 276: [4027, 3052, 4262, 5618, 2918, 5377, 8464, 5995, 6539, 1246],\n"," 277: [2571, 2959, 1196, 3578, 318, 1210, 4226, 2858, 2028, 4306],\n"," 278: [356, 296, 1271, 5418, 2797, 1307, 1721, 318, 2858, 4995],\n"," 279: [4226, 3481, 6711, 2858, 4993, 4034, 4973, 2329, 1732, 1206],\n"," 280: [2959, 296, 1196, 3578, 318, 1210, 4226, 2762, 593, 589],\n"," 281: [253, 1, 733, 196, 48, 111, 160, 555, 198, 1092],\n"," 282: [141, 832, 6, 104, 1210, 1356, 1393, 58, 52, 788],\n"," 285: [1196, 1240, 589, 1200, 2571, 1198, 1214, 541, 32, 2028],\n"," 286: [32, 1356, 1617, 924, 858, 1610, 1199, 296, 608, 1197],\n"," 287: [593, 349, 589, 318, 165, 296, 161, 10, 377, 292],\n"," 288: [1193, 541, 858, 1252, 923, 912, 608, 750, 1208, 527],\n"," 289: [2406, 1380, 1285, 1297, 1282, 2371, 1198, 1291, 2081, 1073],\n"," 290: [239, 256, 2142, 1373, 1036, 4799, 1206, 107, 1348, 146],\n"," 291: [4027, 2054, 370, 318, 2858, 1380, 2761, 3053, 912, 188],\n"," 292: [3481, 2542, 293, 778, 4262, 1748, 4848, 923, 1921, 1219],\n"," 293: [380, 480, 589, 110, 356, 377, 292, 364, 47, 50],\n"," 294: [593, 480, 589, 590, 10, 329, 1, 500, 527, 34],\n"," 295: [1259, 914, 1285, 1101, 1297, 342, 440, 2174, 902, 2081],\n"," 296: [3481, 1258, 3996, 8636, 4979, 1748, 2395, 1219, 5618, 8874],\n"," 297: [733, 736, 494, 832, 95, 6, 802, 653, 3, 58],\n"," 298: [2567, 1136, 1215, 1196, 1220, 1060, 1968, 2571, 368, 2001],\n"," 300: [2324, 593, 2396, 3104, 457, 2243, 36, 1962, 1674, 34],\n"," 301: [1193, 904, 903, 908, 111, 913, 1207, 1206, 1219, 1304],\n"," 302: [2572, 2485, 2716, 2125, 2355, 2710, 2336, 2006, 2700, 1597],\n"," 303: [1036, 50, 2329, 1704, 1270, 6539, 1089, 1, 6377, 4963],\n"," 304: [1358, 1704, 2028, 590, 2324, 608, 858, 1617, 318, 593],\n"," 305: [34, 339, 11, 440, 364, 318, 350, 474, 454, 595],\n"," 306: [1721, 2997, 3148, 1617, 608, 778, 2028, 1221, 1183, 858],\n"," 307: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 308: [539, 597, 150, 356, 587, 508, 593, 357, 339, 11],\n"," 309: [733, 608, 1073, 32, 494, 832, 36, 6, 104, 653],\n"," 310: [1242, 2289, 2243, 2355, 1271, 1090, 1095, 3114, 2916, 2890],\n"," 311: [457, 593, 589, 318, 110, 296, 161, 10, 377, 292],\n"," 312: [1617, 1219, 1193, 903, 1198, 920, 1207, 1258, 4784, 953],\n"," 313: [965, 2186, 1617, 2858, 2935, 931, 1066, 2324, 2208, 929],\n"," 314: [608, 1208, 913, 1206, 1230, 910, 1213, 924, 1212, 50],\n"," 315: [17, 236, 474, 497, 141, 266, 21, 509, 22, 277],\n"," 316: [608, 62, 648, 494, 832, 36, 6, 17, 805, 1210],\n"," 317: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 318, 593],\n"," 318: [4226, 2329, 2692, 32, 293, 4878, 223, 6502, 4262, 4848],\n"," 319: [786, 708, 362, 1028, 1029, 79, 1035, 1012, 1022, 661],\n"," 320: [318, 1198, 50, 1291, 858, 2329, 1200, 1704, 6874, 1222],\n"," 321: [150, 590, 318, 165, 110, 161, 10, 377, 434, 47],\n"," 322: [1197, 1079, 1234, 1278, 1220, 1028, 2797, 1097, 2406, 1968],\n"," 323: [2804, 2951, 1997, 1256, 346, 2363, 3508, 2273, 2571, 861],\n"," 324: [296, 1196, 318, 2858, 2762, 356, 2028, 4306, 593, 589],\n"," 325: [3394, 3861, 2447, 1268, 4571, 2622, 1248, 2248, 2021, 4896],\n"," 326: [34, 508, 339, 527, 318, 500, 282, 350, 62, 141],\n"," 327: [141, 832, 36, 6, 17, 104, 805, 653, 112, 3],\n"," 328: [1210, 1198, 2028, 593, 1356, 1617, 1291, 858, 1610, 1036],\n"," 329: [1466, 1673, 608, 25, 50, 296, 1120, 290, 1912, 1041],\n"," 330: [4226, 3481, 4027, 6711, 2997, 4034, 4973, 1258, 1732, 2692],\n"," 331: [5952, 296, 1196, 3578, 1210, 2762, 356, 2028, 4306, 593],\n"," 332: [589, 165, 10, 377, 329, 344, 500, 474, 527, 204],\n"," 333: [2997, 3148, 1617, 2804, 1961, 2918, 1259, 1220, 2028, 1221],\n"," 334: [1079, 1097, 2791, 914, 1285, 1101, 1265, 1394, 2918, 2396],\n"," 335: [1193, 541, 608, 1208, 903, 527, 111, 913, 1221, 1136],\n"," 336: [342, 261, 265, 372, 537, 247, 531, 147, 427, 318],\n"," 337: [1219, 2268, 648, 858, 1252, 2764, 1221, 2810, 3000, 924],\n"," 338: [2571, 2959, 4993, 1196, 7153, 3578, 1210, 4226, 2858, 2762],\n"," 339: [480, 589, 110, 588, 356, 595, 316, 10, 377, 292],\n"," 340: [1028, 2797, 2406, 1968, 2791, 1380, 1641, 2100, 3039, 1784],\n"," 341: [2028, 965, 2186, 919, 2935, 931, 1066, 2208, 929, 947],\n"," 342: [2396, 1699, 1704, 593, 1429, 3671, 923, 1148, 2791, 3897],\n"," 343: [318, 165, 296, 595, 377, 292, 364, 329, 47, 50],\n"," 344: [1200, 2571, 1214, 541, 2028, 593, 1374, 1356, 1617, 924],\n"," 345: [1307, 1079, 1220, 1259, 2791, 1380, 914, 1101, 1663, 342],\n"," 346: [3081, 1210, 2125, 2571, 2394, 1586, 2336, 780, 2424, 2006],\n"," 347: [4558, 7004, 3394, 2410, 3861, 3524, 2447, 2118, 4489, 1268],\n"," 348: [3481, 1732, 1206, 3052, 223, 5902, 2395, 5618, 5669, 2918],\n"," 349: [34, 357, 11, 440, 500, 350, 474, 497, 62, 39],\n"," 350: [427, 1059, 2245, 1131, 1132, 1658, 724, 2657, 2291, 327],\n"," 351: [541, 1252, 1206, 1219, 1267, 910, 1212, 1228, 2019, 1254],\n"," 352: [786, 708, 1210, 362, 1196, 1028, 1097, 260, 1029, 79],\n"," 353: [260, 1240, 1210, 1198, 1374, 1356, 1617, 924, 1291, 858],\n"," 354: [3147, 2273, 4701, 4025, 3793, 2353, 4896, 4886, 3753, 4018],\n"," 355: [380, 480, 589, 588, 161, 10, 377, 592, 292, 364],\n"," 356: [1, 62, 736, 141, 1073, 494, 802, 104, 805, 653],\n"," 357: [1197, 1270, 2797, 1097, 1259, 914, 919, 1641, 2100, 3039],\n"," 358: [5952, 2959, 4993, 7153, 3578, 4226, 2762, 2028, 1036, 1198],\n"," 359: [527, 2707, 589, 1176, 593, 2712, 16, 2700, 1046, 3623],\n"," 360: [260, 733, 608, 62, 1073, 494, 832, 6, 802, 104],\n"," 361: [1193, 904, 541, 923, 912, 750, 903, 908, 527, 111],\n"," 362: [296, 3578, 1210, 2858, 356, 2028, 4306, 589, 1036, 47],\n"," 363: [2571, 3578, 318, 1210, 4226, 2858, 2762, 356, 2028, 4306],\n"," 364: [480, 589, 356, 377, 364, 47, 500, 474, 527, 454],\n"," 365: [539, 440, 377, 318, 282, 350, 474, 588, 454, 62],\n"," 366: [1307, 1270, 1028, 2797, 1097, 1968, 1259, 2791, 1380, 914],\n"," 367: [1704, 590, 2324, 1961, 1617, 593, 2396, 2762, 3104, 2289],\n"," 368: [6711, 2858, 2542, 4034, 2329, 4878, 6502, 4262, 2762, 4848],\n"," 369: [480, 589, 110, 356, 377, 364, 47, 50, 1, 500],\n"," 370: [260, 733, 608, 32, 648, 494, 832, 36, 95, 25],\n"," 371: [2571, 5952, 2959, 7153, 3578, 318, 1210, 4226, 2858, 2762],\n"," 372: [296, 7153, 318, 4226, 2858, 356, 2028, 4306, 593, 260],\n"," 373: [4027, 2054, 370, 2858, 1019, 1380, 2761, 1193, 1207, 3053],\n"," 375: [1307, 1197, 1079, 1278, 1220, 1270, 1028, 1097, 2406, 1968],\n"," 376: [1196, 1198, 1374, 924, 1291, 858, 1610, 1036, 2916, 1199],\n"," 377: [2028, 608, 1961, 1617, 593, 1094, 2762, 2194, 3104, 2289],\n"," 378: [1240, 589, 1200, 2571, 1198, 1214, 541, 32, 2028, 593],\n"," 379: [5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 380: [97, 2858, 714, 2571, 1094, 1147, 1304, 924, 1090, 4002],\n"," 381: [282, 236, 497, 141, 509, 224, 367, 265, 736, 230],\n"," 382: [457, 593, 150, 480, 589, 318, 110, 161, 356, 377],\n"," 383: [2959, 1210, 356, 4306, 1036, 110, 47, 1291, 2329, 527],\n"," 384: [380, 593, 349, 589, 296, 595, 316, 10, 434, 329],\n"," 385: [260, 733, 1073, 832, 36, 802, 805, 653, 1210, 1356],\n"," 386: [1094, 3104, 2289, 1225, 1641, 1299, 2355, 2336, 1207, 1090],\n"," 387: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 388: [5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 2858],\n"," 389: [3481, 2542, 4973, 1258, 1732, 1206, 1089, 1214, 6502, 5902],\n"," 390: [1036, 47, 527, 1214, 1270, 32, 6539, 1222, 1089, 541],\n"," 391: [349, 161, 292, 434, 47, 231, 50, 474, 527, 454],\n"," 392: [161, 316, 329, 1, 500, 474, 185, 204, 442, 225],\n"," 393: [1, 25, 17, 802, 653, 1210, 112, 1356, 58, 7],\n"," 394: [648, 1230, 2764, 2810, 52, 2706, 16, 1080, 3000, 1234],\n"," 395: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226],\n"," 396: [5952, 296, 4993, 7153, 3578, 318, 4226, 2762, 356, 593],\n"," 397: [5952, 4993, 1196, 7153, 1210, 4306, 589, 260, 1036, 1198],\n"," 398: [508, 318, 17, 236, 474, 39, 161, 141, 21, 296],\n"," 399: [1210, 58, 7, 52, 14, 858, 783, 1183, 41, 140],\n"," 400: [965, 2186, 919, 2935, 931, 1066, 2208, 929, 1207, 947],\n"," 401: [1193, 904, 1252, 923, 912, 750, 1208, 903, 908, 111],\n"," 402: [720, 2858, 1265, 852, 2891, 3254, 4002, 608, 4223, 736],\n"," 403: [5952, 2959, 4993, 1196, 3578, 318, 1210, 2858, 2028, 4306],\n"," 404: [593, 595, 434, 231, 50, 500, 474, 527, 32, 288],\n"," 405: [1, 608, 494, 832, 95, 6, 17, 805, 653, 1210],\n"," 406: [457, 380, 593, 480, 589, 590, 318, 110, 588, 356],\n"," 407: [1196, 1210, 1374, 1617, 2916, 1197, 318, 1376, 110, 1213],\n"," 408: [912, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358, 3347],\n"," 409: [508, 339, 457, 527, 110, 364, 318, 500, 17, 282],\n"," 410: [708, 362, 260, 531, 1015, 1367, 837, 1042, 1032, 1282],\n"," 411: [260, 32, 494, 36, 95, 25, 6, 17, 802, 104],\n"," 412: [1252, 1429, 3671, 7153, 1148, 2791, 3897, 1587, 627, 837],\n"," 413: [1252, 912, 608, 750, 1208, 903, 908, 527, 913, 1247],\n"," 414: [2901, 2976, 2028, 1552, 1917, 3753, 3005, 1387, 2320, 1620],\n"," 415: [457, 380, 593, 150, 480, 589, 318, 110, 161, 356],\n"," 416: [589, 318, 165, 110, 296, 595, 316, 329, 253, 50],\n"," 417: [1193, 904, 541, 858, 1252, 923, 912, 608, 750, 1208],\n"," 418: [527, 593, 353, 232, 307, 290, 306, 778, 509, 356],\n"," 419: [923, 1206, 1267, 910, 922, 1228, 2019, 1250, 235, 1952],\n"," 420: [50, 318, 25, 223, 32, 593, 608, 293, 47, 555],\n"," 421: [380, 593, 349, 318, 165, 110, 296, 316, 377, 292],\n"," 422: [1, 608, 141, 1073, 32, 832, 36, 95, 6, 17],\n"," 424: [858, 111, 1221, 1207, 1219, 1225, 1213, 593, 50, 296],\n"," 425: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210],\n"," 426: [2502, 5902, 4848, 1921, 2395, 8464, 5995, 3911, 3175, 3000],\n"," 427: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 428: [256, 1073, 4799, 1206, 1031, 1348, 5373, 8117, 1792, 1333],\n"," 429: [1358, 1704, 2028, 590, 2324, 1961, 858, 318, 593, 2396],\n"," 430: [410, 193, 324, 534, 2359, 2858, 2336, 1183, 1252, 2997],\n"," 431: [2571, 2959, 296, 1196, 3578, 1210, 4226, 2858, 2762, 593],\n"," 432: [539, 587, 357, 39, 161, 141, 266, 252, 367, 380],\n"," 433: [19, 274, 2195, 1092, 575, 2403, 3578, 590, 2329, 3623],\n"," 434: [296, 318, 4226, 47, 50, 2329, 32, 1136, 1089, 1213],\n"," 435: [3481, 4027, 6711, 2858, 2997, 4993, 4034, 4973, 5952, 1258],\n"," 436: [349, 590, 165, 161, 10, 292, 1, 527, 204, 225],\n"," 437: [1193, 858, 923, 912, 1207, 1206, 1304, 1230, 1225, 924],\n"," 438: [904, 1252, 923, 912, 608, 1208, 903, 908, 527, 111],\n"," 439: [1097, 2762, 1080, 919, 2858, 3545, 3534, 2971, 8917, 3101],\n"," 440: [1196, 3578, 1210, 4306, 589, 260, 3996, 1704, 6874, 1136],\n"," 441: [590, 318, 588, 356, 595, 364, 253, 1, 500, 474],\n"," 443: [965, 2186, 919, 1617, 2935, 931, 1066, 2324, 2208, 929],\n"," 444: [1079, 1234, 1220, 1968, 1380, 1641, 1285, 1136, 1784, 1101],\n"," 445: [2028, 965, 2186, 919, 1617, 2858, 2935, 931, 1066, 2324],\n"," 446: [590, 165, 110, 377, 364, 47, 153, 50, 1, 500],\n"," 447: [1193, 904, 541, 858, 1252, 750, 1208, 903, 908, 111],\n"," 448: [720, 1265, 852, 3624, 2891, 3254, 4448, 3578, 4002, 608],\n"," 449: [4993, 1176, 750, 4226, 1046, 6296, 5992, 4848, 3911, 1721],\n"," 450: [2959, 6711, 1258, 2692, 1206, 7361, 4878, 4011, 778, 8636],\n"," 451: [97, 2858, 714, 1094, 1147, 1304, 3753, 5378, 1394, 800],\n"," 452: [5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226, 2858],\n"," 453: [457, 593, 150, 480, 589, 590, 318, 110, 588, 161],\n"," 454: [380, 150, 480, 349, 318, 165, 161, 356, 316, 10],\n"," 455: [141, 1073, 494, 95, 17, 104, 653, 112, 1356, 1393],\n"," 456: [539, 597, 356, 587, 508, 357, 527, 377, 500, 480],\n"," 458: [2605, 2706, 2581, 1210, 1777, 2485, 2125, 2571, 2394, 1586],\n"," 459: [318, 1198, 50, 858, 4886, 1213, 1221, 4995, 8961, 33794],\n"," 460: [480, 589, 318, 356, 377, 364, 47, 50, 1, 500],\n"," 461: [62, 141, 494, 36, 95, 25, 6, 802, 104, 653],\n"," 462: [597, 150, 356, 587, 34, 508, 357, 339, 440, 527],\n"," 463: [590, 1961, 2396, 1094, 3104, 2243, 1962, 2355, 1247, 1271],\n"," 464: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 465: [2542, 5952, 3793, 2502, 32, 2571, 3052, 4886, 223, 1214],\n"," 466: [2571, 2959, 296, 4993, 1196, 7153, 3578, 318, 4226, 2858],\n"," 467: [2571, 5952, 2959, 296, 4993, 1196, 7153, 1210, 4226, 2858],\n"," 468: [50, 318, 36, 25, 223, 608, 293, 47, 246, 111],\n"," 469: [2959, 3481, 2542, 2329, 1258, 1732, 1206, 7361, 2502, 1089],\n"," 470: [2542, 4993, 3793, 2502, 5349, 293, 4886, 4011, 223, 6502],\n"," 471: [539, 597, 150, 587, 508, 593, 357, 339, 11, 440],\n"," 472: [508, 440, 527, 377, 110, 500, 17, 236, 474, 497],\n"," 473: [1, 36, 802, 104, 805, 653, 1210, 112, 1393, 3],\n"," 474: [1198, 32, 2028, 1374, 1617, 1291, 858, 1610, 1036, 1199],\n"," 475: [1732, 2858, 296, 1573, 593, 2762, 1617, 2908, 2028, 1639],\n"," 476: [4034, 2502, 3052, 4886, 223, 6502, 1219, 5618, 2918, 3499],\n"," 477: [3578, 2858, 356, 4306, 589, 110, 1704, 1270, 480, 6874],\n"," 479: [904, 541, 858, 923, 912, 903, 908, 913, 1221, 260],\n"," 480: [6711, 2542, 4034, 2329, 1258, 1732, 7361, 1089, 3052, 293],\n"," 481: [4027, 2997, 4973, 1258, 1732, 2692, 1206, 778, 4979, 5902],\n"," 482: [4027, 6711, 2858, 2997, 4034, 4973, 1258, 2692, 1206, 3996],\n"," 483: [508, 11, 440, 377, 282, 236, 588, 497, 62, 39],\n"," 484: [62, 1073, 36, 104, 1210, 3, 58, 7, 52, 14],\n"," 485: [1358, 1704, 2028, 590, 2324, 1961, 1242, 2194, 3104, 2243],\n"," 486: [2571, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 487: [2355, 1265, 2567, 1136, 1215, 1196, 1220, 1343, 1968, 368],\n"," 488: [318, 165, 110, 588, 296, 356, 595, 316, 377, 292],\n"," 489: [541, 1252, 923, 912, 903, 908, 913, 1219, 1304, 1230],\n"," 490: [260, 2571, 1214, 541, 32, 593, 1374, 1356, 1617, 924],\n"," 491: [3481, 4027, 6711, 2997, 4993, 4034, 2329, 3793, 1732, 2692],\n"," 492: [431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 49530,\n"," 306,\n"," 31696,\n"," 2858,\n"," 8644],\n"," 493: [4993, 5952, 1258, 1206, 2502, 223, 8636, 1214, 4979, 6502],\n"," 494: [588, 595, 10, 364, 434, 329, 153, 1, 474, 32],\n"," 495: [539, 597, 356, 587, 34, 508, 593, 357, 11, 457],\n"," 496: [2858, 1617, 2908, 2959, 1639, 39, 431, 1206, 1923, 4720],\n"," 497: [5952, 2959, 296, 3578, 2858, 356, 2028, 589, 260, 1036],\n"," 498: [6985, 7234, 7072, 3742, 1209, 1260, 3307, 1348, 8125, 6666],\n"," 499: [780, 733, 736, 141, 1073, 648, 494, 832, 95, 17],\n"," 500: [590, 2324, 1961, 858, 1242, 1094, 2762, 2194, 3104, 457],\n"," 501: [1307, 1259, 914, 1641, 3039, 1285, 1101, 1663, 1297, 342],\n"," 502: [1777, 2125, 3578, 2571, 1586, 2355, 780, 2424, 1197, 2006],\n"," 503: [1196, 1210, 260, 1036, 110, 47, 50, 1291, 1214, 1200],\n"," 504: [508, 110, 17, 480, 474, 588, 497, 62, 595, 39],\n"," 505: [508, 593, 357, 527, 500, 282, 350, 236, 474, 497],\n"," 506: [2571, 2028, 1617, 1610, 2916, 1199, 608, 1197, 1676, 1376],\n"," 507: [904, 912, 1219, 913, 1193, 928, 950, 903, 1198, 920],\n"," 508: [2571, 5952, 2959, 296, 4993, 7153, 3578, 318, 1210, 4226],\n"," 509: [4027, 2542, 2997, 4993, 4973, 3052, 4878, 1214, 4979, 3897],\n"," 510: [380, 349, 589, 165, 110, 316, 10, 377, 592, 292],\n"," 511: [457, 593, 480, 589, 318, 588, 356, 10, 377, 364],\n"," 512: [1584, 431, 4308, 11, 45186, 53322, 214, 1213, 49530, 31696],\n"," 513: [1358, 1962, 110, 3114, 1678, 1956, 2890, 1213, 1293, 1250],\n"," 515: [904, 1219, 913, 1193, 950, 903, 1198, 920, 1258, 953],\n"," 516: [1036, 50, 6377, 1080, 33794, 608, 7361, 2918, 1193, 16],\n"," 517: [36, 25, 527, 593, 608, 246, 111, 337, 353, 112],\n"," 518: [1200, 1198, 541, 32, 1374, 1356, 1617, 924, 1291, 858],\n"," 520: [371, 544, 68, 514, 270, 71, 315, 1302, 248, 440],\n"," 521: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226],\n"," 522: [904, 1252, 923, 912, 750, 903, 908, 527, 913, 1247],\n"," 523: [904, 1252, 923, 912, 750, 903, 908, 111, 913, 1136],\n"," 524: [3481, 1258, 3793, 1732, 7361, 32, 3996, 5349, 293, 4886],\n"," 525: [380, 480, 589, 161, 356, 377, 292, 364, 434, 47],\n"," 526: [62, 141, 17, 653, 1210, 112, 1393, 3, 58, 52],\n"," 527: [539, 597, 150, 587, 508, 593, 357, 11, 440, 457],\n"," 529: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 2858, 2762],\n"," 530: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 531: [457, 593, 480, 349, 589, 165, 356, 377, 592, 364],\n"," 532: [1307, 1721, 608, 2804, 2918, 1259, 1704, 1220, 2301, 4350],\n"," 533: [2329, 1732, 293, 4886, 4878, 4011, 778, 4262, 5669, 3081],\n"," 534: [5464, 610, 2987, 1089, 5679, 2140, 1968, 1172, 593, 1193],\n"," 535: [7153, 4226, 2858, 2762, 2329, 1214, 1200, 3996, 1704, 1270],\n"," 536: [597, 356, 587, 11, 440, 527, 590, 110, 364, 318],\n"," 537: [1, 141, 1073, 32, 648, 832, 95, 802, 104, 653],\n"," 539: [457, 110, 161, 356, 595, 10, 344, 253, 231, 1],\n"," 540: [26, 637, 1042, 711, 708, 49278, 786, 719, 1752, 609],\n"," 541: [1234, 914, 919, 1285, 1663, 1297, 1304, 342, 2302, 2144],\n"," 542: [950, 1258, 1271, 2028, 1345, 969, 47, 1023, 2206, 1580],\n"," 543: [3481, 2542, 4034, 2329, 1732, 3052, 4011, 223, 4979, 3897],\n"," 544: [3578, 318, 1210, 4226, 2858, 2762, 356, 2028, 4306, 593],\n"," 545: [3578, 4306, 6539, 4963, 5445, 1527, 1784, 5418, 6333, 2628],\n"," 546: [26, 637, 1042, 711, 724, 708, 786, 12, 719, 1752],\n"," 547: [318, 165, 296, 10, 292, 47, 253, 50, 474, 288],\n"," 548: [371, 544, 68, 514, 270, 71, 315, 248, 342, 105],\n"," 549: [480, 589, 110, 356, 377, 364, 47, 50, 1, 500],\n"," 550: [111, 1206, 1213, 593, 1228, 235, 1199, 1080, 1172, 1175],\n"," 551: [2324, 608, 1961, 1617, 318, 2396, 1242, 1094, 2762, 2194],\n"," 552: [4027, 6711, 4993, 5952, 3793, 1732, 2692, 7361, 32, 3996],\n"," 553: [150, 590, 588, 595, 1, 527, 34, 204, 225, 21],\n"," 554: [541, 858, 1252, 923, 912, 750, 1208, 908, 1247, 1221],\n"," 555: [3481, 4034, 4973, 2692, 7361, 1214, 6502, 5902, 541, 5445],\n"," 556: [590, 380, 1, 592, 253, 555, 1020, 1036, 647, 852],\n"," 557: [733, 32, 648, 832, 36, 95, 25, 17, 104, 805],\n"," 558: [2542, 2997, 4973, 2329, 1258, 1732, 2692, 1206, 7361, 3052],\n"," 559: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1777, 2485, 2716],\n"," 560: [3481, 4027, 6711, 4993, 4973, 5952, 3793, 7361, 2502, 3996],\n"," 561: [318, 1198, 50, 858, 527, 1704, 1136, 1222, 1089, 1],\n"," 562: [376, 1, 524, 596, 281, 257, 708, 7, 289, 262],\n"," 563: [62, 736, 494, 832, 95, 6, 802, 104, 805, 653],\n"," 564: [1358, 1704, 2028, 590, 2324, 593, 1242, 1094, 2762, 2289],\n"," 565: [2571, 5952, 2959, 4993, 1196, 7153, 318, 1210, 4226, 2028],\n"," 566: [19, 21, 2028, 1784, 1004, 274, 2195, 1092, 575, 1610],\n"," 567: [17, 282, 509, 22, 224, 50, 265, 376, 151, 230],\n"," 568: [260, 1073, 832, 802, 805, 1210, 1356, 1393, 52, 788],\n"," 569: [150, 480, 589, 318, 110, 161, 356, 595, 316, 377],\n"," 570: [1358, 1704, 2028, 590, 2324, 1961, 858, 1617, 593, 2396],\n"," 571: [2959, 3481, 4027, 6711, 4993, 4034, 5952, 1258, 1732, 1206],\n"," 572: [5147,\n"," 33880,\n"," 4973,\n"," 3089,\n"," 2585,\n"," 3507,\n"," 6339,\n"," 6235,\n"," 27773,\n"," 2019],\n"," 573: [1961, 858, 1242, 2194, 3104, 2243, 1954, 1962, 1674, 1097],\n"," 574: [150, 590, 318, 110, 161, 356, 595, 316, 10, 592],\n"," 575: [1, 733, 32, 648, 36, 25, 6, 17, 104, 805],\n"," 576: [19, 1004, 274, 2195, 1092, 575, 2403, 3578, 1527, 3623],\n"," 577: [410, 193, 324, 2858, 2997, 532, 1564, 1148, 212, 327],\n"," 578: [2028, 593, 1617, 457, 50, 1197, 318, 47, 1213, 1527],\n"," 579: [1240, 1200, 2028, 1374, 1356, 1617, 924, 858, 1610, 2916],\n"," 580: [4027, 318, 2858, 1019, 2761, 1193, 1207, 3053, 912, 188],\n"," 581: [150, 34, 593, 339, 11, 440, 377, 590, 500, 17],\n"," 582: [541, 923, 750, 1221, 1136, 1206, 1213, 924, 922, 1148],\n"," 583: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 584: [593, 150, 590, 318, 165, 110, 588, 296, 161, 595],\n"," 585: [1208, 1221, 1212, 1250, 1233, 1952, 1204, 1263, 3435, 3683],\n"," 586: [410, 193, 324, 2359, 2336, 1252, 532, 3112, 2341, 212],\n"," 587: [2571, 5952, 2959, 4993, 7153, 3578, 4226, 2858, 2762, 2028],\n"," 588: [150, 11, 110, 17, 282, 350, 236, 161, 141, 349],\n"," 589: [1252, 923, 912, 903, 908, 111, 913, 1247, 1221, 1136],\n"," 590: [1214, 541, 32, 1374, 1356, 1617, 924, 858, 1199, 1197],\n"," 591: [720, 3624, 2891, 608, 736, 2108, 6016, 2976, 1537, 2609],\n"," 592: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 593: [377, 329, 1, 500, 474, 527, 32, 204, 442, 733],\n"," 594: [736, 141, 32, 494, 36, 95, 25, 6, 17, 802],\n"," 595: [2028, 2324, 1617, 318, 2396, 1094, 3104, 457, 1641, 2355],\n"," 596: [4027, 1732, 1639, 2997, 1500, 2890, 2692, 2108, 5004, 4086],\n"," 597: [720, 852, 3624, 2891, 3254, 4448, 3578, 4002, 3745, 4223],\n"," 598: [1617, 904, 912, 913, 1193, 928, 950, 903, 1198, 920],\n"," 599: [2028, 2858, 2324, 1207, 364, 593, 318, 2731, 3114, 2700],\n"," 600: [2571, 5952, 296, 4993, 1196, 7153, 318, 2762, 356, 2028],\n"," 601: [590, 350, 497, 161, 509, 224, 50, 105, 265, 376],\n"," 602: [1196, 1240, 589, 1200, 2571, 1214, 541, 32, 2028, 1374],\n"," 603: [260, 3083, 2080, 912, 899, 2137, 3448, 1094, 3189, 3174],\n"," 604: [1, 141, 494, 17, 802, 653, 1356, 3, 58, 7],\n"," 605: [593, 246, 111, 300, 232, 6, 16, 509, 31, 175],\n"," 606: [1240, 541, 32, 593, 1374, 1356, 1617, 924, 1291, 1036],\n"," 607: [2571, 2959, 296, 4993, 1196, 7153, 3578, 1210, 2858, 2762],\n"," 608: [2571, 5952, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 609: [1, 474, 204, 442, 733, 586, 786, 196, 553, 6],\n"," 610: [541, 1252, 912, 608, 750, 1208, 908, 111, 913, 1221],\n"," 612: [260, 608, 832, 25, 17, 802, 104, 805, 653, 1210],\n"," 613: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 614: [1210, 589, 2571, 1214, 541, 32, 593, 1374, 1356, 1617],\n"," 616: [1196, 4306, 858, 2329, 1214, 1200, 3996, 1704, 1270, 6874],\n"," 617: [318, 282, 266, 509, 22, 277, 224, 367, 105, 376],\n"," 618: [2324, 1961, 858, 1242, 2194, 3104, 2289, 2243, 36, 1962],\n"," 619: [1196, 2571, 541, 32, 593, 1374, 1356, 1617, 924, 1291],\n"," 620: [3578, 2959, 2858, 527, 2707, 589, 2571, 4993, 3157, 1176],\n"," 621: [2542, 2329, 5952, 2692, 4878, 4011, 3897, 1748, 4848, 7153],\n"," 622: [1200, 1214, 1374, 1356, 1617, 1291, 858, 1036, 2916, 1199],\n"," 623: [508, 357, 527, 282, 236, 497, 586, 292, 22, 224],\n"," 624: [2762, 3081, 1210, 1777, 2716, 2125, 3578, 2571, 2394, 2396],\n"," 625: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 318],\n"," 627: [6539, 6377, 3793, 4886, 780, 4995, 5989, 6378, 2959, 3408],\n"," 628: [539, 597, 356, 587, 34, 508, 593, 357, 339, 11],\n"," 629: [539, 597, 356, 587, 508, 593, 357, 440, 457, 527],\n"," 630: [260, 608, 62, 141, 1073, 832, 36, 6, 17, 1210],\n"," 631: [904, 541, 858, 1252, 923, 912, 608, 1208, 903, 908],\n"," 632: [4022, 356, 1625, 1610, 1833, 2706, 3101, 1552, 479, 260],\n"," 633: [1247, 910, 924, 1212, 1148, 235, 1952, 969, 1199, 899],\n"," 634: [1234, 2797, 2791, 1380, 914, 919, 3039, 1663, 1297, 2918],\n"," 635: [5418, 1307, 318, 4995, 260, 3083, 608, 2080, 912, 899],\n"," 636: [4226, 2959, 2858, 2997, 1258, 1732, 7361, 2502, 32, 3996],\n"," 637: [2572, 2683, 2605, 2706, 2581, 3081, 1210, 1777, 2485, 2716],\n"," 638: [593, 457, 377, 364, 480, 474, 588, 497, 595, 161],\n"," 639: [2571, 1196, 3578, 1210, 4226, 2858, 2762, 2028, 589, 260],\n"," 640: [296, 2762, 593, 260, 1036, 50, 2329, 527, 1200, 3996],\n"," 641: [150, 165, 110, 588, 356, 292, 364, 434, 47, 253],\n"," 642: [1358, 2324, 1962, 1641, 527, 110, 1299, 1610, 1584, 1207],\n"," 643: [2948, 2947, 2951, 5060, 3508, 2186, 2949, 861, 1198, 2406],\n"," 644: [32, 1374, 1356, 1617, 924, 858, 1610, 2916, 1199, 296],\n"," 645: [593, 349, 329, 344, 50, 1, 288, 225, 339, 733],\n"," 646: [150, 349, 589, 318, 165, 110, 161, 316, 10, 377],\n"," 647: [708, 362, 1196, 1028, 1097, 260, 1029, 79, 1035, 1012],\n"," 648: [750, 1208, 908, 527, 1221, 1136, 260, 1206, 1219, 1304],\n"," 649: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 650: [2959, 4027, 6711, 4973, 5952, 1732, 2692, 1206, 7361, 32],\n"," 651: [904, 1208, 111, 913, 1304, 1230, 924, 593, 922, 1234],\n"," 652: [296, 1271, 2797, 1721, 318, 2858, 260, 1246, 3083, 608],\n"," 653: [431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 49530,\n"," 306,\n"," 31696,\n"," 2858,\n"," 8644],\n"," 654: [589, 377, 364, 47, 50, 1, 500, 474, 527, 454],\n"," 655: [1079, 1234, 1278, 1220, 2797, 2406, 1968, 2791, 914, 1641],\n"," 656: [357, 457, 527, 590, 17, 282, 497, 454, 62, 586],\n"," 657: [3793, 2692, 1206, 2502, 293, 4886, 4011, 1748, 4848, 5618],\n"," 658: [3481, 4027, 6711, 2997, 4993, 4034, 4973, 5952, 1258, 3793],\n"," 659: [2959, 3578, 2028, 110, 50, 1291, 2329, 1270, 480, 6539],\n"," 660: [5952, 4993, 1196, 7153, 1210, 4226, 589, 260, 1036, 1198],\n"," 661: [539, 357, 339, 11, 17, 350, 236, 474, 497, 62],\n"," 662: [1252, 923, 1208, 903, 908, 527, 913, 1247, 1136, 1207],\n"," 663: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 664: [7445, 7254, 3897, 1831, 3701, 7022, 180, 5952, 4993, 2424],\n"," 665: [904, 858, 527, 913, 1247, 1221, 1136, 1207, 260, 1219],\n"," 666: [3578, 318, 1210, 2762, 356, 2028, 4306, 593, 589, 260],\n"," 667: [762, 662, 280, 1476, 828, 467, 8327, 836, 2019, 733],\n"," 668: [1193, 904, 541, 858, 1252, 912, 608, 1208, 903, 908],\n"," 669: [595, 10, 377, 364, 434, 47, 50, 1, 500, 474],\n"," 670: [1, 260, 608, 736, 32, 648, 494, 832, 95, 25],\n"," 671: [904, 913, 1207, 910, 1234, 1148, 1276, 1250, 969, 1179],\n"," 672: [5952, 4993, 7153, 318, 4226, 4306, 1036, 1214, 1200, 3996],\n"," 674: [1198, 32, 2028, 1617, 1291, 858, 1610, 2916, 1199, 296],\n"," 675: [2858, 3408, 2762, 1833, 50, 2706, 1249, 2683, 1127, 3101],\n"," 676: [539, 597, 356, 587, 34, 508, 593, 357, 440, 457],\n"," 677: [349, 165, 595, 329, 47, 153, 50, 1, 32, 288],\n"," 678: [904, 111, 1207, 1230, 910, 922, 1212, 1148, 1617, 1288],\n"," 679: [1307, 1197, 1079, 1278, 1220, 1028, 2797, 1097, 2406, 1968],\n"," 680: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 919, 1617, 903],\n"," 681: [4022, 356, 1625, 1610, 2762, 1833, 50, 2706, 1249, 593],\n"," 683: [4963, 3147, 2762, 2353, 4896, 4995, 597, 5989, 2959, 3408],\n"," 684: [527, 1097, 1193, 2762, 1080, 919, 2028, 2858, 3545, 3534],\n"," 685: [62, 494, 832, 95, 802, 104, 112, 1393, 58, 7],\n"," 686: [1356, 1610, 1199, 296, 1676, 318, 1148, 1127, 47, 110],\n"," 688: [2542, 4973, 7361, 2502, 3996, 293, 4878, 223, 8636, 3897],\n"," 689: [589, 165, 10, 377, 364, 329, 47, 253, 50, 1],\n"," 690: [608, 62, 1073, 32, 494, 832, 36, 25, 17, 805],\n"," 691: [2567, 1215, 1343, 1500, 1457, 1079, 373, 1515, 1270, 3114],\n"," 693: [539, 34, 508, 11, 440, 377, 590, 500, 282, 236],\n"," 694: [904, 541, 858, 1252, 923, 608, 750, 1208, 903, 908],\n"," 695: [2572, 2762, 2605, 2706, 3081, 1210, 1777, 2716, 3578, 2355],\n"," 696: [2605, 1210, 1777, 2485, 2716, 2125, 3578, 2571, 2394, 1586],\n"," 697: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 2858, 919, 1617],\n"," 698: [3481, 4027, 2997, 4034, 4973, 2692, 2502, 5349, 3052, 293],\n"," 699: [2087, 2143, 1006, 1354, 3020, 2161, 2683, 2023, 1221, 858],\n"," 700: [110, 296, 595, 364, 434, 329, 253, 231, 50, 500],\n"," 701: [380, 593, 150, 480, 349, 589, 590, 318, 165, 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858, 1617, 318],\n"," 719: [1020, 1036, 647, 852, 1569, 1035, 541, 1042, 1021, 1566],\n"," 720: [589, 165, 110, 595, 316, 10, 592, 434, 329, 153],\n"," 721: [597, 150, 356, 508, 593, 357, 339, 440, 457, 527],\n"," 723: [296, 1196, 1210, 2858, 2028, 593, 260, 1036, 1198, 50],\n"," 724: [780, 62, 736, 494, 832, 36, 95, 25, 6, 802],\n"," 725: [2863, 2804, 2948, 2947, 1136, 2160, 2951, 1197, 2918, 1997],\n"," 726: [260, 1240, 589, 1200, 2571, 1214, 541, 32, 2028, 1374],\n"," 727: [349, 589, 588, 161, 316, 10, 592, 292, 434, 329],\n"," 728: [260, 1200, 1198, 1214, 541, 32, 593, 1374, 1356, 1617],\n"," 729: [539, 597, 356, 587, 508, 593, 357, 440, 457, 527],\n"," 730: [457, 593, 480, 589, 110, 161, 356, 377, 292, 364],\n"," 731: [1234, 1278, 1220, 1028, 2791, 1380, 1641, 3039, 1297, 2396],\n"," 732: [1465, 1231, 457, 2943, 531, 1056, 593, 3082, 1277, 474],\n"," 733: [2959, 2858, 527, 3157, 1176, 4022, 750, 593, 4226, 2712],\n"," 734: [762, 662, 608, 1476, 828, 467, 8327, 836, 2797, 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1982, 724, 1562, 2953, 1210, 575, 1573],\n"," 768: [5464, 610, 2987, 1089, 5679, 7445, 2140, 1968, 1172, 7254],\n"," 769: [34, 377, 364, 595, 161, 380, 589, 105, 736, 733],\n"," 770: [608, 62, 32, 36, 25, 6, 104, 1210, 112, 1393],\n"," 771: [1197, 1079, 1234, 1278, 1220, 1259, 1380, 2100, 1285, 1136],\n"," 772: [4226, 2959, 4027, 2858, 2329, 5952, 1258, 2692, 7361, 2502],\n"," 773: [786, 362, 1028, 1029, 79, 1035, 1012, 1022, 661, 1198],\n"," 774: [2571, 5952, 2959, 296, 4993, 1196, 3578, 1210, 4226, 2858],\n"," 775: [508, 593, 357, 527, 110, 282, 62, 21, 509, 292],\n"," 776: [2324, 608, 1961, 1617, 318, 2396, 1242, 2762, 2194, 3104],\n"," 777: [1210, 97, 2571, 1304, 3753, 5378, 1287, 3947, 4643, 920],\n"," 780: [1704, 2289, 2243, 2858, 1299, 2355, 1247, 1207, 1960, 1272],\n"," 781: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 782: [608, 25, 50, 296, 1120, 110, 1968, 1912, 1041, 3125],\n"," 783: [2959, 296, 3578, 1210, 4226, 2858, 2762, 356, 2028, 589],\n"," 784: 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253, 50, 1, 32, 34, 204, 442, 21],\n"," 800: [733, 608, 494, 832, 36, 95, 25, 17, 805, 653],\n"," 801: [318, 2858, 2762, 356, 1036, 50, 858, 1240, 1222, 1213],\n"," 802: [150, 34, 357, 339, 11, 110, 364, 17, 282, 236],\n"," 803: [1358, 1617, 3104, 527, 2355, 2336, 1095, 3114, 2916, 2571],\n"," 804: [457, 380, 150, 480, 349, 589, 590, 318, 165, 588],\n"," 805: [2959, 296, 318, 4226, 2858, 2762, 356, 2028, 589, 1036],\n"," 806: [539, 597, 587, 508, 17, 350, 474, 497, 62, 141],\n"," 807: [457, 593, 480, 589, 110, 356, 595, 316, 377, 364],\n"," 808: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 809: [1833, 2706, 593, 2683, 3101, 1552, 479, 608, 1210, 3675],\n"," 810: [597, 150, 587, 34, 508, 593, 357, 339, 11, 440],\n"," 811: [260, 608, 32, 832, 36, 6, 802, 104, 805, 1210],\n"," 812: [34, 357, 590, 110, 39, 161, 266, 21, 296, 509],\n"," 813: [1358, 1704, 590, 608, 1961, 1617, 318, 593, 1242, 1094],\n"," 814: [1197, 1079, 1270, 1028, 2797, 1097, 1968, 1259, 1380, 914],\n"," 815: [2959, 3578, 318, 4226, 2858, 1036, 47, 50, 1291, 2329],\n"," 816: [380, 480, 589, 165, 110, 356, 377, 364, 434, 329],\n"," 817: [2028, 590, 608, 858, 1617, 593, 2396, 1242, 2762, 3104],\n"," 818: [371, 544, 68, 514, 270, 71, 315, 1302, 248, 1196],\n"," 819: [720, 2858, 1265, 852, 3624, 2891, 4448, 3578, 4002, 608],\n"," 820: [3481, 2542, 4034, 2329, 4011, 223, 778, 3897, 6502, 4262],\n"," 821: [2571, 4993, 4963, 3578, 5349, 1580, 6539, 2273, 6377, 4701],\n"," 822: [36, 32, 593, 608, 47, 555, 337, 353, 290, 6],\n"," 823: [786, 708, 362, 79, 531, 852, 919, 1270, 1015, 837],\n"," 824: [923, 527, 111, 913, 1267, 1230, 1213, 50, 296, 1148],\n"," 825: [1617, 904, 912, 1219, 913, 928, 950, 903, 1207, 1258],\n"," 826: [2329, 2692, 2502, 5349, 293, 4886, 8636, 2762, 2395, 5669],\n"," 827: [593, 589, 318, 110, 296, 434, 329, 47, 253, 50],\n"," 828: [593, 150, 590, 318, 588, 161, 356, 595, 329, 344],\n"," 829: [608, 25, 104, 112, 1356, 1393, 58, 52, 788, 14],\n"," 830: [1307, 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2943, 531, 1056, 509, 2804, 613, 3469],\n"," 846: [36, 608, 111, 337, 232, 290, 778, 52, 288, 468],\n"," 847: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 588],\n"," 848: [50, 318, 36, 223, 527, 32, 593, 608, 293, 47],\n"," 849: [371, 318, 544, 68, 514, 270, 71, 315, 1302, 248],\n"," 851: [5952, 2959, 4993, 7153, 3578, 318, 4226, 2858, 2028, 593],\n"," 852: [150, 165, 110, 316, 10, 292, 329, 153, 50, 1],\n"," 853: [260, 832, 25, 17, 802, 805, 1210, 1356, 1393, 858],\n"," 854: [1358, 1704, 590, 1617, 318, 2396, 1242, 1094, 3104, 2289],\n"," 855: [1358, 1704, 2028, 590, 1961, 858, 2396, 1242, 1094, 2762],\n"," 856: [260, 1196, 1240, 1210, 1200, 1198, 1214, 541, 32, 593],\n"," 857: [2959, 296, 318, 1210, 4226, 2858, 2762, 4306, 260, 1198],\n"," 859: [318, 2858, 4306, 47, 50, 2329, 527, 1214, 6874, 32],\n"," 860: [410, 193, 324, 534, 2858, 2336, 1183, 1252, 2997, 532],\n"," 861: [5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 862: [508, 593, 11, 590, 110, 17, 497, 62, 161, 141],\n"," 863: [260, 1196, 1240, 1200, 1198, 1214, 541, 1374, 924, 1291],\n"," 864: [4014,\n"," 8645,\n"," 3148,\n"," 4296,\n"," 6870,\n"," 2023,\n"," 40819,\n"," 3252,\n"," 5364,\n"," 30707],\n"," 865: [5952, 4993, 2762, 2028, 589, 1036, 1198, 1291, 858, 2329],\n"," 866: [1240, 589, 2571, 541, 2028, 1356, 1617, 924, 1291, 1610],\n"," 867: [6711, 2542, 1258, 1206, 7361, 2502, 32, 1089, 3052, 293],\n"," 868: [260, 1196, 1240, 1200, 2571, 1198, 1214, 541, 2028, 1374],\n"," 869: [1639, 2997, 3996, 1500, 2890, 1747, 5004, 4086, 1213, 912],\n"," 870: [50, 36, 32, 608, 47, 555, 353, 235, 307, 290],\n"," 871: [910, 1212, 1148, 2019, 1617, 1254, 1250, 2858, 1233, 1952],\n"," 872: [904, 541, 858, 923, 912, 608, 750, 903, 908, 527],\n"," 873: [912, 527, 3244, 260, 2918, 1240, 3347, 1197, 8405, 3189],\n"," 874: [2273, 4701, 4025, 2353, 4018, 5299, 6378, 3977, 3052, 3510],\n"," 875: [539, 597, 34, 11, 440, 457, 377, 110, 364, 500],\n"," 876: [150, 587, 34, 508, 357, 11, 110, 364, 17, 350],\n"," 878: [1304, 910, 1212, 1234, 1148, 1276, 1617, 1254, 2858, 1233],\n"," 879: [858, 1699, 1429, 7153, 923, 2541, 1587, 627, 837, 1208],\n"," 881: [349, 434, 1, 204, 21, 733, 300, 786, 196, 1036],\n"," 882: [733, 608, 1073, 494, 832, 36, 6, 17, 802, 104],\n"," 883: [539, 597, 150, 356, 587, 34, 508, 593, 357, 339],\n"," 884: [260, 780, 733, 608, 494, 832, 36, 95, 25, 6],\n"," 885: [1234, 1278, 1394, 1663, 1297, 2144, 2371, 440, 2150, 902],\n"," 886: [2324, 1954, 110, 2355, 1247, 1207, 1272, 3114, 2890, 1183],\n"," 887: [1358, 2324, 608, 1961, 858, 2396, 1242, 1094, 2762, 2194],\n"," 888: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 889: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 890: [2901, 1732, 2976, 593, 1617, 2908, 1639, 2396, 2320, 1620],\n"," 891: [593, 858, 2329, 6874, 6539, 1136, 1222, 1089, 1213, 6377],\n"," 892: [337, 112, 232, 235, 307, 290, 306, 509, 31, 288],\n"," 893: [2959, 296, 4993, 7153, 1210, 4226, 2858, 2762, 356, 4306],\n"," 894: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 895: [457, 593, 480, 349, 318, 165, 110, 292, 47, 50],\n"," 896: [318, 246, 111, 555, 337, 300, 232, 6, 16, 509],\n"," 897: [457, 150, 480, 349, 589, 318, 588, 161, 356, 377],\n"," 898: [1240, 1200, 1198, 2028, 593, 1374, 1617, 924, 1291, 858],\n"," 899: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 900: [3, 58, 628, 140, 135, 1405, 76, 748, 743, 671],\n"," 901: [5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226, 2858],\n"," 902: [110, 527, 32, 541, 6377, 1221, 1961, 4973, 4878, 2502],\n"," 903: [608, 1073, 832, 36, 25, 6, 17, 104, 805, 653],\n"," 904: [2028, 1961, 858, 593, 2396, 1242, 2762, 2194, 3104, 2289],\n"," 905: [762, 662, 280, 828, 467, 8327, 836, 2019, 1291, 441],\n"," 906: [1258, 3793, 5349, 3052, 4886, 47, 8636, 1214, 4979, 6502],\n"," 907: [588, 161, 595, 316, 292, 434, 329, 1, 34, 185],\n"," 908: [32, 593, 1617, 1199, 296, 608, 50, 1197, 318, 1148],\n"," 909: [4226, 3481, 4027, 6711, 2542, 4034, 4973, 1732, 2692, 1206],\n"," 910: [2571, 1196, 3578, 1210, 2762, 356, 2028, 589, 260, 1036],\n"," 911: [786, 708, 1097, 1029, 79, 1012, 1022, 661, 531, 852],\n"," 912: [2028, 965, 2186, 919, 2858, 2935, 931, 1066, 2324, 2208],\n"," 913: [1465, 2943, 531, 1056, 3082, 1277, 590, 613, 4709, 2397],\n"," 914: [1270, 1246, 280, 608, 828, 8327, 2019, 1291, 2797, 2028],\n"," 915: [1220, 2406, 1380, 914, 919, 1641, 1285, 1136, 1101, 1663],\n"," 916: [2571, 5952, 296, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 917: [260, 32, 494, 6, 802, 104, 805, 1210, 112, 1356],\n"," 918: [608, 141, 32, 36, 25, 6, 17, 805, 112, 58],\n"," 919: [26, 637, 1042, 711, 724, 708, 49278, 12, 719, 609],\n"," 920: [527, 3707, 1233, 2918, 1358, 3347, 2396, 1197, 8405, 3189],\n"," 921: [1230, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358, 3347],\n"," 922: [4025, 3793, 2353, 5989, 6378, 3408, 1704, 2028, 3052, 2763],\n"," 923: [260, 62, 736, 32, 648, 832, 36, 25, 17, 802],\n"," 924: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 593],\n"," 925: [2863, 2948, 2918, 1256, 346, 1580, 2363, 2186, 2949, 2273],\n"," 926: [858, 2396, 1252, 1699, 1704, 593, 1429, 3671, 1148, 2541],\n"," 927: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 4226],\n"," 928: [7153, 2028, 1036, 110, 1291, 858, 527, 1214, 1200, 1704],\n"," 930: [34, 357, 11, 440, 110, 364, 17, 497, 454, 62],\n"," 931: [1, 260, 608, 62, 141, 648, 832, 36, 95, 25],\n"," 932: [150, 356, 508, 339, 440, 527, 590, 110, 318, 500],\n"," 933: [1374, 924, 858, 1199, 608, 1197, 1376, 1148, 110, 1213],\n"," 934: [2959, 7254, 1006, 1354, 1967, 3020, 2683, 318, 2023, 1221],\n"," 935: [19, 21, 1784, 274, 2195, 1092, 575, 1527, 590, 2329],\n"," 936: [318, 1270, 1, 1213, 1527, 2716, 1580, 2918, 2628, 3114],\n"," 937: [2571, 296, 4993, 1196, 3578, 1210, 4226, 2858, 356, 2028],\n"," 938: [480, 589, 165, 110, 588, 356, 595, 10, 377, 592],\n"," 939: [62, 494, 25, 17, 802, 104, 805, 653, 1210, 112],\n"," 940: [431,\n"," 11,\n"," 45186,\n"," 53322,\n"," 1213,\n"," 49530,\n"," 31696,\n"," 8644,\n"," 3298,\n"," 1573],\n"," 941: [370, 256, 19, 410, 420, 333, 248, 355, 455, 466],\n"," 942: [150, 480, 589, 590, 110, 161, 356, 377, 364, 47],\n"," 943: [466, 104, 543, 88, 520, 1552, 2717, 2735, 743, 1982],\n"," 945: [780, 608, 648, 832, 36, 95, 6, 104, 805, 1210],\n"," 946: [1200, 541, 2028, 1036, 1199, 296, 318, 1148, 1270, 47],\n"," 947: [5952, 2959, 296, 4993, 7153, 3578, 318, 4226, 2858, 2762],\n"," 948: [1358, 590, 1961, 1242, 2194, 3104, 2289, 2243, 1954, 1962],\n"," 949: [1732, 2890, 1747, 4086, 4242, 246, 5303, 1468, 608, 4034],\n"," 950: [4226, 2959, 3481, 4027, 2542, 4993, 4034, 4973, 5952, 3793],\n"," 951: [260, 25, 802, 805, 1210, 1356, 1393, 3, 58, 52],\n"," 952: [1, 608, 141, 32, 36, 25, 6, 104, 653, 112],\n"," 953: [1343, 2396, 1457, 373, 1515, 1967, 73, 919, 1197, 892],\n"," 954: [5952, 296, 4993, 7153, 318, 4226, 2858, 356, 4306, 593],\n"," 955: [1230, 912, 3244, 3707, 260, 1233, 1240, 1358, 3347, 8405],\n"," 956: [316, 1, 204, 733, 786, 196, 173, 6, 1036, 780],\n"," 957: [1704, 2324, 2396, 1094, 2762, 2194, 3104, 2289, 2243, 1962],\n"," 958: [4993, 4963, 3147, 3578, 1580, 6539, 356, 2273, 6377, 4701],\n"," 960: [50, 318, 36, 25, 223, 32, 293, 246, 111, 555],\n"," 961: [904, 541, 858, 1252, 923, 912, 608, 750, 1208, 903],\n"," 962: [356, 1625, 1610, 2762, 1833, 50, 1249, 593, 1127, 3101],\n"," 963: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 964: [5952, 4993, 7153, 2858, 2762, 4306, 1198, 50, 1291, 1214],\n"," 965: [25, 527, 293, 555, 337, 353, 112, 235, 307, 6],\n"," 966: [2329, 1258, 7361, 3996, 3052, 4011, 778, 47, 4262, 2762],\n"," 967: [5952, 4993, 1196, 7153, 3578, 1210, 4226, 2028, 4306, 589],\n"," 968: [380, 589, 588, 161, 356, 595, 10, 377, 592, 364],\n"," 969: [260, 608, 648, 494, 832, 95, 6, 802, 104, 805],\n"," 970: [457, 480, 349, 589, 165, 588, 161, 356, 595, 316],\n"," 971: [3481, 6711, 2542, 7361, 2502, 32, 1089, 4878, 4011, 778],\n"," 972: [1961, 3104, 2289, 2243, 36, 1674, 260, 1299, 2355, 1960],\n"," 973: [3949, 55820, 1222, 7361, 1234, 858, 5147, 6874, 2858, 2959],\n"," 974: [371, 318, 544, 68, 514, 270, 71, 315, 248, 440],\n"," 975: [318, 36, 25, 223, 527, 593, 293, 47, 246, 555],\n"," 976: [457, 593, 480, 589, 110, 356, 316, 10, 377, 292],\n"," 977: [1219, 1304, 1212, 2019, 1254, 1233, 1204, 1394, 1284, 2396],\n"," 978: [527, 1097, 1193, 356, 2762, 1080, 2028, 2858, 3534, 32],\n"," 979: [610, 5679, 7445, 2140, 1172, 7254, 593, 3897, 1193, 1831],\n"," 980: [2571, 5952, 1196, 3578, 318, 2028, 4306, 589, 1036, 1198],\n"," 982: [733, 62, 494, 832, 36, 6, 17, 802, 104, 805],\n"," 983: [4993, 4963, 3578, 2273, 4025, 2353, 3753, 780, 5989, 6378],\n"," 984: [2571, 5952, 4993, 7153, 3578, 4226, 2858, 4306, 589, 260],\n"," 985: [4226, 2959, 3481, 6711, 2858, 2542, 2997, 4034, 2329, 1258],\n"," 986: [34, 508, 364, 17, 497, 62, 595, 39, 141, 21],\n"," 987: [539, 597, 356, 587, 34, 508, 593, 357, 11, 440],\n"," 988: [4027, 6711, 4034, 2329, 1732, 7361, 8636, 4979, 6502, 5902],\n"," 989: [923, 750, 1208, 903, 111, 913, 1247, 1221, 1207, 1206],\n"," 990: [349, 110, 161, 595, 316, 292, 329, 47, 153, 253],\n"," 991: [3949, 55820, 1213, 7361, 858, 5147, 6874, 2858, 2959, 923],\n"," 992: [786, 708, 1210, 362, 1028, 1029, 79, 1035, 1012, 1022],\n"," 993: [2571, 2959, 296, 7153, 318, 2858, 2762, 356, 593, 589],\n"," 994: [261, 265, 372, 537, 247, 531, 147, 427, 481, 45],\n"," 995: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 593],\n"," 996: [587, 440, 364, 17, 282, 480, 588, 497, 595, 141],\n"," 997: [260, 608, 832, 802, 104, 805, 1210, 1356, 1393, 58],\n"," 998: [1732, 318, 1500, 2692, 5004, 4086, 1682, 4242, 5303, 1],\n"," 999: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 1001: [380, 593, 349, 590, 318, 165, 296, 292, 434, 47],\n"," 1002: [1, 733, 608, 62, 648, 494, 832, 36, 95, 25],\n"," 1003: [2581,\n"," 3081,\n"," 1210,\n"," 1777,\n"," 2485,\n"," 2716,\n"," 2125,\n"," 3578,\n"," 2394,\n"," 2396],\n"," 1004: [527, 1080, 919, 3545, 3534, 2971, 5504, 1968, 8917, 3101],\n"," 1005: [34, 508, 11, 590, 17, 282, 497, 266, 261, 509],\n"," 1006: [296, 50, 36, 25, 223, 593, 293, 47, 246, 111],\n"," 1007: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 1008: [3481, 4027, 2542, 4034, 1258, 1206, 2502, 1089, 4011, 223],\n"," 1009: [4993,\n"," 4963,\n"," 3147,\n"," 3578,\n"," 5349,\n"," 6539,\n"," 2762,\n"," 2273,\n"," 4701,\n"," 3793],\n"," 1011: [762, 662, 280, 1476, 828, 467, 8327, 836, 2797, 1242],\n"," 1012: [356, 1625, 3408, 1610, 2762, 1833, 50, 2706, 1249, 2683],\n"," 1013: [2959, 3578, 4226, 2858, 2762, 1036, 47, 1291, 2329, 1214],\n"," 1014: [1193, 923, 912, 750, 1208, 903, 111, 913, 1221, 1136],\n"," 1015: [3468, 4034, 5013, 3022, 3983, 4226, 919, 1084, 3307, 694],\n"," 1016: [904, 541, 858, 1252, 912, 750, 1208, 903, 908, 913],\n"," 1017: [2571, 2959, 296, 3578, 318, 4226, 2858, 2762, 356, 2028],\n"," 1018: [4027, 2692, 2502, 8636, 4979, 5902, 4262, 50, 1748, 923],\n"," 1019: [62, 1073, 32, 494, 832, 36, 25, 6, 17, 802],\n"," 1020: [1079, 1270, 2797, 1097, 1259, 2791, 919, 1641, 3039, 1285],\n"," 1021: [1193, 904, 541, 1252, 912, 608, 750, 1208, 903, 908],\n"," 1022: [1193, 356, 2762, 1080, 2028, 2858, 3545, 3534, 2971, 32],\n"," 1023: [296, 318, 1210, 4226, 2762, 356, 2028, 4306, 589, 1036],\n"," 1025: [1193, 904, 541, 1252, 750, 1208, 903, 527, 913, 1206],\n"," 1026: [6711, 2858, 2542, 2997, 4034, 1732, 2692, 1206, 32, 3996],\n"," 1027: [2571, 5952, 4993, 1196, 7153, 318, 1210, 2858, 2762, 356],\n"," 1028: [589, 318, 110, 588, 595, 377, 364, 344, 47, 253],\n"," 1029: [36, 25, 223, 527, 32, 608, 293, 246, 111, 300],\n"," 1030: [4963,\n"," 3147,\n"," 3578,\n"," 5349,\n"," 6377,\n"," 4701,\n"," 4025,\n"," 4246,\n"," 4896,\n"," 4886],\n"," 1031: [457, 380, 150, 349, 589, 318, 588, 296, 161, 356],\n"," 1032: [1, 260, 733, 608, 62, 141, 32, 832, 36, 25],\n"," 1033: [2571, 5952, 2959, 4993, 7153, 3578, 4226, 2858, 1036, 858],\n"," 1034: [2571, 2959, 296, 4993, 1196, 318, 1210, 4226, 2858, 2762],\n"," 1035: [2140, 2087, 2143, 1006, 1354, 1967, 3020, 2161, 4878, 912],\n"," 1037: [508, 11, 440, 457, 527, 590, 110, 364, 318, 17],\n"," 1038: [786, 362, 79, 1198, 852, 1291, 1367, 837, 653, 745],\n"," 1039: [1196, 1240, 589, 1200, 2571, 1198, 1214, 32, 1374, 1356],\n"," 1040: [466, 104, 543, 88, 520, 1552, 2717, 2735, 743, 1982],\n"," 1041: [356, 4306, 1270, 480, 32, 1136, 1240, 1222, 541, 1213],\n"," 1043: [260, 733, 608, 1073, 832, 802, 104, 805, 653, 1210],\n"," 1044: [608, 62, 141, 494, 832, 36, 6, 17, 104, 805],\n"," 1045: [5952,\n"," 1214,\n"," 1200,\n"," 3996,\n"," 480,\n"," 1240,\n"," 1080,\n"," 33794,\n"," 2716,\n"," 4973],\n"," 1046: [589, 318, 110, 588, 161, 10, 377, 292, 364, 329],\n"," 1047: [1222,\n"," 1234,\n"," 5147,\n"," 4873,\n"," 1080,\n"," 2585,\n"," 55805,\n"," 3507,\n"," 6339,\n"," 5792],\n"," 1048: [3578, 356, 260, 110, 858, 527, 1214, 3996, 480, 1136],\n"," 1050: [36, 608, 246, 111, 112, 232, 235, 307, 290, 306],\n"," 1051: [608, 32, 494, 832, 36, 95, 25, 6, 805, 653],\n"," 1052: [21, 2028, 1004, 274, 2195, 1092, 575, 1610, 2403, 3623],\n"," 1053: [2028, 593, 1374, 1356, 1617, 858, 1199, 296, 608, 457]})"]},"metadata":{},"execution_count":10}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 리스트를 작성한다\n","# 각 사용자의 소속 확률이 가장 높은 토픽을 얻고, 해당 토픽 안에서 확률이 높은 아이템을 저장해 나간다\n","\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","pred_user2items = defaultdict(list)\n","for i, (user_id, data) in enumerate(movielens_train_high_rating.groupby(\"user_id\")):\n"," evaluated_movie_ids = user_evaluated_movies[user_id]\n"," # 사용자의 소속 확률이 가장 높은 토픽을 얻는다\n"," user_topic = sorted(lda_topics[i], key=lambda x: -x[1])[0][0]\n"," # 해당 토픽 안에서 확률이 높은 아이템을 얻는다\n"," topic_movies = lda_model.get_topic_terms(user_topic, topn=len(movies))\n","\n"," for token_id, score in topic_movies:\n"," movie_id = int(common_dictionary.id2token[token_id])\n"," if movie_id not in evaluated_movie_ids:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":11,"id":"1701bd43","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":288},"id":"1701bd43","executionInfo":{"status":"ok","timestamp":1672118441268,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"ee59bd31-4381-4245-f7ae-752409de578c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"16eddf46","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"16eddf46","executionInfo":{"status":"ok","timestamp":1672118441268,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"da0309a9-ae28-406a-bcf1-f5d45c559abc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n","1212 1240 Terminator, The (1984) \n","\n"," genre \\\n","1171 [Action, Adventure, Sci-Fi] \n","1173 [Action, Adventure] \n","1212 [Action, Sci-Fi, Thriller] \n","\n"," tag \n","1171 [lucas, george lucas, george lucas, gfei own i... \n","1173 [egypt, lucas, seen more than once, dvd collec... \n","1212 [arnold schwarzenegger, sci-fi, time travel, d... "],"text/html":["\n","
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
12121240Terminator, The (1984)[Action, Sci-Fi, Thriller][arnold schwarzenegger, sci-fi, time travel, d...
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(1198, 1196, 1240)\n","movies[movies.movie_id.isin([1198, 1196, 1240])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/LDA_content.ipynb b/chapter5/colab/LDA_content.ipynb index 4ed495f..11cf8eb 100644 --- a/chapter5/colab/LDA_content.ipynb +++ b/chapter5/colab/LDA_content.ipynb @@ -1,4001 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0a902087", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_content.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "98853cdc", - "metadata": {}, - "source": [ - "# Latent Dirichlet Allocation (LDA)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dd0a4718", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "38f1eabd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ea40c411", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 50\n", - "# 에폭 수\n", - "n_epochs = 30" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7aa2bed1", - "metadata": {}, - "outputs": [], - "source": [ - "movie_content = movies.copy()\n", - "# tag가 부여되어 있지 않은 영화가 있지만, genre는 모든 영화에 부여되어 있다\n", - "# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 하여 비슷한 영화를 찾아서 추천한다\n", - "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", - "movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n", - "movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: list(map(str, x)))\n", - "\n", - "# 태그와 장르 데이터를 사용해 lda를 학습한다\n", - "tag_genre_data = movie_content.tag_genre.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5804af1", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install gensim==4.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c448ef53", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "from gensim.corpora.dictionary import Dictionary\n", - "\n", - "# LDA의 입력으로 사용할 데이터를 작성한다\n", - "common_dictionary = Dictionary(tag_genre_data)\n", - "common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a4449eb1", - "metadata": {}, - "outputs": [], - "source": [ - "import gensim\n", - "\n", - "# LDA 학습\n", - "lda_model = gensim.models.LdaModel(\n", - " common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a5911f4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "word=drugs, score=0.12014076858758926\n", - "word=japan, score=0.0761302188038826\n", - "word=quentin tarantino, score=0.05024522915482521\n", - "word=will smith, score=0.03854871168732643\n", - "word=tarantino, score=0.029550844803452492\n", - "word=tommy lee jones, score=0.02945614792406559\n", - "word=akira kurosawa, score=0.028017211705446243\n", - "word=samurai, score=0.025930559262633324\n", - "word=ummarti2007, score=0.02379794418811798\n", - "word=addiction, score=0.018428761512041092\n" - ] - } - ], - "source": [ - "# topic0인 단어 목록\n", - "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", - " word = common_dictionary.id2token[token_id]\n", - " print(f'word={word}, score={score}')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "858bf7ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 0.509997),\n", - " (1, 0.010000061),\n", - " (2, 0.010000061),\n", - " (3, 0.010000061),\n", - " (4, 0.010000061),\n", - " (5, 0.010000061),\n", - " (6, 0.010000061),\n", - " (7, 0.010000061),\n", - " (8, 0.010000061),\n", - " (9, 0.010000061),\n", - " (10, 0.010000061),\n", - " (11, 0.010000061),\n", - " (12, 0.010000061),\n", - " (13, 0.010000061),\n", - " (14, 0.010000061),\n", - " (15, 0.010000061),\n", - " (16, 0.010000061),\n", - " (17, 0.010000061),\n", - " (18, 0.010000061),\n", - " (19, 0.010000061),\n", - " (20, 0.010000061),\n", - " (21, 0.010000061),\n", - " (22, 0.010000061),\n", - " (23, 0.010000061),\n", - " (24, 0.010000061),\n", - " (25, 0.010000061),\n", - " (26, 0.010000061),\n", - " (27, 0.010000061),\n", - " (28, 0.010000061),\n", - " (29, 0.010000061),\n", - " (30, 0.010000061),\n", - " (31, 0.010000061),\n", - " (32, 0.010000061),\n", - " (33, 0.010000061),\n", - " (34, 0.010000061),\n", - " (35, 0.010000061),\n", - " (36, 0.010000061),\n", - " (37, 0.010000061),\n", - " (38, 0.010000061),\n", - " (39, 0.010000061),\n", - " (40, 0.010000061),\n", - " (41, 0.010000061),\n", - " (42, 0.010000061),\n", - " (43, 0.010000061),\n", - " (44, 0.010000061),\n", - " (45, 0.010000061),\n", - " (46, 0.010000061),\n", - " (47, 0.010000061),\n", - " (48, 0.010000061),\n", - " (49, 0.010000061)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# samurai라는 단어의 토픽(각 토픽에 소속될 확률)\n", - "lda_model[common_dictionary.doc2bow(['samurai'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e03ab49d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretagtag_genretopictopic_score
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...[pixar, pixar, pixar, animation, pixar, animat...200.642943
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...[for children, game, animals, joe johnston, ro...10.473445
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...[funniest movies, comedinha de velhinhos engra...380.601124
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie][girl movie, Comedy, Drama, Romance]330.755000
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...[steve martin, pregnancy, remake, steve martin...210.600993
........................
1067665088Bedtime Stories (2008)[Adventure, Children, Comedy]NaN[Adventure, Children, Comedy]10.755000
1067765091Manhattan Melodrama (1934)[Crime, Drama, Romance]NaN[Crime, Drama, Romance]330.388229
1067865126Choke (2008)[Comedy, Drama][chuck palahniuk, based on book][chuck palahniuk, based on book, Comedy, Drama]330.505002
1067965130Revolutionary Road (2008)[Drama, Romance][toplist08][toplist08, Drama, Romance]330.673333
1068065133Blackadder Back & Forth (1999)[Comedy]NaN[Comedy]330.510000
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10681 rows × 7 columns

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" - ], - "text/plain": [ - " movie_id title \\\n", - "0 1 Toy Story (1995) \n", - "1 2 Jumanji (1995) \n", - "2 3 Grumpier Old Men (1995) \n", - "3 4 Waiting to Exhale (1995) \n", - "4 5 Father of the Bride Part II (1995) \n", - "... ... ... \n", - "10676 65088 Bedtime Stories (2008) \n", - "10677 65091 Manhattan Melodrama (1934) \n", - "10678 65126 Choke (2008) \n", - "10679 65130 Revolutionary Road (2008) \n", - "10680 65133 Blackadder Back & Forth (1999) \n", - "\n", - " genre \\\n", - "0 [Adventure, Animation, Children, Comedy, Fantasy] \n", - "1 [Adventure, Children, Fantasy] \n", - "2 [Comedy, Romance] \n", - "3 [Comedy, Drama, Romance] \n", - "4 [Comedy] \n", - "... ... \n", - "10676 [Adventure, Children, Comedy] \n", - "10677 [Crime, Drama, Romance] \n", - "10678 [Comedy, Drama] \n", - "10679 [Drama, Romance] \n", - "10680 [Comedy] \n", - "\n", - " tag \\\n", - "0 [pixar, pixar, pixar, animation, pixar, animat... \n", - "1 [for children, game, animals, joe johnston, ro... \n", - 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] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 각 영화의 토픽을 저장한다\n", - "lda_topics = lda_model[common_corpus]\n", - "\n", - "# 각 영화에 가장 확률이 높은 토픽을 1개 추출해서 저장한다\n", - "movie_topics = []\n", - "movie_topic_scores = []\n", - "for movie_index, lda_topic in enumerate(lda_topics):\n", - " sorted_topic = sorted(lda_topics[movie_index], key=lambda x: -x[1])\n", - " # 가장 확률이 높은 토픽\n", - " movie_topic, topic_score = sorted_topic[0]\n", - " movie_topics.append(movie_topic)\n", - " movie_topic_scores.append(topic_score)\n", - "movie_content[\"topic\"] = movie_topics\n", - "movie_content[\"topic_score\"] = movie_topic_scores\n", - "movie_content" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "961bb095", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {1: [3759, 3964, 3775, 1022, 1030, 2096, 48, 2687, 6414, 2566],\n", - " 2: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 6934, 2848],\n", - 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" 59362,\n", - " 6757,\n", - " 56700],\n", - " 1020: [1256,\n", - " 3742,\n", - " 7091,\n", - " 25777,\n", - " 1358,\n", - " 1186,\n", - " 34002,\n", - " 8734,\n", - " 25766,\n", - " 34018],\n", - " 1021: [3759, 3964, 3775, 1030, 2096, 48, 2687, 6414, 2566, 4154],\n", - " 1022: [364, 3759, 3964, 3775, 1022, 1030, 2096, 48, 2687, 6414],\n", - " 1023: [3100,\n", - " 1933,\n", - " 1929,\n", - " 1935,\n", - " 4292,\n", - " 1946,\n", - " 1943,\n", - " 8153,\n", - " 7771,\n", - " 1937],\n", - " 1025: [1012,\n", - " 27850,\n", - " 6775,\n", - " 1051,\n", - " 44195,\n", - " 7976,\n", - " 1206,\n", - " 7762,\n", - " 60530,\n", - " 59290],\n", - " 1026: [318, 1245, 333, 4881, 3683, 26350, 4262, 3529, 2249, 2023],\n", - " 1027: [1256,\n", - " 3742,\n", - " 7091,\n", - " 913,\n", - " 25777,\n", - " 1358,\n", - " 1186,\n", - " 34002,\n", - " 8734,\n", - " 25766],\n", - " 1028: [3652,\n", - " 1921,\n", - " 1464,\n", - " 6952,\n", - " 4794,\n", - " 3970,\n", - " 6530,\n", - " 4210,\n", - " 4046,\n", - " 3075],\n", - " 1029: [54787,\n", - " 56169,\n", - " 33819,\n", - " 8915,\n", - " 2881,\n", - " 55946,\n", - " 2810,\n", - " 6555,\n", - " 6809,\n", - " 5282],\n", - " 1030: [2675,\n", - " 54193,\n", - " 6513,\n", - " 8302,\n", - " 8294,\n", - " 6875,\n", - " 56941,\n", - " 8273,\n", - " 2556,\n", - " 2426],\n", - " 1031: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 6934, 2848],\n", - " 1032: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 6934, 2848],\n", - " 1033: [3759, 3964, 3775, 1022, 1030, 2096, 48, 2687, 6414, 2566],\n", - " 1034: [1, 3086, 3510, 3114, 2014, 4350, 5816, 1270, 2012, 4896],\n", - " 1035: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 2848, 1198],\n", - " 1037: [3869, 370, 466, 43919, 520, 8512, 743, 719, 3785, 6888],\n", - " 1038: [2675,\n", - " 54193,\n", - " 6513,\n", - " 8302,\n", - " 8294,\n", - " 6875,\n", - " 56941,\n", - " 8273,\n", - " 2556,\n", - " 2426],\n", - " 1039: [1020,\n", - " 6104,\n", - " 788,\n", - " 6807,\n", - " 2788,\n", - " 38038,\n", - " 1136,\n", - " 2727,\n", - " 3751,\n", - " 4164],\n", - " 1040: [63859,\n", - " 26495,\n", - " 1848,\n", - " 1525,\n", - " 32153,\n", - " 2041,\n", - " 8811,\n", - " 26792,\n", - " 5006,\n", - " 48159],\n", - " 1041: [2115, 76, 1587, 1779, 8644, 4643, 377, 6934, 2848, 39435],\n", - " 1043: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 6934, 2848],\n", - " 1044: [2115, 76, 2006, 1587, 1779, 8644, 4643, 377, 6934, 2848],\n", - " 1045: [1256,\n", - " 3742,\n", - " 7091,\n", - " 913,\n", - " 25777,\n", - " 1358,\n", - " 1186,\n", - " 34002,\n", - " 8734,\n", - " 25766],\n", - " 1046: [2375, 4022, 8529, 993, 5341, 4468, 7456, 4192, 2501, 2822],\n", - " 1047: [1223, 1148, 1020, 6104, 745, 788, 6807, 2788, 38038, 2727],\n", - " 1048: [2990,\n", - " 3984,\n", - " 5872,\n", - " 2948,\n", - " 2376,\n", - " 2991,\n", - " 63113,\n", - " 2949,\n", - " 1722,\n", - " 3633],\n", - " 1050: [1060, 413, 663, 3211, 3484, 6938, 2342, 46850, 2471, 2435],\n", - " 1051: [1060, 413, 663, 3211, 3484, 6938, 2342, 46850, 2471, 2435],\n", - " 1052: [3100,\n", - " 1933,\n", - " 1929,\n", - " 1935,\n", - " 4292,\n", - " 1946,\n", - " 1943,\n", - " 8153,\n", - " 7771,\n", - " 1937],\n", - " 1053: [2115, 76, 1587, 1779, 4643, 377, 6934, 2848, 39435, 1059]})" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "from collections import Counter\n", - "\n", - "# 각 사용자에 대한 추천 리스트를 작성한다\n", - "# 사용자가 높게 평가한 영화가, 어떤 토픽에 많이 소속되어 있는지 카운트한다\n", - "# 가장 많은 토픽을 사용자가 좋아하는 토픽으로 간주하고, 해당 토픽의 영화를 추천한다\n", - "\n", - "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "\n", - "movie_id2index = dict(zip(movie_content.movie_id.tolist(), range(len(movie_content))))\n", - "pred_user2items = defaultdict(list)\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " # 사용자가 높이 평가한 영화\n", - " evaluated_movie_ids = user_evaluated_movies[user_id]\n", - " # 최근 열람한 영화를 얻는다\n", - " movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-10:]\n", - "\n", - " movie_indexes = [movie_id2index[id] for id in movie_ids]\n", - " \n", - " # 최근 열람한 영화의 토픽을 얻고, 풀현 횟수를 카운트한다\n", - " topic_counter = Counter([movie_topics[i] for i in movie_indexes])\n", - " # 가장 출현 횟수가 많았던 토픽을 얻는다\n", - " frequent_topic = topic_counter.most_common(1)[0][0]\n", - " # 해당 토픽의 영화 중에서도 점수가 높은 것을 추천한다\n", - " topic_movies = (\n", - " movie_content[movie_content.topic == frequent_topic]\n", - " .sort_values(\"topic_score\", ascending=False)\n", - " .movie_id.tolist()\n", - " )\n", - "\n", - " for movie_id in topic_movies:\n", - " if movie_id not in evaluated_movie_ids:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "8d238322", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "d6877799", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
7576Screamers (1995)[Action, Sci-Fi, Thriller][philip k. dick, artificial intelligence, post...
19222006Mask of Zorro, The (1998)[Action, Adventure, Romance][california, mexico, funny, banderas, anthony ...
20312115Indiana Jones and the Temple of Doom (1984)[Action, Adventure][lucas, want it, dvd collection, harrison ford...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "75 76 Screamers (1995) \n", - "1922 2006 Mask of Zorro, The (1998) \n", - "2031 2115 Indiana Jones and the Temple of Doom (1984) \n", - "\n", - " genre \\\n", - "75 [Action, Sci-Fi, Thriller] \n", - "1922 [Action, Adventure, Romance] \n", - "2031 [Action, Adventure] \n", - "\n", - " tag \n", - "75 [philip k. dick, artificial intelligence, post... \n", - "1922 [california, mexico, funny, banderas, anthony ... \n", - "2031 [lucas, want it, dvd collection, harrison ford... " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(2115, 76, 2006)\n", - "movies[movies.movie_id.isin([2115, 76, 2006])]" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "129f2877", - "metadata": {}, - "outputs": [], - "source": [ - "# 여기에서는 각 사용자의 평가 이력으로부터 1개의 토픽을 픽업했지만, 토픽의 확률값을 사용해서 가중치를 주면서 아이템을 추출할 수도 있습니다." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a30f15c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"0a902087","metadata":{"id":"0a902087"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_content.ipynb)"]},{"cell_type":"markdown","id":"98853cdc","metadata":{"id":"98853cdc"},"source":["# Latent Dirichlet Allocation (LDA)"]},{"cell_type":"code","execution_count":1,"id":"dd0a4718","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dd0a4718","executionInfo":{"status":"ok","timestamp":1672118379265,"user_tz":-540,"elapsed":3985,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c3c7a4e6-690d-431c-a767-3ff7cd0aea7a"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:19:32-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 106MB/s in 0.6s \n","\n","2022-12-27 05:19:33 (106 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"38f1eabd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"38f1eabd","executionInfo":{"status":"ok","timestamp":1672118460251,"user_tz":-540,"elapsed":80989,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"7dbb3693-16e5-4e38-fd8e-c20f1595a08f"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"ea40c411","metadata":{"id":"ea40c411","executionInfo":{"status":"ok","timestamp":1672118460251,"user_tz":-540,"elapsed":11,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 50\n","# 에폭 수\n","n_epochs = 30"]},{"cell_type":"code","execution_count":4,"id":"7aa2bed1","metadata":{"id":"7aa2bed1","executionInfo":{"status":"ok","timestamp":1672118460252,"user_tz":-540,"elapsed":12,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["movie_content = movies.copy()\n","# tag가 부여되어 있지 않은 영화가 있지만, genre는 모든 영화에 부여되어 있다\n","# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 하여 비슷한 영화를 찾아서 추천한다\n","# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n","movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n","movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: list(map(str, x)))\n","\n","# 태그와 장르 데이터를 사용해 lda를 학습한다\n","tag_genre_data = movie_content.tag_genre.tolist()"]},{"cell_type":"code","execution_count":5,"id":"d5804af1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d5804af1","executionInfo":{"status":"ok","timestamp":1672118468216,"user_tz":-540,"elapsed":7975,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"04a68cd3-8b0e-45ee-a1ee-d1a952a63437"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.4 MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":6,"id":"c448ef53","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c448ef53","executionInfo":{"status":"ok","timestamp":1672118469654,"user_tz":-540,"elapsed":1442,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"040b678f-bb33-4b0e-eea1-9c147aa589c6"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["from gensim.corpora.dictionary import Dictionary\n","\n","# LDA의 입력으로 사용할 데이터를 작성한다\n","common_dictionary = Dictionary(tag_genre_data)\n","common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n"]},{"cell_type":"code","execution_count":7,"id":"a4449eb1","metadata":{"id":"a4449eb1","executionInfo":{"status":"ok","timestamp":1672118564370,"user_tz":-540,"elapsed":94718,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import gensim\n","\n","# LDA 학습\n","lda_model = gensim.models.LdaModel(\n"," common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n",")"]},{"cell_type":"code","execution_count":8,"id":"a5911f4e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a5911f4e","executionInfo":{"status":"ok","timestamp":1672118564371,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"851e44c7-0605-4c9b-cd83-701340697d26"},"outputs":[{"output_type":"stream","name":"stdout","text":["word=coen brothers, score=0.06292896717786789\n","word=bechdel test:fail, score=0.04510311409831047\n","word=based on book, score=0.04402820020914078\n","word=philip k. dick, score=0.04182061925530434\n","word=kidnapping, score=0.03534778952598572\n","word=quirky, score=0.02876952663064003\n","word=david lynch, score=0.022975284606218338\n","word=fascism, score=0.01680714637041092\n","word=sure thing, score=0.015899477526545525\n","word=robert altman, score=0.015737345442175865\n"]}],"source":["# topic0인 단어 목록\n","for token_id, score in lda_model.get_topic_terms(0, topn=10):\n"," word = common_dictionary.id2token[token_id]\n"," print(f'word={word}, score={score}')"]},{"cell_type":"code","execution_count":9,"id":"858bf7ea","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"858bf7ea","executionInfo":{"status":"ok","timestamp":1672118564371,"user_tz":-540,"elapsed":11,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"df48bb09-d2ad-4570-fa8f-e5b38679a98c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, 0.01000004),\n"," (1, 0.01000004),\n"," (2, 0.01000004),\n"," (3, 0.01000004),\n"," (4, 0.01000004),\n"," (5, 0.01000004),\n"," (6, 0.01000004),\n"," (7, 0.01000004),\n"," (8, 0.01000004),\n"," (9, 0.01000004),\n"," (10, 0.01000004),\n"," (11, 0.01000004),\n"," (12, 0.01000004),\n"," (13, 0.01000004),\n"," (14, 0.01000004),\n"," (15, 0.01000004),\n"," (16, 0.01000004),\n"," (17, 0.01000004),\n"," (18, 0.01000004),\n"," (19, 0.01000004),\n"," (20, 0.01000004),\n"," (21, 0.01000004),\n"," (22, 0.01000004),\n"," (23, 0.01000004),\n"," (24, 0.01000004),\n"," (25, 0.01000004),\n"," (26, 0.01000004),\n"," (27, 0.01000004),\n"," (28, 0.01000004),\n"," (29, 0.01000004),\n"," (30, 0.01000004),\n"," (31, 0.01000004),\n"," (32, 0.509998),\n"," (33, 0.01000004),\n"," (34, 0.01000004),\n"," (35, 0.01000004),\n"," (36, 0.01000004),\n"," (37, 0.01000004),\n"," (38, 0.01000004),\n"," (39, 0.01000004),\n"," (40, 0.01000004),\n"," (41, 0.01000004),\n"," (42, 0.01000004),\n"," (43, 0.01000004),\n"," (44, 0.01000004),\n"," (45, 0.01000004),\n"," (46, 0.01000004),\n"," (47, 0.01000004),\n"," (48, 0.01000004),\n"," (49, 0.01000004)]"]},"metadata":{},"execution_count":9}],"source":["# samurai라는 단어의 토픽(각 토픽에 소속될 확률)\n","lda_model[common_dictionary.doc2bow(['samurai'])]"]},{"cell_type":"code","execution_count":10,"id":"e03ab49d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":554},"id":"e03ab49d","executionInfo":{"status":"ok","timestamp":1672118571397,"user_tz":-540,"elapsed":7036,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"04f31642-f84a-4a5b-a7a7-17f5e79ad431"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","1 2 Jumanji (1995) \n","2 3 Grumpier Old Men (1995) \n","3 4 Waiting to Exhale (1995) \n","4 5 Father of the Bride Part II (1995) \n","... ... ... \n","10676 65088 Bedtime Stories (2008) \n","10677 65091 Manhattan Melodrama (1934) \n","10678 65126 Choke (2008) \n","10679 65130 Revolutionary Road (2008) \n","10680 65133 Blackadder Back & Forth (1999) \n","\n"," genre \\\n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","1 [Adventure, Children, Fantasy] \n","2 [Comedy, Romance] \n","3 [Comedy, Drama, Romance] \n","4 [Comedy] \n","... ... \n","10676 [Adventure, Children, Comedy] \n","10677 [Crime, Drama, Romance] \n","10678 [Comedy, Drama] \n","10679 [Drama, Romance] \n","10680 [Comedy] \n","\n"," tag \\\n","0 [pixar, pixar, pixar, animation, pixar, animat... \n","1 [for children, game, animals, joe johnston, ro... \n","2 [funniest movies, comedinha de velhinhos engra... \n","3 [girl movie] \n","4 [steve martin, pregnancy, remake, steve martin... \n","... ... \n","10676 NaN \n","10677 NaN \n","10678 [chuck palahniuk, based on book] \n","10679 [toplist08] \n","10680 NaN \n","\n"," tag_genre topic topic_score \n","0 [pixar, pixar, pixar, animation, pixar, animat... 34 0.777594 \n","1 [for children, game, animals, joe johnston, ro... 40 0.275520 \n","2 [funniest movies, comedinha de velhinhos engra... 7 0.501525 \n","3 [girl movie, Comedy, Drama, Romance] 49 0.755000 \n","4 [steve martin, pregnancy, remake, steve martin... 34 0.692084 \n","... ... ... ... \n","10676 [Adventure, Children, Comedy] 34 0.755000 \n","10677 [Crime, Drama, Romance] 49 0.456076 \n","10678 [chuck palahniuk, based on book, Comedy, Drama] 49 0.505002 \n","10679 [toplist08, Drama, Romance] 49 0.673333 \n","10680 [Comedy] 17 0.510000 \n","\n","[10681 rows x 7 columns]"],"text/html":["\n","
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movie_idtitlegenretagtag_genretopictopic_score
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...[pixar, pixar, pixar, animation, pixar, animat...340.777594
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...[for children, game, animals, joe johnston, ro...400.275520
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...[funniest movies, comedinha de velhinhos engra...70.501525
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie][girl movie, Comedy, Drama, Romance]490.755000
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...[steve martin, pregnancy, remake, steve martin...340.692084
........................
1067665088Bedtime Stories (2008)[Adventure, Children, Comedy]NaN[Adventure, Children, Comedy]340.755000
1067765091Manhattan Melodrama (1934)[Crime, Drama, Romance]NaN[Crime, Drama, Romance]490.456076
1067865126Choke (2008)[Comedy, Drama][chuck palahniuk, based on book][chuck palahniuk, based on book, Comedy, Drama]490.505002
1067965130Revolutionary Road (2008)[Drama, Romance][toplist08][toplist08, Drama, Romance]490.673333
1068065133Blackadder Back & Forth (1999)[Comedy]NaN[Comedy]170.510000
\n","

10681 rows × 7 columns

\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":10}],"source":["# 각 영화의 토픽을 저장한다\n","lda_topics = lda_model[common_corpus]\n","\n","# 각 영화에 가장 확률이 높은 토픽을 1개 추출해서 저장한다\n","movie_topics = []\n","movie_topic_scores = []\n","for movie_index, lda_topic in enumerate(lda_topics):\n"," sorted_topic = sorted(lda_topics[movie_index], key=lambda x: -x[1])\n"," # 가장 확률이 높은 토픽\n"," movie_topic, topic_score = sorted_topic[0]\n"," movie_topics.append(movie_topic)\n"," movie_topic_scores.append(topic_score)\n","movie_content[\"topic\"] = movie_topics\n","movie_content[\"topic_score\"] = movie_topic_scores\n","movie_content"]},{"cell_type":"code","execution_count":11,"id":"961bb095","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"961bb095","executionInfo":{"status":"ok","timestamp":1672118572985,"user_tz":-540,"elapsed":1592,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e05ef7b3-811d-4c1e-a76b-32c2721be3ad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 2: [1196, 5378, 33493, 2628, 1200, 1580, 377, 1584, 1438, 788],\n"," 3: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 4: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 5: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n"," 6: [6152, 7236, 3702, 2528, 4460, 6678, 4166, 2117, 1527, 1682],\n"," 7: [25766, 910, 116, 5231, 2136, 5682, 2937, 464, 7080, 2494],\n"," 8: [3683, 4881, 1394, 5756, 33823, 2583, 76, 663, 1464, 45028],\n"," 9: [1513, 45221, 4452, 5802, 1285, 6807, 1602, 5439, 6816, 4102],\n"," 10: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n"," 11: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 12: [1210, 1196, 260, 5378, 33493, 1200, 1580, 377, 1584, 1438],\n"," 13: [260, 5378, 33493, 780, 2628, 1200, 377, 1584, 1438, 788],\n"," 14: [780, 2628, 1200, 1580, 377, 1584, 1438, 788, 8644, 736],\n"," 16: [5378, 33493, 780, 2628, 377, 1584, 1438, 788, 8644, 736],\n"," 17: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 18: [5378, 33493, 2628, 1200, 1584, 8644, 1690, 4812, 3699, 2311],\n"," 19: [104,\n"," 59900,\n"," 1777,\n"," 2014,\n"," 27830,\n"," 60487,\n"," 3979,\n"," 1241,\n"," 6593,\n"," 27873],\n"," 22: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 23: [1210, 1196, 5378, 33493, 2628, 1200, 1580, 1584, 1438, 788],\n"," 24: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 26: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 27: [1210, 1196, 260, 5378, 33493, 2628, 1200, 1580, 377, 1584],\n"," 28: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 29: [597, 34, 1059, 3619, 605, 1552, 5942, 6996, 5245, 457],\n"," 30: [135, 5146, 2810, 27731, 42217, 1537, 3484, 4850, 1717, 1343],\n"," 33: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 34: [5378, 33493, 1580, 8644, 4812, 2311, 6882, 3527, 2193, 2115],\n"," 35: [2848, 1411, 7069, 3723, 26, 3598, 2820, 497, 3719, 2622],\n"," 36: [262,\n"," 1130,\n"," 5335,\n"," 60530,\n"," 59290,\n"," 27513,\n"," 3012,\n"," 4514,\n"," 8025,\n"," 5833],\n"," 37: [2249, 3833, 32019, 1785, 18, 3744, 27734, 1729, 1635, 1213],\n"," 38: [5087,\n"," 54193,\n"," 6875,\n"," 8302,\n"," 8294,\n"," 2675,\n"," 6513,\n"," 25993,\n"," 50259,\n"," 5842],\n"," 40: [555, 2249, 7438, 3833, 6874, 32019, 1785, 18, 3744, 27734],\n"," 41: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 42: [1513, 45221, 4452, 5802, 1285, 6807, 1602, 5439, 6816, 4102],\n"," 43: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 44: [1513, 45221, 4452, 5802, 1285, 6807, 1602, 5439, 6816, 4102],\n"," 45: [48161, 993, 930, 2178, 2375, 904, 2186, 290, 43710, 2236],\n"," 46: [1210, 1196, 260, 5378, 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1584],\n"," 61: [1302, 5644, 1940, 3100, 1393, 7771, 1943, 1929, 1225, 1942],\n"," 62: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 63: [555, 1089, 296, 2249, 7438, 3833, 6874, 32019, 1785, 18],\n"," 64: [1210, 1196, 5378, 33493, 2628, 1200, 1580, 377, 1584, 1438],\n"," 65: [1030,\n"," 3759,\n"," 5109,\n"," 3775,\n"," 3964,\n"," 3611,\n"," 1881,\n"," 63237,\n"," 8713,\n"," 48159],\n"," 66: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n"," 67: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 68: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 69: [1302, 5644, 1961, 1940, 3100, 1393, 7771, 1943, 1929, 1225],\n"," 70: [48161, 993, 2178, 524, 2375, 2186, 290, 43710, 2236, 47644],\n"," 71: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 72: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 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3964, 3611, 1025, 1881, 1848],\n"," 1002: [7378, 6475, 4676, 4520, 4502, 6464, 4585, 542, 4619, 445],\n"," 1003: [1513,\n"," 45221,\n"," 4452,\n"," 5802,\n"," 1285,\n"," 6807,\n"," 1602,\n"," 5439,\n"," 6816,\n"," 4102],\n"," 1004: [5378,\n"," 33493,\n"," 1200,\n"," 1584,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699],\n"," 1005: [1196,\n"," 5378,\n"," 33493,\n"," 2628,\n"," 1200,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812],\n"," 1006: [8749,\n"," 6921,\n"," 3134,\n"," 7176,\n"," 6649,\n"," 8126,\n"," 4046,\n"," 7238,\n"," 26151,\n"," 6985],\n"," 1007: [1302,\n"," 5644,\n"," 1961,\n"," 1940,\n"," 3100,\n"," 1393,\n"," 7771,\n"," 1943,\n"," 1929,\n"," 1225],\n"," 1008: [5378,\n"," 33493,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699,\n"," 2311,\n"," 6882],\n"," 1009: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 1011: [5378,\n"," 33493,\n"," 2628,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699,\n"," 2311],\n"," 1012: [1046,\n"," 2550,\n"," 1789,\n"," 1103,\n"," 1340,\n"," 4722,\n"," 4942,\n"," 2665,\n"," 47836,\n"," 4518],\n"," 1013: [5378, 33493, 2628, 1200, 1580, 1584, 1438, 788, 8644, 736],\n"," 1014: [1348,\n"," 27850,\n"," 2783,\n"," 1694,\n"," 3872,\n"," 8194,\n"," 4113,\n"," 2454,\n"," 2064,\n"," 26306],\n"," 1015: [48161, 993, 930, 2178, 524, 2375, 2186, 290, 43710, 2236],\n"," 1016: [1266,\n"," 2401,\n"," 44168,\n"," 5959,\n"," 2921,\n"," 3508,\n"," 4945,\n"," 54787,\n"," 4785,\n"," 7889],\n"," 1017: [260, 780, 1200, 1580, 377, 1584, 1438, 788, 8644, 736],\n"," 1018: [1603,\n"," 2364,\n"," 45981,\n"," 26603,\n"," 5556,\n"," 5700,\n"," 57473,\n"," 5746,\n"," 1969,\n"," 5691],\n"," 1019: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 1020: [1046,\n"," 2550,\n"," 1789,\n"," 1340,\n"," 4722,\n"," 4942,\n"," 2665,\n"," 47836,\n"," 4518,\n"," 2650],\n"," 1021: [1030,\n"," 3759,\n"," 5109,\n"," 3775,\n"," 3964,\n"," 3611,\n"," 1025,\n"," 1881,\n"," 1848,\n"," 63237],\n"," 1022: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1023: [1210, 5378, 33493, 780, 2628, 1200, 1580, 377, 1584, 1438],\n"," 1025: [45221,\n"," 4452,\n"," 5802,\n"," 1285,\n"," 6807,\n"," 1602,\n"," 5439,\n"," 6816,\n"," 4102,\n"," 6212],\n"," 1026: [3061,\n"," 345,\n"," 1734,\n"," 7091,\n"," 7058,\n"," 34002,\n"," 8734,\n"," 34018,\n"," 4763,\n"," 2432],\n"," 1027: [1210, 1196, 260, 5378, 33493, 780, 2628, 1580, 377, 1584],\n"," 1028: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1029: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 1030: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 377, 1584],\n"," 1031: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1032: [8749,\n"," 6921,\n"," 3134,\n"," 7176,\n"," 6649,\n"," 8126,\n"," 4046,\n"," 7238,\n"," 26151,\n"," 6985],\n"," 1033: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 1034: [1210, 1196, 260, 5378, 780, 2628, 1200, 1580, 377, 1584],\n"," 1035: [1210, 1196, 260, 5378, 33493, 2628, 1200, 377, 1584, 1438],\n"," 1037: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1038: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1039: [4740,\n"," 7211,\n"," 5077,\n"," 7221,\n"," 838,\n"," 3454,\n"," 6501,\n"," 3501,\n"," 27509,\n"," 1075],\n"," 1040: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1041: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1043: [1210, 1196, 260, 5378, 33493, 2628, 1200, 1580, 377, 1584],\n"," 1044: [1196,\n"," 5378,\n"," 33493,\n"," 2628,\n"," 1200,\n"," 1580,\n"," 377,\n"," 1584,\n"," 1438,\n"," 8644],\n"," 1045: [262,\n"," 180,\n"," 1130,\n"," 5335,\n"," 60530,\n"," 59290,\n"," 27513,\n"," 3012,\n"," 4514,\n"," 8025],\n"," 1046: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1047: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1048: [555, 1089, 2249, 3833, 32019, 1785, 18, 3744, 27734, 1729],\n"," 1050: [104,\n"," 59900,\n"," 1777,\n"," 2014,\n"," 27830,\n"," 60487,\n"," 3979,\n"," 1241,\n"," 1022,\n"," 6593],\n"," 1051: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1052: [1302,\n"," 5644,\n"," 1961,\n"," 1940,\n"," 3100,\n"," 1393,\n"," 7771,\n"," 1943,\n"," 1929,\n"," 1225],\n"," 1053: [5378, 780, 2628, 377, 1438, 788, 736, 1690, 4812, 3699]})"]},"metadata":{},"execution_count":11}],"source":["from collections import defaultdict\n","from collections import Counter\n","\n","# 각 사용자에 대한 추천 리스트를 작성한다\n","# 사용자가 높게 평가한 영화가, 어떤 토픽에 많이 소속되어 있는지 카운트한다\n","# 가장 많은 토픽을 사용자가 좋아하는 토픽으로 간주하고, 해당 토픽의 영화를 추천한다\n","\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","movie_id2index = dict(zip(movie_content.movie_id.tolist(), range(len(movie_content))))\n","pred_user2items = defaultdict(list)\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," # 사용자가 높이 평가한 영화\n"," evaluated_movie_ids = user_evaluated_movies[user_id]\n"," # 최근 열람한 영화를 얻는다\n"," movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-10:]\n","\n"," movie_indexes = [movie_id2index[id] for id in movie_ids]\n"," \n"," # 최근 열람한 영화의 토픽을 얻고, 풀현 횟수를 카운트한다\n"," topic_counter = Counter([movie_topics[i] for i in movie_indexes])\n"," # 가장 출현 횟수가 많았던 토픽을 얻는다\n"," frequent_topic = topic_counter.most_common(1)[0][0]\n"," # 해당 토픽의 영화 중에서도 점수가 높은 것을 추천한다\n"," topic_movies = (\n"," movie_content[movie_content.topic == frequent_topic]\n"," .sort_values(\"topic_score\", ascending=False)\n"," .movie_id.tolist()\n"," )\n","\n"," for movie_id in topic_movies:\n"," if movie_id not in evaluated_movie_ids:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":12,"id":"8d238322","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"8d238322","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":10,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"68a428f8-984a-4fab-b1e3-8d24e4c5369a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":13,"id":"d6877799","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"d6877799","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6892e806-a4b2-4a5b-dfd9-da01db2f2d4a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","75 76 Screamers (1995) \n","1922 2006 Mask of Zorro, The (1998) \n","2031 2115 Indiana Jones and the Temple of Doom (1984) \n","\n"," genre \\\n","75 [Action, Sci-Fi, Thriller] \n","1922 [Action, Adventure, Romance] \n","2031 [Action, Adventure] \n","\n"," tag \n","75 [philip k. dick, artificial intelligence, post... \n","1922 [california, mexico, funny, banderas, anthony ... \n","2031 [lucas, want it, dvd collection, harrison ford... "],"text/html":["\n","
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movie_idtitlegenretag
7576Screamers (1995)[Action, Sci-Fi, Thriller][philip k. dick, artificial intelligence, post...
19222006Mask of Zorro, The (1998)[Action, Adventure, Romance][california, mexico, funny, banderas, anthony ...
20312115Indiana Jones and the Temple of Doom (1984)[Action, Adventure][lucas, want it, dvd collection, harrison ford...
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\n"," "]},"metadata":{},"execution_count":13}],"source":["# user_id=2에 대한 추천(2115, 76, 2006)\n","movies[movies.movie_id.isin([2115, 76, 2006])]"]},{"cell_type":"code","execution_count":14,"id":"129f2877","metadata":{"id":"129f2877","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 여기에서는 각 사용자의 평가 이력으로부터 1개의 토픽을 픽업했지만, 토픽의 확률값을 사용해서 가중치를 주면서 아이템을 추출할 수도 있습니다."]},{"cell_type":"code","execution_count":14,"id":"6a30f15c","metadata":{"id":"6a30f15c","executionInfo":{"status":"ok","timestamp":1672118572987,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/MF.ipynb b/chapter5/colab/MF.ipynb index fbbe1db..9c5ef59 100644 --- a/chapter5/colab/MF.ipynb +++ b/chapter5/colab/MF.ipynb @@ -1,1607 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "88a3be74", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/MF.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "47e2fec5", - "metadata": {}, - "source": [ - "# Matrix Factorization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "99e09acf", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab用のnotebookです。このnotebook1枚でデータのダウンロードから、レコメンドまで完結するようになっています。（予測評価は含めていません。）\n", - "# MovieLensデータがまだダウンロードされてなければこのセルを実行して、ダウンロードしてください\n", - "# MovieLensデータの分析は、data_download.ipynbをご参照ください\n", - "\n", - "# データのダウンロードと解凍\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "179ce864", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielensのデータの読み込み（データ量が多いため、読み込みに時間がかかる場合があります）\n", - "import pandas as pd\n", - "\n", - "# movieIDとタイトル名のみ使用\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genreをlist形式で保持する\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# ユーザが付与した映画のタグ情報の読み込み\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tagを小文字にする\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tagを映画ごとにlist形式で保持する\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# タグ情報を結合する\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 評価値データの読み込み\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# データ量が多いため、ユーザー数を1000に絞って、試していく\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 映画のデータと評価のデータを結合する\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 学習用とテスト用にデータを分割する\n", - "# 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする\n", - "# まずは、それぞれのユーザが評価した映画の順序を計算する\n", - "# 直近付与した映画から順番を付与していく(1始まり)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8c10d538", - "metadata": {}, - "outputs": [], - "source": [ - "# 因子数\n", - "factors = 5\n", - "# 評価数の閾値\n", - "minimum_num_rating = 100\n", - "# バイアス項の使用\n", - "use_biase = False\n", - "# 学習率\n", - "lr_all = 0.005\n", - "# エポック数\n", - "n_epochs = 50" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af591e1a", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install surprise" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "735ff486", - "metadata": {}, - "outputs": [], - "source": [ - "from surprise import SVD, Reader\n", - "from surprise import Dataset as SurpriseDataset\n", - "\n", - "# 評価数がminimum_num_rating件以上ある映画に絞る\n", - "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", - " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", - ")\n", - "\n", - "# Surprise用にデータを加工\n", - "reader = Reader(rating_scale=(0.5, 5))\n", - "data_train = SurpriseDataset.load_from_df(\n", - " filtered_movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n", - ").build_full_trainset()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cbc512a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Surpriseで行列分解を学習\n", - "# SVDという名前だが、特異値分解ではなく、Matrix Factorizationが実行される\n", - "matrix_factorization = SVD(n_factors=factors, n_epochs=n_epochs, lr_all=lr_all, biased=use_biase)\n", - "matrix_factorization.fit(data_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "48876224", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "def get_top_n(predictions, n=10):\n", - " # 各ユーザーごとに、予測されたアイテムを格納する\n", - " top_n = defaultdict(list)\n", - " for uid, iid, true_r, est, _ in predictions:\n", - " top_n[uid].append((iid, est))\n", - "\n", - " # ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する\n", - " for uid, user_ratings in top_n.items():\n", - " user_ratings.sort(key=lambda x: x[1], reverse=True)\n", - " top_n[uid] = [d[0] for d in user_ratings[:n]]\n", - "\n", - " return top_n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a2b23304", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {1: [110, 260, 590, 733, 780, 858, 1073, 1210, 1356, 1148],\n", - " 22: [2571, 318, 7153, 260, 5952, 4993, 2959, 1198, 1136, 1196],\n", - " 26: [318, 260, 2571, 1198, 7153, 5952, 1148, 527, 4993, 1036],\n", - " 30: [858, 912, 1584, 1234, 1704, 1233, 1250, 2324, 3147, 1148],\n", - " 34: [1036, 7153, 4993, 4306, 5952, 1291, 2115, 1234, 1101, 1527],\n", - " 38: [527, 1036, 1240, 589, 260, 1198, 780, 4306, 2028, 1148],\n", - " 50: [50, 2959, 1233, 2028, 3578, 2858, 2571, 1148, 1234, 912],\n", - " 53: [318, 260, 2571, 2959, 1233, 1234, 593, 4226, 296, 4973],\n", - 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" 732: [912, 858, 1207, 919, 904, 318, 1242, 1233, 1250, 1247],\n", - " 916: [296, 608, 2858, 1199, 1089, 541, 50, 1136, 1214, 6874],\n", - " 736: [260, 5952, 7153, 1136, 318, 2571, 1148, 50, 4993, 4226],\n", - " 961: [318, 593, 2959, 50, 296, 1233, 912, 4973, 4226, 858],\n", - " 974: [318, 260, 858, 50, 1213, 904, 1196, 1234, 4973, 750],\n", - " 443: [527, 50, 1233, 912, 4226, 1193, 858, 904, 1148, 4973],\n", - " 1012: [318, 527, 1233, 912, 2324, 50, 858, 1234, 4973, 1247],\n", - " 515: [318, 527, 1233, 858, 593, 2324, 2858, 1193, 4973, 904],\n", - " 288: [858, 1148, 527, 912, 1207, 1234, 1233, 318, 2324, 1247],\n", - " 978: [1035, 318, 2324, 3578, 527, 1242, 356, 2000, 1704, 780],\n", - " 28: [318, 260, 2571, 527, 50, 7153, 1198, 4993, 3578, 1234],\n", - " 458: [858, 318, 912, 1233, 904, 4973, 1247, 1207, 1213, 1234],\n", - " 475: [318, 50, 527, 296, 593, 2858, 2571, 1233, 2028, 3578],\n", - " 592: [318, 50, 260, 2571, 527, 2959, 1233, 4226, 296, 2858],\n", - " 598: [318, 260, 50, 1148, 1233, 527, 2571, 1234, 7153, 1136],\n", - " 943: [912, 318, 2858, 608, 1233, 4226, 527, 593, 50, 1148],\n", - " 55: [858, 923, 2019, 912, 904, 111, 1213, 1219, 750, 1252],\n", - " 520: [50, 1233, 912, 527, 904, 1148, 4226, 858, 260, 1193],\n", - " 697: [318, 2959, 3578, 527, 2571, 50, 110, 1240, 356, 2028],\n", - " 849: [260, 1148, 527, 1198, 50, 904, 1234, 1233, 318, 2324],\n", - " 1003: [260, 858, 1148, 5952, 7153, 527, 1196, 1198, 50, 904],\n", - " 705: [2959, 296, 318, 50, 593, 608, 527, 1233, 4226, 4973],\n", - " 231: [260, 1196, 1213, 318, 858, 750, 1221, 5952, 7153, 904],\n", - " 699: [260, 858, 527, 904, 4226, 1233, 1234, 4973, 1193, 1148],\n", - " 448: [318, 912, 1233, 858, 50, 608, 4226, 1193, 296, 4973],\n", - " 750: [318, 858, 912, 1233, 904, 4973, 1193, 50, 4226, 1213],\n", - " 782: [318, 50, 260, 2959, 2571, 296, 527, 7153, 1196, 1233],\n", - " 108: [912, 858, 318, 2324, 1247, 1233, 1207, 593, 4973, 1193],\n", - " 29: [318, 527, 3578, 1036, 356, 1198, 457, 1240, 1148, 150]})" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 学習データに出てこないユーザーとアイテムの組み合わせを準備\n", - "data_test = data_train.build_anti_testset(None)\n", - "predictions = matrix_factorization.test(data_test)\n", - "pred_user2items = get_top_n(predictions, n=10)\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "afa174f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2のユーザーが学習データで評価を付けた映画一覧\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "06bb1a90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
34893578Gladiator (2000)[Action, Adventure, Drama][revenge, crowe's best, gfei own it, dvd, glad...
\n", - "
" - ], - "text/plain": [ - " movie_id title genre \\\n", - "2487 2571 Matrix, The (1999) [Action, Sci-Fi, Thriller] \n", - "2874 2959 Fight Club (1999) [Action, Crime, Drama, Thriller] \n", - "3489 3578 Gladiator (2000) [Action, Adventure, Drama] \n", - "\n", - " tag \n", - "2487 [artificial intelligence, hackers, heroine in ... \n", - "2874 [based on a book, chuck palahniuk, edward nort... \n", - "3489 [revenge, crowe's best, gfei own it, dvd, glad... " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2に対するおすすめ(3578, 2571, 2959)\n", - "movies[movies.movie_id.isin([3578, 2571, 2959])]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"88a3be74","metadata":{"id":"88a3be74"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/MF.ipynb)"]},{"cell_type":"markdown","id":"47e2fec5","metadata":{"id":"47e2fec5"},"source":["# Matrix Factorization"]},{"cell_type":"code","execution_count":1,"id":"99e09acf","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"99e09acf","executionInfo":{"status":"ok","timestamp":1672119437385,"user_tz":-540,"elapsed":3804,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"294107d4-f398-41b9-a605-09d7c15bb44f"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:35:29-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.1MB/s in 1.0s \n","\n","2022-12-27 05:35:30 (63.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"179ce864","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"179ce864","executionInfo":{"status":"ok","timestamp":1672119502987,"user_tz":-540,"elapsed":65608,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"4e5a1760-788a-4246-e64d-8cda917e1a11"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"8c10d538","metadata":{"id":"8c10d538","executionInfo":{"status":"ok","timestamp":1672119502988,"user_tz":-540,"elapsed":28,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 5\n","# 평가수 임곗값\n","minimum_num_rating = 100\n","# 바이어스 항 사용\n","use_biase = False\n","# 학습률\n","lr_all = 0.005\n","# 에폭 수\n","n_epochs = 50"]},{"cell_type":"code","execution_count":4,"id":"af591e1a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"af591e1a","executionInfo":{"status":"ok","timestamp":1672119547233,"user_tz":-540,"elapsed":44271,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f5f3025d-2ead-479f-c315-94433d318640"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Collecting scikit-surprise\n"," Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n","\u001b[K |████████████████████████████████| 771 kB 4.8 MB/s \n","\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626477 sha256=a922027e3c7f6d8735b7192824bc8d1a46af450d003dde385ce514ec0ae33d2d\n"," Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"]}],"source":["!pip install surprise"]},{"cell_type":"code","execution_count":5,"id":"735ff486","metadata":{"id":"735ff486","executionInfo":{"status":"ok","timestamp":1672119548405,"user_tz":-540,"elapsed":1188,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from surprise import SVD, Reader\n","from surprise import Dataset as SurpriseDataset\n","\n","# 평가 수가 minimum_num_rating 건 이상 있는 영화로 줄인다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","# Surprise용으로 데이터를 가공한다\n","reader = Reader(rating_scale=(0.5, 5))\n","data_train = SurpriseDataset.load_from_df(\n"," filtered_movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n",").build_full_trainset()"]},{"cell_type":"code","execution_count":6,"id":"cbc512a3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cbc512a3","executionInfo":{"status":"ok","timestamp":1672119549729,"user_tz":-540,"elapsed":1327,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b776c506-902c-4525-a5de-88ecc825e60b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6}],"source":["# Surprise로 행렬 분해를 학습한다\n","# 이름은 SVD지만, 특이값 분석이 아니라 Matrix Factorization가 실행된다\n","matrix_factorization = SVD(n_factors=factors, n_epochs=n_epochs, lr_all=lr_all, biased=use_biase)\n","matrix_factorization.fit(data_train)"]},{"cell_type":"code","execution_count":7,"id":"48876224","metadata":{"id":"48876224","executionInfo":{"status":"ok","timestamp":1672119549730,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from collections import defaultdict\n","\n","def get_top_n(predictions, n=10):\n"," # 각 사용자별로 예측된 아이템을 저장한다\n"," top_n = defaultdict(list)\n"," for uid, iid, true_r, est, _ in predictions:\n"," top_n[uid].append((iid, est))\n","\n"," # 사용자별로 아이템을 예측 평갓값 순으로 나열해 상위 n개를 저장한다\n"," for uid, user_ratings in top_n.items():\n"," user_ratings.sort(key=lambda x: x[1], reverse=True)\n"," top_n[uid] = [d[0] for d in user_ratings[:n]]\n","\n"," return top_n"]},{"cell_type":"code","execution_count":8,"id":"a2b23304","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a2b23304","executionInfo":{"status":"ok","timestamp":1672119551833,"user_tz":-540,"elapsed":2114,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"2318faf1-5003-42d9-f6aa-bc551e8ab571"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [110, 260, 590, 733, 802, 858, 1073, 1210, 1148, 1246],\n"," 22: [318, 260, 608, 1196, 912, 1234, 1213, 858, 904, 2959],\n"," 26: [260, 1210, 318, 527, 1196, 50, 593, 1234, 3578, 1704],\n"," 30: [1148, 912, 457, 3578, 1234, 2762, 1704, 3147, 1233, 2324],\n"," 34: [2324, 1148, 1234, 1207, 4306, 1233, 904, 497, 1247, 17],\n"," 38: [1380, 50, 920, 1036, 1234, 1210, 2324, 2081, 165, 589],\n"," 50: [260, 1207, 904, 2858, 1148, 4993, 1704, 5952, 912, 1233],\n"," 53: [318, 1233, 858, 912, 1193, 904, 2019, 4973, 4226, 1234],\n"," 54: [1210, 1380, 3578, 260, 1196, 1704, 527, 2324, 1234, 1198],\n"," 67: [2959, 1234, 1089, 858, 1213, 1036, 912, 4226, 1221, 1201],\n"," 71: [318, 260, 50, 593, 912, 1234, 527, 1196, 858, 904],\n"," 76: [1721, 597, 587, 3578, 356, 539, 780, 500, 527, 786],\n"," 88: [318, 1036, 50, 589, 1234, 3147, 2000, 457, 593, 733],\n"," 89: [593, 1234, 2959, 858, 912, 1213, 47, 4226, 1036, 1221],\n"," 94: [260, 1704, 1148, 4993, 1207, 2028, 17, 904, 2324, 2762],\n"," 95: [50, 260, 1196, 1210, 47, 1234, 1213, 2959, 4226, 1198],\n"," 100: [1199, 1206, 2019, 541, 1221, 750, 924, 1208, 923, 1219],\n"," 101: [3147, 3578, 1234, 1036, 919, 1242, 2000, 733, 1035, 2324],\n"," 102: [50, 260, 1196, 593, 1210, 1213, 2959, 1234, 4226, 1221],\n"," 103: [318, 1148, 1193, 858, 2858, 904, 912, 2571, 4226, 7153],\n"," 111: [50, 260, 1210, 1036, 47, 3578, 1196, 1234, 2571, 1198],\n"," 116: [1721, 1380, 920, 3578, 919, 527, 1704, 457, 2324, 832],\n"," 118: [260, 1196, 1210, 1234, 904, 5952, 4993, 912, 1704, 7153],\n"," 139: [50, 2542, 2692, 4011, 1200, 4226, 6874, 858, 1193, 1221],\n"," 143: [912, 1233, 1207, 2019, 908, 750, 1247, 1213, 1250, 1234],\n"," 144: [111, 1193, 908, 904, 2502, 1234, 1288, 923, 1225, 1247],\n"," 162: [318, 50, 593, 1234, 608, 912, 1213, 904, 4226, 2324],\n"," 172: [318, 260, 2571, 50, 1148, 527, 3578, 1234, 1198, 4993],\n"," 173: [3578, 1704, 2324, 1148, 2762, 2571, 4306, 2028, 3147, 4993],\n"," 187: [780, 3147, 587, 2396, 62, 11, 736, 1242, 733, 1641],\n"," 193: [3578, 1035, 919, 1234, 2324, 1704, 1036, 3147, 1242, 912],\n"," 208: [733, 780, 597, 919, 457, 3578, 1234, 1036, 2000, 3147],\n"," 210: [912, 50, 1234, 527, 858, 2324, 904, 919, 1193, 1207],\n"," 214: [318, 1207, 1233, 904, 2858, 1247, 2028, 912, 1288, 5952],\n"," 228: [3147, 733, 736, 2000, 802, 1610, 11, 786, 2706, 1242],\n"," 232: [2571, 7153, 5952, 4993, 2858, 1288, 1148, 1136, 4226, 1221],\n"," 236: [2959, 858, 1193, 1234, 1233, 1036, 4226, 4011, 1213, 912],\n"," 259: [318, 1036, 1240, 589, 1234, 380, 3578, 4011, 110, 1213],\n"," 260: [50, 318, 2959, 1234, 2571, 4226, 3578, 1213, 4011, 110],\n"," 267: [1148, 1213, 904, 36, 908, 4011, 912, 1197, 1304, 1250],\n"," 268: [1721, 1035, 919, 920, 1380, 1641, 3147, 2671, 1242, 3578],\n"," 271: [527, 3578, 1704, 356, 150, 318, 17, 2324, 590, 2762],\n"," 274: [527, 593, 608, 912, 50, 318, 904, 296, 1213, 4226],\n"," 281: [1234, 858, 912, 1233, 1193, 2959, 919, 904, 4973, 4226],\n"," 287: [318, 260, 2571, 50, 1148, 3578, 527, 1198, 4993, 1234],\n"," 292: [1219, 2019, 904, 908, 1199, 923, 1201, 1233, 1208, 778],\n"," 296: [260, 1234, 1196, 1148, 1198, 912, 1136, 1210, 608, 908],\n"," 303: [111, 1199, 2019, 923, 1208, 1148, 904, 1193, 1206, 908],\n"," 307: [4993, 1288, 7153, 1148, 5952, 2858, 318, 1136, 293, 4226],\n"," 310: [3578, 1210, 904, 150, 4306, 1148, 1233, 6539, 1242, 4226],\n"," 315: [1721, 494, 653, 1917, 2628, 1584, 3, 786, 832, 802],\n"," 320: [50, 1221, 1213, 318, 1201, 858, 608, 1234, 1198, 2019],\n"," 326: [260, 858, 1148, 527, 912, 1193, 904, 2858, 1233, 318],\n"," 332: [527, 912, 1207, 904, 919, 1233, 4973, 1394, 1035, 1247],\n"," 339: [260, 527, 50, 1234, 3578, 2571, 2324, 1210, 1196, 1704],\n"," 343: [318, 3578, 3147, 733, 1242, 1234, 2000, 1036, 527, 597],\n"," 355: [260, 1196, 1234, 4226, 1213, 527, 912, 2571, 858, 904],\n"," 364: [858, 912, 1234, 1233, 2959, 1193, 1213, 904, 4226, 4973],\n"," 365: [858, 912, 904, 1233, 318, 4973, 1394, 1207, 608, 919],\n"," 368: [1221, 1213, 1193, 318, 1233, 2858, 111, 1208, 904, 1222],\n"," 381: [260, 1148, 912, 1207, 1247, 1193, 2028, 2396, 3578, 904],\n"," 382: [527, 318, 1207, 1233, 1148, 904, 912, 1247, 1704, 2028],\n"," 383: [1219, 923, 1199, 1288, 904, 1094, 1394, 1136, 2019, 1080],\n"," 391: [1234, 50, 3578, 527, 1233, 1148, 3147, 2324, 1036, 912],\n"," 396: [858, 593, 608, 912, 1221, 1193, 50, 904, 296, 1213],\n"," 398: [1704, 318, 1210, 17, 2324, 3578, 1207, 6539, 919, 4993],\n"," 406: [318, 527, 1207, 912, 1233, 904, 1234, 919, 2324, 1148],\n"," 409: [780, 1917, 3578, 380, 2628, 733, 786, 2001, 2000, 3147],\n"," 410: [3147, 1148, 3578, 2762, 1233, 2000, 2396, 2028, 1230, 457],\n"," 416: [318, 912, 527, 919, 1207, 904, 1035, 1233, 1234, 4973],\n"," 418: [260, 1148, 1288, 5952, 7153, 527, 541, 923, 1199, 1196],\n"," 419: [2959, 1206, 6874, 2502, 7153, 223, 2019, 293, 1080, 1196],\n"," 421: [527, 1035, 1704, 1028, 17, 919, 364, 1721, 1207, 2324],\n"," 424: [1207, 1219, 1233, 4973, 1394, 111, 5952, 2019, 858, 1288],\n"," 426: [2502, 1374, 1242, 17, 1278, 2791, 1356, 112, 1376, 1073],\n"," 432: [912, 608, 904, 2858, 4973, 923, 1233, 858, 260, 1219],\n"," 434: [1288, 1148, 318, 17, 904, 34, 1207, 923, 509, 1358],\n"," 436: [260, 2571, 1234, 4226, 1213, 858, 1198, 1148, 1196, 912],\n"," 438: [4993, 7153, 50, 5952, 1288, 4226, 47, 1213, 6874, 527],\n"," 441: [260, 318, 527, 3578, 1210, 1704, 2571, 356, 1148, 4993],\n"," 446: [50, 260, 858, 4226, 2571, 1213, 1193, 1233, 1148, 1221],\n"," 452: [1036, 2959, 2000, 380, 1234, 589, 733, 919, 593, 1035],\n"," 456: [541, 1221, 2959, 2571, 1136, 4226, 858, 750, 2019, 1213],\n"," 459: [50, 2502, 1201, 1221, 223, 1206, 1213, 2692, 6, 1198],\n"," 460: [527, 50, 1234, 318, 3578, 260, 2324, 356, 912, 1036],\n"," 463: [1234, 1213, 4011, 912, 1148, 1304, 380, 919, 1247, 7153],\n"," 468: [260, 1196, 4993, 5952, 7153, 1210, 2571, 1288, 2858, 318],\n"," 469: [733, 858, 1252, 1288, 1207, 1247, 1193, 50, 904, 1617],\n"," 472: [1721, 919, 527, 1207, 2324, 3578, 500, 2396, 1246, 920],\n"," 476: [1193, 858, 904, 912, 1288, 1247, 1213, 111, 908, 1207],\n"," 480: [111, 1089, 1213, 923, 908, 778, 1208, 1233, 1201, 1193],\n"," 482: [1233, 1222, 912, 1230, 2019, 4973, 1252, 750, 2692, 904],\n"," 493: [912, 904, 1234, 593, 1196, 1207, 1233, 919, 1148, 1247],\n"," 494: [2959, 2502, 2542, 1206, 858, 1200, 1221, 2997, 2692, 1201],\n"," 496: [260, 1196, 318, 50, 527, 1234, 1198, 1704, 110, 2324],\n"," 501: [527, 1207, 318, 904, 1233, 2324, 1247, 4973, 2858, 1035],\n"," 504: [1233, 1148, 904, 1193, 2858, 912, 1247, 1207, 858, 1288],\n"," 505: [527, 1704, 3578, 2324, 1207, 1234, 919, 1148, 904, 912],\n"," 511: [527, 318, 1704, 356, 260, 3578, 2324, 919, 1035, 17],\n"," 516: [1233, 1193, 912, 904, 1148, 1247, 1252, 1207, 50, 923],\n"," 525: [260, 1210, 1196, 527, 50, 356, 1704, 3578, 2324, 1234],\n"," 530: [912, 919, 1035, 1207, 593, 1234, 904, 4973, 527, 1394],\n"," 531: [527, 1207, 919, 1035, 2324, 912, 1704, 2396, 1233, 904],\n"," 533: [1213, 111, 2329, 4011, 58, 778, 1090, 2194, 4995, 150],\n"," 536: [527, 318, 1704, 2324, 1207, 3578, 919, 356, 1035, 590],\n"," 543: [2028, 1234, 1233, 1288, 36, 1242, 497, 4011, 1250, 2329],\n"," 547: [260, 1210, 1196, 1704, 318, 4993, 5952, 17, 2571, 7153],\n"," 549: [912, 919, 1035, 1207, 527, 1233, 904, 1234, 4973, 364],\n"," 553: [858, 541, 778, 1206, 1221, 1193, 1732, 4226, 1089, 1200],\n"," 556: [260, 1196, 1210, 5952, 4993, 7153, 2571, 608, 1198, 1704],\n"," 558: [1233, 4973, 904, 919, 1193, 1250, 2019, 908, 1225, 750],\n"," 562: [1035, 1721, 919, 1380, 920, 2000, 1101, 3147, 2706, 786],\n"," 567: [780, 786, 733, 2000, 1917, 3147, 1721, 2001, 802, 3578],\n"," 571: [296, 608, 2019, 912, 1213, 750, 1233, 1193, 2959, 111],\n"," 572: [318, 1148, 36, 3578, 1036, 1233, 1704, 2019, 1225, 1304],\n"," 577: [356, 1036, 2959, 919, 47, 3578, 2324, 1213, 1380, 110],\n"," 581: [912, 1233, 904, 1207, 4973, 858, 2858, 1247, 923, 1193],\n"," 585: [1221, 4973, 1233, 1208, 4226, 2502, 3897, 2542, 1250, 7153],\n"," 593: [1210, 260, 1196, 1036, 2571, 1240, 1198, 3578, 1234, 2959],\n"," 601: [1035, 2324, 1704, 3578, 1234, 590, 1207, 6539, 50, 4306],\n"," 604: [2959, 2502, 1221, 1089, 2542, 1200, 1201, 1206, 1213, 541],\n"," 605: [593, 1213, 912, 1221, 858, 1234, 4226, 904, 2019, 2959],\n"," 609: [1234, 912, 260, 2324, 904, 919, 1233, 1207, 858, 3578],\n"," 628: [1234, 527, 50, 3578, 593, 2324, 260, 912, 1148, 1233],\n"," 629: [260, 4993, 318, 2571, 7153, 1148, 5952, 1288, 527, 2858],\n"," 634: [527, 3578, 50, 1234, 296, 318, 2324, 1704, 1213, 912],\n"," 645: [3147, 1193, 1233, 858, 2959, 1036, 2692, 733, 4011, 1242],\n"," 650: [111, 912, 923, 1221, 1136, 2959, 296, 1213, 1233, 750],\n"," 656: [527, 3578, 260, 1704, 2324, 1148, 1234, 2762, 2571, 2028],\n"," 657: [1394, 1199, 2019, 1233, 750, 1136, 1080, 1288, 1222, 1148],\n"," 669: [527, 3578, 919, 1035, 2324, 1234, 1704, 1207, 912, 260],\n"," 678: [3578, 2571, 47, 2324, 1704, 1148, 4011, 2000, 4226, 36],\n"," 683: [3147, 318, 50, 1240, 457, 4011, 2959, 1234, 2706, 1610],\n"," 688: [1288, 1221, 1208, 223, 293, 1148, 3996, 1079, 318, 1197],\n"," 689: [527, 912, 1207, 904, 919, 1234, 1233, 4973, 1035, 260],\n"," 693: [1035, 919, 912, 1207, 1233, 1234, 904, 4973, 2324, 1242],\n"," 701: [858, 912, 2959, 1233, 4973, 318, 593, 1193, 1234, 919],\n"," 716: [5952, 111, 1288, 7153, 4993, 923, 1136, 1208, 1219, 1080],\n"," 719: [3147, 1233, 1641, 2396, 919, 2706, 1242, 1035, 1230, 912],\n"," 720: [1233, 3147, 858, 1193, 912, 1242, 1234, 1230, 2959, 1222],\n"," 734: [527, 318, 3578, 2324, 1704, 150, 912, 904, 1148, 2762],\n"," 735: [318, 260, 1196, 1234, 527, 1210, 912, 1198, 1213, 2324],\n"," 739: [296, 2502, 858, 1036, 1234, 318, 1213, 1201, 1220, 4011],\n"," 745: [1234, 912, 919, 2324, 356, 4973, 904, 1201, 1304, 2019],\n"," 755: [858, 1233, 912, 2019, 1193, 750, 4973, 904, 1221, 2858],\n"," 758: [527, 1704, 17, 356, 260, 3578, 318, 1210, 2324, 1028],\n"," 767: [1193, 50, 1233, 1234, 1148, 4226, 2959, 912, 4011, 1213],\n"," 769: [260, 1196, 4993, 1210, 5952, 7153, 2571, 1288, 1148, 2858],\n"," 775: [260, 1210, 1148, 1288, 5952, 6539, 7153, 527, 1207, 1196],\n"," 785: [858, 2959, 1234, 912, 1213, 1193, 4226, 1233, 904, 1221],\n"," 786: [589, 293, 2692, 1148, 380, 4993, 7153, 1288, 296, 36],\n"," 789: [260, 1210, 1704, 3578, 17, 1196, 2324, 4993, 6539, 2571],\n"," 792: [527, 260, 1704, 4993, 1148, 3578, 17, 2571, 2028, 1210],\n"," 795: [260, 2571, 1196, 1221, 4226, 1213, 2959, 7153, 858, 1136],\n"," 798: [36, 4011, 1233, 2329, 223, 163, 1358, 1997, 4027, 16],\n"," 799: [919, 1035, 1234, 2324, 912, 3578, 260, 1207, 1704, 50],\n"," 802: [2571, 3578, 1036, 2000, 1148, 1240, 3147, 1917, 2001, 1291],\n"," 806: [1207, 912, 1233, 904, 2396, 1247, 923, 919, 4973, 1394],\n"," 807: [260, 858, 527, 593, 912, 50, 1234, 1213, 4226, 2324],\n"," 816: [527, 1721, 2396, 62, 1207, 1584, 539, 919, 597, 500],\n"," 827: [260, 1148, 527, 904, 4993, 318, 2858, 2571, 7153, 1196],\n"," 828: [356, 539, 920, 1035, 1380, 1721, 786, 919, 1101, 3578],\n"," 846: [2959, 858, 1193, 2542, 2502, 1233, 1036, 1234, 4011, 3147],\n"," 859: [50, 1221, 47, 1213, 608, 318, 1201, 750, 1234, 2502],\n"," 862: [1035, 1721, 919, 1028, 1207, 62, 590, 2324, 2396, 920],\n"," 870: [50, 260, 1234, 2571, 3578, 1148, 1198, 2324, 4226, 1036],\n"," 876: [1035, 919, 912, 1207, 1234, 364, 904, 2324, 1233, 4973],\n"," 881: [1035, 1380, 919, 920, 2324, 1234, 1210, 3578, 1704, 260],\n"," 885: [912, 4973, 1234, 1233, 2858, 4226, 1394, 923, 750, 2324],\n"," 886: [7153, 1148, 4993, 5952, 4226, 4011, 111, 6874, 1291, 1213],\n"," 889: [1148, 318, 1233, 527, 4993, 1288, 2762, 2028, 1247, 2858],\n"," 890: [380, 165, 318, 3147, 1148, 2001, 36, 1610, 1270, 1198],\n"," 891: [1288, 111, 1136, 1221, 1148, 750, 2019, 1213, 1199, 1208],\n"," 896: [912, 1219, 1394, 4973, 904, 858, 2019, 111, 2858, 923],\n"," 897: [1233, 912, 318, 858, 1193, 904, 4973, 1207, 1247, 1234],\n"," 898: [527, 318, 1148, 1704, 3578, 4993, 2762, 2028, 2324, 1207],\n"," 908: [1148, 593, 912, 1207, 1225, 1247, 1193, 3578, 50, 904],\n"," 922: [1242, 733, 2000, 919, 457, 1234, 1233, 1035, 1036, 2324],\n"," 930: [919, 1207, 1233, 1035, 912, 3147, 2396, 1242, 3578, 2324],\n"," 938: [3578, 50, 1234, 1036, 356, 527, 2324, 3147, 919, 1198],\n"," 956: [1196, 1210, 2959, 260, 6874, 1240, 1089, 223, 1201, 2571],\n"," 958: [1394, 912, 1035, 919, 4973, 608, 364, 1207, 1073, 904],\n"," 968: [527, 912, 1207, 904, 919, 1233, 1234, 2324, 1247, 1035],\n"," 977: [5952, 7153, 4993, 2997, 2019, 1219, 1233, 4034, 3996, 3897],\n"," 987: [1721, 1035, 919, 597, 539, 2396, 1641, 3147, 500, 1242],\n"," 990: [608, 912, 1394, 1035, 858, 2959, 4973, 919, 1089, 1234],\n"," 996: [1233, 912, 1207, 904, 1193, 1247, 2396, 858, 4973, 919],\n"," 1004: [527, 3578, 1234, 2324, 1148, 912, 1036, 1704, 919, 1233],\n"," 1005: [1148,\n"," 4993,\n"," 1704,\n"," 2028,\n"," 3578,\n"," 2762,\n"," 2571,\n"," 1288,\n"," 1207,\n"," 7153],\n"," 1008: [1234, 1213, 1233, 904, 2019, 1089, 750, 4011, 908, 2324],\n"," 1029: [2959, 1036, 589, 1240, 2502, 2542, 1234, 4011, 1213, 1089],\n"," 1037: [858, 1233, 912, 1193, 318, 904, 4973, 2019, 750, 4226],\n"," 1050: [260, 2571, 7153, 5952, 4993, 1288, 541, 2858, 1136, 1196],\n"," 4: [356, 260, 1210, 527, 1196, 3578, 1704, 17, 318, 2324],\n"," 8: [296, 2502, 2692, 1221, 318, 1201, 1213, 750, 1193, 1288],\n"," 18: [50, 4226, 1221, 1200, 293, 2692, 1213, 6874, 858, 1148],\n"," 36: [318, 1234, 912, 47, 1193, 2542, 4973, 1233, 1035, 1201],\n"," 47: [380, 1234, 356, 47, 2000, 457, 3147, 2324, 6, 1304],\n"," 59: [527, 260, 1704, 1207, 1148, 2324, 904, 912, 1234, 3578],\n"," 62: [356, 1035, 3578, 919, 1234, 2324, 457, 50, 1036, 527],\n"," 77: [318, 3578, 50, 1234, 260, 2324, 1210, 1704, 1036, 919],\n"," 79: [858, 912, 2959, 1221, 1213, 2019, 4973, 750, 4226, 1089],\n"," 80: [1233, 858, 1193, 912, 1234, 904, 1148, 4226, 4973, 1247],\n"," 119: [912, 50, 1234, 919, 904, 4973, 1233, 527, 608, 1207],\n"," 122: [541, 912, 923, 1199, 1219, 908, 1233, 750, 924, 2019],\n"," 125: [1201, 1288, 778, 1617, 908, 1304, 2329, 1090, 1234, 1252],\n"," 126: [4973, 908, 1035, 2019, 1233, 17, 750, 58, 551, 1199],\n"," 132: [912, 1234, 919, 1233, 858, 50, 1035, 1193, 1207, 904],\n"," 141: [3578, 318, 1704, 150, 2324, 919, 1035, 1234, 260, 1721],\n"," 154: [318, 4226, 858, 1234, 1221, 4011, 1233, 1136, 7153, 2019],\n"," 155: [1233, 912, 2019, 904, 4973, 1234, 4226, 750, 1221, 1222],\n"," 163: [2571, 2959, 1200, 541, 1206, 6874, 1240, 1221, 293, 7153],\n"," 164: [1233, 858, 912, 1193, 4973, 904, 1234, 1222, 1207, 919],\n"," 170: [1221, 6874, 1200, 1201, 1213, 2502, 541, 1089, 260, 589],\n"," 171: [912, 919, 1035, 364, 1207, 1234, 1233, 1242, 2324, 593],\n"," 176: [318, 527, 1207, 260, 904, 912, 1233, 1148, 2324, 1704],\n"," 182: [260, 1148, 904, 1233, 1584, 1035, 1288, 1198, 1252, 1210],\n"," 190: [527, 1288, 17, 1704, 2858, 1148, 34, 509, 318, 1358],\n"," 197: [2959, 296, 912, 2502, 608, 4973, 2019, 1222, 593, 1213],\n"," 198: [1233, 2019, 4973, 904, 908, 4011, 1201, 2542, 1198, 1247],\n"," 199: [527, 1035, 919, 318, 1207, 1704, 2324, 364, 356, 1028],\n"," 212: [2858, 2502, 1222, 1233, 2542, 3897, 1230, 1079, 1247, 4011],\n"," 221: [2959, 1240, 1234, 3578, 4011, 2542, 3147, 6, 2502, 4226],\n"," 223: [356, 364, 110, 260, 858, 1210, 1148, 1246, 1252, 1288],\n"," 226: [912, 1193, 2959, 1233, 2019, 4973, 750, 1252, 1230, 1247],\n"," 241: [2959, 858, 296, 50, 1193, 912, 2502, 1233, 318, 2542],\n"," 247: [1035, 919, 318, 3578, 1234, 2324, 1721, 1380, 527, 3147],\n"," 249: [858, 1234, 1233, 912, 1193, 904, 260, 1148, 2019, 1221],\n"," 254: [7153, 4226, 2019, 1213, 4993, 1148, 608, 1198, 1201, 904],\n"," 264: [318, 912, 260, 904, 858, 608, 527, 1234, 1233, 4973],\n"," 273: [1234, 1233, 912, 1193, 858, 904, 1148, 1207, 1242, 919],\n"," 275: [1219, 1394, 912, 4973, 858, 923, 2019, 1221, 908, 1213],\n"," 276: [1221, 2019, 1148, 1233, 908, 904, 1201, 1230, 912, 1266],\n"," 293: [527, 356, 3578, 2324, 1234, 919, 1035, 1704, 260, 50],\n"," 294: [2959, 593, 1213, 858, 1234, 1036, 1089, 1221, 4226, 1240],\n"," 295: [858, 912, 904, 1233, 318, 4973, 1193, 1250, 593, 923],\n"," 318: [1288, 1094, 1233, 1207, 924, 1199, 1358, 1219, 908, 1252],\n"," 321: [318, 527, 919, 912, 1207, 1035, 1234, 2324, 904, 1233],\n"," 345: [318, 50, 858, 1233, 4226, 1213, 1221, 912, 2019, 1148],\n"," 346: [3147, 733, 2000, 1242, 1233, 318, 1036, 1193, 858, 589],\n"," 348: [1234, 260, 1233, 912, 904, 1136, 1247, 908, 1225, 2324],\n"," 349: [260, 1704, 1210, 1196, 4993, 1148, 2324, 5952, 3578, 7153],\n"," 354: [527, 1207, 919, 2324, 260, 1234, 904, 1233, 1035, 1148],\n"," 359: [608, 111, 1394, 1199, 2019, 912, 1221, 858, 904, 1208],\n"," 366: [1233, 318, 1193, 1148, 912, 858, 904, 1247, 1230, 1207],\n"," 369: [2571, 50, 1240, 260, 47, 1210, 1036, 589, 6874, 1196],\n"," 384: [3578, 527, 1704, 260, 380, 2324, 1210, 1148, 2762, 2571],\n"," 390: [1221, 912, 2019, 750, 1233, 1089, 908, 541, 111, 1222],\n"," 392: [858, 912, 1233, 2019, 2858, 904, 608, 1193, 4973, 750],\n"," 403: [2959, 858, 912, 2019, 608, 4973, 1221, 1213, 1193, 1089],\n"," 407: [318, 36, 293, 1288, 4011, 4226, 1193, 3147, 527, 2858],\n"," 414: [858, 1193, 2502, 1222, 2019, 1233, 750, 1221, 2542, 912],\n"," 431: [858, 912, 1234, 1233, 904, 1193, 593, 527, 1207, 4226],\n"," 454: [260, 527, 1210, 318, 1196, 1704, 3578, 4993, 2324, 356],\n"," 465: [912, 2019, 750, 1394, 1233, 904, 541, 1199, 1201, 1136],\n"," 481: [1193, 1233, 1234, 1148, 750, 1222, 2692, 1247, 4973, 3147],\n"," 485: [2019, 750, 912, 4973, 2502, 1233, 1206, 2542, 1394, 1201],\n"," 503: [260, 50, 1196, 608, 1210, 1234, 912, 47, 904, 1089],\n"," 513: [7153, 5952, 4993, 4226, 4973, 3897, 1358, 1250, 235, 1213],\n"," 517: [260, 1210, 1196, 593, 527, 1234, 2571, 1198, 1213, 5952],\n"," 545: [1199, 1230, 912, 908, 1234, 1148, 1997, 1250, 36, 3147],\n"," 552: [260, 47, 2571, 1221, 593, 1196, 1240, 1198, 1036, 858],\n"," 554: [260, 1196, 1288, 1199, 1210, 1136, 541, 923, 1080, 1221],\n"," 555: [1036, 1221, 36, 1304, 2692, 541, 2324, 1200, 904, 6],\n"," 561: [50, 293, 318, 1198, 1148, 1288, 223, 36, 1136, 1221],\n"," 565: [318, 527, 1234, 912, 1233, 50, 2324, 904, 593, 260],\n"," 591: [1233, 2396, 1230, 912, 1193, 858, 1252, 1073, 4973, 3897],\n"," 617: [1704, 4993, 3578, 318, 2571, 2028, 2628, 2762, 5952, 1288],\n"," 621: [1233, 2542, 1080, 1278, 4011, 2329, 2692, 3897, 1387, 7153],\n"," 622: [858, 1148, 1193, 3578, 50, 1234, 1233, 4226, 1036, 912],\n"," 623: [1233, 912, 858, 1193, 50, 904, 1234, 1148, 4226, 2858],\n"," 633: [1199, 4973, 924, 2997, 1080, 778, 1247, 1079, 2329, 1148],\n"," 636: [2858, 1234, 1247, 4226, 1288, 1207, 1230, 36, 1252, 1242],\n"," 638: [1148, 4993, 1704, 2571, 7153, 1288, 2028, 5952, 2858, 2762],\n"," 641: [356, 3578, 50, 1210, 260, 527, 1234, 2324, 110, 1196],\n"," 646: [318, 50, 260, 1234, 527, 912, 858, 4226, 904, 1213],\n"," 654: [919, 527, 1035, 912, 1234, 2324, 1207, 904, 50, 3578],\n"," 655: [527, 593, 912, 3578, 50, 1234, 2324, 4226, 904, 1233],\n"," 658: [50, 858, 318, 1221, 1213, 1193, 2019, 750, 541, 2502],\n"," 660: [1233, 1207, 923, 912, 1247, 1148, 2396, 1252, 1230, 1250],\n"," 661: [50, 2571, 1148, 1036, 1193, 1234, 4226, 858, 1233, 4011],\n"," 677: [1207, 912, 904, 1233, 2324, 260, 1148, 1234, 919, 1247],\n"," 714: [912, 1234, 1233, 593, 1207, 1193, 1247, 1250, 1213, 1242],\n"," 715: [2959, 858, 1221, 50, 541, 2019, 750, 1193, 1213, 4226],\n"," 723: [912, 858, 4973, 1394, 904, 593, 1207, 2019, 919, 1234],\n"," 731: [3578, 50, 1148, 1234, 1198, 3147, 457, 4993, 4306, 36],\n"," 742: [1233, 1193, 1288, 1230, 36, 858, 904, 2692, 1252, 527],\n"," 743: [527, 318, 3578, 260, 1210, 1704, 2324, 1035, 110, 593],\n"," 752: [912, 318, 858, 4973, 904, 1233, 608, 2019, 1193, 593],\n"," 772: [1394, 912, 1219, 904, 858, 923, 318, 2019, 1207, 908],\n"," 780: [3578, 912, 919, 904, 1207, 1704, 1035, 1148, 4226, 1247],\n"," 788: [1234, 912, 919, 1233, 1193, 1213, 296, 904, 1035, 4973],\n"," 814: [858, 1193, 912, 1222, 4973, 1230, 1252, 2019, 904, 750],\n"," 823: [593, 50, 318, 1234, 356, 2324, 919, 527, 47, 3578],\n"," 826: [1148, 1199, 2692, 2502, 293, 36, 1247, 1198, 1225, 1266],\n"," 833: [1233, 1193, 904, 2019, 912, 4226, 7153, 50, 1247, 4973],\n"," 838: [2019, 1394, 750, 1199, 1221, 924, 1222, 1080, 1250, 1252],\n"," 842: [858, 1148, 527, 912, 919, 1207, 1247, 1193, 904, 1234],\n"," 852: [50, 3578, 1234, 1036, 260, 110, 527, 1210, 2324, 1704],\n"," 878: [2858, 7153, 1148, 5952, 4993, 1233, 2571, 4226, 1617, 2997],\n"," 887: [260, 858, 1148, 1252, 1288, 5952, 7153, 111, 527, 541],\n"," 895: [527, 1704, 3578, 1721, 318, 17, 2324, 62, 587, 1584],\n"," 899: [318, 50, 858, 260, 1233, 1234, 912, 1193, 4226, 1148],\n"," 902: [912, 1234, 1233, 4973, 904, 1221, 2019, 919, 908, 2324],\n"," 907: [1233, 1193, 1148, 858, 904, 912, 2858, 1247, 1230, 4226],\n"," 928: [912, 1233, 904, 858, 2019, 1207, 111, 1394, 1247, 1219],\n"," 936: [318, 1234, 1213, 2324, 904, 1304, 1148, 750, 908, 1250],\n"," 964: [1233, 912, 904, 4973, 1234, 1207, 1247, 1148, 1250, 1252],\n"," 970: [858, 912, 1073, 1233, 919, 1035, 2959, 104, 4973, 2791],\n"," 972: [260, 1234, 1148, 912, 904, 1233, 50, 1198, 919, 1250],\n"," 988: [7153, 1221, 908, 36, 6874, 4034, 1732, 1222, 4027, 2329],\n"," 1001: [318, 527, 912, 919, 1234, 260, 1207, 2324, 1035, 904],\n"," 1013: [1233, 1193, 1230, 1148, 912, 1252, 1247, 904, 3147, 1207],\n"," 1018: [50, 1221, 1213, 858, 2019, 750, 1193, 1201, 912, 1234],\n"," 1020: [912, 1230, 2959, 3147, 1233, 318, 1090, 1242, 2692, 2542],\n"," 1028: [260, 1210, 527, 1196, 3578, 1704, 318, 2324, 17, 110],\n"," 1031: [50, 1234, 318, 912, 858, 1233, 1193, 2959, 4226, 1213],\n"," 1035: [2502, 2542, 50, 1625, 6, 1220, 1234, 1201, 4011, 1278],\n"," 1038: [1148, 150, 527, 912, 1207, 1247, 2396, 904, 1234, 2762],\n"," 1045: [1035, 1234, 4973, 608, 1394, 904, 1207, 1233, 908, 1250],\n"," 1046: [318, 3578, 1234, 1036, 50, 527, 2324, 3147, 919, 1242],\n"," 35: [2997, 858, 231, 2019, 924, 1221, 1199, 541, 1193, 2502],\n"," 72: [318, 50, 2571, 1036, 1234, 1148, 1193, 858, 1233, 4226],\n"," 91: [1233, 111, 1199, 2019, 858, 4973, 1252, 7153, 5952, 908],\n"," 106: [1721, 597, 539, 587, 3147, 500, 62, 2396, 150, 3578],\n"," 128: [260, 858, 1148, 1288, 5952, 7153, 608, 912, 923, 1207],\n"," 158: [260, 318, 2571, 1196, 4226, 7153, 1213, 1221, 1198, 1148],\n"," 160: [47, 111, 541, 593, 608, 912, 1219, 1221, 1193, 50],\n"," 175: [1207, 908, 1247, 1199, 1148, 3897, 1230, 1073, 2324, 1242],\n"," 196: [3578, 1148, 1704, 3147, 733, 2762, 2324, 50, 4993, 36],\n"," 203: [2959, 858, 3578, 4011, 4226, 1193, 912, 3147, 2571, 1233],\n"," 206: [858, 923, 1199, 1207, 1247, 1193, 2858, 1233, 1394, 2019],\n"," 207: [2858, 1288, 7153, 5952, 4993, 111, 260, 1148, 1136, 2571],\n"," 215: [527, 2329, 3147, 1246, 58, 1090, 337, 1035, 1201, 509],\n"," 255: [50, 1234, 3578, 1036, 2324, 527, 260, 1198, 47, 919],\n"," 265: [356, 597, 527, 150, 1704, 587, 318, 2000, 457, 3147],\n"," 340: [1380, 3578, 2959, 2324, 920, 858, 1968, 2081, 4011, 1304],\n"," 404: [3578, 1036, 1234, 50, 3147, 593, 1242, 733, 2000, 2324],\n"," 433: [1148, 1233, 318, 2858, 1288, 1193, 2571, 7153, 4993, 904],\n"," 440: [1234, 912, 1233, 919, 904, 4973, 4011, 2324, 2019, 1250],\n"," 444: [318, 3578, 527, 1234, 2324, 1704, 1148, 3147, 150, 260],\n"," 450: [912, 1234, 2324, 1035, 2019, 923, 1704, 1247, 364, 1250],\n"," 479: [260, 1196, 5952, 1210, 7153, 4993, 1219, 1288, 1221, 904],\n"," 491: [2997, 541, 1193, 750, 1233, 2502, 296, 1208, 924, 1732],\n"," 506: [2858, 7153, 1221, 2019, 5952, 2571, 4226, 4993, 1233, 904],\n"," 523: [2858, 1233, 904, 912, 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1208],\n"," 188: [318, 260, 3578, 2324, 1234, 1704, 50, 919, 912, 1207],\n"," 217: [318, 527, 1207, 912, 1233, 919, 904, 1035, 1234, 1247],\n"," 253: [1148, 1233, 318, 1288, 904, 4993, 1193, 527, 7153, 1247],\n"," 262: [589, 2000, 1234, 1148, 36, 2324, 2001, 1270, 1304, 1917],\n"," 277: [318, 260, 50, 2571, 1148, 4226, 1196, 1198, 1234, 2858],\n"," 289: [1233, 1199, 541, 6377, 1198, 1073, 364, 778, 1035, 2194],\n"," 300: [1394, 4973, 1207, 1219, 1250, 1234, 593, 2858, 1035, 4226],\n"," 302: [4993, 7153, 1198, 5952, 36, 1036, 293, 4226, 1234, 2324],\n"," 308: [318, 912, 919, 1233, 1207, 1035, 1234, 904, 527, 858],\n"," 311: [318, 527, 919, 1035, 2324, 1234, 912, 1207, 260, 593],\n"," 328: [1288, 5952, 7153, 111, 1221, 1136, 4993, 296, 2019, 4226],\n"," 329: [318, 912, 1234, 50, 858, 919, 1233, 904, 1193, 1035],\n"," 344: [1233, 1193, 1230, 858, 1252, 923, 904, 1247, 4973, 1207],\n"," 347: [2858, 111, 5952, 608, 7153, 923, 2019, 904, 318, 1136],\n"," 358: [50, 2959, 1036, 47, 4011, 1234, 858, 4226, 2542, 3578],\n"," 474: [318, 1148, 4993, 7153, 2858, 1288, 1233, 50, 5952, 527],\n"," 483: [3578, 919, 1035, 1721, 2324, 1704, 1207, 1234, 3147, 1242],\n"," 509: [260, 1148, 912, 1207, 1221, 1230, 1247, 1198, 3578, 904],\n"," 578: [2959, 2502, 6874, 2542, 47, 50, 2692, 1206, 293, 1221],\n"," 589: [318, 5952, 4993, 7153, 2858, 2571, 1288, 4226, 912, 1213],\n"," 643: [4973, 1394, 2324, 2019, 4226, 1242, 2329, 1246, 1198, 2959],\n"," 672: [318, 1234, 4226, 5952, 7153, 4993, 1240, 1036, 1148, 1136],\n"," 679: [919, 1207, 1035, 912, 1233, 2396, 318, 527, 904, 364],\n"," 680: [318, 527, 3578, 2324, 1704, 919, 1234, 150, 457, 593],\n"," 691: [1148, 1233, 2762, 904, 1247, 1207, 1193, 1234, 3147, 912],\n"," 702: [2571, 4993, 7153, 5952, 1148, 2858, 1288, 50, 4226, 1198],\n"," 708: [318, 1148, 1234, 527, 1233, 1193, 4226, 1198, 3578, 904],\n"," 730: [858, 912, 1193, 50, 1234, 3147, 1233, 1148, 1242, 4011],\n"," 751: [318, 527, 1234, 50, 919, 1035, 260, 2324, 356, 912],\n"," 773: [527, 4993, 1704, 2028, 17, 150, 509, 2762, 1288, 497],\n"," 803: [62, 736, 597, 3147, 802, 653, 1148, 586, 527, 509],\n"," 809: [1210, 318, 2571, 7153, 1704, 1198, 1288, 593, 1234, 3578],\n"," 820: [1288, 318, 1233, 111, 904, 1221, 1213, 1247, 1198, 1617],\n"," 824: [4973, 364, 318, 527, 923, 1183, 588, 1213, 50, 595],\n"," 863: [858, 1193, 912, 2019, 4226, 1221, 2959, 750, 904, 1136],\n"," 865: [527, 1234, 1148, 912, 904, 1233, 1198, 858, 2324, 1193],\n"," 867: [912, 593, 357, 1193, 17, 1682, 58, 1148, 2291, 2762],\n"," 911: [608, 318, 527, 912, 904, 5952, 593, 50, 1207, 4973],\n"," 915: [527, 3578, 1704, 318, 110, 2324, 150, 1196, 1198, 17],\n"," 939: [527, 919, 1035, 2324, 912, 1207, 1234, 1704, 904, 3578],\n"," 946: [912, 1221, 1193, 908, 1234, 4226, 2959, 296, 1213, 1233],\n"," 954: [527, 318, 593, 1704, 50, 356, 2324, 1234, 110, 1198],\n"," 957: [1148, 5952, 7153, 4993, 1199, 923, 750, 2019, 904, 1094],\n"," 971: [1252, 541, 923, 1247, 1193, 904, 1208, 1233, 1207, 1148],\n"," 986: [260, 1210, 1196, 3578, 1234, 1704, 2324, 1198, 1036, 2571],\n"," 992: [2858, 527, 1148, 4993, 904, 5952, 1288, 7153, 1233, 923],\n"," 92: [593, 919, 50, 1035, 1380, 1234, 920, 2324, 296, 2959],\n"," 107: [1193, 318, 2019, 1148, 750, 904, 2858, 4226, 1252, 1230],\n"," 131: [2959, 1200, 1206, 6874, 2571, 541, 1240, 2502, 1221, 293],\n"," 138: [223, 288, 1221, 1288, 111, 1527, 1213, 2502, 2997, 2692],\n"," 145: [2959, 1221, 858, 1213, 608, 2019, 318, 4226, 750, 541],\n"," 183: [356, 150, 3578, 2324, 17, 4306, 50, 1148, 6539, 2762],\n"," 209: [1233, 912, 1193, 923, 858, 904, 2858, 4973, 2019, 1247],\n"," 230: [318, 858, 1193, 904, 2019, 4226, 111, 7153, 4973, 908],\n"," 263: [904, 1234, 1233, 1148, 2324, 1247, 4011, 1250, 919, 1225],\n"," 305: [318, 919, 1234, 912, 1207, 3578, 1233, 2324, 1035, 904],\n"," 314: [50, 1213, 2019, 4226, 608, 7153, 2571, 1233, 2959, 1148],\n"," 319: [858, 608, 912, 1394, 2019, 4973, 2959, 1221, 1233, 1193],\n"," 325: [2571, 2692, 733, 4011, 780, 3147, 3578, 293, 2959, 36],\n"," 341: [5952, 7153, 111, 541, 608, 912, 1221, 1193, 50, 904],\n"," 471: [318, 1233, 912, 904, 1193, 858, 1207, 527, 1148, 1234],\n"," 488: [318, 1036, 3147, 2000, 3578, 733, 50, 1234, 1242, 2959],\n"," 495: [527, 3578, 1704, 318, 780, 17, 1148, 2028, 11, 356],\n"," 532: [231, 2959, 1732, 2502, 924, 1222, 750, 2019, 2542, 1193],\n"," 564: [1148, 527, 912, 919, 1207, 1247, 2028, 3578, 1234, 2762],\n"," 574: [356, 3578, 2000, 318, 733, 3147, 50, 1234, 1101, 110],\n"," 590: [912, 858, 904, 1234, 4973, 527, 1233, 1207, 4226, 919],\n"," 603: [1035, 919, 364, 62, 2396, 500, 1028, 1641, 920, 1207],\n"," 674: [2959, 296, 2502, 2692, 2542, 858, 1221, 750, 50, 4226],\n"," 753: [858, 1148, 541, 912, 1221, 1193, 50, 904, 4226, 1136],\n"," 810: [1035, 919, 912, 593, 318, 1234, 858, 50, 4973, 2324],\n"," 830: [318, 3578, 17, 4993, 1148, 2762, 6539, 4306, 1234, 2571],\n"," 841: [50, 318, 1148, 7153, 2858, 858, 1233, 296, 1198, 1136],\n"," 856: [318, 50, 912, 593, 858, 1234, 904, 1233, 1213, 4973],\n"," 921: [50, 318, 593, 260, 1234, 296, 1036, 3578, 2571, 1198],\n"," 933: [1233, 912, 904, 4973, 1207, 858, 1193, 2858, 1394, 924],\n"," 976: [912, 904, 608, 4973, 1233, 858, 260, 2858, 1207, 923],\n"," 10: [1148, 2858, 1288, 1247, 1207, 1230, 904, 923, 2396, 318],\n"," 19: [1704, 4993, 3578, 2571, 2324, 5952, 7153, 2028, 2762, 4306],\n"," 41: [318, 527, 2324, 593, 1234, 3578, 1704, 590, 150, 1207],\n"," 43: [780, 1148, 1252, 150, 527, 1207, 1230, 1247, 1584, 2028],\n"," 44: [858, 1148, 1252, 7153, 111, 541, 912, 923, 1199, 1207],\n"," 45: [2959, 318, 1234, 2542, 4011, 296, 1193, 2502, 2000, 733],\n"," 51: [47, 1221, 50, 1089, 2959, 1213, 318, 1201, 1394, 2019],\n"," 56: [608, 1199, 1394, 1183, 1233, 1073, 318, 778, 2997, 2959],\n"," 61: [919, 1035, 2396, 1233, 904, 150, 2324, 1242, 1246, 364],\n"," 68: [318, 527, 3578, 1035, 1234, 912, 150, 1233, 1704, 3147],\n"," 69: [1207, 904, 1233, 923, 1148, 2028, 919, 4973, 1704, 1250],\n"," 78: [2019, 1221, 1213, 3897, 7153, 1266, 50, 5952, 2329, 4011],\n"," 110: [2000, 733, 597, 3578, 3147, 786, 1036, 165, 587, 1101],\n"," 115: [1233, 923, 904, 1148, 1207, 1288, 912, 1252, 4973, 2028],\n"," 129: [858, 541, 608, 912, 1221, 1193, 50, 904, 908, 1234],\n"," 149: [1200, 47, 1206, 1201, 1089, 1240, 2571, 1213, 223, 2542],\n"," 150: [318, 1148, 1233, 2571, 1193, 260, 904, 4993, 2858, 1288],\n"," 168: [50, 318, 593, 296, 2959, 1234, 858, 912, 1213, 47],\n"," 169: [260, 858, 1148, 1252, 7153, 47, 527, 541, 593, 608],\n"," 178: [3578, 1234, 2324, 919, 50, 1035, 912, 1198, 1207, 1036],\n"," 186: [1199, 2019, 2858, 923, 750, 924, 1221, 858, 1208, 2997],\n"," 201: [318, 260, 1035, 919, 1207, 3578, 17, 1234, 912, 904],\n"," 239: [318, 50, 912, 593, 1234, 260, 527, 858, 904, 1233],\n"," 248: [260, 858, 912, 904, 2858, 318, 1233, 50, 2019, 608],\n"," 256: [318, 527, 260, 1148, 1234, 50, 3578, 1233, 2324, 904],\n"," 257: [318, 50, 593, 1234, 2959, 296, 912, 4011, 47, 1193],\n"," 272: [1206, 111, 541, 1199, 2019, 750, 924, 1136, 608, 1080],\n"," 279: [858, 608, 912, 1221, 1233, 750, 2019, 4973, 1193, 1394],\n"," 280: [318, 3147, 1233, 3578, 1242, 1207, 1148, 1234, 919, 150],\n"," 285: [858, 541, 923, 1199, 1193, 1208, 2858, 1233, 750, 924],\n"," 298: [912, 1394, 608, 923, 4973, 904, 1233, 858, 1219, 2019],\n"," 301: [111, 7153, 5952, 1233, 4226, 1193, 904, 1148, 4993, 924],\n"," 304: [527, 318, 1704, 1207, 3578, 1584, 62, 2028, 1148, 17],\n"," 333: [150, 2396, 1207, 500, 3578, 919, 11, 3147, 1035, 1961],\n"," 334: [318, 527, 3578, 2324, 1704, 593, 1035, 50, 150, 1207],\n"," 338: [50, 2959, 608, 1213, 1221, 858, 260, 912, 1089, 4226],\n"," 350: [2858, 527, 923, 904, 1233, 1207, 1288, 5952, 1148, 4993],\n"," 351: [1219, 5952, 7153, 2019, 4993, 1199, 4226, 1233, 541, 1080],\n"," 353: [1136, 1221, 2858, 6874, 260, 1080, 750, 223, 2997, 1208],\n"," 378: [318, 1233, 1193, 2858, 904, 1148, 912, 2019, 1247, 4226],\n"," 386: [1148, 1234, 1233, 50, 2571, 1207, 904, 1193, 4306, 919],\n"," 397: [260, 1207, 1234, 919, 904, 150, 1035, 1148, 1233, 17],\n"," 420: [541, 1206, 1199, 2997, 924, 750, 2019, 1208, 2858, 1221],\n"," 439: [260, 858, 593, 912, 1221, 50, 904, 1234, 4226, 296],\n"," 449: [296, 318, 110, 1036, 1234, 1198, 1240, 1213, 4226, 2324],\n"," 478: [50, 593, 1234, 912, 919, 2959, 296, 1036, 858, 2324],\n"," 487: [1233, 912, 858, 1234, 1193, 904, 593, 1148, 527, 1207],\n"," 489: [912, 4973, 593, 4226, 2959, 1242, 908, 1148, 2324, 1252],\n"," 500: [858, 912, 2019, 1233, 1221, 1193, 904, 50, 750, 4973],\n"," 510: [260, 904, 1207, 912, 2858, 1233, 1148, 1247, 923, 5952],\n"," 521: [260, 527, 1210, 1234, 1196, 3578, 1198, 2324, 2571, 110],\n"," 522: [50, 2858, 4226, 7153, 5952, 2019, 904, 912, 750, 2571],\n"," 524: [1233, 912, 904, 1207, 527, 1193, 1148, 1247, 260, 1234],\n"," 527: [912, 318, 1233, 1234, 1207, 904, 919, 858, 1193, 1247],\n"," 529: [1233, 1193, 858, 1230, 2858, 1252, 1148, 2019, 750, 923],\n"," 535: [1233, 2858, 3897, 1230, 2396, 1247, 1207, 1288, 1148, 1094],\n"," 550: [3578, 3147, 2324, 1242, 2028, 1704, 4306, 1246, 6377, 4973],\n"," 560: [858, 2502, 1221, 1222, 2019, 1213, 912, 1201, 750, 4973],\n"," 573: [260, 1196, 1210, 912, 1207, 904, 1219, 919, 1035, 5952],\n"," 579: [1240, 6874, 110, 4226, 223, 1221, 7153, 1200, 1213, 4993],\n"," 582: [1233, 923, 924, 4973, 750, 2858, 1148, 3897, 2997, 1199],\n"," 583: [1233, 2858, 1193, 858, 2019, 923, 111, 924, 904, 1288],\n"," 584: [318, 919, 3578, 1234, 1035, 2324, 593, 3147, 912, 1242],\n"," 587: [111, 923, 2858, 2019, 1199, 912, 858, 1233, 4973, 904],\n"," 588: [260, 1148, 4993, 2571, 7153, 1288, 2858, 5952, 1233, 904],\n"," 596: [1094, 3114, 5952, 4993, 1148, 1394, 7153, 1250, 1358, 1246],\n"," 602: [2858, 318, 1148, 7153, 1233, 1288, 1193, 2571, 1136, 5952],\n"," 618: [1233, 912, 904, 1193, 1207, 1247, 858, 1148, 4973, 923],\n"," 624: [589, 260, 858, 1210, 1148, 1252, 1288, 5952, 7153, 32],\n"," 627: [260, 7153, 1148, 5952, 1288, 1136, 1196, 527, 904, 1233],\n"," 635: [858, 1148, 912, 1207, 1247, 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229: [318, 50, 593, 912, 1196, 296, 858, 1213, 904, 1234],\n"," 237: [527, 17, 1721, 150, 1028, 590, 1035, 62, 919, 508],\n"," 242: [858, 1148, 1252, 1288, 527, 912, 923, 1207, 1230, 1247],\n"," 252: [318, 4973, 923, 1247, 1234, 858, 1148, 1219, 1250, 1394],\n"," 266: [858, 111, 541, 1221, 50, 4226, 1136, 296, 1213, 750],\n"," 270: [2858, 318, 527, 904, 5952, 1148, 1233, 4993, 7153, 1288],\n"," 282: [1233, 318, 912, 1193, 904, 858, 1207, 1247, 1148, 4973],\n"," 297: [318, 50, 296, 1196, 1213, 593, 4226, 1221, 2858, 858],\n"," 309: [1148, 1233, 318, 527, 1247, 1193, 2858, 1288, 904, 2762],\n"," 316: [1148, 1233, 318, 1230, 1193, 1288, 1247, 2762, 3147, 2858],\n"," 327: [593, 1221, 50, 2959, 296, 1213, 318, 912, 4226, 2019],\n"," 356: [318, 1233, 912, 904, 1148, 1193, 858, 527, 1247, 1234],\n"," 393: [1233, 318, 1193, 912, 1148, 3147, 1207, 904, 1247, 858],\n"," 394: [1148, 1288, 7153, 527, 1247, 1193, 2571, 904, 1234, 4226],\n"," 395: [780, 733, 1148, 2000, 1233, 1193, 1036, 589, 2571, 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5952, 904, 7153, 912, 527, 1213],\n"," 763: [318, 3578, 1234, 1704, 2324, 2571, 50, 2762, 1233, 1198],\n"," 770: [1233, 2396, 923, 1230, 1207, 1252, 1247, 3897, 904, 1193],\n"," 796: [318, 912, 904, 2858, 1233, 858, 527, 4973, 923, 1207],\n"," 800: [318, 50, 1148, 2571, 1233, 1193, 858, 4226, 1234, 904],\n"," 829: [1148, 1252, 1288, 5952, 7153, 111, 527, 608, 912, 923],\n"," 836: [527, 318, 904, 1207, 912, 1233, 1148, 2858, 1247, 1704],\n"," 882: [858, 912, 1207, 1193, 904, 1233, 318, 4973, 1247, 919],\n"," 900: [296, 50, 318, 1221, 1213, 912, 2019, 1196, 593, 4226],\n"," 903: [318, 1148, 527, 1233, 904, 3578, 2762, 1207, 1247, 1234],\n"," 914: [318, 1233, 912, 1234, 527, 1193, 904, 1148, 858, 1207],\n"," 918: [1148, 2571, 1288, 318, 1233, 4993, 1230, 3147, 1193, 2762],\n"," 920: [2959, 1193, 2019, 296, 1233, 750, 318, 50, 1213, 4226],\n"," 934: [318, 1148, 1233, 2858, 527, 904, 1193, 7153, 1288, 4993],\n"," 945: [527, 1207, 2396, 1233, 904, 923, 1247, 912, 1148, 2858],\n"," 950: [912, 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912, 1207, 1247, 2028, 3578, 904],\n"," 931: [11, 597, 3147, 802, 1148, 2000, 587, 2396, 539, 500],\n"," 969: [527, 318, 1704, 260, 1207, 1148, 150, 2028, 2324, 904],\n"," 12: [1148, 318, 1233, 1193, 1230, 1247, 2571, 527, 1288, 904],\n"," 370: [1148, 318, 1233, 527, 260, 2571, 1193, 904, 1234, 4993],\n"," 597: [318, 1233, 904, 858, 1193, 50, 260, 1234, 1148, 527],\n"," 195: [1233, 912, 904, 318, 2396, 1222, 923, 1242, 3147, 1148],\n"," 337: [858, 912, 50, 296, 608, 2019, 2959, 4973, 904, 1193],\n"," 910: [608, 1221, 1219, 111, 2019, 912, 750, 1394, 4973, 541],\n"," 63: [318, 260, 50, 1148, 1196, 2571, 4993, 1234, 7153, 5952],\n"," 70: [1233, 1247, 923, 1250, 919, 2858, 1394, 2396, 1230, 1225],\n"," 99: [2997, 2019, 2959, 1221, 1208, 1233, 1222, 2858, 1732, 2502],\n"," 121: [260, 4993, 5952, 7153, 1704, 1148, 1234, 110, 3578, 4226],\n"," 130: [2959, 2019, 1221, 912, 750, 1222, 2502, 1213, 4226, 318],\n"," 244: [318, 1233, 260, 1207, 4973, 923, 2019, 4226, 908, 1250],\n"," 291: [912, 904, 4973, 608, 1233, 923, 1394, 318, 2858, 1219],\n"," 335: [318, 608, 1233, 50, 2959, 1213, 296, 1221, 1193, 593],\n"," 361: [1233, 912, 904, 318, 923, 1193, 1207, 1247, 4973, 1148],\n"," 470: [2019, 260, 912, 1196, 904, 1193, 908, 1233, 1201, 7153],\n"," 497: [296, 2959, 1221, 2019, 608, 541, 1206, 750, 1213, 50],\n"," 620: [260, 318, 2571, 7153, 4993, 5952, 2858, 50, 1148, 1196],\n"," 648: [3147, 780, 1230, 733, 1242, 2762, 3578, 2692, 2571, 11],\n"," 666: [260, 527, 318, 1288, 904, 923, 111, 1148, 912, 1207],\n"," 710: [260, 912, 904, 1233, 1148, 1247, 1234, 4973, 2324, 923],\n"," 794: [2571, 4993, 1288, 260, 5952, 2858, 318, 1136, 293, 541],\n"," 854: [260, 318, 2571, 1148, 4993, 7153, 1288, 5952, 3578, 1704],\n"," 861: [318, 912, 1233, 904, 1193, 4973, 1234, 50, 2019, 1207],\n"," 819: [1233, 912, 1207, 904, 2396, 4973, 1193, 923, 1247, 858],\n"," 87: [589, 260, 858, 1148, 1252, 1288, 4995, 5952, 6377, 7153],\n"," 317: [1233, 318, 858, 912, 904, 2858, 1148, 2019, 4973, 1247],\n"," 324: [296, 608, 1221, 50, 1196, 593, 260, 1201, 2019, 858],\n"," 387: [318, 912, 50, 593, 1234, 858, 260, 904, 527, 1233],\n"," 451: [296, 260, 50, 1221, 2858, 318, 541, 1136, 111, 1213],\n"," 502: [527, 150, 1207, 62, 1584, 1704, 1721, 17, 2028, 318],\n"," 559: [260, 1148, 527, 904, 2858, 4993, 318, 1233, 2571, 7153],\n"," 973: [858, 1233, 1193, 2019, 912, 750, 318, 4973, 904, 1221],\n"," 1015: [318, 912, 1233, 858, 1207, 1193, 527, 1234, 1247, 4973],\n"," 1017: [318, 2571, 1148, 50, 260, 4226, 1233, 1193, 2858, 1288],\n"," 85: [260, 1196, 1210, 318, 110, 1234, 47, 1198, 1704, 2324],\n"," 306: [318, 1233, 912, 858, 1193, 904, 1234, 1207, 4973, 1247],\n"," 653: [318, 1233, 912, 904, 858, 1193, 50, 1148, 1234, 2858],\n"," 371: [318, 50, 260, 2571, 4226, 1234, 593, 1213, 1198, 858],\n"," 566: [780, 3147, 736, 104, 733, 802, 1233, 2000, 11, 597],\n"," 331: [296, 858, 1221, 2019, 1089, 912, 1222, 750, 2997, 608],\n"," 665: [50, 318, 858, 2959, 1221, 1213, 1233, 1136, 2571, 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1288, 1221, 858],\n"," 234: [296, 589, 1240, 1036, 47, 2502, 380, 2542, 1200, 6874],\n"," 430: [296, 50, 318, 2959, 593, 858, 1213, 4226, 260, 1234],\n"," 1027: [318, 912, 1233, 1234, 858, 1193, 50, 904, 593, 1207],\n"," 140: [1233, 904, 858, 4973, 923, 1207, 2019, 1247, 608, 908],\n"," 373: [919, 356, 1234, 593, 3578, 2324, 50, 527, 1380, 457],\n"," 926: [2959, 296, 1206, 2997, 858, 1221, 2502, 2019, 541, 1222],\n"," 781: [260, 858, 1148, 541, 608, 912, 923, 1221, 1193, 50],\n"," 825: [318, 912, 1207, 904, 1148, 1247, 527, 3147, 1242, 2396],\n"," 913: [1035, 912, 318, 1234, 858, 1233, 364, 4973, 3147, 1207],\n"," 453: [318, 3578, 356, 527, 2324, 1704, 1234, 260, 50, 1036],\n"," 540: [260, 47, 593, 1221, 1196, 50, 1234, 4226, 2959, 296],\n"," 66: [50, 1221, 858, 2019, 318, 4226, 593, 750, 608, 541],\n"," 185: [318, 527, 1207, 1233, 3578, 1148, 3147, 150, 2324, 2762],\n"," 222: [296, 2959, 50, 858, 318, 1221, 4226, 1193, 1213, 2019],\n"," 498: [1233, 2858, 858, 1193, 2019, 111, 750, 923, 904, 1136],\n"," 994: [912, 608, 904, 1394, 527, 1207, 4973, 1219, 260, 923],\n"," 165: [318, 2571, 260, 50, 7153, 1148, 2858, 4226, 1288, 1136],\n"," 202: [260, 858, 1148, 150, 912, 919, 1207, 1247, 1193, 3578],\n"," 180: [260, 1148, 1288, 5952, 7153, 527, 912, 923, 1207, 1225],\n"," 261: [318, 912, 527, 904, 1207, 1233, 4973, 919, 1234, 260],\n"," 435: [318, 1233, 527, 912, 904, 1207, 1148, 1247, 2858, 1193],\n"," 322: [318, 50, 858, 912, 1233, 1193, 4226, 904, 1213, 260],\n"," 238: [260, 7153, 4993, 318, 5952, 1288, 1148, 2571, 527, 904],\n"," 425: [2858, 923, 1233, 1199, 924, 904, 1288, 2019, 912, 1208],\n"," 512: [858, 1233, 912, 2019, 1193, 4973, 904, 750, 923, 1221],\n"," 725: [296, 50, 858, 318, 1221, 1213, 4226, 2019, 1193, 750],\n"," 732: [1148, 527, 4993, 1288, 2028, 1233, 318, 2762, 1247, 7153],\n"," 916: [296, 541, 1221, 2571, 7153, 111, 1136, 1206, 260, 2858],\n"," 736: [2571, 50, 296, 318, 2959, 4226, 1221, 541, 260, 7153],\n"," 961: [318, 50, 1233, 912, 1234, 858, 904, 1148, 593, 260],\n"," 974: [318, 50, 260, 912, 1234, 593, 904, 858, 4226, 1148],\n"," 443: [50, 296, 2571, 260, 1234, 4226, 1036, 1198, 858, 2959],\n"," 1012: [318, 912, 1234, 1233, 858, 919, 904, 527, 50, 1207],\n"," 515: [318, 593, 50, 1234, 858, 904, 1233, 4973, 527, 919],\n"," 288: [260, 527, 912, 1207, 1234, 318, 2324, 1704, 1233, 3578],\n"," 978: [1207, 1035, 527, 912, 318, 364, 2396, 1233, 904, 1247],\n"," 28: [318, 260, 50, 527, 2571, 1148, 1234, 4226, 593, 1198],\n"," 458: [527, 1207, 904, 318, 912, 1233, 1247, 923, 260, 1148],\n"," 475: [858, 1233, 1193, 318, 912, 2019, 50, 750, 4226, 1221],\n"," 592: [318, 2571, 50, 1148, 260, 4226, 1193, 1233, 7153, 858],\n"," 598: [912, 318, 858, 1233, 904, 4973, 1193, 2019, 608, 2858],\n"," 943: [2858, 318, 260, 1148, 1233, 1288, 7153, 904, 4993, 5952],\n"," 55: [858, 2959, 912, 2019, 1233, 4973, 608, 750, 296, 1213],\n"," 520: [1233, 858, 1193, 912, 904, 2019, 4973, 1148, 1247, 50],\n"," 697: [1233, 2396, 923, 1230, 1207, 1252, 1193, 1247, 912, 904],\n"," 849: [858, 1148, 593, 912, 1193, 2571, 50, 1234, 4226, 1233],\n"," 1003: [260, 1148, 1288, 7153, 50, 2858, 4993, 318, 5952, 527],\n"," 705: [912, 608, 858, 318, 4973, 593, 904, 50, 1213, 2019],\n"," 231: [296, 318, 260, 593, 2959, 1213, 1196, 4226, 1221, 47],\n"," 699: [858, 912, 1234, 1233, 593, 1193, 904, 4226, 1213, 260],\n"," 448: [318, 1233, 904, 912, 1148, 1193, 260, 858, 1247, 527],\n"," 750: [318, 527, 260, 1148, 904, 912, 1233, 1234, 1207, 50],\n"," 782: [318, 50, 858, 912, 1233, 1234, 1193, 4226, 904, 593],\n"," 108: [296, 858, 2959, 1221, 2019, 50, 1213, 318, 750, 912],\n"," 29: [318, 1036, 50, 589, 3578, 380, 2571, 1234, 1240, 3147]})"]},"metadata":{},"execution_count":8}],"source":["# 학습 데이터에 나오지 않는 사용자와 아이템의 조합을 준비한다\n","data_test = data_train.build_anti_testset(None)\n","predictions = matrix_factorization.test(data_test)\n","pred_user2items = get_top_n(predictions, n=10)\n","pred_user2items"]},{"cell_type":"code","execution_count":9,"id":"afa174f5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"afa174f5","executionInfo":{"status":"ok","timestamp":1672119551834,"user_tz":-540,"elapsed":26,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"cc01c6d1-c6f5-4616-a63f-2add1448726a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
34893578Gladiator (2000)[Action, Adventure, Drama][revenge, crowe's best, gfei own it, dvd, glad...
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\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2에 대한 추천(3578, 2571, 2959)\n","movies[movies.movie_id.isin([3578, 2571, 2959])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/NMF.ipynb b/chapter5/colab/NMF.ipynb index cd11431..0f8192f 100644 --- a/chapter5/colab/NMF.ipynb +++ b/chapter5/colab/NMF.ipynb @@ -1,1881 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b9224173", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/NMF.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "f8caaceb", - "metadata": {}, - "source": [ - "# 비부값 행렬 분석" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9cb2f773", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed3c6a08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8e6879e3", - "metadata": {}, - "outputs": [], - "source": [ - "# 결손값을 채우는 방법\n", - "fillna_with_zero = True\n", - "# 인자 수\n", - "factors = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "52281452", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [5., 0., 3., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [5., 0., 0., ..., 0., 0., 0.]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", - "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", - "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", - "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", - "if fillna_with_zero:\n", - " matrix = user_movie_matrix.fillna(0).to_numpy()\n", - "else:\n", - " matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n", - "matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "708dda3c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "NMF(n_components=5)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.decomposition import NMF\n", - "\n", - "# NMF 실행\n", - "nmf = NMF(n_components=factors)\n", - "nmf.fit(matrix)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "687a16b1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.02183305, 0.53589333, 0. , 0. , 0. ],\n", - " [0.21316287, 0.10881497, 0. , 0. , 0. ],\n", - " [0. , 0.02976101, 0.05290754, 0.15364687, 0.02014396],\n", - " ...,\n", - " [0.13635948, 0.14281692, 0. , 0. , 0.00656248],\n", - " [0.09754 , 0.01077277, 0. , 0.13238006, 0.13515664],\n", - " [0.99759019, 0. , 0. , 0.64704357, 0.05969731]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "P = nmf.fit_transform(matrix)\n", - "P" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a731dbb1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.96418693, 0.50120485, 0.39873535, ..., 0. , 0. ,\n", - " 0. ],\n", - " [1.15067665, 0.96078243, 0.40197059, ..., 0. , 0.00289228,\n", - " 0.00216921],\n", - " [0.47179502, 0. , 0. , ..., 0. , 0. ,\n", - " 0. ],\n", - " [0.79016702, 0.3041748 , 0. , ..., 0.02803924, 0.01446954,\n", - " 0.01085215],\n", - " [0.29612837, 0.30057646, 0.17845097, ..., 0. , 0. ,\n", - " 0. ]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Q = nmf.components_\n", - "Q" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "36669c15", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[6.59524137e-01, 5.25819727e-01, 2.24118966e-01, ...,\n", - " 0.00000000e+00, 1.54995325e-03, 1.16246494e-03],\n", - " [5.43902575e-01, 2.11385779e-01, 1.28735991e-01, ...,\n", - " 0.00000000e+00, 3.14723309e-04, 2.36042481e-04],\n", - " [1.86578695e-01, 8.13841578e-02, 1.55577588e-02, ...,\n", - " 4.30814090e-03, 2.30927602e-03, 1.73195702e-03],\n", - " ...,\n", - " [4.34114948e-01, 2.07532549e-01, 1.12950629e-01, ...,\n", - " 0.00000000e+00, 4.13066441e-04, 3.09799830e-04],\n", - " [3.48608841e-01, 1.40129388e-01, 6.73418166e-02, ...,\n", - " 3.71183579e-03, 1.94663585e-03, 1.45997689e-03],\n", - " [2.48840417e+00, 7.14754992e-01, 4.08427521e-01, ...,\n", - " 1.81426079e-02, 9.36242017e-03, 7.02181513e-03]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "# 예측 평갓값 행렬\n", - "pred_matrix = np.dot(P, Q)\n", - "pred_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "08eb2849", - "metadata": {}, - "outputs": [], - "source": [ - "# 학습용에 나타나지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다\n", - "average_score = movielens_train.rating.mean()\n", - "movie_rating_predict = movielens_test.copy()\n", - "pred_results = []\n", - "for i, row in movielens_test.iterrows():\n", - " user_id = row[\"user_id\"]\n", - " if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n", - " pred_results.append(average_score)\n", - " continue\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " pred_score = pred_matrix[user_index, movie_index]\n", - " pred_results.append(pred_score)\n", - "movie_rating_predict[\"rating_pred\"] = pred_results" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7aa822a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {139: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n", - " 149: [4226, 2571, 2762, 47, 3996, 318, 6539, 5445, 4963, 1704],\n", - " 182: [47, 6874, 2329, 3793, 4011, 7361, 4878, 7438, 6333, 2502],\n", - " 215: [2329, 527, 6711, 3147, 2683, 8360, 5669, 6502, 3948, 1246],\n", - " 281: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", - " 326: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n", - " 351: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n", - " 357: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n", - " 426: [2502,\n", - " 33794,\n", - " 3949,\n", - " 7147,\n", - " 33493,\n", - " 5902,\n", - 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" 231: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n", - " 699: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n", - " 448: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n", - " 750: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n", - " 782: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n", - " 860: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n", - " 768: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n", - " 127: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n", - " 894: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", - "pred_user2items = defaultdict(list)\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "for user_id in movielens_train.user_id.unique():\n", - " if user_id not in user_id2index:\n", - " continue\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", - " for movie_index in movie_indexes:\n", - " movie_id = user_movie_matrix.columns[movie_index]\n", - " if movie_id not in user_evaluated_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "27b87c98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2의 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1940df09", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_user2items[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d1786a85", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "2874 2959 Fight Club (1999) \n", - "4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n", - "5852 5952 Lord of the Rings: The Two Towers, The (2002) \n", - "\n", - " genre \\\n", - "2874 [Action, Crime, Drama, Thriller] \n", - "4899 [Action, Adventure, Fantasy] \n", - "5852 [Action, Adventure, Fantasy] \n", - "\n", - " tag \n", - "2874 [based on a book, chuck palahniuk, edward nort... \n", - "4899 [based on a book, big budget, new zealand, sce... \n", - "5852 [based on a book, big budget, new zealand, sce... " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(2959, 4993, 5952)\n", - "movies[movies.movie_id.isin([2959, 4993, 5952])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "441ff6da", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"b9224173","metadata":{"id":"b9224173"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/NMF.ipynb)"]},{"cell_type":"markdown","id":"f8caaceb","metadata":{"id":"f8caaceb"},"source":["# 비부값 행렬 분석"]},{"cell_type":"code","execution_count":1,"id":"9cb2f773","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cb2f773","executionInfo":{"status":"ok","timestamp":1672119380127,"user_tz":-540,"elapsed":4459,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3b3441b6-3940-4b5a-ef0c-99ca4e312e13"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:36:13-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 64.1MB/s in 1.0s \n","\n","2022-12-27 05:36:14 (64.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"ed3c6a08","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ed3c6a08","executionInfo":{"status":"ok","timestamp":1672119449934,"user_tz":-540,"elapsed":69811,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"740a21e4-a8ae-42cc-f142-e51190fa0f81"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"8e6879e3","metadata":{"id":"8e6879e3","executionInfo":{"status":"ok","timestamp":1672119449934,"user_tz":-540,"elapsed":21,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 결손값을 채우는 방법\n","fillna_with_zero = True\n","# 인자 수\n","factors = 5"]},{"cell_type":"code","execution_count":4,"id":"52281452","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"52281452","executionInfo":{"status":"ok","timestamp":1672119449935,"user_tz":-540,"elapsed":20,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1bb4e599-cc7a-4fc5-cb95-4ef09185a7a2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," ...,\n"," [5., 0., 3., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," [5., 0., 0., ..., 0., 0., 0.]])"]},"metadata":{},"execution_count":4}],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","if fillna_with_zero:\n"," matrix = user_movie_matrix.fillna(0).to_numpy()\n","else:\n"," matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n","matrix"]},{"cell_type":"code","execution_count":5,"id":"708dda3c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"708dda3c","executionInfo":{"status":"ok","timestamp":1672119463518,"user_tz":-540,"elapsed":13601,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e404fb9c-9bd5-4466-95a6-79c39e5ea205"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n"," warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n"," warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["NMF(n_components=5)"]},"metadata":{},"execution_count":5}],"source":["from sklearn.decomposition import NMF\n","\n","# NMF 실행\n","nmf = NMF(n_components=factors)\n","nmf.fit(matrix)\n"]},{"cell_type":"code","execution_count":6,"id":"687a16b1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"687a16b1","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":12450,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"64397446-be94-4869-bc6c-61d351679634"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n"," warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n"," warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[0.02183306, 0.53589472, 0. , 0. , 0. ],\n"," [0.21316649, 0.10881363, 0. , 0. , 0. ],\n"," [0. , 0.02976153, 0.05290897, 0.15364729, 0.02014201],\n"," ...,\n"," [0.13635649, 0.14281654, 0. , 0. , 0.00656846],\n"," [0.09753435, 0.01077205, 0. , 0.13237952, 0.13516597],\n"," [0.99758645, 0. , 0. , 0.64704016, 0.0597298 ]])"]},"metadata":{},"execution_count":6}],"source":["P = nmf.fit_transform(matrix)\n","P"]},{"cell_type":"code","execution_count":7,"id":"a731dbb1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a731dbb1","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f6e9c43b-428e-4a65-e742-25a037fbe084"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1.96418272, 0.50117827, 0.39871676, ..., 0. , 0. ,\n"," 0. ],\n"," [1.15067071, 0.96077987, 0.40196914, ..., 0. , 0.00289232,\n"," 0.00216924],\n"," [0.47176639, 0. , 0. , ..., 0. , 0. ,\n"," 0. ],\n"," [0.79016059, 0.30417491, 0. , ..., 0.02803924, 0.01446954,\n"," 0.01085215],\n"," [0.29616062, 0.30060155, 0.17846866, ..., 0. , 0. ,\n"," 0. ]])"]},"metadata":{},"execution_count":7}],"source":["Q = nmf.components_\n","Q"]},{"cell_type":"code","execution_count":8,"id":"36669c15","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"36669c15","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":3,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6c3b7cb4-feb2-460f-8f90-42484580128c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[6.59522475e-01, 5.25819116e-01, 2.24118350e-01, ...,\n"," 0.00000000e+00, 1.54997784e-03, 1.16248338e-03],\n"," [5.43906588e-01, 2.11380357e-01, 1.28732773e-01, ...,\n"," 0.00000000e+00, 3.14723598e-04, 2.36042698e-04],\n"," [1.86577692e-01, 8.13846441e-02, 1.55579328e-02, ...,\n"," 4.30815294e-03, 2.30928521e-03, 1.73196391e-03],\n"," ...,\n"," [4.34109190e-01, 2.07528655e-01, 1.12947724e-01, ...,\n"," 0.00000000e+00, 4.13070817e-04, 3.09803112e-04],\n"," [3.48602292e-01, 1.40129296e-01, 6.73415036e-02, ...,\n"," 3.71182091e-03, 1.94662680e-03, 1.45997010e-03],\n"," [2.48839731e+00, 7.14736905e-01, 4.08414332e-01, ...,\n"," 1.81425136e-02, 9.36237290e-03, 7.02177967e-03]])"]},"metadata":{},"execution_count":8}],"source":["import numpy as np\n","# 예측 평갓값 행렬\n","pred_matrix = np.dot(P, Q)\n","pred_matrix"]},{"cell_type":"code","execution_count":9,"id":"08eb2849","metadata":{"id":"08eb2849","executionInfo":{"status":"ok","timestamp":1672119476234,"user_tz":-540,"elapsed":287,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용에 나타나지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다\n","average_score = movielens_train.rating.mean()\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results"]},{"cell_type":"code","execution_count":10,"id":"7aa822a0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7aa822a0","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":1330,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"12551108-c9b0-445a-fe32-eb39af8205c8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 149: [4226, 2571, 2762, 47, 3996, 318, 6539, 5445, 4963, 1704],\n"," 182: [47, 6874, 2329, 3793, 4011, 7361, 4878, 7438, 6333, 2502],\n"," 215: [2329, 527, 6711, 3147, 2683, 8360, 5669, 6502, 3948, 1246],\n"," 281: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 326: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 351: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n"," 357: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 426: [2502,\n"," 33794,\n"," 3949,\n"," 7147,\n"," 33493,\n"," 5902,\n"," 44191,\n"," 33166,\n"," 4848,\n"," 6016],\n"," 456: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 459: [318, 4886, 2997, 50, 4995, 4973, 7361, 4878, 3897, 2502],\n"," 494: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 517: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 524: [7153, 3996, 6539, 5349, 4886, 3793, 7361, 593, 7438, 527],\n"," 556: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 588: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 589: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 590: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 4306, 3996, 6539],\n"," 601: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 621: [5952, 7153, 4306, 2329, 4995, 4011, 4878, 1682, 3897, 7438],\n"," 634: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 672: [4993, 5952, 4226, 7153, 4306, 3996, 318, 6539, 5445, 6874],\n"," 701: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 719: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 745: [5952, 7153, 4306, 6539, 5445, 6874, 2329, 6377, 356, 4973],\n"," 757: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 775: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 780: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 4306, 3996],\n"," 785: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 788: [296, 4973, 4878, 3897, 1089, 2502, 1136, 1961, 2542, 4034],\n"," 870: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 987: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 988: [7153, 6539, 5445, 6874, 4963, 2329, 6377, 4995, 7361, 4027],\n"," 1020: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 1: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 22: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 26: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 30: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 34: [4993, 5952, 4226, 2858, 7153, 4306, 3996, 6539, 5445, 6874],\n"," 38: [4226, 2858, 4306, 6539, 5445, 4886, 2329, 3793, 2997, 50],\n"," 50: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 53: [2959, 4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996],\n"," 54: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 67: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 71: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 76: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 88: [4226, 296, 47, 4306, 3996, 318, 1704, 2997, 50, 4995],\n"," 89: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 94: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 95: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 100: [2959, 4993, 4226, 7153, 3578, 2762, 4306, 5445, 6874, 4963],\n"," 101: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 102: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 103: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 111: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 116: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 118: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 143: [7153, 6539, 6874, 6377, 50, 7361, 4027, 7438, 8961, 6711],\n"," 144: [1704, 4886, 3897, 5989, 2502, 2706, 3147, 1784, 3114, 4979],\n"," 162: [4226, 318, 4963, 1704, 2329, 2997, 50, 4011, 7361, 4878],\n"," 172: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 173: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 187: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 193: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 208: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 210: [2959, 5952, 47, 6539, 5445, 6874, 4963, 5349, 4886, 2329],\n"," 214: [2959, 5952, 2858, 7153, 4306, 318, 6539, 6874, 2329, 6377],\n"," 228: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 232: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 236: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 259: [3578, 318, 6377, 4011, 4027, 1089, 8961, 1961, 2542, 4034],\n"," 260: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 267: [4306, 1704, 4886, 4011, 4878, 1682, 5989, 2502, 6711, 4034],\n"," 268: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 271: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 274: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306],\n"," 287: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 292: [6874, 4963, 527, 6333, 5989, 2542, 3147, 5418, 1517, 3481],\n"," 296: [3996, 1704, 6377, 1136, 2683, 1270, 1784, 1258, 364, 1517],\n"," 303: [6539, 4963, 1704, 5349, 2329, 6377, 2997, 50, 4995, 4973],\n"," 307: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 310: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539],\n"," 315: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 320: [318, 6874, 1704, 5349, 2329, 3793, 2997, 50, 7361, 4878],\n"," 332: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 339: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 343: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 355: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 364: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 365: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 368: [2858, 2762, 318, 1704, 2329, 4995, 2028, 4878, 593, 7438],\n"," 381: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 382: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 383: [2959, 47, 4306, 6539, 6874, 4963, 5349, 2329, 356, 4011],\n"," 391: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 396: [4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 3996, 318],\n"," 398: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 406: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 409: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 410: 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4963, 6377, 4973, 4011, 7361, 1682, 3897, 7438],\n"," 468: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 469: [2959, 47, 2329, 50, 4995, 4011, 7361, 4878, 1682, 3897],\n"," 472: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 476: [1704, 4886, 4995, 7438, 2502, 2291, 1923, 1721, 4034, 1270],\n"," 480: [7153, 318, 6539, 6874, 1704, 2329, 6377, 7361, 4878, 1682],\n"," 482: [2858, 2762, 3996, 5445, 6874, 2997, 50, 4973, 1682, 3897],\n"," 493: [4993, 5952, 7153, 593, 7438, 6333, 2502, 1136, 6365, 1258],\n"," 496: [2959, 4993, 5952, 2858, 7153, 3578, 4306, 3996, 318, 6539],\n"," 501: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 504: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 505: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 511: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 516: [6377, 50, 7361, 1682, 4027, 2542, 33794, 364, 3481, 2918],\n"," 525: [2959, 4993, 5952, 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4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 581: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 585: [4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996, 6539],\n"," 593: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 604: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 605: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 609: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 628: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 629: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 645: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 650: [2959, 5952, 7153, 296, 4306, 6539, 5445, 6874, 4963, 4886],\n"," 656: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 657: [4306, 4963, 4886, 3793, 4011, 7438, 6333, 2502, 1136, 8961],\n"," 669: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 678: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 683: [2959, 5952, 4226, 2858, 2762, 296, 3996, 318, 6874, 4963],\n"," 688: [3996, 318, 5445, 4973, 2028, 7361, 4878, 1682, 3897, 2502],\n"," 689: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 693: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 716: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 720: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 734: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 735: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 739: [4993, 5952, 4226, 2858, 7153, 3578, 296, 4306, 3996, 318],\n"," 755: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 758: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 767: [2959, 4226, 2858, 296, 47, 4306, 5445, 4963, 1704, 4886],\n"," 769: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 786: [2959, 4993, 5952, 2858, 7153, 296, 47, 6539, 5445, 6874],\n"," 789: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 792: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 795: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 798: [2329, 6377, 4011, 7361, 4027, 2502, 2706, 4034, 1784, 3481],\n"," 799: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 802: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 806: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 807: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 816: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 827: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 828: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 846: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 859: [2858, 47, 4306, 318, 6874, 4963, 2329, 6377, 2997, 50],\n"," 862: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 876: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 881: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 885: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 4306, 3996],\n"," 886: [4993, 5952, 4226, 7153, 4306, 6539, 5445, 6874, 4963, 5349],\n"," 889: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 890: [4993, 5952, 4226, 7153, 47, 4306, 318, 6539, 5445, 6874],\n"," 891: [6539, 5445, 6874, 4963, 2329, 6377, 4995, 4011, 7361, 593],\n"," 896: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 897: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 898: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 908: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 922: [4226, 2858, 296, 3996, 5445, 6874, 1704, 2329, 3793, 2997],\n"," 930: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 938: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 956: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 958: [4993, 5952, 4226, 2858, 7153, 3578, 296, 47, 4306, 3996],\n"," 968: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 977: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 4306, 3996, 6539],\n"," 990: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 996: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 1004: [4993,\n"," 5952,\n"," 4226,\n"," 7153,\n"," 3578,\n"," 4306,\n"," 3996,\n"," 6539,\n"," 5445,\n"," 6874],\n"," 1005: [2959,\n"," 4993,\n"," 5952,\n"," 4226,\n"," 2571,\n"," 2858,\n"," 7153,\n"," 3578,\n"," 2762,\n"," 4306],\n"," 1008: [6539,\n"," 4963,\n"," 4995,\n"," 2028,\n"," 4011,\n"," 1682,\n"," 3897,\n"," 4027,\n"," 1089,\n"," 5989],\n"," 1029: [2959,\n"," 4993,\n"," 5952,\n"," 4226,\n"," 2571,\n"," 2858,\n"," 7153,\n"," 3578,\n"," 2762,\n"," 4306],\n"," 1037: [2959,\n"," 4993,\n"," 5952,\n"," 4226,\n"," 2571,\n"," 2858,\n"," 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[47, 6539, 6377, 7361, 3897, 1089, 7438, 527, 2291, 1961],\n"," 392: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 403: [2959, 4993, 5952, 2858, 3578, 4306, 3996, 318, 6539, 6874],\n"," 407: [4226, 2858, 318, 1704, 5349, 4995, 4973, 4011, 7361, 4878],\n"," 414: [4226, 7153, 3578, 4306, 318, 6539, 5445, 5349, 4886, 2329],\n"," 431: [2959, 4226, 2571, 2858, 3578, 2762, 296, 47, 3996, 5445],\n"," 454: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 465: [5952, 2571, 7153, 5445, 4963, 4886, 3793, 2028, 3897, 6333],\n"," 481: [2762, 6377, 2997, 4973, 4027, 527, 5989, 8961, 1961, 3147],\n"," 485: [4993, 5952, 4226, 7153, 47, 4306, 3996, 6539, 5445, 6874],\n"," 503: [47, 3996, 4963, 1704, 5349, 50, 4011, 3897, 4027, 1089],\n"," 513: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539],\n"," 545: [3578, 4306, 6539, 5445, 4963, 5349, 3793, 1682, 6333, 2706],\n"," 552: [4993, 5952, 2571, 7153, 3578, 47, 3996, 5445, 6874, 4963],\n"," 554: [4226, 2571, 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6874, 1704],\n"," 970: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 972: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 1001: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1013: [2959, 4226, 2858, 3578, 2762, 47, 6539, 5445, 6874, 1704],\n"," 1018: [3578, 4963, 1704, 6377, 50, 4995, 4027, 527, 2502, 8961],\n"," 1028: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 1031: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1035: [3996, 6539, 4963, 1704, 2329, 2997, 50, 4995, 4973, 4011],\n"," 1038: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1045: [5952,\n"," 3996,\n"," 4973,\n"," 6711,\n"," 2542,\n"," 33794,\n"," 5418,\n"," 2692,\n"," 1580,\n"," 480],\n"," 1046: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 35: [5952, 7153, 3578, 296, 318, 6539, 1704, 5349, 4886, 6377],\n"," 72: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 91: [2959, 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2858, 7153, 3578, 2762, 4306],\n"," 1006: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1026: [2858, 296, 47, 3996, 318, 6539, 5445, 356, 2997, 50],\n"," 1047: [5952, 6539, 1704, 593, 7438, 5989, 2291, 1961, 2683, 5418],\n"," 65: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 3996],\n"," 135: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 152: [2959, 4993, 5952, 4226, 2858, 7153, 296, 4306, 3996, 6539],\n"," 166: [2959, 4226, 2571, 2858, 7153, 3578, 296, 47, 4306, 3996],\n"," 179: [2959, 5952, 4226, 2858, 7153, 2762, 47, 318, 6539, 6874],\n"," 188: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 217: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 253: [2959, 4993, 5952, 4226, 7153, 2762, 296, 47, 4306, 318],\n"," 262: [2858, 2329, 6377, 1682, 3897, 4027, 1089, 5989, 1136, 6711],\n"," 277: [2959, 4226, 2571, 2858, 3578, 47, 4306, 3996, 318, 6539],\n"," 289: [6377, 4878, 7438, 6333, 2502, 2542, 6365, 2683, 5418, 364],\n"," 300: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 47, 4306, 3996],\n"," 302: [4993, 5952, 4226, 7153, 47, 4306, 3996, 6539, 5445, 6874],\n"," 308: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 311: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 328: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 4306],\n"," 329: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 344: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 347: [2959, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47, 3996],\n"," 358: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 3996, 6539],\n"," 474: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 483: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 509: [4993, 3578, 4306, 318, 6539, 5445, 6377, 2997, 4973, 4878],\n"," 578: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n"," 643: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 47, 4306],\n"," 679: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 680: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 318],\n"," 691: [2959, 4993, 5952, 4226, 2858, 7153, 2762, 47, 4306, 3996],\n"," 702: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 708: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 730: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 751: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 773: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n"," 803: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 47, 4306, 3996],\n"," 809: [2959, 4226, 2571, 7153, 3578, 296, 4306, 3996, 318, 6539],\n"," 820: [2762, 318, 1704, 2329, 4995, 4011, 3897, 527, 5989, 2542],\n"," 824: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 4306],\n"," 863: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 4306, 3996, 6539],\n"," 865: [4993, 5952, 2762, 3996, 6539, 5445, 6874, 5349, 4886, 2329],\n"," 867: [2762, 47, 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386: [4993, 5952, 2571, 7153, 47, 4306, 6539, 5445, 6874, 4963],\n"," 397: [4993, 5952, 7153, 47, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 420: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 439: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 449: [4993, 5952, 4226, 7153, 296, 4306, 3996, 318, 6539, 5445],\n"," 478: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 487: [4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539, 5445],\n"," 489: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 500: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 510: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 521: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 522: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 527: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 529: [2959, 4993, 5952, 2571, 2858, 7153, 3578, 2762, 47, 3996],\n"," 535: [4226, 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4963],\n"," 839: [2959, 2571, 2858, 3578, 296, 47, 4306, 3996, 318, 6874],\n"," 840: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 851: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 47, 3996, 318],\n"," 855: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 857: [2959, 4226, 2858, 2762, 296, 4306, 3996, 318, 5445, 1704],\n"," 868: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 4306, 3996],\n"," 904: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 905: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 906: [7153, 3578, 47, 4306, 318, 6539, 5445, 5349, 4886, 6377],\n"," 924: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 925: [4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47, 4306],\n"," 927: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 940: [2959, 5952, 4226, 7153, 3578, 2762, 296, 47, 4306, 3996],\n"," 948: [2959, 7153, 3578, 4306, 6539, 5445, 6874, 4963, 5349, 4886],\n"," 953: [2959, 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3996,\n"," 6539],\n"," 1039: [2959,\n"," 4993,\n"," 5952,\n"," 4226,\n"," 2571,\n"," 2858,\n"," 7153,\n"," 3578,\n"," 2762,\n"," 4306],\n"," 1040: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 1053: [2959, 4993, 5952, 4226, 7153, 296, 47, 3996, 318, 6874],\n"," 704: [2858, 7153, 296, 318, 6539, 6874, 2329, 6377, 356, 2997],\n"," 934: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 42: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 73: [4993, 5952, 4226, 7153, 3578, 4306, 3996, 6539, 5445, 6874],\n"," 82: [2959, 2858, 7153, 3578, 2762, 47, 3996, 318, 5445, 1704],\n"," 159: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 161: [4306, 4963, 1704, 2329, 356, 2997, 4995, 2028, 4011, 1682],\n"," 192: [4993, 5952, 7153, 2762, 3996, 318, 6539, 2329, 356, 4995],\n"," 216: [2959, 4226, 7153, 3578, 296, 6539, 5445, 6874, 1704, 6377],\n"," 219: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n"," 290: [2959, 4993, 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4306],\n"," 213: [2959, 4993, 5952, 4226, 7153, 3578, 4306, 3996, 318, 6539],\n"," 243: [4993, 5952, 4226, 7153, 3578, 296, 47, 4306, 3996, 318],\n"," 323: [2959, 4226, 2571, 2858, 3578, 2762, 47, 4306, 3996, 318],\n"," 352: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 429: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 625: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 808: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 843: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 847: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 963: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 975: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 998: [4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47, 318],\n"," 75: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 47, 4306],\n"," 427: [2959, 4993, 5952, 4226, 2858, 7153, 296, 4306, 3996, 5445],\n"," 466: 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296],\n"," 113: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 120: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 123: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 157: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 194: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 200: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 204: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 205: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 225: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 229: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 237: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 47, 4306],\n"," 242: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 252: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 266: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 4306],\n"," 270: 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4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 594: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 607: [2959, 4993, 2571, 2858, 7153, 3578, 2762, 296, 47, 4306],\n"," 631: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 651: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 664: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 685: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 690: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 696: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 4306, 3996],\n"," 724: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 738: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 762: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 763: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 770: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 796: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 800: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 829: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 836: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 882: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 900: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 903: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 914: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 918: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 920: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 945: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 950: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 952: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 982: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 989: [2959, 4993, 5952, 4226, 7153, 2762, 47, 4306, 3996, 318],\n"," 1016: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 1019: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1044: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1051: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 917: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 951: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 997: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 174: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 676: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 764: [4993, 5952, 4226, 2571, 7153, 3578, 47, 4306, 3996, 6539],\n"," 1052: [4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47, 4306],\n"," 5: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 27: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 33: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 134: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 142: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 156: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 167: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 177: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 224: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 269: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 312: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 360: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 385: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 411: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 464: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47],\n"," 568: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 612: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 630: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 706: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 737: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 749: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 756: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 811: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 853: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 884: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 955: [2959, 4993, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47],\n"," 1032: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1043: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 370: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 670: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 923: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 931: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 969: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 12: [4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996, 318],\n"," 597: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 195: [2959, 5952, 2571, 7153, 47, 318, 6539, 5445, 6874, 1704],\n"," 337: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 910: [2571, 3578, 2762, 6539, 5445, 4963, 5349, 356, 2997, 4973],\n"," 63: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 70: [2959, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47, 3996],\n"," 99: [2959, 5952, 2571, 2858, 7153, 3578, 47, 4306, 3996, 6539],\n"," 121: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 47, 4306],\n"," 130: [2959, 4226, 2571, 3578, 2762, 47, 4306, 318, 6539, 5445],\n"," 244: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 291: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 335: [2959, 5952, 2571, 2858, 7153, 2762, 296, 47, 4306, 318],\n"," 361: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 4306, 3996],\n"," 470: [4993, 7153, 3578, 4306, 4963, 1704, 5349, 4886, 3793, 356],\n"," 497: [2959, 5952, 2858, 3578, 296, 47, 3996, 6539, 5445, 6874],\n"," 620: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 648: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 666: [3578, 2762, 4306, 318, 6539, 5445, 4963, 1704, 5349, 4886],\n"," 710: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 794: [2959, 4993, 5952, 2571, 2858, 3578, 296, 47, 4306, 3996],\n"," 854: [4993, 5952, 4226, 2571, 7153, 3578, 47, 4306, 3996, 318],\n"," 861: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 47, 4306],\n"," 87: [4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996, 318],\n"," 317: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 324: [2858, 2762, 296, 47, 4306, 3996, 318, 6539, 6874, 4963],\n"," 502: [4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306, 3996],\n"," 559: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 973: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47],\n"," 1015: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 1017: [2959, 4226, 2571, 2858, 3578, 2762, 296, 47, 4306, 3996],\n"," 85: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 318],\n"," 306: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 653: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 371: [2959, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47, 4306],\n"," 566: [2959, 4993, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47],\n"," 331: [5952, 3578, 2762, 296, 47, 4306, 3996, 5445, 1704, 5349],\n"," 665: [2959, 4993, 2571, 7153, 3578, 2762, 47, 4306, 318, 6539],\n"," 872: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 879: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 486: [2959, 4993, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 864: [4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306, 3996],\n"," 52: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 288: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 9: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 117: [2959, 4993, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 220: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 544: [4226, 2858, 3578, 2762, 4306, 3996, 318, 6539, 5445, 4963],\n"," 999: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 458: [2959, 4993, 5952, 2571, 7153, 296, 47, 4306, 3996, 318],\n"," 974: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 546: [2959, 4226, 2571, 2858, 2762, 296, 47, 6874, 4963, 1704],\n"," 55: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 363: [4226, 2571, 2858, 3578, 2762, 47, 4306, 3996, 318, 6539],\n"," 445: [2959, 4993, 4226, 2571, 2858, 3578, 2762, 296, 47, 4306],\n"," 492: [2959, 4993, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 234: [4993, 5952, 7153, 3578, 2762, 296, 47, 318, 6539, 6874],\n"," 1027: [4993, 5952, 2571, 2858, 7153, 2762, 47, 4306, 3996, 318],\n"," 140: [2959, 4993, 5952, 2571, 3578, 2762, 296, 47, 4306, 3996],\n"," 926: [2959, 4993, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47],\n"," 781: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 825: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 913: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 453: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 540: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 66: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306, 3996],\n"," 185: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 222: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 498: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 994: [2959, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 165: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 202: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 180: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 93: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 261: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 435: [4993, 5952, 2858, 7153, 3578, 2762, 296, 4306, 3996, 318],\n"," 322: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 238: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 425: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 512: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 725: [4993, 5952, 4226, 7153, 3578, 296, 47, 4306, 3996, 318],\n"," 732: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 108: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 592: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 443: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 916: [5952, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47, 4306],\n"," 736: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47],\n"," 961: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306],\n"," 1012: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 515: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 978: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 28: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 475: [4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 598: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 943: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 520: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 697: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 849: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1003: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 296, 47, 4306],\n"," 705: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 48: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 29: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 231: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 699: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n"," 448: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 750: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n"," 782: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 860: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 768: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 127: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 894: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]})"]},"metadata":{},"execution_count":10}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," if user_id not in user_id2index:\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":11,"id":"27b87c98","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"27b87c98","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":25,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3b53ae05-8f2b-4bcd-e3e5-72cac813ffcf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2의 사용자가 학습 데이터에 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"1940df09","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1940df09","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c9d4698b-99ae-48dd-8628-92b6791aaa0e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]"]},"metadata":{},"execution_count":12}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":13,"id":"d1786a85","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"d1786a85","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9bf45053-0677-4bd4-b3e2-e27b4f1c6384"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","2874 2959 Fight Club (1999) \n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","5852 5952 Lord of the Rings: The Two Towers, The (2002) \n","\n"," genre \\\n","2874 [Action, Crime, Drama, Thriller] \n","4899 [Action, Adventure, Fantasy] \n","5852 [Action, Adventure, Fantasy] \n","\n"," tag \n","2874 [based on a book, chuck palahniuk, edward nort... \n","4899 [based on a book, big budget, new zealand, sce... \n","5852 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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\n"," "]},"metadata":{},"execution_count":13}],"source":["# user_id=2에 대한 추천(2959, 4993, 5952)\n","movies[movies.movie_id.isin([2959, 4993, 5952])]"]},{"cell_type":"code","execution_count":13,"id":"441ff6da","metadata":{"id":"441ff6da","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":4,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/Popularity.ipynb b/chapter5/colab/Popularity.ipynb index 82b7723..4ba90e7 100644 --- a/chapter5/colab/Popularity.ipynb +++ b/chapter5/colab/Popularity.ipynb @@ -1,1812 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b058b784", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Popularity.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "4ffa2166", - "metadata": {}, - "source": [ - "# 인기도 순 추천\n", - "## 인기도의 정의\n", - "* 여기에서는 평갓값이 높은 것을 인기가 높은 영화로 간주합니다\n", - "* 인기도는 '클릭 수가 많은 것', '구매가 많은 것', '평갓값이 높은 것' 등 다양하게 정의할 수 있으므로 자사 서비스에 맞춰 적합한 정의를 사용합니다.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dddeebb1", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "373c704d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "95116bab", - "metadata": {}, - "outputs": [], - "source": [ - "# 평가 수의 임곗값\n", - "minimum_num_rating = 200\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "035cfe3b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rating
sizemean
movie_idtitle
4095Cry Freedom (1987)15.0
7227Trouble with Angels, The (1966)15.0
27255Wind Will Carry Us, The (Bad ma ra khahad bord) (1999)15.0
4453Michael Jordan to the Max (2000)25.0
3415Mirror, The (Zerkalo) (1975)15.0
\n", - "
" - ], - "text/plain": [ - " rating \n", - " size mean\n", - "movie_id title \n", - "4095 Cry Freedom (1987) 1 5.0\n", - "7227 Trouble with Angels, The (1966) 1 5.0\n", - "27255 Wind Will Carry Us, The (Bad ma ra khahad bord)... 1 5.0\n", - "4453 Michael Jordan to the Max (2000) 2 5.0\n", - "3415 Mirror, The (Zerkalo) (1975) 1 5.0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "# 평갓값의 평균이 높은 영화를 확인한다\n", - "# 평가 수가 1건인 영화가 상위에 여럿 나타난다\n", - "movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", - "movie_stats.sort_values(by=('rating', 'mean'), ascending=False).head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "fcfef4c3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rating
sizemean
movie_idtitle
318Shawshank Redemption, The (1994)4244.491745
50Usual Suspects, The (1995)3344.459581
912Casablanca (1942)1634.444785
904Rear Window (1954)1294.441860
2019Seven Samurai (Shichinin no samurai) (1954)1044.408654
\n", - "
" - ], - "text/plain": [ - " rating \n", - " size mean\n", - "movie_id title \n", - "318 Shawshank Redemption, The (1994) 424 4.491745\n", - "50 Usual Suspects, The (1995) 334 4.459581\n", - "912 Casablanca (1942) 163 4.444785\n", - "904 Rear Window (1954) 129 4.441860\n", - "2019 Seven Samurai (Shichinin no samurai) (1954) 104 4.408654" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 임곗값을 도입해 평가 수가 적은 영화를 제거한다\n", - "# 쇼생크 탈출이나 7인의 사무라이 등 익숙한 영화가 상위에 나타난다\n", - "movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", - "atleast_flg = movie_stats['rating']['size'] >= 100\n", - "movies_sorted_by_rating = movie_stats[atleast_flg].sort_values(by=('rating', 'mean'), ascending=False)\n", - "movies_sorted_by_rating.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2827d6f8", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "# 각 아이템별로 평균 평갓값을 계싼하고, 해당 평균 평갓값을 예측값으로 사용한다\n", - "movie_rating_average = movielens_train.groupby(\"movie_id\").agg({\"rating\": np.mean})\n", - "# 테스트 데이터에 예측값을 저장한다. 테스트 데이터에만 존재하는 아이템의 예측 평갓값은 0으로 한다.\n", - "movie_rating_predict = movielens_test.merge(\n", - " movie_rating_average, on=\"movie_id\", how=\"left\", suffixes=(\"_test\", \"_pred\")\n", - ").fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e18fcf5b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {139: [50, 858, 1193, 1214, 1200, 1291, 457, 150, 2396, 34],\n", - " 149: [318, 527, 260, 1193, 593, 2571, 1136, 2028, 1197, 2762],\n", - " 182: [260, 541, 1198, 1214, 1200, 47, 1036, 1291, 1210, 1240],\n", - " 215: [527, 590, 1073, 539, 253, 2683, 231],\n", - " 281: [858, 260, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 1136],\n", - " 326: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n", - " 351: [541, 2959, 2028, 2762, 1200, 3578, 47, 1291, 1240, 589],\n", - " 357: [318, 50, 858, 260, 1193, 593, 541, 2959, 1196, 2858],\n", - 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" 719: [858, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617, 1136],\n", - " 745: [1136, 356, 457, 34, 1073, 588, 357, 21, 539, 380],\n", - " 757: [318, 858, 527, 1193, 593, 2571, 2959, 2858, 1617, 2028],\n", - " 775: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 780: [2571, 2959, 2858, 3578, 1, 595, 1580, 165, 10, 316],\n", - " 785: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n", - " 788: [260, 1193, 541, 1196, 1136, 1197, 608, 296, 1210, 150],\n", - " 870: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n", - " 987: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n", - " 988: [527, 110, 2396, 733, 34, 1073, 480, 25, 539, 141],\n", - " 1020: [318, 50, 260, 2571, 541, 2959, 1196, 2858, 1198, 1136],\n", - " 1: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 22: [318, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n", - " 26: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 30: [50, 858, 2571, 541, 2959, 2858, 1617, 2028, 1197, 296],\n", - 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" 331: [858, 527, 260, 1193, 593, 541, 1196, 1198, 1617, 1136],\n", - " 665: [318, 50, 858, 527, 260, 593, 2571, 2959, 1196, 1198],\n", - " 872: [318, 858, 527, 260, 593, 2571, 541, 2959, 1196, 2858],\n", - " 879: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n", - " 486: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 864: [318, 50, 858, 260, 1193, 593, 2571, 541, 1196, 1198],\n", - " 52: [318, 858, 527, 260, 593, 2571, 541, 2959, 1196, 2858],\n", - " 288: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 9: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 117: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 220: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 544: [318, 858, 527, 260, 1193, 593, 541, 2858, 1198, 1617],\n", - " 999: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 458: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n", - " 974: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 546: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 55: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n", - " 363: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2858],\n", - " 445: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 492: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 234: [318, 858, 260, 1193, 593, 541, 1196, 1198, 1136, 2028],\n", - " 1027: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n", - " 140: [858, 260, 2571, 541, 2959, 1196, 1198, 1617, 1136, 2028],\n", - " 926: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 781: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 825: [318, 50, 858, 527, 260, 2571, 541, 2959, 1196, 2858],\n", - " 913: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n", - " 453: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 540: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 66: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n", - " 185: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 222: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 498: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 994: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 165: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 202: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 180: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 93: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 261: [318, 50, 858, 527, 260, 593, 541, 2959, 1196, 2858],\n", - " 435: [318, 50, 858, 527, 260, 1193, 593, 1196, 2858, 1198],\n", - " 322: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 238: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 425: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 512: [50, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 1198],\n", - " 725: [318, 50, 858, 527, 260, 1193, 593, 541, 1196, 1198],\n", - 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" 697: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 849: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 1003: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n", - " 705: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 48: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 29: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 231: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n", - " 699: [858, 527, 260, 1193, 593, 2571, 541, 1196, 1198, 1617],\n", - " 448: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 750: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n", - " 782: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 860: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 768: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 127: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n", - " 894: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959]})" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 평갓값이 높은 10작품으로 한다\n", - "# 단, 평가 건수가 작으면 노이즈가 크므로 minimum_num_rating건 이상 평가가 있는 영화로 필터링한다\n", - "pred_user2items = defaultdict(list)\n", - "user_watched_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "movie_stats = movielens_train.groupby(\"movie_id\").agg({\"rating\": [np.size, np.mean]})\n", - "atleast_flg = movie_stats[\"rating\"][\"size\"] >= minimum_num_rating\n", - "movies_sorted_by_rating = (\n", - " movie_stats[atleast_flg].sort_values(by=(\"rating\", \"mean\"), ascending=False).index.tolist()\n", - ")\n", - "\n", - "for user_id in movielens_train.user_id.unique():\n", - " for movie_id in movies_sorted_by_rating:\n", - " if movie_id not in user_watched_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f242af0f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "44ab20ed", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[318, 50, 527, 1193, 593, 2571, 541, 2959, 1196, 2858]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_user2items[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8c8b00e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
4950Usual Suspects, The (1995)[Crime, Mystery, Thriller][kevin spacey, ensemble cast, complicated, mus...
315318Shawshank Redemption, The (1994)[Drama][based on a short story, directorial debut, fr...
523527Schindler's List (1993)[Drama, War][speilberg, drama, holocaust, steven spielberg...
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" - ], - "text/plain": [ - " movie_id title genre \\\n", - "49 50 Usual Suspects, The (1995) [Crime, Mystery, Thriller] \n", - "315 318 Shawshank Redemption, The (1994) [Drama] \n", - "523 527 Schindler's List (1993) [Drama, War] \n", - "\n", - " tag \n", - "49 [kevin spacey, ensemble cast, complicated, mus... \n", - "315 [based on a short story, directorial debut, fr... \n", - "523 [speilberg, drama, holocaust, steven spielberg... " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(318, 50, 527)\n", - "movies[movies.movie_id.isin([318, 50, 527])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9deafa9b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"b058b784","metadata":{"id":"b058b784"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Popularity.ipynb)"]},{"cell_type":"markdown","id":"4ffa2166","metadata":{"id":"4ffa2166"},"source":["# 인기도 순 추천\n","## 인기도의 정의\n","* 여기에서는 평갓값이 높은 것을 인기가 높은 영화로 간주합니다\n","* 인기도는 '클릭 수가 많은 것', '구매가 많은 것', '평갓값이 높은 것' 등 다양하게 정의할 수 있으므로 자사 서비스에 맞춰 적합한 정의를 사용합니다.\n"]},{"cell_type":"code","execution_count":1,"id":"dddeebb1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dddeebb1","executionInfo":{"status":"ok","timestamp":1672119640719,"user_tz":-540,"elapsed":3067,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"306ffbe3-8bf3-43c5-c8e0-9fd045d29c13"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:40:34-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 92.1MB/s in 0.7s \n","\n","2022-12-27 05:40:35 (92.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"373c704d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"373c704d","executionInfo":{"status":"ok","timestamp":1672119702658,"user_tz":-540,"elapsed":61943,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5d00a5b5-d98f-456c-df97-ce9f96a23767"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"95116bab","metadata":{"id":"95116bab","executionInfo":{"status":"ok","timestamp":1672119702658,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 평가 수의 임곗값\n","minimum_num_rating = 200\n"]},{"cell_type":"code","execution_count":4,"id":"035cfe3b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"035cfe3b","executionInfo":{"status":"ok","timestamp":1672119702658,"user_tz":-540,"elapsed":22,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"54cf3a99-c9e4-4249-bdcc-a3e9245763a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" rating \n"," size mean\n","movie_id title \n","4095 Cry Freedom (1987) 1 5.0\n","7227 Trouble with Angels, The (1966) 1 5.0\n","27255 Wind Will Carry Us, The (Bad ma ra khahad bord)... 1 5.0\n","4453 Michael Jordan to the Max (2000) 2 5.0\n","3415 Mirror, The (Zerkalo) (1975) 1 5.0"],"text/html":["\n","
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rating
sizemean
movie_idtitle
4095Cry Freedom (1987)15.0
7227Trouble with Angels, The (1966)15.0
27255Wind Will Carry Us, The (Bad ma ra khahad bord) (1999)15.0
4453Michael Jordan to the Max (2000)25.0
3415Mirror, The (Zerkalo) (1975)15.0
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\n"," "]},"metadata":{},"execution_count":4}],"source":["import numpy as np\n","\n","# 평갓값의 평균이 높은 영화를 확인한다\n","# 평가 수가 1건인 영화가 상위에 여럿 나타난다\n","movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n","movie_stats.sort_values(by=('rating', 'mean'), ascending=False).head()"]},{"cell_type":"code","execution_count":5,"id":"fcfef4c3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"fcfef4c3","executionInfo":{"status":"ok","timestamp":1672119702659,"user_tz":-540,"elapsed":7,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3b1d6192-9d79-434e-a9ff-349d906d2b04"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" rating \n"," size mean\n","movie_id title \n","318 Shawshank Redemption, The (1994) 424 4.491745\n","50 Usual Suspects, The (1995) 334 4.459581\n","912 Casablanca (1942) 163 4.444785\n","904 Rear Window (1954) 129 4.441860\n","2019 Seven Samurai (Shichinin no samurai) (1954) 104 4.408654"],"text/html":["\n","
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rating
sizemean
movie_idtitle
318Shawshank Redemption, The (1994)4244.491745
50Usual Suspects, The (1995)3344.459581
912Casablanca (1942)1634.444785
904Rear Window (1954)1294.441860
2019Seven Samurai (Shichinin no samurai) (1954)1044.408654
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\n"," "]},"metadata":{},"execution_count":5}],"source":["# 임곗값을 도입해 평가 수가 적은 영화를 제거한다\n","# 쇼생크 탈출이나 7인의 사무라이 등 익숙한 영화가 상위에 나타난다\n","movie_stats = movielens_train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n","atleast_flg = movie_stats['rating']['size'] >= 100\n","movies_sorted_by_rating = movie_stats[atleast_flg].sort_values(by=('rating', 'mean'), ascending=False)\n","movies_sorted_by_rating.head()"]},{"cell_type":"code","execution_count":6,"id":"2827d6f8","metadata":{"id":"2827d6f8","executionInfo":{"status":"ok","timestamp":1672119703181,"user_tz":-540,"elapsed":528,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np \n","# 각 아이템별로 평균 평갓값을 계싼하고, 해당 평균 평갓값을 예측값으로 사용한다\n","movie_rating_average = movielens_train.groupby(\"movie_id\").agg({\"rating\": np.mean})\n","# 테스트 데이터에 예측값을 저장한다. 테스트 데이터에만 존재하는 아이템의 예측 평갓값은 0으로 한다.\n","movie_rating_predict = movielens_test.merge(\n"," movie_rating_average, on=\"movie_id\", how=\"left\", suffixes=(\"_test\", \"_pred\")\n",").fillna(0)"]},{"cell_type":"code","execution_count":7,"id":"e18fcf5b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e18fcf5b","executionInfo":{"status":"ok","timestamp":1672119703182,"user_tz":-540,"elapsed":529,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"eef6631e-96dd-4508-aee6-8f8a1b0ca23a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [50, 858, 1193, 1214, 1200, 1291, 457, 150, 2396, 34],\n"," 149: [318, 527, 260, 1193, 593, 2571, 1136, 2028, 1197, 2762],\n"," 182: [260, 541, 1198, 1214, 1200, 47, 1036, 1291, 1210, 1240],\n"," 215: [527, 590, 1073, 539, 253, 2683, 231],\n"," 281: [858, 260, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 326: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 351: [541, 2959, 2028, 2762, 1200, 3578, 47, 1291, 1240, 589],\n"," 357: [318, 50, 858, 260, 1193, 593, 541, 2959, 1196, 2858],\n"," 426: [1073, 141, 329],\n"," 456: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 459: [318, 50, 858, 1193, 1198, 1617, 1197, 608, 457, 1265],\n"," 494: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 517: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 524: [527, 260, 1193, 593, 1196, 1198, 1617, 1136, 1197, 608],\n"," 556: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 588: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 589: [318, 2571, 2959, 2858, 1617, 1136, 2028, 1197, 2762, 3578],\n"," 590: [858, 527, 1193, 541, 2959, 2858, 1617, 1136, 1197, 1214],\n"," 601: [50, 858, 2571, 541, 2959, 2858, 1617, 1136, 2028, 608],\n"," 621: [110, 34, 595, 25, 141, 597, 253, 587, 500, 454],\n"," 634: [318, 50, 858, 527, 1193, 2571, 2959, 2858, 2028, 296],\n"," 672: [318, 1193, 541, 1136, 1214, 1200, 1036, 1240, 457, 1270],\n"," 701: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 719: [858, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 745: [1136, 356, 457, 34, 1073, 588, 357, 21, 539, 380],\n"," 757: [318, 858, 527, 1193, 593, 2571, 2959, 2858, 1617, 2028],\n"," 775: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 780: [2571, 2959, 2858, 3578, 1, 595, 1580, 165, 10, 316],\n"," 785: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 788: [260, 1193, 541, 1196, 1136, 1197, 608, 296, 1210, 150],\n"," 870: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 987: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 988: [527, 110, 2396, 733, 34, 1073, 480, 25, 539, 141],\n"," 1020: [318, 50, 260, 2571, 541, 2959, 1196, 2858, 1198, 1136],\n"," 1: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 22: [318, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 26: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 30: [50, 858, 2571, 541, 2959, 2858, 1617, 2028, 1197, 296],\n"," 34: [1193, 2858, 1617, 1214, 1036, 1291, 2396, 1270, 2716, 1097],\n"," 38: [50, 858, 527, 260, 1193, 541, 1196, 2858, 1198, 1617],\n"," 50: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 53: [318, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 1198],\n"," 54: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 67: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 71: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 76: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 88: [318, 50, 858, 527, 1193, 593, 541, 1617, 2028, 608],\n"," 89: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 94: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 95: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 100: [1193, 541, 2959, 1617, 1136, 1197, 2762, 1214, 1200, 3578],\n"," 101: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 102: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 103: [318, 858, 1193, 593, 2571, 2959, 2858, 1617, 2028, 1197],\n"," 111: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 116: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 118: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 143: [50, 541, 1617, 1036, 1240, 589, 349, 165, 253, 587],\n"," 144: [1193, 1197, 2396, 590, 1073, 595, 349, 539, 587, 39],\n"," 162: [318, 50, 1193, 593, 541, 1617, 1197, 608, 1200, 1036],\n"," 172: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 173: [2571, 541, 2959, 2858, 1617, 1136, 2028, 1197, 2762, 110],\n"," 187: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 193: [858, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 1136, 2028],\n"," 208: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 210: [50, 858, 527, 260, 1193, 2959, 1196, 1198, 1617, 1136],\n"," 214: [318, 1193, 541, 2959, 2858, 2028, 1214, 1200, 32, 349],\n"," 228: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 232: [1193, 2571, 2959, 2858, 1617, 1136, 2028, 1197, 2762, 3578],\n"," 236: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 259: [318, 858, 1193, 541, 110, 3578, 1036, 1, 1240, 589],\n"," 260: [318, 50, 527, 2571, 2959, 2858, 1617, 2028, 1197, 2762],\n"," 267: [1197, 2396, 2716, 364, 1265, 34, 1073, 588, 595, 349],\n"," 268: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 271: [318, 50, 858, 527, 1193, 593, 2571, 2959, 2858, 1198],\n"," 274: [318, 50, 858, 527, 1193, 593, 2571, 2959, 1198, 1617],\n"," 287: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 292: [527, 1617, 110, 1036, 457, 2396, 34, 590, 595, 349],\n"," 296: [260, 1196, 1198, 1617, 1136, 1197, 608, 110, 1291, 1210],\n"," 303: [50, 1193, 1617, 1197, 1036, 1, 457, 2396, 1270, 733],\n"," 307: [318, 50, 858, 527, 1193, 593, 2959, 2858, 1617, 1136],\n"," 310: [1193, 2959, 3578, 47, 1210, 150, 588, 141, 10, 500],\n"," 315: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 320: [318, 50, 858, 1193, 541, 1198, 1197, 608, 1200, 1291],\n"," 332: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 339: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 343: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 355: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 364: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 365: [318, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 368: [318, 527, 1193, 593, 2858, 2028, 2762, 2396, 34, 349],\n"," 381: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 382: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 383: [527, 2959, 1136, 1197, 110, 1200, 47, 1036, 1291, 1210],\n"," 391: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 396: [318, 50, 858, 527, 1193, 593, 1617, 1136, 1197, 608],\n"," 398: [318, 50, 858, 1193, 2571, 541, 2959, 2858, 1617, 2028],\n"," 406: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 409: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 410: [260, 2571, 2959, 2858, 1617, 1136, 2028, 296, 2762, 3578],\n"," 416: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 418: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 419: [2959, 1196, 2028, 3578, 1210, 1, 356, 150, 34, 25],\n"," 421: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 424: [50, 858, 593, 2571, 2959, 2028, 1197, 296, 2762, 1214],\n"," 432: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 434: [318, 50, 1136, 1197, 296, 47, 150, 2396, 32, 2716],\n"," 436: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 438: [50, 527, 2959, 2028, 608, 47, 150, 1270, 588, 357],\n"," 441: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 446: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 452: [50, 858, 260, 1193, 593, 541, 2959, 1196, 2858, 1198],\n"," 460: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 463: [2396, 364, 1265, 590, 588, 595, 357, 539, 380, 165],\n"," 468: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 469: [50, 858, 1193, 2959, 1617, 1197, 110, 47, 733, 1073],\n"," 472: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 476: [858, 1193, 1197, 1, 150, 1270, 2716, 1265, 34, 1097],\n"," 480: [318, 527, 1193, 1036, 457, 150, 2396, 733, 364, 34],\n"," 482: [50, 593, 541, 2858, 1617, 1197, 608, 2762, 1214, 110],\n"," 493: [593, 1196, 1198, 1136, 1214, 1200, 1240, 589, 733, 1265],\n"," 496: [318, 50, 858, 527, 260, 1193, 541, 2959, 1196, 2858],\n"," 501: [318, 50, 858, 527, 541, 2959, 2858, 1617, 296, 2762],\n"," 504: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 505: [50, 527, 1193, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 511: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 516: [50, 1193, 1197, 608, 1036, 2396, 364, 1073, 349, 25],\n"," 525: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 530: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 531: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 533: [150, 1097, 349, 21, 380, 165, 10, 2683, 367, 288],\n"," 536: [318, 50, 858, 527, 1193, 2571, 541, 2959, 1196, 2858],\n"," 543: [2028, 21, 10, 288],\n"," 547: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 549: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 553: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 558: [1193, 541, 2762, 316, 329],\n"," 562: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 567: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 571: [1193, 593, 541, 2959, 608, 296, 2762, 1214, 110, 356],\n"," 572: [318, 3578, 1036, 150, 2396, 34, 1097, 590, 588, 357],\n"," 577: [527, 260, 1193, 2571, 541, 2959, 2858, 1617, 1136, 2028],\n"," 581: [50, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 585: [2762, 110, 3578, 47, 733, 597, 253, 292, 288, 344],\n"," 593: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 604: [1193, 2571, 541, 2959, 1196, 2858, 1198, 1617, 1136, 2028],\n"," 605: [858, 593, 2571, 2959, 2858, 1617, 2028, 1197, 2762, 3578],\n"," 609: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 628: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 629: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 645: [50, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 650: [527, 593, 2959, 1617, 1136, 2028, 296, 110, 1200, 457],\n"," 656: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 657: [1136, 1200, 1240, 39, 316, 329, 288],\n"," 669: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 678: [2571, 2959, 2858, 1617, 2028, 2762, 3578, 47, 1, 2396],\n"," 683: [318, 50, 527, 1193, 593, 541, 2959, 2858, 1617, 1136],\n"," 688: [318, 1617, 2028, 1197, 457, 1265, 34, 1073, 588, 595],\n"," 689: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 693: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 716: [858, 1193, 1136, 110, 1036, 150, 733, 590, 1073, 595],\n"," 720: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 734: [318, 858, 527, 1193, 2571, 541, 2959, 2858, 1617, 1136],\n"," 735: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 739: [318, 858, 527, 260, 1193, 541, 1196, 2858, 1198, 1136],\n"," 755: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 758: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 767: [50, 1193, 541, 2959, 2858, 1617, 1136, 2028, 608, 296],\n"," 769: [858, 260, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 2028],\n"," 786: [50, 858, 1193, 593, 541, 2959, 2858, 1617, 2028, 608],\n"," 789: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 792: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 795: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 798: [2396, 590, 595, 25, 21, 141, 292, 329, 153],\n"," 799: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 802: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 806: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 807: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 816: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 827: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 828: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 846: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 859: [318, 50, 527, 1193, 2858, 1617, 608, 1214, 47, 457],\n"," 862: [50, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 876: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 881: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 885: [2959, 2858, 1617, 2762, 3578, 364, 357, 539, 141, 597],\n"," 886: [1197, 110, 1291, 1, 364, 588, 595, 25, 165, 367],\n"," 889: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 890: [318, 50, 858, 1193, 593, 541, 1196, 1198, 1617, 1136],\n"," 891: [858, 593, 1136, 349, 25, 21, 141, 165, 10, 454],\n"," 896: [318, 858, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 2028],\n"," 897: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 898: [318, 50, 858, 527, 1193, 593, 2959, 2858, 1198, 1617],\n"," 908: [318, 50, 1193, 593, 2959, 2858, 1617, 1136, 1197, 608],\n"," 922: [50, 858, 527, 260, 1193, 593, 541, 1196, 2858, 1198],\n"," 930: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 938: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 956: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 958: [50, 858, 527, 260, 1193, 593, 541, 1196, 2858, 1198],\n"," 968: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 977: [2959, 2762, 110, 3578, 356, 457, 150, 2396, 32, 1270],\n"," 990: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 996: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 1004: [527, 541, 1197, 1214, 1200, 3578, 1036, 1291, 2396, 2716],\n"," 1005: [50, 858, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 1008: [1136, 2028, 1240, 589, 349, 25, 21, 165, 10, 454],\n"," 1029: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 1037: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 1050: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 4: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 8: [318, 858, 1193, 296, 110, 1, 356, 150, 2396, 2716],\n"," 18: [50, 858, 1193, 1617, 1197, 1200, 588, 595, 21, 141],\n"," 36: [318, 527, 1193, 541, 1136, 1197, 1214, 110, 1200, 47],\n"," 47: [527, 1193, 1617, 1197, 608, 47, 356, 457, 1265, 1097],\n"," 59: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 62: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 77: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 79: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 80: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 119: [50, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1617],\n"," 122: [541, 1617, 2028, 47, 1, 32, 364, 595, 349, 1580],\n"," 125: [1617, 1197, 357, 10, 329, 153],\n"," 126: [357, 21, 329],\n"," 132: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 141: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 154: [318, 858, 527, 2858, 1617, 1136, 2028, 1197, 3578, 2396],\n"," 155: [527, 1197, 608, 1200, 1291, 2396, 733, 364, 590, 588],\n"," 163: [318, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 164: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 170: [858, 527, 260, 1193, 593, 541, 1196, 1198, 1617, 2028],\n"," 171: [858, 527, 260, 1193, 593, 2571, 541, 1196, 1198, 1617],\n"," 176: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 190: [318, 50, 527, 1193, 2858, 1617, 608, 47, 457, 733],\n"," 197: [318, 50, 260, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 198: [1198, 1291, 457, 349, 480, 357, 25, 21, 539, 141],\n"," 199: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 212: [2858, 110, 3578, 1036, 2396, 364, 1073, 25, 141, 597],\n"," 221: [1193, 541, 2959, 1617, 1136, 1197, 608, 2762, 1214, 1200],\n"," 223: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 226: [527, 1193, 2959, 2858, 1, 150, 2396, 364, 34, 1097],\n"," 241: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 247: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 249: [858, 527, 260, 1193, 2858, 1617, 110, 1210, 1, 457],\n"," 254: [1198, 1617, 1197, 608, 110, 1036, 1291, 1, 457, 150],\n"," 264: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 273: [858, 1193, 541, 2959, 1196, 1198, 1617, 1136, 1197, 608],\n"," 275: [858, 527, 260, 541, 2959, 1198, 1136, 1197, 1214, 1200],\n"," 276: [110, 2716, 208],\n"," 293: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 294: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 295: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 2858],\n"," 318: [1200, 32, 25, 380, 454, 316, 329, 288, 434, 344],\n"," 321: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 345: [318, 50, 858, 527, 593, 541, 1617, 608, 1214, 1200],\n"," 346: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 348: [260, 1196, 1136, 1197, 1210, 2396, 1265, 34, 1073, 595],\n"," 349: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 354: [50, 527, 260, 1193, 541, 1196, 1198, 1617, 1136, 2028],\n"," 359: [318, 50, 858, 527, 260, 1193, 593, 541, 1196, 1617],\n"," 366: [318, 50, 858, 527, 1193, 593, 2571, 541, 2959, 2858],\n"," 369: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 384: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 390: [527, 541, 1197, 1214, 47, 1036, 150, 32, 1270, 2716],\n"," 392: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 403: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 407: [318, 527, 1193, 1196, 2858, 1617, 1197, 110, 1210, 2716],\n"," 414: [318, 50, 858, 527, 260, 1193, 541, 1196, 1198, 1136],\n"," 431: [858, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 454: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 465: [260, 2571, 541, 1196, 1198, 1136, 2028, 1197, 1214, 1200],\n"," 481: [527, 1193, 541, 1197, 608, 2762, 110, 150, 2396, 1073],\n"," 485: [1136, 2028, 1197, 110, 47, 1036, 1210, 150, 1270, 2716],\n"," 503: [50, 260, 1193, 1196, 1617, 1197, 608, 1214, 110, 1200],\n"," 513: [2959, 110, 3578, 47, 1291, 1, 733, 25, 165, 253],\n"," 545: [3578, 2396, 1073, 595, 25, 21, 141, 39, 2683, 288],\n"," 552: [858, 527, 260, 593, 2571, 541, 1196, 1198, 1617, 2028],\n"," 554: [50, 858, 260, 2571, 541, 1196, 1617, 1136, 1197, 3578],\n"," 555: [541, 1197, 1214, 1200, 1036, 457, 2396, 364, 34, 1097],\n"," 561: [318, 50, 858, 527, 1193, 1198, 1617, 1136, 1197, 608],\n"," 565: [318, 50, 527, 260, 593, 2571, 541, 2959, 1196, 2028],\n"," 591: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 617: [318, 858, 1193, 2571, 2959, 2858, 1617, 2028, 608, 2762],\n"," 622: [50, 858, 527, 1193, 1617, 1136, 1197, 608, 1214, 1200],\n"," 623: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 633: [2762, 1036, 1291, 1240, 589, 1270, 1097, 595, 357, 25],\n"," 636: [541, 2959, 2858, 1197, 608, 1214, 110, 1200, 47, 1],\n"," 638: [858, 1193, 593, 2571, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 641: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 646: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 654: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 655: [50, 527, 593, 2571, 2959, 2858, 1617, 2028, 2762, 110],\n"," 658: [318, 50, 858, 527, 260, 1193, 541, 1196, 1198, 1617],\n"," 660: [858, 260, 1196, 1198, 1617, 1136, 1197, 1214, 110, 1200],\n"," 661: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 677: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 714: [1193, 593, 541, 1196, 1214, 1200, 47, 1036, 1210, 1],\n"," 715: 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1193, 541, 1196, 1198, 1136, 1197, 608],\n"," 895: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 899: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 902: [527, 541, 110, 150, 32, 1265, 34, 590, 357, 25],\n"," 907: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 928: [858, 527, 2028, 1214, 110, 1200, 1036, 1291, 2396, 2716],\n"," 936: [318, 1197, 1, 457, 1270, 2716, 733, 364, 34, 1097],\n"," 964: [50, 541, 2858, 1198, 1617, 1136, 1197, 2762, 1214, 1200],\n"," 970: [858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 972: [50, 260, 541, 1196, 1198, 1136, 1197, 1214, 1200, 1036],\n"," 1001: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 1013: [1193, 541, 2959, 2858, 1617, 1136, 1197, 608, 2762, 1214],\n"," 1018: [50, 858, 527, 1193, 1198, 1200, 3578, 1036, 1291, 1240],\n"," 1028: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 1031: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 1035: [50, 527, 260, 1193, 541, 1196, 1198, 1617, 1197, 608],\n"," 1038: [318, 50, 858, 527, 2571, 541, 2959, 2858, 1198, 1617],\n"," 1045: [608, 1214, 1200, 1240, 2716, 34, 349, 480, 357, 25],\n"," 1046: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 35: [318, 50, 858, 527, 260, 1193, 593, 541, 1198, 1617],\n"," 72: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 91: [858, 593, 2959, 1617, 296, 2762, 110, 3578, 457, 589],\n"," 106: [318, 50, 858, 527, 1193, 593, 541, 2959, 2858, 1198],\n"," 128: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 158: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 160: [318, 50, 527, 1193, 593, 541, 2959, 1617, 1136, 1197],\n"," 175: [1291, 34, 1073, 141, 165, 10, 780, 316, 2683, 329],\n"," 196: [50, 1193, 541, 2858, 1617, 2762, 1200, 3578, 47, 2396],\n"," 203: [858, 527, 1193, 2571, 2959, 2858, 2028, 2762, 3578, 2396],\n"," 206: [318, 50, 858, 260, 1193, 2959, 2858, 1198, 1136, 1197],\n"," 207: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 255: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 265: [318, 50, 527, 260, 1193, 593, 541, 2959, 1196, 2858],\n"," 340: [858, 1193, 2571, 2959, 2858, 1617, 2028, 2762, 3578, 1],\n"," 404: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 433: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 440: [260, 541, 1196, 1617, 1136, 3578, 1210, 1, 1240, 589],\n"," 444: [318, 50, 858, 527, 260, 593, 2959, 1617, 1136, 2028],\n"," 450: [2959, 1136, 150, 364, 1265, 34, 21, 141, 165, 10],\n"," 479: [858, 260, 541, 1196, 1198, 1197, 3578, 47, 1036, 1291],\n"," 491: [50, 1193, 593, 541, 1198, 1617, 1136, 2028, 608, 296],\n"," 506: [2571, 2959, 2858, 1617, 2028, 1197, 608, 2762, 110, 3578],\n"," 523: [593, 2571, 2959, 1196, 2858, 1198, 1617, 1136, 2028, 1197],\n"," 539: [858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 541: [318, 858, 1193, 296, 2762, 3578, 150, 364, 595, 357],\n"," 616: [858, 1193, 541, 1196, 1617, 1136, 1197, 1214, 1200, 1240],\n"," 647: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 659: [50, 1193, 2959, 1617, 2028, 110, 3578, 1291, 1, 150],\n"," 695: [858, 1193, 541, 2959, 1196, 2858, 1617, 1136, 2762, 1214],\n"," 700: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 707: [318, 50, 858, 260, 1193, 2571, 541, 1196, 1198, 1617],\n"," 748: [858, 260, 1193, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 759: [318, 50, 858, 527, 1193, 593, 2959, 2858, 1617, 1136],\n"," 784: [1193, 1617, 1200, 150, 34, 595, 349, 25, 21, 539],\n"," 790: [1193, 2571, 2959, 2858, 1617, 2028, 608, 2762, 3578, 356],\n"," 835: [858, 527, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 844: [318, 50, 527, 1193, 541, 2959, 1617, 1136, 2028, 1197],\n"," 871: [2571, 2959, 2858, 1617, 2028, 1197, 2762, 110, 3578, 1036],\n"," 875: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 892: [1193, 2571, 541, 2959, 2858, 1198, 1617, 1136, 2028, 2762],\n"," 919: [318, 50, 858, 527, 1193, 593, 541, 2959, 1196, 2858],\n"," 935: [318, 858, 527, 1193, 593, 541, 2959, 2858, 1617, 1136],\n"," 942: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 960: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 1006: [50, 858, 1193, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 1026: [318, 50, 858, 527, 2858, 1617, 296, 110, 47, 356],\n"," 1047: [260, 1193, 593, 1196, 1617, 1214, 1200, 1240, 32, 2716],\n"," 65: [50, 858, 527, 1193, 593, 541, 2959, 1617, 2028, 608],\n"," 135: [318, 50, 527, 1193, 2571, 2959, 2858, 1617, 2028, 608],\n"," 152: [527, 1193, 2959, 2858, 1136, 1197, 608, 296, 1, 150],\n"," 166: [318, 50, 858, 527, 1193, 2571, 541, 2959, 2858, 1198],\n"," 179: [318, 527, 593, 2959, 2858, 1136, 2762, 47, 1291, 356],\n"," 188: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 217: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 253: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 262: [1193, 541, 2858, 1136, 1214, 1200, 1, 589, 150, 32],\n"," 277: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 289: [541, 1198, 1291, 1240, 589, 733, 364, 1073, 588, 292],\n"," 300: [593, 2959, 2858, 1198, 1136, 1197, 3578, 47, 1036, 1291],\n"," 302: [858, 593, 541, 1198, 1214, 1200, 47, 1036, 1, 356],\n"," 308: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 311: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 328: [858, 527, 1193, 593, 2959, 1198, 1617, 1136, 2028, 1197],\n"," 329: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 344: [318, 50, 858, 527, 1193, 593, 2571, 541, 2959, 2858],\n"," 347: [318, 858, 527, 1193, 593, 2959, 2858, 1198, 1136, 2028],\n"," 358: [50, 858, 527, 1193, 541, 2959, 1198, 1617, 1136, 2028],\n"," 474: [318, 50, 858, 527, 1193, 2959, 2858, 1198, 1617, 1136],\n"," 483: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 509: [318, 260, 1196, 1198, 1197, 608, 1214, 110, 1200, 3578],\n"," 578: [318, 50, 527, 1193, 593, 2959, 1617, 2028, 1197, 2762],\n"," 643: [2959, 2858, 1198, 2762, 3578, 47, 1036, 1291, 1, 150],\n"," 679: [318, 50, 527, 1193, 2571, 541, 2959, 2858, 1198, 1617],\n"," 680: [318, 50, 858, 527, 1193, 593, 541, 2959, 1198, 1617],\n"," 691: [50, 858, 1193, 541, 2959, 2858, 1617, 2762, 1214, 1200],\n"," 702: [50, 858, 2571, 2959, 2858, 1198, 1617, 2028, 1197, 2762],\n"," 708: [318, 527, 1193, 593, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 730: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 751: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 773: [318, 50, 858, 527, 1193, 2959, 1198, 1617, 2028, 608],\n"," 803: [50, 527, 2571, 2959, 1617, 1136, 1197, 3578, 47, 1],\n"," 809: [318, 858, 1193, 593, 2571, 2959, 1198, 1136, 2028, 1197],\n"," 820: [318, 527, 1198, 1617, 608, 2762, 457, 34, 590, 588],\n"," 824: [318, 50, 527, 2959, 2858, 1198, 1617, 2028, 296, 2762],\n"," 863: [858, 260, 1193, 541, 2959, 1196, 1198, 1136, 1197, 2762],\n"," 865: [858, 527, 1193, 541, 1198, 1617, 1136, 2028, 608, 2762],\n"," 867: [1193, 593, 2762, 1214, 47, 1036, 32, 1265, 357, 539],\n"," 911: [318, 50, 858, 527, 1193, 593, 541, 2959, 1617, 1136],\n"," 915: [318, 858, 527, 1193, 2959, 1196, 1198, 1617, 1136, 296],\n"," 939: [50, 858, 527, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 946: [318, 527, 1193, 541, 2959, 2858, 1136, 2028, 296, 2762],\n"," 954: [318, 50, 858, 527, 1193, 593, 541, 2858, 1198, 1617],\n"," 957: [2762, 3578, 2396, 733, 364, 34, 588, 595, 349, 357],\n"," 971: [527, 1193, 541, 2762, 1214, 1200, 3578, 47, 1036, 1291],\n"," 986: [858, 260, 1193, 2571, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 992: [50, 858, 527, 1193, 541, 2959, 2858, 1198, 1617, 1136],\n"," 92: [50, 858, 1193, 593, 541, 2959, 1617, 1136, 1197, 296],\n"," 107: [318, 50, 1193, 593, 2959, 2858, 1198, 1136, 1197, 608],\n"," 131: [318, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 138: [858, 527, 1193, 1197, 608, 1291, 1, 457, 150, 2396],\n"," 145: [318, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 183: [50, 858, 2571, 541, 2959, 2858, 1617, 1197, 608, 296],\n"," 209: [318, 50, 858, 527, 1193, 2571, 541, 2959, 1196, 2858],\n"," 230: [318, 858, 1193, 2571, 1198, 2762, 47, 1036, 1291, 1210],\n"," 263: [1617, 2028, 356, 150, 733, 34, 1073, 595, 357, 25],\n"," 305: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 314: [50, 2571, 2959, 1197, 608, 1214, 1200, 47, 1036, 1291],\n"," 319: [318, 50, 858, 527, 1193, 2959, 2858, 1198, 1617, 1136],\n"," 325: [527, 260, 593, 2571, 541, 2959, 1196, 2858, 1617, 2028],\n"," 341: [50, 1193, 541, 1136, 2028, 1197, 608, 2762, 3578, 47],\n"," 471: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 488: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 495: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 532: [318, 527, 1193, 593, 541, 2959, 1136, 1197, 608, 1214],\n"," 564: [527, 593, 2571, 541, 2959, 2858, 1136, 2028, 1197, 2762],\n"," 574: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 603: [50, 260, 1193, 593, 1198, 1617, 1197, 2762, 3578, 47],\n"," 674: [318, 50, 858, 527, 2959, 2858, 1198, 1617, 1136, 2028],\n"," 753: [50, 858, 527, 1193, 2571, 541, 2959, 1196, 1617, 1136],\n"," 810: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 830: [318, 50, 2571, 541, 2959, 1617, 1136, 608, 2762, 1214],\n"," 841: [318, 50, 858, 527, 593, 2959, 2858, 1198, 1617, 1136],\n"," 856: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 921: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 933: [858, 527, 1193, 2858, 1136, 1197, 608, 2762, 110, 3578],\n"," 976: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 37: [858, 527, 260, 1193, 541, 1196, 2858, 1198, 1617, 1197],\n"," 83: [318, 50, 527, 593, 2571, 2959, 2858, 1617, 2028, 296],\n"," 248: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 387: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 428: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 451: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 2858],\n"," 584: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 874: [318, 50, 858, 527, 541, 1617, 608, 1214, 1200, 1036],\n"," 995: [50, 858, 527, 260, 593, 2571, 541, 2959, 1196, 2858],\n"," 10: [318, 50, 858, 260, 2571, 2959, 1196, 2858, 1198, 1136],\n"," 19: [593, 2571, 2959, 2858, 1617, 2028, 296, 2762, 1214, 1200],\n"," 41: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 43: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 44: [858, 260, 1193, 593, 2571, 541, 2959, 1196, 1198, 1617],\n"," 45: [318, 527, 1193, 541, 2959, 1136, 1197, 296, 1214, 3578],\n"," 51: [318, 50, 2571, 541, 2959, 1196, 1198, 1136, 1197, 110],\n"," 56: [318, 2959, 1197, 608, 2762, 1200, 47, 1240, 457, 150],\n"," 61: [50, 858, 260, 2571, 541, 2959, 1196, 1198, 1136, 2028],\n"," 68: [318, 50, 858, 527, 593, 2959, 1196, 1198, 1136, 2028],\n"," 69: [50, 858, 260, 593, 2571, 541, 2959, 1196, 1198, 1617],\n"," 78: 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260, 1193, 593, 541, 2959, 1196, 1198],\n"," 285: [318, 50, 858, 527, 1193, 593, 2571, 541, 2959, 1196],\n"," 298: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 301: [1193, 2959, 1617, 2028, 1197, 1214, 110, 1200, 3578, 47],\n"," 304: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 333: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 334: [318, 50, 858, 527, 1193, 593, 541, 2959, 2858, 1617],\n"," 338: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 350: [858, 527, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 353: [318, 858, 527, 260, 1193, 2858, 1198, 1617, 1136, 1197],\n"," 378: [318, 50, 527, 1193, 593, 2571, 541, 2959, 2858, 1198],\n"," 386: [50, 1193, 2571, 541, 1136, 1197, 47, 32, 2716, 588],\n"," 397: [50, 260, 541, 1196, 1198, 1617, 1136, 1197, 608, 1214],\n"," 420: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 439: [318, 50, 858, 260, 593, 2571, 541, 2959, 1196, 2858],\n"," 449: [318, 858, 1193, 541, 1198, 1617, 1136, 2028, 608, 296],\n"," 478: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 487: [858, 527, 260, 1193, 593, 541, 1196, 1198, 1617, 1136],\n"," 489: [593, 2571, 541, 2959, 1196, 2858, 1198, 1617, 2028, 1197],\n"," 500: [50, 858, 260, 1193, 2571, 2959, 1196, 2858, 1198, 1136],\n"," 510: [50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198],\n"," 521: [527, 260, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 522: [50, 527, 2571, 2959, 2858, 1617, 2028, 2762, 1200, 3578],\n"," 527: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 529: [318, 50, 858, 1193, 593, 2571, 2959, 1196, 2858, 1198],\n"," 535: [2858, 1617, 2762, 1214, 1200, 1240, 457, 2396, 32, 1270],\n"," 550: [593, 2571, 2959, 2028, 1197, 110, 1200, 3578, 47, 1036],\n"," 560: [858, 260, 2571, 541, 1196, 1198, 2028, 1197, 1214, 110],\n"," 573: [858, 260, 1193, 541, 2959, 1196, 1198, 1136, 1197, 296],\n"," 579: [858, 1193, 1617, 2028, 1197, 608, 110, 1200, 1, 1240],\n"," 582: [2571, 541, 2959, 2858, 1136, 1197, 2762, 3578, 47, 1036],\n"," 583: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 587: [50, 858, 2571, 541, 2959, 2858, 1198, 1617, 2028, 1197],\n"," 596: [50, 593, 2571, 541, 2959, 1197, 296, 1214, 110, 3578],\n"," 602: [318, 50, 527, 1193, 2571, 541, 2959, 1196, 2858, 1617],\n"," 618: [858, 260, 1193, 541, 2959, 1196, 1198, 1136, 1197, 296],\n"," 624: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 627: [527, 260, 1193, 541, 2959, 1196, 1198, 1617, 1136, 1197],\n"," 635: [318, 50, 858, 260, 1193, 2571, 2959, 1196, 1198, 1617],\n"," 639: [50, 858, 527, 260, 1193, 2571, 541, 1196, 2858, 1198],\n"," 640: [50, 527, 260, 1193, 593, 1617, 1136, 1197, 608, 296],\n"," 642: [527, 1198, 110, 733, 364, 1073, 588, 595, 349, 380],\n"," 649: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 652: [318, 50, 858, 260, 593, 2571, 541, 2959, 1196, 2858],\n"," 662: [527, 2571, 2959, 1198, 1136, 2762, 1200, 3578, 47, 1036],\n"," 671: [318, 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905: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 906: [318, 541, 1198, 1214, 110, 1200, 3578, 47, 1036, 1291],\n"," 924: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1198],\n"," 925: [50, 527, 1193, 2571, 541, 1196, 2858, 1617, 2028, 608],\n"," 927: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 940: [318, 858, 527, 1193, 593, 541, 2959, 1196, 1198, 1617],\n"," 948: [260, 1193, 541, 2959, 1196, 1136, 1197, 1214, 1200, 3578],\n"," 953: [318, 50, 858, 527, 1193, 593, 2959, 2858, 1198, 1617],\n"," 966: [318, 50, 858, 527, 593, 1198, 2028, 608, 2762, 3578],\n"," 967: [50, 858, 527, 260, 541, 1196, 1198, 1617, 1136, 2028],\n"," 979: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 980: [318, 527, 1193, 2571, 1196, 1198, 1617, 2028, 1197, 110],\n"," 983: [50, 527, 260, 1193, 593, 541, 1196, 1617, 2028, 608],\n"," 984: [50, 527, 260, 2571, 541, 2858, 1198, 1617, 1136, 1197],\n"," 991: [858, 1193, 593, 2571, 541, 2959, 2858, 1617, 2028, 608],\n"," 1009: [50, 858, 527, 260, 593, 541, 2959, 1196, 1198, 1617],\n"," 1011: [318, 858, 527, 2571, 2959, 2858, 1197, 2762, 110, 3578],\n"," 1014: [318, 50, 1193, 2571, 2959, 1196, 1198, 1136, 1197, 2762],\n"," 1021: [318, 50, 527, 1193, 541, 1617, 1136, 608, 296, 110],\n"," 1030: [318, 50, 527, 260, 1193, 541, 2959, 1196, 2858, 1617],\n"," 1033: [858, 1193, 2571, 541, 2959, 2858, 1617, 608, 1214, 3578],\n"," 1039: [1193,\n"," 2571,\n"," 2959,\n"," 1196,\n"," 2858,\n"," 1198,\n"," 1136,\n"," 1197,\n"," 2762,\n"," 1214],\n"," 1040: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 1053: [318, 50, 858, 527, 1193, 593, 2959, 1617, 1136, 2028],\n"," 704: [318, 858, 527, 1193, 593, 2858, 1198, 1617, 1136, 1197],\n"," 934: [318, 50, 858, 527, 1193, 593, 2571, 2959, 1196, 2858],\n"," 42: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 73: [527, 1193, 1617, 1136, 1197, 1214, 110, 3578, 1291, 1210],\n"," 82: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 159: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 161: [858, 1193, 1617, 2028, 1036, 356, 457, 150, 2396, 32],\n"," 192: [318, 260, 1196, 1198, 2028, 1197, 2762, 110, 1200, 1036],\n"," 216: [858, 527, 2959, 1617, 2028, 608, 296, 1214, 1200, 3578],\n"," 219: [318, 50, 858, 593, 2571, 541, 1196, 1198, 1617, 1136],\n"," 290: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 379: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 389: [50, 858, 260, 1193, 541, 1196, 1198, 1136, 1197, 608],\n"," 400: [318, 858, 527, 260, 1193, 541, 1196, 1198, 1136, 1197],\n"," 462: [858, 527, 2571, 541, 2959, 1196, 2858, 1198, 1617, 1136],\n"," 507: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 600: [318, 50, 527, 260, 1193, 593, 2571, 541, 1196, 1198],\n"," 721: [50, 858, 527, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 793: [318, 50, 858, 527, 1193, 593, 541, 2959, 2858, 1198],\n"," 912: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 932: [318, 50, 858, 527, 1193, 2571, 541, 2959, 1196, 2858],\n"," 949: [50, 1193, 593, 541, 1198, 608, 1214, 1200, 47, 1036],\n"," 1025: [50, 527, 1193, 593, 541, 2959, 1617, 296, 2762, 3578],\n"," 46: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 74: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 342: [318, 50, 260, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 508: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 2858],\n"," 580: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 774: [527, 260, 1193, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 783: [50, 858, 527, 1193, 2959, 2858, 1617, 2028, 1197, 296],\n"," 1002: [318, 50, 527, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 1023: [318, 50, 858, 1193, 1617, 1136, 2028, 1197, 608, 296],\n"," 1048: [858, 527, 260, 1193, 541, 1617, 1136, 1197, 608, 1214],\n"," 23: [318, 858, 1193, 2571, 541, 2959, 1196, 2858, 1198, 1617],\n"," 96: [318, 2959, 47, 356, 150, 1270, 733, 364, 1073, 588],\n"," 124: [318, 50, 858, 527, 593, 2959, 2858, 1617, 1197, 296],\n"," 136: [50, 527, 260, 593, 2571, 541, 2959, 1196, 2858, 1617],\n"," 148: [318, 858, 527, 1193, 593, 2571, 2959, 2858, 1198, 1617],\n"," 189: [318, 50, 858, 527, 1193, 2571, 2959, 1196, 2858, 1617],\n"," 213: [318, 858, 260, 1193, 541, 2959, 1198, 1136, 1197, 110],\n"," 243: [318, 50, 527, 1193, 593, 541, 1617, 1136, 1197, 296],\n"," 323: [318, 50, 2571, 541, 2959, 2858, 2028, 2762, 110, 3578],\n"," 352: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 429: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 625: [318, 50, 858, 527, 1193, 2571, 541, 2959, 2858, 1198],\n"," 808: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 843: [318, 50, 593, 2571, 2959, 2858, 1617, 2028, 608, 296],\n"," 847: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 963: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 975: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 998: [318, 50, 858, 527, 260, 1193, 593, 541, 1196, 2858],\n"," 75: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 427: [1193, 541, 2959, 2858, 1136, 1197, 608, 296, 110, 356],\n"," 466: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 801: [318, 50, 858, 2858, 1617, 608, 2762, 1036, 1240, 356],\n"," 848: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 888: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 191: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 227: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 245: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 380: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 408: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 668: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 747: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 754: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 11: [318, 50, 858, 527, 1193, 593, 541, 2959, 2858, 1136],\n"," 16: [318, 50, 858, 527, 1193, 593, 2571, 2959, 2858, 1198],\n"," 81: [318, 50, 858, 527, 260, 593, 1196, 2858, 1198, 1617],\n"," 86: [318, 50, 527, 260, 1193, 593, 541, 2959, 2858, 1617],\n"," 97: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 151: [858, 1193, 593, 541, 2959, 2858, 1198, 1617, 2028, 1197],\n"," 235: [527, 1196, 1136, 2028, 1197, 608, 2762, 1214, 110, 1200],\n"," 251: [318, 50, 527, 1193, 2571, 2959, 2858, 1617, 1136, 2028],\n"," 258: [318, 50, 858, 527, 593, 2571, 541, 2959, 2858, 1198],\n"," 278: [318, 50, 858, 1193, 593, 2571, 541, 2959, 1196, 2858],\n"," 388: [318, 858, 527, 260, 1193, 593, 541, 2959, 1196, 2858],\n"," 551: [318, 50, 1193, 2571, 541, 2959, 2858, 1617, 1136, 1197],\n"," 606: [318, 50, 527, 1193, 593, 541, 2959, 2858, 1617, 1136],\n"," 614: [318, 50, 527, 1193, 593, 2571, 541, 2959, 2858, 1617],\n"," 681: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 686: [318, 527, 1193, 2959, 296, 110, 47, 1, 356, 150],\n"," 711: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 718: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 873: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 962: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 985: [318, 50, 858, 527, 1193, 593, 2571, 2959, 2858, 1198],\n"," 993: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 184: [318, 858, 527, 260, 1193, 593, 2571, 541, 1196, 2858],\n"," 246: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 373: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 430: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 534: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 805: [318, 50, 858, 527, 1193, 541, 2959, 2858, 1617, 2028],\n"," 58: [858, 1193, 2959, 1196, 1136, 608, 1214, 1200, 3578, 47],\n"," 112: [2571, 2959, 2858, 1617, 2028, 2762, 3578, 356, 589, 2396],\n"," 367: [50, 527, 260, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 548: [50, 858, 527, 1193, 593, 2571, 541, 2959, 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1617],\n"," 137: [858, 260, 1193, 541, 1617, 1136, 1197, 608, 1214, 110],\n"," 153: [318, 50, 1193, 593, 2571, 541, 1196, 2858, 1198, 1617],\n"," 211: [318, 527, 1193, 2571, 541, 1136, 296, 2762, 3578, 47],\n"," 286: [318, 858, 527, 1193, 1617, 1136, 1197, 608, 296, 2762],\n"," 330: [318, 50, 858, 1193, 593, 541, 1198, 1617, 1136, 2028],\n"," 336: [318, 50, 858, 527, 1193, 541, 2959, 2858, 1198, 1617],\n"," 372: [318, 50, 858, 527, 260, 1193, 593, 541, 2858, 1617],\n"," 376: [318, 50, 858, 527, 2959, 1196, 2858, 1198, 1136, 1197],\n"," 412: [50, 527, 260, 1193, 541, 2959, 1198, 1617, 1136, 1197],\n"," 447: [858, 1193, 541, 2959, 1196, 2858, 1198, 2028, 2762, 1214],\n"," 467: [858, 260, 1193, 593, 2571, 541, 2959, 1196, 2858, 1198],\n"," 490: [50, 260, 1193, 593, 2571, 541, 2959, 2858, 1617, 1136],\n"," 518: [50, 858, 541, 2959, 2858, 1198, 1617, 1136, 1197, 296],\n"," 608: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 619: [318, 50, 527, 1193, 593, 2571, 541, 2959, 1196, 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858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 969: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 12: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 597: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 195: [318, 50, 260, 593, 2571, 541, 2959, 1196, 1198, 1136],\n"," 337: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 910: [527, 260, 1193, 2571, 541, 1196, 1198, 2028, 1197, 608],\n"," 63: [318, 50, 260, 593, 2571, 541, 2959, 1196, 1198, 1136],\n"," 70: [50, 260, 593, 2571, 2959, 1196, 2858, 1136, 2028, 1197],\n"," 99: [50, 260, 593, 2571, 2959, 1196, 2858, 1198, 2028, 1197],\n"," 121: [260, 2959, 2858, 2028, 2762, 1214, 110, 3578, 47, 1036],\n"," 130: [318, 260, 2571, 2959, 1196, 1617, 2028, 1197, 2762, 1214],\n"," 244: [318, 260, 2571, 2959, 1196, 1198, 1617, 2028, 296, 2762],\n"," 291: [318, 50, 260, 593, 2571, 541, 2959, 2858, 1198, 1617],\n"," 335: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 361: [318, 527, 260, 1193, 593, 2571, 541, 2959, 1196, 1198],\n"," 470: [260, 1193, 1196, 2028, 1197, 110, 3578, 1036, 1291, 1210],\n"," 497: [50, 260, 1193, 541, 2959, 2858, 1198, 1617, 1136, 2028],\n"," 620: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 648: [527, 260, 2571, 2959, 1198, 1136, 1197, 296, 2762, 1214],\n"," 666: [318, 50, 527, 260, 593, 1617, 2028, 1197, 608, 2762],\n"," 710: [50, 260, 593, 2571, 541, 2959, 1196, 1136, 2028, 1197],\n"," 794: [318, 50, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 854: [318, 50, 260, 1193, 2571, 541, 1196, 1198, 1617, 1136],\n"," 861: [318, 50, 527, 260, 1193, 593, 541, 2959, 1196, 2858],\n"," 87: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1198],\n"," 317: [318, 50, 858, 527, 260, 593, 2571, 2959, 1196, 2858],\n"," 324: [318, 50, 858, 527, 260, 1193, 593, 1196, 2858, 1198],\n"," 502: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 559: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 973: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 1015: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 1017: [318, 50, 858, 527, 260, 1193, 593, 2571, 2959, 2858],\n"," 85: [318, 858, 260, 1193, 541, 2959, 1196, 1198, 1617, 1136],\n"," 306: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 653: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 371: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 566: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 331: [858, 527, 260, 1193, 593, 541, 1196, 1198, 1617, 1136],\n"," 665: [318, 50, 858, 527, 260, 593, 2571, 2959, 1196, 1198],\n"," 872: [318, 858, 527, 260, 593, 2571, 541, 2959, 1196, 2858],\n"," 879: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 486: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 864: [318, 50, 858, 260, 1193, 593, 2571, 541, 1196, 1198],\n"," 52: [318, 858, 527, 260, 593, 2571, 541, 2959, 1196, 2858],\n"," 288: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 9: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 117: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 220: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 544: [318, 858, 527, 260, 1193, 593, 541, 2858, 1198, 1617],\n"," 999: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 458: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n"," 974: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 546: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 55: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n"," 363: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2858],\n"," 445: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 492: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 234: [318, 858, 260, 1193, 593, 541, 1196, 1198, 1136, 2028],\n"," 1027: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 140: [858, 260, 2571, 541, 2959, 1196, 1198, 1617, 1136, 2028],\n"," 926: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 781: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 825: [318, 50, 858, 527, 260, 2571, 541, 2959, 1196, 2858],\n"," 913: [318, 50, 858, 260, 1193, 2571, 541, 2959, 1196, 2858],\n"," 453: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 540: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 66: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 185: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 222: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 498: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 994: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 165: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 202: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 180: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 93: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 261: [318, 50, 858, 527, 260, 593, 541, 2959, 1196, 2858],\n"," 435: [318, 50, 858, 527, 260, 1193, 593, 1196, 2858, 1198],\n"," 322: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 238: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 425: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 512: [50, 858, 260, 1193, 593, 2571, 541, 2959, 1196, 1198],\n"," 725: [318, 50, 858, 527, 260, 1193, 593, 541, 1196, 1198],\n"," 732: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n"," 108: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 592: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 443: [50, 858, 527, 260, 1193, 2571, 541, 2959, 1196, 1198],\n"," 916: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 736: [318, 50, 858, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 961: [318, 50, 858, 260, 593, 2571, 541, 2959, 1196, 1136],\n"," 1012: [318, 50, 858, 527, 260, 1193, 2571, 541, 2959, 1196],\n"," 515: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 978: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 28: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 475: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 598: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 943: [318, 50, 858, 527, 260, 593, 2571, 541, 2959, 1196],\n"," 520: [50, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 697: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 849: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 1003: [318, 50, 858, 527, 260, 1193, 593, 541, 2959, 1196],\n"," 705: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 48: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 29: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 231: [318, 858, 527, 260, 1193, 593, 2571, 541, 2959, 1196],\n"," 699: [858, 527, 260, 1193, 593, 2571, 541, 1196, 1198, 1617],\n"," 448: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 750: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 1196],\n"," 782: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 860: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 768: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 127: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959],\n"," 894: [318, 50, 858, 527, 260, 1193, 593, 2571, 541, 2959]})"]},"metadata":{},"execution_count":7}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 평갓값이 높은 10작품으로 한다\n","# 단, 평가 건수가 작으면 노이즈가 크므로 minimum_num_rating건 이상 평가가 있는 영화로 필터링한다\n","pred_user2items = defaultdict(list)\n","user_watched_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","movie_stats = movielens_train.groupby(\"movie_id\").agg({\"rating\": [np.size, np.mean]})\n","atleast_flg = movie_stats[\"rating\"][\"size\"] >= minimum_num_rating\n","movies_sorted_by_rating = (\n"," movie_stats[atleast_flg].sort_values(by=(\"rating\", \"mean\"), ascending=False).index.tolist()\n",")\n","\n","for user_id in movielens_train.user_id.unique():\n"," for movie_id in movies_sorted_by_rating:\n"," if movie_id not in user_watched_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":8,"id":"f242af0f","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"f242af0f","executionInfo":{"status":"ok","timestamp":1672119703182,"user_tz":-540,"elapsed":29,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"dcaede65-6ebd-4874-c57b-35af1e18a837"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
4950Usual Suspects, The (1995)[Crime, Mystery, Thriller][kevin spacey, ensemble cast, complicated, mus...
315318Shawshank Redemption, The (1994)[Drama][based on a short story, directorial debut, fr...
523527Schindler's List (1993)[Drama, War][speilberg, drama, holocaust, steven spielberg...
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\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2에 대한 추천(318, 50, 527)\n","movies[movies.movie_id.isin([318, 50, 527])]"]},{"cell_type":"code","execution_count":10,"id":"9deafa9b","metadata":{"id":"9deafa9b","executionInfo":{"status":"ok","timestamp":1672119703183,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/RF.ipynb b/chapter5/colab/RF.ipynb index 06f8fda..3e14165 100644 --- a/chapter5/colab/RF.ipynb +++ b/chapter5/colab/RF.ipynb @@ -1,4915 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0d5889b3", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/RF.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "4935d845", - "metadata": {}, - "source": [ - "# RandomForest(회귀 모델)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "52eedd0b", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d3c02fa7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "683d7c01-91e8-4835-b9cc-332b16c13966", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1048NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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10535.0NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1000 rows × 6673 columns

\n", - "
" - ], - "text/plain": [ - "movie_id 1 2 3 4 5 6 7 8 9 \\\n", - "user_id \n", - "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "5 1.0 NaN NaN NaN NaN NaN 3.0 NaN NaN \n", - "... ... ... ... ... ... ... ... ... ... \n", - "1048 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1050 NaN 3.0 NaN NaN NaN 3.0 NaN NaN NaN \n", - "1051 5.0 NaN 3.0 NaN 3.0 NaN 4.0 NaN NaN \n", - "1052 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1053 5.0 NaN NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - "movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n", - "user_id ... \n", - "1 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "2 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "3 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "5 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "... ... ... ... ... ... ... ... ... ... ... \n", - "1048 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1050 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1051 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1052 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1053 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - "movie_id 63992 64716 \n", - "user_id \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "... ... ... \n", - "1048 NaN NaN \n", - "1050 NaN NaN \n", - "1051 NaN NaN \n", - "1052 NaN NaN \n", - "1053 NaN NaN \n", - "\n", - "[1000 rows x 6673 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다\n", - "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", - "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", - "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", - "user_movie_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1f34610c", - "metadata": {}, - "outputs": [], - "source": [ - "# 학습에 사용하는 학습용 데이터 안의 사용자와 영화의 조합을 얻는다\n", - "train_keys = movielens_train[[\"user_id\", \"movie_id\"]]\n", - "# 학습용 데이터 안의 평갓값을 학습의 정답 데이터로 얻는다\n", - "train_y = movielens_train.rating.values\n", - "\n", - "# 평갓값을 예측할 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다\n", - "test_keys = movielens_test[[\"user_id\", \"movie_id\"]]\n", - "# 순위 형식의 추천 리스트 작성을 위해 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 얻는다\n", - "train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[[\"user_id\", \"movie_id\"]]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9f5053fe", - "metadata": {}, - "outputs": [], - "source": [ - "# 특징량을 작성한다\n", - "train_x = train_keys.copy()\n", - "test_x = test_keys.copy()\n", - "train_all_x = train_all_keys.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3b756eca", - "metadata": {}, - "outputs": [], - "source": [ - "# 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값\n", - "# 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량으로 추가한다\n", - "aggregators = [\"min\", \"max\", \"mean\"]\n", - "user_features = movielens_train.groupby(\"user_id\").rating.agg(aggregators).to_dict()\n", - "movie_features = movielens_train.groupby(\"movie_id\").rating.agg(aggregators).to_dict()\n", - "for agg in aggregators:\n", - " train_x[f\"u_{agg}\"] = train_x[\"user_id\"].map(user_features[agg])\n", - " test_x[f\"u_{agg}\"] = test_x[\"user_id\"].map(user_features[agg])\n", - " train_all_x[f\"u_{agg}\"] = train_all_x[\"user_id\"].map(user_features[agg])\n", - " train_x[f\"m_{agg}\"] = train_x[\"movie_id\"].map(movie_features[agg])\n", - " test_x[f\"m_{agg}\"] = test_x[\"movie_id\"].map(movie_features[agg])\n", - " train_all_x[f\"m_{agg}\"] = train_all_x[\"movie_id\"].map(movie_features[agg])\n", - "# 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다\n", - "average_rating = train_y.mean()\n", - "test_x.fillna(average_rating, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3499ae5a", - "metadata": {}, - "outputs": [], - "source": [ - "import itertools\n", - "\n", - "# 영화가 특정한 genre인지 나타내는 특징량을 추가한다\n", - "movie_genres = movies[[\"movie_id\", \"genre\"]]\n", - "genres = set(list(itertools.chain(*movie_genres.genre)))\n", - "for genre in genres:\n", - " movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n", - "movie_genres.drop(\"genre\", axis=1, inplace=True)\n", - "train_x = train_x.merge(movie_genres, on=\"movie_id\")\n", - "test_x = test_x.merge(movie_genres, on=\"movie_id\")\n", - "train_all_x = train_all_x.merge(movie_genres, on=\"movie_id\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "eca70ebb", - "metadata": {}, - "outputs": [], - "source": [ - "# 특징량으로 사용하지 않는 정보는 삭제한다\n", - "train_x = train_x.drop(columns=[\"user_id\", \"movie_id\"])\n", - "test_x = test_x.drop(columns=[\"user_id\", \"movie_id\"])\n", - "train_all_x = train_all_x.drop(columns=[\"user_id\", \"movie_id\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "78bde7fd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestRegressor(n_jobs=-1, random_state=0)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor as RFR\n", - "\n", - "# Random Forest를 사용한 학습\n", - "reg = RFR(n_jobs=-1, random_state=0)\n", - "reg.fit(train_x.values, train_y)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ca01da90", - "metadata": {}, - "outputs": [], - "source": [ - "# 테스트용 데이터 안의 사용와 영화의 조합에 대해 평갓값을 예측한다\n", - "test_pred = reg.predict(test_x.values)\n", - "\n", - "movie_rating_predict = test_keys.copy()\n", - "movie_rating_predict[\"rating_pred\"] = test_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3ec280f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {139: [7986, 6565, 5817, 6356, 4828, 6538, 4270, 26974, 631, 3588],\n", - " 149: [5914, 640, 555, 1672, 1570, 5996, 5928, 1326, 6689, 719],\n", - " 182: [6729,\n", - " 4956,\n", - " 5971,\n", - " 8379,\n", - 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" 1593,\n", - " 26745,\n", - " 25753,\n", - " 1045,\n", - " 8604,\n", - " 1103,\n", - " 1040,\n", - " 6746],\n", - " 231: [627, 58, 56949, 1032, 33836, 1113, 5499, 157, 164, 719],\n", - " 699: [485, 468, 301, 73, 69, 436, 50794, 8537, 378, 37741],\n", - " 448: [4722, 4168, 5111, 4477, 4886, 6686, 5523, 4085, 4102, 1841],\n", - " 750: [3968, 4453, 4509, 5295, 3849, 5202, 5214, 4121, 1352, 4683],\n", - " 782: [2930, 2885, 2473, 2408, 3061, 3519, 2610, 2443, 3396, 1380],\n", - " 860: [4366, 5236, 4521, 4355, 5823, 5187, 4517, 4995, 5464, 4531],\n", - " 768: [960, 2017, 1997, 1126, 1780, 1400, 1983, 928, 961, 1237],\n", - " 127: [2824, 3189, 3605, 3252, 7373, 8636, 3592, 58047, 269, 3528],\n", - " 894: [486, 118, 177, 1014, 830, 900, 38, 862, 416, 298]})" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "import numpy as np\n", - "\n", - "# 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합에 대해 평갓값을 예측한다\n", - "train_all_pred = reg.predict(train_all_x.values)\n", - "\n", - "pred_train_all = train_all_keys.copy()\n", - "pred_train_all[\"rating_pred\"] = train_all_pred\n", - "pred_matrix = pred_train_all.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating_pred\")\n", - "\n", - "# 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서\n", - "# 예측 평갓값이 높은 순으로 10편의 영화를 순위 형식으로 추천 리스트로 만든다\n", - "pred_user2items = defaultdict(list)\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "for user_id in movielens_train.user_id.unique():\n", - " movie_indexes = np.argsort(-pred_matrix.loc[user_id, :]).values\n", - " for movie_index in movie_indexes:\n", - " movie_id = user_movie_matrix.columns[movie_index]\n", - " if movie_id not in (user_evaluated_movies[user_id]):\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c3a25c25", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a178ecdc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[4210, 4961, 4105, 2351, 4032, 1586, 2804, 5159, 4840, 3350]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_user2items[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "09555a1f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
40134105Evil Dead, The (1981)[Fantasy, Horror][directorial debut, bruce campbell, cult class...
41184210Manhunter (1986)[Action, Crime, Drama, Horror, Thriller][hannibal lecter, serial killer, ei muista, er...
48674961Pornstar: The Legend of Ron Jeremy (2001)[Documentary][pornography]
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" - ], - "text/plain": [ - " movie_id title \\\n", - "4013 4105 Evil Dead, The (1981) \n", - "4118 4210 Manhunter (1986) \n", - "4867 4961 Pornstar: The Legend of Ron Jeremy (2001) \n", - "\n", - " genre \\\n", - "4013 [Fantasy, Horror] \n", - "4118 [Action, Crime, Drama, Horror, Thriller] \n", - "4867 [Documentary] \n", - "\n", - " tag \n", - "4013 [directorial debut, bruce campbell, cult class... \n", - "4118 [hannibal lecter, serial killer, ei muista, er... \n", - "4867 [pornography] " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(4210, 4961, 4105)\n", - "movies[movies.movie_id.isin([4210, 4961, 4105])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a42fa17", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","metadata":{"id":"0d5889b3"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/RF.ipynb)"],"id":"0d5889b3"},{"cell_type":"markdown","metadata":{"id":"4935d845"},"source":["# RandomForest(회귀 모델)"],"id":"4935d845"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4283,"status":"ok","timestamp":1672119776862,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"52eedd0b","outputId":"d53d23e4-53b5-440a-b5d5-5432ccedb2d6"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2022-12-27 05:42:50-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 64.4MB/s in 1.0s \n","\n","2022-12-27 05:42:51 (64.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"],"id":"52eedd0b"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":73811,"status":"ok","timestamp":1672119850668,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"d3c02fa7","outputId":"2703b391-837b-4ced-8876-73fe4b6b7a9e"},"outputs":[{"name":"stdout","output_type":"stream","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"],"id":"d3c02fa7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1672119850668,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"683d7c01-91e8-4835-b9cc-332b16c13966","outputId":"3df466b9-dbeb-47f1-ecd2-105e2288c100"},"outputs":[{"data":{"text/html":["\n","
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movie_id12345678910...62000621136229362344623946280162803631136399264716
user_id
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51.0NaNNaNNaNNaNNaN3.0NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
1048NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1050NaN3.0NaNNaNNaN3.0NaNNaNNaN3.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10515.0NaN3.0NaN3.0NaN4.0NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1052NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10535.0NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1000 rows × 6673 columns

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NaN NaN NaN NaN NaN NaN NaN NaN \n","1050 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1051 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1052 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1053 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","\n","movie_id 63992 64716 \n","user_id \n","1 NaN NaN \n","2 NaN NaN \n","3 NaN NaN \n","4 NaN NaN \n","5 NaN NaN \n","... ... ... \n","1048 NaN NaN \n","1050 NaN NaN \n","1051 NaN NaN \n","1052 NaN NaN \n","1053 NaN NaN \n","\n","[1000 rows x 6673 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","user_movie_matrix"],"id":"683d7c01-91e8-4835-b9cc-332b16c13966"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1f34610c"},"outputs":[],"source":["# 학습에 사용하는 학습용 데이터 안의 사용자와 영화의 조합을 얻는다\n","train_keys = movielens_train[[\"user_id\", \"movie_id\"]]\n","# 학습용 데이터 안의 평갓값을 학습의 정답 데이터로 얻는다\n","train_y = movielens_train.rating.values\n","\n","# 평갓값을 예측할 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다\n","test_keys = movielens_test[[\"user_id\", \"movie_id\"]]\n","# 순위 형식의 추천 리스트 작성을 위해 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 얻는다\n","train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[[\"user_id\", \"movie_id\"]]\n"],"id":"1f34610c"},{"cell_type":"code","execution_count":null,"metadata":{"id":"9f5053fe"},"outputs":[],"source":["# 특징량을 작성한다\n","train_x = train_keys.copy()\n","test_x = test_keys.copy()\n","train_all_x = train_all_keys.copy()"],"id":"9f5053fe"},{"cell_type":"code","execution_count":null,"metadata":{"id":"3b756eca"},"outputs":[],"source":["# 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값\n","# 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량으로 추가한다\n","aggregators = [\"min\", \"max\", \"mean\"]\n","user_features = movielens_train.groupby(\"user_id\").rating.agg(aggregators).to_dict()\n","movie_features = movielens_train.groupby(\"movie_id\").rating.agg(aggregators).to_dict()\n","for agg in aggregators:\n"," train_x[f\"u_{agg}\"] = train_x[\"user_id\"].map(user_features[agg])\n"," test_x[f\"u_{agg}\"] = test_x[\"user_id\"].map(user_features[agg])\n"," train_all_x[f\"u_{agg}\"] = train_all_x[\"user_id\"].map(user_features[agg])\n"," train_x[f\"m_{agg}\"] = train_x[\"movie_id\"].map(movie_features[agg])\n"," test_x[f\"m_{agg}\"] = test_x[\"movie_id\"].map(movie_features[agg])\n"," train_all_x[f\"m_{agg}\"] = train_all_x[\"movie_id\"].map(movie_features[agg])\n","# 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다\n","average_rating = train_y.mean()\n","test_x.fillna(average_rating, inplace=True)"],"id":"3b756eca"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1492,"status":"ok","timestamp":1672119853162,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"3499ae5a","outputId":"ae542425-5ea1-46bd-fe3f-b68df0abae71"},"outputs":[{"name":"stderr","output_type":"stream","text":[":7: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n"]}],"source":["import itertools\n","\n","# 영화가 특정한 genre인지 나타내는 특징량을 추가한다\n","movie_genres = movies[[\"movie_id\", \"genre\"]]\n","genres = set(list(itertools.chain(*movie_genres.genre)))\n","for genre in genres:\n"," movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n","movie_genres.drop(\"genre\", axis=1, inplace=True)\n","train_x = train_x.merge(movie_genres, on=\"movie_id\")\n","test_x = test_x.merge(movie_genres, on=\"movie_id\")\n","train_all_x = train_all_x.merge(movie_genres, on=\"movie_id\")"],"id":"3499ae5a"},{"cell_type":"code","execution_count":null,"metadata":{"id":"eca70ebb"},"outputs":[],"source":["# 특징량으로 사용하지 않는 정보는 삭제한다\n","train_x = train_x.drop(columns=[\"user_id\", \"movie_id\"])\n","test_x = test_x.drop(columns=[\"user_id\", \"movie_id\"])\n","train_all_x = train_all_x.drop(columns=[\"user_id\", \"movie_id\"])"],"id":"eca70ebb"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47235,"status":"ok","timestamp":1672119900826,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"78bde7fd","outputId":"6aa184b5-c138-4343-8e8c-918a5383c6a4"},"outputs":[{"data":{"text/plain":["RandomForestRegressor(n_jobs=-1, random_state=0)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.ensemble import RandomForestRegressor as RFR\n","\n","# Random Forest를 사용한 학습\n","reg = RFR(n_jobs=-1, random_state=0)\n","reg.fit(train_x.values, train_y)\n"],"id":"78bde7fd"},{"cell_type":"code","execution_count":null,"metadata":{"id":"ca01da90"},"outputs":[],"source":["# 테스트용 데이터 안의 사용와 영화의 조합에 대해 평갓값을 예측한다\n","test_pred = reg.predict(test_x.values)\n","\n","movie_rating_predict = test_keys.copy()\n","movie_rating_predict[\"rating_pred\"] = test_pred"],"id":"ca01da90"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"3ec280f6","outputId":"2757cf67-bb2c-4111-8871-e88cbe01853b"},"outputs":[{"data":{"text/plain":["defaultdict(list,\n"," {139: [7986, 6356, 6565, 4270, 6538, 5817, 4828, 3588, 631, 26974],\n"," 149: [5914, 1672, 1570, 555, 5928, 616, 640, 5996, 1326, 1671],\n"," 182: [4956,\n"," 5971,\n"," 6729,\n"," 8379,\n"," 6713,\n"," 5662,\n"," 30894,\n"," 6890,\n"," 7647,\n"," 5090],\n"," 215: [5598, 4626, 4679, 6286, 4458, 4802, 4887, 848, 3274, 5572],\n"," 281: [8482, 1497, 715, 880, 1483, 4531, 6583, 5472, 6722, 1992],\n"," 326: [2269, 2211, 2393, 1084, 1643, 1711, 1336, 1186, 1156, 523],\n"," 351: [6312, 6058, 7458, 7117, 7523, 7340, 6008, 8485, 7836, 8369],\n"," 357: [56949,\n"," 56587,\n"," 59037,\n"," 13,\n"," 2569,\n"," 2243,\n"," 2993,\n"," 2013,\n"," 56801,\n"," 126],\n"," 426: [3718, 3035, 3620, 7817, 3892, 3387, 3496, 3223, 3546, 5723],\n"," 456: [5060, 5634, 5688, 4975, 5909, 5275, 4988, 6568, 1880, 6672],\n"," 459: [6005,\n"," 42004,\n"," 8827,\n"," 40851,\n"," 33903,\n"," 47810,\n"," 56333,\n"," 1940,\n"," 42021,\n"," 27773],\n"," 494: [26144, 8722, 7153, 9, 6874, 8191, 7111, 8, 32596, 7030],\n"," 517: [2770, 3423, 218, 3468, 6772, 6250, 6333, 6005, 3525, 5930],\n"," 524: [3186, 3338, 3992, 4486, 4535, 3135, 2727, 3939, 2665, 3924],\n"," 556: [26, 1783, 1265, 1210, 1215, 32011, 58293, 1135, 166, 82],\n"," 588: [1938, 2898, 2010, 2454, 2824, 2478, 2381, 1809, 1873, 2529],\n"," 589: [7704, 6375, 25850, 5668, 423, 4701, 1661, 910, 729, 854],\n"," 590: [59985,\n"," 6281,\n"," 51088,\n"," 5372,\n"," 32596,\n"," 5427,\n"," 39381,\n"," 53318,\n"," 8939,\n"," 7223],\n"," 601: [6330, 335, 6423, 7150, 8873, 6569, 7075, 8746, 6672, 7025],\n"," 621: [2043, 2443, 2455, 2103, 2906, 2342, 2492, 2412, 2116, 3008],\n"," 634: [559, 840, 767, 1350, 1750, 1496, 1704, 1296, 854, 1342],\n"," 672: [2210, 4272, 1149, 2275, 4926, 1874, 1667, 1370, 4565, 1351],\n"," 701: [2533, 3270, 3802, 4470, 253, 500, 1203, 1237, 5046, 104],\n"," 719: [423, 967, 957, 918, 909, 1585, 893, 1574, 969, 1353],\n"," 745: [1185, 1128, 599, 3, 1759, 116, 5333, 1583, 4634, 691],\n"," 757: [521, 58306, 60688, 550, 757, 1084, 526, 1218, 273, 53921],\n"," 775: [8595,\n"," 6914,\n"," 31687,\n"," 8093,\n"," 8363,\n"," 8998,\n"," 58803,\n"," 8253,\n"," 32019,\n"," 6934],\n"," 780: [4178, 4729, 4108, 4893, 6702, 5529, 4102, 5516, 4483, 4077],\n"," 785: [605, 423, 579, 466, 801, 326, 518, 880, 270, 1086],\n"," 788: [1081, 1885, 907, 553, 876, 58047, 54281, 65, 251, 2340],\n"," 870: [6862, 7375, 7373, 8961, 7368, 7361, 7355, 8981, 3395, 8957],\n"," 987: [6214, 5114, 6927, 5881, 704, 3760, 5302, 513, 765, 6183],\n"," 988: [927, 465, 246, 53956, 1525, 55765, 614, 220, 1615, 1518],\n"," 1020: [2693,\n"," 2748,\n"," 3208,\n"," 3158,\n"," 2687,\n"," 2879,\n"," 3884,\n"," 3821,\n"," 42015,\n"," 45852],\n"," 1: [100, 854, 449, 1025, 802, 196, 1100, 31689, 685, 1136],\n"," 22: [26138,\n"," 6806,\n"," 9005,\n"," 26745,\n"," 8932,\n"," 6887,\n"," 8128,\n"," 7383,\n"," 8256,\n"," 26171],\n"," 26: [520, 4823, 5843, 4845, 4284, 3476, 376, 38, 4296, 4701],\n"," 30: [4723, 3876, 1498, 2163, 5324, 6412, 5341, 2603, 45722, 1378],\n"," 34: [7384, 6709, 6590, 7820, 8743, 7068, 6597, 7321, 3549, 6581],\n"," 38: [881, 4662, 3827, 250, 46, 781, 39381, 1034, 708, 485],\n"," 50: [5480, 4776, 4772, 4771, 5452, 4768, 4766, 4765, 4757, 4756],\n"," 53: [6744, 5792, 6429, 6430, 5788, 6436, 5786, 7163, 5785, 7162],\n"," 54: [48043, 8939, 352, 27790, 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3351, 4384, 3893, 3330, 3359, 4069, 3364, 3716],\n"," 111: [6886, 8754, 33138, 33312, 19, 60069, 2440, 14, 3165, 911],\n"," 116: [4521, 7323, 6346, 6204, 8789, 4001, 3189, 4009, 8782, 6441],\n"," 118: [51091,\n"," 47629,\n"," 48780,\n"," 47970,\n"," 54256,\n"," 53953,\n"," 59315,\n"," 48516,\n"," 59985,\n"," 60522],\n"," 143: [40148,\n"," 7168,\n"," 25995,\n"," 7297,\n"," 8270,\n"," 5379,\n"," 8913,\n"," 55280,\n"," 8784,\n"," 7156],\n"," 144: [3627, 3096, 4440, 100, 4366, 3038, 3760, 5004, 3392, 3043],\n"," 162: [2659, 3342, 2193, 4084, 226, 2130, 3414, 2141, 2607, 88],\n"," 172: [2512, 3118, 5189, 2639, 3269, 2504, 6724, 4471, 2597, 7000],\n"," 173: [8016,\n"," 36525,\n"," 30820,\n"," 8905,\n"," 8605,\n"," 26491,\n"," 8125,\n"," 8131,\n"," 41997,\n"," 30848],\n"," 187: [3300, 4307, 4224, 2474, 4102, 4188, 4405, 3362, 3877, 3287],\n"," 193: [3841,\n"," 3147,\n"," 3684,\n"," 4231,\n"," 7084,\n"," 4007,\n"," 3038,\n"," 4350,\n"," 61132,\n"," 5949],\n"," 208: [53953,\n"," 54256,\n"," 60037,\n"," 58303,\n"," 58418,\n"," 53999,\n"," 502,\n"," 55442,\n"," 513,\n"," 58291],\n"," 210: [3831, 4428, 3978, 3824, 3985, 3896, 3814, 4267, 4371, 3972],\n"," 214: [535, 463, 436, 1665, 5682, 1554, 6870, 1035, 668, 1658],\n"," 228: [7139,\n"," 7211,\n"," 6870,\n"," 8609,\n"," 3628,\n"," 6884,\n"," 7084,\n"," 7216,\n"," 31594,\n"," 6874],\n"," 232: [45880, 197, 2154, 2335, 2027, 1855, 2284, 1640, 2395, 2745],\n"," 236: [1624, 2278, 2427, 2178, 6614, 5470, 1533, 2033, 3503, 2717],\n"," 259: [1769, 2834, 2937, 2884, 2286, 2559, 2577, 2669, 2429, 1912],\n"," 260: [4867, 5685, 6279, 6383, 4722, 6346, 5847, 5522, 5918, 6252],\n"," 267: [67, 34405, 81, 72, 802, 6241, 5164, 1255, 5334, 6973],\n"," 268: [425, 5688, 1647, 19, 6875, 441, 1202, 414, 7728, 50872],\n"," 271: [1020, 519, 970, 1151, 7844, 2410, 1804, 32587, 49530, 7720],\n"," 274: [31923,\n"," 7577,\n"," 5611,\n"," 5016,\n"," 48780,\n"," 5009,\n"," 4126,\n"," 48883,\n"," 26318,\n"," 5478],\n"," 287: [64716,\n"," 4777,\n"," 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5826],\n"," 364: [5736,\n"," 4767,\n"," 52241,\n"," 27340,\n"," 26391,\n"," 5343,\n"," 6291,\n"," 6433,\n"," 5901,\n"," 51088],\n"," 365: [2616, 3587, 2605, 2936, 2624, 2693, 3147, 6345, 3330, 3098],\n"," 368: [7202,\n"," 8639,\n"," 7104,\n"," 31032,\n"," 8501,\n"," 7086,\n"," 2107,\n"," 8941,\n"," 1428,\n"," 7844],\n"," 381: [6203, 5295, 1642, 1233, 2447, 2023, 6025, 6425, 5193, 5692],\n"," 382: [7334, 6545, 6810, 5816, 7573, 6646, 5648, 2399, 1942, 6232],\n"," 383: [2118, 7728, 2110, 2359, 8698, 2926, 2069, 2170, 2431, 3239],\n"," 391: [2977, 3408, 3705, 3104, 3773, 3543, 3395, 2830, 2837, 2970],\n"," 396: [2461, 3073, 3284, 2405, 2550, 2921, 2876, 2961, 2410, 3050],\n"," 398: [813, 3180, 1238, 1279, 7204, 6077, 43919, 748, 3735, 3350],\n"," 406: [5398,\n"," 4047,\n"," 3993,\n"," 4602,\n"," 26828,\n"," 4510,\n"," 7386,\n"," 5300,\n"," 3941,\n"," 5321],\n"," 409: [3412,\n"," 2762,\n"," 26425,\n"," 7364,\n"," 2191,\n"," 1409,\n"," 41573,\n"," 3387,\n"," 3232,\n"," 2695],\n"," 410: [6130, 7075, 2387, 2470, 2570, 17, 2628, 2342, 2005, 6005],\n"," 416: [1293, 1826, 2530, 1151, 647, 1459, 1799, 1214, 570, 8800],\n"," 418: [4502,\n"," 42197,\n"," 160,\n"," 896,\n"," 42009,\n"," 44665,\n"," 4641,\n"," 59725,\n"," 4677,\n"," 53000],\n"," 419: [6772, 3599, 5999, 3752, 3157, 6970, 4257, 6690, 3016, 6525],\n"," 421: [5572, 5321, 6333, 5648, 5512, 5339, 5688, 5944, 5324, 5651],\n"," 424: [457, 456, 455, 453, 452, 448, 447, 446, 445, 444],\n"," 432: [4300,\n"," 4229,\n"," 4236,\n"," 4807,\n"," 4867,\n"," 4458,\n"," 3676,\n"," 54286,\n"," 4612,\n"," 2875],\n"," 434: [8365,\n"," 8157,\n"," 8584,\n"," 26152,\n"," 7079,\n"," 6997,\n"," 33649,\n"," 7781,\n"," 27563,\n"," 8768],\n"," 436: [5362, 4739, 207, 165, 4654, 5516, 618, 5913, 219, 70],\n"," 438: [4187, 4397, 4470, 4169, 4265, 5485, 510, 992, 2363, 4692],\n"," 441: [1727, 1845, 2798, 2744, 1904, 2409, 2202, 2272, 2512, 1650],\n"," 446: [870, 1281, 4645, 6166, 1333, 4840, 4580, 4833, 6037, 5472],\n"," 452: [3774, 3726, 4395, 2676, 4333, 3992, 2073, 3173, 3157, 3740],\n"," 460: [3580, 4153, 3732, 3564, 3573, 3014, 3653, 2405, 4094, 4637],\n"," 463: [3552, 3624, 3547, 4274, 4126, 4182, 4622, 4371, 3755, 4063],\n"," 468: [7058, 5915, 8197, 51925, 459, 42002, 143, 8235, 54995, 188],\n"," 469: [3504, 3566, 3499, 4792, 4428, 4444, 4144, 3937, 3600, 4714],\n"," 472: [118, 932, 702, 680, 230, 695, 65, 934, 147, 664],\n"," 476: [355, 702, 844, 5580, 7440, 27731, 6597, 577, 124, 38992],\n"," 480: [2333, 1805, 4932, 2446, 4276, 2118, 1814, 2283, 1793, 2906],\n"," 482: [3161, 3169, 3168, 3155, 3152, 3145, 3142, 3173, 3135, 3133],\n"," 493: [26064,\n"," 41569,\n"," 801,\n"," 1550,\n"," 45730,\n"," 59615,\n"," 3286,\n"," 46976,\n"," 58293,\n"," 8874],\n"," 496: [8799, 2590, 2723, 5840, 4600, 2137, 2082, 2545, 2087, 5009],\n"," 501: [50804,\n"," 47099,\n"," 58972,\n"," 48142,\n"," 54997,\n"," 47254,\n"," 62394,\n"," 48678,\n"," 48518,\n"," 50068],\n"," 504: [5859, 5992, 6240, 5662, 6405, 6312, 6777, 6063, 6261, 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3357],\n"," 93: [8207,\n"," 8235,\n"," 8484,\n"," 34143,\n"," 31429,\n"," 4975,\n"," 47382,\n"," 38499,\n"," 32139,\n"," 8831],\n"," 261: [7034, 8966, 4629, 5248, 6303, 4212, 4526, 4566, 4837, 3925],\n"," 435: [1825, 2048, 2578, 2574, 2042, 2040, 2038, 2037, 2569, 2035],\n"," 322: [6247, 5343, 5996, 6221, 6568, 6118, 5447, 6510, 6433, 5764],\n"," 238: [33004,\n"," 51080,\n"," 31435,\n"," 49280,\n"," 31359,\n"," 31590,\n"," 55118,\n"," 31685,\n"," 44191,\n"," 62803],\n"," 425: [3918, 4448, 4393, 4972, 4637, 4588, 4334, 4743, 4686, 4153],\n"," 512: [55250,\n"," 32296,\n"," 1055,\n"," 2294,\n"," 1862,\n"," 43921,\n"," 54612,\n"," 27873,\n"," 34321,\n"," 51925],\n"," 725: [4389, 4705, 4164, 4695, 4969, 4252, 4409, 3901, 4003, 4087],\n"," 732: [3819, 3088, 3204, 3962, 3702, 3051, 2845, 3101, 3221, 3361],\n"," 108: [3640,\n"," 2847,\n"," 4124,\n"," 49649,\n"," 50796,\n"," 53468,\n"," 1388,\n"," 1011,\n"," 1009,\n"," 51086],\n"," 592: [44665,\n"," 26150,\n"," 27340,\n"," 26172,\n"," 48738,\n"," 42728,\n"," 56715,\n"," 26002,\n"," 7584,\n"," 26119],\n"," 443: [45726,\n"," 25788,\n"," 5494,\n"," 40412,\n"," 4603,\n"," 4679,\n"," 4546,\n"," 6198,\n"," 62394,\n"," 42734],\n"," 916: [7621,\n"," 8864,\n"," 8860,\n"," 33171,\n"," 33237,\n"," 33312,\n"," 8852,\n"," 8841,\n"," 33493,\n"," 33499],\n"," 736: [6093, 6957, 8371, 6794, 8377, 7027, 8459, 8365, 7394, 7646],\n"," 961: [1583,\n"," 1785,\n"," 6790,\n"," 1145,\n"," 7405,\n"," 27689,\n"," 7572,\n"," 26492,\n"," 1626,\n"," 27416],\n"," 1012: [6482,\n"," 6094,\n"," 6377,\n"," 6216,\n"," 6125,\n"," 7134,\n"," 7155,\n"," 7163,\n"," 7486,\n"," 6326],\n"," 515: [7124, 6191, 6299, 6159, 3871, 7445, 1880, 6339, 7102, 1374],\n"," 978: [729, 1655, 1587, 546, 1334, 1662, 1582, 709, 1262, 1261],\n"," 28: [3132, 3825, 2566, 1867, 149, 55269, 3064, 3797, 3728, 58975],\n"," 475: [4205, 3515, 1696, 345, 575, 1050, 810, 2300, 2236, 213],\n"," 598: [27803,\n"," 25764,\n"," 47644,\n"," 54513,\n"," 57538,\n"," 27768,\n"," 59306,\n"," 31594,\n"," 32179,\n"," 25805],\n"," 943: [1251, 1365, 1373, 1152, 1797, 1144, 1785, 1125, 1421, 1428],\n"," 520: [4039, 4104, 4795, 3600, 3738, 3518, 3527, 4677, 4251, 2461],\n"," 697: [938, 2166, 1693, 1669, 1046, 1284, 1468, 1968, 981, 964],\n"," 849: [5024, 4970, 4968, 5602, 4961, 4960, 4958, 4957, 4951, 5606],\n"," 1003: [7194,\n"," 7828,\n"," 8484,\n"," 31367,\n"," 7013,\n"," 8827,\n"," 7237,\n"," 8811,\n"," 6855,\n"," 7047],\n"," 705: [2421, 2306, 1754, 2445, 2365, 2600, 1857, 2095, 2677, 2864],\n"," 48: [3073, 2613, 7134, 5172, 8275, 2754, 2542, 3743, 3204, 6244],\n"," 29: [7943,\n"," 1103,\n"," 7698,\n"," 1593,\n"," 1040,\n"," 1045,\n"," 26745,\n"," 25753,\n"," 8604,\n"," 1733],\n"," 231: [627, 58, 1032, 56949, 33836, 1113, 5499, 164, 719, 157],\n"," 699: [485, 468, 301, 37741, 436, 8537, 50794, 73, 378, 69],\n"," 448: [4722, 4168, 5111, 4477, 4886, 6686, 5523, 4085, 1841, 5099],\n"," 750: [3968, 4453, 5295, 4509, 5202, 3849, 4121, 5214, 4599, 4683],\n"," 782: [2473, 2885, 2930, 2408, 3061, 3519, 2610, 2975, 2443, 3135],\n"," 860: [4366, 4521, 5823, 4355, 5236, 4517, 5187, 4531, 5464, 5560],\n"," 768: [960, 2017, 1126, 1997, 961, 1371, 1575, 1465, 1145, 1780],\n"," 127: [47099,\n"," 3189,\n"," 2824,\n"," 7373,\n"," 3528,\n"," 3605,\n"," 58047,\n"," 3252,\n"," 3592,\n"," 8636],\n"," 894: [118, 177, 900, 830, 1014, 486, 868, 298, 640, 969]})"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["from collections import defaultdict\n","import numpy as np\n","\n","# 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합에 대해 평갓값을 예측한다\n","train_all_pred = reg.predict(train_all_x.values)\n","\n","pred_train_all = train_all_keys.copy()\n","pred_train_all[\"rating_pred\"] = train_all_pred\n","pred_matrix = pred_train_all.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating_pred\")\n","\n","# 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서\n","# 예측 평갓값이 높은 순으로 10편의 영화를 순위 형식으로 추천 리스트로 만든다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," movie_indexes = np.argsort(-pred_matrix.loc[user_id, :]).values\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in (user_evaluated_movies[user_id]):\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"],"id":"3ec280f6"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c3a25c25","outputId":"aa1f9306-1bdb-43c6-f692-1731569f185f"},"outputs":[{"data":{"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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\n"," "],"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# user_id=2인 사용자가 학습 데이터에 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"],"id":"c3a25c25"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"a178ecdc","outputId":"9254da3c-39f8-4c2d-f850-120f1e9b7e68"},"outputs":[{"data":{"text/plain":["[4210, 4961, 4105, 2351, 3573, 4032, 1586, 5159, 4840, 2804]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["pred_user2items[2]"],"id":"a178ecdc"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"09555a1f","outputId":"0a669df8-2100-4815-af2c-0ab68484aab4"},"outputs":[{"data":{"text/html":["\n","
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movie_idtitlegenretag
40134105Evil Dead, The (1981)[Fantasy, Horror][directorial debut, bruce campbell, cult class...
41184210Manhunter (1986)[Action, Crime, Drama, Horror, Thriller][hannibal lecter, serial killer, ei muista, er...
48674961Pornstar: The Legend of Ron Jeremy (2001)[Documentary][pornography]
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\n"," "],"text/plain":[" movie_id title \\\n","4013 4105 Evil Dead, The (1981) \n","4118 4210 Manhunter (1986) \n","4867 4961 Pornstar: The Legend of Ron Jeremy (2001) \n","\n"," genre \\\n","4013 [Fantasy, Horror] \n","4118 [Action, Crime, Drama, Horror, Thriller] \n","4867 [Documentary] \n","\n"," tag \n","4013 [directorial debut, bruce campbell, cult class... \n","4118 [hannibal lecter, serial killer, ei muista, er... \n","4867 [pornography] "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# user_id=2에 대한 추천(4210, 4961, 4105)\n","movies[movies.movie_id.isin([4210, 4961, 4105])]"],"id":"09555a1f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7a42fa17"},"outputs":[],"source":[],"id":"7a42fa17"}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/Random.ipynb b/chapter5/colab/Random.ipynb index 5719d24..5c4d1b4 100644 --- a/chapter5/colab/Random.ipynb +++ b/chapter5/colab/Random.ipynb @@ -1,5152 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9dbb7b61", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Random.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "b8e120c7", - "metadata": {}, - "source": [ - "# 무작위 추천\n", - "0.5부터 5사이의 균등 난수를 예측 평갓값으로 한다" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "2e403fbc", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9827acbb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "bdfd3743", - "metadata": {}, - "outputs": [], - "source": [ - "# 사용자 ID와 아이템 ID에 대해 0부터 시작하는 인덱스를 할당한다\n", - "unique_user_ids = sorted(movielens_train.user_id.unique())\n", - "unique_movie_ids = sorted(movielens_train.movie_id.unique())\n", - "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", - "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b26a7e38", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "# 사용자 x 아이템 행결에서 각 셀의 예측 평갓값은 0.5~5.0의 균등 난수로 한다\n", - "pred_matrix = np.random.uniform(0.5, 5.0, (len(unique_user_ids), len(unique_movie_ids)))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "39dc3945", - "metadata": {}, - "outputs": [], - "source": [ - "# rmse 평가용으로 테스트 데이터에 나타나는 사용자와 아이템의 예측 평갓값을 저장한다.\n", - "movie_rating_predict = movielens_test.copy()\n", - "pred_results = []\n", - "for i, row in movielens_test.iterrows():\n", - " user_id = row[\"user_id\"]\n", - " # 테스트 데이터의 아이템 ID가 학습용에 나타나지 않는 경우에도 난수를 저장한다\n", - " if row[\"movie_id\"] not in movie_id2index:\n", - " pred_results.append(np.random.uniform(0.5, 5.0))\n", - " continue\n", - " # 테스트 데이터에 나타난 사용자 ID와 아이템 ID의 인덱스를 얻어, 평갓값 행렬의 값을 얻는다\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " pred_score = pred_matrix[user_index, movie_index]\n", - " pred_results.append(pred_score)\n", - "movie_rating_predict[\"rating_pred\"] = pred_results" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8f3c304f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {1: [38992, 32213, 847, 3932, 6217, 1782, 3592, 2131, 4758, 206],\n", - " 2: [5656, 714, 4560, 40946, 2729, 7842, 5569, 4551, 570, 2922],\n", - " 3: [32584, 5791, 7027, 7156, 6316, 32770, 7369, 4104, 1407, 8584],\n", - " 4: [1244,\n", - " 1513,\n", - " 2674,\n", - " 47778,\n", - " 42728,\n", - " 6653,\n", - " 8659,\n", - " 1917,\n", - " 1306,\n", - " 40946],\n", - " 5: [3071, 628, 5322, 2197, 5323, 5802, 6510, 53000, 1377, 3107],\n", - " 6: [4310, 589, 26085, 6126, 1880, 6653, 1103, 3401, 29, 6973],\n", - " 7: [2018, 990, 1534, 44828, 2949, 2833, 4351, 31685, 1841, 5345],\n", - " 8: [959, 49274, 40629, 1980, 3754, 3075, 62113, 1979, 1121, 6196],\n", - " 9: [2976, 5936, 51931, 2758, 6554, 309, 8977, 355, 839, 6155],\n", - " 10: [7828, 2010, 2983, 1552, 1884, 3634, 5098, 4701, 4970, 3849],\n", - " 11: [7307, 7893, 6322, 3083, 2214, 32302, 2488, 2463, 5796, 379],\n", - " 12: [2918, 367, 60688, 3065, 1480, 5944, 276, 6760, 65, 534],\n", - " 13: [2279, 2359, 1482, 15, 1564, 58975, 181, 963, 40629, 5119],\n", - " 14: [27513, 1589, 3159, 204, 1660, 2575, 8906, 3735, 2750, 4572],\n", - 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" 31685,\n", - " 4770,\n", - " 55946,\n", - " 3555,\n", - " 5803,\n", - " 8197,\n", - " 417,\n", - " 1336]})" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 순위 평가용 데이터를 작성한다\n", - "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 10작품을 무작위로 선택한다\n", - "# 키는 사용자 ID, 값은 추천 아이템 ID 리스트\n", - "pred_user2items = defaultdict(list)\n", - "# 사용자가 이미 평가한 영화를 얻는다\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "for user_id in unique_user_ids:\n", - " user_index = user_id2index[user_id]\n", - " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", - " for movie_index in movie_indexes:\n", - " movie_id = unique_movie_ids[movie_index]\n", - " if movie_id not in user_evaluated_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "093cd4cf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1ecf4f28", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
movie_idtitlegenretag
702714Dead Man (1995)[Drama, Mystery, Western][jim jarmusch, beautiful, iggy pop!!!, johnny ...
44674560Watchers (1988)[Horror, Sci-Fi][based on a book, dean koontz]
55585656Festival in Cannes (2001)[Drama]NaN
\n", - "
" - ], - "text/plain": [ - " movie_id title genre \\\n", - "702 714 Dead Man (1995) [Drama, Mystery, Western] \n", - "4467 4560 Watchers (1988) [Horror, Sci-Fi] \n", - "5558 5656 Festival in Cannes (2001) [Drama] \n", - "\n", - " tag \n", - "702 [jim jarmusch, beautiful, iggy pop!!!, johnny ... \n", - "4467 [based on a book, dean koontz] \n", - "5558 NaN " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(5656, 714, 4560)\n", - "movies[movies.movie_id.isin([5656, 714, 4560])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dff2694d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"9dbb7b61","metadata":{"id":"9dbb7b61"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Random.ipynb)"]},{"cell_type":"markdown","id":"b8e120c7","metadata":{"id":"b8e120c7"},"source":["# 무작위 추천\n","0.5부터 5사이의 균등 난수를 예측 평갓값으로 한다"]},{"cell_type":"code","execution_count":1,"id":"2e403fbc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2e403fbc","executionInfo":{"status":"ok","timestamp":1672119742747,"user_tz":-540,"elapsed":4113,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"859a6757-7ce5-4d2d-eb0e-25a6a2337197"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:40:34-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 43.4MB/s in 1.4s \n","\n","2022-12-27 05:40:35 (43.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"9827acbb","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9827acbb","executionInfo":{"status":"ok","timestamp":1672119815437,"user_tz":-540,"elapsed":72694,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"8a425bfa-3cdf-4403-e7f1-b37e01615bd7"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"bdfd3743","metadata":{"id":"bdfd3743","executionInfo":{"status":"ok","timestamp":1672119815438,"user_tz":-540,"elapsed":36,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 사용자 ID와 아이템 ID에 대해 0부터 시작하는 인덱스를 할당한다\n","unique_user_ids = sorted(movielens_train.user_id.unique())\n","unique_movie_ids = sorted(movielens_train.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))\n"]},{"cell_type":"code","execution_count":4,"id":"b26a7e38","metadata":{"id":"b26a7e38","executionInfo":{"status":"ok","timestamp":1672119815438,"user_tz":-540,"elapsed":32,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np\n","# 사용자 x 아이템 행결에서 각 셀의 예측 평갓값은 0.5~5.0의 균등 난수로 한다\n","pred_matrix = np.random.uniform(0.5, 5.0, (len(unique_user_ids), len(unique_movie_ids)))\n"]},{"cell_type":"code","execution_count":5,"id":"39dc3945","metadata":{"id":"39dc3945","executionInfo":{"status":"ok","timestamp":1672119815895,"user_tz":-540,"elapsed":486,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# rmse 평가용으로 테스트 데이터에 나타나는 사용자와 아이템의 예측 평갓값을 저장한다.\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," # 테스트 데이터의 아이템 ID가 학습용에 나타나지 않는 경우에도 난수를 저장한다\n"," if row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(np.random.uniform(0.5, 5.0))\n"," continue\n"," # 테스트 데이터에 나타난 사용자 ID와 아이템 ID의 인덱스를 얻어, 평갓값 행렬의 값을 얻는다\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results"]},{"cell_type":"code","execution_count":6,"id":"8f3c304f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8f3c304f","executionInfo":{"status":"ok","timestamp":1672119817002,"user_tz":-540,"elapsed":1116,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e844acbc-bde3-4540-cda2-b067799b9f8b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [4191, 8738, 1405, 1344, 1320, 3669, 3450, 673, 1413, 6118],\n"," 2: [56176, 4893, 761, 2072, 2951, 2540, 2294, 3596, 5362, 4664],\n"," 3: [7880, 1592, 26082, 460, 7474, 2090, 3825, 5684, 2748, 1615],\n"," 4: [5686, 663, 8645, 30820, 1078, 5528, 3180, 3824, 7177, 31193],\n"," 5: [4518, 1179, 26270, 720, 3735, 4556, 29, 2307, 1974, 27912],\n"," 6: [4800, 4234, 4682, 3255, 6808, 6954, 4840, 8501, 2630, 4082],\n"," 7: [5056,\n"," 1258,\n"," 8870,\n"," 6883,\n"," 2695,\n"," 7068,\n"," 54997,\n"," 40955,\n"," 7299,\n"," 33679],\n"," 8: [5650, 4720, 6718, 2803, 5788, 6927, 4836, 4401, 2029, 525],\n"," 9: [44937, 4541, 1928, 8872, 31, 59103, 3663, 3936, 4574, 4630],\n"," 10: [7440, 99, 1215, 54612, 6268, 1027, 5735, 4408, 1615, 6269],\n"," 11: [2639,\n"," 2085,\n"," 55269,\n"," 3167,\n"," 8722,\n"," 44193,\n"," 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2069, 7190],\n"," 29: [405, 888, 5398, 828, 1732, 1419, 7170, 153, 2761, 5955],\n"," 30: [1454, 1006, 717, 37380, 820, 40851, 5071, 1728, 3183, 113],\n"," 33: [33154, 2081, 2467, 2286, 4086, 4092, 1376, 4515, 637, 31952],\n"," 34: [34332, 922, 6014, 747, 8593, 4772, 55442, 3156, 2546, 3948],\n"," 35: [46335,\n"," 2423,\n"," 1468,\n"," 820,\n"," 8016,\n"," 1947,\n"," 33358,\n"," 8754,\n"," 59141,\n"," 3596],\n"," 36: [125, 77, 57640, 2969, 681, 2014, 1200, 4203, 62801, 168],\n"," 37: [4392, 4415, 438, 198, 56801, 3475, 2393, 54286, 1477, 3425],\n"," 38: [5507, 3501, 7649, 272, 1133, 4885, 3465, 1078, 2530, 54997],\n"," 40: [3268, 3102, 56339, 3704, 48997, 2470, 6988, 500, 4228, 4614],\n"," 41: [197, 7308, 1554, 2550, 2916, 230, 2288, 1507, 3466, 6679],\n"," 42: [6180, 6454, 3545, 4254, 1919, 2188, 416, 189, 1177, 6678],\n"," 43: [3304, 5202, 2919, 468, 732, 113, 455, 3141, 2316, 27808],\n"," 44: [4015, 6229, 103, 4372, 745, 62801, 7394, 3090, 50798, 8221],\n"," 45: [1791, 52328, 364, 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1034, 1207, 95],\n"," 61: [957, 55080, 7618, 1008, 4465, 4610, 926, 2503, 3032, 1983],\n"," 62: [2243, 708, 6855, 37853, 351, 5569, 54094, 4803, 8253, 5012],\n"," 63: [238, 2834, 5962, 2384, 6784, 3219, 4014, 2823, 7915, 2432],\n"," 64: [779, 2330, 8969, 5066, 41569, 2278, 49961, 1581, 6786, 1999],\n"," 65: [3438,\n"," 48319,\n"," 7941,\n"," 1869,\n"," 6791,\n"," 57243,\n"," 2353,\n"," 2092,\n"," 2395,\n"," 1038],\n"," 66: [2908, 2679, 7250, 40, 4611, 3494, 6988, 5975, 197, 518],\n"," 67: [6419, 3150, 2493, 4045, 2242, 2435, 4792, 582, 4426, 5829],\n"," 68: [2641,\n"," 2198,\n"," 5529,\n"," 43679,\n"," 43987,\n"," 1352,\n"," 3418,\n"," 25888,\n"," 26138,\n"," 3400],\n"," 69: [5047, 3794, 2457, 56286, 2202, 4240, 848, 6855, 59333, 2151],\n"," 70: [2817, 1250, 2863, 7327, 3035, 223, 40870, 1283, 2593, 2764],\n"," 71: [5629, 34583, 4159, 8768, 87, 31086, 6590, 3785, 1945, 50685],\n"," 72: [4613,\n"," 3659,\n"," 4556,\n"," 44399,\n"," 32584,\n"," 6059,\n"," 37729,\n"," 7892,\n"," 639,\n"," 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4722],\n"," 98: [2916,\n"," 5322,\n"," 57910,\n"," 4338,\n"," 3777,\n"," 48997,\n"," 6892,\n"," 6744,\n"," 26111,\n"," 753],\n"," 99: [4307, 219, 3790, 1681, 27317, 4199, 6219, 1678, 33358, 165],\n"," 100: [40339, 58103, 234, 2340, 846, 7155, 5318, 7373, 4727, 6100],\n"," 101: [59404,\n"," 2919,\n"," 4519,\n"," 151,\n"," 2557,\n"," 3507,\n"," 5981,\n"," 27812,\n"," 4002,\n"," 33722],\n"," 102: [1302,\n"," 56805,\n"," 3837,\n"," 42009,\n"," 1963,\n"," 5080,\n"," 220,\n"," 6041,\n"," 7383,\n"," 59900],\n"," 103: [2269, 3972, 102, 769, 4015, 5529, 2903, 3174, 27846, 6807],\n"," 104: [8593, 3372, 8738, 2066, 338, 8493, 3861, 1884, 3643, 5860],\n"," 105: [7646, 3846, 5787, 954, 4806, 648, 251, 354, 1267, 2236],\n"," 106: [7646, 32291, 1135, 3816, 2693, 15, 1843, 2790, 47610, 5816],\n"," 107: [7169,\n"," 30810,\n"," 4562,\n"," 4267,\n"," 6973,\n"," 3454,\n"," 6536,\n"," 2118,\n"," 3889,\n"," 2422],\n"," 108: [42015,\n"," 555,\n"," 7562,\n"," 2168,\n"," 53466,\n"," 60514,\n"," 3035,\n"," 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7254,\n"," 2266],\n"," 1029: [7151,\n"," 4830,\n"," 8119,\n"," 2545,\n"," 3510,\n"," 1676,\n"," 5494,\n"," 4001,\n"," 2184,\n"," 1976],\n"," 1030: [847,\n"," 26614,\n"," 3705,\n"," 4729,\n"," 5410,\n"," 4956,\n"," 3466,\n"," 3741,\n"," 26122,\n"," 2794],\n"," 1031: [34321,\n"," 3105,\n"," 4895,\n"," 2231,\n"," 3808,\n"," 2589,\n"," 813,\n"," 45186,\n"," 4095,\n"," 5283],\n"," 1032: [47099,\n"," 49957,\n"," 3605,\n"," 30820,\n"," 7176,\n"," 1693,\n"," 54771,\n"," 198,\n"," 40723,\n"," 2163],\n"," 1033: [663,\n"," 8183,\n"," 7162,\n"," 3473,\n"," 55830,\n"," 4772,\n"," 41863,\n"," 5749,\n"," 2513,\n"," 1892],\n"," 1034: [7981,\n"," 2212,\n"," 6378,\n"," 2310,\n"," 6714,\n"," 60522,\n"," 2177,\n"," 1500,\n"," 2912,\n"," 1873],\n"," 1035: [334, 85, 40, 3704, 42004, 2886, 3214, 3964, 32153, 670],\n"," 1037: [3869,\n"," 2892,\n"," 43460,\n"," 1150,\n"," 3604,\n"," 5352,\n"," 4144,\n"," 1747,\n"," 1078,\n"," 7757],\n"," 1038: [39435,\n"," 917,\n"," 6316,\n"," 2883,\n"," 5398,\n"," 31049,\n"," 33649,\n"," 781,\n"," 4682,\n"," 5703],\n"," 1039: [37386,\n"," 5738,\n"," 4886,\n"," 2796,\n"," 5451,\n"," 4723,\n"," 1563,\n"," 215,\n"," 2026,\n"," 27815],\n"," 1040: [7209,\n"," 38600,\n"," 2488,\n"," 2032,\n"," 4247,\n"," 5598,\n"," 2744,\n"," 778,\n"," 26078,\n"," 2772],\n"," 1041: [59315,\n"," 2844,\n"," 1144,\n"," 4707,\n"," 3296,\n"," 615,\n"," 33880,\n"," 6852,\n"," 4248,\n"," 8493],\n"," 1043: [3408, 665, 5128, 2025, 5111, 7482, 2930, 2910, 5574, 1461],\n"," 1044: [56885,\n"," 4888,\n"," 54997,\n"," 44161,\n"," 3703,\n"," 5231,\n"," 3304,\n"," 8253,\n"," 6612,\n"," 590],\n"," 1045: [572,\n"," 6405,\n"," 62113,\n"," 6672,\n"," 3672,\n"," 7386,\n"," 2311,\n"," 43987,\n"," 1843,\n"," 3707],\n"," 1046: [1733,\n"," 6927,\n"," 3481,\n"," 30820,\n"," 56563,\n"," 803,\n"," 1977,\n"," 986,\n"," 34336,\n"," 5437],\n"," 1047: [4831,\n"," 2902,\n"," 55288,\n"," 2964,\n"," 1914,\n"," 6053,\n"," 917,\n"," 2617,\n"," 4981,\n"," 4488],\n"," 1048: [7757, 342, 3069, 769, 784, 6887, 52973, 1224, 26828, 514],\n"," 1050: [1007, 2788, 3200, 3897, 932, 4629, 4377, 2749, 1719, 5318],\n"," 1051: [6587,\n"," 31658,\n"," 6230,\n"," 60069,\n"," 8914,\n"," 1865,\n"," 48877,\n"," 2784,\n"," 1547,\n"," 5927],\n"," 1052: [2449,\n"," 31437,\n"," 5127,\n"," 2917,\n"," 6458,\n"," 3306,\n"," 3235,\n"," 3615,\n"," 3895,\n"," 5279],\n"," 1053: [7743, 390, 5422, 4039, 43708, 7308, 256, 82, 8130, 3940]})"]},"metadata":{},"execution_count":6}],"source":["from collections import defaultdict\n","\n","# 순위 평가용 데이터를 작성한다\n","# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 10작품을 무작위로 선택한다\n","# 키는 사용자 ID, 값은 추천 아이템 ID 리스트\n","pred_user2items = defaultdict(list)\n","# 사용자가 이미 평가한 영화를 얻는다\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in unique_user_ids:\n"," user_index = user_id2index[user_id]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = unique_movie_ids[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":7,"id":"093cd4cf","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"093cd4cf","executionInfo":{"status":"ok","timestamp":1672119817003,"user_tz":-540,"elapsed":19,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"8f80e008-2422-4456-9c47-9c8bff03db04"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":7}],"source":["# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":8,"id":"1ecf4f28","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"1ecf4f28","executionInfo":{"status":"ok","timestamp":1672119817004,"user_tz":-540,"elapsed":16,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"a42a26a6-8ac2-4426-f9d8-92c2b0524147"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title genre \\\n","702 714 Dead Man (1995) [Drama, Mystery, Western] \n","4467 4560 Watchers (1988) [Horror, Sci-Fi] \n","5558 5656 Festival in Cannes (2001) [Drama] \n","\n"," tag \n","702 [jim jarmusch, beautiful, iggy pop!!!, johnny ... \n","4467 [based on a book, dean koontz] \n","5558 NaN "],"text/html":["\n","
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movie_idtitlegenretag
702714Dead Man (1995)[Drama, Mystery, Western][jim jarmusch, beautiful, iggy pop!!!, johnny ...
44674560Watchers (1988)[Horror, Sci-Fi][based on a book, dean koontz]
55585656Festival in Cannes (2001)[Drama]NaN
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\n","
\n"," "]},"metadata":{},"execution_count":8}],"source":["# user_id=2에 대한 추천(5656, 714, 4560)\n","movies[movies.movie_id.isin([5656, 714, 4560])]"]},{"cell_type":"code","execution_count":8,"id":"dff2694d","metadata":{"id":"dff2694d","executionInfo":{"status":"ok","timestamp":1672119817004,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/SVD.ipynb b/chapter5/colab/SVD.ipynb index 4035a2b..b2c9df3 100644 --- a/chapter5/colab/SVD.ipynb +++ b/chapter5/colab/SVD.ipynb @@ -1,2001 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4fb1700b", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/SVD.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "05fd5c37", - "metadata": {}, - "source": [ - "# 특이값 분석(SVD)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae97b569", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "395a5042", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1bcee149", - "metadata": {}, - "outputs": [], - "source": [ - "# 결손값을 채우는 방법\n", - "fillna_with_zero = True\n", - "# 인자 수\n", - "factors = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "2cf8dfe4", - "metadata": {}, - "outputs": [], - "source": [ - "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", - "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", - "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", - "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", - "if fillna_with_zero:\n", - " matrix = user_movie_matrix.fillna(0).to_numpy()\n", - "else:\n", - " matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "22c0909b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ユーザー数=1000, アイテム数=6673, 密度=0.02\n" - ] - } - ], - "source": [ - "# 희소 정보\n", - "user_num = len(user_movie_matrix.index)\n", - "item_num = len(user_movie_matrix.columns)\n", - "non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n", - "non_null_ratio = non_null_num / (user_num*item_num)\n", - "\n", - "print(f'ユーザー数={user_num}, アイテム数={item_num}, 密度={non_null_ratio:.2f}')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7f1f97fa", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from scipy.sparse.linalg import svds\n", - "import numpy as np\n", - "\n", - "# 인자 수 k로 특이점 분석을 수행한다\n", - "P, S, Qt = svds(matrix, k=factors)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "65a9563b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "P: (1000, 5), S: (5,), Qt: (5, 6673), pred_matrix: (1000, 6673)\n" - ] - } - ], - "source": [ - "# 예측 평갓값 행렬\n", - "pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n", - "\n", - "print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "75ee52d8", - "metadata": {}, - "outputs": [], - "source": [ - "# 학습용에 나타나지 않은 사용자와 영화 예측 평갓값은 평균 평갓값으로 한다\n", - "average_score = movielens_train.rating.mean()\n", - "movie_rating_predict = movielens_test.copy()\n", - "pred_results = []\n", - "for i, row in movielens_test.iterrows():\n", - " user_id = row[\"user_id\"]\n", - " if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n", - " pred_results.append(average_score)\n", - " continue\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_index = movie_id2index[row[\"movie_id\"]]\n", - " pred_score = pred_matrix[user_index, movie_index]\n", - " pred_results.append(pred_score)\n", - "movie_rating_predict[\"rating_pred\"] = pred_results" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2a761397", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "defaultdict(list,\n", - " {139: [4993, 4226, 5952, 7153, 4306, 3996, 5349, 4963, 6539, 4886],\n", - " 149: [2571, 4226, 2762, 3996, 5349, 47, 2028, 4963, 6539, 4886],\n", - " 182: [3793, 47, 6874, 2329, 4011, 7361, 2706, 4878, 2502, 6333],\n", - " 215: [2329, 3147, 6711, 527, 2683, 3948, 8360, 5218, 6502, 1246],\n", - " 281: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 326: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 351: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n", - " 357: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n", - " 426: [2502,\n", - " 33794,\n", - " 7147,\n", - " 3949,\n", - " 5902,\n", - " 4848,\n", - " 33166,\n", - " 33493,\n", - " 2791,\n", - 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" 943: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 520: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 697: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 849: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 1003: [2959,\n", - " 4993,\n", - " 3578,\n", - " 4226,\n", - " 5952,\n", - " 2858,\n", - " 7153,\n", - " 4306,\n", - " 3996,\n", - " 3793],\n", - " 705: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n", - " 48: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 29: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 231: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 699: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n", - " 448: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n", - " 750: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n", - " 782: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 860: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 768: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 127: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n", - " 894: [2959,\n", - " 2571,\n", - " 4993,\n", - " 3578,\n", - " 4226,\n", - " 5952,\n", - " 2858,\n", - " 2762,\n", - " 7153,\n", - " 4306]})" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 각 사용자에 대한 추천 영화는 해당 사용자 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", - "pred_user2items = defaultdict(list)\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "for user_id in movielens_train.user_id.unique():\n", - " if user_id not in user_id2index:\n", - " continue\n", - " user_index = user_id2index[row[\"user_id\"]]\n", - " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", - " for movie_index in movie_indexes:\n", - " movie_id = user_movie_matrix.columns[movie_index]\n", - " if movie_id not in user_evaluated_movies[user_id]:\n", - " pred_user2items[user_id].append(movie_id)\n", - " if len(pred_user2items[user_id]) == 10:\n", - " break\n", - "\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "14a45925", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "47543b66", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_user2items[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ac73e03a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "2487 2571 Matrix, The (1999) \n", - "2874 2959 Fight Club (1999) \n", - "4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n", - "\n", - " genre \\\n", - "2487 [Action, Sci-Fi, Thriller] \n", - "2874 [Action, Crime, Drama, Thriller] \n", - "4899 [Action, Adventure, Fantasy] \n", - "\n", - " tag \n", - "2487 [artificial intelligence, hackers, heroine in ... \n", - "2874 [based on a book, chuck palahniuk, edward nort... \n", - "4899 [based on a book, big budget, new zealand, sce... " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(2959, 4993, 5952)\n", - "movies[movies.movie_id.isin([2959, 2571, 4993])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93ce1b7a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"4fb1700b","metadata":{"id":"4fb1700b"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/SVD.ipynb)"]},{"cell_type":"markdown","id":"05fd5c37","metadata":{"id":"05fd5c37"},"source":["# 특이값 분석(SVD)"]},{"cell_type":"code","execution_count":1,"id":"ae97b569","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ae97b569","executionInfo":{"status":"ok","timestamp":1672119890093,"user_tz":-540,"elapsed":4522,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"52b03925-db6b-47e9-e26c-764d0eba7a5b"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:43:01-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 65.4MB/s in 1.0s \n","\n","2022-12-27 05:43:02 (65.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"395a5042","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"395a5042","executionInfo":{"status":"ok","timestamp":1672119956404,"user_tz":-540,"elapsed":66319,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"d2cab3b4-2205-46e7-c70a-e37c5d6b611c"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"1bcee149","metadata":{"id":"1bcee149","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":30,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 결손값을 채우는 방법\n","fillna_with_zero = True\n","# 인자 수\n","factors = 5"]},{"cell_type":"code","execution_count":4,"id":"2cf8dfe4","metadata":{"id":"2cf8dfe4","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":25,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","if fillna_with_zero:\n"," matrix = user_movie_matrix.fillna(0).to_numpy()\n","else:\n"," matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n"]},{"cell_type":"code","execution_count":5,"id":"22c0909b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"22c0909b","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":22,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"73d96ca8-6fd5-42b8-c085-568f67d0d2e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["ユーザー数=1000, アイテム数=6673, 密度=0.02\n"]}],"source":["# 희소 정보\n","user_num = len(user_movie_matrix.index)\n","item_num = len(user_movie_matrix.columns)\n","non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n","non_null_ratio = non_null_num / (user_num*item_num)\n","\n","print(f'ユーザー数={user_num}, アイテム数={item_num}, 密度={non_null_ratio:.2f}')"]},{"cell_type":"code","execution_count":6,"id":"7f1f97fa","metadata":{"scrolled":true,"id":"7f1f97fa","executionInfo":{"status":"ok","timestamp":1672119957450,"user_tz":-540,"elapsed":607,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse.linalg import svds\n","import numpy as np\n","\n","# 인자 수 k로 특이점 분석을 수행한다\n","P, S, Qt = svds(matrix, k=factors)"]},{"cell_type":"code","execution_count":7,"id":"65a9563b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"65a9563b","executionInfo":{"status":"ok","timestamp":1672119957451,"user_tz":-540,"elapsed":4,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b73e0f1a-457a-4e3f-866f-78010e4c4bd3"},"outputs":[{"output_type":"stream","name":"stdout","text":["P: (1000, 5), S: (5,), Qt: (5, 6673), pred_matrix: (1000, 6673)\n"]}],"source":["# 예측 평갓값 행렬\n","pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n","\n","print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")"]},{"cell_type":"code","execution_count":8,"id":"75ee52d8","metadata":{"id":"75ee52d8","executionInfo":{"status":"ok","timestamp":1672119957898,"user_tz":-540,"elapsed":449,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용에 나타나지 않은 사용자와 영화 예측 평갓값은 평균 평갓값으로 한다\n","average_score = movielens_train.rating.mean()\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results"]},{"cell_type":"code","execution_count":9,"id":"2a761397","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2a761397","executionInfo":{"status":"ok","timestamp":1672119959233,"user_tz":-540,"elapsed":1343,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"275c7093-7ffa-4628-d041-649854a18e3a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [4993, 4226, 5952, 7153, 4306, 3996, 5349, 4963, 6539, 4886],\n"," 149: [2571, 4226, 2762, 3996, 5349, 47, 2028, 4963, 6539, 4886],\n"," 182: [3793, 47, 6874, 2329, 4011, 7361, 2706, 4878, 2502, 6333],\n"," 215: [2329, 3147, 6711, 527, 2683, 3948, 8360, 5218, 6502, 1246],\n"," 281: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 326: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 351: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 357: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 426: [2502,\n"," 33794,\n"," 7147,\n"," 3949,\n"," 5902,\n"," 4848,\n"," 33166,\n"," 33493,\n"," 2791,\n"," 5995],\n"," 456: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 459: [4886, 318, 2997, 4995, 4973, 50, 3897, 7361, 1923, 4878],\n"," 494: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 517: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 524: [7153, 3996, 3793, 5349, 6539, 4886, 7361, 593, 1923, 5989],\n"," 556: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 588: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 589: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 590: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 601: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 621: [5952, 7153, 4306, 4995, 2329, 3897, 1682, 4011, 4878, 3147],\n"," 634: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 672: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 4963, 6539],\n"," 701: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 719: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 745: [5952, 7153, 4306, 6539, 5445, 356, 6377, 6874, 4973, 2329],\n"," 757: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 775: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 780: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996],\n"," 785: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 788: [296, 4973, 3897, 1089, 4878, 2502, 1961, 1196, 1136, 4034],\n"," 870: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 987: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 988: [7153, 4963, 6539, 5445, 6377, 6874, 4995, 2329, 4027, 7361],\n"," 1020: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306],\n"," 1: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 22: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 26: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 30: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 34: [4993, 4226, 5952, 2858, 7153, 4306, 3996, 5349, 4963, 6539],\n"," 38: [4226, 2858, 4306, 3793, 2028, 6539, 4886, 5445, 2997, 2329],\n"," 50: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 53: [2959, 2571, 4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 54: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 67: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 71: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 76: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 88: [4226, 4306, 3996, 47, 2028, 296, 1704, 318, 2997, 4995],\n"," 89: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 94: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 95: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 100: [2959, 4993, 3578, 4226, 2762, 7153, 4306, 3793, 5349, 4963],\n"," 101: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 102: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 103: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 111: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 116: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 118: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 143: [7153, 6539, 6377, 6874, 50, 4027, 7361, 6711, 7438, 1617],\n"," 144: [4886, 1704, 3897, 2706, 5989, 2502, 3147, 1784, 3114, 2396],\n"," 162: [4226, 4963, 1704, 318, 2997, 2329, 50, 3897, 4011, 4027],\n"," 172: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 173: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 187: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 193: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 208: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 210: [2959, 5952, 3793, 5349, 47, 2028, 4963, 6539, 4886, 5445],\n"," 214: [2959, 5952, 2858, 7153, 4306, 2028, 6539, 318, 6377, 6874],\n"," 228: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 232: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 236: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 259: [3578, 318, 6377, 4011, 4027, 1089, 1961, 4034, 2542, 3481],\n"," 260: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 267: [4306, 4886, 1704, 1682, 4011, 4878, 5989, 2502, 3147, 6711],\n"," 268: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 271: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 274: [2959, 2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793],\n"," 287: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 292: [4963, 6874, 5989, 6333, 3147, 527, 1517, 2542, 1617, 3481],\n"," 296: [3996, 1704, 6377, 1270, 1196, 1136, 2716, 2683, 1517, 2918],\n"," 303: [5349, 4963, 6539, 1704, 2997, 6377, 4995, 4973, 2329, 50],\n"," 307: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 3996, 47],\n"," 310: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 315: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 320: [3793, 5349, 1704, 318, 2997, 6874, 2329, 50, 3897, 4027],\n"," 332: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 339: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 343: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 355: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 364: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 365: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 368: [2858, 2762, 2028, 1704, 318, 4995, 2329, 593, 4878, 5989],\n"," 381: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 382: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 383: [2959, 4306, 5349, 47, 4963, 6539, 356, 6874, 2329, 3897],\n"," 391: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 396: [4993, 3578, 4226, 5952, 2762, 7153, 3996, 3793, 5349, 47],\n"," 398: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 406: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 409: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 410: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 416: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 418: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 419: [2959, 3578, 7153, 2028, 6539, 4886, 1704, 356, 6377, 6874],\n"," 421: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 424: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 432: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 434: [4226, 47, 296, 318, 2997, 4973, 2329, 50, 3897, 1682],\n"," 436: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 438: [2959, 4993, 4226, 5952, 7153, 5349, 47, 2028, 4963, 6539],\n"," 441: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 446: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 452: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 3996, 47, 2028],\n"," 460: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 463: [7153, 4963, 6377, 6874, 4973, 3897, 1682, 4011, 7361, 2291],\n"," 468: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 469: [2959, 47, 4995, 2329, 50, 3897, 1682, 4011, 7361, 1089],\n"," 472: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 476: [4886, 1704, 4995, 1270, 1923, 2291, 2502, 2716, 4034, 1517],\n"," 480: [7153, 6539, 1704, 318, 6377, 6874, 2329, 1682, 7361, 1089],\n"," 482: [2858, 2762, 3996, 5445, 2997, 6874, 4973, 50, 3897, 1682],\n"," 493: [4993, 5952, 7153, 593, 2502, 6333, 1196, 1136, 7438, 589],\n"," 496: [2959, 4993, 3578, 5952, 2858, 7153, 4306, 3996, 3793, 5349],\n"," 501: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 504: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 505: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 511: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 516: [6377, 50, 1682, 4027, 7361, 2918, 2542, 3052, 3481, 364],\n"," 525: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 530: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 531: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 533: [4886, 4995, 2329, 4011, 2706, 4878, 3147, 2683, 6365, 1732],\n"," 536: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 543: [2028, 2329, 3897, 4011, 1923, 5989, 4034, 2542, 3052, 3481],\n"," 547: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 549: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 553: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 558: [2762, 7153, 4886, 2997, 6874, 4973, 2329, 4011, 7361, 4878],\n"," 562: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 567: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 571: [2959, 4993, 5952, 2762, 7153, 3996, 296, 356, 6874, 4027],\n"," 572: [3578, 3996, 1704, 318, 4973, 1961, 4034, 2918, 3481, 1784],\n"," 577: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 581: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 585: [4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793, 5349],\n"," 593: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 604: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 605: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 609: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 628: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 629: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 645: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 650: [2959, 5952, 7153, 4306, 2028, 296, 4963, 6539, 4886, 5445],\n"," 656: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 657: [4306, 3793, 4963, 4886, 4011, 2502, 6333, 1136, 1517, 7438],\n"," 669: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 678: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 683: [2959, 4226, 5952, 2858, 2762, 3996, 296, 4963, 1704, 318],\n"," 688: [3996, 2028, 5445, 318, 4973, 3897, 1682, 7361, 4878, 2291],\n"," 689: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 693: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 716: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 4963, 6539],\n"," 720: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 734: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 735: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 739: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793, 5349],\n"," 755: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 758: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 767: [2959, 4226, 2858, 4306, 3793, 47, 2028, 296, 4963, 4886],\n"," 769: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 786: [2959, 4993, 5952, 2858, 7153, 5349, 47, 2028, 296, 4963],\n"," 789: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 792: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 795: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 798: [6377, 2329, 4011, 4027, 7361, 2706, 2502, 4034, 3052, 3481],\n"," 799: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 802: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 806: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 807: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 816: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 827: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 828: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 846: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 859: [2858, 4306, 47, 4963, 318, 2997, 6377, 6874, 4995, 2329],\n"," 862: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 876: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 881: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 885: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 886: [4993, 4226, 5952, 7153, 4306, 3793, 5349, 4963, 6539, 4886],\n"," 889: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 890: [4993, 4226, 5952, 7153, 4306, 5349, 47, 4963, 6539, 4886],\n"," 891: [4963, 6539, 5445, 6377, 6874, 4995, 2329, 4011, 4027, 7361],\n"," 896: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 897: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 898: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 908: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 922: [4226, 2858, 3996, 3793, 2028, 296, 5445, 1704, 2997, 6874],\n"," 930: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 938: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 956: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 958: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793, 47],\n"," 968: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 977: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 990: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 996: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 1004: [4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 7153,\n"," 4306,\n"," 3996,\n"," 3793,\n"," 5349,\n"," 4963],\n"," 1005: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306],\n"," 1008: [2028,\n"," 4963,\n"," 6539,\n"," 4995,\n"," 3897,\n"," 1682,\n"," 4011,\n"," 4027,\n"," 1089,\n"," 2706],\n"," 1029: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306],\n"," 1037: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 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[2329, 1682, 7361, 4878, 7438, 1617, 5418, 8961, 1527, 8636],\n"," 126: [4973, 7361, 4878, 6711, 2542, 3052, 6365, 3948, 3751, 5378],\n"," 132: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 141: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 154: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 2028, 4963],\n"," 155: [4226, 7153, 3793, 5349, 4963, 6539, 4886, 5445, 6377, 6874],\n"," 163: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 164: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 170: [4993, 3578, 5952, 7153, 3996, 5349, 2028, 6539, 5445, 1704],\n"," 171: [2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 6539],\n"," 176: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 190: [4226, 2858, 47, 4963, 1704, 318, 2997, 6377, 4973, 2329],\n"," 197: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 198: [2997, 4973, 4011, 5989, 2291, 6333, 3147, 4034, 2542, 1291],\n"," 199: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 212: [3578, 2858, 4886, 1704, 6377, 2329, 3897, 1682, 4011, 2706],\n"," 221: [2959, 4993, 3578, 4226, 2762, 7153, 4306, 3996, 5349, 4963],\n"," 223: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 226: [2959, 2858, 7153, 4963, 6539, 2997, 6377, 6874, 4973, 1682],\n"," 241: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 247: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 249: [2858, 3996, 2997, 6874, 4973, 3897, 6333, 527, 7438, 1617],\n"," 254: [4993, 4226, 7153, 3793, 4963, 6539, 4886, 1704, 6377, 6874],\n"," 264: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 273: [2959, 4226, 2762, 4306, 3996, 5349, 47, 296, 4963, 4886],\n"," 275: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3793, 5349, 47],\n"," 276: [4963, 6539, 4995, 4027, 2716, 1517, 2918, 3052, 110, 5418],\n"," 293: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 294: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 295: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 3996],\n"," 318: [4226, 2329, 1923, 4878, 3147, 32, 2692, 293, 223, 3949],\n"," 321: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 345: [4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47],\n"," 346: [2959, 2571, 4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 348: [1580, 1196, 1136, 2918, 3052, 3481, 5418, 1265, 1210, 6365],\n"," 349: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 354: [4226, 3793, 47, 2028, 296, 4886, 2997, 6874, 2329, 50],\n"," 359: [5349, 47, 2028, 4886, 1704, 356, 318, 6874, 2329, 50],\n"," 366: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 369: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 384: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 390: [47, 6539, 6377, 3897, 7361, 1580, 1089, 1270, 2291, 1961],\n"," 392: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 403: [2959, 4993, 3578, 5952, 2858, 4306, 3996, 5349, 2028, 6539],\n"," 407: [4226, 2858, 5349, 1704, 318, 4995, 4973, 3897, 4011, 4027],\n"," 414: [3578, 4226, 7153, 4306, 3793, 5349, 2028, 6539, 4886, 5445],\n"," 431: [2959, 2571, 3578, 4226, 2858, 2762, 3996, 3793, 5349, 47],\n"," 454: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 465: [2571, 5952, 7153, 3793, 2028, 4963, 4886, 5445, 3897, 2291],\n"," 481: [2762, 2997, 6377, 4973, 4027, 5989, 1961, 3147, 527, 2918],\n"," 485: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47, 2028],\n"," 503: [3996, 5349, 47, 4963, 1704, 50, 3897, 4011, 4027, 1089],\n"," 513: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 545: [3578, 4306, 3793, 5349, 4963, 6539, 5445, 1682, 2706, 1923],\n"," 552: [2571, 4993, 3578, 5952, 7153, 3996, 3793, 5349, 47, 2028],\n"," 554: [2571, 3578, 4226, 5349, 47, 6539, 4886, 5445, 4995, 2329],\n"," 555: [5445, 1704, 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4306],\n"," 35: [3578, 5952, 7153, 3793, 5349, 296, 6539, 4886, 1704, 356],\n"," 72: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 91: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 106: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 128: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 158: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 160: [2959, 4993, 4226, 5952, 2762, 7153, 4306, 5349, 47, 296],\n"," 175: [4306, 3996, 3793, 5349, 4963, 6539, 5445, 1704, 4995, 3897],\n"," 196: [4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 3793],\n"," 203: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 206: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 207: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 255: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 265: [2959, 4993, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 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7153, 4306],\n"," 659: [2959, 3578, 3793, 5349, 2028, 4963, 6539, 4886, 2329, 50],\n"," 695: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 700: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 707: [2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 748: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 759: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 784: [4973, 1682, 4011, 4027, 7361, 1089, 5989, 2502, 3147, 6711],\n"," 790: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 835: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 844: [2959, 4993, 4226, 5952, 7153, 4306, 3996, 5349, 47, 2028],\n"," 871: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 875: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 892: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 919: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 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4306,\n"," 3996],\n"," 1017: [2959,\n"," 2571,\n"," 3578,\n"," 4226,\n"," 2858,\n"," 2762,\n"," 4306,\n"," 3996,\n"," 3793,\n"," 5349],\n"," 85: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 306: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 653: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 371: [2959, 2571, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 566: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 4306, 3996],\n"," 331: [3578, 5952, 2762, 4306, 3996, 3793, 5349, 47, 2028, 296],\n"," 665: [2959, 2571, 4993, 3578, 2762, 7153, 4306, 3793, 5349, 47],\n"," 872: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 879: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 486: [2959, 2571, 4993, 3578, 4226, 2858, 2762, 7153, 4306, 3996],\n"," 864: [2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 52: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 288: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 9: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 117: [2959, 2571, 4993, 3578, 4226, 2858, 2762, 7153, 3996, 3793],\n"," 220: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 544: [3578, 4226, 2858, 2762, 4306, 3996, 5349, 2028, 4963, 6539],\n"," 999: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 458: [2959, 2571, 4993, 5952, 7153, 4306, 3996, 5349, 47, 2028],\n"," 974: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 546: [2959, 2571, 4226, 2858, 2762, 47, 2028, 296, 4963, 1704],\n"," 55: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 363: [2571, 3578, 4226, 2858, 2762, 4306, 3996, 3793, 5349, 47],\n"," 445: [2959, 2571, 4993, 3578, 4226, 2858, 2762, 4306, 3996, 3793],\n"," 492: [2959, 2571, 4993, 3578, 4226, 2858, 2762, 7153, 4306, 5349],\n"," 234: [4993, 3578, 5952, 2762, 7153, 3793, 5349, 47, 2028, 296],\n"," 1027: [2571, 4993, 5952, 2858, 2762, 7153, 4306, 3996, 3793, 47],\n"," 140: [2959, 2571, 4993, 3578, 5952, 2762, 4306, 3996, 3793, 5349],\n"," 926: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 4306, 3996],\n"," 781: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 825: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 913: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 5349],\n"," 453: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 540: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996],\n"," 66: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 185: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 222: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 498: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 994: [2959, 2571, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 165: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 202: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 180: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996],\n"," 93: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 261: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 435: [4993, 3578, 5952, 2858, 2762, 7153, 4306, 3996, 3793, 5349],\n"," 322: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 238: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 425: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 512: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 725: [4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47],\n"," 732: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 108: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 592: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 443: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 916: [2571, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 5349],\n"," 736: [2959, 2571, 4993, 4226, 5952, 2858, 2762, 7153, 4306, 5349],\n"," 961: [2959, 2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793],\n"," 1012: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2762,\n"," 7153,\n"," 4306,\n"," 3996],\n"," 515: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 978: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 28: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 475: [2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 598: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 943: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 520: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 697: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 849: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 1003: [2959,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 7153,\n"," 4306,\n"," 3996,\n"," 3793],\n"," 705: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 48: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 29: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 231: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 699: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 448: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 750: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 782: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 860: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 768: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 127: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 894: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306]})"]},"metadata":{},"execution_count":9}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 영화는 해당 사용자 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," if user_id not in user_id2index:\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":10,"id":"14a45925","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"14a45925","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":33,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"27d652fa-b864-4756-d5fc-74bd77a05a82"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":11,"id":"47543b66","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"47543b66","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"779b6217-68dc-416f-eadc-345a38857d60"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306]"]},"metadata":{},"execution_count":11}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":12,"id":"ac73e03a","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":187},"id":"ac73e03a","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1fb5be98-7592-4696-d496-571b130f9b1d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","2487 2571 Matrix, The (1999) \n","2874 2959 Fight Club (1999) \n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","\n"," genre \\\n","2487 [Action, Sci-Fi, Thriller] \n","2874 [Action, Crime, Drama, Thriller] \n","4899 [Action, Adventure, Fantasy] \n","\n"," tag \n","2487 [artificial intelligence, hackers, heroine in ... \n","2874 [based on a book, chuck palahniuk, edward nort... \n","4899 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(2959, 4993, 5952)\n","movies[movies.movie_id.isin([2959, 2571, 4993])]"]},{"cell_type":"code","execution_count":12,"id":"93ce1b7a","metadata":{"id":"93ce1b7a","executionInfo":{"status":"ok","timestamp":1672119959235,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/UMCF.ipynb b/chapter5/colab/UMCF.ipynb index 4732c61..7f20a42 100644 --- a/chapter5/colab/UMCF.ipynb +++ b/chapter5/colab/UMCF.ipynb @@ -1,4552 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "66d2e720", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/UMCF.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "f8caaceb", - "metadata": {}, - "source": [ - "# 사용자-사용자 메모리 기반 협조 필터링" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "9cb2f773", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ed3c6a08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "06409f5a", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "# 피어슨 상관 계수\n", - "def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float:\n", - " u_diff = u - np.mean(u)\n", - " v_diff = v - np.mean(v)\n", - " numerator = np.dot(u_diff, v_diff)\n", - " denominator = np.sqrt(sum(u_diff ** 2)) * np.sqrt(sum(v_diff ** 2))\n", - " if denominator == 0:\n", - " return 0.0\n", - " return numerator / denominator\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ff148555-35f5-4362-87fb-75f0a8d5d33f", - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "\n", - "# 평갓값을 사용자 x 화면의 행렬로 변환한다\n", - "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", - "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", - "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", - "\n", - "# 예측 대상 사용자와 영화의 조합\n", - "movie_rating_predict = movielens_test.copy()\n", - "pred_user2items = defaultdict(list)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fc0361fa", - "metadata": {}, - "outputs": [], - "source": [ - "# 우직하게 유사도를 계산한다(이 계산은 매우 무겁습니다. 그렇기 떄문에 도중에 break를 넣었습니다. 속도 향상을 위해 라이브러리르 사용한 구현도 다음 셀에서 소개합니다)\n", - "\n", - "# 예측 대상 사용자 ID\n", - "test_users = movie_rating_predict.user_id.unique()\n", - "\n", - "# 예측 대상 사용자(사용자 1)에 주목한다\n", - "for user1_id in test_users:\n", - " similar_users = []\n", - " similarities = []\n", - " avgs = []\n", - "\n", - " # 사용자 1과 평갓값 행렬 안의 기타 사용자(사용자 2)와의 유사도를 산출한다\n", - " for user2_id in user_movie_matrix.index:\n", - " if user1_id == user2_id:\n", - " continue\n", - "\n", - " # 사용자 1과 사용자 2의 평갓값 벡터\n", - " u_1 = user_movie_matrix.loc[user1_id, :].to_numpy()\n", - " u_2 = user_movie_matrix.loc[user2_id, :].to_numpy()\n", - "\n", - " # `u_1`과 `u_2`로부터, 동시에 결손값이 없는 요소만 추출한 벡터를 얻는다\n", - " common_items = ~np.isnan(u_1) & ~np.isnan(u_2)\n", - "\n", - " # 공통으로 평가한 아이템이 없으면 스킵\n", - " if not common_items.any():\n", - " continue\n", - "\n", - " u_1, u_2 = u_1[common_items], u_2[common_items]\n", - "\n", - " # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출\n", - " rho_12 = peason_coefficient(u_1, u_2)\n", - "\n", - " # 사용자 1과의 유사도가 0 보다 큰 경우, 사용자 2를 유사 사용자로 간주한다\n", - " if rho_12 > 0:\n", - " similar_users.append(user2_id)\n", - " similarities.append(rho_12)\n", - " avgs.append(np.mean(u_2))\n", - "\n", - " # 사용자 1의 평균 평갓값\n", - " avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy())\n", - "\n", - " # 예측 대상 영화의 ID\n", - " test_movies = movie_rating_predict[movie_rating_predict[\"user_id\"] == user1_id].movie_id.values\n", - " # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다\n", - " movie_rating_predict.loc[(movie_rating_predict[\"user_id\"] == user1_id), \"rating_pred\"] = avg_1\n", - "\n", - " if similar_users:\n", - " for movie_id in test_movies:\n", - " if movie_id in movie_id2index:\n", - " r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy()\n", - " rating_exists = ~np.isnan(r_xy)\n", - "\n", - " # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않은 경우는 스킵한다\n", - " if not rating_exists.any():\n", - " continue\n", - "\n", - " r_xy = r_xy[rating_exists]\n", - " rho_1x = np.array(similarities)[rating_exists]\n", - " avg_x = np.array(avgs)[rating_exists]\n", - " r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum()\n", - "\n", - " # 예측 평갓값을 저장\n", - " movie_rating_predict.loc[\n", - " (movie_rating_predict[\"user_id\"] == user1_id)\n", - " & (movie_rating_predict[\"movie_id\"] == movie_id),\n", - " \"rating_pred\",\n", - " ] = r_hat_1y\n", - " break # 계산이 무거우므로, for 루프는 1번만 수행한 뒤 종료합니다. 각 변수에 어떤 값이 들어있는지 확인하면 알고리즘을 더 깊이 이해할 수 있습니다." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "665ebafd", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install surprise" - ] - 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" 894: [299,\n", - " 4103,\n", - " 30803,\n", - " 4307,\n", - " 55280,\n", - " 57243,\n", - " 1244,\n", - " 49932,\n", - " 7327,\n", - " 4437]})" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# surprise를 사용한 구현\n", - "\n", - "from surprise import KNNWithMeans, Reader\n", - "from surprise import Dataset as SurpriseDataset\n", - "# surprise용으로 데이터를 가공한다\n", - "reader = Reader(rating_scale=(0.5, 5))\n", - "data_train = SurpriseDataset.load_from_df(\n", - " movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n", - ").build_full_trainset()\n", - "\n", - "sim_options = {\"name\": \"pearson\", \"user_based\": True} # 유사도를 계산하는 방법을 정의한다 # False로 하면 아이템 기반이 된다\n", - "knn = KNNWithMeans(k=30, min_k=1, sim_options=sim_options)\n", - "knn.fit(data_train)\n", - "\n", - "# 학습 데이터셋에서 평갓값이 없는 사용자와 아이템의 조합을 준비한다\n", - "data_test = data_train.build_anti_testset(None)\n", - "predictions = knn.test(data_test)\n", - "\n", - "def get_top_n(predictions, n=10):\n", - " # 각 사용자별로 예측된 아이템을 저장한다\n", - " top_n = defaultdict(list)\n", - " for uid, iid, true_r, est, _ in predictions:\n", - " top_n[uid].append((iid, est))\n", - "\n", - " ユーザーごとに、アイテムを予測評価値順に並べ上位n個を格納する\n", - " # 사용자별로 아이템을 예측 평갓값 순으로 나열하고, 상위 n개를 저장한다\n", - " for uid, user_ratings in top_n.items():\n", - " user_ratings.sort(key=lambda x: x[1], reverse=True)\n", - " top_n[uid] = [d[0] for d in user_ratings[:n]]\n", - "\n", - " return top_n\n", - "\n", - "pred_user2items = get_top_n(predictions, n=10)\n", - "\n", - "average_score = movielens_train.rating.mean()\n", - "pred_results = []\n", - "for _, row in movielens_test.iterrows():\n", - " user_id = row[\"user_id\"]\n", - " movie_id = row[\"movie_id\"]\n", - " # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자와 영화에 대한 예측 평갓값은 전체의 평균 평갓값으로 한다\n", - " if user_id not in user_id2index or movie_id not in movie_id2index:\n", - " pred_results.append(average_score)\n", - " continue\n", - " pred_score = knn.predict(uid=user_id, iid=movie_id).est\n", - " pred_results.append(pred_score)\n", - "movie_rating_predict[\"rating_pred\"] = pred_results\n", - "\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1fccc723", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "6150 2 648 2.0 868244699 \n", - "6531 2 733 3.0 868244562 \n", - "6813 2 736 3.0 868244698 \n", - "7113 2 780 3.0 868244698 \n", - "7506 2 786 3.0 868244562 \n", - "7661 2 802 2.0 868244603 \n", - "7779 2 858 2.0 868245645 \n", - "8077 2 1049 3.0 868245920 \n", - "8127 2 1073 3.0 868244562 \n", - "8381 2 1210 4.0 868245644 \n", - "8771 2 1356 3.0 868244603 \n", - "9097 2 1544 3.0 868245920 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "6150 Mission: Impossible (1996) \n", - "6531 Rock, The (1996) \n", - "6813 Twister (1996) \n", - "7113 Independence Day (a.k.a. ID4) (1996) \n", - "7506 Eraser (1996) \n", - "7661 Phenomenon (1996) \n", - "7779 Godfather, The (1972) \n", - "8077 Ghost and the Darkness, The (1996) \n", - "8127 Willy Wonka & the Chocolate Factory (1971) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "8771 Star Trek: First Contact (1996) \n", - "9097 Lost World: Jurassic Park, The (Jurassic Park ... \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "6150 [Action, Adventure, Mystery, Thriller] \n", - "6531 [Action, Adventure, Thriller] \n", - "6813 [Action, Adventure, Romance, Thriller] \n", - "7113 [Action, Adventure, Sci-Fi, War] \n", - "7506 [Action, Drama, Thriller] \n", - "7661 [Drama, Romance] \n", - "7779 [Crime, Drama] \n", - "8077 [Action, Adventure] \n", - "8127 [Children, Comedy, Fantasy, Musical] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "8771 [Action, Adventure, Sci-Fi, Thriller] \n", - "9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "6150 [confusing, confusing plot, memorable sequence... 12.0 \n", - "6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n", - "6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n", - "7113 [action, alien invasion, aliens, will smith, a... 14.0 \n", - "7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n", - "7661 [interesting concept, own, john travolta, john... 15.0 \n", - "7779 [oscar (best picture), marlon brando, classic,... 9.0 \n", - "8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n", - "8127 [based on a book, roald dahl, based on a book,... 20.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n", - "8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n", - "9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n", - "movielens_train[movielens_train.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "874b6e3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[3473, 4135, 4850, 2425, 2704, 137, 27255, 26425, 8712, 31116]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_user2items[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c9b77fbd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
33863473Jonah Who Will Be 25 in the Year 2000 (Jonas q...[Comedy][cerebral, humorous, intersecting lives, intim...
40434135Monster Squad, The (1987)[Adventure, Comedy, Horror][can't remember]
47564850Spriggan (Supurigan) (1998)[Action, Animation, Sci-Fi][library, anime, spectacle, over expositive]
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "3386 3473 Jonah Who Will Be 25 in the Year 2000 (Jonas q... \n", - "4043 4135 Monster Squad, The (1987) \n", - "4756 4850 Spriggan (Supurigan) (1998) \n", - "\n", - " genre \\\n", - "3386 [Comedy] \n", - "4043 [Adventure, Comedy, Horror] \n", - "4756 [Action, Animation, Sci-Fi] \n", - "\n", - " tag \n", - "3386 [cerebral, humorous, intersecting lives, intim... \n", - "4043 [can't remember] \n", - "4756 [library, anime, spectacle, over expositive] " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(1198, 1196, 1097)\n", - "movies[movies.movie_id.isin([3473, 4135, 4850])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99c25fe6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"66d2e720","metadata":{"id":"66d2e720"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/UMCF.ipynb)"]},{"cell_type":"markdown","id":"f8caaceb","metadata":{"id":"f8caaceb"},"source":["# 사용자-사용자 메모리 기반 협조 필터링"]},{"cell_type":"code","execution_count":1,"id":"9cb2f773","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cb2f773","executionInfo":{"status":"ok","timestamp":1672120577940,"user_tz":-540,"elapsed":3786,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1da0f943-c87e-4e98-9eca-45f624108983"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:54:30-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 91.0MB/s in 0.7s \n","\n","2022-12-27 05:54:31 (91.0 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"ed3c6a08","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ed3c6a08","executionInfo":{"status":"ok","timestamp":1672120647715,"user_tz":-540,"elapsed":69778,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"0c864b95-f3a3-4b20-ab41-d26580399203"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"06409f5a","metadata":{"id":"06409f5a","executionInfo":{"status":"ok","timestamp":1672120647715,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np\n","# 피어슨 상관 계수\n","def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float:\n"," u_diff = u - np.mean(u)\n"," v_diff = v - np.mean(v)\n"," numerator = np.dot(u_diff, v_diff)\n"," denominator = np.sqrt(sum(u_diff ** 2)) * np.sqrt(sum(v_diff ** 2))\n"," if denominator == 0:\n"," return 0.0\n"," return numerator / denominator\n"]},{"cell_type":"code","execution_count":4,"id":"ff148555-35f5-4362-87fb-75f0a8d5d33f","metadata":{"id":"ff148555-35f5-4362-87fb-75f0a8d5d33f","executionInfo":{"status":"ok","timestamp":1672120647716,"user_tz":-540,"elapsed":23,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from collections import defaultdict\n","\n","# 평갓값을 사용자 x 화면의 행렬로 변환한다\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","\n","# 예측 대상 사용자와 영화의 조합\n","movie_rating_predict = movielens_test.copy()\n","pred_user2items = defaultdict(list)"]},{"cell_type":"code","execution_count":5,"id":"fc0361fa","metadata":{"id":"fc0361fa","executionInfo":{"status":"ok","timestamp":1672120648148,"user_tz":-540,"elapsed":453,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 우직하게 유사도를 계산한다(이 계산은 매우 무겁습니다. 그렇기 떄문에 도중에 break를 넣었습니다. 속도 향상을 위해 라이브러리르 사용한 구현도 다음 셀에서 소개합니다)\n","\n","# 예측 대상 사용자 ID\n","test_users = movie_rating_predict.user_id.unique()\n","\n","# 예측 대상 사용자(사용자 1)에 주목한다\n","for user1_id in test_users:\n"," similar_users = []\n"," similarities = []\n"," avgs = []\n","\n"," # 사용자 1과 평갓값 행렬 안의 기타 사용자(사용자 2)와의 유사도를 산출한다\n"," for user2_id in user_movie_matrix.index:\n"," if user1_id == user2_id:\n"," continue\n","\n"," # 사용자 1과 사용자 2의 평갓값 벡터\n"," u_1 = user_movie_matrix.loc[user1_id, :].to_numpy()\n"," u_2 = user_movie_matrix.loc[user2_id, :].to_numpy()\n","\n"," # `u_1`과 `u_2`로부터, 동시에 결손값이 없는 요소만 추출한 벡터를 얻는다\n"," common_items = ~np.isnan(u_1) & ~np.isnan(u_2)\n","\n"," # 공통으로 평가한 아이템이 없으면 스킵\n"," if not common_items.any():\n"," continue\n","\n"," u_1, u_2 = u_1[common_items], u_2[common_items]\n","\n"," # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출\n"," rho_12 = peason_coefficient(u_1, u_2)\n","\n"," # 사용자 1과의 유사도가 0 보다 큰 경우, 사용자 2를 유사 사용자로 간주한다\n"," if rho_12 > 0:\n"," similar_users.append(user2_id)\n"," similarities.append(rho_12)\n"," avgs.append(np.mean(u_2))\n","\n"," # 사용자 1의 평균 평갓값\n"," avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy())\n","\n"," # 예측 대상 영화의 ID\n"," test_movies = movie_rating_predict[movie_rating_predict[\"user_id\"] == user1_id].movie_id.values\n"," # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다\n"," movie_rating_predict.loc[(movie_rating_predict[\"user_id\"] == user1_id), \"rating_pred\"] = avg_1\n","\n"," if similar_users:\n"," for movie_id in test_movies:\n"," if movie_id in movie_id2index:\n"," r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy()\n"," rating_exists = ~np.isnan(r_xy)\n","\n"," # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않은 경우는 스킵한다\n"," if not rating_exists.any():\n"," continue\n","\n"," r_xy = r_xy[rating_exists]\n"," rho_1x = np.array(similarities)[rating_exists]\n"," avg_x = np.array(avgs)[rating_exists]\n"," r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum()\n","\n"," # 예측 평갓값을 저장\n"," movie_rating_predict.loc[\n"," (movie_rating_predict[\"user_id\"] == user1_id)\n"," & (movie_rating_predict[\"movie_id\"] == movie_id),\n"," \"rating_pred\",\n"," ] = r_hat_1y\n"," break # 계산이 무거우므로, for 루프는 1번만 수행한 뒤 종료합니다. 각 변수에 어떤 값이 들어있는지 확인하면 알고리즘을 더 깊이 이해할 수 있습니다."]},{"cell_type":"code","execution_count":6,"id":"665ebafd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"665ebafd","executionInfo":{"status":"ok","timestamp":1672120691833,"user_tz":-540,"elapsed":43701,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"114ccdfc-023b-4a0b-d695-4b2c631818a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Collecting scikit-surprise\n"," Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n","\u001b[K |████████████████████████████████| 771 kB 33.9 MB/s \n","\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626503 sha256=0e6db95d2a4c33d1ddb4f8a1bdfe526bfa9ce95d16c0f4728ccec069f280cbd1\n"," Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"]}],"source":["!pip install surprise"]},{"cell_type":"code","execution_count":8,"id":"b42f291a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b42f291a","executionInfo":{"status":"ok","timestamp":1672121035097,"user_tz":-540,"elapsed":203038,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"d9542b8c-d6ab-44a2-ca47-dbb7e74fc6e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Computing the pearson similarity matrix...\n","Done computing similarity matrix.\n"]},{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [6319, 2721, 2131, 3415, 1121, 2509, 4221, 4418, 4509, 5101],\n"," 149: [2494,\n"," 26425,\n"," 137,\n"," 2930,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 2425,\n"," 31116],\n"," 182: [6319, 3415, 3473, 146, 1121, 2509, 4221, 4418, 4509, 6122],\n"," 215: [1564, 3365, 1204, 1131, 2931, 2938, 1192, 1489, 2679, 1147],\n"," 281: [137,\n"," 1477,\n"," 26425,\n"," 6319,\n"," 27255,\n"," 2911,\n"," 4850,\n"," 5069,\n"," 2494,\n"," 31116],\n"," 326: [1192, 2679, 3341, 7064, 32316, 3415, 3473, 4208, 4453, 146],\n"," 351: [3473,\n"," 6122,\n"," 2911,\n"," 137,\n"," 27255,\n"," 3777,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116],\n"," 357: [7064,\n"," 3473,\n"," 4850,\n"," 2911,\n"," 27255,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116,\n"," 1444],\n"," 426: [26425,\n"," 137,\n"," 2930,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 31116,\n"," 1444,\n"," 3415],\n"," 456: [3473,\n"," 2911,\n"," 27255,\n"," 1477,\n"," 26425,\n"," 31116,\n"," 3823,\n"," 1444,\n"," 3415,\n"," 4928],\n"," 459: [2494,\n"," 2911,\n"," 2704,\n"," 27255,\n"," 3777,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 220,\n"," 31116],\n"," 494: [6319,\n"," 2494,\n"," 2911,\n"," 2704,\n"," 27255,\n"," 1477,\n"," 1621,\n"," 26425,\n"," 2721,\n"," 31116],\n"," 517: [7064, 3415, 1121, 2509, 4221, 4418, 4509, 4732, 6122, 3645],\n"," 524: [3415, 3473, 1121, 2509, 4221, 4418, 4509, 6122, 4850, 5069],\n"," 556: [26425,\n"," 137,\n"," 1477,\n"," 3473,\n"," 6319,\n"," 27255,\n"," 2911,\n"," 4850,\n"," 31116,\n"," 1444],\n"," 588: [7064,\n"," 3473,\n"," 27741,\n"," 2911,\n"," 27255,\n"," 1477,\n"," 26425,\n"," 2721,\n"," 31116,\n"," 1444],\n"," 589: [6319, 3415, 3473, 6023, 1121, 2509, 4135, 4221, 4418, 4509],\n"," 590: [2930,\n"," 26425,\n"," 4850,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 31116,\n"," 1444,\n"," 3415,\n"," 3645],\n"," 601: [3341, 6319, 3415, 3473, 1121, 2509, 4221, 4418, 4509, 4732],\n"," 621: [3415, 3473, 6271, 6918, 7067, 7749, 7759, 8256, 8724, 8785],\n"," 634: [2203,\n"," 1192,\n"," 2679,\n"," 3341,\n"," 7064,\n"," 7933,\n"," 32316,\n"," 2721,\n"," 3415,\n"," 4208],\n"," 672: [26425,\n"," 3002,\n"," 6168,\n"," 3777,\n"," 137,\n"," 2930,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911],\n"," 701: [8533, 2203, 678, 1658, 2618, 5283, 1273, 973, 2679, 6187],\n"," 719: [6319, 4356, 6016, 4850, 5305, 5840, 2977, 1477, 26425, 850],\n"," 745: [3473,\n"," 2911,\n"," 137,\n"," 27255,\n"," 1477,\n"," 2930,\n"," 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1444,\n"," 3415,\n"," 3645],\n"," 566: [2704,\n"," 5840,\n"," 3473,\n"," 1366,\n"," 2977,\n"," 2419,\n"," 3823,\n"," 4356,\n"," 55269,\n"," 6122],\n"," 331: [7064,\n"," 2704,\n"," 26425,\n"," 1477,\n"," 4850,\n"," 5069,\n"," 31116,\n"," 1444,\n"," 8125,\n"," 8199],\n"," 665: [3415, 3473, 6023, 1121, 2509, 4221, 4418, 4509, 6122, 4850],\n"," 872: [1046, 2075, 2931, 2938, 2679, 3676, 5304, 6783, 7064, 8042],\n"," 879: [260, 858, 1210, 1276, 28, 111, 306, 527, 541, 778],\n"," 486: [1046, 5438, 2852, 4276, 4633, 3516, 955, 1192, 2550, 6187],\n"," 864: [2704,\n"," 26425,\n"," 2494,\n"," 2930,\n"," 27741,\n"," 27255,\n"," 6122,\n"," 34164,\n"," 850,\n"," 31116],\n"," 52: [3365, 6273, 2618, 26131, 2075, 2931, 2938, 1203, 5122, 6316],\n"," 288: [324, 2060, 5255, 2931, 2679, 3270, 1646, 1281, 5304, 7933],\n"," 9: [61, 2679, 1646, 1946, 3341, 4920, 7064, 99, 715, 1493],\n"," 117: [2721,\n"," 3473,\n"," 2911,\n"," 1444,\n"," 27255,\n"," 1477,\n"," 1621,\n"," 26425,\n"," 31116,\n"," 3415],\n"," 220: [5017, 328, 2931, 1192, 1489, 5159, 3341, 3831, 2731, 5304],\n"," 544: [1046, 3516, 2721, 3415, 3090, 5288, 1121, 2509, 4221, 4418],\n"," 999: [242, 2936, 328, 1658, 506, 2075, 2852, 2931, 3115, 4276],\n"," 458: [4850,\n"," 5069,\n"," 2930,\n"," 26425,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 850,\n"," 31116],\n"," 974: [927, 1236, 8580, 4466, 6669, 6162, 2625, 80, 2833, 2704],\n"," 546: [1148, 1252, 1276, 232, 593, 1199, 1230, 50, 904, 1248],\n"," 55: [3415, 121, 212, 3511, 1121, 2509, 4221, 4418, 4509, 5101],\n"," 363: [1148, 242, 608, 1046, 50, 904, 1260, 3365, 4432, 1213],\n"," 445: [3341, 3415, 3473, 121, 1121, 2509, 4135, 4221, 4418, 4509],\n"," 492: [926, 1046, 50, 678, 7193, 1131, 1233, 2075, 2931, 2938],\n"," 234: [615, 1684, 6319, 7933, 32316, 2721, 1121, 2509, 4135, 4221],\n"," 1027: [2075,\n"," 2721,\n"," 3415,\n"," 3473,\n"," 1121,\n"," 2509,\n"," 4221,\n"," 4418,\n"," 4509,\n"," 4850],\n"," 140: [3516,\n"," 2679,\n"," 5304,\n"," 6783,\n"," 7064,\n"," 8125,\n"," 32316,\n"," 2721,\n"," 3415,\n"," 3473],\n"," 926: [5017, 2075, 2931, 1489, 2696, 5304, 6783, 7064, 7933, 8596],\n"," 781: [260, 858, 1210, 1148, 5952, 7153, 232, 242, 306, 527],\n"," 825: [1046,\n"," 2931,\n"," 2938,\n"," 1489,\n"," 2679,\n"," 2696,\n"," 3097,\n"," 5434,\n"," 45950,\n"," 49932],\n"," 913: [3415, 3473, 4616, 3480, 5077, 4850, 4939, 1572, 6241, 6271],\n"," 453: [1260, 2203, 6695, 8968, 2938, 5007, 1192, 408, 6006, 2714],\n"," 540: [7064, 2721, 3415, 6023, 4850, 1572, 4405, 6271, 6918, 7067],\n"," 66: [1046, 2075, 2852, 2931, 2938, 3516, 1192, 1489, 2550, 3929],\n"," 185: [4387, 6595, 6219, 3473, 3090, 869, 3857, 4846, 220, 3777],\n"," 222: [4135, 2704, 137, 26425, 4850, 1184, 2425, 2721, 6772, 2935],\n"," 498: [7064, 8199, 3415, 3473, 3645, 4928, 6241, 6271, 7067, 7749],\n"," 994: [2704, 3694, 3695, 7064, 6122, 3823, 1444, 3415, 6023, 8195],\n"," 165: [3473,\n"," 3700,\n"," 2704,\n"," 1444,\n"," 27255,\n"," 7297,\n"," 1477,\n"," 26425,\n"," 31116,\n"," 27648],\n"," 202: [1148, 28, 299, 321, 904, 1348, 2648, 2804, 1611, 2618],\n"," 180: [260, 3684, 28, 47, 308, 326, 527, 912, 926, 1046],\n"," 93: [242, 1172, 908, 1248, 1260, 2206, 3365, 4327, 5017, 5292],\n"," 261: [5017, 1192, 2679, 7064, 7933, 8199, 927, 2043, 3415, 3473],\n"," 435: [324, 3477, 5255, 2075, 2931, 1468, 2550, 3918, 7064, 32316],\n"," 322: [26131,\n"," 40826,\n"," 7064,\n"," 2721,\n"," 3415,\n"," 3473,\n"," 7063,\n"," 6023,\n"," 3511,\n"," 1121],\n"," 238: [1646, 3341, 3415, 3473, 212, 6023, 8580, 3511, 1121, 2509],\n"," 425: [1046, 2075, 2852, 250, 615, 1684, 49932, 2721, 715, 3415],\n"," 512: [1056, 2931, 3516, 1192, 2500, 8916, 1646, 4920, 49932, 680],\n"," 725: [2494,\n"," 26425,\n"," 1477,\n"," 27255,\n"," 2911,\n"," 31116,\n"," 1444,\n"," 3415,\n"," 3645,\n"," 668],\n"," 732: [2721,\n"," 3473,\n"," 27741,\n"," 2911,\n"," 2977,\n"," 27255,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116],\n"," 108: [2721, 3415, 3473, 1184, 6023, 4135, 4850, 4939, 3645, 668],\n"," 592: [1046, 904, 4226, 2618, 2858, 2959, 7360, 44761, 2931, 2938],\n"," 443: [1489, 2679, 2721, 3415, 3384, 336, 2102, 6023, 8580, 4135],\n"," 916: [2704,\n"," 26425,\n"," 2930,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 7064,\n"," 1046,\n"," 2721,\n"," 31116],\n"," 736: [858, 7153, 326, 951, 1260, 324, 2959, 6296, 2931, 29],\n"," 961: [242, 4432, 2075, 2852, 2931, 2938, 2360, 3516, 932, 1837],\n"," 1012: [2721,\n"," 3473,\n"," 34164,\n"," 2704,\n"," 3694,\n"," 3695,\n"," 2930,\n"," 26425,\n"," 31116,\n"," 1444],\n"," 515: [2721, 715, 3415, 3645, 4928, 6271, 7067, 7749, 7759, 8256],\n"," 978: [1148, 8533, 321, 1172, 1260, 3365, 4432, 324, 2060, 2618],\n"," 28: [5438, 2075, 3516, 188, 1034, 615, 3097, 2721, 1361, 146],\n"," 475: [3473,\n"," 2704,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116,\n"," 1444,\n"," 3415,\n"," 3645,\n"," 668],\n"," 598: [1046, 506, 1056, 2931, 1192, 1489, 250, 2696, 1929, 199],\n"," 943: [4135, 6122, 4850, 2704, 1477, 26425, 1046, 850, 7064, 2721],\n"," 520: [8132,\n"," 26425,\n"," 27255,\n"," 2970,\n"," 31116,\n"," 5017,\n"," 5304,\n"," 7064,\n"," 8199,\n"," 3415],\n"," 697: [2347, 28, 1719, 2721, 328, 4103, 30803, 4307, 55280, 57243],\n"," 849: [260, 1210, 1148, 28, 527, 541, 1199, 1207, 1193, 1197],\n"," 1003: [3516, 250, 7064, 7933, 32316, 2721, 715, 5034, 212, 1121],\n"," 705: [2721, 3415, 3473, 121, 212, 6023, 1121, 2509, 4221, 4418],\n"," 48: [242, 562, 1103, 1199, 1206, 1207, 1244, 904, 913, 1086],\n"," 29: [4135,\n"," 4850,\n"," 26425,\n"," 1184,\n"," 1827,\n"," 1870,\n"," 3360,\n"," 6772,\n"," 8712,\n"," 31116],\n"," 231: [250, 2714, 1646, 3415, 1904, 6791, 3076, 212, 1121, 2271],\n"," 699: [1489, 2679, 1147, 615, 3341, 2731, 5304, 6783, 7064, 8125],\n"," 448: [7064,\n"," 2704,\n"," 26425,\n"," 2494,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 3694,\n"," 3695,\n"," 850],\n"," 750: [213, 242, 926, 26131, 613, 2075, 2931, 2938, 4633, 1192],\n"," 782: [5017,\n"," 26131,\n"," 4356,\n"," 7064,\n"," 8199,\n"," 2721,\n"," 3224,\n"," 3415,\n"," 6023,\n"," 8195],\n"," 860: [5840,\n"," 26425,\n"," 3473,\n"," 59315,\n"," 54995,\n"," 3823,\n"," 4327,\n"," 8712,\n"," 31116,\n"," 1444],\n"," 768: [213, 2704, 26425, 1034, 1361, 2419, 2852, 1477, 2714, 2935],\n"," 127: [1833, 3503, 2721, 2732, 82, 1237, 6881, 2704, 3694, 3695],\n"," 894: [299,\n"," 4103,\n"," 30803,\n"," 4307,\n"," 55280,\n"," 57243,\n"," 1244,\n"," 49932,\n"," 7327,\n"," 4437]})"]},"metadata":{},"execution_count":8}],"source":["# surprise를 사용한 구현\n","\n","from surprise import KNNWithMeans, Reader\n","from surprise import Dataset as SurpriseDataset\n","# surprise용으로 데이터를 가공한다\n","reader = Reader(rating_scale=(0.5, 5))\n","data_train = SurpriseDataset.load_from_df(\n"," movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n",").build_full_trainset()\n","\n","sim_options = {\"name\": \"pearson\", \"user_based\": True} # 유사도를 계산하는 방법을 정의한다 # False로 하면 아이템 기반이 된다\n","knn = KNNWithMeans(k=30, min_k=1, sim_options=sim_options)\n","knn.fit(data_train)\n","\n","# 학습 데이터셋에서 평갓값이 없는 사용자와 아이템의 조합을 준비한다\n","data_test = data_train.build_anti_testset(None)\n","predictions = knn.test(data_test)\n","\n","def get_top_n(predictions, n=10):\n"," # 각 사용자별로 예측된 아이템을 저장한다\n"," top_n = defaultdict(list)\n"," for uid, iid, true_r, est, _ in predictions:\n"," top_n[uid].append((iid, est))\n","\n"," # 사용자별로 아이템을 예측 평갓값 순으로 나열하고, 상위 n개를 저장한다\n"," for uid, user_ratings in top_n.items():\n"," user_ratings.sort(key=lambda x: x[1], reverse=True)\n"," top_n[uid] = [d[0] for d in user_ratings[:n]]\n","\n"," return top_n\n","\n","pred_user2items = get_top_n(predictions, n=10)\n","\n","average_score = movielens_train.rating.mean()\n","pred_results = []\n","for _, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," movie_id = row[\"movie_id\"]\n"," # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자와 영화에 대한 예측 평갓값은 전체의 평균 평갓값으로 한다\n"," if user_id not in user_id2index or movie_id not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," pred_score = knn.predict(uid=user_id, iid=movie_id).est\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":9,"id":"1fccc723","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"1fccc723","executionInfo":{"status":"ok","timestamp":1672121035097,"user_tz":-540,"elapsed":30,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6420b02f-837f-4f24-adc1-0c986fe8d916"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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\n"," "]},"metadata":{},"execution_count":9}],"source":["# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":10,"id":"874b6e3e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"874b6e3e","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":19,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b39c148b-c27c-4f83-a2b5-ddf05e1f8545"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[3473, 4135, 4850, 2425, 2704, 137, 27255, 26425, 8712, 31116]"]},"metadata":{},"execution_count":10}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":11,"id":"c9b77fbd","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"c9b77fbd","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9a9700f9-87ac-459c-b415-1df2a8dceb5c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","3386 3473 Jonah Who Will Be 25 in the Year 2000 (Jonas q... \n","4043 4135 Monster Squad, The (1987) \n","4756 4850 Spriggan (Supurigan) (1998) \n","\n"," genre \\\n","3386 [Comedy] \n","4043 [Adventure, Comedy, Horror] \n","4756 [Action, Animation, Sci-Fi] \n","\n"," tag \n","3386 [cerebral, humorous, intersecting lives, intim... \n","4043 [can't remember] \n","4756 [library, anime, spectacle, over expositive] "],"text/html":["\n","
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movie_idtitlegenretag
33863473Jonah Who Will Be 25 in the Year 2000 (Jonas q...[Comedy][cerebral, humorous, intersecting lives, intim...
40434135Monster Squad, The (1987)[Adventure, Comedy, Horror][can't remember]
47564850Spriggan (Supurigan) (1998)[Action, Animation, Sci-Fi][library, anime, spectacle, over expositive]
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\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2에 대한 추천(1198, 1196, 1097)\n","movies[movies.movie_id.isin([3473, 4135, 4850])]"]},{"cell_type":"code","execution_count":11,"id":"99c25fe6","metadata":{"id":"99c25fe6","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/Word2vec.ipynb b/chapter5/colab/Word2vec.ipynb index c33b451..7e2426b 100644 --- a/chapter5/colab/Word2vec.ipynb +++ b/chapter5/colab/Word2vec.ipynb @@ -1,1543 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8dc6cdc1", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Word2vec.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "caaaa856", - "metadata": {}, - "source": [ - "# Word2vec" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "20b107d9", - "metadata": {}, - "outputs": [], - "source": [ - "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", - "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", - "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", - "\n", - "# 데이터 다운로드와 압축 풀기\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9f40fb34", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", - "import pandas as pd\n", - "\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "\n", - "\n", - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "\n", - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "\n", - "\n", - "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "\n", - "\n", - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "\n", - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", - "\n", - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dc9b021b", - "metadata": {}, - "outputs": [], - "source": [ - "# 인자 수\n", - "factors = 100\n", - "# 에폭 수\n", - "n_epochs = 30\n", - "# window 크기\n", - "window = 100\n", - "# 스킵 그램\n", - "use_skip_gram = 1\n", - "# 계층적 소프트맥스\n", - "use_hierarchial_softmax = 0\n", - "# 사용할 단어의 출현 횟수의 임곗값\n", - "min_count = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9f4dd49e", - "metadata": {}, - "outputs": [], - "source": [ - "movie_content = movies.copy()\n", - "# tag가 부여되지 않은 영화는 있지만, genre는 모든 영화에 부여되어 있다\n", - "# tag와 genre를 결합한 것을 영화의 콘텐츠 정보로 해서 비슷한 영화를 찾아 추천한다\n", - "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", - "movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n", - "movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: set(map(str, x)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d04f27c", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install gensim==4.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5994a870", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recsys-test3.7.8/lib/python3.7/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "import gensim\n", - "\n", - "# 태그와 장르 데이터를 사용해 word2vec을 학습한다\n", - "tag_genre_data = movie_content.tag_genre.tolist()\n", - "model = gensim.models.word2vec.Word2Vec(\n", - " tag_genre_data,\n", - " vector_size=factors,\n", - " window=window,\n", - " sg=use_skip_gram,\n", - " hs=use_hierarchial_softmax,\n", - " epochs=n_epochs,\n", - " min_count=min_count,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cad9e002", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('studio ghibli', 0.8167880177497864),\n", - " ('zibri studio', 0.8076589107513428),\n", - " ('pelicula anime', 0.7783956527709961),\n", - " ('hayao miyazaki', 0.7779476046562195),\n", - " ('miyazaki', 0.754835307598114),\n", - " ('japan', 0.6330965161323547),\n", - " ('Animation', 0.5467621088027954),\n", - " ('girl', 0.528790295124054),\n", - " ('wilderness', 0.5270888805389404),\n", - " ('dragon', 0.5217347741127014)]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# anime 태크와 비슷한 태그를 확인한다\n", - "model.wv.most_similar('anime')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "36426496", - "metadata": {}, - "outputs": [], - "source": [ - "# 각 영화의 벡터를 계산한다\n", - "# 각 영화에 부여되어 있는 태그/장르 벡터의 평균을 영화의 벡터로 한다\n", - "movie_vectors = []\n", - "tag_genre_in_model = set(model.wv.key_to_index.keys())\n", - "\n", - "titles = []\n", - "ids = []\n", - "\n", - "for i, tag_genre in enumerate(tag_genre_data):\n", - " # word2vec 모델에서 사용할 수 있는 태그/장르로 필터링한다\n", - " input_tag_genre = set(tag_genre) & tag_genre_in_model\n", - " if len(input_tag_genre) == 0:\n", - " # word2vec에 기반해 벡터 계산할 수 없는 영화에는 무작위 벡터를 부여한다\n", - " vector = np.random.randn(model.vector_size)\n", - " else:\n", - " vector = model.wv[input_tag_genre].mean(axis=0)\n", - " titles.append(movie_content.iloc[i][\"title\"])\n", - " ids.append(movie_content.iloc[i][\"movie_id\"])\n", - " movie_vectors.append(vector)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e0ef174b", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# 후속 유사도 계산을 쉽게 할 수 있도록 numpy 배열로 저장한다\n", - "movie_vectors = np.array(movie_vectors)\n", - "\n", - "# 정규화한 벡터\n", - "sum_vec = np.sqrt(np.sum(movie_vectors ** 2, axis=1))\n", - "movie_norm_vectors = movie_vectors / sum_vec.reshape((-1, 1))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0986ff67", - "metadata": {}, - "outputs": [], - "source": [ - "# 아직 평가하지 않은 영화에서 유사도가 높은 아이템을 반환하는 함수\n", - "def find_similar_items(vec, evaluated_movie_ids, topn=10):\n", - " score_vec = np.dot(movie_norm_vectors, vec)\n", - " similar_indexes = np.argsort(-score_vec)\n", - " similar_items = []\n", - " for similar_index in similar_indexes:\n", - " if ids[similar_index] not in evaluated_movie_ids:\n", - " similar_items.append(ids[similar_index])\n", - 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" 912: [7247, 1028, 8651, 6345, 1380, 2565, 26422, 6316, 588, 1282],\n", - " 913: [1250, 920, 1090, 1276, 912, 1207, 3100, 1939, 1084, 1953],\n", - " 914: [32325, 63310, 32844, 8673, 3117, 39768, 60894, 2755, 621, 1724],\n", - " 915: [26746, 996, 517, 2764, 4086, 27828, 4869, 54958, 60566, 1432],\n", - " 916: [7361, 3020, 2692, 50, 6003, 32, 1748, 2571, 48082, 1206],\n", - " 917: [31610, 6079, 26812, 2904, 4058, 4059, 7880, 6055, 4060, 6049],\n", - " 918: [7007, 1616, 1573, 367, 7381, 3981, 517, 3130, 380, 3269],\n", - " 919: [1382, 1626, 1004, 4224, 4044, 5815, 990, 4466, 26688, 7725],\n", - " 920: [1214, 589, 480, 1690, 3471, 8810, 1196, 2288, 1210, 2366],\n", - " 921: [5452, 6557, 6559, 258, 64652, 4306, 6137, 6625, 6332, 2054],\n", - " 922: [2161, 5452, 7247, 7369, 44022, 7164, 2761, 32031, 2138, 3988],\n", - " 923: [852, 1777, 1393, 8643, 1307, 3270, 1883, 543, 11, 3361],\n", - " 924: [6993, 6750, 52, 7084, 26228, 4392, 7044, 6724, 6584, 3194],\n", - " 925: [3499, 1200, 1036, 1240, 1219, 2664, 2366, 1253, 2288, 1690],\n", - " 926: [4027, 1265, 296, 38061, 1301, 2580, 33004, 7361, 1676, 1089],\n", - " 927: [1091, 367, 7007, 5989, 33437, 380, 7438, 37477, 3275, 51575],\n", - " 928: [1301, 4467, 1274, 2021, 1206, 1161, 316, 2529, 3204, 1214],\n", - " 930: [4242, 4804, 2587, 1784, 4529, 26527, 11, 3358, 6558, 3752],\n", - " 931: [1432, 4466, 26688, 5252, 7040, 46430, 8593, 2540, 7031, 60237],\n", - " 932: [8961, 2987, 31696, 367, 7046, 1784, 1198, 2804, 4306, 551],\n", - " 933: [1597, 2764, 2389, 31696, 51709, 532, 8373, 1658, 7445, 2912],\n", - " 934: [258, 126, 62376, 56, 8713, 2047, 44225, 5038, 5892, 558],\n", - " 935: [2115, 1544, 494, 3204, 95, 1918, 380, 165, 2002, 1210],\n", - " 936: [2580, 1220, 51575, 3275, 47044, 5989, 3254, 3418, 1884, 5010],\n", - " 937: [1199, 4467, 6003, 48082, 1206, 3535, 27904, 27706, 1658, 7361],\n", - " 938: [377, 1036, 1214, 1387, 1240, 1625, 2912, 4210, 1610, 480],\n", - " 939: [377, 2912, 1036, 2268, 517, 2529, 47, 1214, 1909, 1617],\n", - " 940: [377, 2529, 1953, 2912, 1321, 316, 1036, 3729, 517, 4064],\n", - " 941: [5000, 6724, 5788, 56060, 31347, 26211, 6245, 1964, 5970, 27826],\n", - " 942: [377, 480, 1610, 1214, 1036, 1200, 589, 1580, 1544, 2529],\n", - " 943: [1339, 1982, 3461, 6966, 7387, 1214, 7891, 273, 8092, 382],\n", - " 945: [55668, 61414, 6376, 2386, 61312, 3124, 26360, 984, 39416, 4012],\n", - " 946: [596, 1025, 783, 1022, 2081, 5218, 2096, 2080, 1030, 594],\n", - " 947: [914, 7714, 30816, 945, 1947, 26422, 2987, 1250, 8008, 6345],\n", - " 948: [7153, 260, 6947, 7090, 2826, 3578, 7438, 2987, 1210, 31660],\n", - " 949: [27660, 27904, 1208, 2010, 27706, 48082, 1301, 1274, 2529, 6003],\n", - " 950: [49932, 5105, 5867, 7044, 3863, 4546, 48997, 4919, 3476, 27317],\n", - " 951: [27797, 6424, 478, 403, 44058, 4879, 2484, 166, 3876, 5968],\n", - " 952: [2138, 2045, 5452, 258, 2139, 36397, 1367, 6557, 64652, 1025],\n", - " 953: [5944, 329, 2393, 1375, 1376, 2528, 3175, 26513, 1356, 6645],\n", - " 954: [6995, 485, 4133, 26513, 6264, 5040, 208, 3889, 57223, 442],\n", - " 955: [7386, 1784, 6003, 6787, 7160, 1693, 5945, 3100, 3386, 6947],\n", - " 956: [1408, 1378, 1304, 6421, 47117, 3037, 3100, 54997, 47956, 8481],\n", - " 957: [532, 3210, 1235, 4027, 1923, 4467, 319, 3948, 1091, 2717],\n", - " 958: [628, 5954, 1617, 50, 8783, 1213, 8950, 2912, 3476, 27773],\n", - " 960: [3386, 1617, 7160, 4776, 5989, 3006, 36517, 6796, 2396, 6787],\n", - " 961: [1953, 1276, 1784, 1263, 912, 3100, 3504, 858, 1084, 3468],\n", - " 962: [2764, 27828, 7160, 1645, 3148, 2803, 5388, 2467, 2389, 517],\n", - " 963: [990, 4434, 1488, 3770, 7007, 227, 1427, 4044, 8241, 27316],\n", - " 964: [1784, 7044, 4027, 3275, 532, 5945, 6953, 1805, 3020, 7160],\n", - " 965: [2908, 7160, 25, 1271, 2313, 5673, 5015, 64957, 1354, 1094],\n", - " 966: [3996, 7482, 6874, 7090, 3578, 377, 2826, 31660, 6947, 8795],\n", - " 967: [543, 31685, 1393, 3752, 367, 1091, 4306, 33836, 2804, 586],\n", - " 968: [377, 1693, 5445, 3204, 836, 1625, 1036, 1721, 32, 1240],\n", - " 969: [3259, 440, 8643, 31694, 236, 39, 4132, 11, 539, 4464],\n", - " 970: [2001, 2490, 1918, 377, 7007, 5027, 2000, 163, 7381, 2002],\n", - " 971: [30816, 928, 6660, 4308, 1235, 3307, 48082, 953, 920, 7215],\n", - " 972: [1208, 1262, 5010, 3476, 36529, 1953, 4826, 7044, 1233, 26131],\n", - " 973: [909, 7080, 7835, 1256, 4357, 6724, 912, 7831, 914, 969],\n", - " 974: [601, 3657, 706, 1471, 3655, 5814, 32342, 62245, 713, 3641],\n", - " 975: [1784, 1213, 3362, 6, 858, 2762, 1617, 1625, 3275, 1953],\n", - " 976: [7160, 7044, 2594, 1206, 6003, 1693, 3148, 517, 110, 2313],\n", - " 977: [5137, 6948, 7484, 3142, 7124, 8962, 43708, 5385, 33677, 32314],\n", - " 978: [7055, 26084, 7050, 34018, 7049, 7132, 2565, 6785, 914, 3199],\n", - " 979: [1617, 2912, 1784, 5989, 50, 7007, 2580, 1214, 31, 1616],\n", - " 980: [1198, 1196, 1097, 480, 5952, 110, 3578, 527, 589, 33493],\n", - " 982: [1221, 1208, 2023, 919, 1094, 1784, 1084, 1235, 2580, 1953],\n", - " 983: [2161, 551, 4014, 8961, 4294, 919, 44022, 2716, 7247, 37729],\n", - " 984: [1276, 1617, 628, 923, 2858, 8601, 1358, 954, 36, 4027],\n", - " 985: [33437, 1693, 7458, 110, 5989, 1408, 6385, 2826, 1721, 733],\n", - " 986: [26989, 4869, 8093, 7007, 63772, 45412, 49007, 1523, 459, 1432],\n", - " 987: [7149, 8643, 1784, 27797, 33145, 33649, 2587, 3481, 2396, 2580],\n", - " 988: [7123, 7044, 247, 49932, 5105, 928, 39381, 3863, 1264, 6927],\n", - " 989: [1089, 1729, 3275, 1953, 6, 7438, 2542, 4262, 4011, 3020],\n", - " 990: [1784, 2396, 1094, 1183, 7162, 1721, 8533, 6947, 7160, 920],\n", - " 991: [2420, 2133, 34332, 8961, 2470, 442, 2275, 44022, 2005, 8633],\n", - " 992: [2717, 7007, 2389, 7001, 2371, 39446, 1214, 1625, 3461, 6323],\n", - " 993: [56921, 6185, 32029, 3204, 5859, 3476, 1616, 7007, 51709, 46855],\n", - " 994: [1235, 2065, 235, 2395, 1259, 1265, 2858, 6184, 2997, 5673],\n", - " 995: [1244, 7460, 7044, 1235, 5673, 319, 3925, 1273, 3168, 5792],\n", - " 996: [864, 56060, 6570, 7139, 8937, 4529, 26527, 7395, 26803, 53887],\n", - " 997: [7007, 31600, 6491, 26488, 61818, 1432, 2429, 49007, 3269, 5553],\n", - " 998: [1094, 2396, 1185, 8970, 1225, 30812, 6385, 2313, 2908, 2971],\n", - " 999: [1214, 2366, 2163, 3471, 2413, 2288, 3701, 2021, 2662, 1321],\n", - " 1001: [1721, 2987, 1097, 1693, 1198, 1101, 6947, 7153, 34, 1408],\n", - " 1002: [5541, 3868, 3869, 2723, 7102, 3760, 2027, 2791, 1772, 2000],\n", - " 1003: [47937, 59738, 59604, 49528, 5630, 52005, 6736, 58332, 60832, 47830],\n", - " 1004: [1036, 1610, 1625, 1214, 1387, 2529, 5445, 1208, 3204, 6],\n", - " 1005: [50792, 1727, 2100, 4621, 1091, 11, 1784, 1883, 1101, 1777],\n", - " 1006: [1176, 6660, 2291, 64957, 235, 531, 1271, 4014, 2313, 2731],\n", - " 1007: [1658, 11, 1265, 1784, 2997, 5673, 1235, 440, 6155, 2671],\n", - " 1008: [1206, 2529, 1240, 1748, 4467, 1690, 32587, 2021, 1301, 1129],\n", - " 1009: [2987, 51575, 5445, 32, 377, 442, 589, 1784, 165, 733],\n", - " 1011: [2594, 3204, 5445, 1748, 292, 2529, 2762, 198, 8950, 1301],\n", - " 1012: [1805, 2594, 3476, 1690, 32, 1129, 31, 1693, 1784, 1206],\n", - " 1013: [11, 380, 1393, 1097, 1265, 1721, 2987, 1580, 1270, 5989],\n", - " 1014: [1784, 5945, 4529, 26527, 7044, 56715, 532, 7367, 1658, 1235],\n", - " 1015: [928, 913, 1253, 3435, 1219, 1086, 908, 1212, 1248, 1254],\n", - " 1016: [3168, 1953, 1784, 3100, 2912, 7044, 3504, 1254, 1235, 1304],\n", - " 1017: [260, 2826, 1198, 480, 2193, 316, 3997, 589, 1200, 1240],\n", - " 1018: [6736, 63836, 32743, 27338, 8774, 6066, 62788, 2315, 54910, 8894],\n", - " 1019: [26989, 3184, 27797, 4635, 27695, 8927, 438, 1112, 1432, 2438],\n", - " 1020: [912, 903, 922, 7080, 1254, 920, 1253, 969, 1256, 909],\n", - " 1021: [2081, 783, 1025, 2096, 1023, 31687, 3034, 1030, 5218, 31193],\n", - " 1022: [588, 596, 1025, 2080, 5218, 2081, 364, 661, 1029, 2096],\n", - " 1023: [2313, 2728, 590, 40278, 6385, 7160, 33166, 1293, 1693, 1262],\n", - " 1025: [3210, 4027, 1235, 5673, 1784, 1259, 26788, 2729, 2580, 7285],\n", - " 1026: [50, 1953, 6, 1617, 1263, 912, 3362, 1276, 903, 3476],\n", - " 1027: [367, 2716, 2133, 7004, 4262, 1573, 2366, 1091, 1784, 1101],\n", - " 1028: [7007, 648, 3386, 1036, 95, 3256, 377, 733, 494, 6989],\n", - " 1029: [1783, 1422, 52005, 43853, 27828, 3800, 712, 39703, 5424, 3716],\n", - " 1030: [3275, 532, 2952, 2580, 1784, 1658, 2764, 4027, 6708, 3685],\n", - " 1031: [1544, 4638, 780, 3638, 3204, 1580, 2947, 1196, 1374, 377],\n", - " 1032: [1772, 2804, 485, 7369, 6951, 2161, 2413, 4133, 543, 26950],\n", - " 1033: [920, 912, 969, 3307, 909, 899, 1276, 1035, 1250, 7080],\n", - " 1034: [5010, 110, 7458, 260, 8977, 6947, 733, 1408, 1210, 1198],\n", - " 1035: [2094, 43928, 57640, 52722, 5254, 442, 34332, 839, 8865, 2490],\n", - " 1037: [2671, 1784, 5299, 8969, 440, 1307, 186, 1393, 11, 6942],\n", - " 1038: [37846, 7312, 6262, 649, 7370, 60286, 4702, 26976, 46959, 47148],\n", - " 1039: [33166, 1953, 2912, 6, 7981, 3362, 3683, 4034, 2203, 628],\n", - " 1040: [420, 2000, 2001, 5049, 7369, 7102, 2053, 1772, 7004, 6014],\n", - " 1041: [1270, 6377, 31923, 2987, 4306, 1197, 4774, 44022, 7720, 349],\n", - " 1043: [5027, 7108, 861, 26688, 4466, 7007, 1036, 3452, 2001, 26746],\n", - " 1044: [2161, 2005, 31660, 2054, 7046, 2987, 4306, 8961, 1270, 2716],\n", - " 1045: [1235, 923, 3925, 922, 908, 6650, 1247, 2729, 235, 5792],\n", - " 1046: [2987, 1198, 1387, 1097, 377, 1214, 3471, 589, 2366, 1200],\n", - " 1047: [539, 357, 1777, 2671, 11, 4246, 1101, 236, 186, 597],\n", - " 1048: [380, 51255, 6503, 543, 51575, 2723, 2371, 4956, 1265, 2791],\n", - " 1050: [2764, 2455, 2396, 1784, 1277, 532, 1645, 7149, 4280, 7215],\n", - " 1051: [8969, 39, 2396, 1380, 1784, 357, 3481, 8643, 3259, 3210],\n", - " 1052: [7215, 4464, 1183, 3386, 1161, 5989, 1277, 26788, 4326, 6297],\n", - " 1053: [733, 1262, 3654, 2728, 1208, 349, 377, 1287, 150, 6947]}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 사용자가 평갓값 4 이상을 부여한 영화\n", - "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", - "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", - "\n", - "id2index = dict(zip(ids, range(len(ids))))\n", - "pred_user2items = dict()\n", - "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", - " evaluated_movie_ids = user_evaluated_movies[user_id]\n", - " movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", - "\n", - " movie_indexes = [id2index[id] for id in movie_ids]\n", - " user_vector = movie_norm_vectors[movie_indexes].mean(axis=0)\n", - " recommended_items = find_similar_items(user_vector, evaluated_movie_ids, topn=10)\n", - " pred_user2items[user_id] = recommended_items\n", - "pred_user2items" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "625d2894", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp \\\n", - "4732 2 110 5.0 868245777 \n", - "5246 2 260 5.0 868244562 \n", - "5798 2 590 5.0 868245608 \n", - "8381 2 1210 4.0 868245644 \n", - "\n", - " title \\\n", - "4732 Braveheart (1995) \n", - "5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n", - "5798 Dances with Wolves (1990) \n", - "8381 Star Wars: Episode VI - Return of the Jedi (1983) \n", - "\n", - " genre \\\n", - "4732 [Action, Drama, War] \n", - "5246 [Action, Adventure, Sci-Fi] \n", - "5798 [Adventure, Drama, Western] \n", - "8381 [Action, Adventure, Sci-Fi] \n", - "\n", - " tag timestamp_rank \n", - "4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n", - "5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n", - "5798 [afi 100, lame, native, biopic, american india... 11.0 \n", - "8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n", - "movielens_train_high_rating[movielens_train_high_rating.user_id==2]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2e3be9df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
10751097E.T. the Extra-Terrestrial (1982)[Children, Drama, Sci-Fi][speilberg, steven spielberg, spielberg/lucas,...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "1075 1097 E.T. the Extra-Terrestrial (1982) \n", - "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", - "1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n", - "\n", - " genre \\\n", - "1075 [Children, Drama, Sci-Fi] \n", - "1171 [Action, Adventure, Sci-Fi] \n", - "1173 [Action, Adventure] \n", - "\n", - " tag \n", - "1075 [speilberg, steven spielberg, spielberg/lucas,... \n", - "1171 [lucas, george lucas, george lucas, gfei own i... \n", - "1173 [egypt, lucas, seen more than once, dvd collec... " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# user_id=2에 대한 추천(1198, 1196, 1097)\n", - "movies[movies.movie_id.isin([1198, 1196, 1097])]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83e8a9a4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"8dc6cdc1","metadata":{"id":"8dc6cdc1"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Word2vec.ipynb)"]},{"cell_type":"markdown","id":"caaaa856","metadata":{"id":"caaaa856"},"source":["# Word2vec"]},{"cell_type":"code","execution_count":1,"id":"20b107d9","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"20b107d9","executionInfo":{"status":"ok","timestamp":1672120480929,"user_tz":-540,"elapsed":3734,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"622537ab-1a59-4ac9-b565-f8de5c655b7d"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:54:34-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 115MB/s in 0.5s \n","\n","2022-12-27 05:54:35 (115 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"9f40fb34","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9f40fb34","executionInfo":{"status":"ok","timestamp":1672120548661,"user_tz":-540,"elapsed":67736,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3dd4700f-df2c-4a1b-94bd-15d24ce926b5"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"dc9b021b","metadata":{"id":"dc9b021b","executionInfo":{"status":"ok","timestamp":1672120548662,"user_tz":-540,"elapsed":21,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 100\n","# 에폭 수\n","n_epochs = 30\n","# window 크기\n","window = 100\n","# 스킵 그램\n","use_skip_gram = 1\n","# 계층적 소프트맥스\n","use_hierarchial_softmax = 0\n","# 사용할 단어의 출현 횟수의 임곗값\n","min_count = 5"]},{"cell_type":"code","execution_count":4,"id":"9f4dd49e","metadata":{"id":"9f4dd49e","executionInfo":{"status":"ok","timestamp":1672120548662,"user_tz":-540,"elapsed":20,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["movie_content = movies.copy()\n","# tag가 부여되지 않은 영화는 있지만, genre는 모든 영화에 부여되어 있다\n","# tag와 genre를 결합한 것을 영화의 콘텐츠 정보로 해서 비슷한 영화를 찾아 추천한다\n","# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n","movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n","movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: set(map(str, x)))"]},{"cell_type":"code","execution_count":5,"id":"1d04f27c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1d04f27c","executionInfo":{"status":"ok","timestamp":1672120556096,"user_tz":-540,"elapsed":7453,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"0fc2192f-4c86-42b7-b72b-c4d4f87a4481"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.2 MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":6,"id":"5994a870","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5994a870","executionInfo":{"status":"ok","timestamp":1672120587871,"user_tz":-540,"elapsed":31781,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"67b68963-8de4-428e-d623-1d0737d1a20c"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["import gensim\n","\n","# 태그와 장르 데이터를 사용해 word2vec을 학습한다\n","tag_genre_data = movie_content.tag_genre.tolist()\n","model = gensim.models.word2vec.Word2Vec(\n"," tag_genre_data,\n"," vector_size=factors,\n"," window=window,\n"," sg=use_skip_gram,\n"," hs=use_hierarchial_softmax,\n"," epochs=n_epochs,\n"," min_count=min_count,\n",")\n"]},{"cell_type":"code","execution_count":7,"id":"cad9e002","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cad9e002","executionInfo":{"status":"ok","timestamp":1672120587872,"user_tz":-540,"elapsed":6,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f942a765-555c-4378-8318-de39e5184d32"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('studio ghibli', 0.8121964335441589),\n"," ('zibri studio', 0.7996001243591309),\n"," ('miyazaki', 0.7846846580505371),\n"," ('pelicula anime', 0.7555471658706665),\n"," ('hayao miyazaki', 0.7477614879608154),\n"," ('japan', 0.6425853967666626),\n"," ('Animation', 0.5504586696624756),\n"," ('curse', 0.5254901647567749),\n"," ('steampunk', 0.5219841599464417),\n"," ('dragon', 0.5078659057617188)]"]},"metadata":{},"execution_count":7}],"source":["# anime 태크와 비슷한 태그를 확인한다\n","model.wv.most_similar('anime')"]},{"cell_type":"code","execution_count":8,"id":"36426496","metadata":{"id":"36426496","executionInfo":{"status":"ok","timestamp":1672120591388,"user_tz":-540,"elapsed":3520,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 각 영화의 벡터를 계산한다\n","# 각 영화에 부여되어 있는 태그/장르 벡터의 평균을 영화의 벡터로 한다\n","movie_vectors = []\n","tag_genre_in_model = set(model.wv.key_to_index.keys())\n","\n","titles = []\n","ids = []\n","\n","for i, tag_genre in enumerate(tag_genre_data):\n"," # word2vec 모델에서 사용할 수 있는 태그/장르로 필터링한다\n"," input_tag_genre = set(tag_genre) & tag_genre_in_model\n"," if len(input_tag_genre) == 0:\n"," # word2vec에 기반해 벡터 계산할 수 없는 영화에는 무작위 벡터를 부여한다\n"," vector = np.random.randn(model.vector_size)\n"," else:\n"," vector = model.wv[input_tag_genre].mean(axis=0)\n"," titles.append(movie_content.iloc[i][\"title\"])\n"," ids.append(movie_content.iloc[i][\"movie_id\"])\n"," movie_vectors.append(vector)\n"]},{"cell_type":"code","execution_count":9,"id":"e0ef174b","metadata":{"id":"e0ef174b","executionInfo":{"status":"ok","timestamp":1672120591389,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np\n","\n","# 후속 유사도 계산을 쉽게 할 수 있도록 numpy 배열로 저장한다\n","movie_vectors = np.array(movie_vectors)\n","\n","# 정규화한 벡터\n","sum_vec = np.sqrt(np.sum(movie_vectors ** 2, axis=1))\n","movie_norm_vectors = movie_vectors / sum_vec.reshape((-1, 1))\n"]},{"cell_type":"code","execution_count":10,"id":"0986ff67","metadata":{"id":"0986ff67","executionInfo":{"status":"ok","timestamp":1672120591389,"user_tz":-540,"elapsed":4,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 아직 평가하지 않은 영화에서 유사도가 높은 아이템을 반환하는 함수\n","def find_similar_items(vec, evaluated_movie_ids, topn=10):\n"," score_vec = np.dot(movie_norm_vectors, vec)\n"," similar_indexes = np.argsort(-score_vec)\n"," similar_items = []\n"," for similar_index in similar_indexes:\n"," if ids[similar_index] not in evaluated_movie_ids:\n"," similar_items.append(ids[similar_index])\n"," if len(similar_items) == topn:\n"," break\n"," return similar_items"]},{"cell_type":"code","execution_count":11,"id":"9e00ac0c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9e00ac0c","executionInfo":{"status":"ok","timestamp":1672120598439,"user_tz":-540,"elapsed":7054,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"670e4e86-95a5-48f6-a732-eca7e3fb4526"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{1: [4306, 2054, 7369, 5500, 5452, 2080, 2470, 6559, 36708, 44399],\n"," 2: [1198, 1196, 1097, 3578, 1204, 2028, 7153, 3471, 1287, 527],\n"," 3: [27797, 3874, 61071, 3194, 6384, 58494, 1415, 60494, 4766, 45707],\n"," 4: [2125, 1073, 1441, 551, 1028, 4184, 7834, 837, 4306, 2953],\n"," 5: [1276, 3504, 3168, 2729, 928, 1193, 3741, 2730, 922, 1161],\n"," 6: [5445, 198, 541, 292, 5881, 1214, 2594, 1240, 1129, 1690],\n"," 7: [2935, 955, 4357, 922, 947, 6840, 3307, 946, 950, 7080],\n"," 8: [33437, 517, 3269, 4956, 2764, 36529, 7386, 49007, 5027, 5686],\n"," 9: [2890, 665, 3168, 319, 1162, 1953, 1202, 33166, 4399, 2542],\n"," 10: [1950, 1953, 3504, 7215, 2396, 920, 1230, 1925, 912, 858],\n"," 11: [1784, 339, 3259, 1393, 1307, 7, 1597, 11, 3893, 1101],\n"," 12: [1805, 1438, 517, 2594, 51709, 3510, 1597, 8585, 1061, 8963],\n"," 13: [1784, 1084, 1276, 1230, 356, 1213, 909, 920, 858, 1953],\n"," 14: [586, 3489, 2804, 8516, 3988, 2125, 2161, 48082, 2054, 1894],\n"," 16: [2288, 5522, 5881, 674, 748, 3699, 1274, 5046, 2528, 1301],\n"," 17: [1253, 1254, 3551, 2529, 1387, 1086, 2871, 1276, 913, 3384],\n"," 18: [59615, 60072, 517, 2947, 3204, 3947, 1805, 4956, 6503, 1127],\n"," 19: [2080, 4306, 7247, 2161, 1028, 2137, 2125, 2054, 2102, 2081],\n"," 22: [1784, 1240, 5445, 1625, 2490, 5881, 5464, 1693, 32587, 858],\n"," 23: [1073, 4014, 1265, 1197, 7064, 4886, 4306, 6620, 6377, 588],\n"," 24: [1883, 3386, 1213, 1784, 1953, 161, 7445, 36529, 8781, 4776],\n"," 26: [1036, 6, 1240, 1214, 2912, 1200, 2529, 1387, 480, 593],\n"," 27: [2058, 165, 7007, 3386, 555, 377, 2912, 517, 996, 1729],\n"," 28: [2248, 6558, 4091, 3210, 1513, 5969, 1257, 2918, 2134, 54621],\n"," 29: [2887, 46970, 32596, 367, 1091, 6503, 442, 1866, 2723, 4369],\n"," 30: [8937, 3360, 43558, 1883, 2587, 4956, 7160, 6565, 973, 8533],\n"," 33: [2396, 1095, 6993, 1784, 1358, 1104, 3362, 1230, 1959, 3504],\n"," 34: [8387, 27735, 5459, 1772, 7004, 2887, 2366, 2428, 3710, 3701],\n"," 35: [1625, 1921, 1748, 1199, 2997, 1089, 50, 29, 48082, 2594],\n"," 36: [53000, 51709, 45722, 31696, 32, 32587, 3981, 1587, 5881, 37382],\n"," 37: [3168, 319, 1213, 1235, 1729, 18, 3275, 1265, 1658, 532],\n"," 38: [33437, 2490, 50792, 51255, 2000, 51709, 59594, 5027, 8373, 37477],\n"," 40: [21, 2580, 2587, 1923, 1265, 532, 3948, 1658, 4027, 7381],\n"," 41: [709, 1025, 2080, 6316, 7247, 2099, 107, 596, 1031, 46062],\n"," 42: [909, 3307, 920, 1784, 3504, 1959, 750, 1247, 3095, 946],\n"," 43: [1878, 4527, 4603, 4664, 4672, 4736, 4746, 4828, 4875, 4877],\n"," 44: [2791, 3671, 3752, 5541, 1923, 466, 1091, 2723, 3552, 2109],\n"," 45: [2912, 6, 1625, 4226, 1213, 32587, 296, 8783, 628, 1912],\n"," 46: [480, 1210, 1200, 260, 316, 1544, 1196, 1240, 1198, 2987],\n"," 47: [1380, 1784, 1091, 4027, 3752, 1265, 1393, 2804, 3259, 3481],\n"," 48: [53972, 27735, 442, 51255, 2793, 6503, 57528, 51709, 36519, 2490],\n"," 50: [2671, 1101, 1393, 5299, 3270, 1307, 3259, 11, 236, 8969],\n"," 51: [2871, 33437, 3006, 2764, 3100, 2912, 4349, 3386, 1267, 1084],\n"," 52: [3435, 908, 2186, 903, 8044, 942, 2203, 7223, 1254, 922],\n"," 53: [4027, 2912, 8783, 31, 1784, 2594, 7285, 1358, 7044, 3424],\n"," 54: [2987, 2366, 1073, 1097, 2580, 7007, 2804, 8961, 2716, 6377],\n"," 55: [1387, 2366, 1590, 1214, 39446, 3551, 517, 8957, 480, 8783],\n"," 56: [1073, 1197, 1265, 4027, 2529, 6, 1235, 1276, 1953, 920],\n"," 57: [4434, 6574, 5235, 5181, 286, 1626, 464, 27316, 30896, 8241],\n"," 58: [531, 1022, 588, 2161, 1025, 8580, 364, 4306, 42015, 4886],\n"," 59: [1036, 1610, 2912, 377, 1276, 2529, 1387, 1953, 47, 3551],\n"," 60: [1276, 3468, 1254, 1953, 908, 2871, 912, 928, 903, 913],\n"," 61: [1784, 4027, 31, 50, 3100, 858, 1263, 6787, 3362, 1230],\n"," 62: [1036, 1127, 733, 377, 7007, 1610, 780, 3654, 1214, 480],\n"," 63: [1784, 532, 224, 1235, 5673, 27808, 1357, 5945, 52, 337],\n"," 64: [1270, 31660, 316, 2987, 1610, 2011, 1408, 7007, 380, 1127],\n"," 65: [4886, 8907, 53121, 44022, 64652, 52287, 5218, 6377, 5452, 40339],\n"," 66: [1394, 1202, 1235, 319, 5792, 4914, 750, 1079, 608, 101],\n"," 67: [3227, 2438, 5416, 64327, 8330, 1805, 2764, 1783, 43853, 4242],\n"," 68: [7215, 1784, 2912, 2764, 4280, 3006, 5553, 7311, 4326, 1264],\n"," 69: [1959, 7215, 1947, 1784, 1183, 1230, 920, 909, 914, 898],\n"," 70: [6783, 7135, 5673, 4914, 1202, 1300, 34437, 235, 1235, 3819],\n"," 71: [1, 919, 5989, 356, 1721, 1213, 1089, 2987, 1235, 1265],\n"," 72: [1544, 3256, 160, 4638, 736, 1479, 6989, 517, 3204, 798],\n"," 73: [1772, 36708, 4306, 34332, 44022, 8974, 2383, 2054, 7247, 586],\n"," 74: [5693, 2470, 1513, 50792, 2100, 1042, 2468, 2133, 33669, 39],\n"," 75: [7458, 733, 2001, 377, 1918, 5010, 1408, 3654, 3578, 5464],\n"," 76: [990, 1427, 1488, 1626, 798, 548, 26488, 4044, 692, 31255],\n"," 77: [1616, 1690, 7007, 43928, 442, 2490, 208, 2722, 1127, 5881],\n"," 78: [1690, 32239, 48943, 5171, 2594, 31251, 63676, 3080, 6076, 5793],\n"," 79: [8638, 1902, 48082, 5673, 3148, 1658, 2396, 3535, 7160, 7044],\n"," 80: [1784, 4027, 38061, 1884, 532, 5673, 50792, 319, 1235, 1476],\n"," 81: [1953, 1263, 3468, 4007, 1213, 1084, 2890, 1208, 4008, 1304],\n"," 82: [1784, 1380, 3481, 3752, 1307, 4027, 357, 1441, 1088, 2125],\n"," 83: [2366, 1230, 3307, 909, 940, 2936, 1256, 2648, 8235, 955],\n"," 84: [1784, 2115, 4027, 1198, 318, 1291, 356, 3386, 31, 1196],\n"," 85: [1784, 4027, 1358, 1961, 923, 3504, 532, 1617, 7160, 1235],\n"," 86: [316, 1580, 1544, 3471, 1690, 2115, 4121, 377, 349, 8810],\n"," 87: [1784, 4027, 1265, 532, 50792, 2291, 1658, 543, 1923, 2125],\n"," 88: [3130, 1101, 1616, 1784, 7007, 1393, 6193, 2302, 3386, 3175],\n"," 89: [2427, 1227, 1912, 6185, 517, 4262, 2912, 6, 18, 6977],\n"," 90: [62331, 5497, 32825, 8822, 7262, 558, 2041, 26792, 5892, 40339],\n"," 91: [50445, 3623, 26746, 3452, 1488, 51709, 6185, 648, 26488, 377],\n"," 92: [44761, 1127, 5686, 1265, 33004, 1658, 4027, 6003, 7451, 1301],\n"," 93: [49130, 8836, 62155, 2396, 8373, 33437, 42015, 51903, 51884, 3053],\n"," 94: [1101, 2470, 11, 1393, 2133, 2001, 3269, 2000, 5027, 5049],\n"," 95: [1240, 1127, 1200, 260, 1196, 3204, 3471, 2529, 5445, 1214],\n"," 96: [4383, 2389, 517, 1061, 6185, 2764, 33437, 2912, 1597, 5388],\n"," 97: [1721, 1784, 1883, 3259, 1101, 7162, 11, 6385, 41571, 50792],\n"," 98: [38499, 2396, 1611, 1224, 8273, 2020, 3194, 32591, 6117, 44929],\n"," 99: [49932, 5105, 2594, 1219, 8950, 928, 8957, 1076, 3476, 8670],\n"," 100: [2371, 928, 1617, 908, 4027, 1265, 919, 2912, 2291, 1333],\n"," 101: [1883, 6155, 3269, 48943, 49007, 5881, 3259, 8373, 1496, 4564],\n"," 102: [1617, 2912, 50, 1036, 349, 517, 6, 33437, 3386, 733],\n"," 103: [1235, 3168, 5792, 6184, 1273, 57669, 3210, 34437, 2065, 3925],\n"," 104: [349, 377, 316, 2529, 1200, 589, 1127, 480, 592, 3654],\n"," 105: [1274, 2021, 1129, 1127, 1690, 1301, 1200, 2528, 2311, 316],\n"," 106: [1025, 2078, 783, 364, 1030, 1029, 2096, 5218, 1023, 2099],\n"," 107: [920, 1927, 1084, 1287, 909, 1276, 1939, 1950, 1263, 1254],\n"," 108: [27416, 34528, 3152, 2155, 5068, 40870, 6268, 3089, 1952, 1300],\n"," 110: [3623, 165, 5459, 442, 733, 1479, 836, 494, 1580, 3527],\n"," 111: [1208, 1036, 3654, 3471, 1408, 1387, 1090, 1339, 3386, 5010],\n"," 112: [4194, 34528, 6791, 6660, 7352, 1904, 6268, 7135, 5673, 2927],\n"," 113: [39, 8969, 357, 4246, 1307, 186, 2125, 5299, 5066, 339],\n"," 114: [2413, 4467, 1220, 1206, 6798, 26422, 5686, 1199, 4027, 7080],\n"," 115: [1101, 3259, 11, 539, 543, 2302, 2804, 597, 1883, 4975],\n"," 116: [2054, 2987, 2161, 4275, 2804, 2005, 1, 6232, 4306, 32031],\n"," 117: [6558, 2468, 351, 4936, 44193, 5452, 2587, 4341, 52975, 5255],\n"," 118: [3508, 6812, 996, 8094, 7438, 2871, 5184, 4329, 2922, 2002],\n"," 119: [1235, 319, 5792, 1265, 101, 1923, 4914, 4027, 532, 1658],\n"," 120: [442, 380, 3175, 1101, 1580, 5459, 367, 34520, 34332, 208],\n"," 121: [8810, 1127, 2115, 1320, 1690, 33493, 5378, 260, 2628, 1214],\n"," 122: [5673, 48082, 532, 1658, 5945, 54978, 6003, 7444, 3893, 4880],\n"," 123: [1784, 11, 1959, 1101, 3259, 1230, 539, 1721, 7215, 4041],\n"," 124: [40870, 44195, 42730, 36535, 7034, 1271, 8937, 2155, 27843, 33903],\n"," 125: [7044, 56274, 8958, 32591, 1805, 1264, 1357, 4280, 7160, 45],\n"," 126: [2186, 48883, 8950, 7981, 5867, 33437, 5516, 4037, 4837, 50794],\n"," 127: [5758, 6120, 2364, 5478, 5746, 8894, 25798, 53752, 26603, 5556],\n"," 128: [5380, 1224, 2690, 2396, 31694, 838, 3079, 38499, 1059, 3668],\n"," 129: [532, 367, 50792, 1784, 1441, 1658, 1091, 50, 6003, 48082],\n"," 130: [1089, 1199, 6003, 1161, 29, 4027, 2529, 4467, 7361, 48082],\n"," 131: [1729, 1213, 1089, 7160, 3685, 5989, 1961, 337, 4262, 3362],\n"," 132: [47, 2912, 1617, 3499, 1276, 1693, 1193, 2160, 1953, 1259],\n"," 134: [2161, 2716, 5686, 224, 1265, 48082, 2065, 4014, 2580, 5673],\n"," 135: [1084, 920, 1953, 599, 3871, 2871, 1263, 1304, 3468, 3365],\n"," 136: [3269, 4564, 54978, 2413, 1264, 2133, 5553, 5706, 2950, 2455],\n"," 137: [2912, 1350, 8783, 37382, 43871, 5502, 292, 165, 1997, 3551],\n"," 138: [1213, 319, 4027, 3275, 3168, 4034, 1729, 608, 532, 1912],\n"," 139: [34, 4886, 44022, 4306, 60069, 2804, 1282, 1028, 1646, 2018],\n"," 140: [1939, 1944, 1959, 1945, 1927, 920, 1250, 1950, 909, 1931],\n"," 141: [1784, 3481, 2396, 5673, 2671, 1393, 41573, 8949, 32591, 5066],\n"," 142: [1953, 1208, 2912, 3006, 349, 6787, 2529, 1090, 3100, 6],\n"," 143: [26509, 5706, 4290, 5759, 8610, 25931, 5926, 3393, 4578, 7050],\n"," 144: [4956, 1264, 5686, 1267, 58105, 46855, 5000, 7215, 42681, 64622],\n"," 145: [1617, 2912, 47, 1089, 50794, 4034, 4210, 1213, 1036, 1953],\n"," 148: [3681, 1227, 1276, 5653, 8618, 4329, 2921, 7894, 1304, 3365],\n"," 149: [1473, 3184, 33437, 26989, 3275, 2023, 1112, 4635, 438, 8927],\n"," 150: [2313, 5995, 1193, 235, 30812, 923, 1959, 920, 1183, 8197],\n"," 151: [1256, 914, 7080, 909, 1235, 7835, 950, 955, 920, 8235],\n"," 152: [2912, 1249, 1953, 7981, 42632, 1248, 4210, 5867, 1213, 517],\n"," 153: [1206, 50, 1089, 32587, 1805, 3275, 3981, 2529, 3020, 1213],\n"," 154: [1658, 1235, 2580, 3210, 2395, 7361, 2248, 101, 6003, 543],\n"," 155: [1784, 532, 543, 1101, 21, 1883, 3275, 2580, 1230, 1658],\n"," 156: [224, 587, 2291, 337, 5673, 4014, 2065, 1265, 168, 1441],\n"," 157: [4242, 43853, 7007, 64327, 2438, 8330, 5416, 3227, 5553, 1432],\n"," 158: [7044, 4410, 1053, 45, 7442, 41617, 4776, 5563, 5869, 1805],\n"," 159: [1304, 3365, 7218, 1254, 8094, 3873, 7898, 8618, 266, 7894],\n"," 160: [2625, 2450, 2094, 5621, 26950, 4434, 2835, 61818, 5822, 5523],\n"," 161: [150, 2115, 2396, 11, 6947, 1721, 1101, 1894, 356, 7311],\n"," 162: [1127, 2003, 1200, 3471, 2761, 2161, 1270, 27660, 2528, 34],\n"," 163: [7160, 3504, 45, 5878, 5673, 7044, 3168, 1094, 2858, 55444],\n"," 164: [3204, 2826, 1544, 1690, 2021, 2529, 3471, 6078, 517, 61350],\n"," 165: [2468, 1307, 543, 339, 1101, 367, 2125, 1777, 1091, 3422],\n"," 166: [349, 377, 2912, 50794, 7007, 165, 3256, 3654, 836, 1218],\n"," 167: [6297, 4341, 2587, 6753, 2987, 8866, 5027, 8961, 2138, 7004],\n"," 168: [2587, 4341, 26527, 4529, 532, 2223, 4027, 6798, 32009, 2486],\n"," 169: [543, 532, 1091, 367, 4015, 3752, 1658, 6003, 1923, 7367],\n"," 170: [27831, 3275, 51255, 38061, 1089, 532, 4834, 4415, 44761, 5767],\n"," 171: [3210, 1784, 3275, 1968, 2542, 4027, 532, 1658, 3168, 1729],\n"," 172: [1036, 377, 1370, 1200, 1240, 733, 786, 6213, 780, 1214],\n"," 173: [1373, 2393, 5944, 1909, 7812, 32296, 7811, 7649, 6503, 5785],\n"," 174: [1235, 2961, 26527, 4529, 1658, 27797, 1213, 5673, 478, 50949],\n"," 175: [6197, 5056, 3741, 8199, 6842, 7068, 7587, 7123, 25954, 6509],\n"," 176: [377, 1036, 349, 1610, 2912, 480, 733, 1214, 1240, 780],\n"," 177: [3362, 4027, 1213, 1358, 1276, 858, 3468, 1784, 923, 3100],\n"," 178: [1183, 1959, 6947, 1242, 920, 7162, 475, 3100, 7215, 1277],\n"," 179: [1245, 1912, 3683, 6796, 1953, 319, 4034, 7135, 7044, 1234],\n"," 180: [50792, 1912, 1358, 50, 3893, 1658, 1784, 1883, 3275, 2396],\n"," 182: [8961, 32031, 44022, 8865, 34332, 31685, 33437, 2761, 46970, 51575],\n"," 183: [1161, 2324, 1276, 8338, 4349, 7700, 1950, 47306, 1263, 3089],\n"," 184: [1276, 3100, 1213, 1084, 1953, 3362, 1304, 858, 1263, 1090],\n"," 185: [912, 1721, 1947, 920, 899, 1230, 914, 356, 587, 919],\n"," 186: [2389, 7001, 103, 3863, 4383, 1912, 517, 2209, 1625, 2672],\n"," 187: [1265, 587, 1101, 1307, 1784, 2125, 1727, 5299, 6155, 1393],\n"," 188: [2396, 838, 1183, 7215, 4246, 3079, 7579, 1784, 40629, 7162],\n"," 189: [1240, 1200, 1214, 480, 1387, 2529, 5445, 1690, 316, 1036],\n"," 190: [57640, 60074, 34332, 533, 1377, 3438, 54272, 27865, 60040, 61401],\n"," 191: [1136, 5673, 5541, 1235, 50792, 6807, 1080, 3671, 26527, 4529],\n"," 192: [5291, 3741, 2074, 3168, 7700, 26231, 2427, 3091, 1173, 6023],\n"," 193: [1264, 1953, 8094, 3269, 3424, 3685, 7007, 996, 46855, 54978],\n"," 194: [2587, 1473, 3184, 4242, 532, 7004, 5027, 5049, 43558, 27695],\n"," 195: [928, 1276, 3435, 1161, 3741, 923, 1212, 2730, 1253, 922],\n"," 196: [54978, 2369, 26509, 2248, 52227, 6558, 2413, 3688, 5706, 2463],\n"," 197: [4956, 7834, 7831, 1235, 2223, 3210, 61071, 7835, 955, 5969],\n"," 198: [7044, 2594, 319, 50792, 56367, 8950, 34437, 3476, 1658, 4553],\n"," 199: [1036, 1339, 1214, 1387, 349, 480, 1200, 2366, 1610, 1208],\n"," 200: [3489, 588, 364, 4306, 2161, 5444, 4341, 2078, 53974, 1022],\n"," 201: [3556, 27721, 5992, 64957, 3186, 5673, 1176, 2427, 4308, 6660],\n"," 202: [1959, 7215, 912, 1183, 3100, 3654, 1925, 4564, 1035, 4464],\n"," 203: [36708, 7369, 8974, 34332, 3033, 442, 5459, 2053, 3175, 5452],\n"," 204: [4027, 2396, 1784, 3468, 1953, 920, 1208, 3100, 1959, 1094],\n"," 205: [1213, 1263, 2529, 3435, 3468, 3168, 3365, 1253, 4998, 6],\n"," 206: [1250, 1927, 590, 110, 920, 1287, 1939, 1959, 1204, 1262],\n"," 207: [4555, 1879, 33649, 1821, 2333, 4216, 4342, 2029, 3094, 2388],\n"," 208: [1432, 1473, 3184, 6387, 4026, 876, 30896, 27316, 8241, 6714],\n"," 209: [27826, 33649, 51094, 27797, 4555, 43744, 2029, 3124, 56671, 569],\n"," 210: [5952, 2116, 2987, 260, 3997, 2826, 8961, 55995, 2161, 1198],\n"," 211: [920, 1250, 909, 1084, 919, 1945, 1090, 1276, 923, 1204],\n"," 212: [31107, 2434, 7401, 57357, 672, 1364, 4313, 676, 4309, 679],\n"," 213: [1784, 1959, 356, 1183, 3468, 3100, 1193, 1950, 1961, 1953],\n"," 214: [33646, 50792, 3361, 532, 4085, 1883, 26566, 5970, 31, 7346],\n"," 215: [47423, 5945, 48783, 47099, 45, 50274, 45880, 58303, 50068, 58306],\n"," 216: [539, 357, 2671, 186, 11, 1894, 168, 7, 2470, 8969],\n"," 217: [2671, 39, 838, 8969, 357, 3270, 5299, 7, 3079, 2581],\n"," 218: [235, 5673, 34437, 5686, 55901, 3925, 1784, 26527, 4529, 3194],\n"," 219: [909, 1230, 898, 1947, 3307, 3545, 905, 900, 955, 3475],\n"," 220: [1266, 7894, 6428, 1209, 7218, 266, 5440, 4327, 1201, 3494],\n"," 221: [1580, 3471, 2987, 1784, 2115, 1127, 1091, 2003, 4564, 2021],\n"," 222: [33437, 27839, 8963, 6185, 36529, 50, 2490, 54997, 3275, 1625],\n"," 223: [44911, 5161, 5065, 63836, 26791, 1264, 4390, 436, 6736, 1783],\n"," 224: [54978, 7007, 3269, 517, 2587, 367, 5553, 1264, 420, 1805],\n"," 225: [8879, 7215, 4969, 7044, 2764, 5008, 6101, 897, 63033, 1912],\n"," 226: [3210, 6188, 532, 216, 1658, 50792, 1091, 1784, 8641, 2580],\n"," 227: [2223, 4242, 55901, 27695, 1112, 438, 4635, 8927, 5407, 1410],\n"," 228: [3175, 34520, 1091, 5541, 1101, 2001, 5459, 2683, 1580, 2723],\n"," 229: [3275, 367, 7004, 7007, 4262, 20, 33437, 50, 6185, 51575],\n"," 230: [1093, 55444, 6430, 3060, 33677, 8958, 32591, 56286, 54190, 1380],\n"," 231: [3710, 5049, 5706, 4673, 861, 2163, 5370, 2002, 7102, 420],\n"," 232: [1208, 5010, 2529, 1276, 3654, 7438, 3468, 2023, 1953, 5464],\n"," 234: [296, 1912, 1213, 6, 5291, 3020, 8950, 1206, 3476, 7044],\n"," 235: [1249, 6, 1953, 2529, 380, 8665, 349, 377, 2912, 1610],\n"," 236: [6993, 1784, 1912, 2023, 1953, 4085, 5027, 6584, 3362, 1213],\n"," 237: [5621, 4956, 62956, 6317, 4818, 2880, 2879, 51575, 59103, 380],\n"," 238: [6003, 319, 5792, 1173, 48696, 8620, 154, 1202, 7361, 1884],\n"," 239: [260, 1387, 1210, 377, 2115, 1240, 480, 349, 1291, 1200],\n"," 241: [2723, 466, 532, 1923, 671, 3752, 2791, 344, 5500, 367],\n"," 242: [2268, 6879, 1573, 7007, 3386, 454, 1729, 165, 58025, 1479],\n"," 243: [3168, 928, 3741, 2186, 319, 1248, 903, 7700, 7044, 49932],\n"," 244: [3100, 1084, 1959, 4349, 920, 1250, 1090, 6787, 110, 7311],\n"," 245: [8973, 26599, 7034, 27727, 6538, 36276, 7486, 5617, 27416, 6620],\n"," 246: [3275, 367, 1784, 532, 21, 2580, 1091, 2723, 4085, 4262],\n"," 247: [1784, 1597, 3257, 1393, 2764, 3259, 3386, 1883, 2396, 3893],\n"," 248: [1953, 2729, 3504, 1213, 1206, 319, 1263, 1276, 1617, 2912],\n"," 249: [7891, 53953, 31270, 4196, 2118, 7001, 26492, 3696, 26693, 6120],\n"," 250: [7215, 8643, 4280, 1496, 1408, 7046, 4564, 362, 515, 1727],\n"," 251: [1373, 2393, 329, 5944, 1909, 2094, 4121, 3175, 1479, 7649],\n"," 252: [356, 940, 8235, 1198, 1035, 1784, 920, 110, 1, 1250],\n"," 253: [3257, 1883, 8905, 2912, 6977, 5824, 7438, 1304, 6185, 2764],\n"," 254: [2528, 1129, 1690, 198, 27660, 1748, 2529, 5046, 1301, 2009],\n"," 255: [1784, 539, 1393, 1101, 2671, 5299, 4205, 236, 3259, 1721],\n"," 256: [1784, 532, 2396, 2160, 1953, 2912, 1193, 7215, 7347, 7160],\n"," 257: [1276, 2912, 6, 2871, 3654, 377, 3468, 1617, 1213, 1387],\n"," 258: [380, 2947, 33679, 377, 648, 95, 3623, 1370, 165, 3082],\n"," 259: [1259, 6798, 5686, 4564, 8202, 4085, 6297, 1081, 5673, 7247],\n"," 260: [1690, 5445, 1274, 1387, 2529, 2916, 2288, 2985, 3527, 1129],\n"," 261: [5445, 1214, 2529, 1240, 541, 1129, 589, 6979, 2528, 1690],\n"," 262: [5618, 8253, 31660, 7064, 48082, 7577, 7067, 25805, 4294, 8235],\n"," 263: [1091, 4306, 34332, 1101, 50792, 7007, 8636, 1573, 2723, 3752],\n"," 264: [5299, 3270, 2671, 339, 1784, 11, 468, 852, 8969, 7],\n"," 265: [8593, 46430, 5252, 64231, 6280, 60237, 2540, 7040, 7031, 8733],\n"," 266: [5686, 1224, 1059, 1883, 8643, 30850, 970, 5975, 7215, 4464],\n"," 267: [1953, 1263, 1276, 1161, 1250, 1693, 6787, 1207, 4349, 3504],\n"," 268: [1544, 2001, 1918, 2000, 2826, 2490, 2002, 442, 5459, 163],\n"," 269: [2080, 4306, 2054, 42734, 1022, 5452, 588, 64652, 2733, 6137],\n"," 270: [1196, 1198, 2115, 1291, 33493, 316, 1127, 1200, 5378, 2628],\n"," 271: [2662, 6882, 1690, 26513, 2722, 5523, 5822, 64234, 208, 3175],\n"," 272: [6003, 3535, 2313, 1213, 1193, 3148, 3476, 3342, 7123, 27706],\n"," 273: [316, 780, 1616, 7007, 5445, 1917, 208, 1690, 442, 1580],\n"," 274: [6183, 6750, 7835, 7055, 7049, 5742, 3144, 61123, 63119, 6696],\n"," 275: [1276, 1084, 1953, 912, 1254, 3424, 920, 7080, 969, 2871],\n"," 276: [38061, 51255, 51709, 2723, 532, 40732, 5945, 2490, 6902, 4956],\n"," 277: [1198, 260, 1200, 1339, 589, 1387, 1210, 1240, 377, 2987],\n"," 278: [3194, 3874, 1264, 6344, 2396, 26228, 27797, 7011, 427, 509],\n"," 279: [49932, 7044, 2594, 1094, 48082, 7123, 4848, 3476, 5673, 6440],\n"," 280: [586, 7395, 5945, 7247, 837, 1073, 7834, 6297, 6385, 42015],\n"," 281: [6584, 532, 2371, 7007, 6993, 3275, 1912, 3685, 5553, 911],\n"," 282: [1784, 1573, 50792, 4027, 5693, 1380, 3259, 587, 7701, 3481],\n"," 285: [1235, 1, 2065, 3429, 4306, 7206, 2291, 471, 1079, 7834],\n"," 286: [1129, 5445, 6979, 5903, 5881, 1690, 2528, 442, 198, 2529],\n"," 287: [1090, 1287, 1208, 3100, 1276, 1953, 2028, 3578, 1250, 1204],\n"," 288: [56915, 2045, 5076, 8850, 32062, 8534, 3647, 1551, 1595, 7166],\n"," 289: [48696, 47423, 48738, 57669, 52604, 50274, 7044, 36529, 48304, 6187],\n"," 290: [2987, 1220, 6377, 4306, 4121, 2366, 380, 40339, 44022, 377],\n"," 291: [1950, 1953, 1942, 3504, 920, 3095, 1276, 1263, 1213, 1254],\n"," 292: [40339, 5218, 45074, 52287, 47264, 44022, 5444, 42734, 2141, 4016],\n"," 293: [1036, 3386, 2912, 7007, 517, 1339, 1213, 6, 3130, 1208],\n"," 294: [47044, 4034, 27831, 6003, 38061, 319, 44761, 5027, 6503, 4011],\n"," 295: [8923, 26265, 4464, 65126, 2366, 1264, 7215, 1277, 1019, 7247],\n"," 296: [1784, 33437, 6620, 1358, 1953, 36535, 1617, 110, 1693, 5686],\n"," 297: [4341, 36708, 2587, 7048, 3752, 2788, 2706, 1091, 7102, 6753],\n"," 298: [27660, 7099, 8426, 6350, 8253, 5072, 5618, 4850, 31184, 31660],\n"," 300: [3360, 1957, 5644, 914, 1035, 6565, 1925, 3098, 3361, 477],\n"," 301: [1784, 1805, 6787, 6003, 532, 3510, 1747, 5989, 1883, 1617],\n"," 302: [415, 2133, 4121, 3763, 1610, 6558, 8634, 3584, 3269, 349],\n"," 303: [1208, 2010, 1206, 1884, 1235, 923, 1161, 928, 6184, 1254],\n"," 304: [5015, 1950, 2908, 3111, 3152, 1185, 1942, 7078, 2929, 25952],\n"," 305: [1073, 7247, 8643, 1028, 515, 2804, 6798, 3489, 2087, 34],\n"," 306: [2133, 1968, 3210, 1513, 1380, 3552, 2248, 1257, 1923, 1777],\n"," 307: [1208, 593, 1090, 2648, 1253, 1953, 1250, 2529, 3551, 7387],\n"," 308: [586, 1073, 3988, 2804, 837, 30793, 2054, 2161, 2953, 1302],\n"," 309: [50792, 1784, 1883, 1265, 1101, 3275, 1721, 1409, 5989, 367],\n"," 310: [532, 45, 59594, 51903, 5673, 50792, 51255, 42015, 4880, 3481],\n"," 311: [3257, 1721, 1883, 1408, 1727, 50792, 1101, 42015, 55995, 1496],\n"," 312: [7044, 2076, 5105, 49932, 3741, 1104, 7700, 3730, 3788, 5291],\n"," 313: [912, 1784, 1235, 7215, 1247, 920, 1721, 1276, 4027, 1358],\n"," 314: [1235, 4914, 955, 6650, 2729, 6783, 5792, 3168, 1202, 951],\n"," 315: [54978, 6815, 3168, 1264, 7044, 1597, 1805, 26803, 5945, 4880],\n"," 316: [3227, 2438, 8330, 64327, 5416, 1432, 3184, 1473, 1112, 4635],\n"," 317: [5445, 1748, 1214, 913, 1387, 1240, 2529, 32587, 6, 1625],\n"," 318: [2161, 7164, 56915, 2045, 8866, 5452, 51698, 6557, 2138, 32031],\n"," 319: [2528, 26513, 5944, 6979, 6645, 3699, 47997, 8644, 208, 173],\n"," 320: [1690, 8644, 1129, 2528, 5502, 3300, 6645, 2662, 1590, 2288],\n"," 321: [1200, 1240, 1214, 1690, 5445, 780, 260, 1127, 1196, 1544],\n"," 322: [337, 48082, 47725, 1801, 6003, 1658, 36477, 8643, 5051, 1073],\n"," 323: [909, 1084, 3435, 1953, 920, 25773, 1284, 942, 25757, 900],\n"," 324: [30810, 1265, 1784, 6003, 1883, 7361, 48082, 1658, 5673, 2594],\n"," 325: [33437, 27831, 3020, 1249, 4901, 7981, 2819, 36529, 4383, 6185],\n"," 326: [7783, 7278, 55668, 1825, 60286, 794, 3162, 5745, 4352, 2374],\n"," 327: [1883, 26422, 5686, 7088, 8190, 6139, 8183, 6117, 26527, 4529],\n"," 328: [1690, 5445, 480, 1127, 2528, 4370, 8644, 1210, 3471, 1129],\n"," 329: [50792, 26491, 1378, 2587, 53349, 3184, 1473, 1304, 3836, 5706],\n"," 330: [2133, 2011, 1198, 4564, 2406, 2987, 4799, 380, 1097, 1259],\n"," 331: [48082, 2997, 4878, 1199, 1265, 3556, 6003, 4873, 1235, 1921],\n"," 332: [1101, 1580, 1784, 1544, 1894, 11, 5989, 34332, 532, 4306],\n"," 333: [5686, 7044, 7123, 1208, 3476, 1161, 3342, 2594, 2427, 665],\n"," 334: [6558, 2369, 2133, 2468, 5500, 2587, 3210, 46970, 1541, 5969],\n"," 335: [6003, 1784, 6620, 2858, 2997, 356, 38061, 48082, 1225, 6385],\n"," 336: [42007, 236, 539, 186, 1101, 33669, 339, 2671, 5957, 61071],\n"," 337: [1617, 2858, 2959, 1259, 1953, 2912, 6003, 3020, 7361, 6],\n"," 338: [1953, 32587, 858, 1276, 50, 1036, 1213, 923, 1287, 7438],\n"," 339: [1036, 377, 7007, 733, 5027, 1610, 1370, 37477, 6, 996],\n"," 340: [5673, 1235, 2125, 1247, 5299, 2671, 2065, 1658, 1784, 1441],\n"," 341: [5051, 5878, 5673, 3320, 1784, 3194, 1873, 2952, 31956, 6192],\n"," 342: [3275, 1953, 4262, 1221, 6, 1617, 5464, 1213, 3735, 33437],\n"," 343: [377, 780, 1127, 1036, 1610, 3256, 3204, 1200, 1240, 1544],\n"," 344: [1276, 1250, 1953, 1084, 2728, 1950, 2871, 1254, 1262, 1263],\n"," 345: [2987, 2161, 2193, 4275, 2046, 45722, 3889, 8810, 1544, 6539],\n"," 346: [1784, 2912, 48082, 8783, 50792, 1721, 928, 31, 2160, 7215],\n"," 347: [2161, 2116, 2087, 2005, 7164, 31660, 551, 2054, 7046, 1282],\n"," 348: [3257, 31, 4326, 8533, 2396, 1727, 7701, 27831, 1883, 20],\n"," 349: [8643, 8905, 7247, 4564, 5452, 1277, 30816, 56167, 5975, 1380],\n"," 350: [1244, 1247, 5673, 3307, 955, 2065, 919, 7136, 235, 909],\n"," 351: [3521, 1202, 6783, 4914, 3168, 7135, 3819, 5673, 5792, 2303],\n"," 352: [974, 1784, 3259, 339, 1101, 539, 440, 50802, 4305, 6155],\n"," 353: [32587, 2912, 1953, 1036, 1089, 6185, 3275, 163, 1617, 7438],\n"," 354: [1912, 1711, 8373, 8948, 7162, 8533, 1277, 7160, 38798, 1784],\n"," 355: [1953, 1089, 1213, 1276, 6, 2912, 2529, 50794, 1036, 1208],\n"," 356: [11, 2396, 8943, 3874, 1784, 27797, 2268, 5051, 1457, 5673],\n"," 357: [65133, 7249, 7231, 1516, 7220, 1528, 1532, 7213, 7207, 7191],\n"," 358: [1690, 1127, 2288, 33493, 1129, 1320, 5445, 2393, 2021, 5944],\n"," 359: [5673, 48082, 1464, 1784, 1658, 30810, 6385, 3067, 5617, 7044],\n"," 360: [2396, 1277, 1224, 3079, 2690, 31694, 40629, 7579, 39, 6027],\n"," 361: [1264, 7698, 7831, 9011, 1254, 7044, 5854, 6847, 3424, 7835],\n"," 362: [1748, 1625, 2594, 6003, 2858, 32587, 3476, 2291, 6953, 2997],\n"," 363: [1089, 3275, 50, 1036, 1206, 1625, 1213, 2529, 1617, 165],\n"," 364: [4262, 3275, 1213, 1617, 5464, 48516, 6, 858, 1089, 1693],\n"," 365: [532, 6424, 27797, 4242, 4602, 1639, 1484, 7415, 166, 5968],\n"," 366: [3087, 36708, 2683, 2423, 344, 367, 2804, 2723, 34332, 4184],\n"," 367: [1925, 5975, 4326, 2919, 920, 4337, 4033, 6787, 7162, 7303],\n"," 368: [4956, 4085, 8916, 3039, 7831, 33639, 81, 6297, 8528, 1091],\n"," 369: [1036, 1089, 50, 5027, 37477, 7438, 6, 2490, 1213, 1617],\n"," 370: [5706, 2468, 26509, 26527, 4529, 5926, 61071, 2371, 2133, 2163],\n"," 371: [1270, 4027, 1197, 2692, 2987, 2580, 1235, 1073, 1, 356],\n"," 372: [260, 1200, 480, 1214, 33493, 1240, 2115, 2628, 5378, 1127],\n"," 373: [4306, 1784, 356, 595, 1028, 1, 6297, 1197, 4027, 919],\n"," 375: [420, 4306, 36708, 2723, 5452, 34332, 2887, 5493, 5497, 44022],\n"," 376: [1274, 5445, 1690, 2528, 27660, 1129, 2916, 198, 316, 2529],\n"," 377: [1263, 1208, 1230, 1213, 912, 909, 3168, 928, 7044, 356],\n"," 378: [1276, 2028, 1262, 3468, 1953, 3654, 599, 2529, 969, 1233],\n"," 379: [1625, 593, 1214, 2912, 1387, 8950, 6, 1240, 3551, 5445],\n"," 380: [6934, 442, 8636, 208, 3889, 2094, 5254, 61350, 2826, 34150],\n"," 381: [3545, 1256, 7080, 1947, 1250, 909, 3424, 3507, 2971, 25757],\n"," 382: [517, 480, 2987, 1693, 3269, 4306, 2764, 5445, 5881, 367],\n"," 383: [7748, 1273, 30803, 6023, 3925, 3741, 1916, 7700, 2964, 1176],\n"," 384: [1784, 1101, 2302, 539, 1883, 4621, 1393, 3259, 532, 4069],\n"," 385: [2396, 3386, 1277, 1225, 1095, 30812, 2268, 2132, 4464, 1959],\n"," 386: [8533, 6942, 8969, 4776, 7149, 2764, 3257, 60336, 45707, 65091],\n"," 387: [5283, 1091, 46970, 3617, 367, 7346, 47122, 35836, 3263, 4015],\n"," 388: [1625, 2959, 2762, 296, 3476, 2692, 1617, 2912, 1748, 3510],\n"," 389: [5971, 8253, 7099, 26776, 27731, 26662, 37857, 31660, 2294, 6093],\n"," 390: [2987, 1148, 6377, 38038, 44022, 2139, 2366, 1223, 2138, 2761],\n"," 391: [1101, 11, 3259, 1894, 597, 3269, 3257, 3270, 539, 2470],\n"," 392: [858, 1213, 1036, 3275, 4262, 5445, 1240, 1573, 2912, 1089],\n"," 393: [5027, 2764, 6185, 7007, 517, 4383, 380, 2490, 996, 1432],\n"," 394: [39, 186, 2248, 3270, 2100, 236, 5299, 357, 1307, 587],\n"," 395: [1625, 6, 1276, 1693, 2912, 150, 1617, 1213, 1036, 2529],\n"," 396: [3948, 231, 344, 3752, 2706, 6763, 466, 43919, 367, 5541],\n"," 397: [1053, 1683, 7094, 427, 41617, 509, 4410, 2396, 3457, 7044],\n"," 398: [1210, 940, 1291, 2115, 2716, 2193, 2987, 1270, 2968, 7153],\n"," 399: [2133, 1091, 7007, 1968, 3130, 2587, 367, 50792, 1573, 7381],\n"," 400: [1199, 6003, 1921, 4467, 2987, 1206, 4027, 1748, 27904, 27660],\n"," 401: [62788, 54910, 2315, 5478, 6966, 25807, 7845, 7245, 48678, 330],\n"," 402: [36529, 5464, 5010, 33437, 733, 32029, 163, 2023, 8972, 1408],\n"," 403: [1265, 4027, 1923, 532, 3948, 2371, 6807, 1080, 367, 4011],\n"," 404: [1240, 1036, 1200, 2366, 1214, 2529, 786, 1690, 6185, 2490],\n"," 405: [2138, 1713, 5452, 6297, 6557, 4306, 2090, 2045, 31700, 2139],\n"," 406: [3257, 1597, 3269, 1101, 1393, 736, 3259, 380, 11, 517],\n"," 407: [165, 23, 8373, 648, 8665, 7007, 40959, 33679, 996, 5027],\n"," 408: [608, 1235, 296, 4027, 50792, 1658, 3168, 1245, 1784, 1288],\n"," 409: [6557, 5452, 2733, 31700, 6137, 47384, 7262, 61071, 26828, 2045],\n"," 410: [3259, 1393, 8533, 1883, 1784, 168, 1721, 6155, 3270, 2396],\n"," 411: [1213, 5673, 5954, 8951, 5051, 532, 45, 6724, 36529, 4034],\n"," 412: [2529, 1206, 2959, 1748, 1240, 628, 5445, 8950, 1214, 480],\n"," 413: [928, 903, 6650, 913, 4914, 3435, 3788, 908, 1254, 6783],\n"," 414: [3020, 4027, 33437, 50, 6003, 7367, 3556, 1245, 1358, 5673],\n"," 415: [5673, 2396, 5686, 48082, 1235, 4027, 1784, 1658, 8273, 3210],\n"," 416: [1101, 1307, 339, 1541, 2406, 1091, 6155, 2100, 1777, 539],\n"," 417: [1235, 1265, 4027, 919, 1259, 2065, 30810, 6620, 1073, 2291],\n"," 418: [532, 7044, 6815, 7235, 3685, 1912, 6584, 36529, 20, 54978],\n"," 419: [1960, 2728, 1287, 1262, 2067, 1408, 1959, 1293, 151, 1250],\n"," 420: [5673, 337, 235, 1784, 5989, 3897, 6620, 5686, 8533, 2324],\n"," 421: [1784, 5989, 1393, 1, 1721, 4027, 2987, 7215, 1101, 4306],\n"," 422: [377, 349, 1127, 1291, 2115, 1275, 316, 1370, 7007, 161],\n"," 424: [7088, 7700, 6660, 4422, 2927, 3091, 5291, 2732, 5147, 26055],\n"," 425: [3275, 2023, 2580, 4027, 858, 4262, 367, 21, 532, 784],\n"," 426: [33493, 1274, 51709, 53519, 494, 52281, 1301, 7108, 33437, 2393],\n"," 427: [2912, 1276, 1227, 1953, 33437, 2871, 1213, 1209, 2764, 2529],\n"," 428: [1214, 589, 1690, 377, 1387, 480, 8810, 1320, 2288, 4121],\n"," 429: [5027, 517, 2764, 5388, 5867, 532, 6185, 7325, 1783, 1432],\n"," 430: [517, 47044, 380, 3623, 33679, 377, 996, 165, 5027, 648],\n"," 431: [2054, 44022, 2137, 2139, 661, 2080, 49647, 2138, 1025, 2116],\n"," 432: [2764, 517, 7160, 4776, 1805, 6185, 6977, 5767, 1912, 26989],\n"," 433: [5299, 2671, 539, 236, 186, 2424, 42007, 1307, 6155, 36525],\n"," 434: [34150, 31221, 8636, 1591, 43928, 3879, 442, 5609, 7845, 31696],\n"," 435: [198, 1129, 1690, 27660, 1240, 3863, 1301, 2594, 2288, 5853],\n"," 436: [5027, 6185, 996, 37477, 163, 2490, 3386, 33437, 5507, 7438],\n"," 437: [1912, 7044, 3275, 4027, 1234, 36529, 6003, 1805, 27831, 3362],\n"," 438: [1499, 1626, 4044, 50445, 4224, 547, 5880, 1004, 2835, 990],\n"," 439: [1784, 1263, 3194, 7160, 5686, 30812, 1213, 26527, 4529, 1276],\n"," 440: [1953, 6, 48516, 1276, 4262, 1208, 3275, 3020, 2023, 3362],\n"," 441: [1036, 1625, 2912, 1214, 5445, 1240, 2529, 2959, 32587, 1690],\n"," 443: [2912, 1617, 50, 1089, 1953, 47, 3362, 6, 1213, 48516],\n"," 444: [1784, 7215, 1721, 224, 593, 1956, 4920, 3259, 3100, 587],\n"," 445: [4993, 2116, 2987, 6947, 1204, 110, 260, 1198, 34, 1287],\n"," 446: [1240, 50, 5445, 2529, 1089, 1625, 1214, 1206, 32, 32587],\n"," 447: [1019, 2097, 1301, 2021, 7842, 4467, 2987, 3461, 924, 1073],\n"," 448: [3168, 1784, 1235, 54978, 3342, 26803, 1264, 32591, 2580, 26199],\n"," 449: [1339, 532, 39446, 3275, 51709, 2288, 2455, 517, 50804, 8874],\n"," 450: [4673, 7004, 798, 173, 2625, 51709, 8810, 2367, 2490, 7007],\n"," 451: [46855, 1178, 7700, 5686, 1208, 26131, 8190, 4826, 1222, 1262],\n"," 452: [380, 8810, 3204, 3701, 5027, 1690, 2001, 517, 7007, 1320],\n"," 453: [235, 4014, 1176, 2427, 4973, 1273, 6660, 1235, 199, 3168],\n"," 454: [838, 3079, 4246, 7579, 31694, 8969, 3270, 168, 8643, 468],\n"," 455: [3386, 165, 2058, 50, 33437, 2912, 3275, 7007, 1617, 996],\n"," 456: [2594, 3863, 1805, 2455, 4390, 1264, 53519, 5881, 32587, 52281],\n"," 458: [1, 8961, 6377, 1812, 8783, 4306, 551, 27660, 1073, 45730],\n"," 459: [1748, 3204, 1387, 2662, 2160, 53000, 27660, 2288, 43928, 1274],\n"," 460: [1036, 377, 1387, 1339, 1214, 2912, 480, 1208, 517, 1610],\n"," 461: [1784, 1265, 543, 6003, 4027, 4306, 5989, 337, 11, 1101],\n"," 462: [1276, 2268, 1207, 1953, 920, 913, 1784, 1950, 969, 1959],\n"," 463: [2371, 3275, 1912, 51255, 2467, 4262, 908, 1213, 1234, 380],\n"," 464: [4306, 4121, 8961, 3836, 380, 2761, 1772, 2468, 34332, 2161],\n"," 465: [3476, 3168, 42632, 7044, 7981, 48516, 7160, 48304, 45, 36529],\n"," 466: [1196, 260, 2011, 1200, 1240, 2987, 5378, 480, 1274, 33493],\n"," 467: [377, 1274, 2288, 2528, 1129, 173, 2529, 338, 442, 5445],\n"," 468: [8970, 30812, 337, 1185, 6003, 2313, 7160, 2396, 2291, 1094],\n"," 469: [45186, 51884, 8533, 44195, 39381, 37733, 48780, 50005, 59315, 5686],\n"," 470: [8950, 1921, 49932, 1464, 2594, 48082, 1884, 3863, 1161, 3788],\n"," 471: [2081, 2080, 2139, 1022, 2078, 1028, 1025, 6377, 1029, 596],\n"," 472: [1025, 2078, 2137, 6232, 2081, 2080, 2139, 2033, 1566, 1282],\n"," 473: [1405, 36708, 367, 3752, 2587, 2788, 2706, 8596, 7048, 532],\n"," 474: [2530, 5758, 6835, 3576, 2614, 61350, 8830, 2364, 8615, 6574],\n"," 475: [1235, 1206, 319, 4467, 1321, 532, 2010, 7387, 2455, 2413],\n"," 476: [51709, 32031, 26513, 5881, 53000, 1690, 34150, 8865, 2761, 31696],\n"," 477: [33166, 1208, 3020, 288, 1206, 5464, 1953, 778, 3535, 3275],\n"," 479: [2672, 2594, 8950, 1625, 5388, 7001, 3863, 2529, 5867, 5445],\n"," 480: [442, 173, 377, 798, 8810, 4121, 33493, 786, 1320, 6503],\n"," 481: [1912, 2764, 2587, 2264, 1658, 81, 6708, 175, 431, 1597],\n"," 482: [4956, 62956, 62999, 51698, 7247, 52287, 57504, 58105, 60937, 32296],\n"," 483: [1597, 1393, 8533, 31, 11, 1784, 1101, 6155, 6942, 42015],\n"," 484: [50792, 4027, 7444, 7007, 1573, 4621, 5686, 1221, 1265, 1208],\n"," 485: [924, 8950, 1206, 2529, 1301, 1276, 47, 928, 5105, 2010],\n"," 486: [40819, 7160, 6787, 2313, 356, 5989, 7395, 4033, 40278, 33166],\n"," 487: [4027, 1235, 5673, 235, 1247, 1265, 1784, 2065, 608, 1916],\n"," 488: [1544, 1240, 316, 3471, 1200, 2366, 1097, 1214, 780, 1690],\n"," 489: [1927, 920, 1953, 1959, 1090, 1950, 3100, 1925, 1944, 912],\n"," 490: [3361, 3360, 586, 4085, 3552, 5139, 2133, 1777, 4322, 4936],\n"," 491: [16, 1953, 6, 48516, 3735, 3006, 3275, 1084, 50, 7160],\n"," 492: [920, 3100, 3468, 1953, 858, 1263, 1222, 1959, 1204, 356],\n"," 493: [58303, 4776, 50274, 56001, 56607, 1810, 3194, 7044, 25, 51903],\n"," 494: [7235, 319, 4210, 27317, 2912, 49932, 3863, 1617, 6283, 1805],\n"," 495: [1784, 8643, 3259, 31, 4280, 3526, 7215, 4464, 2396, 3260],\n"," 496: [50, 1213, 2912, 1729, 3275, 7438, 2959, 2542, 1617, 1912],\n"," 497: [6, 1953, 3020, 50, 1213, 2023, 3535, 2529, 48516, 1199],\n"," 498: [1235, 1784, 7215, 2804, 5673, 969, 3210, 1259, 6003, 3504],\n"," 499: [1276, 235, 6783, 1304, 1235, 3168, 1213, 47306, 7700, 923],\n"," 500: [3435, 3334, 913, 6047, 942, 8044, 7216, 7223, 2182, 7217],\n"," 501: [349, 494, 3256, 2115, 733, 165, 1544, 50445, 836, 1370],\n"," 502: [50792, 532, 2316, 1784, 31, 224, 367, 1441, 7444, 8373],\n"," 503: [33437, 8917, 54272, 380, 3752, 1091, 36708, 32596, 44022, 34332],\n"," 504: [2470, 2125, 1101, 1088, 1307, 3270, 1188, 2245, 5299, 1777],\n"," 505: [1249, 1784, 1721, 50792, 1573, 1883, 36529, 31660, 2316, 48082],\n"," 506: [31804, 4956, 6503, 103, 170, 2793, 2413, 1676, 2662, 6966],\n"," 507: [1883, 7386, 3130, 2950, 1277, 2467, 6798, 8905, 6297, 1496],\n"," 508: [1214, 589, 1240, 480, 260, 377, 8810, 1036, 1210, 1320],\n"," 509: [48082, 59387, 53161, 46976, 55814, 57464, 58490, 42015, 54259, 47610],\n"," 510: [1393, 1784, 2396, 1721, 1101, 1959, 11, 1183, 539, 920],\n"," 511: [3257, 26989, 63772, 45412, 54978, 1408, 1784, 3418, 49007, 3269],\n"," 512: [6660, 2927, 2732, 8197, 4194, 30803, 1211, 1305, 7088, 7748],\n"," 513: [6241, 59985, 31613, 5271, 2556, 56908, 195, 8671, 2591, 4147],\n"," 515: [903, 930, 1267, 1254, 1086, 1084, 2186, 1212, 913, 1250],\n"," 516: [1690, 2288, 2662, 1909, 3300, 1320, 2722, 3175, 5459, 7845],\n"," 517: [6977, 27828, 517, 2912, 6185, 33905, 39896, 26159, 694, 6585],\n"," 518: [36529, 1358, 36517, 48082, 3504, 337, 4027, 5673, 1199, 6787],\n"," 520: [1235, 1784, 858, 1953, 1230, 1276, 3168, 4027, 3504, 608],\n"," 521: [33437, 1625, 2912, 2058, 1617, 6185, 7007, 5867, 517, 4383],\n"," 522: [3468, 1276, 2871, 1953, 3362, 3168, 1254, 908, 4329, 1912],\n"," 523: [1784, 4027, 3504, 1235, 5673, 1244, 3685, 532, 6440, 6993],\n"," 524: [6787, 36517, 1207, 1193, 1213, 3504, 45, 1950, 4326, 3168],\n"," 525: [1036, 50794, 377, 4210, 2529, 2912, 1610, 47, 1214, 1387],\n"," 526: [3386, 6787, 1784, 2268, 2302, 150, 1101, 1393, 37477, 3100],\n"," 527: [4306, 2987, 2161, 1073, 6377, 1282, 595, 594, 4886, 551],\n"," 529: [2858, 3168, 1199, 30810, 2997, 48082, 149, 6003, 1235, 3556],\n"," 530: [48943, 49007, 3269, 25789, 51314, 8503, 8583, 5499, 4964, 5524],\n"," 531: [517, 26488, 5388, 7007, 26746, 7445, 27828, 4238, 5824, 2764],\n"," 532: [1212, 3741, 49932, 3788, 928, 149, 7123, 26231, 7044, 2203],\n"," 533: [60074, 57640, 62956, 60161, 60069, 61160, 61132, 60937, 54278, 53993],\n"," 534: [49278, 50792, 1625, 733, 7007, 3275, 1883, 4776, 1805, 42015],\n"," 535: [2732, 7700, 3645, 6650, 8239, 4194, 6035, 7238, 5333, 7088],\n"," 536: [1198, 1196, 1721, 316, 1259, 1784, 3100, 356, 1127, 3578],\n"," 537: [3386, 527, 1959, 6787, 356, 3100, 1090, 150, 30812, 110],\n"," 539: [1784, 1441, 1393, 2125, 2671, 1101, 186, 3752, 468, 4246],\n"," 540: [1970, 54910, 62788, 4138, 26265, 7117, 5206, 4561, 7719, 5862],\n"," 541: [52005, 47830, 50858, 54958, 60566, 2159, 26746, 6736, 1422, 8062],\n"," 542: [969, 7215, 1276, 1784, 1959, 1035, 1950, 1287, 1953, 3100],\n"," 543: [62999, 54272, 7247, 62956, 49274, 52287, 53460, 44022, 33615, 34332],\n"," 544: [1729, 1213, 1036, 6, 288, 1206, 2529, 2912, 32587, 4262],\n"," 545: [1091, 2987, 2699, 5459, 4275, 442, 2490, 1049, 2001, 4121],\n"," 546: [2587, 1473, 3184, 43558, 46970, 4242, 532, 7004, 56274, 1432],\n"," 547: [3386, 95, 6503, 7007, 5445, 1544, 165, 3256, 1573, 6185],\n"," 548: [51255, 367, 51575, 2371, 380, 2723, 3275, 32587, 3301, 7007],\n"," 549: [2912, 1617, 1036, 517, 2371, 47, 377, 3551, 50, 1610],\n"," 550: [3307, 235, 7215, 2291, 8235, 2871, 2936, 928, 3468, 1084],\n"," 551: [235, 923, 3089, 928, 5995, 1084, 3504, 47306, 3741, 6620],\n"," 552: [3210, 31, 3071, 62, 2729, 5792, 4027, 2912, 3688, 7346],\n"," 553: [4011, 5027, 532, 37477, 2001, 5541, 5049, 2490, 7007, 3275],\n"," 554: [3168, 3741, 319, 1202, 6783, 7700, 7587, 2427, 6581, 8197],\n"," 555: [5673, 2010, 48082, 665, 5792, 4956, 8235, 1253, 2729, 6650],\n"," 556: [6798, 7247, 50796, 6245, 26265, 1605, 6297, 1057, 8919, 55732],\n"," 557: [1912, 3685, 27797, 50949, 478, 3876, 4879, 166, 5968, 2484],\n"," 558: [49130, 52460, 58047, 7215, 1408, 42015, 4326, 62155, 54997, 46855],\n"," 559: [7001, 5853, 541, 8830, 8136, 1214, 517, 32213, 6283, 1240],\n"," 560: [3740, 6581, 5026, 7720, 911, 8202, 2483, 1215, 1235, 31660],\n"," 561: [592, 1377, 43928, 27660, 839, 2641, 46530, 2640, 153, 3877],\n"," 562: [1597, 2764, 2802, 3210, 6385, 7149, 532, 1805, 6344, 7160],\n"," 563: [1784, 3362, 4027, 3168, 1358, 858, 1276, 923, 3100, 1213],\n"," 564: [5010, 5881, 33437, 1625, 37382, 161, 4776, 1784, 2058, 58025],\n"," 565: [5541, 2470, 2723, 36708, 380, 466, 2133, 34332, 3033, 4306],\n"," 566: [5673, 1784, 3504, 356, 1207, 2324, 7215, 1721, 6787, 1244],\n"," 567: [2587, 42015, 52973, 4776, 38499, 2396, 1224, 970, 2580, 3538],\n"," 568: [2987, 34, 2161, 4306, 55995, 1049, 52287, 8961, 1101, 32031],\n"," 569: [2912, 1036, 377, 1953, 3386, 6, 1610, 1387, 3551, 2529],\n"," 570: [1276, 3468, 6796, 858, 1084, 5989, 1784, 1221, 3362, 3100],\n"," 571: [1690, 1214, 1127, 2987, 1580, 1301, 480, 2021, 1909, 8810],\n"," 572: [532, 6815, 26788, 1784, 54978, 26803, 4529, 26527, 47274, 4681],\n"," 573: [5673, 3168, 7215, 4027, 1247, 1953, 34437, 3148, 5666, 3504],\n"," 574: [2105, 3745, 2987, 1544, 52287, 40339, 2528, 44022, 4306, 1049],\n"," 575: [43853, 3800, 5424, 712, 39703, 3227, 64327, 5416, 2438, 8330],\n"," 576: [1953, 858, 1276, 1084, 3100, 1263, 912, 1721, 30812, 2023],\n"," 577: [5049, 7004, 5027, 4085, 4673, 532, 2381, 37477, 3710, 380],\n"," 578: [2625, 61350, 1544, 8371, 27618, 60514, 2826, 61818, 4638, 48943],\n"," 579: [4306, 44022, 8961, 2161, 6003, 1, 32031, 6377, 33437, 380],\n"," 580: [380, 2490, 53972, 51255, 3275, 2001, 33679, 5418, 51709, 1918],\n"," 581: [1025, 4886, 2081, 6377, 2294, 1, 2080, 661, 1881, 2139],\n"," 582: [1253, 922, 2936, 1256, 2648, 7080, 8235, 2186, 3629, 8125],\n"," 583: [319, 5867, 2729, 6003, 1676, 532, 1784, 46855, 7007, 2529],\n"," 584: [2470, 5452, 1777, 4306, 2005, 7369, 2054, 586, 36708, 1894],\n"," 585: [4242, 7415, 1034, 2438, 5416, 64327, 3227, 8330, 43853, 403],\n"," 586: [319, 7361, 5617, 1202, 1658, 1199, 5792, 7044, 3925, 3168],\n"," 587: [1198, 1097, 5378, 33493, 2628, 1200, 2987, 2115, 1127, 3471],\n"," 588: [5969, 27797, 3874, 4956, 61071, 8598, 1484, 5970, 4280, 1873],\n"," 589: [6283, 51709, 1690, 1625, 2912, 1387, 7387, 33437, 1249, 1127],\n"," 590: [2115, 3471, 2987, 3175, 2717, 1270, 2003, 2133, 4121, 480],\n"," 591: [671, 6888, 5541, 4388, 532, 2723, 36708, 4591, 2163, 2804],\n"," 592: [2819, 1249, 1213, 4262, 163, 1729, 517, 2912, 50, 48516],\n"," 593: [517, 6, 2912, 1625, 2529, 1240, 1036, 1617, 3551, 50794],\n"," 594: [2987, 2161, 8961, 34, 44022, 6377, 1049, 2138, 2054, 6232],\n"," 595: [2023, 3275, 1912, 3893, 3362, 36529, 3685, 2580, 50, 3006],\n"," 596: [58303, 46559, 2313, 57669, 40278, 1950, 33903, 7700, 1259, 31410],\n"," 597: [7326, 63033, 39703, 712, 3800, 5424, 60526, 3386, 4776, 1597],\n"," 598: [3660, 3058, 3933, 6966, 5478, 4096, 3696, 5774, 63458, 7266],\n"," 599: [7080, 914, 4357, 7055, 1947, 25757, 915, 900, 26084, 4912],\n"," 600: [1953, 296, 1089, 3275, 750, 2529, 1276, 2366, 532, 4027],\n"," 601: [32296, 49278, 1375, 1376, 442, 5944, 2393, 26513, 6503, 5459],\n"," 602: [595, 1, 6093, 4886, 594, 588, 2761, 4294, 4306, 1282],\n"," 603: [5995, 3435, 923, 3741, 2313, 1950, 4432, 1953, 928, 912],\n"," 604: [2587, 7004, 5621, 861, 7048, 420, 34332, 5541, 27735, 7381],\n"," 605: [1259, 4027, 1784, 3168, 1235, 2858, 6620, 6385, 1276, 48082],\n"," 606: [1784, 1953, 1263, 1213, 296, 356, 1276, 3275, 5989, 3100],\n"," 607: [32587, 2023, 3275, 5010, 6, 1953, 3578, 1208, 1210, 1274],\n"," 608: [1136, 532, 2723, 1080, 466, 2791, 231, 1405, 3785, 6807],\n"," 609: [1784, 1721, 1959, 1883, 3100, 920, 2396, 912, 7215, 1234],\n"," 610: [1242, 1959, 1925, 1950, 1272, 6787, 7303, 1947, 3545, 920],\n"," 612: [420, 2470, 1894, 2001, 2471, 380, 7004, 5553, 1918, 7048],\n"," 613: [3424, 1276, 1084, 969, 2871, 1953, 1254, 1250, 913, 2804],\n"," 614: [1214, 589, 377, 3471, 780, 1127, 1690, 316, 1610, 5445],\n"," 616: [1200, 1214, 1339, 2826, 45730, 1240, 8783, 3994, 50445, 5502],\n"," 617: [5553, 4869, 517, 1783, 49007, 26989, 2764, 970, 1597, 55603],\n"," 618: [44761, 49651, 4011, 50792, 3275, 6708, 1304, 2542, 1234, 1953],\n"," 619: [1690, 380, 480, 208, 5445, 1127, 780, 3527, 1544, 2528],\n"," 620: [40819, 30812, 45, 6620, 41571, 1784, 2313, 509, 7160, 6003],\n"," 621: [51698, 57504, 52287, 36397, 27186, 31660, 62956, 56915, 5069, 32031],\n"," 622: [380, 5541, 466, 1784, 367, 543, 1091, 2791, 2723, 2716],\n"," 623: [4121, 3269, 7394, 4275, 1049, 62331, 2887, 3401, 181, 32076],\n"," 624: [4306, 36708, 367, 1, 8974, 44022, 4591, 1405, 1772, 7369],\n"," 625: [349, 2947, 3204, 3638, 1240, 1610, 3256, 26398, 1127, 3654],\n"," 627: [6714, 55603, 420, 532, 2468, 2587, 4242, 861, 2723, 1473],\n"," 628: [3100, 1231, 6787, 527, 1242, 1276, 356, 1090, 1959, 1250],\n"," 629: [1176, 2427, 1305, 5291, 5617, 34155, 7700, 8197, 2731, 5051],\n"," 630: [2133, 2406, 2716, 2470, 367, 4621, 1894, 3269, 1777, 1101],\n"," 631: [909, 1927, 900, 1929, 1950, 1944, 926, 3475, 1939, 912],\n"," 632: [2912, 4919, 3551, 928, 1248, 2917, 5424, 712, 3800, 39703],\n"," 633: [4998, 3741, 8094, 7981, 2912, 3424, 7587, 928, 7076, 3735],\n"," 634: [5944, 26513, 27618, 4121, 8371, 990, 4434, 3175, 3593, 4638],\n"," 635: [2587, 3263, 2082, 4341, 1777, 33646, 3361, 5103, 4799, 586],\n"," 636: [8533, 4033, 26614, 4901, 4234, 33437, 5010, 3053, 41571, 1249],\n"," 637: [7438, 5464, 8783, 50, 32587, 2912, 165, 377, 517, 33437],\n"," 638: [3641, 39659, 2777, 2773, 2771, 2760, 2758, 2755, 2744, 2743],\n"," 639: [8961, 44022, 32031, 2139, 33615, 2081, 8360, 5218, 34, 364],\n"," 640: [1200, 260, 5378, 2628, 1127, 2115, 1690, 2011, 2987, 1580],\n"," 641: [1200, 1240, 1580, 1214, 1690, 208, 2011, 1127, 5445, 2528],\n"," 642: [1274, 7458, 31660, 44022, 380, 34319, 27660, 442, 33437, 51575],\n"," 643: [2396, 1959, 1950, 1961, 920, 1964, 1185, 1293, 1956, 1936],\n"," 644: [1488, 5027, 4044, 7007, 442, 1499, 990, 2490, 377, 648],\n"," 645: [48943, 32076, 459, 3269, 1129, 4434, 55603, 6714, 5171, 26326],\n"," 646: [4886, 1282, 4306, 2143, 2116, 2987, 6377, 27660, 2761, 1],\n"," 647: [442, 4133, 2826, 6264, 2625, 4044, 60514, 8782, 8371, 3889],\n"," 648: [27564, 7738, 7270, 56908, 4817, 1564, 7707, 25832, 26521, 8671],\n"," 649: [367, 1091, 3752, 51575, 7381, 1573, 7004, 2723, 6014, 420],\n"," 650: [27797, 2484, 44058, 4879, 5968, 403, 166, 3876, 50949, 478],\n"," 651: [1235, 27416, 955, 5673, 3168, 7584, 3504, 27826, 953, 52],\n"," 652: [4912, 26084, 1947, 7055, 6732, 6345, 1380, 2941, 1932, 7714],\n"," 653: [3386, 924, 150, 1784, 1393, 1876, 8905, 1208, 4995, 1276],\n"," 654: [733, 3654, 3386, 5010, 3256, 836, 1408, 377, 1127, 2490],\n"," 655: [61071, 5686, 3037, 1772, 8919, 4956, 5452, 6724, 32591, 1220],\n"," 656: [7007, 5553, 55603, 6714, 50792, 49007, 5027, 3257, 26989, 3269],\n"," 657: [6783, 7135, 47306, 3801, 2065, 1276, 1202, 1658, 2927, 1916],\n"," 658: [5010, 33437, 1262, 36529, 40278, 527, 4033, 33162, 8977, 27397],\n"," 659: [3476, 49932, 8950, 1625, 2594, 7044, 628, 2912, 5105, 6],\n"," 660: [110, 3468, 3100, 1953, 3654, 2728, 1090, 2944, 1262, 1276],\n"," 661: [8830, 6120, 5758, 4689, 2364, 8894, 2793, 5556, 25798, 7845],\n"," 662: [1276, 3424, 969, 3194, 1235, 1084, 6993, 954, 1253, 8923],\n"," 663: [2366, 1208, 1387, 3654, 1610, 377, 2871, 1276, 2529, 1214],\n"," 664: [2138, 2143, 2161, 558, 5892, 2162, 7743, 2054, 26326, 673],\n"," 665: [6385, 5673, 5617, 2065, 4014, 6660, 4840, 5686, 5013, 6620],\n"," 666: [7387, 2912, 3476, 7001, 2455, 4210, 2288, 1387, 1321, 27317],\n"," 667: [54978, 1264, 6570, 4280, 1805, 26803, 2594, 3863, 53887, 26199],\n"," 668: [2529, 1301, 1748, 680, 1208, 1200, 541, 1274, 1690, 1240],\n"," 669: [1387, 1097, 1200, 1214, 3471, 1198, 1240, 3578, 2987, 260],\n"," 670: [2138, 517, 2987, 7160, 6297, 4280, 1264, 33437, 5027, 30793],\n"," 671: [3338, 4236, 27899, 6299, 7124, 59590, 34072, 6331, 8464, 4350],\n"," 672: [4027, 1784, 628, 1883, 2912, 1235, 1265, 1203, 31, 1597],\n"," 674: [26513, 5944, 61350, 6645, 47997, 208, 2808, 3959, 674, 27618],\n"," 675: [1784, 50, 7007, 2912, 532, 3275, 33437, 1213, 50792, 6787],\n"," 676: [539, 6155, 440, 1307, 5299, 42007, 2671, 3270, 597, 36525],\n"," 677: [165, 7007, 1616, 1370, 1036, 1573, 349, 8810, 3527, 23],\n"," 678: [7215, 517, 49007, 5867, 1496, 6003, 2467, 6724, 26265, 7007],\n"," 679: [2011, 32, 1200, 2716, 3471, 2021, 1097, 2987, 5445, 5459],\n"," 680: [733, 20, 5027, 6185, 33437, 7007, 161, 8665, 23, 996],\n"," 681: [2987, 3175, 380, 1198, 316, 260, 2094, 1544, 1196, 4275],\n"," 683: [26547, 861, 5621, 6709, 1918, 7004, 50153, 7108, 6582, 6387],\n"," 684: [1265, 48082, 2997, 2692, 4027, 38061, 1658, 7293, 4878, 4975],\n"," 685: [1196, 4027, 296, 1200, 33493, 165, 5378, 3275, 1240, 7438],\n"," 686: [2366, 6283, 2871, 912, 1321, 1127, 1610, 3740, 940, 1953],\n"," 688: [2028, 1208, 3654, 1263, 2871, 2944, 1231, 920, 3100, 5010],\n"," 689: [377, 836, 7007, 733, 1616, 3256, 6281, 49278, 6185, 1597],\n"," 690: [1784, 1883, 1101, 1393, 1721, 1894, 339, 11, 3270, 3259],\n"," 691: [6812, 2404, 4265, 798, 442, 4543, 6078, 4044, 2524, 26488],\n"," 693: [7215, 2803, 3259, 6879, 805, 3386, 1784, 3148, 3893, 3260],\n"," 694: [594, 6093, 7064, 4886, 4294, 4306, 1022, 3114, 6377, 1025],\n"," 695: [1784, 11, 224, 8373, 3258, 532, 8643, 6003, 2826, 469],\n"," 696: [1784, 1658, 48082, 587, 224, 532, 50792, 1265, 3893, 50],\n"," 697: [2438, 3227, 64327, 5416, 8330, 1432, 55603, 6714, 62721, 1824],\n"," 698: [969, 1247, 1235, 471, 6650, 8235, 4914, 8459, 915, 3037],\n"," 699: [4262, 1617, 1213, 7981, 33437, 33166, 1625, 1953, 3020, 1729],\n"," 700: [27706, 53161, 29, 5105, 7123, 7444, 26265, 49932, 3863, 48082],\n"," 701: [54978, 4390, 3101, 26746, 4088, 1645, 5553, 532, 2764, 6815],\n"," 702: [2315, 54910, 62788, 43011, 220, 27402, 7245, 7117, 8701, 1433],\n"," 703: [3210, 3243, 5541, 2133, 6558, 586, 2797, 5500, 7834, 7102],\n"," 704: [527, 8253, 31660, 1259, 31658, 4432, 36535, 928, 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543],\n"," 721: [7215, 2764, 5553, 60526, 2803, 49007, 4280, 1783, 3259, 5424],\n"," 723: [1784, 2858, 6003, 224, 50, 33437, 2366, 6953, 3168, 48082],\n"," 724: [5673, 1784, 1235, 2065, 3358, 1230, 1380, 2291, 4014, 3422],\n"," 725: [61071, 56671, 4984, 4971, 3778, 7269, 7278, 59000, 7312, 4825],\n"," 726: [1953, 1276, 1254, 1084, 6796, 2912, 4034, 2268, 3006, 3362],\n"," 727: [1784, 1393, 31, 1597, 2302, 1101, 532, 4306, 1883, 6385],\n"," 728: [1200, 260, 480, 3471, 1214, 316, 1097, 2529, 1387, 377],\n"," 729: [5957, 539, 2671, 8969, 6155, 357, 236, 3259, 5299, 42007],\n"," 730: [367, 4306, 8961, 1580, 34332, 1270, 2987, 1091, 442, 36708],\n"," 731: [2396, 43744, 4305, 6765, 7149, 50802, 2620, 64243, 1309, 7824],\n"," 732: [4973, 9010, 27790, 59118, 48082, 47725, 1784, 5051, 8533, 8254],\n"," 733: [5952, 480, 2826, 7458, 316, 260, 377, 1200, 1210, 1198],\n"," 734: [25840, 969, 3307, 8235, 4406, 1408, 2936, 3037, 7072, 940],\n"," 735: [1784, 1597, 5989, 1393, 7160, 224, 1961, 6385, 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1219, 2664, 2366, 1240, 2288, 1253, 3551],\n"," 926: [4027, 38061, 296, 1265, 2580, 33004, 3275, 367, 30810, 1527],\n"," 927: [1091, 7007, 33437, 367, 380, 7438, 3275, 733, 37477, 377],\n"," 928: [1274, 1206, 1301, 2021, 2529, 2010, 1161, 4467, 3654, 1690],\n"," 930: [2587, 4242, 27797, 43558, 4602, 11, 4341, 3752, 4132, 1784],\n"," 931: [1432, 4466, 26688, 1473, 3184, 4869, 33905, 7893, 6585, 5138],\n"," 932: [2987, 8961, 367, 31696, 7046, 4306, 1198, 1784, 1101, 7007],\n"," 933: [1597, 51709, 103, 2389, 517, 532, 2763, 4383, 2764, 26746],\n"," 934: [258, 5892, 56, 126, 62376, 2047, 8713, 44225, 1881, 5458],\n"," 935: [2115, 1544, 494, 380, 1918, 165, 95, 836, 8810, 50445],\n"," 936: [47044, 5010, 2580, 532, 50792, 3254, 1220, 5989, 60647, 20],\n"," 937: [1199, 48082, 6003, 4467, 27904, 1206, 1658, 5867, 3476, 8950],\n"," 938: [1036, 1214, 377, 1387, 1610, 4210, 1240, 480, 1200, 1625],\n"," 939: [377, 2912, 517, 1036, 2371, 1214, 2268, 5445, 480, 3386],\n"," 940: [377, 2529, 316, 4064, 1953, 50794, 50445, 1690, 3740, 7483],\n"," 941: [5000, 5788, 6724, 56060, 1964, 2971, 31347, 1264, 26211, 6117],\n"," 942: [377, 480, 1214, 1200, 1610, 1036, 589, 1127, 1544, 1580],\n"," 943: [1339, 6966, 1214, 1982, 273, 3461, 8092, 7387, 7891, 382],\n"," 945: [6085, 2165, 1133, 3162, 56671, 5097, 2172, 6415, 61312, 5807],\n"," 946: [596, 1025, 1022, 783, 2081, 5218, 2080, 2096, 594, 1030],\n"," 947: [7714, 914, 30816, 26422, 2987, 1947, 1250, 945, 2971, 8008],\n"," 948: [7153, 260, 7090, 6947, 3578, 7438, 2826, 2987, 7458, 33437],\n"," 949: [48082, 1208, 27904, 27660, 5686, 6003, 8235, 27706, 1274, 2010],\n"," 950: [49932, 5105, 27317, 5867, 4546, 3863, 7123, 26231, 7044, 7001],\n"," 951: [27797, 5968, 403, 3876, 2484, 44058, 478, 50949, 4879, 166],\n"," 952: [2138, 2045, 5452, 49647, 6557, 2139, 1367, 258, 2054, 2080],\n"," 953: [5944, 2393, 329, 26513, 2528, 3175, 1376, 1356, 1375, 2722],\n"," 954: [4133, 26513, 8782, 31660, 6995, 208, 6264, 62331, 8371, 485],\n"," 955: [7386, 6787, 6003, 5945, 1784, 7160, 4280, 3386, 33437, 6947],\n"," 956: [8094, 1408, 1304, 54783, 47117, 1378, 6421, 52321, 7894, 3100],\n"," 957: [532, 50792, 319, 3210, 101, 1235, 4027, 1923, 2717, 3948],\n"," 958: [50, 5954, 628, 1213, 8783, 1617, 2912, 1358, 8950, 4027],\n"," 960: [3386, 1617, 7160, 4776, 2396, 6787, 36517, 3006, 6796, 4326],\n"," 961: [1276, 1953, 1784, 3504, 3468, 3100, 1084, 912, 1263, 1304],\n"," 962: [1645, 2467, 2764, 2803, 27828, 52604, 6086, 5388, 5630, 1783],\n"," 963: [4434, 1488, 173, 990, 7007, 5700, 48943, 2625, 996, 3770],\n"," 964: [532, 4027, 45, 3275, 1784, 7044, 1805, 2858, 509, 7160],\n"," 965: [2908, 7160, 1354, 25, 45, 1094, 55814, 2313, 5673, 7044],\n"," 966: [3996, 7482, 6874, 7090, 377, 1198, 3997, 7458, 3578, 33437],\n"," 967: [31685, 543, 3752, 1091, 367, 33836, 1393, 4306, 224, 1777],\n"," 968: [377, 1693, 836, 5445, 1036, 2912, 2490, 33437, 1625, 1240],\n"," 969: [3259, 39, 4280, 31694, 4132, 2396, 440, 5066, 8643, 3079],\n"," 970: [7007, 2490, 2001, 5027, 1918, 163, 6503, 377, 2002, 6185],\n"," 971: [30816, 953, 48082, 928, 9011, 5686, 1235, 1103, 920, 7215],\n"," 972: [5010, 1208, 1262, 36529, 1953, 4826, 3476, 26131, 1276, 7044],\n"," 973: [7835, 7831, 909, 7080, 1084, 912, 1269, 1256, 7834, 6798],\n"," 974: [3894, 6224, 58439, 6164, 6161, 34473, 8804, 3711, 6129, 6127],\n"," 975: [6, 1784, 1213, 3362, 858, 3275, 1089, 2959, 1953, 1625],\n"," 976: [7160, 1206, 7044, 2594, 110, 4349, 27838, 2427, 6003, 3148],\n"," 977: [3142, 5137, 6430, 33677, 43708, 32314, 7484, 6948, 7124, 27812],\n"," 978: [7055, 26084, 7049, 34018, 7050, 7132, 2565, 3199, 7835, 1083],\n"," 979: [31, 7007, 50, 6003, 1617, 1358, 2366, 2912, 1276, 1214],\n"," 980: [1196, 1198, 1097, 480, 5952, 589, 33493, 527, 110, 3578],\n"," 982: [1221, 2023, 1208, 919, 1263, 5686, 1953, 1230, 1784, 48082],\n"," 983: [551, 2161, 8961, 4014, 2143, 919, 34332, 2005, 367, 594],\n"," 984: [1276, 923, 628, 4027, 1358, 47306, 36, 912, 3504, 1617],\n"," 985: [33437, 7458, 5989, 1693, 356, 5010, 1408, 380, 2987, 33679],\n"," 986: [26989, 4869, 7007, 8093, 49007, 5553, 1432, 6714, 55603, 3184],\n"," 987: [2587, 27797, 7149, 3874, 4602, 1457, 6798, 42015, 970, 33145],\n"," 988: [7044, 7123, 49932, 5867, 1264, 928, 3194, 4919, 3863, 3476],\n"," 989: [1089, 3275, 6, 1729, 1953, 1276, 2542, 4262, 7438, 3468],\n"," 990: [1784, 2396, 7162, 1094, 1183, 1721, 8533, 6947, 7160, 6385],\n"," 991: [2470, 2420, 2133, 2275, 442, 8633, 34332, 3269, 4401, 2005],\n"," 992: [2717, 7007, 7001, 2389, 1214, 2467, 39446, 2827, 6120, 2371],\n"," 993: [56921, 33437, 733, 2058, 3476, 7007, 6185, 51709, 6213, 32029],\n"," 994: [1235, 2065, 235, 1259, 2395, 1265, 2997, 2858, 48082, 6184],\n"," 995: [1244, 5673, 7044, 319, 7460, 3925, 5792, 3168, 48082, 6783],\n"," 996: [56060, 864, 6570, 4529, 26527, 64327, 8330, 5416, 2438, 3227],\n"," 997: [6491, 31600, 26488, 49007, 48943, 7007, 1432, 56921, 61818, 3269],\n"," 998: [2396, 1094, 1185, 30812, 8970, 6385, 1225, 1932, 2971, 1277],\n"," 999: [1214, 2366, 3471, 3701, 1127, 2413, 1200, 2010, 2163, 2021],\n"," 1001: [1721, 2987, 1097, 1693, 1198, 1101, 34, 6947, 7153, 1408],\n"," 1002: [3869, 5541, 3868, 2723, 2027, 671, 3760, 7102, 3785, 2791],\n"," 1003: [6736, 50800, 5630, 27338, 44911, 32743, 1645, 8774, 59604, 5474],\n"," 1004: [1036, 1610, 1625, 1214, 2529, 1387, 1208, 1200, 1276, 2912],\n"," 1005: [50792, 4621, 1784, 1101, 1883, 1091, 2248, 2100, 1727, 7701],\n"," 1006: [4014, 1176, 6660, 64957, 2291, 235, 1271, 531, 3556, 8970],\n"," 1007: [1658, 1265, 11, 6155, 543, 5673, 3874, 2997, 1912, 1784],\n"," 1008: [1206, 2529, 1748, 1240, 1690, 32587, 1129, 2021, 1274, 1089],\n"," 1009: [2987, 32, 5445, 5010, 442, 380, 733, 377, 589, 3418],\n"," 1011: [5445, 2594, 292, 1748, 2529, 3204, 1690, 8950, 2762, 198],\n"," 1012: [1693, 1625, 1805, 442, 2594, 1690, 32, 1206, 1784, 1616],\n"," 1013: [380, 1097, 1393, 11, 1721, 1265, 2987, 2366, 5989, 316],\n"," 1014: [532, 56715, 5945, 55901, 7044, 4529, 26527, 4027, 1235, 5686],\n"," 1015: [913, 928, 1086, 3435, 1219, 1253, 908, 1212, 2186, 1248],\n"," 1016: [3168, 1953, 1784, 4034, 1254, 3100, 1235, 1084, 2303, 319],\n"," 1017: [260, 480, 1198, 2193, 2826, 3997, 1200, 316, 589, 1127],\n"," 1018: [63836, 6736, 32743, 8774, 27338, 48591, 45361, 8265, 5474, 54910],\n"," 1019: [3184, 27797, 403, 4879, 50949, 3876, 166, 5968, 2484, 478],\n"," 1020: [912, 1254, 903, 922, 926, 969, 1276, 920, 898, 909],\n"," 1021: [783, 2081, 1025, 2096, 1023, 3034, 1030, 2099, 31687, 5218],\n"," 1022: [596, 1025, 2080, 2081, 661, 588, 364, 5218, 1029, 2096],\n"," 1023: [2313, 2728, 33166, 590, 1276, 33437, 6617, 40278, 27838, 7160],\n"," 1025: [3210, 1235, 4027, 1784, 1259, 5673, 2248, 2580, 26491, 532],\n"," 1026: [1276, 1953, 6, 3362, 50, 912, 913, 1617, 903, 1263],\n"," 1027: [7004, 367, 2366, 2716, 2133, 380, 37477, 45722, 4262, 1091],\n"," 1028: [7007, 1036, 3386, 1479, 3256, 6989, 95, 377, 648, 3623],\n"," 1029: [1783, 1422, 2952, 5867, 52005, 43853, 6977, 712, 5424, 3800],\n"," 1030: [532, 3275, 2952, 1784, 6708, 4242, 3893, 2580, 7007, 4027],\n"," 1031: [1544, 1127, 780, 3638, 4638, 2947, 1196, 1200, 1374, 2094],\n"," 1032: [1772, 6951, 2161, 2804, 2413, 7369, 2701, 2403, 543, 4133],\n"," 1033: [920, 1276, 912, 969, 3307, 899, 2936, 1084, 954, 909],\n"," 1034: [5010, 110, 7458, 8977, 260, 1210, 1408, 733, 6947, 8810],\n"," 1035: [2094, 57640, 52722, 43928, 5254, 442, 34332, 2641, 2640, 839],\n"," 1037: [2671, 1784, 5299, 1393, 186, 8969, 1307, 1101, 468, 6155],\n"," 1038: [7037, 7278, 1825, 7269, 1870, 1871, 304, 55668, 7097, 3308],\n"," 1039: [33166, 1953, 7981, 3683, 4034, 319, 2912, 1912, 2203, 5954],\n"," 1040: [420, 2000, 2001, 5049, 7102, 4121, 6014, 2053, 7369, 5027],\n"," 1041: [2987, 1270, 6377, 4306, 4774, 1197, 31923, 380, 349, 6283],\n"," 1043: [7108, 861, 5027, 26688, 4466, 7007, 2001, 26547, 6185, 2002],\n"," 1044: [2161, 2005, 31660, 2987, 7046, 2054, 4275, 4306, 8961, 2162],\n"," 1045: [1235, 923, 2729, 235, 6650, 1276, 1247, 319, 3925, 922],\n"," 1046: [2987, 1387, 1097, 1198, 1214, 3471, 1339, 1200, 377, 2366],\n"," 1047: [539, 357, 2671, 186, 1777, 236, 4205, 11, 1101, 2581],\n"," 1048: [380, 543, 51255, 2723, 2000, 6503, 2371, 1091, 4956, 51575],\n"," 1050: [2764, 532, 4280, 2396, 1277, 1784, 7149, 1873, 7215, 18],\n"," 1051: [1784, 8969, 357, 2396, 3481, 39, 8643, 3259, 339, 1380],\n"," 1052: [4464, 1277, 7215, 1183, 7160, 337, 3386, 1161, 4326, 2580],\n"," 1053: [733, 1262, 2028, 3654, 2728, 349, 1208, 377, 6, 1287]}"]},"metadata":{},"execution_count":11}],"source":["# 사용자가 평갓값 4 이상을 부여한 영화\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","id2index = dict(zip(ids, range(len(ids))))\n","pred_user2items = dict()\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," evaluated_movie_ids = user_evaluated_movies[user_id]\n"," movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n","\n"," movie_indexes = [id2index[id] for id in movie_ids]\n"," user_vector = movie_norm_vectors[movie_indexes].mean(axis=0)\n"," recommended_items = find_similar_items(user_vector, evaluated_movie_ids, topn=10)\n"," pred_user2items[user_id] = recommended_items\n","pred_user2items"]},{"cell_type":"code","execution_count":12,"id":"625d2894","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"625d2894","executionInfo":{"status":"ok","timestamp":1672120598440,"user_tz":-540,"elapsed":22,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f01e6c60-648a-4277-bf74-76458665e08c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":13,"id":"2e3be9df","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"2e3be9df","executionInfo":{"status":"ok","timestamp":1672120598440,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"0eb28455-c022-41ba-f747-8e6ce568e81e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","1075 1097 E.T. the Extra-Terrestrial (1982) \n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n","\n"," genre \\\n","1075 [Children, Drama, Sci-Fi] \n","1171 [Action, Adventure, Sci-Fi] \n","1173 [Action, Adventure] \n","\n"," tag \n","1075 [speilberg, steven spielberg, spielberg/lucas,... \n","1171 [lucas, george lucas, george lucas, gfei own i... \n","1173 [egypt, lucas, seen more than once, dvd collec... "],"text/html":["\n","
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movie_idtitlegenretag
10751097E.T. the Extra-Terrestrial (1982)[Children, Drama, Sci-Fi][speilberg, steven spielberg, spielberg/lucas,...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
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\n"," "]},"metadata":{},"execution_count":13}],"source":["# user_id=2에 대한 추천(1198, 1196, 1097)\n","movies[movies.movie_id.isin([1198, 1196, 1097])]"]},{"cell_type":"code","execution_count":13,"id":"83e8a9a4","metadata":{"id":"83e8a9a4","executionInfo":{"status":"ok","timestamp":1672120598440,"user_tz":-540,"elapsed":6,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/chapter5/colab/data_download.ipynb b/chapter5/colab/data_download.ipynb index 80476ae..5cdda30 100644 --- a/chapter5/colab/data_download.ipynb +++ b/chapter5/colab/data_download.ipynb @@ -1,1057 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/data_download.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Movielens 데이터 다운로드" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2021-11-30 00:17:51-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", - "files.grouplens.org (files.grouplens.org) をDNSに問いあわせています... 128.101.65.152\n", - "files.grouplens.org (files.grouplens.org)|128.101.65.152|:443 に接続しています... 接続しました。\n", - "HTTP による接続要求を送信しました、応答を待っています... 200 OK\n", - "長さ: 65566137 (63M) [application/zip]\n", - "`../data/ml-10m.zip' に保存中\n", - "\n", - "ml-10m.zip 100%[===================>] 62.53M 778KB/s 時間 72s \n", - "\n", - "2021-11-30 00:19:03 (895 KB/s) - `../data/ml-10m.zip' へ保存完了 [65566137/65566137]\n", - "\n", - "Archive: ../data/ml-10m.zip\n", - " creating: ../data/ml-10M100K/\n", - " inflating: ../data/ml-10M100K/allbut.pl \n", - " inflating: ../data/ml-10M100K/movies.dat \n", - " inflating: ../data/ml-10M100K/ratings.dat \n", - " inflating: ../data/ml-10M100K/README.html \n", - " inflating: ../data/ml-10M100K/split_ratings.sh \n", - " inflating: ../data/ml-10M100K/tags.dat \n" - ] - } - ], - "source": [ - "# MovieLens 데이터셋을 data 디렉터리에 다운로드 한 뒤 압축을 푼다\n", - "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", - "!unzip -n ../data/ml-10m.zip -d ../data/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Movielens 데이터 확인" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenre
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy]
12Jumanji (1995)[Adventure, Children, Fantasy]
23Grumpier Old Men (1995)[Comedy, Romance]
34Waiting to Exhale (1995)[Comedy, Drama, Romance]
45Father of the Bride Part II (1995)[Comedy]
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "0 1 Toy Story (1995) \n", - "1 2 Jumanji (1995) \n", - "2 3 Grumpier Old Men (1995) \n", - "3 4 Waiting to Exhale (1995) \n", - "4 5 Father of the Bride Part II (1995) \n", - "\n", - " genre \n", - "0 [Adventure, Animation, Children, Comedy, Fantasy] \n", - "1 [Adventure, Children, Fantasy] \n", - "2 [Comedy, Romance] \n", - "3 [Comedy, Drama, Romance] \n", - "4 [Comedy] " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 영화 정보 로딩(10681작품)\n", - "# movieID와 제목만 사용\n", - "m_cols = ['movie_id', 'title', 'genre']\n", - "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", - "\n", - "# genre를 list 형식으로 저장한다\n", - "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", - "movies.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idtagtimestamp
0154973excellent!1215184630
1201747politics1188263867
2201747satire1188263867
3202424chick flick 2121188263835
4202424hanks1188263835
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" - ], - "text/plain": [ - " user_id movie_id tag timestamp\n", - "0 15 4973 excellent! 1215184630\n", - "1 20 1747 politics 1188263867\n", - "2 20 1747 satire 1188263867\n", - "3 20 2424 chick flick 212 1188263835\n", - "4 20 2424 hanks 1188263835" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", - "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", - "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", - "\n", - "# tag를 소문자로 바꾼다\n", - "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", - "\n", - "user_tagged_movies.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "タグ種類=15241\n", - "タグレコード数=95580\n", - "タグが付いている映画数=7601\n" - ] - } - ], - "source": [ - "print(f'태그 종류={len(user_tagged_movies.tag.unique())}')\n", - "print(f'태그 레코드 수={len(user_tagged_movies)}')\n", - "print(f'태그가 붙어있는 영화 수={len(user_tagged_movies.movie_id.unique())}')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie]
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...
\n", - "
" - ], - "text/plain": [ - " movie_id title \\\n", - "0 1 Toy Story (1995) \n", - "1 2 Jumanji (1995) \n", - "2 3 Grumpier Old Men (1995) \n", - "3 4 Waiting to Exhale (1995) \n", - "4 5 Father of the Bride Part II (1995) \n", - "\n", - " genre \\\n", - "0 [Adventure, Animation, Children, Comedy, Fantasy] \n", - "1 [Adventure, Children, Fantasy] \n", - "2 [Comedy, Romance] \n", - "3 [Comedy, Drama, Romance] \n", - "4 [Comedy] \n", - "\n", - " tag \n", - "0 [pixar, pixar, pixar, animation, pixar, animat... \n", - "1 [for children, game, animals, joe johnston, ro... \n", - "2 [funniest movies, comedinha de velhinhos engra... \n", - "3 [girl movie] \n", - "4 [steve martin, pregnancy, remake, steve martin... " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# tag를 영화별로 list 형식으로 저장한다\n", - "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", - "\n", - "# 태그 정보를 결합한다\n", - "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", - "\n", - "movies.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamp
011225.0838985046
111855.0838983525
212315.0838983392
312925.0838983421
413165.0838983392
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" - ], - "text/plain": [ - " user_id movie_id rating timestamp\n", - "0 1 122 5.0 838985046\n", - "1 1 185 5.0 838983525\n", - "2 1 231 5.0 838983392\n", - "3 1 292 5.0 838983421\n", - "4 1 316 5.0 838983392" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 평갓값 데이터만 로딩한다\n", - "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", - "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", - "ratings.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamp
011225.0838985046
111855.0838983525
212315.0838983392
312925.0838983421
413165.0838983392
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp\n", - "0 1 122 5.0 838985046\n", - "1 1 185 5.0 838983525\n", - "2 1 231 5.0 838983392\n", - "3 1 292 5.0 838983421\n", - "4 1 316 5.0 838983392" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n", - "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", - "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", - "ratings.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idmovie_idratingtimestamptitlegenretag
011225.0838985046Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
11391223.0974302621Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
21491222.51112342322Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
31821223.0943458784Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
42151224.51102493547Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
\n", - "
" - ], - "text/plain": [ - " user_id movie_id rating timestamp title genre \\\n", - "0 1 122 5.0 838985046 Boomerang (1992) [Comedy, Romance] \n", - "1 139 122 3.0 974302621 Boomerang (1992) [Comedy, Romance] \n", - "2 149 122 2.5 1112342322 Boomerang (1992) [Comedy, Romance] \n", - "3 182 122 3.0 943458784 Boomerang (1992) [Comedy, Romance] \n", - "4 215 122 4.5 1102493547 Boomerang (1992) [Comedy, Romance] \n", - "\n", - " tag \n", - "0 [dating, nudity (topless - brief), can't remem... \n", - "1 [dating, nudity (topless - brief), can't remem... \n", - "2 [dating, nudity (topless - brief), can't remem... \n", - "3 [dating, nudity (topless - brief), can't remem... \n", - "4 [dating, nudity (topless - brief), can't remem... " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 영화 데이터와 평가 데이터를 결합한다\n", - "movielens = ratings.merge(movies, on='movie_id')\n", - "movielens.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "unique_users=1000, unique_movies=6736\n" - ] - } - ], - "source": [ - "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_id
min20.00
max1668.00
mean132.83
len1000.00
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" - ], - "text/plain": [ - " movie_id\n", - "min 20.00\n", - "max 1668.00\n", - "mean 132.83\n", - "len 1000.00" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "# 사용자 정보\n", - "movielens.groupby('user_id').agg({'movie_id': len}).agg({'movie_id':[min, max, np.mean, len]})" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_id
min1.000000
max496.000000
mean19.719418
len6736.000000
\n", - "
" - ], - "text/plain": [ - " user_id\n", - "min 1.000000\n", - "max 496.000000\n", - "mean 19.719418\n", - "len 6736.000000" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 영화 정보\n", - "movielens.groupby('movie_id').agg({'user_id': len}).agg({'user_id':[min, max, np.mean, len]})" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "評価値数=132830\n" - ] - } - ], - "source": [ - "print(f'평갓값 수={len(movielens)}')" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_id
rating
0.5851
1.04847
1.51247
2.010292
2.53729
3.031706
3.59661
4.039917
4.56949
5.023631
\n", - "
" - ], - "text/plain": [ - " movie_id\n", - "rating \n", - "0.5 851\n", - "1.0 4847\n", - "1.5 1247\n", - "2.0 10292\n", - "2.5 3729\n", - "3.0 31706\n", - "3.5 9661\n", - "4.0 39917\n", - "4.5 6949\n", - "5.0 23631" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "movielens.groupby('rating').agg({'movie_id': len})" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "movielens.groupby('rating').agg({'movie_id': len}).plot.bar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 데이터 분할" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# 학습용과 데이터용으로 데이터를 나눈다\n", - "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", - "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", - "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", - "\n", - "movielens['timestamp_rank'] = movielens.groupby(\n", - " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", - "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", - "movielens_test = movielens[movielens['timestamp_rank']<= 5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{"cells":[{"cell_type":"markdown","metadata":{"id":"yCY156qw4sti"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/data_download.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"9Us2iHKa4stk"},"source":["# Movielens 데이터 다운로드"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iHYNrOLi4stk","executionInfo":{"status":"ok","timestamp":1672116122989,"user_tz":-540,"elapsed":4342,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"64ab6289-6afa-41f5-810c-4b1a66d2ea0b"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:41:56-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 92.1MB/s in 0.7s \n","\n","2022-12-27 04:41:57 (92.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# MovieLens 데이터셋을 data 디렉터리에 다운로드 한 뒤 압축을 푼다\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"markdown","metadata":{"id":"cD0PCuUO4stl"},"source":["# Movielens 데이터 확인"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"tmhedtRd4stm","executionInfo":{"status":"ok","timestamp":1672116154325,"user_tz":-540,"elapsed":282,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import pandas as pd"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"nG38A4UQ4stm","executionInfo":{"status":"ok","timestamp":1672116156988,"user_tz":-540,"elapsed":290,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"eb04c892-fc66-4fe6-c39e-bb98685c1733"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","1 2 Jumanji (1995) \n","2 3 Grumpier Old Men (1995) \n","3 4 Waiting to Exhale (1995) \n","4 5 Father of the Bride Part II (1995) \n","\n"," genre \n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","1 [Adventure, Children, Fantasy] \n","2 [Comedy, Romance] \n","3 [Comedy, Drama, Romance] \n","4 [Comedy] "],"text/html":["\n","
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movie_idtitlegenre
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy]
12Jumanji (1995)[Adventure, Children, Fantasy]
23Grumpier Old Men (1995)[Comedy, Romance]
34Waiting to Exhale (1995)[Comedy, Drama, Romance]
45Father of the Bride Part II (1995)[Comedy]
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user_idmovie_idtagtimestamp
0154973excellent!1215184630
1201747politics1188263867
2201747satire1188263867
3202424chick flick 2121188263835
4202424hanks1188263835
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\n"," "]},"metadata":{},"execution_count":4}],"source":["# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","user_tagged_movies.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7f5OX7MY4stn","executionInfo":{"status":"ok","timestamp":1672116163746,"user_tz":-540,"elapsed":418,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"4d8f672e-7515-470e-be3e-da7a3844daaf"},"outputs":[{"output_type":"stream","name":"stdout","text":["태그 종류=15241\n","태그 레코드 수=95580\n","태그가 붙어있는 영화 수=7601\n"]}],"source":["print(f'태그 종류={len(user_tagged_movies.tag.unique())}')\n","print(f'태그 레코드 수={len(user_tagged_movies)}')\n","print(f'태그가 붙어있는 영화 수={len(user_tagged_movies.movie_id.unique())}')"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"3ID9tTb04stn","executionInfo":{"status":"ok","timestamp":1672116165669,"user_tz":-540,"elapsed":3,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9680477e-3b7d-49a6-d8a7-a325c8c640ab"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","1 2 Jumanji (1995) \n","2 3 Grumpier Old Men (1995) \n","3 4 Waiting to Exhale (1995) \n","4 5 Father of the Bride Part II (1995) \n","\n"," genre \\\n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","1 [Adventure, Children, Fantasy] \n","2 [Comedy, Romance] \n","3 [Comedy, Drama, Romance] \n","4 [Comedy] \n","\n"," tag \n","0 [pixar, pixar, pixar, animation, pixar, animat... \n","1 [for children, game, animals, joe johnston, ro... \n","2 [funniest movies, comedinha de velhinhos engra... \n","3 [girl movie] \n","4 [steve martin, pregnancy, remake, steve martin... "],"text/html":["\n","
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie]
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...
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\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":6}],"source":["# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","movies.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Y_16BuAv4stn","executionInfo":{"status":"ok","timestamp":1672116226827,"user_tz":-540,"elapsed":58747,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c017c43d-32cb-47dc-8605-f343afdbfb61"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp\n","0 1 122 5.0 838985046\n","1 1 185 5.0 838983525\n","2 1 231 5.0 838983392\n","3 1 292 5.0 838983421\n","4 1 316 5.0 838983392"],"text/html":["\n","
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user_idmovie_idratingtimestamp
011225.0838985046
111855.0838983525
212315.0838983392
312925.0838983421
413165.0838983392
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\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":7}],"source":["# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","ratings.head()\n"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"-uIC1Irx4sto","executionInfo":{"status":"ok","timestamp":1672116226827,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9d7d2f5f-d826-41e2-8f2b-ec6dcebf3b31"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp\n","0 1 122 5.0 838985046\n","1 1 185 5.0 838983525\n","2 1 231 5.0 838983392\n","3 1 292 5.0 838983421\n","4 1 316 5.0 838983392"],"text/html":["\n","
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user_idmovie_idratingtimestamp
011225.0838985046
111855.0838983525
212315.0838983392
312925.0838983421
413165.0838983392
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":8}],"source":["# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","ratings.head()"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"8Cc0l6UH4sto","executionInfo":{"status":"ok","timestamp":1672116226827,"user_tz":-540,"elapsed":7,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b9e06a0e-a797-4067-d4e1-7112653c608d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp title genre \\\n","0 1 122 5.0 838985046 Boomerang (1992) [Comedy, Romance] \n","1 139 122 3.0 974302621 Boomerang (1992) [Comedy, Romance] \n","2 149 122 2.5 1112342322 Boomerang (1992) [Comedy, Romance] \n","3 182 122 3.0 943458784 Boomerang (1992) [Comedy, Romance] \n","4 215 122 4.5 1102493547 Boomerang (1992) [Comedy, Romance] \n","\n"," tag \n","0 [dating, nudity (topless - brief), can't remem... \n","1 [dating, nudity (topless - brief), can't remem... \n","2 [dating, nudity (topless - brief), can't remem... \n","3 [dating, nudity (topless - brief), can't remem... \n","4 [dating, nudity (topless - brief), can't remem... "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretag
011225.0838985046Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
11391223.0974302621Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
21491222.51112342322Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
31821223.0943458784Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
42151224.51102493547Boomerang (1992)[Comedy, Romance][dating, nudity (topless - brief), can't remem...
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\n","
\n"," "]},"metadata":{},"execution_count":9}],"source":["# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","movielens.head()"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R4Z0VEKr4sto","executionInfo":{"status":"ok","timestamp":1672116226827,"user_tz":-540,"elapsed":6,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"05111483-5ee2-4c2d-f512-45f74cc2fd38"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"stlWbEpk4sto","executionInfo":{"status":"ok","timestamp":1672116227311,"user_tz":-540,"elapsed":489,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"a374f238-c767-4ea8-df20-c213ec8c6f8d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id\n","min 20.00\n","max 1668.00\n","mean 132.83\n","len 1000.00"],"text/html":["\n","
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movie_id
min20.00
max1668.00
mean132.83
len1000.00
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\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":11}],"source":["import numpy as np\n","# 사용자 정보\n","movielens.groupby('user_id').agg({'movie_id': len}).agg({'movie_id':[min, max, np.mean, len]})"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"htrtcADB4stp","executionInfo":{"status":"ok","timestamp":1672116227311,"user_tz":-540,"elapsed":16,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c2df6e23-eddc-4d36-c339-da58f0813f10"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id\n","min 1.000000\n","max 496.000000\n","mean 19.719418\n","len 6736.000000"],"text/html":["\n","
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user_id
min1.000000
max496.000000
mean19.719418
len6736.000000
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":12}],"source":["# 영화 정보\n","movielens.groupby('movie_id').agg({'user_id': len}).agg({'user_id':[min, max, np.mean, len]})"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_PY1CxI24stp","executionInfo":{"status":"ok","timestamp":1672116227311,"user_tz":-540,"elapsed":15,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"75c1f4d8-47e9-4ef3-da0e-adf1a481af53"},"outputs":[{"output_type":"stream","name":"stdout","text":["평갓값 수=132830\n"]}],"source":["print(f'평갓값 수={len(movielens)}')"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":394},"id":"KUQfjZpr4stp","executionInfo":{"status":"ok","timestamp":1672116227311,"user_tz":-540,"elapsed":12,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"eaf0d266-7c51-421c-a7a4-de91af9aca15"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id\n","rating \n","0.5 851\n","1.0 4847\n","1.5 1247\n","2.0 10292\n","2.5 3729\n","3.0 31706\n","3.5 9661\n","4.0 39917\n","4.5 6949\n","5.0 23631"],"text/html":["\n","
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movie_id
rating
0.5851
1.04847
1.51247
2.010292
2.53729
3.031706
3.59661
4.039917
4.56949
5.023631
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\n","
\n"," "]},"metadata":{},"execution_count":14}],"source":["movielens.groupby('rating').agg({'movie_id': len})"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":303},"id":"QmkjL5VS4stp","executionInfo":{"status":"ok","timestamp":1672116227312,"user_tz":-540,"elapsed":12,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"43c55ed9-da51-484f-9ca5-168fbc4bac51"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["
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ImJf/rz0H32UOLcPiYgFEdEQEQ01NTV9vTkzs6pRVDGQNIBcIVgSEX+XwjvTIR7Sz9dTfDswKm/x2hQ7WLy2QNzMzDJSTG8iAQuBLRHx13mzVgAdPYJmAMvz4tNTr6JJwBvpcNIq4HxJQ9KJ4/OBVWnePkmT0ram563LzMwycFQRbc4CrgRekLQhxb4F3A4skzQLeBX4Upq3ErgQaAHeAmYCRMRuSd8D1qd2t0bE7jR+NXA/cCzweBrMzCwj3RaDiHga6Krf/3kF2gcwp4t1LQIWFYg3AZ/qLhczM+sbvgLZzMxcDMzMzMXAzMxwMTAzM1wMzMwMFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMxwMTAzM1wMzMyM4p6BvEjS65I25cVukbRd0oY0XJg370ZJLZJeknRBXrwxxVokzc2Lj5a0NsV/KunoUr5AMzPrXjF7BvcDjQXid0bE+DSsBJA0BrgcGJuWuUdSf0n9gfnAZGAMcEVqC/CDtK6TgD3ArN68IDMzO3TdFoOI+Edgd3ftkinA0ojYHxGvAC3AhDS0RMS2iHgHWApMkSTgXODhtPxiYOohvgYzM+ul3pwzuEbSxnQYaUiKjQRey2vTlmJdxYcCeyPiQKd4QZJmS2qS1NTe3t6L1M3MLF9Pi8G9wJ8C44EdwF+VLKODiIgFEdEQEQ01NTVZbNLMrCoc1ZOFImJnx7ikHwGPpsntwKi8prUpRhfxXcBgSUelvYP89mZmlpEe7RlIGpE3eQnQ0dNoBXC5pGMkjQbqgXXAeqA+9Rw6mtxJ5hUREcCTwKVp+RnA8p7kZGZmPdftnoGknwDnAMMktQE3A+dIGg8E0Ap8DSAiNktaBrwIHADmRMS7aT3XAKuA/sCiiNicNnEDsFTS94HngIUle3VmZlaUbotBRFxRINzlF3ZEzAPmFYivBFYWiG8j19vIzMzKxFcgm5mZi4GZmbkYmJkZLgZmZoaLgZmZ4WJgZma4GJiZGS4GZmaGi4GZmeFiYGZmuBiYmRkuBmZmhouBmZnhYmBmZrgYmJkZLgZmZoaLgZmZUUQxkLRI0uuSNuXFTpS0WtLL6eeQFJekuyS1SNoo6Yy8ZWak9i9LmpEXP1PSC2mZuySp1C/SzMwOrtvHXgL3A/8LeCAvNhd4IiJulzQ3Td8ATAbq0zARuBeYKOlEcs9ObiD33ORmSSsiYk9q81VgLbnHYjYCj/f+pVmlqZv7WK+Wb739ohJlYmaddbtnEBH/COzuFJ4CLE7ji4GpefEHImcNMFjSCOACYHVE7E4FYDXQmOYNiog1ERHkCs5UzMwsUz09ZzA8Inak8d8Cw9P4SOC1vHZtKXaweFuBeEGSZktqktTU3t7ew9TNzKyzXp9ATv/RRwlyKWZbCyKiISIaampqstikmVlVKOacQSE7JY2IiB3pUM/rKb4dGJXXrjbFtgPndIo/leK1BdqbWR/r7Tkc8HmcI0lP9wxWAB09gmYAy/Pi01OvoknAG+lw0irgfElDUs+j84FVad4+SZNSL6LpeesyM7OMdLtnIOkn5P6rHyapjVyvoNuBZZJmAa8CX0rNVwIXAi3AW8BMgIjYLel7wPrU7taI6DgpfTW5HkvHkutF5J5EZmYZ67YYRMQVXcw6r0DbAOZ0sZ5FwKIC8SbgU93lYWZmfcdXIJuZmYuBmZm5GJiZGS4GZmaGi4GZmeFiYGZmuBiYmRkuBmZmhouBmZnhYmBmZvT8rqVmZkcM38HVewZmZoaLgZmZ4WJgZma4GJiZGS4GZmaGi4GZmdHLYiCpVdILkjZIakqxEyWtlvRy+jkkxSXpLkktkjZKOiNvPTNS+5clzehqe2Zm1jdKsWfwhYgYHxENaXou8ERE1ANPpGmAyUB9GmYD90KueJB7rvJEYAJwc0cBMTOzbPTFYaIpwOI0vhiYmhd/IHLWAIMljQAuAFZHxO6I2AOsBhr7IC8zM+tCb4tBAP8gqVnS7BQbHhE70vhvgeFpfCTwWt6ybSnWVfxDJM2W1CSpqb29vZepm5lZh97ejuKzEbFd0r8BVkvamj8zIkJS9HIb+etbACwAaGhoKNl6+1JvL3M/3C9xN7PDQ6/2DCJie/r5OvAIuWP+O9PhH9LP11Pz7cCovMVrU6yruJmZZaTHxUDSRySd0DEOnA9sAlYAHT2CZgDL0/gKYHrqVTQJeCMdTloFnC9pSDpxfH6KmZlZRnpzmGg48IikjvU8GBG/kLQeWCZpFvAq8KXUfiVwIdACvAXMBIiI3ZK+B6xP7W6NiN29yMvMzA5Rj4tBRGwDxhWI7wLOKxAPYE4X61oELOppLmZm1jt+noFVFd+33qww347CzMxcDMzMzMXAzMxwMTAzM1wMzMwMFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzfKO6quCbs5lZd7xnYGZmR+6egf8bNjMrnvcMzMyscoqBpEZJL0lqkTS33PmYmVWTiigGkvoD84HJwBjgCkljypuVmVn1qIhiAEwAWiJiW0S8AywFppQ5JzOzqqHcc+rLnIR0KdAYEf8lTV8JTIyIazq1mw3MTpP/DnipF5sdBvyuF8uXSiXkUQk5QGXkUQk5QGXkUQk5QGXkUQk5QGny+ERE1HQOHla9iSJiAbCgFOuS1BQRDaVY1+GeRyXkUCl5VEIOlZJHJeRQKXlUQg59nUelHCbaDozKm65NMTMzy0ClFIP1QL2k0ZKOBi4HVpQ5JzOzqlERh4ki4oCka4BVQH9gUURs7uPNluRwUwlUQh6VkANURh6VkANURh6VkANURh6VkAP0YR4VcQLZzMzKq1IOE5mZWRm5GJiZmYuBmZm5GJhVFEknSjqx3HmUm9+H7LkYWFlJGi7pjDQMr4B8Mv8CkvRxSUsltQNrgXWSXk+xuqzzKRe/D4Vl9RmpimIg6T/njddKekLSXknPSPpkGfKpqC9AyP5LUNJ4SWuAp4C/SMOvJa2RdEZGOXw7b3yMpH8CmiW1SpqYRQ7JT4FHgD+JiPqIOAkYAfyc3H26+lyFfEbK/j5AxbwX2X9GIuKIH4Bn88aXkbu/UT/gEuCJDPMYD6wBtgC/TMPWFDsjwzy+nTc+Bvgn4BWgldw9obLIYUOhbQGTgOfL8HfxGDA5jU8Ansnw9/FyT+b14XtRls9IJbwPlfJepG1n+hmpij2DTj4ZEQsi4r2IeATI8j/i+4FrI+KUiPj3aTgZ+AZwX4Z5/Ie88b9MOY0GvgTcmVEOH4mItZ2DEbEG+EhGOeT7WEQ8nnJYBxyb4babJd0jaaKkj6VhoqR7gOcyzKNDuT4jlfY+QHm/LzL9jFTEFcgZqJV0FyCgRtKAiPhDmjcgwzy6/OVKKscXIHT6EpSU1Zfg45IeAx4AXkuxUcB04BcZ5fBvJa0g93dRK+m4iHgrzcvy72I6MAv4LjAyxdqAvwcWZpRDJXxGKuF9gMp4LyDjz0i1FINv5o03AccDeyT9CdneA6kSvgChAr4EI+LrkiaTe25Fxwd/OzA/IlZmkQMffmZGP8id0wHuzSgHIvcMj3uz3GYBZf+MVMj7ABXwXkD2nxHfjiJjXfxyV2T4BYikz3cKNUfEm+lL8NKImJ9VLnZwki6OiEfLnUe5+X3oe9V4zuADJF2c5fYi4vGIuCoi/iwNV2VZCFIOv+40vJniOyuhEKSHGFV9Dsmny51A1p+RLpT9fYCKeS/65O+z6osBlfNHVhFfPhWSh8qdABnnIGmCpE+n8TGSrpN0YUTcnGUeXSjbZ0TSAwAV8j5AhXxf0Ad/n9VyzgBJJ1P48Eyl/JFVwhcgZJhH+p2MBNZ27J0kr1ZZDjcDk4GjJK0GJgJPAnMlnR4R8zLKYwIQEbFe0higEdia1Wckncf6QAj4gqTB5BL78yzyKETSAxExvZzfF5I+S67b86aI+N8lX381nDOQdANwBbkLV9pSuJbcQ3SWRsTt5cqtg6SZEZFl99Ky5iHp68AcctdcjCfXvXV5mvdsRPT5hWeVkEPa1gtp+8cAvwVqI2Jf6tm1NiJOyyCHPxYkIL8gfRFYlUVBkvQs8CLwN0CQKwY/Ifc5JSJ+3dc5pDwKFiXgVymPTIqSpHURMSGNf5Xc3+ojwPnA35f8e6uvLpiopIHcRVUDCsSPJsOLWbrJ8f+VO4cs8wBeAI5P43Xkem1cm6afq5YcOm+r83aBDRm+F/2B44B9wKAUPxbYmFEO/YD/Sq4YjU+xbVn9HvLyeBb4MXAO8Pn0c0ca/3yZ/i7WAzVp/CPAC6XeXrUcJnoP+Bgf3vUfkeZlQtLGrmYBmd2WokLy6Bfvn7hulXQO8LCkT5DdoapKyAHgnbzuvWd2BCV9lOz+Pg9ExLvAW5J+ExH7ACLiXyVlkkNEvAfcKemh9HMn5TmU3QBcC9wEfDMiNkj618hozyRPP0lDyBVJRUQ7QET8i6QDpd5YtRSDbwBPSHqZ9/v3fxw4CbgmwzyGAxcAezrFBTxTZXnslDQ+IjYARK5r68XAIuDUKsoB4HMRsT/lkP/FOwCYkVEOlVCQAIiINmCapIvI7aVkqoKK0keBZnKfy5A0IiJ2SDqePvhnpSrOGQBI6kfu5Ev+CeT16b+hrHJYCNwXEU8XmPdgRPynaslDUi25/0Z/W2DeWRHxf6ohh0oh6ZiOgtQpPgwYEREvlCGtipCK0lkR8a1y5wIg6ThgeES8UtL1VksxMDOzrvk6AzMzczEwMzMXA7Nek/SNdBy3Y3plx4VSZocLnzMwK4Ikkfu8fKhnjaRWoCEifpd5YmYl4j0Dsy5IqpP0Uro/ziZgoaQmSZslfTe1+Tq5a1ielPRkirVKGpaW3yLpR2mZf+h4XoSkT0vaKGmDpL+UtKlcr9MMXAzMulMP3BMRY4HrI6IBOA34vKTTIuIu4J+BL0TEF7pYfn5afi/wH1P8PuBrETEeyKx7s1lXXAzMDu7VyD1mEOBL6f45zwFjyT0/ujuvdFzURu4Corp0PuGEiPi/Kf5gSTM264FquQLZrKf+BUDSaOC/AZ+OiD2S7gcGFrF8/oVc75Lts5XNiuY9A7PiDCJXGN5IT4SbnDfv98AJxa4oIvYCv5c0MYUuL1mWZj3kPQOzIkTE85KeA7aSu79V/q0qFgC/kPTPXZw3KGQW8KN0E7hfA2+UNGGzQ+SupWZlIOn4jjumSppL7v4/15Y5Lati3jMwK4+LJN1I7jP4KvCV8qZj1c57BmZm5hPIZmbmYmBmZrgYmJkZLgZmZoaLgZmZAf8feBBCCHGGh2EAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}],"source":["movielens.groupby('rating').agg({'movie_id': len}).plot.bar()"]},{"cell_type":"markdown","metadata":{"id":"a_LEPxLP4stp"},"source":["# 데이터 분할"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"xQGdGyRm4stp","executionInfo":{"status":"ok","timestamp":1672116227592,"user_tz":-540,"elapsed":290,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"EqBNWx7v4stp","executionInfo":{"status":"ok","timestamp":1672116227593,"user_tz":-540,"elapsed":3,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file From 74a425d585c0c3c8c1bc06e3e35c93a3d5f720a8 Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Tue, 27 Dec 2022 16:01:28 +0900 Subject: [PATCH 06/17] updated local notebooks --- chapter5/notebook/Association.ipynb | 474 +-- chapter5/notebook/BPR.ipynb | 159 +- chapter5/notebook/FM.ipynb | 259 +- chapter5/notebook/IMF.ipynb | 311 +- chapter5/notebook/Item2vec.ipynb | 29 +- chapter5/notebook/LDA_collaboration.ipynb | 301 +- chapter5/notebook/LDA_content.ipynb | 3398 +-------------------- chapter5/notebook/MF.ipynb | 47 +- chapter5/notebook/NMF.ipynb | 69 +- chapter5/notebook/Popularity.ipynb | 223 +- chapter5/notebook/RF.ipynb | 35 +- chapter5/notebook/Random.ipynb | 18 +- chapter5/notebook/SVD.ipynb | 854 +----- chapter5/notebook/Template.ipynb | 4 +- chapter5/notebook/UMCF.ipynb | 58 +- chapter5/notebook/Word2vec.ipynb | 55 +- chapter5/notebook/data_download.ipynb | 79 +- 17 files changed, 178 insertions(+), 6195 deletions(-) diff --git a/chapter5/notebook/Association.ipynb b/chapter5/notebook/Association.ipynb index bacf611..e36bdbd 100644 --- a/chapter5/notebook/Association.ipynb +++ b/chapter5/notebook/Association.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1b316a16", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c1b8db75", "metadata": {}, "outputs": [], @@ -36,237 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "f29c7aac", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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" - ], - "text/plain": [ - " movie_id title genre \\\n", - "587 593 Silence of the Lambs, The (1991) [Crime, Horror, Thriller] \n", - "\n", - " tag \n", - "587 [based on a book, anthony hopkins, demme, psyc... " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# movie_id=593의 제목 확인(양들의 침묵)\n", "movielens.item_content[movielens.item_content.movie_id == 593]" @@ -428,85 +80,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "470dafd1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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antecedentsconsequentslift
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" - ], - "text/plain": [ - " antecedents consequents lift\n", - "649 (4993) (5952) 5.459770\n", - "648 (5952) (4993) 5.459770\n", - "1462 (1196, 1198) (1291, 260) 4.669188\n", - "1463 (1291, 260) (1196, 1198) 4.669188\n", - "1460 (1291, 1196) (260, 1198) 4.171359" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from mlxtend.frequent_patterns import association_rules\n", "# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n", @@ -516,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "7414301d", "metadata": {}, "outputs": [], @@ -529,18 +106,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "6c2f4856", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.011, Recall@K=0.036\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -552,23 +121,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f50bd7a9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.015, Recall@K=0.048\n", - "rmse=0.000, Precision@K=0.014, Recall@K=0.042\n", - "rmse=0.000, Precision@K=0.014, Recall@K=0.043\n", - "rmse=0.000, Precision@K=0.013, Recall@K=0.040\n", - "rmse=0.000, Precision@K=0.011, Recall@K=0.036\n", - "rmse=0.000, Precision@K=0.010, Recall@K=0.034\n" - ] - } - ], + "outputs": [], "source": [ "# min_support와 정밀도의 관계\n", "for min_support in [0.06, 0.07, 0.08, 0.09, 0.1, 0.11]:\n", diff --git a/chapter5/notebook/BPR.ipynb b/chapter5/notebook/BPR.ipynb index fda56c8..d421bcf 100644 --- a/chapter5/notebook/BPR.ipynb +++ b/chapter5/notebook/BPR.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "37dbe23a", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "376e58db", "metadata": {}, "outputs": [], @@ -36,39 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a660e0c4", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e2c989ef83084bdd98ca0865590dde74", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00 <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable early-stopping.\u001b[0m\n", - "\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable (-x rmse) option.\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mxLearn uses 4 threads for training task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] \u001b[0mBinary file (/var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpj010yd3q.bin) NOT found. Convert text file to binary file.\n", - "\u001b[32m[------------] \u001b[0mNumber of Feature: 1061\n", - "\u001b[32m[------------] \u001b[0mTime cost for reading problem: 0.02 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Initialize model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mModel size: 53.89 KB\n", - "\u001b[32m[------------] \u001b[0mTime cost for model initial: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to train ...\u001b[0m\n", - "\u001b[32m[------------]\u001b[0m Epoch Train mse_loss Time cost (sec)\n", - "\u001b[32m[ \u001b[0m 2%\u001b[32m ]\u001b[0m 1 0.454755 0.01\n", - "\u001b[32m[ \u001b[0m 4%\u001b[32m ]\u001b[0m 2 0.393448 0.01\n", - "\u001b[32m[ \u001b[0m 6%\u001b[32m ]\u001b[0m 3 0.377439 0.01\n", - "\u001b[32m[ \u001b[0m 8%\u001b[32m ]\u001b[0m 4 0.368265 0.01\n", - "\u001b[32m[ \u001b[0m 10%\u001b[32m ]\u001b[0m 5 0.361691 0.01\n", - "\u001b[32m[ \u001b[0m 12%\u001b[32m ]\u001b[0m 6 0.357060 0.01\n", - "\u001b[32m[ \u001b[0m 14%\u001b[32m ]\u001b[0m 7 0.353435 0.01\n", - 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"\u001b[32m[------------] \u001b[0mTime cost for saving model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to save txt model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mTXT Model file: /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmprefexgam\n", - "\u001b[32m[------------] \u001b[0mTime cost for saving txt model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Finish training\u001b[0m\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 0.34 (sec)\u001b[0m\n", - "\u001b[32m\u001b[1m----------------------------------------------------------------------------------------------\n", - " _\n", - " | |\n", - " __ _| | ___ __ _ _ __ _ __\n", - " \\ \\/ / | / _ \\/ _` | '__| '_ \\ \n", - " > <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[32m[------------] \u001b[0mxLearn uses 8 threads for prediction task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Load model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mLoad model from /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpxh6ei75k\n", - "\u001b[32m[------------] \u001b[0mLoss function: squared\n", - "\u001b[32m[------------] \u001b[0mScore function: fm\n", - "\u001b[32m[------------] \u001b[0mNumber of Feature: 1061\n", - "\u001b[32m[------------] \u001b[0mNumber of K: 10\n", - "\u001b[32m[------------] \u001b[0mTime cost for loading model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] \u001b[0mBinary file (/var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpr2wl3i7s.bin) NOT found. Convert text file to binary file.\n", - "\u001b[32m[------------] \u001b[0mTime cost for reading problem: 0.04 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to predict ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mThe test loss is: 6.944547\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 0.07 (sec)\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# FM 추천\n", "from src.fm import FMRecommender\n", @@ -166,18 +49,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "483d6ad4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.054, Precision@K=0.013, Recall@K=0.040\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -189,128 +64,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "fef7f975", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32m\u001b[1m----------------------------------------------------------------------------------------------\n", - " _\n", - " | |\n", - " __ _| | ___ __ _ _ __ _ __\n", - " \\ \\/ / | / _ \\/ _` | '__| '_ \\ \n", - " > <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable early-stopping.\u001b[0m\n", - "\u001b[35m\u001b[1m[ WARNING ] Validation file not found, xLearn has already disable (-x rmse) option.\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mxLearn uses 4 threads for training task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] \u001b[0mBinary file (/var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpmoz52pqt.bin) NOT found. Convert text file to binary file.\n", - "\u001b[32m[------------] \u001b[0mNumber of Feature: 3050\n", - "\u001b[32m[------------] \u001b[0mTime cost for reading problem: 0.20 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Initialize model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mModel size: 154.89 KB\n", - "\u001b[32m[------------] \u001b[0mTime cost for model initial: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to train ...\u001b[0m\n", - "\u001b[32m[------------]\u001b[0m Epoch Train mse_loss Time cost (sec)\n", - "\u001b[32m[ \u001b[0m 2%\u001b[32m ]\u001b[0m 1 0.513912 0.03\n", - "\u001b[32m[ \u001b[0m 4%\u001b[32m ]\u001b[0m 2 0.505483 0.03\n", - "\u001b[32m[ \u001b[0m 6%\u001b[32m ]\u001b[0m 3 0.505344 0.03\n", - "\u001b[32m[ \u001b[0m 8%\u001b[32m ]\u001b[0m 4 0.505056 0.03\n", - "\u001b[32m[ \u001b[0m 10%\u001b[32m ]\u001b[0m 5 0.504832 0.03\n", - "\u001b[32m[ \u001b[0m 12%\u001b[32m ]\u001b[0m 6 0.504601 0.03\n", - "\u001b[32m[ \u001b[0m 14%\u001b[32m ]\u001b[0m 7 0.504817 0.03\n", - 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"\u001b[32m[------------] \u001b[0mTime cost for saving model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to save txt model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mTXT Model file: /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpzy7o5uqs\n", - "\u001b[32m[------------] \u001b[0mTime cost for saving txt model: 0.01 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Finish training\u001b[0m\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 1.86 (sec)\u001b[0m\n", - "\u001b[32m\u001b[1m----------------------------------------------------------------------------------------------\n", - " _\n", - " | |\n", - " __ _| | ___ __ _ _ __ _ __\n", - " \\ \\/ / | / _ \\/ _` | '__| '_ \\ \n", - " > <| |___| __/ (_| | | | | | |\n", - " /_/\\_\\_____/\\___|\\__,_|_| |_| |_|\n", - "\n", - " xLearn -- 0.40 Version --\n", - "----------------------------------------------------------------------------------------------\n", - "\n", - "\u001b[39m\u001b[0m\u001b[32m[------------] \u001b[0mxLearn uses 8 threads for prediction task.\n", - "\u001b[32m\u001b[1m[ ACTION ] Load model ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mLoad model from /var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpgccvgagg\n", - "\u001b[32m[------------] \u001b[0mLoss function: squared\n", - "\u001b[32m[------------] \u001b[0mScore function: fm\n", - "\u001b[32m[------------] \u001b[0mNumber of Feature: 3050\n", - "\u001b[32m[------------] \u001b[0mNumber of K: 10\n", - "\u001b[32m[------------] \u001b[0mTime cost for loading model: 0.00 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Read Problem ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mFirst check if the text file has been already converted to binary format.\n", - "\u001b[32m[------------] \u001b[0mBinary file (/var/folders/31/92gcylk93xvbjbyb75sdmpqm0000gn/T/tmpegzkkg74.bin) NOT found. Convert text file to binary file.\n", - "\u001b[32m[------------] \u001b[0mTime cost for reading problem: 0.55 (sec)\n", - "\u001b[32m\u001b[1m[ ACTION ] Start to predict ...\u001b[0m\n", - "\u001b[32m[------------] \u001b[0mThe test loss is: 7.458863\n", - "\u001b[32m\u001b[1m[ ACTION ] Clear the xLearn environment ...\u001b[0m\n", - "\u001b[32m\u001b[1m[------------] Total time cost: 0.60 (sec)\u001b[0m\n", - "rmse=1.068, Precision@K=0.015, Recall@K=0.050\n" - ] - } - ], + "outputs": [], "source": [ "# 보충 정보 사용\n", "recommend_result = recommender.recommend(movielens, use_side_information=True)\n", diff --git a/chapter5/notebook/IMF.ipynb b/chapter5/notebook/IMF.ipynb index bd57840..8a5439a 100644 --- a/chapter5/notebook/IMF.ipynb +++ b/chapter5/notebook/IMF.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c302ab23", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "4f1322e1", "metadata": {}, "outputs": [], @@ -36,46 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bf47abe9", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "86a3fdbb20244ff5a28180b308cdc261", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/50 [00:00 is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "# Item2vecContent 추천\n", "from src.item2vec import Item2vecRecommender\n", @@ -58,18 +49,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "d63c29a2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.028, Recall@K=0.087\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/LDA_collaboration.ipynb b/chapter5/notebook/LDA_collaboration.ipynb index eaa480b..a49e8ac 100644 --- a/chapter5/notebook/LDA_collaboration.ipynb +++ b/chapter5/notebook/LDA_collaboration.ipynb @@ -5,12 +5,12 @@ "id": "942a9897", "metadata": {}, "source": [ - "# Latent Dirichlet Allocation (LDA)の行動データへの適用" + "# Latent Dirichlet Allocation (LDA)의 행동 데이터로의 적용" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "b8cb8f0b", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "6769fb75", "metadata": {}, "outputs": [], @@ -36,289 +36,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d3a98495", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 03:02:57,133 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n", - "2021-08-24 03:02:57,227 : INFO : built Dictionary(4987 unique tokens: ['185', '231', '292', '316', '329']...) from 997 documents (total 67731 corpus positions)\n", - "2021-08-24 03:02:57,228 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary(4987 unique tokens: ['185', '231', '292', '316', '329']...) from 997 documents (total 67731 corpus positions)\", 'datetime': '2021-08-24T03:02:57.228811', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n", - "2021-08-24 03:02:57,277 : INFO : using symmetric alpha at 0.02\n", - "2021-08-24 03:02:57,278 : INFO : using symmetric eta at 0.02\n", - "2021-08-24 03:02:57,279 : INFO : using serial LDA version on this node\n", - "2021-08-24 03:02:57,299 : INFO : running online (multi-pass) LDA training, 50 topics, 30 passes over the supplied corpus of 997 documents, updating model once every 997 documents, evaluating perplexity every 997 documents, iterating 50x with a convergence threshold of 0.001000\n", - "2021-08-24 03:02:58,120 : INFO : -18.332 per-word bound, 330039.8 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:02:58,121 : INFO : PROGRESS: pass 0, at document #997/997\n", - "2021-08-24 03:02:58,782 : INFO : topic #41 (0.020): 0.006*\"2858\" + 0.006*\"260\" + 0.006*\"296\" + 0.006*\"527\" + 0.005*\"50\" + 0.005*\"1210\" + 0.005*\"593\" + 0.004*\"2762\" + 0.004*\"1193\" + 0.004*\"541\"\n", - "2021-08-24 03:02:58,783 : INFO : topic #48 (0.020): 0.006*\"527\" + 0.005*\"110\" + 0.005*\"593\" + 0.005*\"34\" + 0.004*\"380\" + 0.004*\"356\" + 0.004*\"608\" + 0.004*\"32\" + 0.004*\"150\" + 0.004*\"1210\"\n", - "2021-08-24 03:02:58,784 : INFO : topic #26 (0.020): 0.009*\"608\" + 0.006*\"593\" + 0.006*\"260\" + 0.006*\"318\" + 0.005*\"527\" + 0.005*\"858\" + 0.004*\"36\" + 0.004*\"1\" + 0.004*\"356\" + 0.004*\"541\"\n", - "2021-08-24 03:02:58,785 : INFO : topic #14 (0.020): 0.009*\"593\" + 0.009*\"457\" + 0.009*\"356\" + 0.007*\"260\" + 0.007*\"527\" + 0.006*\"480\" + 0.006*\"62\" + 0.006*\"318\" + 0.006*\"364\" + 0.005*\"380\"\n", - "2021-08-24 03:02:58,786 : INFO : topic #25 (0.020): 0.009*\"260\" + 0.007*\"296\" + 0.007*\"593\" + 0.007*\"110\" + 0.006*\"318\" + 0.005*\"608\" + 0.005*\"858\" + 0.005*\"32\" + 0.005*\"1\" + 0.005*\"780\"\n", - "2021-08-24 03:02:58,787 : INFO : topic diff=23.625265, rho=1.000000\n", - "2021-08-24 03:02:59,471 : INFO : -10.582 per-word bound, 1533.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:02:59,472 : INFO : PROGRESS: pass 1, at document #997/997\n", - "2021-08-24 03:02:59,938 : INFO : topic #42 (0.020): 0.005*\"296\" + 0.004*\"2571\" + 0.004*\"1617\" + 0.003*\"1196\" + 0.003*\"912\" + 0.003*\"608\" + 0.003*\"50\" + 0.003*\"1193\" + 0.003*\"1198\" + 0.003*\"1089\"\n", - "2021-08-24 03:02:59,939 : INFO : topic #43 (0.020): 0.009*\"589\" + 0.008*\"588\" + 0.008*\"457\" + 0.008*\"593\" + 0.008*\"150\" + 0.008*\"380\" + 0.008*\"47\" + 0.008*\"316\" + 0.007*\"296\" + 0.007*\"318\"\n", - "2021-08-24 03:02:59,940 : INFO : topic #7 (0.020): 0.008*\"318\" + 0.008*\"1210\" + 0.008*\"2959\" + 0.008*\"593\" + 0.007*\"32\" + 0.006*\"2571\" + 0.006*\"296\" + 0.006*\"1196\" + 0.006*\"260\" + 0.006*\"527\"\n", - "2021-08-24 03:02:59,940 : INFO : topic #0 (0.020): 0.007*\"780\" + 0.006*\"3101\" + 0.005*\"1214\" + 0.005*\"527\" + 0.005*\"1219\" + 0.005*\"608\" + 0.005*\"457\" + 0.005*\"1617\" + 0.004*\"908\" + 0.004*\"2278\"\n", - "2021-08-24 03:02:59,941 : INFO : topic #16 (0.020): 0.006*\"260\" + 0.005*\"1193\" + 0.005*\"1196\" + 0.004*\"1250\" + 0.004*\"858\" + 0.004*\"919\" + 0.004*\"2858\" + 0.004*\"780\" + 0.004*\"1617\" + 0.004*\"1393\"\n", - "2021-08-24 03:02:59,942 : INFO : topic diff=3.936084, rho=0.577350\n", - "2021-08-24 03:03:00,626 : INFO : -9.637 per-word bound, 796.4 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:00,627 : INFO : PROGRESS: pass 2, at document #997/997\n", - "2021-08-24 03:03:01,078 : INFO : topic #1 (0.020): 0.006*\"4027\" + 0.006*\"47\" + 0.006*\"356\" + 0.006*\"318\" + 0.006*\"2762\" + 0.005*\"5010\" + 0.005*\"3578\" + 0.005*\"1089\" + 0.005*\"593\" + 0.005*\"527\"\n", - "2021-08-24 03:03:01,079 : INFO : topic #13 (0.020): 0.008*\"50\" + 0.006*\"593\" + 0.006*\"527\" + 0.006*\"2858\" + 0.006*\"2571\" + 0.006*\"296\" + 0.005*\"111\" + 0.005*\"858\" + 0.005*\"1193\" + 0.005*\"318\"\n", - "2021-08-24 03:03:01,080 : INFO : topic #38 (0.020): 0.008*\"3967\" + 0.005*\"1258\" + 0.004*\"50\" + 0.004*\"1997\" + 0.004*\"337\" + 0.004*\"858\" + 0.004*\"1387\" + 0.004*\"1219\" + 0.004*\"1225\" + 0.004*\"2657\"\n", - "2021-08-24 03:03:01,081 : INFO : topic #29 (0.020): 0.007*\"260\" + 0.005*\"919\" + 0.005*\"1394\" + 0.005*\"593\" + 0.005*\"296\" + 0.005*\"1221\" + 0.004*\"1097\" + 0.004*\"1288\" + 0.004*\"2804\" + 0.004*\"1196\"\n", - "2021-08-24 03:03:01,081 : INFO : topic #31 (0.020): 0.008*\"2571\" + 0.007*\"4226\" + 0.006*\"356\" + 0.006*\"1961\" + 0.006*\"4995\" + 0.006*\"2762\" + 0.006*\"2502\" + 0.005*\"4816\" + 0.005*\"3174\" + 0.005*\"296\"\n", - "2021-08-24 03:03:01,082 : INFO : topic diff=3.367622, rho=0.500000\n", - "2021-08-24 03:03:01,745 : INFO : -9.125 per-word bound, 558.4 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:01,745 : INFO : PROGRESS: pass 3, at document #997/997\n", - "2021-08-24 03:03:02,211 : INFO : topic #11 (0.020): 0.008*\"356\" + 0.007*\"150\" + 0.007*\"318\" + 0.007*\"364\" + 0.007*\"527\" + 0.006*\"597\" + 0.006*\"586\" + 0.006*\"1\" + 0.006*\"593\" + 0.006*\"780\"\n", - "2021-08-24 03:03:02,212 : INFO : topic #30 (0.020): 0.010*\"318\" + 0.009*\"50\" + 0.008*\"593\" + 0.007*\"32\" + 0.007*\"2858\" + 0.006*\"296\" + 0.006*\"5464\" + 0.006*\"7153\" + 0.006*\"33794\" + 0.005*\"356\"\n", - "2021-08-24 03:03:02,213 : INFO : topic #43 (0.020): 0.008*\"589\" + 0.006*\"588\" + 0.006*\"296\" + 0.006*\"593\" + 0.006*\"47\" + 0.006*\"318\" + 0.006*\"150\" + 0.005*\"380\" + 0.005*\"527\" + 0.005*\"316\"\n", - "2021-08-24 03:03:02,214 : INFO : topic #12 (0.020): 0.007*\"260\" + 0.007*\"4993\" + 0.006*\"1968\" + 0.006*\"1073\" + 0.005*\"5618\" + 0.005*\"1196\" + 0.005*\"1721\" + 0.005*\"4027\" + 0.005*\"4886\" + 0.005*\"3114\"\n", - "2021-08-24 03:03:02,214 : INFO : topic #0 (0.020): 0.008*\"3101\" + 0.007*\"1214\" + 0.007*\"780\" + 0.006*\"1219\" + 0.006*\"2278\" + 0.005*\"1252\" + 0.005*\"908\" + 0.005*\"2273\" + 0.005*\"1617\" + 0.005*\"527\"\n", - "2021-08-24 03:03:02,215 : INFO : topic diff=2.765281, rho=0.447214\n", - "2021-08-24 03:03:02,891 : INFO : -8.818 per-word bound, 451.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:02,892 : INFO : PROGRESS: pass 4, at document #997/997\n", - "2021-08-24 03:03:03,312 : INFO : topic #9 (0.020): 0.005*\"1704\" + 0.004*\"2028\" + 0.004*\"110\" + 0.004*\"356\" + 0.004*\"1196\" + 0.004*\"318\" + 0.004*\"2762\" + 0.004*\"1198\" + 0.004*\"1784\" + 0.004*\"296\"\n", - "2021-08-24 03:03:03,313 : INFO : topic #18 (0.020): 0.012*\"2700\" + 0.011*\"2683\" + 0.009*\"7153\" + 0.009*\"5952\" + 0.009*\"1208\" + 0.008*\"2706\" + 0.007*\"2791\" + 0.007*\"2716\" + 0.007*\"1213\" + 0.006*\"923\"\n", - "2021-08-24 03:03:03,314 : INFO : topic #6 (0.020): 0.007*\"356\" + 0.006*\"2571\" + 0.006*\"1721\" + 0.006*\"2706\" + 0.006*\"2700\" + 0.006*\"1393\" + 0.006*\"455\" + 0.005*\"2762\" + 0.005*\"2716\" + 0.005*\"2396\"\n", - "2021-08-24 03:03:03,314 : INFO : topic #26 (0.020): 0.010*\"608\" + 0.008*\"1247\" + 0.007*\"858\" + 0.006*\"1225\" + 0.006*\"593\" + 0.006*\"1094\" + 0.006*\"1207\" + 0.006*\"36\" + 0.006*\"1193\" + 0.006*\"17\"\n", - "2021-08-24 03:03:03,315 : INFO : topic #11 (0.020): 0.008*\"356\" + 0.007*\"1\" + 0.007*\"364\" + 0.007*\"150\" + 0.007*\"586\" + 0.007*\"318\" + 0.006*\"597\" + 0.006*\"527\" + 0.006*\"2997\" + 0.006*\"593\"\n", - "2021-08-24 03:03:03,316 : INFO : topic diff=2.221588, rho=0.408248\n", - "2021-08-24 03:03:03,987 : INFO : -8.621 per-word bound, 393.6 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:03,988 : INFO : PROGRESS: pass 5, at document #997/997\n", - "2021-08-24 03:03:04,430 : INFO : topic #15 (0.020): 0.014*\"527\" + 0.007*\"593\" + 0.007*\"2791\" + 0.007*\"260\" + 0.007*\"509\" + 0.007*\"357\" + 0.007*\"1923\" + 0.006*\"141\" + 0.006*\"2408\" + 0.006*\"27878\"\n", - "2021-08-24 03:03:04,431 : INFO : topic #39 (0.020): 0.009*\"2571\" + 0.008*\"1196\" + 0.007*\"1210\" + 0.007*\"260\" + 0.006*\"589\" + 0.006*\"480\" + 0.005*\"2628\" + 0.005*\"2028\" + 0.004*\"1270\" + 0.004*\"1291\"\n", - "2021-08-24 03:03:04,431 : INFO : topic #42 (0.020): 0.004*\"3083\" + 0.004*\"2020\" + 0.003*\"3328\" + 0.003*\"49272\" + 0.003*\"2571\" + 0.003*\"296\" + 0.003*\"6920\" + 0.003*\"4426\" + 0.003*\"5225\" + 0.003*\"7301\"\n", - "2021-08-24 03:03:04,432 : INFO : topic #5 (0.020): 0.006*\"318\" + 0.006*\"3578\" + 0.005*\"2572\" + 0.005*\"2571\" + 0.005*\"2513\" + 0.005*\"2858\" + 0.005*\"589\" + 0.005*\"110\" + 0.004*\"1199\" + 0.004*\"7387\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 03:03:04,433 : INFO : topic #45 (0.020): 0.010*\"1196\" + 0.009*\"260\" + 0.008*\"1200\" + 0.007*\"1258\" + 0.007*\"1198\" + 0.007*\"1097\" + 0.006*\"1968\" + 0.006*\"1721\" + 0.006*\"2081\" + 0.005*\"858\"\n", - "2021-08-24 03:03:04,434 : INFO : topic diff=1.760166, rho=0.377964\n", - "2021-08-24 03:03:05,134 : INFO : -8.487 per-word bound, 358.8 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:05,135 : INFO : PROGRESS: pass 6, at document #997/997\n", - "2021-08-24 03:03:05,612 : INFO : topic #16 (0.020): 0.006*\"260\" + 0.006*\"919\" + 0.006*\"1193\" + 0.005*\"1617\" + 0.005*\"1250\" + 0.005*\"1196\" + 0.005*\"2858\" + 0.005*\"908\" + 0.004*\"1012\" + 0.004*\"1021\"\n", - "2021-08-24 03:03:05,613 : INFO : topic #3 (0.020): 0.010*\"1610\" + 0.009*\"589\" + 0.009*\"2571\" + 0.007*\"780\" + 0.006*\"2763\" + 0.006*\"2028\" + 0.006*\"2353\" + 0.006*\"1356\" + 0.006*\"47\" + 0.005*\"110\"\n", - "2021-08-24 03:03:05,614 : INFO : topic #44 (0.020): 0.007*\"1210\" + 0.006*\"1617\" + 0.006*\"1240\" + 0.006*\"2858\" + 0.006*\"593\" + 0.006*\"1090\" + 0.006*\"1387\" + 0.005*\"2028\" + 0.005*\"50\" + 0.005*\"36\"\n", - "2021-08-24 03:03:05,615 : INFO : topic #10 (0.020): 0.008*\"589\" + 0.008*\"1196\" + 0.007*\"1200\" + 0.006*\"2571\" + 0.006*\"1214\" + 0.006*\"47\" + 0.005*\"32\" + 0.005*\"296\" + 0.005*\"4002\" + 0.005*\"541\"\n", - "2021-08-24 03:03:05,616 : INFO : topic #28 (0.020): 0.012*\"260\" + 0.012*\"2571\" + 0.011*\"593\" + 0.010*\"356\" + 0.010*\"2028\" + 0.010*\"1210\" + 0.009*\"318\" + 0.009*\"296\" + 0.008*\"608\" + 0.008*\"110\"\n", - "2021-08-24 03:03:05,616 : INFO : topic diff=1.385117, rho=0.353553\n", - "2021-08-24 03:03:06,434 : INFO : -8.393 per-word bound, 336.2 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:06,435 : INFO : PROGRESS: pass 7, at document #997/997\n", - "2021-08-24 03:03:06,932 : INFO : topic #18 (0.020): 0.015*\"2683\" + 0.014*\"2700\" + 0.011*\"2706\" + 0.009*\"7153\" + 0.009*\"5952\" + 0.008*\"1208\" + 0.008*\"2791\" + 0.008*\"2716\" + 0.007*\"3101\" + 0.007*\"923\"\n", - "2021-08-24 03:03:06,933 : INFO : topic #40 (0.020): 0.030*\"260\" + 0.029*\"1\" + 0.027*\"780\" + 0.027*\"733\" + 0.022*\"62\" + 0.021*\"608\" + 0.020*\"1073\" + 0.019*\"32\" + 0.019*\"736\" + 0.018*\"648\"\n", - "2021-08-24 03:03:06,934 : INFO : topic #43 (0.020): 0.007*\"589\" + 0.006*\"2858\" + 0.006*\"1089\" + 0.005*\"1210\" + 0.005*\"1500\" + 0.005*\"1704\" + 0.005*\"527\" + 0.005*\"588\" + 0.005*\"778\" + 0.005*\"296\"\n", - "2021-08-24 03:03:06,935 : INFO : topic #19 (0.020): 0.008*\"1036\" + 0.007*\"1240\" + 0.007*\"2571\" + 0.006*\"589\" + 0.006*\"480\" + 0.006*\"1214\" + 0.006*\"356\" + 0.006*\"1198\" + 0.006*\"110\" + 0.006*\"1200\"\n", - "2021-08-24 03:03:06,936 : INFO : topic #0 (0.020): 0.009*\"3101\" + 0.008*\"1214\" + 0.007*\"1219\" + 0.006*\"1252\" + 0.006*\"780\" + 0.006*\"848\" + 0.006*\"2278\" + 0.006*\"908\" + 0.006*\"2273\" + 0.006*\"1748\"\n", - "2021-08-24 03:03:06,936 : INFO : topic diff=1.088646, rho=0.333333\n", - "2021-08-24 03:03:07,706 : INFO : -8.323 per-word bound, 320.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:07,706 : INFO : PROGRESS: pass 8, at document #997/997\n", - "2021-08-24 03:03:08,244 : INFO : topic #32 (0.020): 0.009*\"2028\" + 0.008*\"593\" + 0.007*\"527\" + 0.007*\"858\" + 0.006*\"2858\" + 0.006*\"1617\" + 0.005*\"247\" + 0.005*\"45720\" + 0.005*\"36535\" + 0.005*\"1196\"\n", - "2021-08-24 03:03:08,245 : INFO : topic #8 (0.020): 0.007*\"837\" + 0.007*\"1414\" + 0.007*\"1270\" + 0.007*\"2081\" + 0.006*\"1097\" + 0.006*\"260\" + 0.006*\"1713\" + 0.006*\"1210\" + 0.006*\"2064\" + 0.005*\"1689\"\n", - "2021-08-24 03:03:08,246 : INFO : topic #3 (0.020): 0.010*\"1610\" + 0.009*\"589\" + 0.009*\"2571\" + 0.007*\"780\" + 0.006*\"2028\" + 0.006*\"2763\" + 0.006*\"2353\" + 0.006*\"1356\" + 0.006*\"47\" + 0.005*\"380\"\n", - "2021-08-24 03:03:08,246 : INFO : topic #19 (0.020): 0.008*\"1036\" + 0.007*\"1240\" + 0.007*\"2571\" + 0.007*\"589\" + 0.006*\"1214\" + 0.006*\"480\" + 0.006*\"1198\" + 0.006*\"356\" + 0.006*\"110\" + 0.006*\"1200\"\n", - "2021-08-24 03:03:08,248 : INFO : topic #24 (0.020): 0.007*\"1198\" + 0.006*\"1196\" + 0.006*\"1200\" + 0.006*\"1208\" + 0.006*\"1193\" + 0.006*\"1240\" + 0.005*\"1214\" + 0.005*\"1270\" + 0.005*\"1197\" + 0.005*\"1219\"\n", - "2021-08-24 03:03:08,248 : INFO : topic diff=0.857510, rho=0.316228\n", - "2021-08-24 03:03:09,056 : INFO : -8.270 per-word bound, 308.7 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:09,057 : INFO : PROGRESS: pass 9, at document #997/997\n", - "2021-08-24 03:03:09,604 : INFO : topic #33 (0.020): 0.008*\"1036\" + 0.007*\"780\" + 0.007*\"1580\" + 0.007*\"480\" + 0.007*\"1198\" + 0.006*\"1291\" + 0.006*\"1210\" + 0.006*\"1196\" + 0.006*\"3753\" + 0.005*\"1370\"\n", - "2021-08-24 03:03:09,605 : INFO : topic #1 (0.020): 0.012*\"4027\" + 0.010*\"5010\" + 0.010*\"63\" + 0.008*\"47\" + 0.008*\"318\" + 0.008*\"2762\" + 0.008*\"3578\" + 0.008*\"3745\" + 0.008*\"3996\" + 0.008*\"4310\"\n", - "2021-08-24 03:03:09,606 : INFO : topic #49 (0.020): 0.009*\"608\" + 0.008*\"750\" + 0.007*\"1136\" + 0.007*\"260\" + 0.007*\"1196\" + 0.006*\"541\" + 0.006*\"1213\" + 0.006*\"296\" + 0.006*\"858\" + 0.006*\"50\"\n", - "2021-08-24 03:03:09,606 : INFO : topic #26 (0.020): 0.009*\"608\" + 0.009*\"1247\" + 0.008*\"858\" + 0.008*\"1193\" + 0.008*\"1225\" + 0.007*\"1207\" + 0.007*\"1094\" + 0.007*\"527\" + 0.007*\"593\" + 0.006*\"1230\"\n", - "2021-08-24 03:03:09,607 : INFO : topic #7 (0.020): 0.012*\"2959\" + 0.010*\"1200\" + 0.010*\"1210\" + 0.009*\"1136\" + 0.008*\"1196\" + 0.008*\"2858\" + 0.008*\"2571\" + 0.008*\"50\" + 0.007*\"260\" + 0.007*\"5445\"\n", - "2021-08-24 03:03:09,608 : INFO : topic diff=0.679622, rho=0.301511\n", - "2021-08-24 03:03:10,357 : INFO : -8.229 per-word bound, 300.0 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:10,358 : INFO : PROGRESS: pass 10, at document #997/997\n", - "2021-08-24 03:03:10,830 : INFO : topic #39 (0.020): 0.010*\"2571\" + 0.009*\"1196\" + 0.008*\"1210\" + 0.007*\"260\" + 0.006*\"589\" + 0.006*\"480\" + 0.006*\"2628\" + 0.005*\"1676\" + 0.005*\"2916\" + 0.005*\"1270\"\n", - "2021-08-24 03:03:10,831 : INFO : topic #26 (0.020): 0.009*\"608\" + 0.009*\"1247\" + 0.008*\"1193\" + 0.008*\"858\" + 0.008*\"1225\" + 0.007*\"1207\" + 0.007*\"1094\" + 0.007*\"527\" + 0.007*\"593\" + 0.006*\"1230\"\n", - "2021-08-24 03:03:10,831 : INFO : topic #45 (0.020): 0.012*\"1196\" + 0.010*\"1200\" + 0.009*\"260\" + 0.008*\"1198\" + 0.008*\"1097\" + 0.008*\"1258\" + 0.008*\"1968\" + 0.008*\"2081\" + 0.007*\"1721\" + 0.007*\"318\"\n", - "2021-08-24 03:03:10,832 : INFO : topic #23 (0.020): 0.014*\"5952\" + 0.014*\"2959\" + 0.013*\"7153\" + 0.012*\"4226\" + 0.012*\"4993\" + 0.011*\"2571\" + 0.010*\"296\" + 0.009*\"47\" + 0.009*\"2329\" + 0.009*\"318\"\n", - "2021-08-24 03:03:10,833 : INFO : topic #18 (0.020): 0.018*\"2683\" + 0.016*\"2700\" + 0.014*\"2706\" + 0.008*\"3101\" + 0.008*\"2716\" + 0.008*\"2791\" + 0.007*\"1213\" + 0.007*\"2605\" + 0.006*\"2987\" + 0.006*\"7153\"\n", - "2021-08-24 03:03:10,834 : INFO : topic diff=0.542602, rho=0.288675\n", - "2021-08-24 03:03:11,529 : INFO : -8.195 per-word bound, 293.1 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:11,529 : INFO : PROGRESS: pass 11, at document #997/997\n", - "2021-08-24 03:03:12,005 : INFO : topic #49 (0.020): 0.009*\"608\" + 0.008*\"750\" + 0.007*\"1136\" + 0.007*\"260\" + 0.007*\"541\" + 0.007*\"1196\" + 0.006*\"1213\" + 0.006*\"296\" + 0.006*\"1200\" + 0.006*\"858\"\n", - "2021-08-24 03:03:12,006 : INFO : topic #21 (0.020): 0.012*\"858\" + 0.012*\"1221\" + 0.012*\"1193\" + 0.008*\"1641\" + 0.008*\"527\" + 0.008*\"2858\" + 0.008*\"2396\" + 0.008*\"50\" + 0.008*\"2762\" + 0.008*\"1198\"\n", - "2021-08-24 03:03:12,006 : INFO : topic #20 (0.020): 0.007*\"2140\" + 0.007*\"1240\" + 0.006*\"924\" + 0.006*\"2529\" + 0.006*\"260\" + 0.006*\"1374\" + 0.006*\"1127\" + 0.005*\"1019\" + 0.005*\"1196\" + 0.005*\"1200\"\n", - "2021-08-24 03:03:12,007 : INFO : topic #40 (0.020): 0.033*\"260\" + 0.031*\"1\" + 0.030*\"780\" + 0.029*\"733\" + 0.023*\"62\" + 0.023*\"608\" + 0.021*\"1073\" + 0.021*\"736\" + 0.021*\"32\" + 0.019*\"648\"\n", - "2021-08-24 03:03:12,008 : INFO : topic #8 (0.020): 0.008*\"837\" + 0.007*\"2081\" + 0.007*\"1713\" + 0.007*\"1414\" + 0.007*\"1270\" + 0.006*\"1097\" + 0.006*\"1210\" + 0.006*\"260\" + 0.006*\"1689\" + 0.006*\"1376\"\n", - "2021-08-24 03:03:12,009 : INFO : topic diff=0.437222, rho=0.277350\n", - "2021-08-24 03:03:12,750 : INFO : -8.167 per-word bound, 287.5 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:12,751 : INFO : PROGRESS: pass 12, at document #997/997\n", - "2021-08-24 03:03:13,245 : INFO : topic #33 (0.020): 0.008*\"1036\" + 0.008*\"780\" + 0.007*\"1580\" + 0.007*\"1198\" + 0.007*\"480\" + 0.007*\"1291\" + 0.007*\"1196\" + 0.007*\"1210\" + 0.006*\"1608\" + 0.006*\"3753\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 03:03:13,246 : INFO : topic #2 (0.020): 0.013*\"1197\" + 0.010*\"1148\" + 0.009*\"593\" + 0.008*\"1246\" + 0.008*\"296\" + 0.008*\"2797\" + 0.008*\"1198\" + 0.008*\"924\" + 0.008*\"110\" + 0.008*\"1270\"\n", - "2021-08-24 03:03:13,247 : INFO : topic #40 (0.020): 0.033*\"260\" + 0.031*\"1\" + 0.030*\"780\" + 0.029*\"733\" + 0.023*\"608\" + 0.023*\"62\" + 0.021*\"1073\" + 0.021*\"736\" + 0.021*\"32\" + 0.019*\"648\"\n", - "2021-08-24 03:03:13,248 : INFO : topic #31 (0.020): 0.009*\"6378\" + 0.009*\"4816\" + 0.008*\"5481\" + 0.008*\"3174\" + 0.008*\"3977\" + 0.008*\"1961\" + 0.007*\"1580\" + 0.007*\"356\" + 0.007*\"4995\" + 0.006*\"2683\"\n", - "2021-08-24 03:03:13,249 : INFO : topic #20 (0.020): 0.007*\"2140\" + 0.007*\"924\" + 0.007*\"1240\" + 0.006*\"2529\" + 0.006*\"260\" + 0.006*\"1374\" + 0.006*\"1127\" + 0.006*\"1019\" + 0.005*\"1196\" + 0.005*\"1200\"\n", - "2021-08-24 03:03:13,250 : INFO : topic diff=0.355709, rho=0.267261\n", - "2021-08-24 03:03:14,009 : INFO : -8.144 per-word bound, 282.8 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:14,010 : INFO : PROGRESS: pass 13, at document #997/997\n", - "2021-08-24 03:03:14,491 : INFO : topic #11 (0.020): 0.010*\"1\" + 0.009*\"586\" + 0.007*\"364\" + 0.007*\"356\" + 0.007*\"1073\" + 0.007*\"2997\" + 0.007*\"2706\" + 0.006*\"1197\" + 0.006*\"2683\" + 0.006*\"4306\"\n", - "2021-08-24 03:03:14,492 : INFO : topic #22 (0.020): 0.015*\"1354\" + 0.012*\"1357\" + 0.011*\"1704\" + 0.011*\"2858\" + 0.011*\"1784\" + 0.010*\"3720\" + 0.010*\"296\" + 0.010*\"2324\" + 0.010*\"1635\" + 0.009*\"527\"\n", - "2021-08-24 03:03:14,492 : INFO : topic #46 (0.020): 0.009*\"1200\" + 0.009*\"1240\" + 0.007*\"2918\" + 0.007*\"2858\" + 0.006*\"260\" + 0.006*\"1196\" + 0.006*\"1214\" + 0.006*\"2951\" + 0.005*\"1259\" + 0.005*\"599\"\n", - "2021-08-24 03:03:14,493 : INFO : topic #25 (0.020): 0.008*\"43396\" + 0.006*\"6932\" + 0.006*\"41863\" + 0.006*\"260\" + 0.005*\"49961\" + 0.005*\"296\" + 0.005*\"999\" + 0.005*\"44191\" + 0.005*\"1673\" + 0.005*\"27803\"\n", - "2021-08-24 03:03:14,494 : INFO : topic #30 (0.020): 0.012*\"318\" + 0.011*\"5464\" + 0.010*\"50\" + 0.008*\"3869\" + 0.007*\"2997\" + 0.006*\"356\" + 0.006*\"1266\" + 0.006*\"1704\" + 0.006*\"2987\" + 0.006*\"1193\"\n", - "2021-08-24 03:03:14,495 : INFO : topic diff=0.292491, rho=0.258199\n", - "2021-08-24 03:03:15,173 : INFO : -8.124 per-word bound, 278.9 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:15,174 : INFO : PROGRESS: pass 14, at document #997/997\n", - "2021-08-24 03:03:15,626 : INFO : topic #47 (0.020): 0.022*\"150\" + 0.022*\"457\" + 0.022*\"593\" + 0.020*\"296\" + 0.019*\"318\" + 0.018*\"356\" + 0.018*\"590\" + 0.017*\"110\" + 0.016*\"380\" + 0.015*\"480\"\n", - "2021-08-24 03:03:15,626 : INFO : topic #17 (0.020): 0.008*\"1250\" + 0.006*\"457\" + 0.006*\"2527\" + 0.006*\"1610\" + 0.006*\"2393\" + 0.006*\"1183\" + 0.005*\"1090\" + 0.005*\"1954\" + 0.005*\"1210\" + 0.005*\"2167\"\n", - "2021-08-24 03:03:15,628 : INFO : topic #4 (0.020): 0.011*\"1196\" + 0.010*\"1023\" + 0.009*\"28\" + 0.009*\"3093\" + 0.008*\"2022\" + 0.008*\"3114\" + 0.008*\"3094\" + 0.008*\"314\" + 0.008*\"2858\" + 0.007*\"924\"\n", - "2021-08-24 03:03:15,628 : INFO : topic #42 (0.020): 0.005*\"8507\" + 0.005*\"5225\" + 0.005*\"3083\" + 0.004*\"4117\" + 0.004*\"7706\" + 0.004*\"3742\" + 0.004*\"49272\" + 0.004*\"6920\" + 0.004*\"4426\" + 0.004*\"7301\"\n", - "2021-08-24 03:03:15,629 : INFO : topic #39 (0.020): 0.010*\"2571\" + 0.009*\"1196\" + 0.008*\"1210\" + 0.007*\"260\" + 0.007*\"589\" + 0.006*\"480\" + 0.006*\"2628\" + 0.005*\"1676\" + 0.005*\"1580\" + 0.005*\"2916\"\n", - "2021-08-24 03:03:15,630 : INFO : topic diff=0.243063, rho=0.250000\n", - "2021-08-24 03:03:16,303 : INFO : -8.106 per-word bound, 275.6 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:16,304 : INFO : PROGRESS: pass 15, at document #997/997\n", - "2021-08-24 03:03:16,745 : INFO : topic #4 (0.020): 0.011*\"1196\" + 0.010*\"1023\" + 0.010*\"28\" + 0.009*\"3093\" + 0.008*\"2022\" + 0.008*\"3094\" + 0.008*\"314\" + 0.008*\"2858\" + 0.008*\"3114\" + 0.007*\"2443\"\n", - "2021-08-24 03:03:16,746 : INFO : topic #26 (0.020): 0.009*\"608\" + 0.009*\"1193\" + 0.009*\"1247\" + 0.008*\"1225\" + 0.008*\"858\" + 0.007*\"1207\" + 0.007*\"527\" + 0.007*\"1094\" + 0.007*\"593\" + 0.006*\"1230\"\n", - "2021-08-24 03:03:16,747 : INFO : topic #30 (0.020): 0.012*\"318\" + 0.011*\"5464\" + 0.009*\"50\" + 0.008*\"3869\" + 0.007*\"2997\" + 0.006*\"1266\" + 0.006*\"193\" + 0.006*\"59615\" + 0.006*\"49272\" + 0.006*\"1193\"\n", - "2021-08-24 03:03:16,748 : INFO : topic #46 (0.020): 0.010*\"1200\" + 0.009*\"1240\" + 0.008*\"2918\" + 0.007*\"2858\" + 0.006*\"260\" + 0.006*\"1214\" + 0.006*\"599\" + 0.006*\"1259\" + 0.006*\"1196\" + 0.006*\"2951\"\n", - "2021-08-24 03:03:16,749 : INFO : topic #47 (0.020): 0.022*\"150\" + 0.022*\"457\" + 0.022*\"593\" + 0.020*\"296\" + 0.020*\"318\" + 0.018*\"356\" + 0.018*\"590\" + 0.017*\"110\" + 0.016*\"380\" + 0.015*\"480\"\n", - "2021-08-24 03:03:16,749 : INFO : topic diff=0.204050, rho=0.242536\n", - "2021-08-24 03:03:17,419 : INFO : -8.092 per-word bound, 272.8 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:17,420 : INFO : PROGRESS: pass 16, at document #997/997\n", - "2021-08-24 03:03:17,856 : INFO : topic #19 (0.020): 0.009*\"1036\" + 0.008*\"1240\" + 0.008*\"589\" + 0.007*\"110\" + 0.007*\"2571\" + 0.007*\"1198\" + 0.007*\"1214\" + 0.007*\"480\" + 0.006*\"1200\" + 0.006*\"2000\"\n", - "2021-08-24 03:03:17,857 : INFO : topic #13 (0.020): 0.014*\"50\" + 0.010*\"111\" + 0.010*\"290\" + 0.010*\"527\" + 0.009*\"1237\" + 0.008*\"593\" + 0.008*\"2858\" + 0.008*\"1173\" + 0.008*\"296\" + 0.007*\"175\"\n", - "2021-08-24 03:03:17,858 : INFO : topic #11 (0.020): 0.010*\"1\" + 0.009*\"586\" + 0.007*\"364\" + 0.007*\"1073\" + 0.007*\"356\" + 0.007*\"2997\" + 0.007*\"2706\" + 0.006*\"1197\" + 0.006*\"2683\" + 0.006*\"4306\"\n", - "2021-08-24 03:03:17,859 : INFO : topic #36 (0.020): 0.007*\"6365\" + 0.006*\"1580\" + 0.006*\"4874\" + 0.006*\"2291\" + 0.006*\"2011\" + 0.005*\"520\" + 0.005*\"1270\" + 0.005*\"6333\" + 0.005*\"3793\" + 0.005*\"1200\"\n", - "2021-08-24 03:03:17,860 : INFO : topic #1 (0.020): 0.012*\"4027\" + 0.012*\"5010\" + 0.010*\"3745\" + 0.009*\"4310\" + 0.009*\"63\" + 0.009*\"3996\" + 0.008*\"4019\" + 0.008*\"631\" + 0.008*\"5400\" + 0.008*\"3969\"\n", - "2021-08-24 03:03:17,860 : INFO : topic diff=0.172991, rho=0.235702\n", - "2021-08-24 03:03:18,528 : INFO : -8.079 per-word bound, 270.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:18,528 : INFO : PROGRESS: pass 17, at document #997/997\n", - "2021-08-24 03:03:18,985 : INFO : topic #3 (0.020): 0.009*\"1610\" + 0.009*\"589\" + 0.009*\"2571\" + 0.007*\"2028\" + 0.006*\"1356\" + 0.006*\"18\" + 0.006*\"780\" + 0.006*\"2763\" + 0.006*\"58559\" + 0.006*\"54997\"\n", - "2021-08-24 03:03:18,986 : INFO : topic #2 (0.020): 0.013*\"1197\" + 0.011*\"1148\" + 0.009*\"1246\" + 0.009*\"593\" + 0.008*\"2797\" + 0.008*\"296\" + 0.008*\"1198\" + 0.008*\"110\" + 0.008*\"1270\" + 0.008*\"1617\"\n", - "2021-08-24 03:03:18,987 : INFO : topic #13 (0.020): 0.014*\"50\" + 0.010*\"111\" + 0.010*\"290\" + 0.010*\"527\" + 0.009*\"1237\" + 0.008*\"593\" + 0.008*\"1173\" + 0.008*\"2858\" + 0.008*\"296\" + 0.007*\"175\"\n", - "2021-08-24 03:03:18,988 : INFO : topic #21 (0.020): 0.012*\"858\" + 0.012*\"1221\" + 0.012*\"1193\" + 0.008*\"2396\" + 0.008*\"2858\" + 0.008*\"2762\" + 0.008*\"904\" + 0.008*\"1641\" + 0.008*\"527\" + 0.008*\"50\"\n", - "2021-08-24 03:03:18,989 : INFO : topic #33 (0.020): 0.009*\"1036\" + 0.008*\"780\" + 0.008*\"1198\" + 0.008*\"1196\" + 0.008*\"1291\" + 0.007*\"1580\" + 0.007*\"480\" + 0.007*\"1210\" + 0.006*\"1608\" + 0.006*\"260\"\n", - "2021-08-24 03:03:18,990 : INFO : topic diff=0.148018, rho=0.229416\n", - "2021-08-24 03:03:19,753 : INFO : -8.067 per-word bound, 268.2 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:19,754 : INFO : PROGRESS: pass 18, at document #997/997\n", - "2021-08-24 03:03:20,202 : INFO : topic #4 (0.020): 0.011*\"1196\" + 0.010*\"1023\" + 0.010*\"28\" + 0.009*\"2443\" + 0.009*\"2022\" + 0.008*\"3093\" + 0.008*\"3094\" + 0.008*\"314\" + 0.008*\"2858\" + 0.007*\"3114\"\n", - "2021-08-24 03:03:20,203 : INFO : topic #8 (0.020): 0.008*\"837\" + 0.008*\"1713\" + 0.007*\"1414\" + 0.007*\"2081\" + 0.007*\"1270\" + 0.007*\"1210\" + 0.006*\"1097\" + 0.006*\"260\" + 0.006*\"1689\" + 0.006*\"1376\"\n", - "2021-08-24 03:03:20,204 : INFO : topic #16 (0.020): 0.010*\"455\" + 0.009*\"355\" + 0.008*\"837\" + 0.008*\"609\" + 0.008*\"688\" + 0.007*\"1015\" + 0.007*\"919\" + 0.007*\"851\" + 0.006*\"27\" + 0.006*\"1021\"\n", - "2021-08-24 03:03:20,205 : INFO : topic #29 (0.020): 0.008*\"919\" + 0.008*\"1394\" + 0.008*\"260\" + 0.007*\"2064\" + 0.007*\"2804\" + 0.007*\"3751\" + 0.006*\"1022\" + 0.006*\"2395\" + 0.006*\"1222\" + 0.006*\"1097\"\n", - "2021-08-24 03:03:20,206 : INFO : topic #22 (0.020): 0.018*\"1354\" + 0.013*\"1357\" + 0.012*\"1704\" + 0.011*\"2858\" + 0.011*\"1784\" + 0.011*\"3720\" + 0.010*\"2324\" + 0.010*\"1635\" + 0.010*\"527\" + 0.009*\"1682\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 03:03:20,207 : INFO : topic diff=0.127955, rho=0.223607\n", - "2021-08-24 03:03:20,943 : INFO : -8.057 per-word bound, 266.2 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:20,944 : INFO : PROGRESS: pass 19, at document #997/997\n", - "2021-08-24 03:03:21,406 : INFO : topic #47 (0.020): 0.023*\"150\" + 0.022*\"457\" + 0.022*\"593\" + 0.020*\"296\" + 0.020*\"318\" + 0.018*\"356\" + 0.018*\"590\" + 0.017*\"110\" + 0.016*\"380\" + 0.016*\"480\"\n", - "2021-08-24 03:03:21,407 : INFO : topic #49 (0.020): 0.010*\"608\" + 0.009*\"750\" + 0.008*\"1136\" + 0.007*\"541\" + 0.007*\"260\" + 0.007*\"1196\" + 0.007*\"296\" + 0.007*\"1148\" + 0.007*\"1214\" + 0.007*\"1200\"\n", - "2021-08-24 03:03:21,408 : INFO : topic #22 (0.020): 0.018*\"1354\" + 0.013*\"1357\" + 0.012*\"1704\" + 0.011*\"2858\" + 0.011*\"1784\" + 0.011*\"3720\" + 0.010*\"2324\" + 0.010*\"1635\" + 0.010*\"527\" + 0.010*\"1682\"\n", - "2021-08-24 03:03:21,408 : INFO : topic #37 (0.020): 0.007*\"908\" + 0.006*\"913\" + 0.006*\"1256\" + 0.006*\"904\" + 0.005*\"933\" + 0.005*\"903\" + 0.005*\"1269\" + 0.005*\"905\" + 0.005*\"912\" + 0.005*\"910\"\n", - "2021-08-24 03:03:21,409 : INFO : topic #32 (0.020): 0.010*\"2028\" + 0.008*\"593\" + 0.007*\"858\" + 0.007*\"527\" + 0.007*\"590\" + 0.006*\"1617\" + 0.006*\"45720\" + 0.006*\"36535\" + 0.006*\"247\" + 0.006*\"2858\"\n", - "2021-08-24 03:03:21,410 : INFO : topic diff=0.111559, rho=0.218218\n", - "2021-08-24 03:03:22,206 : INFO : -8.047 per-word bound, 264.5 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:22,206 : INFO : PROGRESS: pass 20, at document #997/997\n", - "2021-08-24 03:03:22,680 : INFO : topic #24 (0.020): 0.012*\"1196\" + 0.011*\"1270\" + 0.011*\"1198\" + 0.008*\"1197\" + 0.008*\"1291\" + 0.008*\"1240\" + 0.007*\"1246\" + 0.007*\"1097\" + 0.007*\"1193\" + 0.007*\"1200\"\n", - "2021-08-24 03:03:22,681 : INFO : topic #37 (0.020): 0.007*\"908\" + 0.007*\"913\" + 0.006*\"1256\" + 0.006*\"904\" + 0.005*\"933\" + 0.005*\"903\" + 0.005*\"1269\" + 0.005*\"905\" + 0.005*\"912\" + 0.005*\"910\"\n", - "2021-08-24 03:03:22,682 : INFO : topic #27 (0.020): 0.020*\"587\" + 0.016*\"41\" + 0.016*\"36\" + 0.016*\"371\" + 0.016*\"376\" + 0.014*\"539\" + 0.014*\"11\" + 0.013*\"141\" + 0.013*\"62\" + 0.013*\"17\"\n", - "2021-08-24 03:03:22,683 : INFO : topic #31 (0.020): 0.010*\"6378\" + 0.009*\"4816\" + 0.009*\"3977\" + 0.008*\"5481\" + 0.008*\"3174\" + 0.007*\"1961\" + 0.007*\"1580\" + 0.007*\"356\" + 0.006*\"4995\" + 0.006*\"2683\"\n", - "2021-08-24 03:03:22,684 : INFO : topic #39 (0.020): 0.010*\"2571\" + 0.009*\"1196\" + 0.008*\"1210\" + 0.007*\"260\" + 0.007*\"589\" + 0.006*\"2628\" + 0.006*\"1676\" + 0.006*\"480\" + 0.006*\"1580\" + 0.005*\"1527\"\n", - "2021-08-24 03:03:22,685 : INFO : topic diff=0.098085, rho=0.213201\n", - "2021-08-24 03:03:23,490 : INFO : -8.039 per-word bound, 263.0 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:23,491 : INFO : PROGRESS: pass 21, at document #997/997\n", - "2021-08-24 03:03:23,925 : INFO : topic #37 (0.020): 0.007*\"908\" + 0.007*\"913\" + 0.006*\"1256\" + 0.006*\"904\" + 0.006*\"903\" + 0.005*\"933\" + 0.005*\"1269\" + 0.005*\"905\" + 0.005*\"910\" + 0.005*\"912\"\n", - "2021-08-24 03:03:23,926 : INFO : topic #6 (0.020): 0.008*\"2396\" + 0.008*\"1193\" + 0.007*\"11\" + 0.007*\"356\" + 0.007*\"2700\" + 0.007*\"2571\" + 0.007*\"1721\" + 0.007*\"2706\" + 0.007*\"1584\" + 0.007*\"3793\"\n", - "2021-08-24 03:03:23,927 : INFO : topic #2 (0.020): 0.013*\"1197\" + 0.012*\"1148\" + 0.009*\"1246\" + 0.009*\"593\" + 0.009*\"2797\" + 0.008*\"110\" + 0.008*\"1270\" + 0.008*\"1617\" + 0.007*\"1198\" + 0.007*\"1097\"\n", - "2021-08-24 03:03:23,928 : INFO : topic #14 (0.020): 0.015*\"412\" + 0.008*\"105\" + 0.007*\"1747\" + 0.007*\"27878\" + 0.007*\"2683\" + 0.006*\"2858\" + 0.006*\"111\" + 0.006*\"2791\" + 0.006*\"1584\" + 0.005*\"55\"\n", - "2021-08-24 03:03:23,929 : INFO : topic #30 (0.020): 0.012*\"193\" + 0.011*\"5464\" + 0.010*\"318\" + 0.008*\"59615\" + 0.008*\"3869\" + 0.007*\"50\" + 0.007*\"2997\" + 0.007*\"1266\" + 0.007*\"49272\" + 0.006*\"1193\"\n", - "2021-08-24 03:03:23,929 : INFO : topic diff=0.086907, rho=0.208514\n", - "2021-08-24 03:03:24,636 : INFO : -8.031 per-word bound, 261.6 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:24,637 : INFO : PROGRESS: pass 22, at document #997/997\n", - "2021-08-24 03:03:25,082 : INFO : topic #3 (0.020): 0.010*\"589\" + 0.009*\"1610\" + 0.009*\"2571\" + 0.007*\"2028\" + 0.006*\"1356\" + 0.006*\"18\" + 0.006*\"60072\" + 0.006*\"58559\" + 0.006*\"54997\" + 0.006*\"780\"\n", - "2021-08-24 03:03:25,083 : INFO : topic #29 (0.020): 0.008*\"919\" + 0.008*\"1394\" + 0.008*\"260\" + 0.007*\"2064\" + 0.007*\"2804\" + 0.007*\"3751\" + 0.007*\"1022\" + 0.006*\"2395\" + 0.006*\"1222\" + 0.006*\"3608\"\n", - "2021-08-24 03:03:25,083 : INFO : topic #7 (0.020): 0.010*\"2959\" + 0.010*\"1210\" + 0.009*\"1196\" + 0.008*\"1200\" + 0.008*\"260\" + 0.008*\"50\" + 0.007*\"2571\" + 0.007*\"3578\" + 0.007*\"2858\" + 0.007*\"1136\"\n", - "2021-08-24 03:03:25,084 : INFO : topic #15 (0.020): 0.015*\"527\" + 0.008*\"2791\" + 0.008*\"357\" + 0.008*\"1923\" + 0.008*\"141\" + 0.007*\"509\" + 0.007*\"2408\" + 0.007*\"1665\" + 0.007*\"1288\" + 0.007*\"260\"\n", - "2021-08-24 03:03:25,085 : INFO : topic #5 (0.020): 0.009*\"2699\" + 0.008*\"2572\" + 0.008*\"26\" + 0.007*\"2513\" + 0.007*\"2428\" + 0.007*\"2858\" + 0.007*\"2722\" + 0.006*\"2541\" + 0.005*\"7387\" + 0.005*\"724\"\n", - "2021-08-24 03:03:25,086 : INFO : topic diff=0.077428, rho=0.204124\n", - "2021-08-24 03:03:25,733 : INFO : -8.024 per-word bound, 260.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:25,733 : INFO : PROGRESS: pass 23, at document #997/997\n", - "2021-08-24 03:03:26,139 : INFO : topic #9 (0.020): 0.005*\"1784\" + 0.005*\"1968\" + 0.004*\"1704\" + 0.004*\"2918\" + 0.004*\"1923\" + 0.004*\"2762\" + 0.004*\"1682\" + 0.004*\"1270\" + 0.004*\"1197\" + 0.004*\"2268\"\n", - "2021-08-24 03:03:26,140 : INFO : topic #31 (0.020): 0.010*\"6378\" + 0.009*\"4816\" + 0.009*\"3977\" + 0.008*\"5481\" + 0.008*\"3174\" + 0.007*\"1961\" + 0.007*\"1580\" + 0.007*\"356\" + 0.006*\"4995\" + 0.006*\"2683\"\n", - "2021-08-24 03:03:26,140 : INFO : topic #21 (0.020): 0.012*\"858\" + 0.012*\"1221\" + 0.012*\"1193\" + 0.008*\"2396\" + 0.008*\"904\" + 0.008*\"2762\" + 0.008*\"2858\" + 0.008*\"1097\" + 0.008*\"1641\" + 0.008*\"50\"\n", - "2021-08-24 03:03:26,141 : INFO : topic #32 (0.020): 0.011*\"2028\" + 0.008*\"593\" + 0.007*\"590\" + 0.007*\"858\" + 0.007*\"527\" + 0.006*\"1617\" + 0.006*\"45720\" + 0.006*\"36535\" + 0.006*\"2788\" + 0.006*\"57640\"\n", - "2021-08-24 03:03:26,142 : INFO : topic #30 (0.020): 0.014*\"193\" + 0.011*\"5464\" + 0.010*\"318\" + 0.009*\"59615\" + 0.008*\"3869\" + 0.007*\"50\" + 0.007*\"1266\" + 0.007*\"2997\" + 0.007*\"49272\" + 0.006*\"1193\"\n", - "2021-08-24 03:03:26,143 : INFO : topic diff=0.069387, rho=0.200000\n", - "2021-08-24 03:03:26,835 : INFO : -8.018 per-word bound, 259.2 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:26,836 : INFO : PROGRESS: pass 24, at document #997/997\n", - "2021-08-24 03:03:27,237 : INFO : topic #18 (0.020): 0.023*\"2683\" + 0.017*\"2605\" + 0.017*\"2706\" + 0.016*\"2700\" + 0.014*\"2716\" + 0.014*\"2572\" + 0.013*\"2712\" + 0.012*\"2336\" + 0.012*\"2396\" + 0.011*\"2987\"\n", - "2021-08-24 03:03:27,238 : INFO : topic #40 (0.020): 0.035*\"260\" + 0.032*\"1\" + 0.031*\"780\" + 0.030*\"733\" + 0.024*\"608\" + 0.024*\"62\" + 0.022*\"1073\" + 0.021*\"736\" + 0.021*\"32\" + 0.019*\"648\"\n", - "2021-08-24 03:03:27,239 : INFO : topic #12 (0.020): 0.008*\"260\" + 0.007*\"4993\" + 0.007*\"912\" + 0.007*\"2087\" + 0.006*\"5618\" + 0.006*\"6350\" + 0.006*\"4027\" + 0.006*\"3000\" + 0.006*\"1721\" + 0.005*\"3114\"\n", - "2021-08-24 03:03:27,240 : INFO : topic #22 (0.020): 0.019*\"1354\" + 0.013*\"1357\" + 0.012*\"1704\" + 0.011*\"3720\" + 0.011*\"2858\" + 0.011*\"1784\" + 0.011*\"2324\" + 0.010*\"1635\" + 0.010*\"527\" + 0.010*\"1682\"\n", - "2021-08-24 03:03:27,241 : INFO : topic #19 (0.020): 0.009*\"1036\" + 0.009*\"1240\" + 0.008*\"589\" + 0.008*\"110\" + 0.007*\"1198\" + 0.007*\"2571\" + 0.007*\"480\" + 0.007*\"1214\" + 0.007*\"2000\" + 0.007*\"1200\"\n", - "2021-08-24 03:03:27,242 : INFO : topic diff=0.062576, rho=0.196116\n", - "2021-08-24 03:03:27,872 : INFO : -8.012 per-word bound, 258.2 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:27,873 : INFO : PROGRESS: pass 25, at document #997/997\n", - "2021-08-24 03:03:28,275 : INFO : topic #41 (0.020): 0.010*\"1193\" + 0.009*\"1219\" + 0.008*\"541\" + 0.007*\"1258\" + 0.007*\"1148\" + 0.007*\"527\" + 0.007*\"2858\" + 0.006*\"6104\" + 0.006*\"904\" + 0.006*\"745\"\n", - "2021-08-24 03:03:28,276 : INFO : topic #6 (0.020): 0.009*\"2396\" + 0.008*\"1193\" + 0.007*\"11\" + 0.007*\"356\" + 0.007*\"2700\" + 0.007*\"2571\" + 0.007*\"1721\" + 0.007*\"2706\" + 0.007*\"1584\" + 0.007*\"3793\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 03:03:28,277 : INFO : topic #35 (0.020): 0.011*\"2716\" + 0.008*\"750\" + 0.007*\"32\" + 0.007*\"318\" + 0.007*\"2987\" + 0.006*\"52\" + 0.006*\"457\" + 0.006*\"1136\" + 0.006*\"111\" + 0.006*\"919\"\n", - "2021-08-24 03:03:28,278 : INFO : topic #46 (0.020): 0.011*\"1200\" + 0.010*\"1240\" + 0.007*\"2858\" + 0.007*\"2918\" + 0.006*\"2692\" + 0.006*\"2762\" + 0.006*\"599\" + 0.006*\"260\" + 0.006*\"1214\" + 0.006*\"1261\"\n", - "2021-08-24 03:03:28,279 : INFO : topic #20 (0.020): 0.008*\"2140\" + 0.008*\"2529\" + 0.008*\"1374\" + 0.008*\"2105\" + 0.008*\"924\" + 0.007*\"2761\" + 0.007*\"1748\" + 0.007*\"1376\" + 0.007*\"1127\" + 0.007*\"1019\"\n", - "2021-08-24 03:03:28,280 : INFO : topic diff=0.056690, rho=0.192450\n", - "2021-08-24 03:03:28,912 : INFO : -8.007 per-word bound, 257.3 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:28,913 : INFO : PROGRESS: pass 26, at document #997/997\n", - "2021-08-24 03:03:29,321 : INFO : topic #5 (0.020): 0.010*\"26\" + 0.009*\"2699\" + 0.008*\"2572\" + 0.007*\"2513\" + 0.007*\"2428\" + 0.007*\"2722\" + 0.007*\"2858\" + 0.006*\"2541\" + 0.005*\"724\" + 0.005*\"2719\"\n", - "2021-08-24 03:03:29,322 : INFO : topic #28 (0.020): 0.013*\"2028\" + 0.012*\"260\" + 0.011*\"2571\" + 0.011*\"593\" + 0.010*\"356\" + 0.009*\"110\" + 0.009*\"608\" + 0.009*\"1210\" + 0.008*\"318\" + 0.008*\"296\"\n", - "2021-08-24 03:03:29,322 : INFO : topic #37 (0.020): 0.008*\"908\" + 0.007*\"913\" + 0.006*\"1256\" + 0.006*\"904\" + 0.006*\"903\" + 0.006*\"933\" + 0.005*\"1269\" + 0.005*\"905\" + 0.005*\"910\" + 0.005*\"912\"\n", - "2021-08-24 03:03:29,323 : INFO : topic #46 (0.020): 0.011*\"1200\" + 0.010*\"1240\" + 0.007*\"2858\" + 0.007*\"2918\" + 0.006*\"2692\" + 0.006*\"2762\" + 0.006*\"599\" + 0.006*\"260\" + 0.006*\"1261\" + 0.006*\"1214\"\n", - "2021-08-24 03:03:29,324 : INFO : topic #21 (0.020): 0.012*\"858\" + 0.012*\"1221\" + 0.012*\"1193\" + 0.008*\"2396\" + 0.008*\"904\" + 0.008*\"2762\" + 0.008*\"2858\" + 0.008*\"1097\" + 0.008*\"1641\" + 0.008*\"50\"\n", - "2021-08-24 03:03:29,325 : INFO : topic diff=0.051637, rho=0.188982\n", - "2021-08-24 03:03:29,980 : INFO : -8.002 per-word bound, 256.4 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:29,981 : INFO : PROGRESS: pass 27, at document #997/997\n", - "2021-08-24 03:03:30,388 : INFO : topic #34 (0.020): 0.007*\"920\" + 0.007*\"3456\" + 0.006*\"908\" + 0.006*\"942\" + 0.006*\"2762\" + 0.006*\"50\" + 0.006*\"4022\" + 0.006*\"4226\" + 0.005*\"1732\" + 0.005*\"1248\"\n", - "2021-08-24 03:03:30,389 : INFO : topic #8 (0.020): 0.009*\"837\" + 0.008*\"1713\" + 0.007*\"1414\" + 0.007*\"1210\" + 0.007*\"1270\" + 0.007*\"1376\" + 0.006*\"260\" + 0.006*\"1689\" + 0.006*\"1097\" + 0.006*\"2064\"\n", - "2021-08-24 03:03:30,389 : INFO : topic #12 (0.020): 0.008*\"260\" + 0.007*\"6350\" + 0.007*\"912\" + 0.007*\"2087\" + 0.007*\"4993\" + 0.006*\"7099\" + 0.006*\"5618\" + 0.006*\"3000\" + 0.006*\"4027\" + 0.006*\"1721\"\n", - "2021-08-24 03:03:30,390 : INFO : topic #36 (0.020): 0.008*\"6365\" + 0.007*\"1580\" + 0.007*\"4874\" + 0.006*\"520\" + 0.006*\"2291\" + 0.005*\"6934\" + 0.005*\"2011\" + 0.005*\"541\" + 0.005*\"6333\" + 0.005*\"3793\"\n", - "2021-08-24 03:03:30,391 : INFO : topic #31 (0.020): 0.010*\"6378\" + 0.009*\"4816\" + 0.009*\"3977\" + 0.008*\"5481\" + 0.008*\"3174\" + 0.007*\"1961\" + 0.007*\"1580\" + 0.006*\"356\" + 0.006*\"4995\" + 0.006*\"2683\"\n", - "2021-08-24 03:03:30,392 : INFO : topic diff=0.047233, rho=0.185695\n", - "2021-08-24 03:03:31,034 : INFO : -7.998 per-word bound, 255.6 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:31,035 : INFO : PROGRESS: pass 28, at document #997/997\n", - "2021-08-24 03:03:31,438 : INFO : topic #6 (0.020): 0.009*\"2396\" + 0.008*\"1193\" + 0.007*\"11\" + 0.007*\"356\" + 0.007*\"2571\" + 0.007*\"1721\" + 0.007*\"1584\" + 0.007*\"3793\" + 0.007*\"2700\" + 0.007*\"2706\"\n", - "2021-08-24 03:03:31,439 : INFO : topic #24 (0.020): 0.014*\"1196\" + 0.013*\"1198\" + 0.012*\"1270\" + 0.010*\"1291\" + 0.010*\"1197\" + 0.009*\"1240\" + 0.009*\"1246\" + 0.008*\"1097\" + 0.007*\"1012\" + 0.007*\"260\"\n", - "2021-08-24 03:03:31,440 : INFO : topic #13 (0.020): 0.015*\"50\" + 0.011*\"111\" + 0.011*\"290\" + 0.010*\"527\" + 0.010*\"1173\" + 0.009*\"593\" + 0.009*\"1237\" + 0.009*\"2858\" + 0.009*\"296\" + 0.008*\"36\"\n", - "2021-08-24 03:03:31,440 : INFO : topic #48 (0.020): 0.008*\"4055\" + 0.008*\"5577\" + 0.007*\"5812\" + 0.006*\"2029\" + 0.006*\"5682\" + 0.006*\"6367\" + 0.006*\"448\" + 0.006*\"5447\" + 0.006*\"3712\" + 0.006*\"5991\"\n", - "2021-08-24 03:03:31,441 : INFO : topic #31 (0.020): 0.010*\"6378\" + 0.009*\"4816\" + 0.009*\"3977\" + 0.008*\"5481\" + 0.008*\"3174\" + 0.007*\"1580\" + 0.007*\"1961\" + 0.006*\"356\" + 0.006*\"4995\" + 0.006*\"2683\"\n", - "2021-08-24 03:03:31,442 : INFO : topic diff=0.043400, rho=0.182574\n", - "2021-08-24 03:03:32,092 : INFO : -7.993 per-word bound, 254.8 perplexity estimate based on a held-out corpus of 997 documents with 67731 words\n", - "2021-08-24 03:03:32,092 : INFO : PROGRESS: pass 29, at document #997/997\n", - "2021-08-24 03:03:32,497 : INFO : topic #9 (0.020): 0.005*\"1784\" + 0.005*\"1968\" + 0.004*\"1704\" + 0.004*\"2918\" + 0.004*\"1923\" + 0.004*\"1682\" + 0.004*\"1270\" + 0.004*\"2268\" + 0.004*\"2762\" + 0.004*\"1197\"\n", - "2021-08-24 03:03:32,498 : INFO : topic #16 (0.020): 0.013*\"455\" + 0.010*\"355\" + 0.010*\"837\" + 0.009*\"609\" + 0.008*\"688\" + 0.008*\"1021\" + 0.007*\"851\" + 0.007*\"1015\" + 0.007*\"919\" + 0.007*\"27\"\n", - "2021-08-24 03:03:32,498 : INFO : topic #45 (0.020): 0.015*\"1196\" + 0.012*\"260\" + 0.012*\"1198\" + 0.011*\"2081\" + 0.011*\"1968\" + 0.010*\"318\" + 0.009*\"1307\" + 0.009*\"1200\" + 0.009*\"1961\" + 0.008*\"2396\"\n", - "2021-08-24 03:03:32,499 : INFO : topic #39 (0.020): 0.010*\"2571\" + 0.009*\"1196\" + 0.009*\"1210\" + 0.007*\"260\" + 0.007*\"1676\" + 0.006*\"589\" + 0.006*\"2628\" + 0.006*\"480\" + 0.006*\"1580\" + 0.006*\"1527\"\n", - "2021-08-24 03:03:32,500 : INFO : topic #35 (0.020): 0.011*\"2716\" + 0.008*\"750\" + 0.007*\"32\" + 0.007*\"318\" + 0.007*\"2987\" + 0.006*\"52\" + 0.006*\"457\" + 0.006*\"1136\" + 0.006*\"111\" + 0.006*\"919\"\n", - "2021-08-24 03:03:32,501 : INFO : topic diff=0.039941, rho=0.179605\n", - "2021-08-24 03:03:32,502 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel(num_terms=4987, num_topics=50, decay=0.5, chunksize=2000) in 35.20s', 'datetime': '2021-08-24T03:03:32.502542', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n" - ] - } - ], + "outputs": [], "source": [ "# LDACollaboration 추천\n", "from src.lda_collaboration import LDACollaborationRecommender\n", @@ -328,18 +49,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "106dce69", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.024, Recall@K=0.075\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/LDA_content.ipynb b/chapter5/notebook/LDA_content.ipynb index 2d9af6c..8f2b2f8 100644 --- a/chapter5/notebook/LDA_content.ipynb +++ b/chapter5/notebook/LDA_content.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "dd0a4718", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "38f1eabd", "metadata": {}, "outputs": [], @@ -36,1694 +36,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "56ca8b83", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:24,041 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n", - "2021-08-24 02:38:24,219 : INFO : adding document #10000 to Dictionary(14749 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...)\n", - "2021-08-24 02:38:24,230 : INFO : built Dictionary(15261 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...) from 10681 documents (total 117144 corpus positions)\n", - "2021-08-24 02:38:24,230 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary(15261 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...) from 10681 documents (total 117144 corpus positions)\", 'datetime': '2021-08-24T02:38:24.230595', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n", - "2021-08-24 02:38:24,321 : INFO : using symmetric alpha at 0.02\n", - "2021-08-24 02:38:24,322 : INFO : using symmetric eta at 0.02\n", - "2021-08-24 02:38:24,324 : INFO : using serial LDA version on this node\n", - "2021-08-24 02:38:24,378 : INFO : running online (multi-pass) LDA training, 50 topics, 30 passes over the supplied corpus of 10681 documents, updating model once every 2000 documents, evaluating perplexity every 10681 documents, iterating 50x with a convergence threshold of 0.001000\n", - "2021-08-24 02:38:24,379 : INFO : PROGRESS: pass 0, at document #2000/10681\n", - "2021-08-24 02:38:24,907 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:24,936 : INFO : topic #15 (0.020): 0.027*\"classic\" + 0.016*\"oscar (best picture)\" + 0.011*\"afi 100\" + 0.010*\"Comedy\" + 0.009*\"Drama\" + 0.009*\"coen brothers\" + 0.009*\"submarine\" + 0.009*\"Thriller\" + 0.008*\"action\" + 0.008*\"stephen king\"\n", - "2021-08-24 02:38:24,937 : INFO : topic #3 (0.020): 0.026*\"Drama\" + 0.018*\"classic\" + 0.017*\"Comedy\" + 0.014*\"Romance\" + 0.012*\"war\" + 0.012*\"aliens\" + 0.011*\"oscar (best picture)\" + 0.010*\"sergio leone\" + 0.010*\"video game adaptation\" + 0.009*\"space\"\n", - "2021-08-24 02:38:24,938 : INFO : topic #7 (0.020): 0.025*\"oscar (best picture)\" + 0.018*\"Drama\" + 0.016*\"space\" + 0.015*\"mel gibson\" + 0.012*\"sci-fi\" + 0.012*\"aliens\" + 0.012*\"boxing\" + 0.011*\"action\" + 0.011*\"Action\" + 0.010*\"dvd\"\n", - "2021-08-24 02:38:24,938 : INFO : topic #12 (0.020): 0.041*\"quentin tarantino\" + 0.024*\"Drama\" + 0.019*\"christmas\" + 0.019*\"tarantino\" + 0.012*\"violence\" + 0.011*\"Comedy\" + 0.011*\"stanley kubrick\" + 0.011*\"owned\" + 0.009*\"dystopia\" + 0.009*\"can't remember\"\n", - "2021-08-24 02:38:24,939 : INFO : topic #5 (0.020): 0.024*\"Drama\" + 0.024*\"Comedy\" + 0.021*\"action\" + 0.018*\"Action\" + 0.017*\"tom hanks\" + 0.017*\"meg ryan\" + 0.014*\"robin williams\" + 0.014*\"Thriller\" + 0.013*\"comedy\" + 0.013*\"mel gibson\"\n", - "2021-08-24 02:38:24,940 : INFO : topic diff=46.180958, rho=1.000000\n", - "2021-08-24 02:38:24,941 : INFO : PROGRESS: pass 0, at document #4000/10681\n", - "2021-08-24 02:38:25,306 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:25,333 : INFO : topic #39 (0.020): 0.034*\"bruce willis\" + 0.027*\"disney\" + 0.022*\"Drama\" + 0.019*\"Comedy\" + 0.017*\"avi\" + 0.015*\"end of the world\" + 0.014*\"Thriller\" + 0.014*\"70mm\" + 0.013*\"Sci-Fi\" + 0.013*\"Action\"\n", - "2021-08-24 02:38:25,334 : INFO : topic #47 (0.020): 0.058*\"Drama\" + 0.027*\"Comedy\" + 0.026*\"less than 300 ratings\" + 0.022*\"Thriller\" + 0.020*\"comedy\" + 0.019*\"martial arts\" + 0.019*\"racism\" + 0.018*\"football\" + 0.018*\"jackie chan\" + 0.017*\"Crime\"\n", - "2021-08-24 02:38:25,334 : INFO : topic #35 (0.020): 0.066*\"comedy\" + 0.061*\"Comedy\" + 0.034*\"Romance\" + 0.026*\"funny\" + 0.023*\"Drama\" + 0.021*\"Adventure\" + 0.017*\"parody\" + 0.014*\"can't remember\" + 0.014*\"mel brooks\" + 0.011*\"Action\"\n", - "2021-08-24 02:38:25,335 : INFO : topic #18 (0.020): 0.051*\"surrealism\" + 0.031*\"Comedy\" + 0.016*\"Drama\" + 0.016*\"spoof\" + 0.016*\"Thriller\" + 0.015*\"creepy\" + 0.015*\"Horror\" + 0.013*\"based on a book\" + 0.013*\"robert downey jr\" + 0.012*\"silly\"\n", - "2021-08-24 02:38:25,336 : INFO : topic #9 (0.020): 0.056*\"Drama\" + 0.030*\"Western\" + 0.021*\"Comedy\" + 0.017*\"Romance\" + 0.016*\"british\" + 0.015*\"russell crowe\" + 0.014*\"nudity (topless - brief)\" + 0.014*\"oliver stone\" + 0.013*\"jane austen\" + 0.013*\"drama\"\n", - "2021-08-24 02:38:25,337 : INFO : topic diff=0.443563, rho=0.707107\n", - "2021-08-24 02:38:25,338 : INFO : PROGRESS: pass 0, at document #6000/10681\n", - "2021-08-24 02:38:25,667 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:25,693 : INFO : topic #19 (0.020): 0.037*\"girlie movie\" + 0.035*\"julia roberts\" + 0.028*\"Comedy\" + 0.028*\"mystery\" + 0.026*\"ghosts\" + 0.023*\"romance\" + 0.022*\"Romance\" + 0.021*\"pg-13\" + 0.019*\"wedding\" + 0.016*\"afternoon section\"\n", - "2021-08-24 02:38:25,694 : INFO : topic #47 (0.020): 0.076*\"less than 300 ratings\" + 0.075*\"Drama\" + 0.036*\"Comedy\" + 0.028*\"jackie chan\" + 0.026*\"Thriller\" + 0.024*\"Crime\" + 0.021*\"Action\" + 0.021*\"martial arts\" + 0.015*\"brian de palma\" + 0.014*\"football\"\n", - "2021-08-24 02:38:25,694 : INFO : topic #3 (0.020): 0.044*\"Drama\" + 0.034*\"Comedy\" + 0.029*\"zombies\" + 0.026*\"nudity (topless)\" + 0.023*\"Horror\" + 0.018*\"zombie\" + 0.017*\"Romance\" + 0.016*\"virtual reality\" + 0.013*\"Sci-Fi\" + 0.011*\"Thriller\"\n", - "2021-08-24 02:38:25,695 : INFO : topic #17 (0.020): 0.026*\"sci-fi\" + 0.018*\"Drama\" + 0.014*\"police\" + 0.013*\"Action\" + 0.013*\"no dialogue\" + 0.013*\"oscar (best supporting actress)\" + 0.013*\"computers\" + 0.012*\"writer\" + 0.012*\"action\" + 0.012*\"robots\"\n", - "2021-08-24 02:38:25,696 : INFO : topic #29 (0.020): 0.031*\"Comedy\" + 0.029*\"Action\" + 0.028*\"arnold schwarzenegger\" + 0.025*\"parody\" + 0.024*\"adventure\" + 0.015*\"action\" + 0.014*\"Adventure\" + 0.014*\"Thriller\" + 0.014*\"stupid\" + 0.013*\"Drama\"\n", - "2021-08-24 02:38:25,697 : INFO : topic diff=0.375887, rho=0.577350\n", - "2021-08-24 02:38:25,699 : INFO : PROGRESS: pass 0, at document #8000/10681\n", - "2021-08-24 02:38:25,988 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:26,015 : INFO : topic #11 (0.020): 0.221*\"Documentary\" + 0.066*\"in netflix queue\" + 0.044*\"Drama\" + 0.022*\"gay\" + 0.022*\"to see\" + 0.022*\"Comedy\" + 0.018*\"india\" + 0.017*\"ian mckellen\" + 0.017*\"homosexuality\" + 0.017*\"IMAX\"\n", - "2021-08-24 02:38:26,016 : INFO : topic #4 (0.020): 0.053*\"romance\" + 0.037*\"Comedy\" + 0.036*\"watched 2006\" + 0.027*\"Romance\" + 0.024*\"sean connery\" + 0.024*\"chick flick\" + 0.020*\"car chase\" + 0.019*\"Musical\" + 0.019*\"dance\" + 0.019*\"seen more than once\"\n", - "2021-08-24 02:38:26,017 : INFO : topic #3 (0.020): 0.072*\"zombies\" + 0.043*\"Drama\" + 0.031*\"ingmar bergman\" + 0.027*\"Horror\" + 0.026*\"Comedy\" + 0.026*\"nudity (topless)\" + 0.026*\"zombie\" + 0.022*\"virtual reality\" + 0.016*\"Romance\" + 0.012*\"erlend's dvds\"\n", - "2021-08-24 02:38:26,018 : INFO : topic #0 (0.020): 0.027*\"violence\" + 0.027*\"action\" + 0.025*\"robert rodriguez\" + 0.025*\"philip seymour hoffman\" + 0.025*\"crime\" + 0.023*\"morgan freeman\" + 0.022*\"Thriller\" + 0.021*\"brad pitt\" + 0.020*\"civil war\" + 0.019*\"Drama\"\n", - "2021-08-24 02:38:26,018 : INFO : topic #28 (0.020): 0.082*\"pixar\" + 0.021*\"oscar (best actress)\" + 0.021*\"mockumentary\" + 0.020*\"elijah wood\" + 0.017*\"high fantasy\" + 0.016*\"Comedy\" + 0.016*\"Adventure\" + 0.015*\"rock and roll\" + 0.015*\"Drama\" + 0.015*\"animation\"\n", - "2021-08-24 02:38:26,020 : INFO : topic diff=0.275035, rho=0.500000\n", - "2021-08-24 02:38:26,021 : INFO : PROGRESS: pass 0, at document #10000/10681\n", - "2021-08-24 02:38:26,329 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:26,357 : INFO : topic #21 (0.020): 0.105*\"Drama\" + 0.047*\"criterion\" + 0.024*\"sven's to see list\" + 0.020*\"library\" + 0.019*\"tumey's dvds\" + 0.019*\"deliberate\" + 0.018*\"reflective\" + 0.017*\"Comedy\" + 0.016*\"atmospheric\" + 0.016*\"stylized\"\n", - "2021-08-24 02:38:26,358 : INFO : topic #9 (0.020): 0.136*\"r\" + 0.074*\"Western\" + 0.071*\"Drama\" + 0.064*\"clearplay\" + 0.039*\"history\" + 0.036*\"pg13\" + 0.026*\"Comedy\" + 0.023*\"nudity (topless - brief)\" + 0.021*\"Romance\" + 0.015*\"War\"\n", - "2021-08-24 02:38:26,359 : INFO : topic #19 (0.020): 0.113*\"pg-13\" + 0.038*\"jude law\" + 0.025*\"Comedy\" + 0.024*\"Romance\" + 0.021*\"girlie movie\" + 0.021*\"wedding\" + 0.020*\"julia roberts\" + 0.018*\"want to own\" + 0.018*\"sam peckinpah\" + 0.018*\"infidelity\"\n", - "2021-08-24 02:38:26,360 : INFO : topic #1 (0.020): 0.084*\"remake\" + 0.044*\"Drama\" + 0.037*\"Thriller\" + 0.032*\"jonossa\" + 0.024*\"tumey's dvds\" + 0.023*\"Mystery\" + 0.018*\"movie to see\" + 0.017*\"Romance\" + 0.015*\"Crime\" + 0.014*\"predictable\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:26,361 : INFO : topic #46 (0.020): 0.025*\"natalie portman\" + 0.021*\"kevin spacey\" + 0.018*\"Drama\" + 0.016*\"sci-fi\" + 0.014*\"ridley scott\" + 0.014*\"ewan mcgregor\" + 0.013*\"must see!\" + 0.012*\"steven spielberg\" + 0.010*\"recorded\" + 0.010*\"moving\"\n", - "2021-08-24 02:38:26,362 : INFO : topic diff=0.235645, rho=0.447214\n", - "2021-08-24 02:38:26,531 : INFO : -21.546 per-word bound, 3061396.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:26,531 : INFO : PROGRESS: pass 0, at document #10681/10681\n", - "2021-08-24 02:38:26,638 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:26,664 : INFO : topic #13 (0.020): 0.071*\"time travel\" + 0.050*\"twist ending\" + 0.045*\"holocaust\" + 0.037*\"post-apocalyptic\" + 0.027*\"bill murray\" + 0.022*\"good dialogue\" + 0.016*\"capitalism\" + 0.015*\"michael moore\" + 0.014*\"singing\" + 0.014*\"twist\"\n", - "2021-08-24 02:38:26,664 : INFO : topic #47 (0.020): 0.250*\"less than 300 ratings\" + 0.120*\"Drama\" + 0.070*\"Comedy\" + 0.027*\"kung fu\" + 0.024*\"Crime\" + 0.024*\"Thriller\" + 0.023*\"Romance\" + 0.022*\"nudity (rear)\" + 0.021*\"Action\" + 0.019*\"oppl\"\n", - "2021-08-24 02:38:26,665 : INFO : topic #24 (0.020): 0.034*\"disability\" + 0.028*\"france\" + 0.026*\"mask\" + 0.025*\"tom cruise\" + 0.024*\"nicole kidman\" + 0.021*\"Drama\" + 0.020*\"dustin hoffman\" + 0.020*\"halloween\" + 0.018*\"french\" + 0.017*\"bank robbery\"\n", - "2021-08-24 02:38:26,666 : INFO : topic #0 (0.020): 0.052*\"morgan freeman\" + 0.039*\"tommy lee jones\" + 0.039*\"violent\" + 0.038*\"organized crime\" + 0.037*\"violence\" + 0.037*\"philip seymour hoffman\" + 0.031*\"brad pitt\" + 0.031*\"Thriller\" + 0.030*\"crime\" + 0.021*\"robert rodriguez\"\n", - "2021-08-24 02:38:26,667 : INFO : topic #3 (0.020): 0.242*\"nudity (topless)\" + 0.088*\"zombies\" + 0.040*\"Drama\" + 0.034*\"keira knightley\" + 0.027*\"Horror\" + 0.025*\"Comedy\" + 0.013*\"video game adaptation\" + 0.012*\"r:language\" + 0.012*\"fascism\" + 0.012*\"Thriller\"\n", - "2021-08-24 02:38:26,668 : INFO : topic diff=0.303876, rho=0.408248\n", - "2021-08-24 02:38:26,669 : INFO : PROGRESS: pass 1, at document #2000/10681\n", - "2021-08-24 02:38:26,969 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:26,997 : INFO : topic #30 (0.020): 0.380*\"Drama\" + 0.037*\"Film-Noir\" + 0.024*\"classic\" + 0.013*\"boring\" + 0.011*\"film noir\" + 0.010*\"Comedy\" + 0.010*\"based on a book\" + 0.010*\"small town\" + 0.009*\"erlend's dvds\" + 0.009*\"witch\"\n", - "2021-08-24 02:38:26,998 : INFO : topic #5 (0.020): 0.168*\"Comedy\" + 0.132*\"Action\" + 0.127*\"Thriller\" + 0.112*\"Drama\" + 0.088*\"Horror\" + 0.071*\"Sci-Fi\" + 0.042*\"Adventure\" + 0.031*\"Crime\" + 0.016*\"Romance\" + 0.013*\"can't remember\"\n", - "2021-08-24 02:38:26,999 : INFO : topic #23 (0.020): 0.066*\"mafia\" + 0.029*\"al pacino\" + 0.026*\"robert de niro\" + 0.023*\"classic\" + 0.022*\"marlon brando\" + 0.016*\"scifi\" + 0.015*\"guns\" + 0.015*\"francis ford coppola\" + 0.014*\"Drama\" + 0.013*\"conspiracy\"\n", - "2021-08-24 02:38:27,000 : INFO : topic #18 (0.020): 0.044*\"slasher\" + 0.029*\"franchise\" + 0.025*\"Horror\" + 0.025*\"united states\" + 0.023*\"creepy\" + 0.022*\"unique\" + 0.020*\"shark\" + 0.019*\"jean reno\" + 0.018*\"silly\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:38:27,001 : INFO : topic #21 (0.020): 0.082*\"Drama\" + 0.036*\"criterion\" + 0.021*\"tumey's dvds\" + 0.020*\"library\" + 0.018*\"irreverent\" + 0.018*\"deliberate\" + 0.017*\"lyrical\" + 0.017*\"sven's to see list\" + 0.016*\"reflective\" + 0.016*\"library vhs\"\n", - "2021-08-24 02:38:27,002 : INFO : topic diff=0.480280, rho=0.369094\n", - "2021-08-24 02:38:27,003 : INFO : PROGRESS: pass 1, at document #4000/10681\n", - "2021-08-24 02:38:27,287 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:27,314 : INFO : topic #15 (0.020): 0.063*\"oscar (best picture)\" + 0.047*\"coen brothers\" + 0.047*\"stephen king\" + 0.039*\"racism\" + 0.022*\"espionage\" + 0.020*\"great acting\" + 0.018*\"ensemble cast\" + 0.015*\"multiple storylines\" + 0.015*\"notable nudity\" + 0.013*\"Drama\"\n", - "2021-08-24 02:38:27,315 : INFO : topic #48 (0.020): 0.046*\"surreal\" + 0.035*\"quirky\" + 0.031*\"Drama\" + 0.025*\"Comedy\" + 0.021*\"humorous\" + 0.021*\"underrated\" + 0.020*\"coming of age\" + 0.020*\"woody allen\" + 0.018*\"bittersweet\" + 0.018*\"whimsical\"\n", - "2021-08-24 02:38:27,316 : INFO : topic #33 (0.020): 0.118*\"comic book\" + 0.115*\"superhero\" + 0.053*\"super-hero\" + 0.034*\"adapted from:comic\" + 0.029*\"gene hackman\" + 0.025*\"batman\" + 0.024*\"action\" + 0.023*\"Action\" + 0.019*\"overrated\" + 0.017*\"alter ego\"\n", - "2021-08-24 02:38:27,317 : INFO : topic #34 (0.020): 0.119*\"War\" + 0.097*\"world war ii\" + 0.077*\"drugs\" + 0.076*\"Drama\" + 0.036*\"prison\" + 0.032*\"Action\" + 0.017*\"vhs\" + 0.016*\"war\" + 0.015*\"wwii\" + 0.015*\"notable soundtrack\"\n", - "2021-08-24 02:38:27,318 : INFO : topic #18 (0.020): 0.043*\"surrealism\" + 0.035*\"slasher\" + 0.032*\"creepy\" + 0.027*\"franchise\" + 0.025*\"robert downey jr\" + 0.024*\"united states\" + 0.022*\"Horror\" + 0.019*\"unique\" + 0.018*\"goth\" + 0.016*\"silly\"\n", - "2021-08-24 02:38:27,319 : INFO : topic diff=0.196787, rho=0.369094\n", - "2021-08-24 02:38:27,321 : INFO : PROGRESS: pass 1, at document #6000/10681\n", - "2021-08-24 02:38:27,603 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:27,629 : INFO : topic #17 (0.020): 0.042*\"police\" + 0.038*\"will smith\" + 0.022*\"action\" + 0.022*\"computers\" + 0.019*\"corruption\" + 0.018*\"milla jovovich\" + 0.017*\"sci-fi\" + 0.016*\"no dialogue\" + 0.014*\"writer\" + 0.013*\"luc besson\"\n", - "2021-08-24 02:38:27,630 : INFO : topic #19 (0.020): 0.059*\"ghosts\" + 0.043*\"pg-13\" + 0.037*\"julia roberts\" + 0.032*\"wedding\" + 0.031*\"girlie movie\" + 0.031*\"jude law\" + 0.022*\"afternoon section\" + 0.021*\"want to own\" + 0.020*\"Comedy\" + 0.019*\"prostitution\"\n", - "2021-08-24 02:38:27,631 : INFO : topic #18 (0.020): 0.049*\"creepy\" + 0.038*\"slasher\" + 0.032*\"surrealism\" + 0.028*\"franchise\" + 0.025*\"Horror\" + 0.020*\"silly\" + 0.018*\"robert downey jr\" + 0.018*\"goth\" + 0.017*\"special\" + 0.016*\"united states\"\n", - "2021-08-24 02:38:27,632 : INFO : topic #16 (0.020): 0.094*\"dystopia\" + 0.043*\"sci-fi\" + 0.030*\"futuristmovies.com\" + 0.029*\"military\" + 0.027*\"future\" + 0.021*\"Sci-Fi\" + 0.018*\"weird\" + 0.017*\"joaquin phoenix\" + 0.014*\"court\" + 0.013*\"urbane\"\n", - "2021-08-24 02:38:27,633 : INFO : topic #37 (0.020): 0.047*\"Drama\" + 0.036*\"easily confused with other movie(s) (title)\" + 0.034*\"vietnam war\" + 0.028*\"vietnam\" + 0.027*\"Comedy\" + 0.026*\"oscar (best supporting actress)\" + 0.023*\"oscar (best actress)\" + 0.019*\"cult classic\" + 0.019*\"19th century\" + 0.018*\"want to see again\"\n", - "2021-08-24 02:38:27,634 : INFO : topic diff=0.237005, rho=0.369094\n", - "2021-08-24 02:38:27,635 : INFO : PROGRESS: pass 1, at document #8000/10681\n", - "2021-08-24 02:38:27,905 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:27,932 : INFO : topic #3 (0.020): 0.157*\"nudity (topless)\" + 0.092*\"zombies\" + 0.032*\"ingmar bergman\" + 0.031*\"Drama\" + 0.030*\"zombie\" + 0.024*\"virtual reality\" + 0.023*\"Comedy\" + 0.022*\"Horror\" + 0.016*\"keira knightley\" + 0.015*\"robert altman\"\n", - "2021-08-24 02:38:27,933 : INFO : topic #15 (0.020): 0.036*\"oscar (best picture)\" + 0.036*\"espionage\" + 0.035*\"coen brothers\" + 0.035*\"stephen king\" + 0.028*\"racism\" + 0.022*\"notable nudity\" + 0.021*\"ensemble cast\" + 0.020*\"multiple storylines\" + 0.017*\"tokyo\" + 0.015*\"god\"\n", - "2021-08-24 02:38:27,934 : INFO : topic #4 (0.020): 0.091*\"romance\" + 0.038*\"chick flick\" + 0.034*\"Comedy\" + 0.033*\"new york city\" + 0.029*\"watched 2006\" + 0.029*\"sean connery\" + 0.025*\"Romance\" + 0.024*\"new york\" + 0.021*\"seen more than once\" + 0.021*\"car chase\"\n", - "2021-08-24 02:38:27,935 : INFO : topic #47 (0.020): 0.257*\"less than 300 ratings\" + 0.114*\"Drama\" + 0.063*\"Comedy\" + 0.033*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.022*\"Action\" + 0.019*\"Thriller\" + 0.018*\"kung fu\" + 0.018*\"football\" + 0.017*\"martial arts\"\n", - "2021-08-24 02:38:27,935 : INFO : topic #2 (0.020): 0.095*\"lesbian\" + 0.071*\"satire\" + 0.070*\"Comedy\" + 0.050*\"imdb bottom 100\" + 0.028*\"sexy\" + 0.021*\"ben stiller\" + 0.020*\"amnesia\" + 0.019*\"olympics\" + 0.017*\"john turturro\" + 0.017*\"get\"\n", - "2021-08-24 02:38:27,937 : INFO : topic diff=0.199877, rho=0.369094\n", - "2021-08-24 02:38:27,938 : INFO : PROGRESS: pass 1, at document #10000/10681\n", - "2021-08-24 02:38:28,211 : INFO : merging changes from 2000 documents into a model of 10681 documents\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:28,238 : INFO : topic #22 (0.020): 0.036*\"philip k. dick\" + 0.036*\"oscar (best cinematography)\" + 0.032*\"journalism\" + 0.031*\"angelina jolie\" + 0.030*\"based on a book\" + 0.029*\"classic\" + 0.026*\"cult film\" + 0.022*\"earnest\" + 0.020*\"adapted from:book\" + 0.019*\"political\"\n", - "2021-08-24 02:38:28,239 : INFO : topic #40 (0.020): 0.122*\"magic\" + 0.082*\"nudity (full frontal - notable)\" + 0.057*\"Drama\" + 0.054*\"edward norton\" + 0.051*\"sports\" + 0.035*\"not corv lib\" + 0.033*\"hw drama\" + 0.030*\"baseball\" + 0.025*\"based on a book\" + 0.025*\"don cheadle\"\n", - "2021-08-24 02:38:28,240 : INFO : topic #35 (0.020): 0.137*\"comedy\" + 0.092*\"Comedy\" + 0.088*\"funny\" + 0.044*\"ummarti2006\" + 0.039*\"parody\" + 0.024*\"hilarious\" + 0.021*\"Romance\" + 0.020*\"dani2006\" + 0.020*\"afi 100 (laughs)\" + 0.019*\"spoof\"\n", - "2021-08-24 02:38:28,241 : INFO : topic #48 (0.020): 0.042*\"surreal\" + 0.040*\"quirky\" + 0.029*\"Drama\" + 0.027*\"bittersweet\" + 0.025*\"Comedy\" + 0.022*\"whimsical\" + 0.020*\"humorous\" + 0.020*\"woody allen\" + 0.017*\"peter jackson\" + 0.017*\"male nudity\"\n", - "2021-08-24 02:38:28,242 : INFO : topic #0 (0.020): 0.061*\"violence\" + 0.059*\"crime\" + 0.055*\"brad pitt\" + 0.041*\"morgan freeman\" + 0.039*\"violent\" + 0.029*\"action\" + 0.028*\"Thriller\" + 0.027*\"robert rodriguez\" + 0.023*\"philip seymour hoffman\" + 0.022*\"tommy lee jones\"\n", - "2021-08-24 02:38:28,243 : INFO : topic diff=0.168704, rho=0.369094\n", - "2021-08-24 02:38:28,408 : INFO : -21.175 per-word bound, 2366819.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:28,408 : INFO : PROGRESS: pass 1, at document #10681/10681\n", - "2021-08-24 02:38:28,514 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:28,540 : INFO : topic #45 (0.020): 0.055*\"japan\" + 0.043*\"revenge\" + 0.040*\"Drama\" + 0.032*\"tense\" + 0.028*\"clint eastwood\" + 0.027*\"ummarti2007\" + 0.027*\"gritty\" + 0.026*\"Action\" + 0.023*\"akira kurosawa\" + 0.021*\"samurai\"\n", - "2021-08-24 02:38:28,541 : INFO : topic #27 (0.020): 0.087*\"aliens\" + 0.044*\"family\" + 0.041*\"childhood\" + 0.037*\"gambling\" + 0.035*\"to-rent\" + 0.028*\"mother-son relationship\" + 0.027*\"poker\" + 0.027*\"steven spielberg\" + 0.024*\"hospital\" + 0.019*\"terrible\"\n", - "2021-08-24 02:38:28,542 : INFO : topic #5 (0.020): 0.176*\"Action\" + 0.158*\"Thriller\" + 0.128*\"Horror\" + 0.111*\"Comedy\" + 0.110*\"Sci-Fi\" + 0.089*\"Drama\" + 0.053*\"Adventure\" + 0.027*\"Crime\" + 0.013*\"betamax\" + 0.008*\"70mm\"\n", - "2021-08-24 02:38:28,543 : INFO : topic #16 (0.020): 0.087*\"dystopia\" + 0.043*\"military\" + 0.034*\"sci-fi\" + 0.026*\"Sci-Fi\" + 0.025*\"betrayal\" + 0.024*\"future\" + 0.020*\"travel\" + 0.019*\"imdb top 250\" + 0.018*\"weird\" + 0.018*\"futuristmovies.com\"\n", - "2021-08-24 02:38:28,543 : INFO : topic #24 (0.020): 0.037*\"disability\" + 0.037*\"france\" + 0.035*\"tom cruise\" + 0.029*\"nicole kidman\" + 0.027*\"mask\" + 0.025*\"french\" + 0.024*\"dustin hoffman\" + 0.021*\"halloween\" + 0.021*\"golden palm\" + 0.018*\"bank robbery\"\n", - "2021-08-24 02:38:28,545 : INFO : topic diff=0.234428, rho=0.369094\n", - "2021-08-24 02:38:28,546 : INFO : PROGRESS: pass 2, at document #2000/10681\n", - "2021-08-24 02:38:28,865 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:28,892 : INFO : topic #48 (0.020): 0.042*\"quirky\" + 0.036*\"surreal\" + 0.029*\"woody allen\" + 0.027*\"bittersweet\" + 0.023*\"humorous\" + 0.022*\"Drama\" + 0.022*\"underrated\" + 0.022*\"Comedy\" + 0.020*\"witty\" + 0.018*\"whimsical\"\n", - "2021-08-24 02:38:28,894 : INFO : topic #14 (0.020): 0.097*\"Mystery\" + 0.064*\"Thriller\" + 0.052*\"Horror\" + 0.034*\"disturbing\" + 0.026*\"atmospheric\" + 0.026*\"tense\" + 0.024*\"ominous\" + 0.022*\"menacing\" + 0.017*\"dvd-r\" + 0.017*\"erlend's dvds\"\n", - "2021-08-24 02:38:28,894 : INFO : topic #33 (0.020): 0.147*\"comic book\" + 0.118*\"superhero\" + 0.053*\"super-hero\" + 0.039*\"batman\" + 0.034*\"adapted from:comic\" + 0.032*\"gene hackman\" + 0.029*\"action\" + 0.025*\"Action\" + 0.024*\"overrated\" + 0.017*\"based on a comic\"\n", - "2021-08-24 02:38:28,895 : INFO : topic #4 (0.020): 0.103*\"romance\" + 0.053*\"chick flick\" + 0.040*\"new york city\" + 0.031*\"Romance\" + 0.031*\"classic\" + 0.028*\"Comedy\" + 0.022*\"new york\" + 0.022*\"seen more than once\" + 0.021*\"sean connery\" + 0.020*\"watched 2006\"\n", - "2021-08-24 02:38:28,896 : INFO : topic #10 (0.020): 0.262*\"Romance\" + 0.254*\"Comedy\" + 0.200*\"Drama\" + 0.019*\"bibliothek\" + 0.013*\"london\" + 0.012*\"directorial debut\" + 0.011*\"clv\" + 0.011*\"divorce\" + 0.009*\"adultery\" + 0.007*\"oppl\"\n", - "2021-08-24 02:38:28,897 : INFO : topic diff=0.393339, rho=0.346261\n", - "2021-08-24 02:38:28,898 : INFO : PROGRESS: pass 2, at document #4000/10681\n", - "2021-08-24 02:38:29,185 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:29,214 : INFO : topic #37 (0.020): 0.043*\"easily confused with other movie(s) (title)\" + 0.037*\"Drama\" + 0.037*\"vietnam\" + 0.037*\"vietnam war\" + 0.034*\"oscar (best supporting actress)\" + 0.026*\"oscar (best actress)\" + 0.026*\"cult classic\" + 0.025*\"want to see again\" + 0.023*\"j netflix\" + 0.017*\"Comedy\"\n", - "2021-08-24 02:38:29,215 : INFO : topic #44 (0.020): 0.110*\"Children\" + 0.092*\"Adventure\" + 0.079*\"Fantasy\" + 0.078*\"disney\" + 0.062*\"Comedy\" + 0.059*\"Animation\" + 0.035*\"animation\" + 0.024*\"Musical\" + 0.020*\"fantasy\" + 0.019*\"children\"\n", - "2021-08-24 02:38:29,216 : INFO : topic #21 (0.020): 0.073*\"Drama\" + 0.043*\"criterion\" + 0.025*\"tumey's dvds\" + 0.024*\"reflective\" + 0.023*\"library\" + 0.020*\"atmospheric\" + 0.020*\"deliberate\" + 0.019*\"satirical\" + 0.019*\"sven's to see list\" + 0.019*\"lyrical\"\n", - "2021-08-24 02:38:29,216 : INFO : topic #11 (0.020): 0.259*\"Documentary\" + 0.105*\"to see\" + 0.070*\"gay\" + 0.064*\"in netflix queue\" + 0.050*\"oscar (best actor)\" + 0.024*\"india\" + 0.021*\"homosexuality\" + 0.021*\"Drama\" + 0.013*\"matter-of-fact\" + 0.013*\"Comedy\"\n", - "2021-08-24 02:38:29,217 : INFO : topic #28 (0.020): 0.115*\"pixar\" + 0.052*\"oscar (best actress)\" + 0.039*\"mockumentary\" + 0.028*\"medieval\" + 0.027*\"dinosaurs\" + 0.024*\"nostalgia\" + 0.017*\"computer animation\" + 0.017*\"christopher guest\" + 0.015*\"golf\" + 0.015*\"animation\"\n", - "2021-08-24 02:38:29,218 : INFO : topic diff=0.125769, rho=0.346261\n", - "2021-08-24 02:38:29,220 : INFO : PROGRESS: pass 2, at document #6000/10681\n", - "2021-08-24 02:38:29,469 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:29,495 : INFO : topic #3 (0.020): 0.263*\"nudity (topless)\" + 0.073*\"zombies\" + 0.026*\"zombie\" + 0.022*\"virtual reality\" + 0.021*\"Drama\" + 0.020*\"Comedy\" + 0.015*\"robert altman\" + 0.014*\"Horror\" + 0.012*\"ingmar bergman\" + 0.012*\"keira knightley\"\n", - "2021-08-24 02:38:29,496 : INFO : topic #31 (0.020): 0.093*\"james bond\" + 0.063*\"bond\" + 0.063*\"007\" + 0.037*\"murder\" + 0.036*\"assassin\" + 0.031*\"franchise\" + 0.027*\"Action\" + 0.025*\"claymation\" + 0.023*\"Thriller\" + 0.020*\"Adventure\"\n", - "2021-08-24 02:38:29,497 : INFO : topic #20 (0.020): 0.101*\"politics\" + 0.076*\"jack nicholson\" + 0.073*\"documentary\" + 0.035*\"terrorism\" + 0.034*\"seen 2008\" + 0.031*\"propaganda\" + 0.031*\"aviation\" + 0.024*\"renee zellweger\" + 0.023*\"assassination\" + 0.019*\"usa\"\n", - "2021-08-24 02:38:29,497 : INFO : topic #35 (0.020): 0.175*\"comedy\" + 0.092*\"Comedy\" + 0.080*\"funny\" + 0.054*\"parody\" + 0.034*\"hilarious\" + 0.024*\"afi 100 (laughs)\" + 0.023*\"spoof\" + 0.021*\"funny as hell\" + 0.017*\"can't remember\" + 0.015*\"john malkovich\"\n", - "2021-08-24 02:38:29,498 : INFO : topic #19 (0.020): 0.084*\"ghosts\" + 0.048*\"pg-13\" + 0.039*\"julia roberts\" + 0.036*\"jude law\" + 0.035*\"wedding\" + 0.024*\"want to own\" + 0.024*\"afternoon section\" + 0.021*\"infidelity\" + 0.021*\"hollywood\" + 0.021*\"girlie movie\"\n", - "2021-08-24 02:38:29,499 : INFO : topic diff=0.159741, rho=0.346261\n", - "2021-08-24 02:38:29,501 : INFO : PROGRESS: pass 2, at document #8000/10681\n", - "2021-08-24 02:38:29,770 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:29,797 : INFO : topic #47 (0.020): 0.277*\"less than 300 ratings\" + 0.118*\"Drama\" + 0.064*\"Comedy\" + 0.045*\"nudity (rear)\" + 0.033*\"jackie chan\" + 0.022*\"Action\" + 0.020*\"kung fu\" + 0.019*\"football\" + 0.015*\"martial arts\" + 0.014*\"brian de palma\"\n", - "2021-08-24 02:38:29,798 : INFO : topic #30 (0.020): 0.540*\"Drama\" + 0.039*\"Film-Noir\" + 0.017*\"boring\" + 0.012*\"small town\" + 0.012*\"tumey's dvds\" + 0.011*\"oscar (best foreign language film)\" + 0.010*\"italy\" + 0.009*\"erlend's dvds\" + 0.009*\"witch\" + 0.008*\"elegant\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:29,798 : INFO : topic #29 (0.020): 0.099*\"action\" + 0.047*\"adventure\" + 0.035*\"sequel\" + 0.034*\"Action\" + 0.031*\"pirates\" + 0.029*\"arnold schwarzenegger\" + 0.026*\"robots\" + 0.026*\"motorcycle\" + 0.020*\"Adventure\" + 0.019*\"death\"\n", - "2021-08-24 02:38:29,799 : INFO : topic #19 (0.020): 0.065*\"ghosts\" + 0.054*\"pg-13\" + 0.034*\"wedding\" + 0.033*\"jude law\" + 0.032*\"julia roberts\" + 0.030*\"sam peckinpah\" + 0.023*\"want to own\" + 0.022*\"prostitution\" + 0.020*\"infidelity\" + 0.019*\"hollywood\"\n", - "2021-08-24 02:38:29,800 : INFO : topic #36 (0.020): 0.083*\"dark comedy\" + 0.066*\"black comedy\" + 0.061*\"bechdel test:fail\" + 0.039*\"funniest movies\" + 0.031*\"cold war\" + 0.028*\"nazis\" + 0.019*\"classic\" + 0.017*\"Comedy\" + 0.014*\"russia\" + 0.014*\"seen more than once\"\n", - "2021-08-24 02:38:29,801 : INFO : topic diff=0.124645, rho=0.346261\n", - "2021-08-24 02:38:29,803 : INFO : PROGRESS: pass 2, at document #10000/10681\n", - "2021-08-24 02:38:30,093 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:30,120 : INFO : topic #45 (0.020): 0.057*\"japan\" + 0.044*\"revenge\" + 0.039*\"tense\" + 0.032*\"clint eastwood\" + 0.031*\"Drama\" + 0.028*\"gritty\" + 0.024*\"forceful\" + 0.024*\"Action\" + 0.023*\"atmospheric\" + 0.023*\"ummarti2007\"\n", - "2021-08-24 02:38:30,121 : INFO : topic #33 (0.020): 0.188*\"comic book\" + 0.102*\"superhero\" + 0.078*\"super-hero\" + 0.037*\"adapted from:comic\" + 0.029*\"action\" + 0.026*\"gene hackman\" + 0.026*\"Action\" + 0.023*\"alter ego\" + 0.018*\"dark\" + 0.017*\"overrated\"\n", - "2021-08-24 02:38:30,122 : INFO : topic #12 (0.020): 0.093*\"quentin tarantino\" + 0.078*\"christmas\" + 0.073*\"heist\" + 0.049*\"tarantino\" + 0.037*\"rape\" + 0.036*\"nonlinear\" + 0.022*\"ireland\" + 0.021*\"blindfold\" + 0.018*\"xmas theme\" + 0.016*\"terry gilliam\"\n", - "2021-08-24 02:38:30,123 : INFO : topic #34 (0.020): 0.185*\"War\" + 0.109*\"Drama\" + 0.093*\"world war ii\" + 0.060*\"drugs\" + 0.044*\"war\" + 0.038*\"Action\" + 0.024*\"prison\" + 0.022*\"history\" + 0.018*\"Adventure\" + 0.017*\"netwatch\"\n", - "2021-08-24 02:38:30,125 : INFO : topic #48 (0.020): 0.046*\"quirky\" + 0.043*\"surreal\" + 0.029*\"bittersweet\" + 0.023*\"woody allen\" + 0.022*\"whimsical\" + 0.022*\"humorous\" + 0.020*\"Comedy\" + 0.019*\"Drama\" + 0.018*\"peter jackson\" + 0.017*\"coming of age\"\n", - "2021-08-24 02:38:30,126 : INFO : topic diff=0.100648, rho=0.346261\n", - "2021-08-24 02:38:30,289 : INFO : -21.073 per-word bound, 2206047.4 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:30,289 : INFO : PROGRESS: pass 2, at document #10681/10681\n", - "2021-08-24 02:38:30,397 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:30,424 : INFO : topic #49 (0.020): 0.120*\"anime\" + 0.048*\"high school\" + 0.048*\"psychology\" + 0.041*\"serial killer\" + 0.027*\"teen\" + 0.021*\"japan\" + 0.021*\"do zassania\" + 0.020*\"cannibalism\" + 0.017*\"philosophical\" + 0.015*\"california\"\n", - "2021-08-24 02:38:30,425 : INFO : topic #39 (0.020): 0.083*\"bruce willis\" + 0.050*\"africa\" + 0.041*\"apocalypse\" + 0.032*\"predictable\" + 0.029*\"love\" + 0.026*\"avi\" + 0.025*\"tragedy\" + 0.023*\"big budget\" + 0.022*\"interesting\" + 0.020*\"good\"\n", - "2021-08-24 02:38:30,426 : INFO : topic #36 (0.020): 0.126*\"bechdel test:fail\" + 0.096*\"dark comedy\" + 0.061*\"black comedy\" + 0.044*\"cold war\" + 0.042*\"nazis\" + 0.024*\"funniest movies\" + 0.018*\"existentialism\" + 0.015*\"author:charles dickens\" + 0.015*\"Comedy\" + 0.014*\"communism\"\n", - "2021-08-24 02:38:30,427 : INFO : topic #30 (0.020): 0.570*\"Drama\" + 0.055*\"Film-Noir\" + 0.015*\"boring\" + 0.009*\"witch\" + 0.009*\"small town\" + 0.008*\"glbt\" + 0.008*\"flying\" + 0.008*\"tumey's dvds\" + 0.008*\"film noir\" + 0.007*\"animal:dog\"\n", - "2021-08-24 02:38:30,429 : INFO : topic #5 (0.020): 0.198*\"Action\" + 0.180*\"Thriller\" + 0.147*\"Horror\" + 0.121*\"Sci-Fi\" + 0.068*\"Comedy\" + 0.067*\"Drama\" + 0.058*\"Adventure\" + 0.020*\"betamax\" + 0.012*\"Crime\" + 0.009*\"can't remember\"\n", - "2021-08-24 02:38:30,431 : INFO : topic diff=0.166890, rho=0.346261\n", - "2021-08-24 02:38:30,432 : INFO : PROGRESS: pass 3, at document #2000/10681\n", - "2021-08-24 02:38:30,755 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:30,789 : INFO : topic #6 (0.020): 0.050*\"martial arts\" + 0.035*\"campy\" + 0.029*\"gothic\" + 0.028*\"australia\" + 0.024*\"silly\" + 0.022*\"australian\" + 0.020*\"sam raimi\" + 0.018*\"seen 2006\" + 0.018*\"freedom\" + 0.017*\"transgender\"\n", - "2021-08-24 02:38:30,790 : INFO : topic #45 (0.020): 0.038*\"japan\" + 0.037*\"tense\" + 0.029*\"clint eastwood\" + 0.028*\"revenge\" + 0.026*\"atmospheric\" + 0.026*\"gritty\" + 0.025*\"Drama\" + 0.024*\"forceful\" + 0.022*\"sweeping\" + 0.021*\"samurai\"\n", - "2021-08-24 02:38:30,791 : INFO : topic #10 (0.020): 0.310*\"Comedy\" + 0.262*\"Romance\" + 0.206*\"Drama\" + 0.018*\"bibliothek\" + 0.012*\"london\" + 0.010*\"directorial debut\" + 0.009*\"divorce\" + 0.008*\"oppl\" + 0.007*\"adultery\" + 0.007*\"poverty\"\n", - "2021-08-24 02:38:30,793 : INFO : topic #41 (0.020): 0.172*\"based on a book\" + 0.082*\"adapted from:book\" + 0.073*\"nudity (full frontal)\" + 0.070*\"netflix\" + 0.070*\"Drama\" + 0.058*\"robin williams\" + 0.039*\"vampire\" + 0.028*\"vampires\" + 0.015*\"book\" + 0.011*\"book was better\"\n", - "2021-08-24 02:38:30,796 : INFO : topic #3 (0.020): 0.317*\"nudity (topless)\" + 0.089*\"zombies\" + 0.024*\"keira knightley\" + 0.015*\"zombie\" + 0.015*\"Drama\" + 0.015*\"ingmar bergman\" + 0.014*\"Comedy\" + 0.012*\"futuristic\" + 0.012*\"horror\" + 0.012*\"Horror\"\n", - "2021-08-24 02:38:30,798 : INFO : topic diff=0.320232, rho=0.327201\n", - "2021-08-24 02:38:30,799 : INFO : PROGRESS: pass 3, at document #4000/10681\n", - "2021-08-24 02:38:31,100 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:31,132 : INFO : topic #4 (0.020): 0.100*\"romance\" + 0.052*\"chick flick\" + 0.036*\"sean connery\" + 0.035*\"new york city\" + 0.034*\"Romance\" + 0.026*\"seen more than once\" + 0.025*\"dance\" + 0.025*\"Comedy\" + 0.024*\"girlie movie\" + 0.023*\"new york\"\n", - "2021-08-24 02:38:31,133 : INFO : topic #33 (0.020): 0.137*\"comic book\" + 0.128*\"superhero\" + 0.060*\"super-hero\" + 0.039*\"gene hackman\" + 0.038*\"adapted from:comic\" + 0.029*\"batman\" + 0.027*\"Action\" + 0.023*\"action\" + 0.020*\"alter ego\" + 0.016*\"based on a comic\"\n", - "2021-08-24 02:38:31,134 : INFO : topic #14 (0.020): 0.093*\"Mystery\" + 0.060*\"Horror\" + 0.056*\"Thriller\" + 0.034*\"disturbing\" + 0.032*\"atmospheric\" + 0.025*\"erlend's dvds\" + 0.024*\"tense\" + 0.024*\"ominous\" + 0.024*\"scary movies to see on halloween\" + 0.022*\"tumey's dvds\"\n", - "2021-08-24 02:38:31,134 : INFO : topic #43 (0.020): 0.240*\"Crime\" + 0.136*\"Drama\" + 0.102*\"Thriller\" + 0.047*\"nudity (topless - notable)\" + 0.041*\"Mystery\" + 0.033*\"shakespeare\" + 0.032*\"Comedy\" + 0.026*\"murder\" + 0.026*\"nicolas cage\" + 0.018*\"remade\"\n", - "2021-08-24 02:38:31,135 : INFO : topic #44 (0.020): 0.111*\"Children\" + 0.099*\"Adventure\" + 0.085*\"Fantasy\" + 0.079*\"disney\" + 0.063*\"Comedy\" + 0.059*\"Animation\" + 0.036*\"animation\" + 0.023*\"fantasy\" + 0.019*\"children\" + 0.016*\"disney animated feature\"\n", - "2021-08-24 02:38:31,136 : INFO : topic diff=0.079228, rho=0.327201\n", - "2021-08-24 02:38:31,138 : INFO : PROGRESS: pass 3, at document #6000/10681\n", - "2021-08-24 02:38:31,383 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:31,409 : INFO : topic #30 (0.020): 0.575*\"Drama\" + 0.037*\"Film-Noir\" + 0.017*\"boring\" + 0.011*\"tumey's dvds\" + 0.011*\"small town\" + 0.010*\"italy\" + 0.009*\"glbt\" + 0.009*\"oscar (best foreign language film)\" + 0.008*\"animal:dog\" + 0.008*\"film noir\"\n", - "2021-08-24 02:38:31,410 : INFO : topic #42 (0.020): 0.143*\"johnny depp\" + 0.076*\"tim burton\" + 0.053*\"martin scorsese\" + 0.039*\"george clooney\" + 0.038*\"narrated\" + 0.024*\"ei muista\" + 0.024*\"monster\" + 0.018*\"alien invasion\" + 0.018*\"brothers\" + 0.013*\"disaster\"\n", - "2021-08-24 02:38:31,411 : INFO : topic #16 (0.020): 0.101*\"dystopia\" + 0.092*\"sci-fi\" + 0.054*\"futuristmovies.com\" + 0.038*\"Sci-Fi\" + 0.037*\"future\" + 0.034*\"military\" + 0.018*\"weird\" + 0.017*\"joaquin phoenix\" + 0.017*\"court\" + 0.015*\"urbane\"\n", - "2021-08-24 02:38:31,412 : INFO : topic #17 (0.020): 0.056*\"police\" + 0.055*\"will smith\" + 0.027*\"corruption\" + 0.026*\"computers\" + 0.022*\"milla jovovich\" + 0.019*\"no dialogue\" + 0.016*\"race\" + 0.016*\"writer\" + 0.016*\"luc besson\" + 0.016*\"torture\"\n", - "2021-08-24 02:38:31,413 : INFO : topic #36 (0.020): 0.109*\"dark comedy\" + 0.073*\"black comedy\" + 0.070*\"bechdel test:fail\" + 0.036*\"nazis\" + 0.029*\"cold war\" + 0.028*\"funniest movies\" + 0.015*\"Comedy\" + 0.015*\"spies\" + 0.013*\"communism\" + 0.012*\"seen more than once\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:31,414 : INFO : topic diff=0.118591, rho=0.327201\n", - "2021-08-24 02:38:31,416 : INFO : PROGRESS: pass 3, at document #8000/10681\n", - "2021-08-24 02:38:31,681 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:31,710 : INFO : topic #48 (0.020): 0.054*\"quirky\" + 0.050*\"surreal\" + 0.028*\"bittersweet\" + 0.026*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"Comedy\" + 0.021*\"peter jackson\" + 0.020*\"whimsical\" + 0.019*\"coming of age\" + 0.018*\"fantasy\"\n", - "2021-08-24 02:38:31,710 : INFO : topic #17 (0.020): 0.062*\"police\" + 0.050*\"will smith\" + 0.033*\"computers\" + 0.023*\"corruption\" + 0.019*\"no dialogue\" + 0.018*\"small-town life\" + 0.017*\"milla jovovich\" + 0.016*\"stage\" + 0.016*\"writer\" + 0.015*\"race\"\n", - "2021-08-24 02:38:31,711 : INFO : topic #12 (0.020): 0.097*\"quentin tarantino\" + 0.096*\"christmas\" + 0.068*\"heist\" + 0.065*\"tarantino\" + 0.048*\"nonlinear\" + 0.042*\"rape\" + 0.023*\"ireland\" + 0.019*\"blindfold\" + 0.018*\"xmas theme\" + 0.016*\"terry gilliam\"\n", - "2021-08-24 02:38:31,712 : INFO : topic #26 (0.020): 0.132*\"oscar (best picture)\" + 0.107*\"classic\" + 0.067*\"oscar (best supporting actor)\" + 0.057*\"oscar (best directing)\" + 0.035*\"matt damon\" + 0.028*\"afi 100 (cheers)\" + 0.023*\"afi 100\" + 0.023*\"oscar (best supporting actress)\" + 0.019*\"national film registry\" + 0.018*\"afi 100 (movie quotes)\"\n", - "2021-08-24 02:38:31,713 : INFO : topic #24 (0.020): 0.059*\"tom cruise\" + 0.042*\"nicole kidman\" + 0.040*\"golden palm\" + 0.036*\"france\" + 0.033*\"french\" + 0.023*\"disability\" + 0.023*\"dustin hoffman\" + 0.023*\"takeshi kitano\" + 0.020*\"sweet\" + 0.020*\"on dvr\"\n", - "2021-08-24 02:38:31,714 : INFO : topic diff=0.090403, rho=0.327201\n", - "2021-08-24 02:38:31,716 : INFO : PROGRESS: pass 3, at document #10000/10681\n", - "2021-08-24 02:38:31,991 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:32,018 : INFO : topic #49 (0.020): 0.138*\"anime\" + 0.056*\"psychology\" + 0.048*\"high school\" + 0.046*\"serial killer\" + 0.037*\"teen\" + 0.024*\"japan\" + 0.014*\"mental illness\" + 0.014*\"cannibalism\" + 0.014*\"old\" + 0.014*\"steve martin\"\n", - "2021-08-24 02:38:32,019 : INFO : topic #7 (0.020): 0.133*\"movie to see\" + 0.053*\"boxing\" + 0.039*\"mel gibson\" + 0.035*\"uma thurman\" + 0.031*\"kidnapping\" + 0.030*\"suburbia\" + 0.024*\"basketball\" + 0.023*\"sylvester stallone\" + 0.020*\"john travolta\" + 0.017*\"dvd\"\n", - "2021-08-24 02:38:32,020 : INFO : topic #13 (0.020): 0.136*\"time travel\" + 0.078*\"twist ending\" + 0.045*\"holocaust\" + 0.041*\"bill murray\" + 0.028*\"post-apocalyptic\" + 0.020*\"michael moore\" + 0.020*\"liam neeson\" + 0.018*\"post apocalyptic\" + 0.016*\"capitalism\" + 0.015*\"mindfuck\"\n", - "2021-08-24 02:38:32,021 : INFO : topic #29 (0.020): 0.108*\"action\" + 0.047*\"adventure\" + 0.045*\"pirates\" + 0.039*\"Action\" + 0.038*\"sequel\" + 0.035*\"watched 2007\" + 0.032*\"motorcycle\" + 0.025*\"robots\" + 0.023*\"Adventure\" + 0.023*\"death\"\n", - "2021-08-24 02:38:32,022 : INFO : topic #3 (0.020): 0.282*\"nudity (topless)\" + 0.110*\"zombies\" + 0.030*\"keira knightley\" + 0.024*\"zombie\" + 0.021*\"ingmar bergman\" + 0.020*\"fascism\" + 0.016*\"virtual reality\" + 0.015*\"robert altman\" + 0.014*\"england\" + 0.011*\"gruesome\"\n", - "2021-08-24 02:38:32,023 : INFO : topic diff=0.072226, rho=0.327201\n", - "2021-08-24 02:38:32,179 : INFO : -21.033 per-word bound, 2145824.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:32,180 : INFO : PROGRESS: pass 3, at document #10681/10681\n", - "2021-08-24 02:38:32,284 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:32,310 : INFO : topic #2 (0.020): 0.099*\"satire\" + 0.087*\"lesbian\" + 0.086*\"Comedy\" + 0.073*\"imdb bottom 100\" + 0.022*\"adolf hitler\" + 0.021*\"olympics\" + 0.017*\"get\" + 0.017*\"john turturro\" + 0.016*\"ben stiller\" + 0.016*\"sexy\"\n", - "2021-08-24 02:38:32,311 : INFO : topic #19 (0.020): 0.110*\"pg-13\" + 0.056*\"ghosts\" + 0.042*\"jude law\" + 0.041*\"wedding\" + 0.032*\"infidelity\" + 0.031*\"julia roberts\" + 0.022*\"scope\" + 0.021*\"want to own\" + 0.021*\"prostitution\" + 0.016*\"jealousy\"\n", - "2021-08-24 02:38:32,312 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.077*\"heist\" + 0.073*\"quentin tarantino\" + 0.050*\"rape\" + 0.039*\"tarantino\" + 0.032*\"nonlinear\" + 0.026*\"blindfold\" + 0.022*\"robbery\" + 0.020*\"xmas theme\" + 0.017*\"ireland\"\n", - "2021-08-24 02:38:32,313 : INFO : topic #17 (0.020): 0.076*\"will smith\" + 0.056*\"police\" + 0.055*\"corruption\" + 0.037*\"torture\" + 0.027*\"action packed\" + 0.024*\"milla jovovich\" + 0.021*\"tattoo\" + 0.020*\"suicide attempt\" + 0.019*\"let down\" + 0.017*\"so bad it's good\"\n", - "2021-08-24 02:38:32,316 : INFO : topic #8 (0.020): 0.080*\"space\" + 0.054*\"christianity\" + 0.031*\"marx brothers\" + 0.030*\"archaeology\" + 0.025*\"sci-fi\" + 0.023*\"harrison ford\" + 0.021*\"1970s\" + 0.018*\"kevin smith\" + 0.017*\"werewolves\" + 0.016*\"western\"\n", - "2021-08-24 02:38:32,319 : INFO : topic diff=0.139988, rho=0.327201\n", - "2021-08-24 02:38:32,324 : INFO : PROGRESS: pass 4, at document #2000/10681\n", - "2021-08-24 02:38:32,624 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:32,671 : INFO : topic #44 (0.020): 0.112*\"Children\" + 0.103*\"Adventure\" + 0.091*\"Fantasy\" + 0.085*\"disney\" + 0.069*\"Comedy\" + 0.056*\"Animation\" + 0.038*\"animation\" + 0.023*\"fantasy\" + 0.020*\"children\" + 0.017*\"movie to see\"\n", - "2021-08-24 02:38:32,672 : INFO : topic #38 (0.020): 0.084*\"true story\" + 0.069*\"tom hanks\" + 0.064*\"biography\" + 0.060*\"based on a true story\" + 0.033*\"drama\" + 0.033*\"music\" + 0.032*\"biopic\" + 0.030*\"overrated\" + 0.023*\"oscar (best actor)\" + 0.021*\"paris\"\n", - "2021-08-24 02:38:32,673 : INFO : topic #13 (0.020): 0.161*\"time travel\" + 0.064*\"twist ending\" + 0.058*\"holocaust\" + 0.029*\"bill murray\" + 0.027*\"good dialogue\" + 0.024*\"post-apocalyptic\" + 0.023*\"post apocalyptic\" + 0.019*\"suspense\" + 0.015*\"liam neeson\" + 0.013*\"dvd\"\n", - "2021-08-24 02:38:32,674 : INFO : topic #42 (0.020): 0.152*\"johnny depp\" + 0.079*\"tim burton\" + 0.071*\"martin scorsese\" + 0.030*\"narrated\" + 0.027*\"george clooney\" + 0.026*\"monster\" + 0.024*\"alien invasion\" + 0.021*\"brothers\" + 0.021*\"disaster\" + 0.015*\"movielens quickpick\"\n", - "2021-08-24 02:38:32,675 : INFO : topic #34 (0.020): 0.170*\"War\" + 0.103*\"Drama\" + 0.102*\"world war ii\" + 0.071*\"drugs\" + 0.051*\"war\" + 0.051*\"prison\" + 0.031*\"history\" + 0.030*\"Action\" + 0.018*\"wwii\" + 0.014*\"notable soundtrack\"\n", - "2021-08-24 02:38:32,677 : INFO : topic diff=0.283192, rho=0.310978\n", - "2021-08-24 02:38:32,684 : INFO : PROGRESS: pass 4, at document #4000/10681\n", - "2021-08-24 02:38:32,969 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:32,997 : INFO : topic #42 (0.020): 0.150*\"johnny depp\" + 0.098*\"tim burton\" + 0.051*\"martin scorsese\" + 0.036*\"narrated\" + 0.030*\"george clooney\" + 0.026*\"monster\" + 0.024*\"ei muista\" + 0.020*\"alien invasion\" + 0.018*\"brothers\" + 0.018*\"disaster\"\n", - "2021-08-24 02:38:32,999 : INFO : topic #10 (0.020): 0.352*\"Comedy\" + 0.250*\"Romance\" + 0.206*\"Drama\" + 0.021*\"bibliothek\" + 0.009*\"directorial debut\" + 0.009*\"london\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.006*\"poverty\" + 0.006*\"betamax\"\n", - "2021-08-24 02:38:33,000 : INFO : topic #44 (0.020): 0.113*\"Children\" + 0.103*\"Adventure\" + 0.087*\"Fantasy\" + 0.079*\"disney\" + 0.066*\"Comedy\" + 0.060*\"Animation\" + 0.036*\"animation\" + 0.025*\"fantasy\" + 0.020*\"children\" + 0.016*\"disney animated feature\"\n", - "2021-08-24 02:38:33,000 : INFO : topic #0 (0.020): 0.091*\"crime\" + 0.063*\"brad pitt\" + 0.060*\"violence\" + 0.041*\"Crime\" + 0.036*\"morgan freeman\" + 0.034*\"violent\" + 0.031*\"organized crime\" + 0.028*\"philip seymour hoffman\" + 0.027*\"action\" + 0.026*\"Thriller\"\n", - "2021-08-24 02:38:33,001 : INFO : topic #13 (0.020): 0.145*\"time travel\" + 0.071*\"twist ending\" + 0.059*\"holocaust\" + 0.033*\"bill murray\" + 0.031*\"post apocalyptic\" + 0.029*\"post-apocalyptic\" + 0.020*\"good dialogue\" + 0.018*\"mindfuck\" + 0.017*\"liam neeson\" + 0.016*\"suspense\"\n", - "2021-08-24 02:38:33,003 : INFO : topic diff=0.060562, rho=0.310978\n", - "2021-08-24 02:38:33,004 : INFO : PROGRESS: pass 4, at document #6000/10681\n", - "2021-08-24 02:38:33,243 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:33,271 : INFO : topic #12 (0.020): 0.102*\"christmas\" + 0.072*\"quentin tarantino\" + 0.069*\"heist\" + 0.066*\"nonlinear\" + 0.049*\"rape\" + 0.048*\"tarantino\" + 0.025*\"terry gilliam\" + 0.019*\"blindfold\" + 0.018*\"xmas theme\" + 0.015*\"gilliam\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:33,271 : INFO : topic #10 (0.020): 0.376*\"Comedy\" + 0.243*\"Romance\" + 0.200*\"Drama\" + 0.022*\"bibliothek\" + 0.010*\"directorial debut\" + 0.008*\"betamax\" + 0.008*\"london\" + 0.006*\"poverty\" + 0.005*\"owen wilson\" + 0.005*\"divorce\"\n", - "2021-08-24 02:38:33,272 : INFO : topic #1 (0.020): 0.105*\"remake\" + 0.054*\"hitchcock\" + 0.040*\"horror\" + 0.039*\"alfred hitchcock\" + 0.031*\"tumey's dvds\" + 0.028*\"Thriller\" + 0.024*\"moody\" + 0.022*\"classic\" + 0.020*\"scary\" + 0.017*\"courtroom drama\"\n", - "2021-08-24 02:38:33,273 : INFO : topic #36 (0.020): 0.111*\"dark comedy\" + 0.074*\"black comedy\" + 0.072*\"bechdel test:fail\" + 0.038*\"nazis\" + 0.030*\"cold war\" + 0.028*\"funniest movies\" + 0.015*\"spies\" + 0.013*\"Comedy\" + 0.013*\"communism\" + 0.012*\"existentialism\"\n", - "2021-08-24 02:38:33,273 : INFO : topic #47 (0.020): 0.199*\"less than 300 ratings\" + 0.108*\"Drama\" + 0.064*\"Comedy\" + 0.064*\"nudity (rear)\" + 0.042*\"jackie chan\" + 0.026*\"kung fu\" + 0.024*\"football\" + 0.021*\"Action\" + 0.020*\"brian de palma\" + 0.018*\"denzel washington\"\n", - "2021-08-24 02:38:33,275 : INFO : topic diff=0.101780, rho=0.310978\n", - "2021-08-24 02:38:33,276 : INFO : PROGRESS: pass 4, at document #8000/10681\n", - "2021-08-24 02:38:33,522 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:33,550 : INFO : topic #27 (0.020): 0.122*\"aliens\" + 0.070*\"steven spielberg\" + 0.054*\"family\" + 0.037*\"michael crichton\" + 0.032*\"gambling\" + 0.024*\"alien\" + 0.021*\"to-rent\" + 0.018*\"didn't finish\" + 0.018*\"drew barrymore\" + 0.018*\"poker\"\n", - "2021-08-24 02:38:33,551 : INFO : topic #5 (0.020): 0.190*\"Action\" + 0.175*\"Thriller\" + 0.152*\"Horror\" + 0.119*\"Sci-Fi\" + 0.059*\"Adventure\" + 0.052*\"Comedy\" + 0.047*\"betamax\" + 0.039*\"Drama\" + 0.023*\"can't remember\" + 0.014*\"70mm\"\n", - "2021-08-24 02:38:33,552 : INFO : topic #20 (0.020): 0.134*\"politics\" + 0.102*\"documentary\" + 0.065*\"jack nicholson\" + 0.043*\"terrorism\" + 0.035*\"seen 2008\" + 0.033*\"propaganda\" + 0.027*\"aviation\" + 0.025*\"political\" + 0.021*\"renee zellweger\" + 0.020*\"own\"\n", - "2021-08-24 02:38:33,553 : INFO : topic #9 (0.020): 0.174*\"r\" + 0.116*\"Western\" + 0.081*\"nudity (topless - brief)\" + 0.079*\"clearplay\" + 0.067*\"Drama\" + 0.025*\"british\" + 0.021*\"pg13\" + 0.020*\"russell crowe\" + 0.017*\"historical\" + 0.017*\"Thriller\"\n", - "2021-08-24 02:38:33,554 : INFO : topic #37 (0.020): 0.055*\"easily confused with other movie(s) (title)\" + 0.042*\"oscar (best supporting actress)\" + 0.034*\"peter sellers\" + 0.032*\"vietnam war\" + 0.032*\"want to see again\" + 0.031*\"vietnam\" + 0.027*\"Drama\" + 0.025*\"j netflix\" + 0.021*\"19th century\" + 0.020*\"father daughter relationship\"\n", - "2021-08-24 02:38:33,555 : INFO : topic diff=0.076975, rho=0.310978\n", - "2021-08-24 02:38:33,557 : INFO : PROGRESS: pass 4, at document #10000/10681\n", - "2021-08-24 02:38:33,813 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:33,841 : INFO : topic #46 (0.020): 0.049*\"samuel l. jackson\" + 0.028*\"kevin spacey\" + 0.028*\"ewan mcgregor\" + 0.024*\"natalie portman\" + 0.022*\"imdb top 250\" + 0.022*\"ridley scott\" + 0.019*\"gary oldman\" + 0.019*\"dvd\" + 0.018*\"mystery\" + 0.018*\"seen more than once\"\n", - "2021-08-24 02:38:33,842 : INFO : topic #36 (0.020): 0.085*\"dark comedy\" + 0.070*\"black comedy\" + 0.061*\"bechdel test:fail\" + 0.037*\"funniest movies\" + 0.034*\"nazis\" + 0.026*\"cold war\" + 0.026*\"existentialism\" + 0.021*\"communism\" + 0.017*\"spies\" + 0.015*\"author:charles dickens\"\n", - "2021-08-24 02:38:33,842 : INFO : topic #18 (0.020): 0.045*\"creepy\" + 0.037*\"franchise\" + 0.036*\"surrealism\" + 0.032*\"lurid\" + 0.029*\"robert downey jr\" + 0.029*\"united states\" + 0.024*\"unique\" + 0.024*\"macabre\" + 0.023*\"slasher\" + 0.021*\"adolescence\"\n", - "2021-08-24 02:38:33,843 : INFO : topic #4 (0.020): 0.102*\"romance\" + 0.052*\"watched 2006\" + 0.044*\"new york city\" + 0.041*\"chick flick\" + 0.037*\"Romance\" + 0.029*\"new york\" + 0.024*\"sean connery\" + 0.023*\"seen more than once\" + 0.023*\"girlie movie\" + 0.023*\"Comedy\"\n", - "2021-08-24 02:38:33,844 : INFO : topic #8 (0.020): 0.081*\"space\" + 0.040*\"marx brothers\" + 0.035*\"sci-fi\" + 0.033*\"christianity\" + 0.026*\"harrison ford\" + 0.022*\"werewolves\" + 0.020*\"kevin smith\" + 0.019*\"spaghetti western\" + 0.018*\"george lucas\" + 0.017*\"fantasy\"\n", - "2021-08-24 02:38:33,845 : INFO : topic diff=0.060451, rho=0.310978\n", - "2021-08-24 02:38:33,989 : INFO : -21.007 per-word bound, 2107763.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:33,990 : INFO : PROGRESS: pass 4, at document #10681/10681\n", - "2021-08-24 02:38:34,087 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:34,114 : INFO : topic #45 (0.020): 0.053*\"japan\" + 0.042*\"revenge\" + 0.039*\"tense\" + 0.030*\"clint eastwood\" + 0.028*\"gritty\" + 0.027*\"corvallis library\" + 0.025*\"forceful\" + 0.024*\"ummarti2007\" + 0.024*\"Drama\" + 0.023*\"atmospheric\"\n", - "2021-08-24 02:38:34,115 : INFO : topic #23 (0.020): 0.054*\"mafia\" + 0.032*\"al pacino\" + 0.028*\"michael caine\" + 0.027*\"virus\" + 0.023*\"guns\" + 0.021*\"robert de niro\" + 0.020*\"nudity (full frontal - brief)\" + 0.017*\"ninja\" + 0.017*\"marlon brando\" + 0.017*\"tumey's dvds\"\n", - "2021-08-24 02:38:34,116 : INFO : topic #19 (0.020): 0.109*\"pg-13\" + 0.057*\"ghosts\" + 0.042*\"jude law\" + 0.041*\"wedding\" + 0.032*\"infidelity\" + 0.031*\"julia roberts\" + 0.023*\"scope\" + 0.022*\"want to own\" + 0.021*\"prostitution\" + 0.016*\"hollywood\"\n", - "2021-08-24 02:38:34,116 : INFO : topic #38 (0.020): 0.095*\"based on a true story\" + 0.087*\"true story\" + 0.069*\"biography\" + 0.050*\"tom hanks\" + 0.035*\"overrated\" + 0.028*\"biopic\" + 0.027*\"paris\" + 0.024*\"music\" + 0.024*\"movie to see\" + 0.023*\"pg\"\n", - "2021-08-24 02:38:34,117 : INFO : topic #37 (0.020): 0.084*\"easily confused with other movie(s) (title)\" + 0.045*\"j netflix\" + 0.027*\"oscar (best supporting actress)\" + 0.026*\"19th century\" + 0.025*\"father daughter relationship\" + 0.024*\"Drama\" + 0.023*\"food\" + 0.021*\"slow\" + 0.020*\"peter sellers\" + 0.019*\"vietnam war\"\n", - "2021-08-24 02:38:34,119 : INFO : topic diff=0.126928, rho=0.310978\n", - "2021-08-24 02:38:34,120 : INFO : PROGRESS: pass 5, at document #2000/10681\n", - "2021-08-24 02:38:34,384 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:34,411 : INFO : topic #10 (0.020): 0.359*\"Comedy\" + 0.259*\"Romance\" + 0.205*\"Drama\" + 0.017*\"bibliothek\" + 0.010*\"london\" + 0.009*\"divorce\" + 0.008*\"oppl\" + 0.006*\"directorial debut\" + 0.005*\"poverty\" + 0.005*\"owen wilson\"\n", - "2021-08-24 02:38:34,412 : INFO : topic #42 (0.020): 0.152*\"johnny depp\" + 0.079*\"tim burton\" + 0.071*\"martin scorsese\" + 0.031*\"narrated\" + 0.028*\"george clooney\" + 0.026*\"monster\" + 0.024*\"alien invasion\" + 0.021*\"brothers\" + 0.020*\"disaster\" + 0.015*\"ei muista\"\n", - "2021-08-24 02:38:34,412 : INFO : topic #27 (0.020): 0.153*\"aliens\" + 0.093*\"steven spielberg\" + 0.046*\"family\" + 0.035*\"michael crichton\" + 0.024*\"childhood\" + 0.023*\"to-rent\" + 0.021*\"gambling\" + 0.021*\"alien\" + 0.018*\"didn't finish\" + 0.017*\"mother-son relationship\"\n", - "2021-08-24 02:38:34,413 : INFO : topic #2 (0.020): 0.113*\"satire\" + 0.097*\"lesbian\" + 0.078*\"Comedy\" + 0.053*\"imdb bottom 100\" + 0.022*\"olympics\" + 0.022*\"sexy\" + 0.019*\"john turturro\" + 0.019*\"get\" + 0.017*\"1\" + 0.015*\"money\"\n", - "2021-08-24 02:38:34,414 : INFO : topic #45 (0.020): 0.040*\"tense\" + 0.039*\"japan\" + 0.031*\"revenge\" + 0.031*\"clint eastwood\" + 0.028*\"atmospheric\" + 0.027*\"gritty\" + 0.027*\"forceful\" + 0.024*\"sweeping\" + 0.022*\"visceral\" + 0.022*\"rousing\"\n", - "2021-08-24 02:38:34,416 : INFO : topic diff=0.261405, rho=0.296950\n", - "2021-08-24 02:38:34,417 : INFO : PROGRESS: pass 5, at document #4000/10681\n", - "2021-08-24 02:38:34,676 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:34,703 : INFO : topic #16 (0.020): 0.143*\"sci-fi\" + 0.096*\"dystopia\" + 0.061*\"futuristmovies.com\" + 0.047*\"Sci-Fi\" + 0.035*\"military\" + 0.027*\"future\" + 0.015*\"court\" + 0.014*\"imdb top 250\" + 0.013*\"joaquin phoenix\" + 0.013*\"1950s\"\n", - "2021-08-24 02:38:34,704 : INFO : topic #18 (0.020): 0.059*\"franchise\" + 0.046*\"surrealism\" + 0.040*\"slasher\" + 0.038*\"creepy\" + 0.029*\"robert downey jr\" + 0.028*\"united states\" + 0.023*\"lurid\" + 0.023*\"no rec?\" + 0.022*\"unique\" + 0.021*\"goth\"\n", - "2021-08-24 02:38:34,704 : INFO : topic #33 (0.020): 0.142*\"comic book\" + 0.130*\"superhero\" + 0.062*\"super-hero\" + 0.040*\"gene hackman\" + 0.039*\"adapted from:comic\" + 0.029*\"batman\" + 0.026*\"Action\" + 0.022*\"action\" + 0.021*\"alter ego\" + 0.017*\"based on a comic\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:34,705 : INFO : topic #49 (0.020): 0.071*\"anime\" + 0.062*\"teen\" + 0.057*\"high school\" + 0.055*\"serial killer\" + 0.054*\"psychology\" + 0.020*\"mental illness\" + 0.016*\"cannibalism\" + 0.016*\"cult film\" + 0.013*\"jodie foster\" + 0.013*\"anthony hopkins\"\n", - "2021-08-24 02:38:34,705 : INFO : topic #1 (0.020): 0.089*\"remake\" + 0.069*\"hitchcock\" + 0.052*\"alfred hitchcock\" + 0.038*\"horror\" + 0.032*\"tumey's dvds\" + 0.029*\"Thriller\" + 0.025*\"classic\" + 0.021*\"moody\" + 0.019*\"courtroom drama\" + 0.017*\"scary\"\n", - "2021-08-24 02:38:34,706 : INFO : topic diff=0.052636, rho=0.296950\n", - "2021-08-24 02:38:34,708 : INFO : PROGRESS: pass 5, at document #6000/10681\n", - "2021-08-24 02:38:34,936 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:34,964 : INFO : topic #7 (0.020): 0.121*\"movie to see\" + 0.074*\"mel gibson\" + 0.051*\"boxing\" + 0.034*\"suburbia\" + 0.031*\"kidnapping\" + 0.030*\"john travolta\" + 0.028*\"uma thurman\" + 0.027*\"sylvester stallone\" + 0.023*\"basketball\" + 0.018*\"dvd\"\n", - "2021-08-24 02:38:34,965 : INFO : topic #10 (0.020): 0.396*\"Comedy\" + 0.241*\"Romance\" + 0.197*\"Drama\" + 0.020*\"bibliothek\" + 0.008*\"betamax\" + 0.007*\"london\" + 0.006*\"directorial debut\" + 0.005*\"poverty\" + 0.005*\"owen wilson\" + 0.005*\"divorce\"\n", - "2021-08-24 02:38:34,966 : INFO : topic #6 (0.020): 0.082*\"martial arts\" + 0.048*\"gothic\" + 0.041*\"campy\" + 0.032*\"silly\" + 0.029*\"australian\" + 0.028*\"australia\" + 0.021*\"sam raimi\" + 0.019*\"don't remember\" + 0.017*\"2.5\" + 0.017*\"bad\"\n", - "2021-08-24 02:38:34,967 : INFO : topic #1 (0.020): 0.107*\"remake\" + 0.054*\"hitchcock\" + 0.044*\"horror\" + 0.040*\"alfred hitchcock\" + 0.030*\"tumey's dvds\" + 0.028*\"Thriller\" + 0.024*\"moody\" + 0.020*\"scary\" + 0.020*\"classic\" + 0.019*\"courtroom drama\"\n", - "2021-08-24 02:38:34,967 : INFO : topic #42 (0.020): 0.144*\"johnny depp\" + 0.077*\"tim burton\" + 0.054*\"martin scorsese\" + 0.039*\"narrated\" + 0.038*\"george clooney\" + 0.028*\"ei muista\" + 0.024*\"monster\" + 0.018*\"alien invasion\" + 0.018*\"brothers\" + 0.014*\"disaster\"\n", - "2021-08-24 02:38:34,969 : INFO : topic diff=0.092987, rho=0.296950\n", - "2021-08-24 02:38:34,970 : INFO : PROGRESS: pass 5, at document #8000/10681\n", - "2021-08-24 02:38:35,224 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:35,251 : INFO : topic #5 (0.020): 0.197*\"Action\" + 0.180*\"Thriller\" + 0.158*\"Horror\" + 0.122*\"Sci-Fi\" + 0.061*\"Adventure\" + 0.047*\"betamax\" + 0.039*\"Comedy\" + 0.025*\"Drama\" + 0.024*\"can't remember\" + 0.014*\"70mm\"\n", - "2021-08-24 02:38:35,253 : INFO : topic #24 (0.020): 0.059*\"tom cruise\" + 0.041*\"golden palm\" + 0.041*\"nicole kidman\" + 0.036*\"france\" + 0.032*\"french\" + 0.027*\"on dvr\" + 0.023*\"dustin hoffman\" + 0.023*\"disability\" + 0.021*\"takeshi kitano\" + 0.020*\"sweet\"\n", - "2021-08-24 02:38:35,254 : INFO : topic #6 (0.020): 0.094*\"martial arts\" + 0.044*\"gothic\" + 0.037*\"campy\" + 0.027*\"silly\" + 0.026*\"australian\" + 0.026*\"australia\" + 0.023*\"sam raimi\" + 0.021*\"don't remember\" + 0.020*\"seen 2006\" + 0.019*\"blindness\"\n", - "2021-08-24 02:38:35,254 : INFO : topic #32 (0.020): 0.074*\"based on a tv show\" + 0.072*\"jim carrey\" + 0.055*\"keanu reeves\" + 0.053*\"memory\" + 0.039*\"video game adaptation\" + 0.034*\"tv\" + 0.033*\"road trip\" + 0.031*\"england\" + 0.029*\"friday night movie\" + 0.027*\"star trek\"\n", - "2021-08-24 02:38:35,255 : INFO : topic #22 (0.020): 0.079*\"national film registry\" + 0.070*\"classic\" + 0.053*\"oscar (best cinematography)\" + 0.026*\"cult film\" + 0.025*\"black and white\" + 0.025*\"tumey's dvds\" + 0.023*\"philip k. dick\" + 0.023*\"afi 100\" + 0.022*\"afi 100 (thrills)\" + 0.021*\"imdb top 250\"\n", - "2021-08-24 02:38:35,256 : INFO : topic diff=0.070783, rho=0.296950\n", - "2021-08-24 02:38:35,257 : INFO : PROGRESS: pass 5, at document #10000/10681\n", - "2021-08-24 02:38:35,516 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:35,543 : INFO : topic #16 (0.020): 0.126*\"sci-fi\" + 0.093*\"dystopia\" + 0.048*\"Sci-Fi\" + 0.048*\"futuristmovies.com\" + 0.034*\"future\" + 0.033*\"military\" + 0.020*\"joaquin phoenix\" + 0.017*\"weird\" + 0.012*\"surprise ending\" + 0.012*\"gfei own it\"\n", - "2021-08-24 02:38:35,544 : INFO : topic #5 (0.020): 0.207*\"Action\" + 0.188*\"Thriller\" + 0.161*\"Horror\" + 0.118*\"Sci-Fi\" + 0.063*\"Adventure\" + 0.039*\"Comedy\" + 0.036*\"betamax\" + 0.023*\"Drama\" + 0.018*\"can't remember\" + 0.011*\"70mm\"\n", - "2021-08-24 02:38:35,545 : INFO : topic #2 (0.020): 0.105*\"lesbian\" + 0.100*\"satire\" + 0.081*\"Comedy\" + 0.054*\"imdb bottom 100\" + 0.028*\"sexy\" + 0.025*\"get\" + 0.022*\"olympics\" + 0.022*\"ben stiller\" + 0.017*\"amnesia\" + 0.016*\"john turturro\"\n", - "2021-08-24 02:38:35,545 : INFO : topic #28 (0.020): 0.140*\"pixar\" + 0.064*\"oscar (best actress)\" + 0.040*\"mockumentary\" + 0.030*\"elijah wood\" + 0.026*\"medieval\" + 0.025*\"cgi\" + 0.024*\"computer animation\" + 0.019*\"dinosaurs\" + 0.018*\"animation\" + 0.016*\"nostalgia\"\n", - "2021-08-24 02:38:35,547 : INFO : topic #49 (0.020): 0.137*\"anime\" + 0.058*\"psychology\" + 0.049*\"high school\" + 0.048*\"serial killer\" + 0.041*\"teen\" + 0.023*\"japan\" + 0.015*\"mental illness\" + 0.015*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.014*\"old\"\n", - "2021-08-24 02:38:35,548 : INFO : topic diff=0.053799, rho=0.296950\n", - "2021-08-24 02:38:35,699 : INFO : -21.001 per-word bound, 2098093.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:35,699 : INFO : PROGRESS: pass 5, at document #10681/10681\n", - "2021-08-24 02:38:35,801 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:35,827 : INFO : topic #40 (0.020): 0.118*\"magic\" + 0.115*\"nudity (full frontal - notable)\" + 0.058*\"not corv lib\" + 0.050*\"edward norton\" + 0.045*\"sports\" + 0.033*\"inspirational\" + 0.031*\"hw drama\" + 0.030*\"Drama\" + 0.029*\"baseball\" + 0.028*\"don cheadle\"\n", - "2021-08-24 02:38:35,828 : INFO : topic #46 (0.020): 0.046*\"samuel l. jackson\" + 0.036*\"imdb top 250\" + 0.031*\"ridley scott\" + 0.028*\"natalie portman\" + 0.026*\"kevin spacey\" + 0.023*\"father-son relationship\" + 0.022*\"ewan mcgregor\" + 0.021*\"owned\" + 0.020*\"dvd\" + 0.019*\"gary oldman\"\n", - "2021-08-24 02:38:35,829 : INFO : topic #13 (0.020): 0.116*\"time travel\" + 0.077*\"twist ending\" + 0.054*\"holocaust\" + 0.039*\"post-apocalyptic\" + 0.032*\"bill murray\" + 0.026*\"post apocalyptic\" + 0.024*\"good dialogue\" + 0.016*\"michael moore\" + 0.016*\"liam neeson\" + 0.016*\"capitalism\"\n", - "2021-08-24 02:38:35,830 : INFO : topic #41 (0.020): 0.253*\"based on a book\" + 0.098*\"adapted from:book\" + 0.096*\"netflix\" + 0.079*\"Drama\" + 0.072*\"nudity (full frontal)\" + 0.035*\"vampire\" + 0.029*\"robin williams\" + 0.024*\"vampires\" + 0.009*\"drinking\" + 0.009*\"book\"\n", - "2021-08-24 02:38:35,831 : INFO : topic #24 (0.020): 0.041*\"tom cruise\" + 0.039*\"france\" + 0.034*\"disability\" + 0.031*\"nicole kidman\" + 0.030*\"golden palm\" + 0.029*\"on dvr\" + 0.027*\"french\" + 0.025*\"dustin hoffman\" + 0.024*\"mask\" + 0.019*\"halloween\"\n", - "2021-08-24 02:38:35,832 : INFO : topic diff=0.118734, rho=0.296950\n", - "2021-08-24 02:38:35,834 : INFO : PROGRESS: pass 6, at document #2000/10681\n", - "2021-08-24 02:38:36,099 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:36,126 : INFO : topic #6 (0.020): 0.065*\"martial arts\" + 0.036*\"campy\" + 0.035*\"gothic\" + 0.029*\"australia\" + 0.027*\"silly\" + 0.023*\"australian\" + 0.022*\"seen 2006\" + 0.021*\"sam raimi\" + 0.018*\"freedom\" + 0.017*\"transgender\"\n", - "2021-08-24 02:38:36,127 : INFO : topic #4 (0.020): 0.112*\"romance\" + 0.052*\"chick flick\" + 0.050*\"Romance\" + 0.042*\"new york city\" + 0.028*\"seen more than once\" + 0.027*\"sean connery\" + 0.025*\"girlie movie\" + 0.023*\"new york\" + 0.022*\"watched 2006\" + 0.022*\"Comedy\"\n", - "2021-08-24 02:38:36,127 : INFO : topic #45 (0.020): 0.041*\"tense\" + 0.040*\"japan\" + 0.032*\"revenge\" + 0.031*\"clint eastwood\" + 0.029*\"atmospheric\" + 0.028*\"forceful\" + 0.027*\"gritty\" + 0.024*\"sweeping\" + 0.022*\"visceral\" + 0.022*\"rousing\"\n", - "2021-08-24 02:38:36,128 : INFO : topic #40 (0.020): 0.117*\"nudity (full frontal - notable)\" + 0.091*\"magic\" + 0.049*\"not corv lib\" + 0.047*\"sports\" + 0.040*\"edward norton\" + 0.039*\"baseball\" + 0.036*\"inspirational\" + 0.035*\"hw drama\" + 0.029*\"Drama\" + 0.024*\"christopher walken\"\n", - "2021-08-24 02:38:36,129 : INFO : topic #20 (0.020): 0.144*\"politics\" + 0.072*\"documentary\" + 0.071*\"jack nicholson\" + 0.043*\"terrorism\" + 0.037*\"love story\" + 0.034*\"propaganda\" + 0.033*\"political\" + 0.031*\"seen 2008\" + 0.030*\"assassination\" + 0.026*\"president\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:36,130 : INFO : topic diff=0.246258, rho=0.284665\n", - "2021-08-24 02:38:36,131 : INFO : PROGRESS: pass 6, at document #4000/10681\n", - "2021-08-24 02:38:36,402 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:36,428 : INFO : topic #7 (0.020): 0.131*\"movie to see\" + 0.080*\"mel gibson\" + 0.058*\"boxing\" + 0.038*\"suburbia\" + 0.031*\"sylvester stallone\" + 0.030*\"uma thurman\" + 0.026*\"basketball\" + 0.026*\"kidnapping\" + 0.025*\"john travolta\" + 0.018*\"ang lee\"\n", - "2021-08-24 02:38:36,429 : INFO : topic #48 (0.020): 0.047*\"quirky\" + 0.044*\"surreal\" + 0.028*\"directorial debut\" + 0.027*\"woody allen\" + 0.025*\"humorous\" + 0.023*\"bittersweet\" + 0.021*\"underrated\" + 0.021*\"coming of age\" + 0.019*\"erlend's dvds\" + 0.018*\"deadpan\"\n", - "2021-08-24 02:38:36,430 : INFO : topic #22 (0.020): 0.089*\"classic\" + 0.071*\"national film registry\" + 0.050*\"oscar (best cinematography)\" + 0.033*\"afi 100\" + 0.030*\"imdb top 250\" + 0.030*\"stanley kubrick\" + 0.028*\"black and white\" + 0.027*\"afi 100 (thrills)\" + 0.026*\"tumey's dvds\" + 0.025*\"cult film\"\n", - "2021-08-24 02:38:36,430 : INFO : topic #33 (0.020): 0.145*\"comic book\" + 0.131*\"superhero\" + 0.063*\"super-hero\" + 0.040*\"gene hackman\" + 0.039*\"adapted from:comic\" + 0.030*\"batman\" + 0.026*\"Action\" + 0.022*\"action\" + 0.021*\"alter ego\" + 0.017*\"based on a comic\"\n", - "2021-08-24 02:38:36,431 : INFO : topic #47 (0.020): 0.211*\"less than 300 ratings\" + 0.114*\"Drama\" + 0.067*\"nudity (rear)\" + 0.056*\"Comedy\" + 0.036*\"jackie chan\" + 0.027*\"kung fu\" + 0.027*\"football\" + 0.018*\"Action\" + 0.017*\"oppl\" + 0.017*\"denzel washington\"\n", - "2021-08-24 02:38:36,432 : INFO : topic diff=0.047856, rho=0.284665\n", - "2021-08-24 02:38:36,434 : INFO : PROGRESS: pass 6, at document #6000/10681\n", - "2021-08-24 02:38:36,663 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:36,690 : INFO : topic #5 (0.020): 0.201*\"Action\" + 0.183*\"Thriller\" + 0.166*\"Horror\" + 0.125*\"Sci-Fi\" + 0.060*\"Adventure\" + 0.049*\"betamax\" + 0.029*\"Comedy\" + 0.027*\"can't remember\" + 0.014*\"Drama\" + 0.013*\"70mm\"\n", - "2021-08-24 02:38:36,690 : INFO : topic #22 (0.020): 0.079*\"classic\" + 0.068*\"national film registry\" + 0.052*\"oscar (best cinematography)\" + 0.029*\"afi 100\" + 0.027*\"imdb top 250\" + 0.027*\"black and white\" + 0.026*\"stanley kubrick\" + 0.026*\"cult film\" + 0.025*\"tumey's dvds\" + 0.024*\"afi 100 (thrills)\"\n", - "2021-08-24 02:38:36,691 : INFO : topic #16 (0.020): 0.140*\"sci-fi\" + 0.094*\"dystopia\" + 0.062*\"futuristmovies.com\" + 0.051*\"Sci-Fi\" + 0.034*\"future\" + 0.032*\"military\" + 0.016*\"weird\" + 0.016*\"joaquin phoenix\" + 0.015*\"court\" + 0.012*\"urbane\"\n", - "2021-08-24 02:38:36,692 : INFO : topic #38 (0.020): 0.086*\"tom hanks\" + 0.075*\"true story\" + 0.055*\"biography\" + 0.052*\"based on a true story\" + 0.041*\"drama\" + 0.031*\"music\" + 0.027*\"overrated\" + 0.025*\"biopic\" + 0.024*\"paris\" + 0.022*\"historical\"\n", - "2021-08-24 02:38:36,693 : INFO : topic #25 (0.020): 0.178*\"Musical\" + 0.093*\"70mm\" + 0.070*\"musical\" + 0.061*\"religion\" + 0.048*\"Drama\" + 0.027*\"rock and roll\" + 0.024*\"friendship\" + 0.022*\"music\" + 0.017*\"adapted from b'way\" + 0.016*\"monty python\"\n", - "2021-08-24 02:38:36,694 : INFO : topic diff=0.086874, rho=0.284665\n", - "2021-08-24 02:38:36,695 : INFO : PROGRESS: pass 6, at document #8000/10681\n", - "2021-08-24 02:38:36,947 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:36,974 : INFO : topic #31 (0.020): 0.092*\"james bond\" + 0.079*\"murder\" + 0.063*\"007\" + 0.059*\"bond\" + 0.034*\"assassin\" + 0.025*\"Action\" + 0.024*\"claymation\" + 0.023*\"Thriller\" + 0.022*\"franchise\" + 0.022*\"tolkien\"\n", - "2021-08-24 02:38:36,975 : INFO : topic #44 (0.020): 0.116*\"Adventure\" + 0.110*\"Children\" + 0.097*\"Fantasy\" + 0.066*\"Comedy\" + 0.061*\"disney\" + 0.059*\"Animation\" + 0.039*\"animation\" + 0.038*\"fantasy\" + 0.019*\"cars\" + 0.018*\"children\"\n", - "2021-08-24 02:38:36,976 : INFO : topic #28 (0.020): 0.124*\"pixar\" + 0.076*\"oscar (best actress)\" + 0.036*\"mockumentary\" + 0.026*\"medieval\" + 0.026*\"elijah wood\" + 0.021*\"nostalgia\" + 0.021*\"dinosaurs\" + 0.020*\"computer animation\" + 0.018*\"high fantasy\" + 0.016*\"animation\"\n", - "2021-08-24 02:38:36,977 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.048*\"surreal\" + 0.027*\"bittersweet\" + 0.026*\"directorial debut\" + 0.026*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"fantasy\" + 0.020*\"peter jackson\" + 0.020*\"whimsical\" + 0.019*\"erlend's dvds\"\n", - "2021-08-24 02:38:36,978 : INFO : topic #19 (0.020): 0.075*\"ghosts\" + 0.058*\"pg-13\" + 0.035*\"wedding\" + 0.035*\"jude law\" + 0.034*\"julia roberts\" + 0.027*\"sam peckinpah\" + 0.026*\"want to own\" + 0.025*\"hollywood\" + 0.023*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:36,979 : INFO : topic diff=0.066436, rho=0.284665\n", - "2021-08-24 02:38:36,981 : INFO : PROGRESS: pass 6, at document #10000/10681\n", - "2021-08-24 02:38:37,229 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:37,257 : INFO : topic #29 (0.020): 0.121*\"action\" + 0.051*\"adventure\" + 0.049*\"Action\" + 0.041*\"pirates\" + 0.040*\"sequel\" + 0.031*\"watched 2007\" + 0.030*\"motorcycle\" + 0.029*\"Adventure\" + 0.025*\"robots\" + 0.023*\"boring\"\n", - "2021-08-24 02:38:37,257 : INFO : topic #22 (0.020): 0.086*\"national film registry\" + 0.057*\"classic\" + 0.049*\"oscar (best cinematography)\" + 0.027*\"philip k. dick\" + 0.025*\"tumey's dvds\" + 0.024*\"black and white\" + 0.024*\"journalism\" + 0.024*\"cult film\" + 0.022*\"angelina jolie\" + 0.021*\"afi 100\"\n", - "2021-08-24 02:38:37,258 : INFO : topic #15 (0.020): 0.050*\"racism\" + 0.046*\"espionage\" + 0.038*\"stephen king\" + 0.037*\"coen brothers\" + 0.028*\"los angeles\" + 0.024*\"ensemble cast\" + 0.023*\"great acting\" + 0.021*\"notable nudity\" + 0.020*\"san francisco\" + 0.017*\"multiple storylines\"\n", - "2021-08-24 02:38:37,259 : INFO : topic #16 (0.020): 0.135*\"sci-fi\" + 0.092*\"dystopia\" + 0.051*\"Sci-Fi\" + 0.049*\"futuristmovies.com\" + 0.034*\"future\" + 0.033*\"military\" + 0.020*\"joaquin phoenix\" + 0.017*\"weird\" + 0.012*\"gfei own it\" + 0.011*\"surprise ending\"\n", - "2021-08-24 02:38:37,260 : INFO : topic #24 (0.020): 0.053*\"tom cruise\" + 0.039*\"france\" + 0.034*\"nicole kidman\" + 0.033*\"golden palm\" + 0.033*\"on dvr\" + 0.029*\"french\" + 0.027*\"dustin hoffman\" + 0.023*\"disability\" + 0.023*\"bank robbery\" + 0.019*\"takeshi kitano\"\n", - "2021-08-24 02:38:37,262 : INFO : topic diff=0.050225, rho=0.284665\n", - "2021-08-24 02:38:37,406 : INFO : -20.998 per-word bound, 2094111.2 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:37,407 : INFO : PROGRESS: pass 6, at document #10681/10681\n", - "2021-08-24 02:38:37,506 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:37,533 : INFO : topic #43 (0.020): 0.319*\"Crime\" + 0.167*\"Drama\" + 0.127*\"Thriller\" + 0.051*\"Mystery\" + 0.038*\"nudity (topless - notable)\" + 0.019*\"nicolas cage\" + 0.018*\"sean penn\" + 0.016*\"murder\" + 0.014*\"shakespeare\" + 0.012*\"remade\"\n", - "2021-08-24 02:38:37,534 : INFO : topic #2 (0.020): 0.103*\"Comedy\" + 0.099*\"satire\" + 0.086*\"lesbian\" + 0.069*\"imdb bottom 100\" + 0.023*\"sexy\" + 0.021*\"olympics\" + 0.020*\"adolf hitler\" + 0.019*\"get\" + 0.016*\"ben stiller\" + 0.016*\"john turturro\"\n", - "2021-08-24 02:38:37,534 : INFO : topic #3 (0.020): 0.370*\"nudity (topless)\" + 0.101*\"zombies\" + 0.034*\"keira knightley\" + 0.020*\"nudity (topless - brief)\" + 0.016*\"zombie\" + 0.015*\"ingmar bergman\" + 0.013*\"fascism\" + 0.013*\"futuristic\" + 0.012*\"virtual reality\" + 0.012*\"r:language\"\n", - "2021-08-24 02:38:37,535 : INFO : topic #8 (0.020): 0.084*\"space\" + 0.050*\"christianity\" + 0.030*\"marx brothers\" + 0.028*\"archaeology\" + 0.026*\"sci-fi\" + 0.025*\"harrison ford\" + 0.020*\"1970s\" + 0.019*\"kevin smith\" + 0.017*\"werewolves\" + 0.017*\"western\"\n", - "2021-08-24 02:38:37,536 : INFO : topic #29 (0.020): 0.104*\"action\" + 0.050*\"adventure\" + 0.046*\"Action\" + 0.040*\"sequel\" + 0.037*\"robots\" + 0.035*\"pirates\" + 0.034*\"death\" + 0.031*\"Adventure\" + 0.028*\"motorcycle\" + 0.027*\"watched 2007\"\n", - "2021-08-24 02:38:37,537 : INFO : topic diff=0.112596, rho=0.284665\n", - "2021-08-24 02:38:37,539 : INFO : PROGRESS: pass 7, at document #2000/10681\n", - "2021-08-24 02:38:37,803 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:37,830 : INFO : topic #32 (0.020): 0.093*\"based on a tv show\" + 0.086*\"jim carrey\" + 0.060*\"keanu reeves\" + 0.048*\"video game adaptation\" + 0.043*\"star trek\" + 0.040*\"tv\" + 0.033*\"england\" + 0.030*\"memory\" + 0.030*\"road trip\" + 0.024*\"seen at the cinema\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:37,832 : INFO : topic #2 (0.020): 0.111*\"satire\" + 0.096*\"lesbian\" + 0.089*\"Comedy\" + 0.053*\"imdb bottom 100\" + 0.025*\"sexy\" + 0.022*\"olympics\" + 0.019*\"get\" + 0.019*\"john turturro\" + 0.016*\"1\" + 0.016*\"queer\"\n", - "2021-08-24 02:38:37,832 : INFO : topic #12 (0.020): 0.126*\"quentin tarantino\" + 0.095*\"christmas\" + 0.083*\"tarantino\" + 0.055*\"heist\" + 0.044*\"nonlinear\" + 0.038*\"rape\" + 0.027*\"blindfold\" + 0.025*\"terry gilliam\" + 0.017*\"xmas theme\" + 0.016*\"ireland\"\n", - "2021-08-24 02:38:37,833 : INFO : topic #28 (0.020): 0.118*\"pixar\" + 0.071*\"oscar (best actress)\" + 0.047*\"mockumentary\" + 0.036*\"dinosaurs\" + 0.028*\"medieval\" + 0.023*\"elijah wood\" + 0.019*\"christopher guest\" + 0.017*\"computer animation\" + 0.016*\"cgi\" + 0.016*\"best war films\"\n", - "2021-08-24 02:38:37,834 : INFO : topic #33 (0.020): 0.162*\"comic book\" + 0.128*\"superhero\" + 0.061*\"super-hero\" + 0.039*\"batman\" + 0.037*\"adapted from:comic\" + 0.037*\"gene hackman\" + 0.027*\"Action\" + 0.026*\"action\" + 0.019*\"based on a comic\" + 0.018*\"alter ego\"\n", - "2021-08-24 02:38:37,835 : INFO : topic diff=0.233039, rho=0.273788\n", - "2021-08-24 02:38:37,837 : INFO : PROGRESS: pass 7, at document #4000/10681\n", - "2021-08-24 02:38:38,101 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:38,129 : INFO : topic #38 (0.020): 0.089*\"tom hanks\" + 0.085*\"true story\" + 0.058*\"based on a true story\" + 0.057*\"biography\" + 0.044*\"drama\" + 0.031*\"music\" + 0.027*\"biopic\" + 0.027*\"oscar (best actor)\" + 0.026*\"historical\" + 0.025*\"overrated\"\n", - "2021-08-24 02:38:38,130 : INFO : topic #24 (0.020): 0.069*\"tom cruise\" + 0.041*\"golden palm\" + 0.033*\"dustin hoffman\" + 0.031*\"france\" + 0.028*\"on dvr\" + 0.025*\"disability\" + 0.024*\"watch\" + 0.023*\"nicole kidman\" + 0.023*\"french\" + 0.019*\"sweet\"\n", - "2021-08-24 02:38:38,131 : INFO : topic #21 (0.020): 0.084*\"Drama\" + 0.048*\"criterion\" + 0.027*\"tumey's dvds\" + 0.027*\"library\" + 0.024*\"reflective\" + 0.021*\"atmospheric\" + 0.021*\"dvd-video\" + 0.021*\"satirical\" + 0.021*\"deliberate\" + 0.021*\"sven's to see list\"\n", - "2021-08-24 02:38:38,131 : INFO : topic #7 (0.020): 0.135*\"movie to see\" + 0.079*\"mel gibson\" + 0.057*\"boxing\" + 0.037*\"suburbia\" + 0.031*\"sylvester stallone\" + 0.031*\"uma thurman\" + 0.026*\"kidnapping\" + 0.026*\"basketball\" + 0.025*\"john travolta\" + 0.017*\"ang lee\"\n", - "2021-08-24 02:38:38,132 : INFO : topic #31 (0.020): 0.093*\"james bond\" + 0.084*\"murder\" + 0.066*\"007\" + 0.063*\"bond\" + 0.042*\"assassin\" + 0.031*\"claymation\" + 0.025*\"Action\" + 0.024*\"Thriller\" + 0.024*\"franchise\" + 0.023*\"aardman\"\n", - "2021-08-24 02:38:38,134 : INFO : topic diff=0.044511, rho=0.273788\n", - "2021-08-24 02:38:38,135 : INFO : PROGRESS: pass 7, at document #6000/10681\n", - "2021-08-24 02:38:38,369 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:38,396 : INFO : topic #11 (0.020): 0.261*\"Documentary\" + 0.140*\"to see\" + 0.074*\"gay\" + 0.070*\"in netflix queue\" + 0.049*\"oscar (best actor)\" + 0.023*\"IMAX\" + 0.021*\"homosexuality\" + 0.021*\"india\" + 0.019*\"matter-of-fact\" + 0.015*\"ian mckellen\"\n", - "2021-08-24 02:38:38,397 : INFO : topic #28 (0.020): 0.118*\"pixar\" + 0.078*\"oscar (best actress)\" + 0.034*\"mockumentary\" + 0.027*\"dinosaurs\" + 0.024*\"nostalgia\" + 0.024*\"medieval\" + 0.021*\"computer animation\" + 0.021*\"high fantasy\" + 0.019*\"elijah wood\" + 0.017*\"cgi\"\n", - "2021-08-24 02:38:38,398 : INFO : topic #8 (0.020): 0.094*\"space\" + 0.041*\"harrison ford\" + 0.035*\"sci-fi\" + 0.023*\"george lucas\" + 0.023*\"spaghetti western\" + 0.023*\"kevin smith\" + 0.022*\"christianity\" + 0.021*\"fantasy\" + 0.021*\"archaeology\" + 0.019*\"sergio leone\"\n", - "2021-08-24 02:38:38,399 : INFO : topic #38 (0.020): 0.085*\"tom hanks\" + 0.075*\"true story\" + 0.055*\"biography\" + 0.053*\"based on a true story\" + 0.044*\"drama\" + 0.031*\"music\" + 0.026*\"biopic\" + 0.024*\"overrated\" + 0.024*\"historical\" + 0.024*\"paris\"\n", - "2021-08-24 02:38:38,400 : INFO : topic #47 (0.020): 0.212*\"less than 300 ratings\" + 0.121*\"Drama\" + 0.066*\"nudity (rear)\" + 0.042*\"Comedy\" + 0.042*\"jackie chan\" + 0.026*\"kung fu\" + 0.024*\"football\" + 0.019*\"brian de palma\" + 0.018*\"denzel washington\" + 0.018*\"Action\"\n", - "2021-08-24 02:38:38,401 : INFO : topic diff=0.082357, rho=0.273788\n", - "2021-08-24 02:38:38,403 : INFO : PROGRESS: pass 7, at document #8000/10681\n", - "2021-08-24 02:38:38,640 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:38,667 : INFO : topic #9 (0.020): 0.189*\"r\" + 0.118*\"Western\" + 0.090*\"nudity (topless - brief)\" + 0.086*\"clearplay\" + 0.059*\"Drama\" + 0.026*\"british\" + 0.023*\"pg13\" + 0.021*\"russell crowe\" + 0.020*\"Thriller\" + 0.018*\"to see\"\n", - "2021-08-24 02:38:38,668 : INFO : topic #0 (0.020): 0.099*\"crime\" + 0.057*\"violence\" + 0.055*\"brad pitt\" + 0.039*\"Crime\" + 0.036*\"violent\" + 0.034*\"morgan freeman\" + 0.026*\"Thriller\" + 0.026*\"philip seymour hoffman\" + 0.025*\"organized crime\" + 0.025*\"action\"\n", - "2021-08-24 02:38:38,668 : INFO : topic #17 (0.020): 0.064*\"police\" + 0.054*\"will smith\" + 0.033*\"computers\" + 0.026*\"corruption\" + 0.019*\"no dialogue\" + 0.018*\"milla jovovich\" + 0.017*\"stage\" + 0.017*\"small-town life\" + 0.016*\"writer\" + 0.016*\"torture\"\n", - "2021-08-24 02:38:38,669 : INFO : topic #44 (0.020): 0.115*\"Adventure\" + 0.110*\"Children\" + 0.098*\"Fantasy\" + 0.067*\"Comedy\" + 0.061*\"disney\" + 0.059*\"Animation\" + 0.041*\"fantasy\" + 0.039*\"animation\" + 0.019*\"cars\" + 0.018*\"children\"\n", - "2021-08-24 02:38:38,670 : INFO : topic #18 (0.020): 0.054*\"creepy\" + 0.047*\"franchise\" + 0.044*\"surrealism\" + 0.035*\"slasher\" + 0.028*\"lurid\" + 0.025*\"adolescence\" + 0.023*\"robert downey jr\" + 0.022*\"goth\" + 0.020*\"unique\" + 0.020*\"united states\"\n", - "2021-08-24 02:38:38,671 : INFO : topic diff=0.063222, rho=0.273788\n", - "2021-08-24 02:38:38,672 : INFO : PROGRESS: pass 7, at document #10000/10681\n", - "2021-08-24 02:38:38,925 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:38,952 : INFO : topic #35 (0.020): 0.151*\"comedy\" + 0.121*\"Comedy\" + 0.088*\"funny\" + 0.047*\"parody\" + 0.040*\"hilarious\" + 0.034*\"ummarti2006\" + 0.023*\"stupid\" + 0.023*\"afi 100 (laughs)\" + 0.020*\"spoof\" + 0.016*\"funny as hell\"\n", - "2021-08-24 02:38:38,953 : INFO : topic #28 (0.020): 0.139*\"pixar\" + 0.072*\"oscar (best actress)\" + 0.039*\"mockumentary\" + 0.030*\"elijah wood\" + 0.026*\"medieval\" + 0.024*\"cgi\" + 0.024*\"computer animation\" + 0.020*\"dinosaurs\" + 0.017*\"animation\" + 0.016*\"nostalgia\"\n", - "2021-08-24 02:38:38,954 : INFO : topic #17 (0.020): 0.063*\"police\" + 0.045*\"corruption\" + 0.044*\"will smith\" + 0.025*\"computers\" + 0.024*\"action packed\" + 0.022*\"milla jovovich\" + 0.021*\"snakes\" + 0.019*\"torture\" + 0.017*\"surveillance\" + 0.016*\"tattoo\"\n", - "2021-08-24 02:38:38,955 : INFO : topic #25 (0.020): 0.209*\"Musical\" + 0.093*\"70mm\" + 0.065*\"musical\" + 0.062*\"religion\" + 0.043*\"Drama\" + 0.028*\"rock and roll\" + 0.023*\"friendship\" + 0.018*\"trains\" + 0.018*\"monty python\" + 0.016*\"music\"\n", - "2021-08-24 02:38:38,956 : INFO : topic #27 (0.020): 0.110*\"aliens\" + 0.067*\"steven spielberg\" + 0.065*\"family\" + 0.044*\"to-rent\" + 0.035*\"gambling\" + 0.029*\"michael crichton\" + 0.022*\"alien\" + 0.022*\"poker\" + 0.020*\"childhood\" + 0.018*\"didn't finish\"\n", - "2021-08-24 02:38:38,957 : INFO : topic diff=0.047011, rho=0.273788\n", - "2021-08-24 02:38:39,104 : INFO : -20.992 per-word bound, 2085772.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:39,105 : INFO : PROGRESS: pass 7, at document #10681/10681\n", - "2021-08-24 02:38:39,214 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:39,240 : INFO : topic #1 (0.020): 0.154*\"remake\" + 0.033*\"horror\" + 0.028*\"Thriller\" + 0.028*\"hitchcock\" + 0.028*\"jonossa\" + 0.026*\"tumey's dvds\" + 0.022*\"moody\" + 0.021*\"alfred hitchcock\" + 0.017*\"courtroom drama\" + 0.014*\"scary\"\n", - "2021-08-24 02:38:39,241 : INFO : topic #0 (0.020): 0.084*\"crime\" + 0.052*\"violence\" + 0.052*\"brad pitt\" + 0.049*\"violent\" + 0.049*\"Crime\" + 0.044*\"morgan freeman\" + 0.034*\"organized crime\" + 0.032*\"tommy lee jones\" + 0.030*\"philip seymour hoffman\" + 0.029*\"Thriller\"\n", - "2021-08-24 02:38:39,242 : INFO : topic #16 (0.020): 0.123*\"sci-fi\" + 0.085*\"dystopia\" + 0.054*\"Sci-Fi\" + 0.043*\"futuristmovies.com\" + 0.039*\"military\" + 0.028*\"future\" + 0.020*\"imdb top 250\" + 0.018*\"betrayal\" + 0.017*\"weird\" + 0.016*\"joaquin phoenix\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:39,243 : INFO : topic #28 (0.020): 0.154*\"pixar\" + 0.060*\"oscar (best actress)\" + 0.040*\"mockumentary\" + 0.032*\"elijah wood\" + 0.024*\"computer animation\" + 0.022*\"medieval\" + 0.020*\"cgi\" + 0.020*\"family drama\" + 0.017*\"social commentary\" + 0.016*\"dinosaurs\"\n", - "2021-08-24 02:38:39,243 : INFO : topic #21 (0.020): 0.095*\"Drama\" + 0.054*\"criterion\" + 0.032*\"library\" + 0.027*\"sven's to see list\" + 0.024*\"tumey's dvds\" + 0.022*\"irreverent\" + 0.020*\"reflective\" + 0.019*\"stylized\" + 0.019*\"dvd-video\" + 0.019*\"deliberate\"\n", - "2021-08-24 02:38:39,245 : INFO : topic diff=0.108012, rho=0.273788\n", - "2021-08-24 02:38:39,246 : INFO : PROGRESS: pass 8, at document #2000/10681\n", - "2021-08-24 02:38:39,522 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:39,547 : INFO : topic #33 (0.020): 0.163*\"comic book\" + 0.129*\"superhero\" + 0.061*\"super-hero\" + 0.039*\"batman\" + 0.038*\"adapted from:comic\" + 0.037*\"gene hackman\" + 0.027*\"Action\" + 0.026*\"action\" + 0.019*\"based on a comic\" + 0.018*\"alter ego\"\n", - "2021-08-24 02:38:39,548 : INFO : topic #27 (0.020): 0.154*\"aliens\" + 0.094*\"steven spielberg\" + 0.052*\"family\" + 0.036*\"michael crichton\" + 0.024*\"childhood\" + 0.024*\"to-rent\" + 0.023*\"gambling\" + 0.021*\"alien\" + 0.019*\"didn't finish\" + 0.017*\"mother-son relationship\"\n", - "2021-08-24 02:38:39,549 : INFO : topic #13 (0.020): 0.161*\"time travel\" + 0.067*\"twist ending\" + 0.059*\"holocaust\" + 0.030*\"bill murray\" + 0.026*\"good dialogue\" + 0.025*\"post-apocalyptic\" + 0.024*\"post apocalyptic\" + 0.018*\"suspense\" + 0.016*\"liam neeson\" + 0.014*\"mindfuck\"\n", - "2021-08-24 02:38:39,550 : INFO : topic #25 (0.020): 0.178*\"Musical\" + 0.083*\"musical\" + 0.069*\"70mm\" + 0.063*\"religion\" + 0.040*\"Drama\" + 0.029*\"friendship\" + 0.023*\"rock and roll\" + 0.022*\"music\" + 0.022*\"monty python\" + 0.015*\"adapted from b'way\"\n", - "2021-08-24 02:38:39,551 : INFO : topic #12 (0.020): 0.125*\"quentin tarantino\" + 0.095*\"christmas\" + 0.082*\"tarantino\" + 0.055*\"heist\" + 0.045*\"nonlinear\" + 0.038*\"rape\" + 0.028*\"blindfold\" + 0.024*\"terry gilliam\" + 0.017*\"xmas theme\" + 0.016*\"ireland\"\n", - "2021-08-24 02:38:39,553 : INFO : topic diff=0.223132, rho=0.264069\n", - "2021-08-24 02:38:39,554 : INFO : PROGRESS: pass 8, at document #4000/10681\n", - "2021-08-24 02:38:39,815 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:39,842 : INFO : topic #49 (0.020): 0.076*\"anime\" + 0.061*\"teen\" + 0.057*\"high school\" + 0.056*\"serial killer\" + 0.055*\"psychology\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.015*\"cult film\" + 0.014*\"jodie foster\" + 0.014*\"japan\"\n", - "2021-08-24 02:38:39,843 : INFO : topic #24 (0.020): 0.069*\"tom cruise\" + 0.041*\"golden palm\" + 0.033*\"dustin hoffman\" + 0.031*\"france\" + 0.027*\"on dvr\" + 0.025*\"disability\" + 0.024*\"watch\" + 0.023*\"nicole kidman\" + 0.023*\"french\" + 0.019*\"sweet\"\n", - "2021-08-24 02:38:39,844 : INFO : topic #0 (0.020): 0.093*\"crime\" + 0.061*\"brad pitt\" + 0.058*\"violence\" + 0.041*\"Crime\" + 0.036*\"morgan freeman\" + 0.034*\"violent\" + 0.031*\"organized crime\" + 0.029*\"thriller\" + 0.027*\"philip seymour hoffman\" + 0.026*\"Thriller\"\n", - "2021-08-24 02:38:39,844 : INFO : topic #26 (0.020): 0.178*\"oscar (best picture)\" + 0.168*\"classic\" + 0.071*\"oscar (best directing)\" + 0.054*\"oscar (best supporting actor)\" + 0.028*\"afi 100\" + 0.023*\"afi 100 (cheers)\" + 0.023*\"matt damon\" + 0.020*\"oscar (best supporting actress)\" + 0.018*\"best picture\" + 0.017*\"submarine\"\n", - "2021-08-24 02:38:39,845 : INFO : topic #14 (0.020): 0.110*\"Mystery\" + 0.060*\"Horror\" + 0.054*\"Thriller\" + 0.037*\"disturbing\" + 0.033*\"atmospheric\" + 0.026*\"ominous\" + 0.025*\"erlend's dvds\" + 0.024*\"scary movies to see on halloween\" + 0.023*\"tense\" + 0.022*\"menacing\"\n", - "2021-08-24 02:38:39,846 : INFO : topic diff=0.041677, rho=0.264069\n", - "2021-08-24 02:38:39,847 : INFO : PROGRESS: pass 8, at document #6000/10681\n", - "2021-08-24 02:38:40,310 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:40,337 : INFO : topic #37 (0.020): 0.053*\"easily confused with other movie(s) (title)\" + 0.043*\"oscar (best supporting actress)\" + 0.042*\"vietnam war\" + 0.037*\"want to see again\" + 0.036*\"vietnam\" + 0.031*\"food\" + 0.027*\"j netflix\" + 0.023*\"19th century\" + 0.022*\"cult classic\" + 0.019*\"alcoholism\"\n", - "2021-08-24 02:38:40,338 : INFO : topic #19 (0.020): 0.091*\"ghosts\" + 0.055*\"pg-13\" + 0.039*\"julia roberts\" + 0.038*\"jude law\" + 0.036*\"wedding\" + 0.029*\"hollywood\" + 0.028*\"afternoon section\" + 0.027*\"want to own\" + 0.025*\"infidelity\" + 0.021*\"prostitution\"\n", - "2021-08-24 02:38:40,339 : INFO : topic #23 (0.020): 0.069*\"mafia\" + 0.047*\"al pacino\" + 0.034*\"robert de niro\" + 0.025*\"guns\" + 0.022*\"poverty\" + 0.022*\"eddie murphy\" + 0.019*\"marlon brando\" + 0.018*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.016*\"michael caine\"\n", - "2021-08-24 02:38:40,340 : INFO : topic #1 (0.020): 0.109*\"remake\" + 0.055*\"hitchcock\" + 0.048*\"horror\" + 0.041*\"alfred hitchcock\" + 0.032*\"tumey's dvds\" + 0.029*\"Thriller\" + 0.024*\"moody\" + 0.021*\"courtroom drama\" + 0.020*\"scary\" + 0.015*\"classic\"\n", - "2021-08-24 02:38:40,340 : INFO : topic #34 (0.020): 0.160*\"War\" + 0.109*\"Drama\" + 0.101*\"world war ii\" + 0.089*\"drugs\" + 0.050*\"war\" + 0.043*\"history\" + 0.037*\"prison\" + 0.029*\"Action\" + 0.014*\"wwii\" + 0.014*\"Adventure\"\n", - "2021-08-24 02:38:40,342 : INFO : topic diff=0.078785, rho=0.264069\n", - "2021-08-24 02:38:40,343 : INFO : PROGRESS: pass 8, at document #8000/10681\n", - "2021-08-24 02:38:40,591 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:40,618 : INFO : topic #36 (0.020): 0.096*\"dark comedy\" + 0.073*\"black comedy\" + 0.070*\"bechdel test:fail\" + 0.039*\"funniest movies\" + 0.034*\"nazis\" + 0.033*\"cold war\" + 0.015*\"russia\" + 0.014*\"author:charles dickens\" + 0.014*\"communism\" + 0.012*\"autumnal\"\n", - "2021-08-24 02:38:40,619 : INFO : topic #26 (0.020): 0.162*\"classic\" + 0.144*\"oscar (best picture)\" + 0.061*\"oscar (best supporting actor)\" + 0.057*\"oscar (best directing)\" + 0.032*\"matt damon\" + 0.025*\"afi 100 (cheers)\" + 0.023*\"oscar (best supporting actress)\" + 0.022*\"afi 100\" + 0.016*\"submarine\" + 0.014*\"national film registry\"\n", - "2021-08-24 02:38:40,620 : INFO : topic #6 (0.020): 0.100*\"martial arts\" + 0.045*\"gothic\" + 0.037*\"campy\" + 0.029*\"silly\" + 0.026*\"australian\" + 0.026*\"australia\" + 0.023*\"sam raimi\" + 0.021*\"don't remember\" + 0.020*\"seen 2006\" + 0.019*\"blindness\"\n", - "2021-08-24 02:38:40,621 : INFO : topic #19 (0.020): 0.075*\"ghosts\" + 0.058*\"pg-13\" + 0.035*\"jude law\" + 0.035*\"wedding\" + 0.034*\"julia roberts\" + 0.026*\"want to own\" + 0.026*\"sam peckinpah\" + 0.026*\"hollywood\" + 0.023*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:40,622 : INFO : topic #42 (0.020): 0.156*\"johnny depp\" + 0.072*\"tim burton\" + 0.051*\"martin scorsese\" + 0.040*\"narrated\" + 0.032*\"george clooney\" + 0.028*\"ei muista\" + 0.024*\"monster\" + 0.018*\"alien invasion\" + 0.017*\"brothers\" + 0.015*\"las vegas\"\n", - "2021-08-24 02:38:40,623 : INFO : topic diff=0.060622, rho=0.264069\n", - "2021-08-24 02:38:40,625 : INFO : PROGRESS: pass 8, at document #10000/10681\n", - "2021-08-24 02:38:40,870 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:40,897 : INFO : topic #4 (0.020): 0.104*\"romance\" + 0.050*\"Romance\" + 0.048*\"watched 2006\" + 0.043*\"new york city\" + 0.042*\"chick flick\" + 0.028*\"new york\" + 0.026*\"sean connery\" + 0.024*\"girlie movie\" + 0.022*\"dance\" + 0.022*\"seen more than once\"\n", - "2021-08-24 02:38:40,898 : INFO : topic #21 (0.020): 0.096*\"Drama\" + 0.055*\"criterion\" + 0.028*\"library\" + 0.026*\"sven's to see list\" + 0.026*\"tumey's dvds\" + 0.022*\"reflective\" + 0.021*\"stylized\" + 0.021*\"atmospheric\" + 0.021*\"deliberate\" + 0.021*\"dvd-video\"\n", - "2021-08-24 02:38:40,899 : INFO : topic #45 (0.020): 0.056*\"japan\" + 0.045*\"tense\" + 0.044*\"revenge\" + 0.034*\"clint eastwood\" + 0.030*\"forceful\" + 0.029*\"gritty\" + 0.028*\"corvallis library\" + 0.027*\"atmospheric\" + 0.024*\"Action\" + 0.021*\"sweeping\"\n", - "2021-08-24 02:38:40,900 : INFO : topic #37 (0.020): 0.072*\"easily confused with other movie(s) (title)\" + 0.036*\"j netflix\" + 0.035*\"oscar (best supporting actress)\" + 0.029*\"want to see again\" + 0.028*\"slow\" + 0.028*\"19th century\" + 0.028*\"food\" + 0.027*\"vietnam war\" + 0.026*\"father daughter relationship\" + 0.025*\"peter sellers\"\n", - "2021-08-24 02:38:40,901 : INFO : topic #48 (0.020): 0.047*\"quirky\" + 0.042*\"surreal\" + 0.034*\"directorial debut\" + 0.028*\"bittersweet\" + 0.023*\"woody allen\" + 0.022*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"fantasy\" + 0.019*\"erlend's dvds\" + 0.018*\"sad\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:40,903 : INFO : topic diff=0.044128, rho=0.264069\n", - "2021-08-24 02:38:41,045 : INFO : -20.992 per-word bound, 2084885.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:41,046 : INFO : PROGRESS: pass 8, at document #10681/10681\n", - "2021-08-24 02:38:41,142 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:41,168 : INFO : topic #6 (0.020): 0.095*\"martial arts\" + 0.033*\"seen 2006\" + 0.032*\"campy\" + 0.031*\"gothic\" + 0.028*\"australia\" + 0.024*\"silly\" + 0.024*\"australian\" + 0.021*\"freedom\" + 0.019*\"greed\" + 0.016*\"blindness\"\n", - "2021-08-24 02:38:41,169 : INFO : topic #1 (0.020): 0.154*\"remake\" + 0.034*\"horror\" + 0.029*\"hitchcock\" + 0.028*\"Thriller\" + 0.028*\"jonossa\" + 0.024*\"tumey's dvds\" + 0.023*\"moody\" + 0.022*\"alfred hitchcock\" + 0.018*\"courtroom drama\" + 0.014*\"scary\"\n", - "2021-08-24 02:38:41,170 : INFO : topic #48 (0.020): 0.041*\"quirky\" + 0.038*\"surreal\" + 0.033*\"directorial debut\" + 0.026*\"bittersweet\" + 0.022*\"library on hold\" + 0.022*\"woody allen\" + 0.020*\"sad\" + 0.019*\"underrated\" + 0.019*\"humorous\" + 0.019*\"coming of age\"\n", - "2021-08-24 02:38:41,171 : INFO : topic #46 (0.020): 0.044*\"overrated\" + 0.042*\"samuel l. jackson\" + 0.038*\"imdb top 250\" + 0.030*\"dvd\" + 0.028*\"owned\" + 0.027*\"ridley scott\" + 0.026*\"seen more than once\" + 0.025*\"natalie portman\" + 0.024*\"kevin spacey\" + 0.021*\"eric's dvds\"\n", - "2021-08-24 02:38:41,172 : INFO : topic #4 (0.020): 0.103*\"romance\" + 0.063*\"new york city\" + 0.050*\"Romance\" + 0.042*\"watched 2006\" + 0.041*\"chick flick\" + 0.029*\"new york\" + 0.025*\"1980s\" + 0.022*\"sean connery\" + 0.021*\"girlie movie\" + 0.020*\"light\"\n", - "2021-08-24 02:38:41,173 : INFO : topic diff=0.103935, rho=0.264069\n", - "2021-08-24 02:38:41,174 : INFO : PROGRESS: pass 9, at document #2000/10681\n", - "2021-08-24 02:38:41,439 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:41,466 : INFO : topic #14 (0.020): 0.122*\"Mystery\" + 0.058*\"Thriller\" + 0.051*\"Horror\" + 0.039*\"disturbing\" + 0.030*\"atmospheric\" + 0.026*\"ominous\" + 0.025*\"tense\" + 0.023*\"menacing\" + 0.022*\"erlend's dvds\" + 0.022*\"dvd-r\"\n", - "2021-08-24 02:38:41,466 : INFO : topic #19 (0.020): 0.068*\"pg-13\" + 0.064*\"ghosts\" + 0.039*\"wedding\" + 0.038*\"julia roberts\" + 0.034*\"jude law\" + 0.034*\"hollywood\" + 0.029*\"want to own\" + 0.024*\"afternoon section\" + 0.023*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:41,467 : INFO : topic #15 (0.020): 0.073*\"coen brothers\" + 0.058*\"stephen king\" + 0.051*\"racism\" + 0.033*\"great acting\" + 0.032*\"adultery\" + 0.028*\"espionage\" + 0.024*\"los angeles\" + 0.018*\"ensemble cast\" + 0.016*\"san francisco\" + 0.015*\"notable nudity\"\n", - "2021-08-24 02:38:41,468 : INFO : topic #22 (0.020): 0.089*\"classic\" + 0.070*\"national film registry\" + 0.048*\"oscar (best cinematography)\" + 0.034*\"afi 100\" + 0.034*\"stanley kubrick\" + 0.030*\"imdb top 250\" + 0.028*\"afi 100 (thrills)\" + 0.027*\"black and white\" + 0.025*\"cult film\" + 0.024*\"70mm\"\n", - "2021-08-24 02:38:41,468 : INFO : topic #31 (0.020): 0.089*\"murder\" + 0.057*\"james bond\" + 0.045*\"assassin\" + 0.042*\"claymation\" + 0.041*\"bond\" + 0.036*\"007\" + 0.029*\"aardman\" + 0.023*\"Thriller\" + 0.022*\"Action\" + 0.021*\"killer-as-protagonist\"\n", - "2021-08-24 02:38:41,470 : INFO : topic diff=0.213692, rho=0.255317\n", - "2021-08-24 02:38:41,471 : INFO : PROGRESS: pass 9, at document #4000/10681\n", - "2021-08-24 02:38:41,717 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:41,743 : INFO : topic #48 (0.020): 0.047*\"quirky\" + 0.044*\"surreal\" + 0.041*\"directorial debut\" + 0.026*\"woody allen\" + 0.024*\"humorous\" + 0.024*\"bittersweet\" + 0.021*\"underrated\" + 0.021*\"coming of age\" + 0.019*\"erlend's dvds\" + 0.018*\"deadpan\"\n", - "2021-08-24 02:38:41,744 : INFO : topic #7 (0.020): 0.144*\"movie to see\" + 0.077*\"mel gibson\" + 0.056*\"boxing\" + 0.036*\"suburbia\" + 0.031*\"uma thurman\" + 0.030*\"sylvester stallone\" + 0.027*\"kidnapping\" + 0.026*\"basketball\" + 0.025*\"john travolta\" + 0.017*\"ang lee\"\n", - "2021-08-24 02:38:41,745 : INFO : topic #27 (0.020): 0.155*\"aliens\" + 0.081*\"steven spielberg\" + 0.059*\"family\" + 0.037*\"michael crichton\" + 0.024*\"alien\" + 0.024*\"gambling\" + 0.022*\"childhood\" + 0.022*\"poker\" + 0.021*\"didn't finish\" + 0.021*\"to-rent\"\n", - "2021-08-24 02:38:41,746 : INFO : topic #3 (0.020): 0.325*\"nudity (topless)\" + 0.075*\"zombies\" + 0.031*\"virtual reality\" + 0.028*\"nudity (topless - brief)\" + 0.019*\"keira knightley\" + 0.014*\"robert altman\" + 0.013*\"ingmar bergman\" + 0.013*\"zombie\" + 0.012*\"futuristic\" + 0.011*\"gruesome\"\n", - "2021-08-24 02:38:41,747 : INFO : topic #6 (0.020): 0.094*\"martial arts\" + 0.051*\"gothic\" + 0.036*\"campy\" + 0.029*\"australia\" + 0.028*\"silly\" + 0.021*\"don't remember\" + 0.019*\"seen 2006\" + 0.019*\"2.5\" + 0.019*\"australian\" + 0.018*\"sam raimi\"\n", - "2021-08-24 02:38:41,748 : INFO : topic diff=0.039438, rho=0.255317\n", - "2021-08-24 02:38:41,750 : INFO : PROGRESS: pass 9, at document #6000/10681\n", - "2021-08-24 02:38:41,979 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:42,006 : INFO : topic #36 (0.020): 0.114*\"dark comedy\" + 0.076*\"bechdel test:fail\" + 0.076*\"black comedy\" + 0.040*\"nazis\" + 0.031*\"cold war\" + 0.029*\"funniest movies\" + 0.015*\"spies\" + 0.014*\"communism\" + 0.013*\"existentialism\" + 0.011*\"russia\"\n", - "2021-08-24 02:38:42,007 : INFO : topic #18 (0.020): 0.055*\"franchise\" + 0.051*\"creepy\" + 0.042*\"slasher\" + 0.040*\"surrealism\" + 0.024*\"lurid\" + 0.024*\"robert downey jr\" + 0.022*\"united states\" + 0.020*\"unique\" + 0.020*\"no rec?\" + 0.020*\"goth\"\n", - "2021-08-24 02:38:42,007 : INFO : topic #14 (0.020): 0.111*\"Mystery\" + 0.061*\"Horror\" + 0.055*\"Thriller\" + 0.038*\"disturbing\" + 0.032*\"atmospheric\" + 0.026*\"erlend's dvds\" + 0.025*\"ominous\" + 0.024*\"tense\" + 0.022*\"menacing\" + 0.020*\"tumey's dvds\"\n", - "2021-08-24 02:38:42,008 : INFO : topic #40 (0.020): 0.117*\"nudity (full frontal - notable)\" + 0.091*\"magic\" + 0.072*\"edward norton\" + 0.056*\"sports\" + 0.051*\"baseball\" + 0.048*\"not corv lib\" + 0.040*\"hw drama\" + 0.029*\"compassionate\" + 0.025*\"christopher walken\" + 0.023*\"inspirational\"\n", - "2021-08-24 02:38:42,009 : INFO : topic #26 (0.020): 0.163*\"classic\" + 0.160*\"oscar (best picture)\" + 0.064*\"oscar (best directing)\" + 0.057*\"oscar (best supporting actor)\" + 0.030*\"matt damon\" + 0.025*\"afi 100\" + 0.023*\"afi 100 (cheers)\" + 0.022*\"oscar (best supporting actress)\" + 0.016*\"best picture\" + 0.016*\"submarine\"\n", - "2021-08-24 02:38:42,010 : INFO : topic diff=0.075169, rho=0.255317\n", - "2021-08-24 02:38:42,012 : INFO : PROGRESS: pass 9, at document #8000/10681\n", - "2021-08-24 02:38:42,248 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:42,275 : INFO : topic #39 (0.020): 0.108*\"bruce willis\" + 0.054*\"interesting\" + 0.033*\"avi\" + 0.030*\"gerard depardieu\" + 0.028*\"love\" + 0.025*\"africa\" + 0.024*\"golden raspberry (worst actor)\" + 0.024*\"big budget\" + 0.024*\"good\" + 0.023*\"end of the world\"\n", - "2021-08-24 02:38:42,276 : INFO : topic #42 (0.020): 0.156*\"johnny depp\" + 0.073*\"tim burton\" + 0.051*\"martin scorsese\" + 0.040*\"narrated\" + 0.032*\"george clooney\" + 0.029*\"ei muista\" + 0.024*\"monster\" + 0.018*\"alien invasion\" + 0.017*\"brothers\" + 0.015*\"las vegas\"\n", - "2021-08-24 02:38:42,277 : INFO : topic #47 (0.020): 0.293*\"less than 300 ratings\" + 0.140*\"Drama\" + 0.058*\"nudity (rear)\" + 0.037*\"jackie chan\" + 0.024*\"kung fu\" + 0.022*\"football\" + 0.019*\"Comedy\" + 0.016*\"brian de palma\" + 0.014*\"denzel washington\" + 0.013*\"oppl\"\n", - "2021-08-24 02:38:42,278 : INFO : topic #2 (0.020): 0.098*\"Comedy\" + 0.095*\"lesbian\" + 0.087*\"satire\" + 0.050*\"imdb bottom 100\" + 0.037*\"sexy\" + 0.028*\"get\" + 0.019*\"ben stiller\" + 0.018*\"olympics\" + 0.017*\"john turturro\" + 0.017*\"amnesia\"\n", - "2021-08-24 02:38:42,279 : INFO : topic #28 (0.020): 0.124*\"pixar\" + 0.089*\"oscar (best actress)\" + 0.036*\"mockumentary\" + 0.026*\"medieval\" + 0.025*\"elijah wood\" + 0.022*\"dinosaurs\" + 0.021*\"nostalgia\" + 0.020*\"computer animation\" + 0.017*\"high fantasy\" + 0.016*\"cgi\"\n", - "2021-08-24 02:38:42,280 : INFO : topic diff=0.058535, rho=0.255317\n", - "2021-08-24 02:38:42,282 : INFO : PROGRESS: pass 9, at document #10000/10681\n", - "2021-08-24 02:38:42,528 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:42,556 : INFO : topic #4 (0.020): 0.105*\"romance\" + 0.051*\"Romance\" + 0.047*\"watched 2006\" + 0.044*\"new york city\" + 0.043*\"chick flick\" + 0.028*\"new york\" + 0.026*\"sean connery\" + 0.024*\"girlie movie\" + 0.023*\"dance\" + 0.021*\"light\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:42,557 : INFO : topic #22 (0.020): 0.086*\"national film registry\" + 0.056*\"classic\" + 0.050*\"oscar (best cinematography)\" + 0.026*\"philip k. dick\" + 0.025*\"tumey's dvds\" + 0.024*\"black and white\" + 0.024*\"imdb top 250\" + 0.023*\"cult film\" + 0.023*\"journalism\" + 0.022*\"afi 100\"\n", - "2021-08-24 02:38:42,558 : INFO : topic #28 (0.020): 0.138*\"pixar\" + 0.080*\"oscar (best actress)\" + 0.039*\"mockumentary\" + 0.029*\"elijah wood\" + 0.026*\"medieval\" + 0.024*\"cgi\" + 0.023*\"computer animation\" + 0.020*\"dinosaurs\" + 0.017*\"nostalgia\" + 0.016*\"animation\"\n", - "2021-08-24 02:38:42,558 : INFO : topic #37 (0.020): 0.072*\"easily confused with other movie(s) (title)\" + 0.036*\"j netflix\" + 0.035*\"oscar (best supporting actress)\" + 0.029*\"want to see again\" + 0.029*\"slow\" + 0.028*\"food\" + 0.028*\"19th century\" + 0.027*\"vietnam war\" + 0.026*\"father daughter relationship\" + 0.026*\"vietnam\"\n", - "2021-08-24 02:38:42,559 : INFO : topic #20 (0.020): 0.144*\"politics\" + 0.102*\"documentary\" + 0.055*\"terrorism\" + 0.053*\"jack nicholson\" + 0.040*\"propaganda\" + 0.040*\"political\" + 0.039*\"love story\" + 0.036*\"Documentary\" + 0.031*\"seen 2008\" + 0.026*\"aviation\"\n", - "2021-08-24 02:38:42,560 : INFO : topic diff=0.042244, rho=0.255317\n", - "2021-08-24 02:38:42,708 : INFO : -20.985 per-word bound, 2075044.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:42,709 : INFO : PROGRESS: pass 9, at document #10681/10681\n", - "2021-08-24 02:38:42,808 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:42,834 : INFO : topic #36 (0.020): 0.116*\"bechdel test:fail\" + 0.102*\"dark comedy\" + 0.067*\"black comedy\" + 0.043*\"nazis\" + 0.042*\"cold war\" + 0.027*\"funniest movies\" + 0.018*\"existentialism\" + 0.015*\"author:charles dickens\" + 0.015*\"communism\" + 0.014*\"russia\"\n", - "2021-08-24 02:38:42,835 : INFO : topic #11 (0.020): 0.308*\"Documentary\" + 0.206*\"to see\" + 0.059*\"in netflix queue\" + 0.059*\"gay\" + 0.025*\"india\" + 0.021*\"oscar (best actor)\" + 0.020*\"IMAX\" + 0.018*\"homosexuality\" + 0.013*\"matter-of-fact\" + 0.011*\"mexico\"\n", - "2021-08-24 02:38:42,836 : INFO : topic #40 (0.020): 0.119*\"magic\" + 0.118*\"nudity (full frontal - notable)\" + 0.058*\"not corv lib\" + 0.054*\"edward norton\" + 0.047*\"sports\" + 0.033*\"hw drama\" + 0.033*\"inspirational\" + 0.032*\"baseball\" + 0.028*\"don cheadle\" + 0.025*\"compassionate\"\n", - "2021-08-24 02:38:42,837 : INFO : topic #25 (0.020): 0.213*\"Musical\" + 0.077*\"musical\" + 0.077*\"religion\" + 0.076*\"70mm\" + 0.036*\"friendship\" + 0.034*\"Drama\" + 0.026*\"rock and roll\" + 0.020*\"trains\" + 0.018*\"music\" + 0.014*\"monty python\"\n", - "2021-08-24 02:38:42,837 : INFO : topic #43 (0.020): 0.319*\"Crime\" + 0.169*\"Drama\" + 0.125*\"Thriller\" + 0.051*\"Mystery\" + 0.039*\"nudity (topless - notable)\" + 0.019*\"nicolas cage\" + 0.018*\"sean penn\" + 0.015*\"shakespeare\" + 0.015*\"murder\" + 0.012*\"remade\"\n", - "2021-08-24 02:38:42,839 : INFO : topic diff=0.100206, rho=0.255317\n", - "2021-08-24 02:38:42,840 : INFO : PROGRESS: pass 10, at document #2000/10681\n", - "2021-08-24 02:38:43,102 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:43,128 : INFO : topic #49 (0.020): 0.086*\"anime\" + 0.063*\"serial killer\" + 0.052*\"high school\" + 0.052*\"psychology\" + 0.035*\"teen\" + 0.021*\"cannibalism\" + 0.019*\"mental illness\" + 0.018*\"japan\" + 0.017*\"jodie foster\" + 0.017*\"anthony hopkins\"\n", - "2021-08-24 02:38:43,129 : INFO : topic #29 (0.020): 0.144*\"action\" + 0.052*\"adventure\" + 0.050*\"Action\" + 0.035*\"sequel\" + 0.031*\"robots\" + 0.031*\"Adventure\" + 0.027*\"arnold schwarzenegger\" + 0.025*\"death\" + 0.023*\"pirates\" + 0.023*\"boring\"\n", - "2021-08-24 02:38:43,131 : INFO : topic #19 (0.020): 0.068*\"pg-13\" + 0.065*\"ghosts\" + 0.039*\"wedding\" + 0.038*\"julia roberts\" + 0.034*\"jude law\" + 0.033*\"hollywood\" + 0.029*\"want to own\" + 0.024*\"afternoon section\" + 0.023*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:43,132 : INFO : topic #37 (0.020): 0.064*\"easily confused with other movie(s) (title)\" + 0.046*\"vietnam\" + 0.044*\"vietnam war\" + 0.034*\"food\" + 0.033*\"j netflix\" + 0.030*\"oscar (best supporting actress)\" + 0.029*\"want to see again\" + 0.023*\"peter sellers\" + 0.021*\"19th century\" + 0.020*\"slow\"\n", - "2021-08-24 02:38:43,133 : INFO : topic #10 (0.020): 0.414*\"Comedy\" + 0.247*\"Romance\" + 0.199*\"Drama\" + 0.012*\"bibliothek\" + 0.008*\"london\" + 0.008*\"oppl\" + 0.008*\"divorce\" + 0.005*\"owen wilson\" + 0.005*\"betamax\" + 0.004*\"world war i\"\n", - "2021-08-24 02:38:43,134 : INFO : topic diff=0.205817, rho=0.247382\n", - "2021-08-24 02:38:43,135 : INFO : PROGRESS: pass 10, at document #4000/10681\n", - "2021-08-24 02:38:43,390 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:43,416 : INFO : topic #23 (0.020): 0.076*\"mafia\" + 0.044*\"al pacino\" + 0.037*\"robert de niro\" + 0.023*\"marlon brando\" + 0.022*\"guns\" + 0.019*\"poverty\" + 0.019*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.017*\"eddie murphy\" + 0.016*\"nudity (full frontal - brief)\"\n", - "2021-08-24 02:38:43,417 : INFO : topic #39 (0.020): 0.136*\"bruce willis\" + 0.052*\"interesting\" + 0.026*\"africa\" + 0.026*\"end of the world\" + 0.025*\"golden raspberry (worst actor)\" + 0.025*\"good\" + 0.025*\"love\" + 0.023*\"apocalypse\" + 0.021*\"predictable\" + 0.021*\"big budget\"\n", - "2021-08-24 02:38:43,418 : INFO : topic #49 (0.020): 0.078*\"anime\" + 0.061*\"teen\" + 0.058*\"high school\" + 0.057*\"serial killer\" + 0.056*\"psychology\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.015*\"cult film\" + 0.014*\"japan\" + 0.014*\"jodie foster\"\n", - "2021-08-24 02:38:43,419 : INFO : topic #16 (0.020): 0.171*\"sci-fi\" + 0.089*\"dystopia\" + 0.067*\"futuristmovies.com\" + 0.058*\"Sci-Fi\" + 0.032*\"military\" + 0.026*\"future\" + 0.013*\"court\" + 0.013*\"joaquin phoenix\" + 0.012*\"saturn award (best science fiction film)\" + 0.012*\"1950s\"\n", - "2021-08-24 02:38:43,420 : INFO : topic #15 (0.020): 0.068*\"stephen king\" + 0.066*\"racism\" + 0.065*\"coen brothers\" + 0.037*\"adultery\" + 0.028*\"great acting\" + 0.026*\"espionage\" + 0.023*\"los angeles\" + 0.020*\"ensemble cast\" + 0.017*\"multiple storylines\" + 0.017*\"notable nudity\"\n", - "2021-08-24 02:38:43,421 : INFO : topic diff=0.037603, rho=0.247382\n", - "2021-08-24 02:38:43,422 : INFO : PROGRESS: pass 10, at document #6000/10681\n", - "2021-08-24 02:38:43,655 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:43,681 : INFO : topic #23 (0.020): 0.069*\"mafia\" + 0.046*\"al pacino\" + 0.033*\"robert de niro\" + 0.025*\"guns\" + 0.022*\"poverty\" + 0.022*\"eddie murphy\" + 0.019*\"marlon brando\" + 0.018*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.016*\"michael caine\"\n", - "2021-08-24 02:38:43,682 : INFO : topic #43 (0.020): 0.288*\"Crime\" + 0.156*\"Drama\" + 0.107*\"Thriller\" + 0.050*\"nudity (topless - notable)\" + 0.043*\"Mystery\" + 0.025*\"shakespeare\" + 0.023*\"nicolas cage\" + 0.017*\"remade\" + 0.016*\"adapted from:play\" + 0.015*\"murder\"\n", - "2021-08-24 02:38:43,683 : INFO : topic #39 (0.020): 0.122*\"bruce willis\" + 0.056*\"interesting\" + 0.031*\"africa\" + 0.030*\"gerard depardieu\" + 0.026*\"golden raspberry (worst actor)\" + 0.024*\"avi\" + 0.023*\"love\" + 0.022*\"good\" + 0.022*\"end of the world\" + 0.022*\"predictable\"\n", - "2021-08-24 02:38:43,683 : INFO : topic #20 (0.020): 0.125*\"politics\" + 0.073*\"documentary\" + 0.072*\"jack nicholson\" + 0.038*\"terrorism\" + 0.034*\"seen 2008\" + 0.032*\"propaganda\" + 0.030*\"love story\" + 0.030*\"aviation\" + 0.030*\"Documentary\" + 0.027*\"political\"\n", - "2021-08-24 02:38:43,684 : INFO : topic #17 (0.020): 0.059*\"will smith\" + 0.059*\"police\" + 0.031*\"corruption\" + 0.028*\"computers\" + 0.022*\"milla jovovich\" + 0.019*\"no dialogue\" + 0.018*\"torture\" + 0.016*\"luc besson\" + 0.015*\"race\" + 0.015*\"writer\"\n", - "2021-08-24 02:38:43,685 : INFO : topic diff=0.072370, rho=0.247382\n", - "2021-08-24 02:38:43,686 : INFO : PROGRESS: pass 10, at document #8000/10681\n", - "2021-08-24 02:38:43,933 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:43,961 : INFO : topic #48 (0.020): 0.051*\"quirky\" + 0.047*\"surreal\" + 0.041*\"directorial debut\" + 0.027*\"bittersweet\" + 0.026*\"woody allen\" + 0.022*\"humorous\" + 0.020*\"fantasy\" + 0.020*\"whimsical\" + 0.019*\"peter jackson\" + 0.019*\"erlend's dvds\"\n", - "2021-08-24 02:38:43,961 : INFO : topic #47 (0.020): 0.294*\"less than 300 ratings\" + 0.142*\"Drama\" + 0.059*\"nudity (rear)\" + 0.037*\"jackie chan\" + 0.024*\"kung fu\" + 0.022*\"football\" + 0.016*\"brian de palma\" + 0.015*\"denzel washington\" + 0.013*\"oppl\" + 0.012*\"Comedy\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:43,962 : INFO : topic #26 (0.020): 0.173*\"classic\" + 0.144*\"oscar (best picture)\" + 0.060*\"oscar (best supporting actor)\" + 0.057*\"oscar (best directing)\" + 0.031*\"matt damon\" + 0.023*\"afi 100 (cheers)\" + 0.023*\"afi 100\" + 0.022*\"oscar (best supporting actress)\" + 0.016*\"submarine\" + 0.016*\"national film registry\"\n", - "2021-08-24 02:38:43,963 : INFO : topic #5 (0.020): 0.223*\"Action\" + 0.196*\"Thriller\" + 0.171*\"Horror\" + 0.133*\"Sci-Fi\" + 0.066*\"Adventure\" + 0.042*\"betamax\" + 0.019*\"can't remember\" + 0.012*\"70mm\" + 0.010*\"courtroom\" + 0.009*\"vhs\"\n", - "2021-08-24 02:38:43,964 : INFO : topic #29 (0.020): 0.134*\"action\" + 0.055*\"Action\" + 0.052*\"adventure\" + 0.040*\"sequel\" + 0.030*\"Adventure\" + 0.028*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.026*\"pirates\" + 0.025*\"boring\" + 0.024*\"motorcycle\"\n", - "2021-08-24 02:38:43,965 : INFO : topic diff=0.056478, rho=0.247382\n", - "2021-08-24 02:38:43,967 : INFO : PROGRESS: pass 10, at document #10000/10681\n", - "2021-08-24 02:38:44,220 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:44,247 : INFO : topic #13 (0.020): 0.143*\"time travel\" + 0.079*\"twist ending\" + 0.049*\"holocaust\" + 0.039*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.021*\"post apocalyptic\" + 0.020*\"liam neeson\" + 0.020*\"michael moore\" + 0.018*\"mindfuck\" + 0.015*\"capitalism\"\n", - "2021-08-24 02:38:44,248 : INFO : topic #10 (0.020): 0.433*\"Comedy\" + 0.239*\"Romance\" + 0.196*\"Drama\" + 0.012*\"bibliothek\" + 0.009*\"betamax\" + 0.009*\"london\" + 0.005*\"owen wilson\" + 0.005*\"divorce\" + 0.004*\"world war i\" + 0.004*\"oppl\"\n", - "2021-08-24 02:38:44,249 : INFO : topic #41 (0.020): 0.239*\"based on a book\" + 0.111*\"adapted from:book\" + 0.076*\"netflix\" + 0.066*\"Drama\" + 0.055*\"nudity (full frontal)\" + 0.044*\"robin williams\" + 0.036*\"vampire\" + 0.034*\"vampires\" + 0.014*\"book\" + 0.010*\"drinking\"\n", - "2021-08-24 02:38:44,250 : INFO : topic #21 (0.020): 0.097*\"Drama\" + 0.055*\"criterion\" + 0.028*\"library\" + 0.026*\"sven's to see list\" + 0.025*\"tumey's dvds\" + 0.022*\"dvd-video\" + 0.022*\"reflective\" + 0.021*\"atmospheric\" + 0.021*\"deliberate\" + 0.021*\"stylized\"\n", - "2021-08-24 02:38:44,251 : INFO : topic #18 (0.020): 0.045*\"creepy\" + 0.041*\"surrealism\" + 0.041*\"franchise\" + 0.030*\"lurid\" + 0.028*\"robert downey jr\" + 0.028*\"united states\" + 0.026*\"slasher\" + 0.024*\"unique\" + 0.022*\"macabre\" + 0.021*\"adolescence\"\n", - "2021-08-24 02:38:44,252 : INFO : topic diff=0.040475, rho=0.247382\n", - "2021-08-24 02:38:44,404 : INFO : -20.984 per-word bound, 2073623.2 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:44,405 : INFO : PROGRESS: pass 10, at document #10681/10681\n", - "2021-08-24 02:38:44,502 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:44,527 : INFO : topic #5 (0.020): 0.243*\"Action\" + 0.210*\"Thriller\" + 0.174*\"Horror\" + 0.138*\"Sci-Fi\" + 0.068*\"Adventure\" + 0.025*\"betamax\" + 0.011*\"can't remember\" + 0.007*\"70mm\" + 0.007*\"courtroom\" + 0.006*\"dvd-video\"\n", - "2021-08-24 02:38:44,528 : INFO : topic #39 (0.020): 0.090*\"bruce willis\" + 0.050*\"interesting\" + 0.045*\"africa\" + 0.039*\"love\" + 0.033*\"apocalypse\" + 0.032*\"predictable\" + 0.025*\"big budget\" + 0.024*\"avi\" + 0.024*\"tragedy\" + 0.022*\"gerard depardieu\"\n", - "2021-08-24 02:38:44,529 : INFO : topic #37 (0.020): 0.082*\"easily confused with other movie(s) (title)\" + 0.043*\"j netflix\" + 0.033*\"food\" + 0.030*\"oscar (best supporting actress)\" + 0.027*\"19th century\" + 0.025*\"want to see again\" + 0.025*\"father daughter relationship\" + 0.024*\"slow\" + 0.024*\"vietnam war\" + 0.022*\"vietnam\"\n", - "2021-08-24 02:38:44,530 : INFO : topic #22 (0.020): 0.087*\"national film registry\" + 0.053*\"classic\" + 0.046*\"oscar (best cinematography)\" + 0.031*\"angelina jolie\" + 0.029*\"black and white\" + 0.025*\"journalism\" + 0.024*\"philip k. dick\" + 0.023*\"tumey's dvds\" + 0.023*\"imdb top 250\" + 0.021*\"cult film\"\n", - "2021-08-24 02:38:44,530 : INFO : topic #19 (0.020): 0.102*\"pg-13\" + 0.062*\"ghosts\" + 0.042*\"jude law\" + 0.040*\"wedding\" + 0.032*\"julia roberts\" + 0.031*\"infidelity\" + 0.024*\"want to own\" + 0.021*\"prostitution\" + 0.020*\"hollywood\" + 0.017*\"scope\"\n", - "2021-08-24 02:38:44,532 : INFO : topic diff=0.097064, rho=0.247382\n", - "2021-08-24 02:38:44,533 : INFO : PROGRESS: pass 11, at document #2000/10681\n", - "2021-08-24 02:38:44,819 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:44,845 : INFO : topic #16 (0.020): 0.184*\"sci-fi\" + 0.084*\"dystopia\" + 0.058*\"futuristmovies.com\" + 0.055*\"Sci-Fi\" + 0.034*\"military\" + 0.032*\"future\" + 0.014*\"sf\" + 0.014*\"saturn award (best science fiction film)\" + 0.012*\"imdb top 250\" + 0.011*\"1950s\"\n", - "2021-08-24 02:38:44,847 : INFO : topic #34 (0.020): 0.168*\"War\" + 0.120*\"Drama\" + 0.097*\"world war ii\" + 0.072*\"drugs\" + 0.053*\"war\" + 0.046*\"history\" + 0.046*\"prison\" + 0.027*\"Action\" + 0.018*\"Adventure\" + 0.016*\"wwii\"\n", - "2021-08-24 02:38:44,848 : INFO : topic #45 (0.020): 0.043*\"tense\" + 0.042*\"japan\" + 0.033*\"revenge\" + 0.032*\"clint eastwood\" + 0.030*\"atmospheric\" + 0.029*\"forceful\" + 0.028*\"gritty\" + 0.024*\"sweeping\" + 0.022*\"visceral\" + 0.022*\"Action\"\n", - "2021-08-24 02:38:44,849 : INFO : topic #23 (0.020): 0.086*\"mafia\" + 0.038*\"al pacino\" + 0.034*\"robert de niro\" + 0.026*\"marlon brando\" + 0.023*\"guns\" + 0.020*\"poverty\" + 0.019*\"scifi\" + 0.019*\"francis ford coppola\" + 0.016*\"tumey's dvds\" + 0.016*\"francis ford copolla\"\n", - "2021-08-24 02:38:44,850 : INFO : topic #2 (0.020): 0.109*\"satire\" + 0.097*\"Comedy\" + 0.095*\"lesbian\" + 0.052*\"imdb bottom 100\" + 0.026*\"sexy\" + 0.021*\"get\" + 0.021*\"olympics\" + 0.018*\"john turturro\" + 0.016*\"1\" + 0.016*\"queer\"\n", - "2021-08-24 02:38:44,852 : INFO : topic diff=0.198465, rho=0.240143\n", - "2021-08-24 02:38:44,854 : INFO : PROGRESS: pass 11, at document #4000/10681\n", - "2021-08-24 02:38:45,118 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:45,145 : INFO : topic #45 (0.020): 0.042*\"tense\" + 0.039*\"japan\" + 0.039*\"clint eastwood\" + 0.030*\"revenge\" + 0.030*\"atmospheric\" + 0.029*\"forceful\" + 0.027*\"gritty\" + 0.024*\"Action\" + 0.024*\"samurai\" + 0.024*\"sweeping\"\n", - "2021-08-24 02:38:45,146 : INFO : topic #47 (0.020): 0.234*\"less than 300 ratings\" + 0.128*\"Drama\" + 0.070*\"nudity (rear)\" + 0.037*\"jackie chan\" + 0.028*\"kung fu\" + 0.027*\"football\" + 0.018*\"oppl\" + 0.018*\"denzel washington\" + 0.016*\"bechdel test:pass\" + 0.016*\"aids\"\n", - "2021-08-24 02:38:45,146 : INFO : topic #26 (0.020): 0.185*\"classic\" + 0.173*\"oscar (best picture)\" + 0.069*\"oscar (best directing)\" + 0.053*\"oscar (best supporting actor)\" + 0.028*\"afi 100\" + 0.023*\"matt damon\" + 0.022*\"afi 100 (cheers)\" + 0.020*\"oscar (best supporting actress)\" + 0.018*\"best picture\" + 0.017*\"national film registry\"\n", - "2021-08-24 02:38:45,147 : INFO : topic #38 (0.020): 0.088*\"tom hanks\" + 0.086*\"true story\" + 0.059*\"based on a true story\" + 0.059*\"biography\" + 0.047*\"drama\" + 0.030*\"music\" + 0.029*\"historical\" + 0.029*\"Drama\" + 0.029*\"oscar (best actor)\" + 0.028*\"biopic\"\n", - "2021-08-24 02:38:45,147 : INFO : topic #19 (0.020): 0.091*\"ghosts\" + 0.059*\"pg-13\" + 0.040*\"julia roberts\" + 0.035*\"jude law\" + 0.031*\"wedding\" + 0.030*\"hollywood\" + 0.029*\"afternoon section\" + 0.028*\"infidelity\" + 0.026*\"want to own\" + 0.024*\"prostitution\"\n", - "2021-08-24 02:38:45,149 : INFO : topic diff=0.036162, rho=0.240143\n", - "2021-08-24 02:38:45,150 : INFO : PROGRESS: pass 11, at document #6000/10681\n", - "2021-08-24 02:38:45,383 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:45,410 : INFO : topic #37 (0.020): 0.056*\"easily confused with other movie(s) (title)\" + 0.043*\"vietnam war\" + 0.042*\"oscar (best supporting actress)\" + 0.039*\"want to see again\" + 0.038*\"vietnam\" + 0.032*\"food\" + 0.028*\"j netflix\" + 0.024*\"19th century\" + 0.020*\"peter sellers\" + 0.020*\"alcoholism\"\n", - "2021-08-24 02:38:45,411 : INFO : topic #18 (0.020): 0.055*\"franchise\" + 0.051*\"creepy\" + 0.042*\"slasher\" + 0.041*\"surrealism\" + 0.024*\"lurid\" + 0.024*\"robert downey jr\" + 0.022*\"united states\" + 0.021*\"unique\" + 0.020*\"no rec?\" + 0.020*\"goth\"\n", - "2021-08-24 02:38:45,412 : INFO : topic #47 (0.020): 0.229*\"less than 300 ratings\" + 0.130*\"Drama\" + 0.068*\"nudity (rear)\" + 0.042*\"jackie chan\" + 0.027*\"kung fu\" + 0.025*\"football\" + 0.019*\"brian de palma\" + 0.019*\"denzel washington\" + 0.016*\"oppl\" + 0.016*\"bechdel test:pass\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:45,413 : INFO : topic #5 (0.020): 0.223*\"Action\" + 0.197*\"Thriller\" + 0.174*\"Horror\" + 0.134*\"Sci-Fi\" + 0.064*\"Adventure\" + 0.042*\"betamax\" + 0.020*\"can't remember\" + 0.012*\"70mm\" + 0.009*\"courtroom\" + 0.008*\"dvd-video\"\n", - "2021-08-24 02:38:45,414 : INFO : topic #49 (0.020): 0.102*\"anime\" + 0.064*\"psychology\" + 0.055*\"serial killer\" + 0.053*\"teen\" + 0.047*\"high school\" + 0.019*\"mental illness\" + 0.018*\"japan\" + 0.016*\"anthony hopkins\" + 0.016*\"cannibalism\" + 0.013*\"jodie foster\"\n", - "2021-08-24 02:38:45,415 : INFO : topic diff=0.069876, rho=0.240143\n", - "2021-08-24 02:38:45,417 : INFO : PROGRESS: pass 11, at document #8000/10681\n", - "2021-08-24 02:38:45,655 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:45,683 : INFO : topic #49 (0.020): 0.118*\"anime\" + 0.061*\"psychology\" + 0.051*\"high school\" + 0.051*\"serial killer\" + 0.050*\"teen\" + 0.021*\"japan\" + 0.018*\"mental illness\" + 0.013*\"cannibalism\" + 0.013*\"steve martin\" + 0.013*\"anthony hopkins\"\n", - "2021-08-24 02:38:45,685 : INFO : topic #47 (0.020): 0.296*\"less than 300 ratings\" + 0.143*\"Drama\" + 0.059*\"nudity (rear)\" + 0.037*\"jackie chan\" + 0.024*\"kung fu\" + 0.022*\"football\" + 0.016*\"brian de palma\" + 0.015*\"denzel washington\" + 0.013*\"oppl\" + 0.012*\"bechdel test:pass\"\n", - "2021-08-24 02:38:45,686 : INFO : topic #18 (0.020): 0.053*\"creepy\" + 0.047*\"franchise\" + 0.046*\"surrealism\" + 0.035*\"slasher\" + 0.028*\"lurid\" + 0.024*\"adolescence\" + 0.023*\"robert downey jr\" + 0.021*\"goth\" + 0.021*\"unique\" + 0.020*\"united states\"\n", - "2021-08-24 02:38:45,686 : INFO : topic #5 (0.020): 0.225*\"Action\" + 0.197*\"Thriller\" + 0.171*\"Horror\" + 0.134*\"Sci-Fi\" + 0.066*\"Adventure\" + 0.040*\"betamax\" + 0.017*\"can't remember\" + 0.012*\"70mm\" + 0.010*\"courtroom\" + 0.009*\"vhs\"\n", - "2021-08-24 02:38:45,687 : INFO : topic #3 (0.020): 0.257*\"nudity (topless)\" + 0.097*\"zombies\" + 0.038*\"nudity (topless - brief)\" + 0.032*\"virtual reality\" + 0.028*\"zombie\" + 0.028*\"ingmar bergman\" + 0.019*\"keira knightley\" + 0.016*\"robert altman\" + 0.013*\"futuristic\" + 0.011*\"gruesome\"\n", - "2021-08-24 02:38:45,688 : INFO : topic diff=0.054763, rho=0.240143\n", - "2021-08-24 02:38:45,690 : INFO : PROGRESS: pass 11, at document #10000/10681\n", - "2021-08-24 02:38:45,957 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:45,984 : INFO : topic #36 (0.020): 0.093*\"dark comedy\" + 0.073*\"black comedy\" + 0.067*\"bechdel test:fail\" + 0.036*\"nazis\" + 0.036*\"funniest movies\" + 0.029*\"cold war\" + 0.024*\"existentialism\" + 0.020*\"communism\" + 0.016*\"spies\" + 0.015*\"author:charles dickens\"\n", - "2021-08-24 02:38:45,985 : INFO : topic #31 (0.020): 0.093*\"james bond\" + 0.092*\"murder\" + 0.056*\"007\" + 0.049*\"bond\" + 0.035*\"assassin\" + 0.031*\"claymation\" + 0.022*\"Thriller\" + 0.021*\"aardman\" + 0.020*\"Action\" + 0.019*\"franchise\"\n", - "2021-08-24 02:38:45,986 : INFO : topic #26 (0.020): 0.166*\"classic\" + 0.132*\"oscar (best picture)\" + 0.055*\"oscar (best directing)\" + 0.054*\"oscar (best supporting actor)\" + 0.038*\"matt damon\" + 0.022*\"afi 100 (cheers)\" + 0.020*\"afi 100\" + 0.020*\"oscar (best supporting actress)\" + 0.015*\"national film registry\" + 0.015*\"submarine\"\n", - "2021-08-24 02:38:45,986 : INFO : topic #30 (0.020): 0.633*\"Drama\" + 0.042*\"Film-Noir\" + 0.014*\"tumey's dvds\" + 0.010*\"small town\" + 0.010*\"witch\" + 0.008*\"glbt\" + 0.008*\"film noir\" + 0.008*\"erlend's dvds\" + 0.008*\"boring\" + 0.007*\"oscar (best foreign language film)\"\n", - "2021-08-24 02:38:45,987 : INFO : topic #16 (0.020): 0.153*\"sci-fi\" + 0.087*\"dystopia\" + 0.059*\"Sci-Fi\" + 0.057*\"futuristmovies.com\" + 0.032*\"future\" + 0.032*\"military\" + 0.018*\"joaquin phoenix\" + 0.016*\"weird\" + 0.012*\"gfei own it\" + 0.011*\"court\"\n", - "2021-08-24 02:38:45,988 : INFO : topic diff=0.039063, rho=0.240143\n", - "2021-08-24 02:38:46,139 : INFO : -20.983 per-word bound, 2073255.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:46,139 : INFO : PROGRESS: pass 11, at document #10681/10681\n", - "2021-08-24 02:38:46,242 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:46,268 : INFO : topic #18 (0.020): 0.042*\"creepy\" + 0.036*\"united states\" + 0.035*\"surrealism\" + 0.034*\"franchise\" + 0.033*\"unique\" + 0.028*\"robert downey jr\" + 0.025*\"lurid\" + 0.022*\"slasher\" + 0.018*\"macabre\" + 0.018*\"hitman\"\n", - "2021-08-24 02:38:46,269 : INFO : topic #8 (0.020): 0.087*\"space\" + 0.048*\"christianity\" + 0.030*\"marx brothers\" + 0.028*\"harrison ford\" + 0.027*\"archaeology\" + 0.020*\"kevin smith\" + 0.020*\"1970s\" + 0.018*\"western\" + 0.017*\"werewolves\" + 0.017*\"spaghetti western\"\n", - "2021-08-24 02:38:46,270 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.042*\"tense\" + 0.042*\"revenge\" + 0.032*\"clint eastwood\" + 0.029*\"forceful\" + 0.029*\"gritty\" + 0.028*\"corvallis library\" + 0.025*\"atmospheric\" + 0.023*\"Action\" + 0.023*\"akira kurosawa\"\n", - "2021-08-24 02:38:46,271 : INFO : topic #9 (0.020): 0.260*\"r\" + 0.134*\"clearplay\" + 0.084*\"Western\" + 0.076*\"nudity (topless - brief)\" + 0.066*\"Drama\" + 0.041*\"pg13\" + 0.028*\"to see\" + 0.025*\"Thriller\" + 0.020*\"movie to see\" + 0.016*\"russell crowe\"\n", - "2021-08-24 02:38:46,272 : INFO : topic #41 (0.020): 0.263*\"based on a book\" + 0.106*\"adapted from:book\" + 0.088*\"netflix\" + 0.074*\"Drama\" + 0.068*\"nudity (full frontal)\" + 0.035*\"vampire\" + 0.033*\"robin williams\" + 0.026*\"vampires\" + 0.011*\"book\" + 0.009*\"drinking\"\n", - "2021-08-24 02:38:46,273 : INFO : topic diff=0.093864, rho=0.240143\n", - "2021-08-24 02:38:46,275 : INFO : PROGRESS: pass 12, at document #2000/10681\n", - "2021-08-24 02:38:46,547 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:46,574 : INFO : topic #23 (0.020): 0.085*\"mafia\" + 0.038*\"al pacino\" + 0.034*\"robert de niro\" + 0.026*\"marlon brando\" + 0.023*\"guns\" + 0.020*\"poverty\" + 0.019*\"scifi\" + 0.019*\"francis ford coppola\" + 0.016*\"tumey's dvds\" + 0.016*\"francis ford copolla\"\n", - "2021-08-24 02:38:46,575 : INFO : topic #35 (0.020): 0.164*\"comedy\" + 0.127*\"Comedy\" + 0.079*\"funny\" + 0.047*\"parody\" + 0.040*\"hilarious\" + 0.025*\"can't remember\" + 0.024*\"stupid\" + 0.020*\"afi 100 (laughs)\" + 0.020*\"spoof\" + 0.019*\"ummarti2006\"\n", - "2021-08-24 02:38:46,576 : INFO : topic #13 (0.020): 0.160*\"time travel\" + 0.068*\"twist ending\" + 0.058*\"holocaust\" + 0.031*\"bill murray\" + 0.026*\"post-apocalyptic\" + 0.025*\"good dialogue\" + 0.024*\"post apocalyptic\" + 0.018*\"suspense\" + 0.016*\"liam neeson\" + 0.015*\"mindfuck\"\n", - "2021-08-24 02:38:46,577 : INFO : topic #24 (0.020): 0.059*\"tom cruise\" + 0.035*\"golden palm\" + 0.035*\"france\" + 0.034*\"dustin hoffman\" + 0.029*\"french\" + 0.027*\"on dvr\" + 0.026*\"watch\" + 0.025*\"disability\" + 0.023*\"nicole kidman\" + 0.021*\"sweet\"\n", - "2021-08-24 02:38:46,578 : INFO : topic #32 (0.020): 0.094*\"based on a tv show\" + 0.085*\"jim carrey\" + 0.061*\"keanu reeves\" + 0.049*\"video game adaptation\" + 0.042*\"star trek\" + 0.040*\"england\" + 0.039*\"tv\" + 0.033*\"memory\" + 0.032*\"road trip\" + 0.023*\"not funny\"\n", - "2021-08-24 02:38:46,579 : INFO : topic diff=0.191544, rho=0.233504\n", - "2021-08-24 02:38:46,581 : INFO : PROGRESS: pass 12, at document #4000/10681\n", - "2021-08-24 02:38:46,836 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:46,864 : INFO : topic #23 (0.020): 0.076*\"mafia\" + 0.044*\"al pacino\" + 0.037*\"robert de niro\" + 0.022*\"marlon brando\" + 0.022*\"guns\" + 0.020*\"poverty\" + 0.018*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.017*\"eddie murphy\" + 0.016*\"nudity (full frontal - brief)\"\n", - "2021-08-24 02:38:46,865 : INFO : topic #10 (0.020): 0.431*\"Comedy\" + 0.236*\"Romance\" + 0.197*\"Drama\" + 0.013*\"bibliothek\" + 0.010*\"betamax\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.004*\"owen wilson\" + 0.004*\"london\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:38:46,866 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.055*\"Action\" + 0.051*\"adventure\" + 0.035*\"sequel\" + 0.032*\"Adventure\" + 0.030*\"robots\" + 0.030*\"arnold schwarzenegger\" + 0.023*\"boring\" + 0.022*\"death\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:38:46,868 : INFO : topic #40 (0.020): 0.120*\"nudity (full frontal - notable)\" + 0.076*\"magic\" + 0.072*\"edward norton\" + 0.062*\"sports\" + 0.050*\"not corv lib\" + 0.047*\"baseball\" + 0.036*\"hw drama\" + 0.031*\"inspirational\" + 0.025*\"christopher walken\" + 0.023*\"compassionate\"\n", - "2021-08-24 02:38:46,869 : INFO : topic #44 (0.020): 0.117*\"Children\" + 0.109*\"Adventure\" + 0.098*\"Fantasy\" + 0.079*\"disney\" + 0.063*\"Animation\" + 0.060*\"Comedy\" + 0.039*\"animation\" + 0.035*\"fantasy\" + 0.020*\"children\" + 0.016*\"disney animated feature\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:46,870 : INFO : topic diff=0.034787, rho=0.233504\n", - "2021-08-24 02:38:46,872 : INFO : PROGRESS: pass 12, at document #6000/10681\n", - "2021-08-24 02:38:47,103 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:47,131 : INFO : topic #19 (0.020): 0.090*\"ghosts\" + 0.058*\"pg-13\" + 0.039*\"julia roberts\" + 0.038*\"jude law\" + 0.036*\"wedding\" + 0.029*\"hollywood\" + 0.028*\"afternoon section\" + 0.027*\"want to own\" + 0.025*\"infidelity\" + 0.021*\"prostitution\"\n", - "2021-08-24 02:38:47,132 : INFO : topic #35 (0.020): 0.171*\"comedy\" + 0.131*\"Comedy\" + 0.080*\"funny\" + 0.052*\"parody\" + 0.038*\"hilarious\" + 0.029*\"can't remember\" + 0.023*\"stupid\" + 0.022*\"afi 100 (laughs)\" + 0.022*\"spoof\" + 0.020*\"funny as hell\"\n", - "2021-08-24 02:38:47,132 : INFO : topic #47 (0.020): 0.232*\"less than 300 ratings\" + 0.131*\"Drama\" + 0.069*\"nudity (rear)\" + 0.042*\"jackie chan\" + 0.028*\"kung fu\" + 0.025*\"football\" + 0.019*\"brian de palma\" + 0.019*\"denzel washington\" + 0.017*\"oppl\" + 0.016*\"bechdel test:pass\"\n", - "2021-08-24 02:38:47,133 : INFO : topic #20 (0.020): 0.126*\"politics\" + 0.073*\"documentary\" + 0.071*\"jack nicholson\" + 0.038*\"terrorism\" + 0.036*\"seen 2008\" + 0.032*\"propaganda\" + 0.031*\"love story\" + 0.030*\"Documentary\" + 0.030*\"aviation\" + 0.027*\"political\"\n", - "2021-08-24 02:38:47,134 : INFO : topic #46 (0.020): 0.059*\"dvd\" + 0.058*\"overrated\" + 0.049*\"seen more than once\" + 0.036*\"owned\" + 0.034*\"samuel l. jackson\" + 0.030*\"imdb top 250\" + 0.026*\"bibliothek\" + 0.025*\"kevin spacey\" + 0.024*\"ridley scott\" + 0.023*\"seen at the cinema\"\n", - "2021-08-24 02:38:47,135 : INFO : topic diff=0.067310, rho=0.233504\n", - "2021-08-24 02:38:47,136 : INFO : PROGRESS: pass 12, at document #8000/10681\n", - "2021-08-24 02:38:47,374 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:47,402 : INFO : topic #17 (0.020): 0.063*\"police\" + 0.055*\"will smith\" + 0.032*\"computers\" + 0.028*\"corruption\" + 0.019*\"milla jovovich\" + 0.019*\"no dialogue\" + 0.017*\"stage\" + 0.017*\"torture\" + 0.016*\"small-town life\" + 0.016*\"action packed\"\n", - "2021-08-24 02:38:47,402 : INFO : topic #25 (0.020): 0.206*\"Musical\" + 0.116*\"70mm\" + 0.067*\"religion\" + 0.060*\"musical\" + 0.027*\"rock and roll\" + 0.024*\"monty python\" + 0.023*\"friendship\" + 0.021*\"music\" + 0.020*\"Drama\" + 0.017*\"trains\"\n", - "2021-08-24 02:38:47,403 : INFO : topic #41 (0.020): 0.248*\"based on a book\" + 0.126*\"adapted from:book\" + 0.067*\"Drama\" + 0.049*\"robin williams\" + 0.046*\"nudity (full frontal)\" + 0.039*\"netflix\" + 0.039*\"vampire\" + 0.038*\"vampires\" + 0.017*\"book\" + 0.011*\"drama\"\n", - "2021-08-24 02:38:47,404 : INFO : topic #43 (0.020): 0.307*\"Crime\" + 0.158*\"Drama\" + 0.110*\"Thriller\" + 0.047*\"nudity (topless - notable)\" + 0.045*\"Mystery\" + 0.022*\"shakespeare\" + 0.021*\"nicolas cage\" + 0.017*\"remade\" + 0.015*\"sean penn\" + 0.015*\"adapted from:play\"\n", - "2021-08-24 02:38:47,405 : INFO : topic #15 (0.020): 0.053*\"racism\" + 0.052*\"stephen king\" + 0.052*\"coen brothers\" + 0.038*\"adultery\" + 0.034*\"espionage\" + 0.025*\"los angeles\" + 0.022*\"notable nudity\" + 0.021*\"ensemble cast\" + 0.021*\"great acting\" + 0.020*\"multiple storylines\"\n", - "2021-08-24 02:38:47,407 : INFO : topic diff=0.053090, rho=0.233504\n", - "2021-08-24 02:38:47,408 : INFO : PROGRESS: pass 12, at document #10000/10681\n", - "2021-08-24 02:38:47,661 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:47,689 : INFO : topic #47 (0.020): 0.336*\"less than 300 ratings\" + 0.151*\"Drama\" + 0.063*\"nudity (rear)\" + 0.028*\"jackie chan\" + 0.019*\"football\" + 0.019*\"kung fu\" + 0.015*\"denzel washington\" + 0.013*\"oppl\" + 0.013*\"brian de palma\" + 0.009*\"bechdel test:pass\"\n", - "2021-08-24 02:38:47,690 : INFO : topic #38 (0.020): 0.085*\"true story\" + 0.066*\"tom hanks\" + 0.061*\"biography\" + 0.052*\"based on a true story\" + 0.042*\"drama\" + 0.036*\"biopic\" + 0.032*\"Drama\" + 0.029*\"pg\" + 0.028*\"historical\" + 0.028*\"paris\"\n", - "2021-08-24 02:38:47,691 : INFO : topic #22 (0.020): 0.084*\"national film registry\" + 0.054*\"classic\" + 0.051*\"oscar (best cinematography)\" + 0.026*\"philip k. dick\" + 0.026*\"imdb top 250\" + 0.026*\"tumey's dvds\" + 0.025*\"black and white\" + 0.023*\"journalism\" + 0.022*\"afi 100\" + 0.022*\"afi 100 (thrills)\"\n", - "2021-08-24 02:38:47,691 : INFO : topic #19 (0.020): 0.102*\"pg-13\" + 0.065*\"ghosts\" + 0.045*\"jude law\" + 0.034*\"wedding\" + 0.030*\"julia roberts\" + 0.028*\"want to own\" + 0.028*\"infidelity\" + 0.023*\"hollywood\" + 0.021*\"prostitution\" + 0.018*\"sam peckinpah\"\n", - "2021-08-24 02:38:47,692 : INFO : topic #36 (0.020): 0.094*\"dark comedy\" + 0.073*\"black comedy\" + 0.067*\"bechdel test:fail\" + 0.036*\"nazis\" + 0.036*\"funniest movies\" + 0.029*\"cold war\" + 0.023*\"existentialism\" + 0.020*\"communism\" + 0.016*\"spies\" + 0.015*\"author:charles dickens\"\n", - "2021-08-24 02:38:47,693 : INFO : topic diff=0.037873, rho=0.233504\n", - "2021-08-24 02:38:47,840 : INFO : -20.982 per-word bound, 2070829.4 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:47,841 : INFO : PROGRESS: pass 12, at document #10681/10681\n", - "2021-08-24 02:38:47,946 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:47,972 : INFO : topic #30 (0.020): 0.660*\"Drama\" + 0.043*\"Film-Noir\" + 0.011*\"tumey's dvds\" + 0.008*\"small town\" + 0.008*\"witch\" + 0.007*\"erlend's dvds\" + 0.007*\"glbt\" + 0.006*\"film noir\" + 0.006*\"animal:dog\" + 0.006*\"flying\"\n", - "2021-08-24 02:38:47,973 : INFO : topic #4 (0.020): 0.104*\"romance\" + 0.060*\"new york city\" + 0.050*\"Romance\" + 0.042*\"chick flick\" + 0.040*\"watched 2006\" + 0.028*\"new york\" + 0.025*\"cars\" + 0.024*\"1980s\" + 0.023*\"sean connery\" + 0.021*\"girlie movie\"\n", - "2021-08-24 02:38:47,973 : INFO : topic #6 (0.020): 0.093*\"martial arts\" + 0.032*\"campy\" + 0.032*\"gothic\" + 0.031*\"seen 2006\" + 0.027*\"australia\" + 0.024*\"silly\" + 0.024*\"australian\" + 0.020*\"cult film\" + 0.019*\"freedom\" + 0.018*\"cult classic\"\n", - "2021-08-24 02:38:47,974 : INFO : topic #19 (0.020): 0.101*\"pg-13\" + 0.063*\"ghosts\" + 0.042*\"jude law\" + 0.040*\"wedding\" + 0.032*\"julia roberts\" + 0.030*\"infidelity\" + 0.024*\"want to own\" + 0.021*\"prostitution\" + 0.020*\"hollywood\" + 0.016*\"scope\"\n", - "2021-08-24 02:38:47,975 : INFO : topic #24 (0.020): 0.046*\"tom cruise\" + 0.038*\"france\" + 0.032*\"disability\" + 0.032*\"golden palm\" + 0.031*\"nicole kidman\" + 0.029*\"on dvr\" + 0.027*\"french\" + 0.026*\"dustin hoffman\" + 0.021*\"mask\" + 0.018*\"bank robbery\"\n", - "2021-08-24 02:38:47,976 : INFO : topic diff=0.091244, rho=0.233504\n", - "2021-08-24 02:38:47,977 : INFO : PROGRESS: pass 13, at document #2000/10681\n", - "2021-08-24 02:38:48,241 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:48,271 : INFO : topic #19 (0.020): 0.069*\"pg-13\" + 0.065*\"ghosts\" + 0.039*\"wedding\" + 0.038*\"julia roberts\" + 0.035*\"jude law\" + 0.033*\"hollywood\" + 0.029*\"want to own\" + 0.024*\"afternoon section\" + 0.024*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:48,272 : INFO : topic #27 (0.020): 0.154*\"aliens\" + 0.093*\"steven spielberg\" + 0.053*\"family\" + 0.036*\"michael crichton\" + 0.025*\"childhood\" + 0.024*\"to-rent\" + 0.024*\"gambling\" + 0.022*\"alien\" + 0.019*\"didn't finish\" + 0.017*\"mother-son relationship\"\n", - "2021-08-24 02:38:48,273 : INFO : topic #25 (0.020): 0.192*\"Musical\" + 0.083*\"musical\" + 0.079*\"70mm\" + 0.065*\"religion\" + 0.030*\"friendship\" + 0.024*\"music\" + 0.024*\"rock and roll\" + 0.022*\"monty python\" + 0.018*\"Drama\" + 0.016*\"trains\"\n", - "2021-08-24 02:38:48,274 : INFO : topic #17 (0.020): 0.068*\"will smith\" + 0.055*\"police\" + 0.037*\"corruption\" + 0.029*\"computers\" + 0.022*\"torture\" + 0.021*\"sandra bullock\" + 0.021*\"milla jovovich\" + 0.020*\"action packed\" + 0.017*\"luc besson\" + 0.015*\"no dialogue\"\n", - "2021-08-24 02:38:48,274 : INFO : topic #21 (0.020): 0.091*\"Drama\" + 0.047*\"criterion\" + 0.028*\"library\" + 0.023*\"tumey's dvds\" + 0.021*\"irreverent\" + 0.021*\"sven's to see list\" + 0.021*\"atmospheric\" + 0.020*\"deliberate\" + 0.020*\"reflective\" + 0.020*\"satirical\"\n", - "2021-08-24 02:38:48,276 : INFO : topic diff=0.185286, rho=0.227387\n", - "2021-08-24 02:38:48,277 : INFO : PROGRESS: pass 13, at document #4000/10681\n", - "2021-08-24 02:38:48,533 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:48,561 : INFO : topic #27 (0.020): 0.156*\"aliens\" + 0.082*\"steven spielberg\" + 0.058*\"family\" + 0.037*\"michael crichton\" + 0.025*\"gambling\" + 0.024*\"alien\" + 0.023*\"childhood\" + 0.022*\"poker\" + 0.022*\"to-rent\" + 0.021*\"didn't finish\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:48,562 : INFO : topic #16 (0.020): 0.178*\"sci-fi\" + 0.088*\"dystopia\" + 0.069*\"futuristmovies.com\" + 0.060*\"Sci-Fi\" + 0.032*\"military\" + 0.026*\"future\" + 0.013*\"saturn award (best science fiction film)\" + 0.013*\"court\" + 0.013*\"joaquin phoenix\" + 0.012*\"sf\"\n", - "2021-08-24 02:38:48,563 : INFO : topic #45 (0.020): 0.043*\"tense\" + 0.040*\"japan\" + 0.039*\"clint eastwood\" + 0.030*\"revenge\" + 0.029*\"atmospheric\" + 0.029*\"forceful\" + 0.028*\"gritty\" + 0.024*\"Action\" + 0.024*\"sweeping\" + 0.024*\"samurai\"\n", - "2021-08-24 02:38:48,564 : INFO : topic #40 (0.020): 0.120*\"nudity (full frontal - notable)\" + 0.077*\"magic\" + 0.072*\"edward norton\" + 0.062*\"sports\" + 0.050*\"not corv lib\" + 0.047*\"baseball\" + 0.036*\"hw drama\" + 0.031*\"inspirational\" + 0.025*\"christopher walken\" + 0.024*\"compassionate\"\n", - "2021-08-24 02:38:48,564 : INFO : topic #21 (0.020): 0.089*\"Drama\" + 0.049*\"criterion\" + 0.027*\"library\" + 0.025*\"tumey's dvds\" + 0.023*\"reflective\" + 0.022*\"atmospheric\" + 0.022*\"dvd-video\" + 0.021*\"satirical\" + 0.021*\"sven's to see list\" + 0.021*\"deliberate\"\n", - "2021-08-24 02:38:48,566 : INFO : topic diff=0.033530, rho=0.227387\n", - "2021-08-24 02:38:48,567 : INFO : PROGRESS: pass 13, at document #6000/10681\n", - "2021-08-24 02:38:48,797 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:48,824 : INFO : topic #25 (0.020): 0.196*\"Musical\" + 0.109*\"70mm\" + 0.073*\"musical\" + 0.064*\"religion\" + 0.027*\"rock and roll\" + 0.025*\"friendship\" + 0.024*\"music\" + 0.018*\"adapted from b'way\" + 0.018*\"monty python\" + 0.016*\"Drama\"\n", - "2021-08-24 02:38:48,825 : INFO : topic #28 (0.020): 0.120*\"pixar\" + 0.091*\"oscar (best actress)\" + 0.035*\"mockumentary\" + 0.027*\"dinosaurs\" + 0.025*\"medieval\" + 0.023*\"nostalgia\" + 0.021*\"computer animation\" + 0.020*\"elijah wood\" + 0.019*\"high fantasy\" + 0.017*\"cgi\"\n", - "2021-08-24 02:38:48,826 : INFO : topic #3 (0.020): 0.298*\"nudity (topless)\" + 0.080*\"zombies\" + 0.052*\"nudity (topless - brief)\" + 0.026*\"virtual reality\" + 0.023*\"zombie\" + 0.016*\"keira knightley\" + 0.015*\"robert altman\" + 0.014*\"ingmar bergman\" + 0.013*\"futuristic\" + 0.011*\"gruesome\"\n", - "2021-08-24 02:38:48,827 : INFO : topic #40 (0.020): 0.118*\"nudity (full frontal - notable)\" + 0.093*\"magic\" + 0.072*\"edward norton\" + 0.056*\"sports\" + 0.050*\"baseball\" + 0.049*\"not corv lib\" + 0.041*\"hw drama\" + 0.029*\"compassionate\" + 0.025*\"christopher walken\" + 0.024*\"inspirational\"\n", - "2021-08-24 02:38:48,828 : INFO : topic #42 (0.020): 0.149*\"johnny depp\" + 0.079*\"tim burton\" + 0.055*\"martin scorsese\" + 0.039*\"narrated\" + 0.037*\"george clooney\" + 0.029*\"ei muista\" + 0.025*\"monster\" + 0.018*\"brothers\" + 0.018*\"alien invasion\" + 0.014*\"disaster\"\n", - "2021-08-24 02:38:48,829 : INFO : topic diff=0.065113, rho=0.227387\n", - "2021-08-24 02:38:48,830 : INFO : PROGRESS: pass 13, at document #8000/10681\n", - "2021-08-24 02:38:49,079 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:49,106 : INFO : topic #19 (0.020): 0.076*\"ghosts\" + 0.060*\"pg-13\" + 0.036*\"jude law\" + 0.035*\"wedding\" + 0.035*\"julia roberts\" + 0.027*\"want to own\" + 0.026*\"hollywood\" + 0.025*\"sam peckinpah\" + 0.024*\"infidelity\" + 0.023*\"afternoon section\"\n", - "2021-08-24 02:38:49,107 : INFO : topic #23 (0.020): 0.061*\"mafia\" + 0.039*\"al pacino\" + 0.028*\"robert de niro\" + 0.023*\"guns\" + 0.023*\"poverty\" + 0.023*\"eddie murphy\" + 0.020*\"mst3k\" + 0.019*\"tumey's dvds\" + 0.018*\"marlon brando\" + 0.017*\"nudity (full frontal - brief)\"\n", - "2021-08-24 02:38:49,108 : INFO : topic #10 (0.020): 0.448*\"Comedy\" + 0.234*\"Romance\" + 0.193*\"Drama\" + 0.014*\"bibliothek\" + 0.013*\"betamax\" + 0.004*\"oppl\" + 0.004*\"owen wilson\" + 0.004*\"divorce\" + 0.004*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:38:49,109 : INFO : topic #41 (0.020): 0.248*\"based on a book\" + 0.125*\"adapted from:book\" + 0.067*\"Drama\" + 0.049*\"robin williams\" + 0.046*\"nudity (full frontal)\" + 0.040*\"netflix\" + 0.039*\"vampire\" + 0.037*\"vampires\" + 0.017*\"book\" + 0.011*\"drama\"\n", - "2021-08-24 02:38:49,109 : INFO : topic #36 (0.020): 0.100*\"dark comedy\" + 0.074*\"black comedy\" + 0.072*\"bechdel test:fail\" + 0.037*\"funniest movies\" + 0.035*\"nazis\" + 0.033*\"cold war\" + 0.014*\"russia\" + 0.014*\"communism\" + 0.014*\"author:charles dickens\" + 0.012*\"cav\"\n", - "2021-08-24 02:38:49,111 : INFO : topic diff=0.051747, rho=0.227387\n", - "2021-08-24 02:38:49,112 : INFO : PROGRESS: pass 13, at document #10000/10681\n", - "2021-08-24 02:38:49,362 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:49,390 : INFO : topic #0 (0.020): 0.090*\"crime\" + 0.061*\"violence\" + 0.056*\"brad pitt\" + 0.042*\"violent\" + 0.041*\"Crime\" + 0.037*\"morgan freeman\" + 0.029*\"Thriller\" + 0.024*\"thriller\" + 0.023*\"philip seymour hoffman\" + 0.023*\"organized crime\"\n", - "2021-08-24 02:38:49,391 : INFO : topic #25 (0.020): 0.218*\"Musical\" + 0.104*\"70mm\" + 0.067*\"musical\" + 0.064*\"religion\" + 0.028*\"rock and roll\" + 0.024*\"friendship\" + 0.019*\"music\" + 0.019*\"monty python\" + 0.018*\"trains\" + 0.016*\"adapted from b'way\"\n", - "2021-08-24 02:38:49,392 : INFO : topic #27 (0.020): 0.118*\"aliens\" + 0.071*\"steven spielberg\" + 0.064*\"family\" + 0.040*\"to-rent\" + 0.034*\"gambling\" + 0.031*\"michael crichton\" + 0.023*\"alien\" + 0.022*\"poker\" + 0.020*\"childhood\" + 0.019*\"didn't finish\"\n", - "2021-08-24 02:38:49,393 : INFO : topic #20 (0.020): 0.142*\"politics\" + 0.099*\"documentary\" + 0.054*\"jack nicholson\" + 0.054*\"terrorism\" + 0.039*\"propaganda\" + 0.039*\"political\" + 0.038*\"love story\" + 0.035*\"Documentary\" + 0.035*\"seen 2008\" + 0.027*\"aviation\"\n", - "2021-08-24 02:38:49,393 : INFO : topic #5 (0.020): 0.234*\"Action\" + 0.202*\"Thriller\" + 0.173*\"Horror\" + 0.131*\"Sci-Fi\" + 0.067*\"Adventure\" + 0.032*\"betamax\" + 0.013*\"can't remember\" + 0.009*\"70mm\" + 0.009*\"courtroom\" + 0.007*\"vhs\"\n", - "2021-08-24 02:38:49,395 : INFO : topic diff=0.036834, rho=0.227387\n", - "2021-08-24 02:38:49,540 : INFO : -20.982 per-word bound, 2070509.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:49,541 : INFO : PROGRESS: pass 13, at document #10681/10681\n", - "2021-08-24 02:38:49,637 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:49,664 : INFO : topic #36 (0.020): 0.111*\"bechdel test:fail\" + 0.103*\"dark comedy\" + 0.068*\"black comedy\" + 0.043*\"nazis\" + 0.041*\"cold war\" + 0.028*\"funniest movies\" + 0.018*\"existentialism\" + 0.015*\"communism\" + 0.015*\"author:charles dickens\" + 0.014*\"russia\"\n", - "2021-08-24 02:38:49,664 : INFO : topic #37 (0.020): 0.082*\"easily confused with other movie(s) (title)\" + 0.043*\"j netflix\" + 0.034*\"food\" + 0.031*\"oscar (best supporting actress)\" + 0.027*\"19th century\" + 0.027*\"want to see again\" + 0.025*\"vietnam war\" + 0.025*\"father daughter relationship\" + 0.024*\"slow\" + 0.023*\"vietnam\"\n", - "2021-08-24 02:38:49,665 : INFO : topic #4 (0.020): 0.104*\"romance\" + 0.059*\"new york city\" + 0.050*\"Romance\" + 0.042*\"chick flick\" + 0.039*\"watched 2006\" + 0.030*\"cars\" + 0.028*\"new york\" + 0.024*\"1980s\" + 0.023*\"sean connery\" + 0.021*\"girlie movie\"\n", - "2021-08-24 02:38:49,666 : INFO : topic #34 (0.020): 0.191*\"War\" + 0.137*\"Drama\" + 0.083*\"world war ii\" + 0.069*\"drugs\" + 0.048*\"history\" + 0.042*\"war\" + 0.033*\"Action\" + 0.027*\"prison\" + 0.025*\"london\" + 0.021*\"Adventure\"\n", - "2021-08-24 02:38:49,666 : INFO : topic #12 (0.020): 0.102*\"christmas\" + 0.081*\"quentin tarantino\" + 0.073*\"heist\" + 0.048*\"rape\" + 0.046*\"tarantino\" + 0.045*\"nonlinear\" + 0.035*\"blindfold\" + 0.020*\"robbery\" + 0.019*\"xmas theme\" + 0.018*\"ireland\"\n", - "2021-08-24 02:38:49,668 : INFO : topic diff=0.088608, rho=0.227387\n", - "2021-08-24 02:38:49,669 : INFO : PROGRESS: pass 14, at document #2000/10681\n", - "2021-08-24 02:38:49,929 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:49,955 : INFO : topic #21 (0.020): 0.091*\"Drama\" + 0.047*\"criterion\" + 0.027*\"library\" + 0.024*\"tumey's dvds\" + 0.021*\"irreverent\" + 0.021*\"sven's to see list\" + 0.021*\"atmospheric\" + 0.020*\"dvd-video\" + 0.020*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:38:49,956 : INFO : topic #5 (0.020): 0.244*\"Action\" + 0.209*\"Thriller\" + 0.167*\"Horror\" + 0.128*\"Sci-Fi\" + 0.065*\"Adventure\" + 0.024*\"betamax\" + 0.017*\"can't remember\" + 0.008*\"70mm\" + 0.007*\"courtroom\" + 0.006*\"dvd-video\"\n", - "2021-08-24 02:38:49,957 : INFO : topic #3 (0.020): 0.314*\"nudity (topless)\" + 0.090*\"zombies\" + 0.062*\"nudity (topless - brief)\" + 0.024*\"keira knightley\" + 0.017*\"zombie\" + 0.016*\"virtual reality\" + 0.016*\"ingmar bergman\" + 0.013*\"futuristic\" + 0.012*\"strippers\" + 0.011*\"horror\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:49,958 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.110*\"Adventure\" + 0.103*\"Fantasy\" + 0.082*\"disney\" + 0.061*\"Animation\" + 0.059*\"Comedy\" + 0.041*\"animation\" + 0.038*\"fantasy\" + 0.021*\"children\" + 0.016*\"fairy tale\"\n", - "2021-08-24 02:38:49,959 : INFO : topic #47 (0.020): 0.270*\"less than 300 ratings\" + 0.141*\"Drama\" + 0.065*\"nudity (rear)\" + 0.029*\"jackie chan\" + 0.027*\"kung fu\" + 0.026*\"oppl\" + 0.020*\"denzel washington\" + 0.018*\"football\" + 0.018*\"aids\" + 0.014*\"bechdel test:pass\"\n", - "2021-08-24 02:38:49,960 : INFO : topic diff=0.179919, rho=0.221727\n", - "2021-08-24 02:38:49,962 : INFO : PROGRESS: pass 14, at document #4000/10681\n", - "2021-08-24 02:38:50,225 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:50,253 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.056*\"Action\" + 0.050*\"adventure\" + 0.036*\"sequel\" + 0.033*\"Adventure\" + 0.030*\"robots\" + 0.029*\"arnold schwarzenegger\" + 0.023*\"boring\" + 0.022*\"death\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:38:50,254 : INFO : topic #1 (0.020): 0.100*\"remake\" + 0.068*\"hitchcock\" + 0.052*\"alfred hitchcock\" + 0.048*\"horror\" + 0.032*\"tumey's dvds\" + 0.030*\"Thriller\" + 0.023*\"moody\" + 0.021*\"courtroom drama\" + 0.018*\"scary\" + 0.015*\"jonossa\"\n", - "2021-08-24 02:38:50,255 : INFO : topic #35 (0.020): 0.171*\"comedy\" + 0.128*\"Comedy\" + 0.080*\"funny\" + 0.050*\"parody\" + 0.039*\"hilarious\" + 0.030*\"can't remember\" + 0.025*\"stupid\" + 0.022*\"spoof\" + 0.022*\"afi 100 (laughs)\" + 0.020*\"funny as hell\"\n", - "2021-08-24 02:38:50,256 : INFO : topic #28 (0.020): 0.125*\"pixar\" + 0.094*\"oscar (best actress)\" + 0.042*\"mockumentary\" + 0.029*\"medieval\" + 0.028*\"dinosaurs\" + 0.023*\"nostalgia\" + 0.019*\"computer animation\" + 0.019*\"elijah wood\" + 0.017*\"christopher guest\" + 0.017*\"golf\"\n", - "2021-08-24 02:38:50,256 : INFO : topic #10 (0.020): 0.434*\"Comedy\" + 0.236*\"Romance\" + 0.197*\"Drama\" + 0.013*\"bibliothek\" + 0.011*\"betamax\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.004*\"owen wilson\" + 0.003*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:38:50,258 : INFO : topic diff=0.032167, rho=0.221727\n", - "2021-08-24 02:38:50,259 : INFO : PROGRESS: pass 14, at document #6000/10681\n", - "2021-08-24 02:38:50,497 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:50,524 : INFO : topic #37 (0.020): 0.057*\"easily confused with other movie(s) (title)\" + 0.044*\"oscar (best supporting actress)\" + 0.043*\"vietnam war\" + 0.040*\"want to see again\" + 0.038*\"vietnam\" + 0.033*\"food\" + 0.029*\"j netflix\" + 0.024*\"19th century\" + 0.020*\"peter sellers\" + 0.020*\"alcoholism\"\n", - "2021-08-24 02:38:50,525 : INFO : topic #42 (0.020): 0.149*\"johnny depp\" + 0.079*\"tim burton\" + 0.055*\"martin scorsese\" + 0.040*\"narrated\" + 0.037*\"george clooney\" + 0.029*\"ei muista\" + 0.025*\"monster\" + 0.019*\"brothers\" + 0.018*\"alien invasion\" + 0.014*\"disaster\"\n", - "2021-08-24 02:38:50,526 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.046*\"directorial debut\" + 0.045*\"surreal\" + 0.025*\"woody allen\" + 0.023*\"bittersweet\" + 0.023*\"humorous\" + 0.022*\"whimsical\" + 0.020*\"coming of age\" + 0.019*\"underrated\" + 0.019*\"erlend's dvds\"\n", - "2021-08-24 02:38:50,526 : INFO : topic #15 (0.020): 0.059*\"racism\" + 0.059*\"coen brothers\" + 0.057*\"stephen king\" + 0.039*\"adultery\" + 0.032*\"espionage\" + 0.025*\"great acting\" + 0.024*\"los angeles\" + 0.021*\"multiple storylines\" + 0.020*\"notable nudity\" + 0.020*\"ensemble cast\"\n", - "2021-08-24 02:38:50,527 : INFO : topic #30 (0.020): 0.653*\"Drama\" + 0.034*\"Film-Noir\" + 0.012*\"tumey's dvds\" + 0.010*\"small town\" + 0.008*\"italy\" + 0.007*\"erlend's dvds\" + 0.007*\"glbt\" + 0.007*\"oscar (best foreign language film)\" + 0.007*\"film noir\" + 0.007*\"animal:dog\"\n", - "2021-08-24 02:38:50,529 : INFO : topic diff=0.063371, rho=0.221727\n", - "2021-08-24 02:38:50,530 : INFO : PROGRESS: pass 14, at document #8000/10681\n", - "2021-08-24 02:38:50,775 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:50,803 : INFO : topic #39 (0.020): 0.110*\"bruce willis\" + 0.054*\"interesting\" + 0.036*\"love\" + 0.031*\"avi\" + 0.029*\"gerard depardieu\" + 0.027*\"africa\" + 0.024*\"golden raspberry (worst actor)\" + 0.024*\"good\" + 0.023*\"predictable\" + 0.023*\"end of the world\"\n", - "2021-08-24 02:38:50,804 : INFO : topic #0 (0.020): 0.100*\"crime\" + 0.059*\"violence\" + 0.056*\"brad pitt\" + 0.039*\"Crime\" + 0.036*\"violent\" + 0.035*\"morgan freeman\" + 0.027*\"organized crime\" + 0.026*\"Thriller\" + 0.026*\"philip seymour hoffman\" + 0.025*\"drama\"\n", - "2021-08-24 02:38:50,805 : INFO : topic #43 (0.020): 0.309*\"Crime\" + 0.158*\"Drama\" + 0.109*\"Thriller\" + 0.048*\"nudity (topless - notable)\" + 0.044*\"Mystery\" + 0.023*\"shakespeare\" + 0.021*\"nicolas cage\" + 0.017*\"remade\" + 0.015*\"sean penn\" + 0.015*\"adapted from:play\"\n", - "2021-08-24 02:38:50,806 : INFO : topic #30 (0.020): 0.654*\"Drama\" + 0.035*\"Film-Noir\" + 0.013*\"tumey's dvds\" + 0.010*\"small town\" + 0.009*\"oscar (best foreign language film)\" + 0.008*\"erlend's dvds\" + 0.008*\"italy\" + 0.007*\"witch\" + 0.006*\"elegant\" + 0.006*\"glbt\"\n", - "2021-08-24 02:38:50,807 : INFO : topic #48 (0.020): 0.051*\"quirky\" + 0.047*\"surreal\" + 0.043*\"directorial debut\" + 0.027*\"bittersweet\" + 0.026*\"woody allen\" + 0.023*\"humorous\" + 0.020*\"whimsical\" + 0.020*\"erlend's dvds\" + 0.019*\"peter jackson\" + 0.019*\"coming of age\"\n", - "2021-08-24 02:38:50,808 : INFO : topic diff=0.050241, rho=0.221727\n", - "2021-08-24 02:38:50,810 : INFO : PROGRESS: pass 14, at document #10000/10681\n", - "2021-08-24 02:38:51,052 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:51,081 : INFO : topic #23 (0.020): 0.057*\"mafia\" + 0.039*\"al pacino\" + 0.027*\"robert de niro\" + 0.026*\"guns\" + 0.025*\"poverty\" + 0.023*\"michael caine\" + 0.020*\"marlon brando\" + 0.019*\"tumey's dvds\" + 0.018*\"eddie murphy\" + 0.017*\"scifi\"\n", - "2021-08-24 02:38:51,082 : INFO : topic #12 (0.020): 0.096*\"quentin tarantino\" + 0.088*\"christmas\" + 0.070*\"heist\" + 0.055*\"tarantino\" + 0.053*\"nonlinear\" + 0.041*\"rape\" + 0.033*\"blindfold\" + 0.021*\"ireland\" + 0.019*\"terry gilliam\" + 0.019*\"xmas theme\"\n", - "2021-08-24 02:38:51,082 : INFO : topic #46 (0.020): 0.077*\"overrated\" + 0.063*\"dvd\" + 0.050*\"seen more than once\" + 0.041*\"owned\" + 0.035*\"samuel l. jackson\" + 0.029*\"imdb top 250\" + 0.027*\"bibliothek\" + 0.026*\"eric's dvds\" + 0.022*\"seen at the cinema\" + 0.022*\"tumey's dvds\"\n", - "2021-08-24 02:38:51,083 : INFO : topic #35 (0.020): 0.153*\"comedy\" + 0.132*\"Comedy\" + 0.086*\"funny\" + 0.047*\"parody\" + 0.039*\"hilarious\" + 0.030*\"ummarti2006\" + 0.023*\"stupid\" + 0.023*\"can't remember\" + 0.020*\"spoof\" + 0.020*\"afi 100 (laughs)\"\n", - "2021-08-24 02:38:51,084 : INFO : topic #48 (0.020): 0.047*\"quirky\" + 0.043*\"surreal\" + 0.040*\"directorial debut\" + 0.027*\"bittersweet\" + 0.024*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"erlend's dvds\" + 0.018*\"sad\" + 0.018*\"peter jackson\"\n", - "2021-08-24 02:38:51,086 : INFO : topic diff=0.035886, rho=0.221727\n", - "2021-08-24 02:38:51,238 : INFO : -20.982 per-word bound, 2070468.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:51,239 : INFO : PROGRESS: pass 14, at document #10681/10681\n", - "2021-08-24 02:38:51,339 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:51,366 : INFO : topic #1 (0.020): 0.150*\"remake\" + 0.039*\"horror\" + 0.033*\"hitchcock\" + 0.029*\"Thriller\" + 0.027*\"jonossa\" + 0.026*\"tumey's dvds\" + 0.025*\"alfred hitchcock\" + 0.024*\"moody\" + 0.018*\"courtroom drama\" + 0.015*\"scary\"\n", - "2021-08-24 02:38:51,367 : INFO : topic #48 (0.020): 0.042*\"quirky\" + 0.039*\"surreal\" + 0.038*\"directorial debut\" + 0.026*\"bittersweet\" + 0.023*\"woody allen\" + 0.020*\"humorous\" + 0.020*\"library on hold\" + 0.020*\"sad\" + 0.019*\"underrated\" + 0.019*\"whimsical\"\n", - "2021-08-24 02:38:51,367 : INFO : topic #24 (0.020): 0.046*\"tom cruise\" + 0.038*\"france\" + 0.032*\"golden palm\" + 0.032*\"nicole kidman\" + 0.031*\"disability\" + 0.030*\"on dvr\" + 0.028*\"french\" + 0.026*\"dustin hoffman\" + 0.021*\"mask\" + 0.018*\"bank robbery\"\n", - "2021-08-24 02:38:51,368 : INFO : topic #31 (0.020): 0.118*\"murder\" + 0.075*\"james bond\" + 0.047*\"assassin\" + 0.045*\"007\" + 0.045*\"bond\" + 0.025*\"claymation\" + 0.025*\"Thriller\" + 0.025*\"Action\" + 0.021*\"killer-as-protagonist\" + 0.019*\"aardman\"\n", - "2021-08-24 02:38:51,368 : INFO : topic #11 (0.020): 0.302*\"Documentary\" + 0.199*\"to see\" + 0.061*\"in netflix queue\" + 0.060*\"gay\" + 0.025*\"oscar (best actor)\" + 0.025*\"india\" + 0.020*\"IMAX\" + 0.019*\"homosexuality\" + 0.014*\"matter-of-fact\" + 0.011*\"mexico\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:51,370 : INFO : topic diff=0.086096, rho=0.221727\n", - "2021-08-24 02:38:51,371 : INFO : PROGRESS: pass 15, at document #2000/10681\n", - "2021-08-24 02:38:51,619 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:51,646 : INFO : topic #31 (0.020): 0.096*\"murder\" + 0.061*\"james bond\" + 0.044*\"assassin\" + 0.042*\"bond\" + 0.040*\"claymation\" + 0.039*\"007\" + 0.028*\"aardman\" + 0.021*\"Thriller\" + 0.021*\"Action\" + 0.021*\"killer-as-protagonist\"\n", - "2021-08-24 02:38:51,646 : INFO : topic #33 (0.020): 0.167*\"comic book\" + 0.130*\"superhero\" + 0.064*\"super-hero\" + 0.039*\"adapted from:comic\" + 0.038*\"batman\" + 0.037*\"gene hackman\" + 0.026*\"action\" + 0.025*\"Action\" + 0.019*\"alter ego\" + 0.019*\"based on a comic\"\n", - "2021-08-24 02:38:51,647 : INFO : topic #17 (0.020): 0.067*\"will smith\" + 0.055*\"police\" + 0.037*\"corruption\" + 0.032*\"sandra bullock\" + 0.029*\"computers\" + 0.022*\"torture\" + 0.021*\"milla jovovich\" + 0.019*\"action packed\" + 0.016*\"luc besson\" + 0.015*\"no dialogue\"\n", - "2021-08-24 02:38:51,648 : INFO : topic #1 (0.020): 0.108*\"remake\" + 0.060*\"hitchcock\" + 0.056*\"alfred hitchcock\" + 0.045*\"horror\" + 0.027*\"Thriller\" + 0.026*\"tumey's dvds\" + 0.025*\"moody\" + 0.020*\"jonossa\" + 0.018*\"courtroom drama\" + 0.015*\"scary\"\n", - "2021-08-24 02:38:51,649 : INFO : topic #25 (0.020): 0.196*\"Musical\" + 0.083*\"musical\" + 0.083*\"70mm\" + 0.065*\"religion\" + 0.030*\"friendship\" + 0.024*\"rock and roll\" + 0.024*\"music\" + 0.022*\"monty python\" + 0.016*\"trains\" + 0.016*\"adapted from b'way\"\n", - "2021-08-24 02:38:51,651 : INFO : topic diff=0.175367, rho=0.216470\n", - "2021-08-24 02:38:51,652 : INFO : PROGRESS: pass 15, at document #4000/10681\n", - "2021-08-24 02:38:51,909 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:51,936 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.110*\"Adventure\" + 0.101*\"Fantasy\" + 0.079*\"disney\" + 0.064*\"Animation\" + 0.057*\"Comedy\" + 0.041*\"animation\" + 0.037*\"fantasy\" + 0.021*\"children\" + 0.016*\"disney animated feature\"\n", - "2021-08-24 02:38:51,937 : INFO : topic #35 (0.020): 0.171*\"comedy\" + 0.129*\"Comedy\" + 0.080*\"funny\" + 0.050*\"parody\" + 0.039*\"hilarious\" + 0.031*\"can't remember\" + 0.025*\"stupid\" + 0.022*\"spoof\" + 0.021*\"afi 100 (laughs)\" + 0.020*\"funny as hell\"\n", - "2021-08-24 02:38:51,938 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.056*\"Action\" + 0.050*\"adventure\" + 0.036*\"sequel\" + 0.033*\"Adventure\" + 0.030*\"robots\" + 0.029*\"arnold schwarzenegger\" + 0.024*\"boring\" + 0.022*\"death\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:38:51,939 : INFO : topic #3 (0.020): 0.313*\"nudity (topless)\" + 0.075*\"zombies\" + 0.061*\"nudity (topless - brief)\" + 0.030*\"virtual reality\" + 0.020*\"keira knightley\" + 0.014*\"zombie\" + 0.014*\"ingmar bergman\" + 0.013*\"robert altman\" + 0.012*\"futuristic\" + 0.011*\"strippers\"\n", - "2021-08-24 02:38:51,940 : INFO : topic #18 (0.020): 0.055*\"franchise\" + 0.048*\"surrealism\" + 0.039*\"slasher\" + 0.039*\"creepy\" + 0.028*\"robert downey jr\" + 0.028*\"united states\" + 0.024*\"lurid\" + 0.023*\"unique\" + 0.021*\"no rec?\" + 0.020*\"goth\"\n", - "2021-08-24 02:38:51,941 : INFO : topic diff=0.031025, rho=0.216470\n", - "2021-08-24 02:38:51,943 : INFO : PROGRESS: pass 15, at document #6000/10681\n", - "2021-08-24 02:38:52,160 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:52,195 : INFO : topic #21 (0.020): 0.091*\"Drama\" + 0.051*\"criterion\" + 0.026*\"tumey's dvds\" + 0.025*\"library\" + 0.023*\"satirical\" + 0.023*\"sven's to see list\" + 0.023*\"atmospheric\" + 0.023*\"dvd-video\" + 0.022*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:38:52,197 : INFO : topic #4 (0.020): 0.110*\"romance\" + 0.052*\"chick flick\" + 0.051*\"Romance\" + 0.038*\"new york city\" + 0.031*\"sean connery\" + 0.030*\"girlie movie\" + 0.026*\"new york\" + 0.024*\"cars\" + 0.022*\"watched 2006\" + 0.021*\"dance\"\n", - "2021-08-24 02:38:52,197 : INFO : topic #20 (0.020): 0.127*\"politics\" + 0.073*\"documentary\" + 0.070*\"jack nicholson\" + 0.039*\"terrorism\" + 0.037*\"seen 2008\" + 0.033*\"propaganda\" + 0.031*\"love story\" + 0.030*\"Documentary\" + 0.030*\"aviation\" + 0.028*\"political\"\n", - "2021-08-24 02:38:52,198 : INFO : topic #5 (0.020): 0.226*\"Action\" + 0.198*\"Thriller\" + 0.174*\"Horror\" + 0.134*\"Sci-Fi\" + 0.064*\"Adventure\" + 0.041*\"betamax\" + 0.019*\"can't remember\" + 0.011*\"70mm\" + 0.009*\"courtroom\" + 0.008*\"dvd-video\"\n", - "2021-08-24 02:38:52,199 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.081*\"quentin tarantino\" + 0.067*\"nonlinear\" + 0.066*\"heist\" + 0.053*\"tarantino\" + 0.047*\"rape\" + 0.030*\"blindfold\" + 0.024*\"terry gilliam\" + 0.018*\"xmas theme\" + 0.015*\"ireland\"\n", - "2021-08-24 02:38:52,200 : INFO : topic diff=0.061644, rho=0.216470\n", - "2021-08-24 02:38:52,202 : INFO : PROGRESS: pass 15, at document #8000/10681\n", - "2021-08-24 02:38:52,447 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:52,474 : INFO : topic #6 (0.020): 0.093*\"martial arts\" + 0.051*\"cult film\" + 0.042*\"gothic\" + 0.035*\"campy\" + 0.027*\"silly\" + 0.026*\"cult classic\" + 0.025*\"australia\" + 0.025*\"australian\" + 0.021*\"sam raimi\" + 0.020*\"seen 2006\"\n", - "2021-08-24 02:38:52,475 : INFO : topic #24 (0.020): 0.060*\"tom cruise\" + 0.040*\"golden palm\" + 0.038*\"nicole kidman\" + 0.036*\"france\" + 0.031*\"french\" + 0.029*\"on dvr\" + 0.025*\"dustin hoffman\" + 0.023*\"disability\" + 0.020*\"sweet\" + 0.019*\"takeshi kitano\"\n", - "2021-08-24 02:38:52,476 : INFO : topic #39 (0.020): 0.110*\"bruce willis\" + 0.054*\"interesting\" + 0.038*\"love\" + 0.031*\"avi\" + 0.029*\"gerard depardieu\" + 0.027*\"africa\" + 0.024*\"golden raspberry (worst actor)\" + 0.024*\"good\" + 0.023*\"predictable\" + 0.023*\"end of the world\"\n", - "2021-08-24 02:38:52,477 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.045*\"tense\" + 0.038*\"clint eastwood\" + 0.034*\"forceful\" + 0.031*\"gritty\" + 0.029*\"revenge\" + 0.026*\"atmospheric\" + 0.025*\"akira kurosawa\" + 0.023*\"Action\" + 0.022*\"visceral\"\n", - "2021-08-24 02:38:52,478 : INFO : topic #31 (0.020): 0.093*\"murder\" + 0.089*\"james bond\" + 0.061*\"007\" + 0.057*\"bond\" + 0.035*\"assassin\" + 0.025*\"claymation\" + 0.023*\"Action\" + 0.022*\"franchise\" + 0.022*\"Thriller\" + 0.019*\"tolkien\"\n", - "2021-08-24 02:38:52,479 : INFO : topic diff=0.049119, rho=0.216470\n", - "2021-08-24 02:38:52,481 : INFO : PROGRESS: pass 15, at document #10000/10681\n", - "2021-08-24 02:38:52,749 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:52,777 : INFO : topic #12 (0.020): 0.096*\"quentin tarantino\" + 0.089*\"christmas\" + 0.070*\"heist\" + 0.055*\"tarantino\" + 0.053*\"nonlinear\" + 0.041*\"rape\" + 0.033*\"blindfold\" + 0.021*\"ireland\" + 0.019*\"terry gilliam\" + 0.019*\"xmas theme\"\n", - "2021-08-24 02:38:52,778 : INFO : topic #28 (0.020): 0.137*\"pixar\" + 0.084*\"oscar (best actress)\" + 0.040*\"mockumentary\" + 0.029*\"elijah wood\" + 0.027*\"medieval\" + 0.023*\"computer animation\" + 0.023*\"cgi\" + 0.021*\"dinosaurs\" + 0.017*\"nostalgia\" + 0.014*\"family drama\"\n", - "2021-08-24 02:38:52,779 : INFO : topic #9 (0.020): 0.228*\"r\" + 0.100*\"clearplay\" + 0.097*\"Western\" + 0.078*\"nudity (topless - brief)\" + 0.060*\"Drama\" + 0.036*\"pg13\" + 0.023*\"Thriller\" + 0.020*\"british\" + 0.018*\"jane austen\" + 0.017*\"russell crowe\"\n", - "2021-08-24 02:38:52,780 : INFO : topic #18 (0.020): 0.045*\"creepy\" + 0.042*\"surrealism\" + 0.042*\"franchise\" + 0.030*\"lurid\" + 0.028*\"robert downey jr\" + 0.028*\"slasher\" + 0.027*\"united states\" + 0.024*\"unique\" + 0.021*\"macabre\" + 0.020*\"adolescence\"\n", - "2021-08-24 02:38:52,781 : INFO : topic #49 (0.020): 0.130*\"anime\" + 0.059*\"psychology\" + 0.051*\"serial killer\" + 0.051*\"high school\" + 0.044*\"teen\" + 0.022*\"japan\" + 0.016*\"mental illness\" + 0.015*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.013*\"steve martin\"\n", - "2021-08-24 02:38:52,783 : INFO : topic diff=0.035055, rho=0.216470\n", - "2021-08-24 02:38:52,939 : INFO : -20.980 per-word bound, 2068832.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:52,940 : INFO : PROGRESS: pass 15, at document #10681/10681\n", - "2021-08-24 02:38:53,042 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:53,069 : INFO : topic #34 (0.020): 0.191*\"War\" + 0.137*\"Drama\" + 0.084*\"world war ii\" + 0.069*\"drugs\" + 0.044*\"history\" + 0.042*\"war\" + 0.033*\"Action\" + 0.028*\"prison\" + 0.026*\"london\" + 0.020*\"Adventure\"\n", - "2021-08-24 02:38:53,070 : INFO : topic #25 (0.020): 0.220*\"Musical\" + 0.087*\"70mm\" + 0.078*\"musical\" + 0.077*\"religion\" + 0.035*\"friendship\" + 0.026*\"rock and roll\" + 0.021*\"music\" + 0.020*\"trains\" + 0.015*\"monty python\" + 0.015*\"adapted from b'way\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:53,071 : INFO : topic #5 (0.020): 0.246*\"Action\" + 0.209*\"Thriller\" + 0.174*\"Horror\" + 0.138*\"Sci-Fi\" + 0.068*\"Adventure\" + 0.026*\"betamax\" + 0.010*\"can't remember\" + 0.007*\"courtroom\" + 0.007*\"70mm\" + 0.006*\"vhs\"\n", - "2021-08-24 02:38:53,072 : INFO : topic #10 (0.020): 0.439*\"Comedy\" + 0.241*\"Romance\" + 0.201*\"Drama\" + 0.009*\"bibliothek\" + 0.008*\"betamax\" + 0.008*\"oppl\" + 0.006*\"owen wilson\" + 0.005*\"divorce\" + 0.003*\"world war i\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:38:53,073 : INFO : topic #44 (0.020): 0.126*\"Adventure\" + 0.125*\"Fantasy\" + 0.114*\"Children\" + 0.073*\"Animation\" + 0.064*\"Comedy\" + 0.044*\"fantasy\" + 0.044*\"disney\" + 0.041*\"animation\" + 0.019*\"movie to see\" + 0.018*\"children\"\n", - "2021-08-24 02:38:53,074 : INFO : topic diff=0.084423, rho=0.216470\n", - "2021-08-24 02:38:53,075 : INFO : PROGRESS: pass 16, at document #2000/10681\n", - "2021-08-24 02:38:53,334 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:53,360 : INFO : topic #21 (0.020): 0.092*\"Drama\" + 0.048*\"criterion\" + 0.027*\"library\" + 0.023*\"tumey's dvds\" + 0.021*\"irreverent\" + 0.021*\"sven's to see list\" + 0.021*\"atmospheric\" + 0.020*\"dvd-video\" + 0.020*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:38:53,361 : INFO : topic #8 (0.020): 0.113*\"space\" + 0.050*\"harrison ford\" + 0.026*\"christianity\" + 0.025*\"kevin smith\" + 0.025*\"george lucas\" + 0.024*\"archaeology\" + 0.021*\"space opera\" + 0.020*\"fantasy\" + 0.019*\"sergio leone\" + 0.018*\"marx brothers\"\n", - "2021-08-24 02:38:53,362 : INFO : topic #37 (0.020): 0.066*\"easily confused with other movie(s) (title)\" + 0.045*\"vietnam\" + 0.043*\"vietnam war\" + 0.035*\"food\" + 0.034*\"j netflix\" + 0.032*\"want to see again\" + 0.032*\"oscar (best supporting actress)\" + 0.024*\"peter sellers\" + 0.022*\"19th century\" + 0.021*\"slow\"\n", - "2021-08-24 02:38:53,362 : INFO : topic #29 (0.020): 0.144*\"action\" + 0.053*\"Action\" + 0.052*\"adventure\" + 0.036*\"sequel\" + 0.033*\"Adventure\" + 0.031*\"robots\" + 0.026*\"boring\" + 0.026*\"arnold schwarzenegger\" + 0.024*\"death\" + 0.024*\"pirates\"\n", - "2021-08-24 02:38:53,363 : INFO : topic #48 (0.020): 0.045*\"quirky\" + 0.041*\"directorial debut\" + 0.038*\"surreal\" + 0.027*\"woody allen\" + 0.027*\"bittersweet\" + 0.024*\"humorous\" + 0.021*\"underrated\" + 0.018*\"whimsical\" + 0.018*\"witty\" + 0.018*\"erlend's dvds\"\n", - "2021-08-24 02:38:53,364 : INFO : topic diff=0.170628, rho=0.211570\n", - "2021-08-24 02:38:53,366 : INFO : PROGRESS: pass 16, at document #4000/10681\n", - "2021-08-24 02:38:53,620 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:53,647 : INFO : topic #18 (0.020): 0.055*\"franchise\" + 0.048*\"surrealism\" + 0.039*\"slasher\" + 0.039*\"creepy\" + 0.028*\"robert downey jr\" + 0.028*\"united states\" + 0.024*\"lurid\" + 0.023*\"unique\" + 0.021*\"no rec?\" + 0.020*\"goth\"\n", - "2021-08-24 02:38:53,648 : INFO : topic #6 (0.020): 0.087*\"martial arts\" + 0.059*\"cult film\" + 0.046*\"gothic\" + 0.034*\"campy\" + 0.030*\"cult classic\" + 0.027*\"australia\" + 0.026*\"silly\" + 0.019*\"seen 2006\" + 0.019*\"don't remember\" + 0.019*\"australian\"\n", - "2021-08-24 02:38:53,649 : INFO : topic #13 (0.020): 0.148*\"time travel\" + 0.073*\"twist ending\" + 0.059*\"holocaust\" + 0.034*\"bill murray\" + 0.030*\"post apocalyptic\" + 0.029*\"post-apocalyptic\" + 0.020*\"good dialogue\" + 0.018*\"mindfuck\" + 0.017*\"liam neeson\" + 0.016*\"suspense\"\n", - "2021-08-24 02:38:53,650 : INFO : topic #10 (0.020): 0.435*\"Comedy\" + 0.236*\"Romance\" + 0.197*\"Drama\" + 0.013*\"bibliothek\" + 0.011*\"betamax\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.004*\"owen wilson\" + 0.003*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:38:53,651 : INFO : topic #0 (0.020): 0.095*\"crime\" + 0.061*\"brad pitt\" + 0.060*\"violence\" + 0.042*\"Crime\" + 0.037*\"morgan freeman\" + 0.036*\"violent\" + 0.032*\"organized crime\" + 0.030*\"thriller\" + 0.027*\"Thriller\" + 0.027*\"philip seymour hoffman\"\n", - "2021-08-24 02:38:53,652 : INFO : topic diff=0.030098, rho=0.211570\n", - "2021-08-24 02:38:53,654 : INFO : PROGRESS: pass 16, at document #6000/10681\n", - "2021-08-24 02:38:53,894 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:53,923 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.081*\"quentin tarantino\" + 0.067*\"nonlinear\" + 0.066*\"heist\" + 0.053*\"tarantino\" + 0.047*\"rape\" + 0.030*\"blindfold\" + 0.024*\"terry gilliam\" + 0.018*\"xmas theme\" + 0.015*\"ireland\"\n", - "2021-08-24 02:38:53,924 : INFO : topic #6 (0.020): 0.084*\"martial arts\" + 0.057*\"cult film\" + 0.044*\"gothic\" + 0.038*\"campy\" + 0.030*\"silly\" + 0.029*\"cult classic\" + 0.027*\"australia\" + 0.026*\"australian\" + 0.020*\"sam raimi\" + 0.018*\"don't remember\"\n", - "2021-08-24 02:38:53,924 : INFO : topic #43 (0.020): 0.292*\"Crime\" + 0.158*\"Drama\" + 0.106*\"Thriller\" + 0.050*\"nudity (topless - notable)\" + 0.042*\"Mystery\" + 0.026*\"shakespeare\" + 0.023*\"nicolas cage\" + 0.017*\"remade\" + 0.016*\"adapted from:play\" + 0.014*\"sean penn\"\n", - "2021-08-24 02:38:53,925 : INFO : topic #19 (0.020): 0.089*\"ghosts\" + 0.059*\"pg-13\" + 0.039*\"julia roberts\" + 0.038*\"jude law\" + 0.036*\"wedding\" + 0.029*\"hollywood\" + 0.028*\"afternoon section\" + 0.027*\"want to own\" + 0.026*\"infidelity\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:38:53,925 : INFO : topic #35 (0.020): 0.170*\"comedy\" + 0.133*\"Comedy\" + 0.080*\"funny\" + 0.051*\"parody\" + 0.038*\"hilarious\" + 0.033*\"can't remember\" + 0.023*\"stupid\" + 0.022*\"spoof\" + 0.020*\"funny as hell\" + 0.019*\"afi 100 (laughs)\"\n", - "2021-08-24 02:38:53,927 : INFO : topic diff=0.059746, rho=0.211570\n", - "2021-08-24 02:38:53,928 : INFO : PROGRESS: pass 16, at document #8000/10681\n", - "2021-08-24 02:38:54,174 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:54,202 : INFO : topic #11 (0.020): 0.272*\"Documentary\" + 0.141*\"to see\" + 0.077*\"in netflix queue\" + 0.067*\"gay\" + 0.045*\"oscar (best actor)\" + 0.023*\"india\" + 0.020*\"homosexuality\" + 0.019*\"matter-of-fact\" + 0.018*\"IMAX\" + 0.015*\"ian mckellen\"\n", - "2021-08-24 02:38:54,203 : INFO : topic #46 (0.020): 0.074*\"dvd\" + 0.067*\"overrated\" + 0.062*\"seen more than once\" + 0.042*\"owned\" + 0.030*\"samuel l. jackson\" + 0.030*\"bibliothek\" + 0.029*\"imdb top 250\" + 0.025*\"eric's dvds\" + 0.023*\"kevin spacey\" + 0.022*\"seen at the cinema\"\n", - "2021-08-24 02:38:54,204 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.045*\"tense\" + 0.038*\"clint eastwood\" + 0.034*\"forceful\" + 0.031*\"gritty\" + 0.029*\"revenge\" + 0.027*\"atmospheric\" + 0.025*\"akira kurosawa\" + 0.023*\"Action\" + 0.022*\"visceral\"\n", - "2021-08-24 02:38:54,204 : INFO : topic #43 (0.020): 0.308*\"Crime\" + 0.159*\"Drama\" + 0.109*\"Thriller\" + 0.048*\"nudity (topless - notable)\" + 0.045*\"Mystery\" + 0.023*\"shakespeare\" + 0.021*\"nicolas cage\" + 0.017*\"remade\" + 0.015*\"sean penn\" + 0.015*\"adapted from:play\"\n", - "2021-08-24 02:38:54,205 : INFO : topic #2 (0.020): 0.102*\"Comedy\" + 0.095*\"lesbian\" + 0.089*\"satire\" + 0.051*\"imdb bottom 100\" + 0.037*\"sexy\" + 0.029*\"get\" + 0.018*\"ben stiller\" + 0.018*\"olympics\" + 0.017*\"john turturro\" + 0.017*\"1\"\n", - "2021-08-24 02:38:54,206 : INFO : topic diff=0.048158, rho=0.211570\n", - "2021-08-24 02:38:54,208 : INFO : PROGRESS: pass 16, at document #10000/10681\n", - "2021-08-24 02:38:54,455 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:54,484 : INFO : topic #34 (0.020): 0.173*\"War\" + 0.125*\"Drama\" + 0.091*\"world war ii\" + 0.067*\"drugs\" + 0.050*\"war\" + 0.042*\"history\" + 0.033*\"Action\" + 0.028*\"prison\" + 0.026*\"london\" + 0.020*\"Adventure\"\n", - "2021-08-24 02:38:54,484 : INFO : topic #37 (0.020): 0.072*\"easily confused with other movie(s) (title)\" + 0.037*\"oscar (best supporting actress)\" + 0.036*\"j netflix\" + 0.033*\"want to see again\" + 0.030*\"vietnam war\" + 0.029*\"food\" + 0.028*\"vietnam\" + 0.028*\"19th century\" + 0.027*\"slow\" + 0.026*\"peter sellers\"\n", - "2021-08-24 02:38:54,485 : INFO : topic #8 (0.020): 0.091*\"space\" + 0.035*\"marx brothers\" + 0.034*\"harrison ford\" + 0.033*\"christianity\" + 0.022*\"kevin smith\" + 0.021*\"spaghetti western\" + 0.021*\"george lucas\" + 0.021*\"werewolves\" + 0.017*\"space opera\" + 0.017*\"vincent price\"\n", - "2021-08-24 02:38:54,485 : INFO : topic #40 (0.020): 0.123*\"magic\" + 0.110*\"nudity (full frontal - notable)\" + 0.066*\"edward norton\" + 0.057*\"not corv lib\" + 0.057*\"sports\" + 0.040*\"hw drama\" + 0.039*\"baseball\" + 0.030*\"compassionate\" + 0.025*\"don cheadle\" + 0.025*\"inspirational\"\n", - "2021-08-24 02:38:54,486 : INFO : topic #16 (0.020): 0.165*\"sci-fi\" + 0.087*\"dystopia\" + 0.060*\"Sci-Fi\" + 0.059*\"futuristmovies.com\" + 0.032*\"future\" + 0.032*\"military\" + 0.017*\"joaquin phoenix\" + 0.015*\"weird\" + 0.012*\"gfei own it\" + 0.011*\"court\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:54,487 : INFO : topic diff=0.033768, rho=0.211570\n", - "2021-08-24 02:38:54,633 : INFO : -20.978 per-word bound, 2065473.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:54,633 : INFO : PROGRESS: pass 16, at document #10681/10681\n", - "2021-08-24 02:38:54,735 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:54,761 : INFO : topic #5 (0.020): 0.245*\"Action\" + 0.209*\"Thriller\" + 0.173*\"Horror\" + 0.138*\"Sci-Fi\" + 0.068*\"Adventure\" + 0.026*\"betamax\" + 0.011*\"can't remember\" + 0.007*\"courtroom\" + 0.007*\"70mm\" + 0.006*\"vhs\"\n", - "2021-08-24 02:38:54,762 : INFO : topic #9 (0.020): 0.256*\"r\" + 0.130*\"clearplay\" + 0.085*\"Western\" + 0.074*\"nudity (topless - brief)\" + 0.067*\"Drama\" + 0.040*\"pg13\" + 0.027*\"to see\" + 0.025*\"Thriller\" + 0.022*\"movie to see\" + 0.016*\"russell crowe\"\n", - "2021-08-24 02:38:54,763 : INFO : topic #29 (0.020): 0.116*\"action\" + 0.055*\"Action\" + 0.049*\"adventure\" + 0.039*\"sequel\" + 0.035*\"Adventure\" + 0.034*\"robots\" + 0.032*\"pirates\" + 0.030*\"boring\" + 0.030*\"death\" + 0.026*\"motorcycle\"\n", - "2021-08-24 02:38:54,764 : INFO : topic #2 (0.020): 0.111*\"Comedy\" + 0.098*\"satire\" + 0.088*\"lesbian\" + 0.063*\"imdb bottom 100\" + 0.026*\"sexy\" + 0.021*\"get\" + 0.020*\"olympics\" + 0.017*\"adolf hitler\" + 0.016*\"ben stiller\" + 0.016*\"john turturro\"\n", - "2021-08-24 02:38:54,764 : INFO : topic #43 (0.020): 0.320*\"Crime\" + 0.169*\"Drama\" + 0.119*\"Thriller\" + 0.049*\"Mystery\" + 0.041*\"nudity (topless - notable)\" + 0.020*\"nicolas cage\" + 0.018*\"sean penn\" + 0.017*\"shakespeare\" + 0.013*\"remade\" + 0.012*\"adapted from:play\"\n", - "2021-08-24 02:38:54,766 : INFO : topic diff=0.082039, rho=0.211570\n", - "2021-08-24 02:38:54,768 : INFO : PROGRESS: pass 17, at document #2000/10681\n", - "2021-08-24 02:38:55,037 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:55,063 : INFO : topic #15 (0.020): 0.070*\"coen brothers\" + 0.057*\"stephen king\" + 0.052*\"racism\" + 0.037*\"adultery\" + 0.033*\"great acting\" + 0.029*\"espionage\" + 0.025*\"los angeles\" + 0.017*\"ensemble cast\" + 0.016*\"san francisco\" + 0.016*\"notable nudity\"\n", - "2021-08-24 02:38:55,064 : INFO : topic #16 (0.020): 0.195*\"sci-fi\" + 0.084*\"dystopia\" + 0.060*\"futuristmovies.com\" + 0.057*\"Sci-Fi\" + 0.033*\"military\" + 0.031*\"future\" + 0.015*\"saturn award (best science fiction film)\" + 0.014*\"sf\" + 0.011*\"1950s\" + 0.011*\"joaquin phoenix\"\n", - "2021-08-24 02:38:55,064 : INFO : topic #40 (0.020): 0.121*\"nudity (full frontal - notable)\" + 0.098*\"magic\" + 0.052*\"not corv lib\" + 0.050*\"sports\" + 0.048*\"edward norton\" + 0.041*\"baseball\" + 0.037*\"hw drama\" + 0.035*\"inspirational\" + 0.024*\"compassionate\" + 0.024*\"christopher walken\"\n", - "2021-08-24 02:38:55,065 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.110*\"Adventure\" + 0.104*\"Fantasy\" + 0.081*\"disney\" + 0.062*\"Animation\" + 0.057*\"Comedy\" + 0.042*\"animation\" + 0.039*\"fantasy\" + 0.021*\"children\" + 0.016*\"fairy tale\"\n", - "2021-08-24 02:38:55,066 : INFO : topic #24 (0.020): 0.059*\"tom cruise\" + 0.035*\"golden palm\" + 0.035*\"france\" + 0.033*\"dustin hoffman\" + 0.029*\"french\" + 0.027*\"on dvr\" + 0.025*\"watch\" + 0.025*\"disability\" + 0.024*\"nicole kidman\" + 0.021*\"sweet\"\n", - "2021-08-24 02:38:55,067 : INFO : topic diff=0.166545, rho=0.206988\n", - "2021-08-24 02:38:55,069 : INFO : PROGRESS: pass 17, at document #4000/10681\n", - "2021-08-24 02:38:55,301 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:55,329 : INFO : topic #11 (0.020): 0.261*\"Documentary\" + 0.162*\"to see\" + 0.074*\"gay\" + 0.069*\"in netflix queue\" + 0.055*\"oscar (best actor)\" + 0.024*\"india\" + 0.021*\"homosexuality\" + 0.015*\"matter-of-fact\" + 0.015*\"IMAX\" + 0.012*\"ian mckellen\"\n", - "2021-08-24 02:38:55,331 : INFO : topic #49 (0.020): 0.083*\"anime\" + 0.059*\"teen\" + 0.057*\"serial killer\" + 0.057*\"high school\" + 0.057*\"psychology\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.016*\"cult film\" + 0.015*\"japan\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:38:55,332 : INFO : topic #31 (0.020): 0.093*\"murder\" + 0.090*\"james bond\" + 0.062*\"007\" + 0.060*\"bond\" + 0.041*\"assassin\" + 0.031*\"claymation\" + 0.023*\"Action\" + 0.023*\"aardman\" + 0.023*\"franchise\" + 0.023*\"Thriller\"\n", - "2021-08-24 02:38:55,332 : INFO : topic #32 (0.020): 0.093*\"based on a tv show\" + 0.074*\"jim carrey\" + 0.060*\"keanu reeves\" + 0.046*\"england\" + 0.043*\"video game adaptation\" + 0.042*\"tv\" + 0.040*\"star trek\" + 0.037*\"road trip\" + 0.033*\"memory\" + 0.027*\"seen at the cinema\"\n", - "2021-08-24 02:38:55,333 : INFO : topic #21 (0.020): 0.091*\"Drama\" + 0.049*\"criterion\" + 0.027*\"library\" + 0.025*\"tumey's dvds\" + 0.023*\"reflective\" + 0.022*\"atmospheric\" + 0.022*\"dvd-video\" + 0.021*\"sven's to see list\" + 0.021*\"satirical\" + 0.021*\"deliberate\"\n", - "2021-08-24 02:38:55,334 : INFO : topic diff=0.029280, rho=0.206988\n", - "2021-08-24 02:38:55,335 : INFO : PROGRESS: pass 17, at document #6000/10681\n", - "2021-08-24 02:38:55,559 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:55,587 : INFO : topic #1 (0.020): 0.113*\"remake\" + 0.057*\"hitchcock\" + 0.051*\"horror\" + 0.043*\"alfred hitchcock\" + 0.030*\"tumey's dvds\" + 0.030*\"Thriller\" + 0.025*\"moody\" + 0.021*\"courtroom drama\" + 0.020*\"scary\" + 0.015*\"3\"\n", - "2021-08-24 02:38:55,588 : INFO : topic #44 (0.020): 0.114*\"Children\" + 0.109*\"Adventure\" + 0.103*\"Fantasy\" + 0.071*\"disney\" + 0.064*\"Animation\" + 0.054*\"Comedy\" + 0.049*\"fantasy\" + 0.042*\"animation\" + 0.021*\"children\" + 0.017*\"fairy tale\"\n", - "2021-08-24 02:38:55,588 : INFO : topic #40 (0.020): 0.119*\"nudity (full frontal - notable)\" + 0.094*\"magic\" + 0.072*\"edward norton\" + 0.057*\"sports\" + 0.050*\"baseball\" + 0.049*\"not corv lib\" + 0.041*\"hw drama\" + 0.029*\"compassionate\" + 0.025*\"christopher walken\" + 0.025*\"inspirational\"\n", - "2021-08-24 02:38:55,589 : INFO : topic #13 (0.020): 0.163*\"time travel\" + 0.070*\"twist ending\" + 0.061*\"holocaust\" + 0.028*\"bill murray\" + 0.027*\"post apocalyptic\" + 0.026*\"post-apocalyptic\" + 0.022*\"mindfuck\" + 0.018*\"good dialogue\" + 0.017*\"very good\" + 0.017*\"liam neeson\"\n", - "2021-08-24 02:38:55,590 : INFO : topic #7 (0.020): 0.160*\"movie to see\" + 0.071*\"mel gibson\" + 0.051*\"boxing\" + 0.033*\"suburbia\" + 0.031*\"kidnapping\" + 0.029*\"uma thurman\" + 0.029*\"john travolta\" + 0.027*\"sylvester stallone\" + 0.022*\"basketball\" + 0.015*\"intense\"\n", - "2021-08-24 02:38:55,591 : INFO : topic diff=0.058335, rho=0.206988\n", - "2021-08-24 02:38:55,592 : INFO : PROGRESS: pass 17, at document #8000/10681\n", - "2021-08-24 02:38:55,821 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:55,849 : INFO : topic #48 (0.020): 0.051*\"quirky\" + 0.047*\"surreal\" + 0.043*\"directorial debut\" + 0.027*\"bittersweet\" + 0.026*\"woody allen\" + 0.023*\"humorous\" + 0.020*\"whimsical\" + 0.020*\"erlend's dvds\" + 0.019*\"peter jackson\" + 0.019*\"coming of age\"\n", - "2021-08-24 02:38:55,850 : INFO : topic #14 (0.020): 0.112*\"Mystery\" + 0.065*\"Horror\" + 0.058*\"Thriller\" + 0.038*\"disturbing\" + 0.033*\"atmospheric\" + 0.028*\"erlend's dvds\" + 0.027*\"tense\" + 0.025*\"ominous\" + 0.024*\"menacing\" + 0.023*\"dvd-r\"\n", - "2021-08-24 02:38:55,851 : INFO : topic #29 (0.020): 0.136*\"action\" + 0.057*\"Action\" + 0.050*\"adventure\" + 0.039*\"sequel\" + 0.032*\"Adventure\" + 0.031*\"boring\" + 0.028*\"robots\" + 0.026*\"arnold schwarzenegger\" + 0.025*\"pirates\" + 0.023*\"motorcycle\"\n", - "2021-08-24 02:38:55,852 : INFO : topic #28 (0.020): 0.126*\"pixar\" + 0.092*\"oscar (best actress)\" + 0.037*\"mockumentary\" + 0.026*\"medieval\" + 0.025*\"elijah wood\" + 0.023*\"dinosaurs\" + 0.021*\"nostalgia\" + 0.021*\"computer animation\" + 0.017*\"high fantasy\" + 0.017*\"cgi\"\n", - "2021-08-24 02:38:55,853 : INFO : topic #9 (0.020): 0.200*\"r\" + 0.111*\"Western\" + 0.093*\"clearplay\" + 0.084*\"nudity (topless - brief)\" + 0.058*\"Drama\" + 0.026*\"pg13\" + 0.025*\"british\" + 0.022*\"Thriller\" + 0.021*\"russell crowe\" + 0.018*\"jane austen\"\n", - "2021-08-24 02:38:55,854 : INFO : topic diff=0.046992, rho=0.206988\n", - "2021-08-24 02:38:55,856 : INFO : PROGRESS: pass 17, at document #10000/10681\n", - "2021-08-24 02:38:56,100 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:56,128 : INFO : topic #48 (0.020): 0.047*\"quirky\" + 0.043*\"surreal\" + 0.040*\"directorial debut\" + 0.027*\"bittersweet\" + 0.024*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"erlend's dvds\" + 0.018*\"sad\" + 0.018*\"peter jackson\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:56,129 : INFO : topic #35 (0.020): 0.153*\"comedy\" + 0.133*\"Comedy\" + 0.085*\"funny\" + 0.047*\"parody\" + 0.039*\"hilarious\" + 0.029*\"ummarti2006\" + 0.025*\"can't remember\" + 0.023*\"stupid\" + 0.020*\"spoof\" + 0.019*\"afi 100 (laughs)\"\n", - "2021-08-24 02:38:56,130 : INFO : topic #3 (0.020): 0.287*\"nudity (topless)\" + 0.102*\"zombies\" + 0.049*\"nudity (topless - brief)\" + 0.026*\"keira knightley\" + 0.023*\"zombie\" + 0.023*\"virtual reality\" + 0.021*\"ingmar bergman\" + 0.016*\"fascism\" + 0.014*\"robert altman\" + 0.012*\"futuristic\"\n", - "2021-08-24 02:38:56,131 : INFO : topic #31 (0.020): 0.099*\"murder\" + 0.091*\"james bond\" + 0.055*\"007\" + 0.050*\"bond\" + 0.035*\"assassin\" + 0.031*\"claymation\" + 0.022*\"Thriller\" + 0.021*\"aardman\" + 0.020*\"Action\" + 0.019*\"franchise\"\n", - "2021-08-24 02:38:56,132 : INFO : topic #2 (0.020): 0.100*\"lesbian\" + 0.099*\"Comedy\" + 0.098*\"satire\" + 0.053*\"imdb bottom 100\" + 0.031*\"sexy\" + 0.026*\"get\" + 0.021*\"olympics\" + 0.020*\"ben stiller\" + 0.016*\"john turturro\" + 0.015*\"amnesia\"\n", - "2021-08-24 02:38:56,133 : INFO : topic diff=0.032990, rho=0.206988\n", - "2021-08-24 02:38:56,281 : INFO : -20.978 per-word bound, 2064813.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:56,282 : INFO : PROGRESS: pass 17, at document #10681/10681\n", - "2021-08-24 02:38:56,386 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:56,411 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.042*\"tense\" + 0.041*\"revenge\" + 0.034*\"clint eastwood\" + 0.029*\"forceful\" + 0.029*\"gritty\" + 0.027*\"corvallis library\" + 0.025*\"atmospheric\" + 0.024*\"Action\" + 0.023*\"akira kurosawa\"\n", - "2021-08-24 02:38:56,413 : INFO : topic #38 (0.020): 0.086*\"true story\" + 0.081*\"based on a true story\" + 0.067*\"biography\" + 0.060*\"tom hanks\" + 0.047*\"Drama\" + 0.034*\"drama\" + 0.031*\"historical\" + 0.029*\"biopic\" + 0.026*\"paris\" + 0.024*\"music\"\n", - "2021-08-24 02:38:56,413 : INFO : topic #25 (0.020): 0.220*\"Musical\" + 0.090*\"70mm\" + 0.077*\"musical\" + 0.076*\"religion\" + 0.035*\"friendship\" + 0.026*\"rock and roll\" + 0.020*\"music\" + 0.020*\"trains\" + 0.016*\"monty python\" + 0.015*\"adapted from b'way\"\n", - "2021-08-24 02:38:56,414 : INFO : topic #28 (0.020): 0.149*\"pixar\" + 0.074*\"oscar (best actress)\" + 0.041*\"mockumentary\" + 0.031*\"elijah wood\" + 0.023*\"medieval\" + 0.023*\"computer animation\" + 0.020*\"cgi\" + 0.019*\"dinosaurs\" + 0.018*\"family drama\" + 0.016*\"social commentary\"\n", - "2021-08-24 02:38:56,415 : INFO : topic #21 (0.020): 0.098*\"Drama\" + 0.053*\"criterion\" + 0.030*\"library\" + 0.026*\"sven's to see list\" + 0.023*\"tumey's dvds\" + 0.021*\"dvd-video\" + 0.021*\"irreverent\" + 0.021*\"atmospheric\" + 0.020*\"reflective\" + 0.020*\"stylized\"\n", - "2021-08-24 02:38:56,416 : INFO : topic diff=0.080245, rho=0.206988\n", - "2021-08-24 02:38:56,418 : INFO : PROGRESS: pass 18, at document #2000/10681\n", - "2021-08-24 02:38:56,686 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:56,712 : INFO : topic #45 (0.020): 0.044*\"japan\" + 0.042*\"tense\" + 0.034*\"revenge\" + 0.033*\"clint eastwood\" + 0.030*\"forceful\" + 0.029*\"atmospheric\" + 0.029*\"gritty\" + 0.024*\"sweeping\" + 0.023*\"Action\" + 0.022*\"corvallis library\"\n", - "2021-08-24 02:38:56,713 : INFO : topic #31 (0.020): 0.097*\"murder\" + 0.062*\"james bond\" + 0.044*\"assassin\" + 0.043*\"bond\" + 0.040*\"007\" + 0.039*\"claymation\" + 0.027*\"aardman\" + 0.022*\"Thriller\" + 0.021*\"Action\" + 0.020*\"killer-as-protagonist\"\n", - "2021-08-24 02:38:56,714 : INFO : topic #27 (0.020): 0.153*\"aliens\" + 0.092*\"steven spielberg\" + 0.054*\"family\" + 0.036*\"michael crichton\" + 0.025*\"to-rent\" + 0.025*\"childhood\" + 0.025*\"gambling\" + 0.022*\"alien\" + 0.019*\"didn't finish\" + 0.017*\"poker\"\n", - "2021-08-24 02:38:56,714 : INFO : topic #25 (0.020): 0.198*\"Musical\" + 0.087*\"70mm\" + 0.083*\"musical\" + 0.065*\"religion\" + 0.030*\"friendship\" + 0.025*\"rock and roll\" + 0.023*\"music\" + 0.022*\"monty python\" + 0.016*\"trains\" + 0.016*\"adapted from b'way\"\n", - "2021-08-24 02:38:56,715 : INFO : topic #7 (0.020): 0.199*\"movie to see\" + 0.065*\"mel gibson\" + 0.045*\"boxing\" + 0.035*\"uma thurman\" + 0.032*\"kidnapping\" + 0.032*\"john travolta\" + 0.029*\"sylvester stallone\" + 0.024*\"suburbia\" + 0.019*\"basketball\" + 0.014*\"do kupienia\"\n", - "2021-08-24 02:38:56,716 : INFO : topic diff=0.162543, rho=0.202691\n", - "2021-08-24 02:38:56,717 : INFO : PROGRESS: pass 18, at document #4000/10681\n", - "2021-08-24 02:38:56,971 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:56,997 : INFO : topic #36 (0.020): 0.127*\"dark comedy\" + 0.083*\"bechdel test:fail\" + 0.079*\"black comedy\" + 0.044*\"nazis\" + 0.033*\"cold war\" + 0.028*\"funniest movies\" + 0.013*\"existentialism\" + 0.013*\"nuclear war\" + 0.013*\"communism\" + 0.013*\"russia\"\n", - "2021-08-24 02:38:56,998 : INFO : topic #3 (0.020): 0.313*\"nudity (topless)\" + 0.076*\"zombies\" + 0.060*\"nudity (topless - brief)\" + 0.029*\"virtual reality\" + 0.020*\"keira knightley\" + 0.014*\"zombie\" + 0.014*\"ingmar bergman\" + 0.013*\"robert altman\" + 0.012*\"futuristic\" + 0.012*\"strippers\"\n", - "2021-08-24 02:38:56,999 : INFO : topic #37 (0.020): 0.059*\"easily confused with other movie(s) (title)\" + 0.043*\"oscar (best supporting actress)\" + 0.042*\"vietnam war\" + 0.041*\"vietnam\" + 0.041*\"want to see again\" + 0.036*\"food\" + 0.033*\"j netflix\" + 0.022*\"19th century\" + 0.020*\"peter sellers\" + 0.018*\"alcoholism\"\n", - "2021-08-24 02:38:57,000 : INFO : topic #23 (0.020): 0.076*\"mafia\" + 0.043*\"al pacino\" + 0.036*\"robert de niro\" + 0.022*\"marlon brando\" + 0.022*\"guns\" + 0.020*\"poverty\" + 0.018*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.017*\"eddie murphy\" + 0.016*\"nudity (full frontal - brief)\"\n", - "2021-08-24 02:38:57,000 : INFO : topic #0 (0.020): 0.096*\"crime\" + 0.061*\"brad pitt\" + 0.060*\"violence\" + 0.042*\"Crime\" + 0.037*\"morgan freeman\" + 0.036*\"violent\" + 0.032*\"organized crime\" + 0.030*\"thriller\" + 0.027*\"Thriller\" + 0.027*\"philip seymour hoffman\"\n", - "2021-08-24 02:38:57,002 : INFO : topic diff=0.028410, rho=0.202691\n", - "2021-08-24 02:38:57,003 : INFO : PROGRESS: pass 18, at document #6000/10681\n", - "2021-08-24 02:38:57,240 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:57,267 : INFO : topic #29 (0.020): 0.138*\"action\" + 0.057*\"Action\" + 0.051*\"adventure\" + 0.038*\"sequel\" + 0.032*\"boring\" + 0.032*\"Adventure\" + 0.030*\"robots\" + 0.028*\"arnold schwarzenegger\" + 0.020*\"motorcycle\" + 0.020*\"death\"\n", - "2021-08-24 02:38:57,268 : INFO : topic #7 (0.020): 0.161*\"movie to see\" + 0.070*\"mel gibson\" + 0.051*\"boxing\" + 0.033*\"suburbia\" + 0.031*\"kidnapping\" + 0.030*\"uma thurman\" + 0.029*\"john travolta\" + 0.027*\"sylvester stallone\" + 0.022*\"basketball\" + 0.015*\"intense\"\n", - "2021-08-24 02:38:57,269 : INFO : topic #23 (0.020): 0.070*\"mafia\" + 0.045*\"al pacino\" + 0.033*\"robert de niro\" + 0.025*\"guns\" + 0.022*\"poverty\" + 0.021*\"eddie murphy\" + 0.020*\"marlon brando\" + 0.017*\"tumey's dvds\" + 0.017*\"francis ford coppola\" + 0.017*\"michael caine\"\n", - "2021-08-24 02:38:57,270 : INFO : topic #3 (0.020): 0.298*\"nudity (topless)\" + 0.081*\"zombies\" + 0.054*\"nudity (topless - brief)\" + 0.026*\"virtual reality\" + 0.023*\"zombie\" + 0.017*\"keira knightley\" + 0.014*\"ingmar bergman\" + 0.014*\"robert altman\" + 0.013*\"futuristic\" + 0.011*\"strippers\"\n", - "2021-08-24 02:38:57,270 : INFO : topic #5 (0.020): 0.227*\"Action\" + 0.200*\"Thriller\" + 0.173*\"Horror\" + 0.134*\"Sci-Fi\" + 0.064*\"Adventure\" + 0.041*\"betamax\" + 0.019*\"can't remember\" + 0.009*\"courtroom\" + 0.008*\"70mm\" + 0.008*\"dvd-video\"\n", - "2021-08-24 02:38:57,272 : INFO : topic diff=0.057110, rho=0.202691\n", - "2021-08-24 02:38:57,273 : INFO : PROGRESS: pass 18, at document #8000/10681\n", - "2021-08-24 02:38:57,557 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:57,584 : INFO : topic #11 (0.020): 0.272*\"Documentary\" + 0.142*\"to see\" + 0.077*\"in netflix queue\" + 0.067*\"gay\" + 0.045*\"oscar (best actor)\" + 0.024*\"india\" + 0.020*\"homosexuality\" + 0.019*\"matter-of-fact\" + 0.018*\"IMAX\" + 0.015*\"ian mckellen\"\n", - "2021-08-24 02:38:57,585 : INFO : topic #37 (0.020): 0.061*\"easily confused with other movie(s) (title)\" + 0.042*\"oscar (best supporting actress)\" + 0.042*\"want to see again\" + 0.037*\"vietnam war\" + 0.035*\"vietnam\" + 0.032*\"peter sellers\" + 0.032*\"food\" + 0.029*\"j netflix\" + 0.023*\"19th century\" + 0.021*\"father daughter relationship\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:57,585 : INFO : topic #15 (0.020): 0.054*\"racism\" + 0.054*\"coen brothers\" + 0.053*\"stephen king\" + 0.039*\"adultery\" + 0.034*\"espionage\" + 0.025*\"los angeles\" + 0.022*\"great acting\" + 0.021*\"notable nudity\" + 0.020*\"multiple storylines\" + 0.020*\"ensemble cast\"\n", - "2021-08-24 02:38:57,586 : INFO : topic #18 (0.020): 0.051*\"creepy\" + 0.046*\"surrealism\" + 0.046*\"franchise\" + 0.035*\"slasher\" + 0.027*\"lurid\" + 0.024*\"robert downey jr\" + 0.022*\"adolescence\" + 0.021*\"united states\" + 0.021*\"unique\" + 0.021*\"goth\"\n", - "2021-08-24 02:38:57,587 : INFO : topic #3 (0.020): 0.264*\"nudity (topless)\" + 0.095*\"zombies\" + 0.046*\"nudity (topless - brief)\" + 0.030*\"virtual reality\" + 0.027*\"zombie\" + 0.026*\"ingmar bergman\" + 0.019*\"keira knightley\" + 0.015*\"robert altman\" + 0.013*\"futuristic\" + 0.011*\"Horror\"\n", - "2021-08-24 02:38:57,588 : INFO : topic diff=0.046030, rho=0.202691\n", - "2021-08-24 02:38:57,590 : INFO : PROGRESS: pass 18, at document #10000/10681\n", - "2021-08-24 02:38:57,824 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:57,851 : INFO : topic #7 (0.020): 0.182*\"movie to see\" + 0.051*\"boxing\" + 0.048*\"mel gibson\" + 0.035*\"uma thurman\" + 0.031*\"kidnapping\" + 0.030*\"suburbia\" + 0.025*\"sylvester stallone\" + 0.023*\"basketball\" + 0.023*\"john travolta\" + 0.015*\"sports\"\n", - "2021-08-24 02:38:57,853 : INFO : topic #0 (0.020): 0.092*\"crime\" + 0.061*\"violence\" + 0.057*\"brad pitt\" + 0.042*\"Crime\" + 0.042*\"violent\" + 0.037*\"morgan freeman\" + 0.029*\"Thriller\" + 0.024*\"thriller\" + 0.024*\"organized crime\" + 0.024*\"philip seymour hoffman\"\n", - "2021-08-24 02:38:57,854 : INFO : topic #42 (0.020): 0.168*\"johnny depp\" + 0.071*\"tim burton\" + 0.055*\"martin scorsese\" + 0.039*\"george clooney\" + 0.038*\"narrated\" + 0.024*\"ei muista\" + 0.023*\"monster\" + 0.017*\"brothers\" + 0.017*\"alien invasion\" + 0.016*\"las vegas\"\n", - "2021-08-24 02:38:57,854 : INFO : topic #19 (0.020): 0.098*\"pg-13\" + 0.067*\"ghosts\" + 0.044*\"jude law\" + 0.035*\"wedding\" + 0.031*\"julia roberts\" + 0.028*\"want to own\" + 0.028*\"infidelity\" + 0.024*\"hollywood\" + 0.021*\"prostitution\" + 0.018*\"sam peckinpah\"\n", - "2021-08-24 02:38:57,855 : INFO : topic #48 (0.020): 0.048*\"quirky\" + 0.043*\"surreal\" + 0.040*\"directorial debut\" + 0.027*\"bittersweet\" + 0.024*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"erlend's dvds\" + 0.018*\"sad\" + 0.018*\"peter jackson\"\n", - "2021-08-24 02:38:57,857 : INFO : topic diff=0.032224, rho=0.202691\n", - "2021-08-24 02:38:58,010 : INFO : -20.978 per-word bound, 2065827.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:58,011 : INFO : PROGRESS: pass 18, at document #10681/10681\n", - "2021-08-24 02:38:58,115 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:58,141 : INFO : topic #18 (0.020): 0.042*\"creepy\" + 0.036*\"surrealism\" + 0.035*\"franchise\" + 0.034*\"united states\" + 0.031*\"unique\" + 0.027*\"robert downey jr\" + 0.025*\"lurid\" + 0.024*\"slasher\" + 0.018*\"macabre\" + 0.017*\"adolescence\"\n", - "2021-08-24 02:38:58,141 : INFO : topic #19 (0.020): 0.097*\"pg-13\" + 0.065*\"ghosts\" + 0.042*\"jude law\" + 0.039*\"wedding\" + 0.033*\"julia roberts\" + 0.030*\"infidelity\" + 0.024*\"want to own\" + 0.021*\"prostitution\" + 0.021*\"hollywood\" + 0.016*\"sam peckinpah\"\n", - "2021-08-24 02:38:58,142 : INFO : topic #8 (0.020): 0.091*\"space\" + 0.045*\"christianity\" + 0.031*\"harrison ford\" + 0.029*\"marx brothers\" + 0.027*\"archaeology\" + 0.021*\"kevin smith\" + 0.019*\"1970s\" + 0.018*\"spaghetti western\" + 0.018*\"western\" + 0.018*\"george lucas\"\n", - "2021-08-24 02:38:58,143 : INFO : topic #7 (0.020): 0.251*\"movie to see\" + 0.043*\"boxing\" + 0.039*\"mel gibson\" + 0.034*\"kidnapping\" + 0.028*\"uma thurman\" + 0.026*\"suburbia\" + 0.024*\"sylvester stallone\" + 0.021*\"basketball\" + 0.018*\"john travolta\" + 0.015*\"do kupienia\"\n", - "2021-08-24 02:38:58,144 : INFO : topic #41 (0.020): 0.260*\"based on a book\" + 0.107*\"adapted from:book\" + 0.083*\"netflix\" + 0.075*\"Drama\" + 0.066*\"nudity (full frontal)\" + 0.036*\"vampire\" + 0.035*\"robin williams\" + 0.027*\"vampires\" + 0.011*\"book\" + 0.009*\"drama\"\n", - "2021-08-24 02:38:58,145 : INFO : topic diff=0.078698, rho=0.202691\n", - "2021-08-24 02:38:58,147 : INFO : PROGRESS: pass 19, at document #2000/10681\n", - "2021-08-24 02:38:58,419 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:58,445 : INFO : topic #35 (0.020): 0.163*\"comedy\" + 0.130*\"Comedy\" + 0.079*\"funny\" + 0.047*\"parody\" + 0.039*\"hilarious\" + 0.031*\"can't remember\" + 0.023*\"stupid\" + 0.020*\"spoof\" + 0.020*\"ummarti2006\" + 0.018*\"seen more than once\"\n", - "2021-08-24 02:38:58,446 : INFO : topic #29 (0.020): 0.144*\"action\" + 0.054*\"Action\" + 0.051*\"adventure\" + 0.036*\"sequel\" + 0.034*\"Adventure\" + 0.030*\"robots\" + 0.029*\"boring\" + 0.026*\"arnold schwarzenegger\" + 0.024*\"death\" + 0.023*\"pirates\"\n", - "2021-08-24 02:38:58,447 : INFO : topic #14 (0.020): 0.127*\"Mystery\" + 0.061*\"Thriller\" + 0.055*\"Horror\" + 0.040*\"disturbing\" + 0.031*\"atmospheric\" + 0.026*\"tense\" + 0.026*\"ominous\" + 0.023*\"erlend's dvds\" + 0.023*\"menacing\" + 0.022*\"dvd-r\"\n", - "2021-08-24 02:38:58,448 : INFO : topic #34 (0.020): 0.169*\"War\" + 0.120*\"Drama\" + 0.097*\"world war ii\" + 0.072*\"drugs\" + 0.051*\"war\" + 0.044*\"prison\" + 0.038*\"history\" + 0.028*\"Action\" + 0.022*\"london\" + 0.018*\"Adventure\"\n", - "2021-08-24 02:38:58,448 : INFO : topic #20 (0.020): 0.142*\"politics\" + 0.075*\"documentary\" + 0.068*\"jack nicholson\" + 0.044*\"terrorism\" + 0.039*\"love story\" + 0.036*\"seen 2008\" + 0.035*\"propaganda\" + 0.033*\"Documentary\" + 0.032*\"political\" + 0.028*\"assassination\"\n", - "2021-08-24 02:38:58,449 : INFO : topic diff=0.158332, rho=0.198652\n", - "2021-08-24 02:38:58,451 : INFO : PROGRESS: pass 19, at document #4000/10681\n", - "2021-08-24 02:38:58,696 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:58,723 : INFO : topic #14 (0.020): 0.118*\"Mystery\" + 0.062*\"Horror\" + 0.058*\"Thriller\" + 0.039*\"disturbing\" + 0.033*\"atmospheric\" + 0.026*\"ominous\" + 0.025*\"tense\" + 0.025*\"erlend's dvds\" + 0.022*\"scary movies to see on halloween\" + 0.022*\"menacing\"\n", - "2021-08-24 02:38:58,724 : INFO : topic #6 (0.020): 0.087*\"martial arts\" + 0.062*\"cult film\" + 0.045*\"gothic\" + 0.034*\"campy\" + 0.030*\"cult classic\" + 0.027*\"australia\" + 0.026*\"silly\" + 0.019*\"seen 2006\" + 0.019*\"australian\" + 0.019*\"don't remember\"\n", - "2021-08-24 02:38:58,725 : INFO : topic #34 (0.020): 0.164*\"War\" + 0.116*\"Drama\" + 0.097*\"world war ii\" + 0.083*\"drugs\" + 0.048*\"war\" + 0.040*\"prison\" + 0.036*\"history\" + 0.030*\"Action\" + 0.020*\"london\" + 0.017*\"Adventure\"\n", - "2021-08-24 02:38:58,726 : INFO : topic #2 (0.020): 0.102*\"satire\" + 0.100*\"Comedy\" + 0.096*\"lesbian\" + 0.050*\"imdb bottom 100\" + 0.032*\"sexy\" + 0.027*\"get\" + 0.019*\"olympics\" + 0.019*\"1\" + 0.017*\"john turturro\" + 0.017*\"charlie chaplin\"\n", - "2021-08-24 02:38:58,727 : INFO : topic #22 (0.020): 0.074*\"national film registry\" + 0.055*\"oscar (best cinematography)\" + 0.048*\"classic\" + 0.035*\"imdb top 250\" + 0.031*\"stanley kubrick\" + 0.030*\"afi 100 (thrills)\" + 0.030*\"afi 100\" + 0.029*\"tumey's dvds\" + 0.027*\"black and white\" + 0.026*\"70mm\"\n", - "2021-08-24 02:38:58,728 : INFO : topic diff=0.027480, rho=0.198652\n", - "2021-08-24 02:38:58,729 : INFO : PROGRESS: pass 19, at document #6000/10681\n", - "2021-08-24 02:38:58,953 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:58,980 : INFO : topic #0 (0.020): 0.102*\"crime\" + 0.061*\"brad pitt\" + 0.060*\"violence\" + 0.042*\"Crime\" + 0.037*\"morgan freeman\" + 0.035*\"violent\" + 0.031*\"organized crime\" + 0.028*\"philip seymour hoffman\" + 0.026*\"Thriller\" + 0.026*\"thriller\"\n", - "2021-08-24 02:38:58,981 : INFO : topic #5 (0.020): 0.228*\"Action\" + 0.200*\"Thriller\" + 0.174*\"Horror\" + 0.134*\"Sci-Fi\" + 0.064*\"Adventure\" + 0.041*\"betamax\" + 0.019*\"can't remember\" + 0.009*\"courtroom\" + 0.008*\"dvd-video\" + 0.007*\"vhs\"\n", - "2021-08-24 02:38:58,982 : INFO : topic #41 (0.020): 0.250*\"based on a book\" + 0.116*\"adapted from:book\" + 0.068*\"Drama\" + 0.058*\"robin williams\" + 0.055*\"nudity (full frontal)\" + 0.046*\"netflix\" + 0.038*\"vampire\" + 0.032*\"vampires\" + 0.016*\"book\" + 0.012*\"drama\"\n", - "2021-08-24 02:38:58,983 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.046*\"directorial debut\" + 0.045*\"surreal\" + 0.025*\"woody allen\" + 0.024*\"bittersweet\" + 0.023*\"humorous\" + 0.022*\"whimsical\" + 0.020*\"coming of age\" + 0.019*\"underrated\" + 0.019*\"erlend's dvds\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:38:58,984 : INFO : topic #21 (0.020): 0.093*\"Drama\" + 0.051*\"criterion\" + 0.026*\"library\" + 0.025*\"tumey's dvds\" + 0.023*\"dvd-video\" + 0.023*\"sven's to see list\" + 0.023*\"satirical\" + 0.022*\"atmospheric\" + 0.022*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:38:58,985 : INFO : topic diff=0.055485, rho=0.198652\n", - "2021-08-24 02:38:58,986 : INFO : PROGRESS: pass 19, at document #8000/10681\n", - "2021-08-24 02:38:59,239 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:59,267 : INFO : topic #10 (0.020): 0.448*\"Comedy\" + 0.234*\"Romance\" + 0.194*\"Drama\" + 0.013*\"bibliothek\" + 0.013*\"betamax\" + 0.004*\"oppl\" + 0.004*\"divorce\" + 0.004*\"owen wilson\" + 0.004*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:38:59,268 : INFO : topic #38 (0.020): 0.082*\"tom hanks\" + 0.081*\"true story\" + 0.058*\"biography\" + 0.051*\"based on a true story\" + 0.047*\"Drama\" + 0.045*\"drama\" + 0.031*\"historical\" + 0.028*\"biopic\" + 0.027*\"music\" + 0.024*\"paris\"\n", - "2021-08-24 02:38:59,268 : INFO : topic #13 (0.020): 0.152*\"time travel\" + 0.068*\"twist ending\" + 0.057*\"holocaust\" + 0.035*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.024*\"post apocalyptic\" + 0.020*\"very good\" + 0.020*\"michael moore\" + 0.019*\"mindfuck\" + 0.016*\"liam neeson\"\n", - "2021-08-24 02:38:59,269 : INFO : topic #17 (0.020): 0.062*\"police\" + 0.055*\"will smith\" + 0.031*\"computers\" + 0.029*\"corruption\" + 0.027*\"sandra bullock\" + 0.019*\"milla jovovich\" + 0.018*\"no dialogue\" + 0.017*\"torture\" + 0.016*\"stage\" + 0.016*\"action packed\"\n", - "2021-08-24 02:38:59,270 : INFO : topic #3 (0.020): 0.266*\"nudity (topless)\" + 0.095*\"zombies\" + 0.044*\"nudity (topless - brief)\" + 0.030*\"virtual reality\" + 0.027*\"zombie\" + 0.026*\"ingmar bergman\" + 0.020*\"keira knightley\" + 0.015*\"robert altman\" + 0.013*\"futuristic\" + 0.011*\"Horror\"\n", - "2021-08-24 02:38:59,271 : INFO : topic diff=0.045148, rho=0.198652\n", - "2021-08-24 02:38:59,272 : INFO : PROGRESS: pass 19, at document #10000/10681\n", - "2021-08-24 02:38:59,553 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:38:59,581 : INFO : topic #26 (0.020): 0.200*\"classic\" + 0.129*\"oscar (best picture)\" + 0.054*\"oscar (best directing)\" + 0.051*\"oscar (best supporting actor)\" + 0.034*\"matt damon\" + 0.023*\"afi 100\" + 0.021*\"afi 100 (cheers)\" + 0.020*\"national film registry\" + 0.018*\"oscar (best supporting actress)\" + 0.015*\"afi 100 (laughs)\"\n", - "2021-08-24 02:38:59,582 : INFO : topic #38 (0.020): 0.084*\"true story\" + 0.067*\"tom hanks\" + 0.061*\"biography\" + 0.053*\"based on a true story\" + 0.047*\"Drama\" + 0.040*\"drama\" + 0.034*\"biopic\" + 0.029*\"historical\" + 0.028*\"pg\" + 0.027*\"paris\"\n", - "2021-08-24 02:38:59,582 : INFO : topic #7 (0.020): 0.185*\"movie to see\" + 0.051*\"boxing\" + 0.049*\"mel gibson\" + 0.035*\"uma thurman\" + 0.031*\"kidnapping\" + 0.030*\"suburbia\" + 0.025*\"sylvester stallone\" + 0.023*\"basketball\" + 0.023*\"john travolta\" + 0.015*\"sports\"\n", - "2021-08-24 02:38:59,583 : INFO : topic #24 (0.020): 0.056*\"tom cruise\" + 0.038*\"france\" + 0.034*\"golden palm\" + 0.034*\"nicole kidman\" + 0.031*\"on dvr\" + 0.029*\"french\" + 0.027*\"dustin hoffman\" + 0.024*\"disability\" + 0.021*\"bank robbery\" + 0.019*\"watch\"\n", - "2021-08-24 02:38:59,584 : INFO : topic #5 (0.020): 0.235*\"Action\" + 0.204*\"Thriller\" + 0.172*\"Horror\" + 0.131*\"Sci-Fi\" + 0.067*\"Adventure\" + 0.033*\"betamax\" + 0.014*\"can't remember\" + 0.009*\"courtroom\" + 0.007*\"vhs\" + 0.007*\"dvd-video\"\n", - "2021-08-24 02:38:59,585 : INFO : topic diff=0.031387, rho=0.198652\n", - "2021-08-24 02:38:59,741 : INFO : -20.978 per-word bound, 2065603.9 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:38:59,742 : INFO : PROGRESS: pass 19, at document #10681/10681\n", - "2021-08-24 02:38:59,864 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:38:59,891 : INFO : topic #46 (0.020): 0.082*\"dvd\" + 0.075*\"overrated\" + 0.047*\"seen more than once\" + 0.046*\"owned\" + 0.037*\"imdb top 250\" + 0.032*\"samuel l. jackson\" + 0.026*\"bibliothek\" + 0.026*\"eric's dvds\" + 0.020*\"ridley scott\" + 0.020*\"tumey's dvds\"\n", - "2021-08-24 02:38:59,892 : INFO : topic #10 (0.020): 0.439*\"Comedy\" + 0.240*\"Romance\" + 0.202*\"Drama\" + 0.009*\"bibliothek\" + 0.009*\"betamax\" + 0.008*\"oppl\" + 0.006*\"owen wilson\" + 0.005*\"divorce\" + 0.003*\"world war i\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:38:59,892 : INFO : topic #24 (0.020): 0.048*\"tom cruise\" + 0.038*\"france\" + 0.032*\"golden palm\" + 0.032*\"nicole kidman\" + 0.031*\"disability\" + 0.030*\"on dvr\" + 0.028*\"french\" + 0.026*\"dustin hoffman\" + 0.020*\"mask\" + 0.018*\"bank robbery\"\n", - "2021-08-24 02:38:59,893 : INFO : topic #21 (0.020): 0.098*\"Drama\" + 0.053*\"criterion\" + 0.030*\"library\" + 0.026*\"sven's to see list\" + 0.023*\"tumey's dvds\" + 0.021*\"dvd-video\" + 0.021*\"irreverent\" + 0.021*\"atmospheric\" + 0.020*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:38:59,894 : INFO : topic #7 (0.020): 0.252*\"movie to see\" + 0.043*\"boxing\" + 0.039*\"mel gibson\" + 0.034*\"kidnapping\" + 0.028*\"uma thurman\" + 0.026*\"suburbia\" + 0.024*\"sylvester stallone\" + 0.021*\"basketball\" + 0.018*\"john travolta\" + 0.015*\"do kupienia\"\n", - "2021-08-24 02:38:59,895 : INFO : topic diff=0.077024, rho=0.198652\n", - "2021-08-24 02:38:59,897 : INFO : PROGRESS: pass 20, at document #2000/10681\n", - "2021-08-24 02:39:00,162 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:00,189 : INFO : topic #28 (0.020): 0.123*\"pixar\" + 0.087*\"oscar (best actress)\" + 0.045*\"mockumentary\" + 0.033*\"dinosaurs\" + 0.028*\"medieval\" + 0.024*\"elijah wood\" + 0.018*\"computer animation\" + 0.018*\"christopher guest\" + 0.017*\"cgi\" + 0.015*\"best war films\"\n", - "2021-08-24 02:39:00,190 : INFO : topic #33 (0.020): 0.169*\"comic book\" + 0.132*\"superhero\" + 0.065*\"super-hero\" + 0.039*\"adapted from:comic\" + 0.037*\"batman\" + 0.037*\"gene hackman\" + 0.026*\"action\" + 0.025*\"Action\" + 0.020*\"alter ego\" + 0.019*\"based on a comic\"\n", - "2021-08-24 02:39:00,191 : INFO : topic #23 (0.020): 0.083*\"mafia\" + 0.039*\"al pacino\" + 0.033*\"robert de niro\" + 0.025*\"marlon brando\" + 0.023*\"guns\" + 0.020*\"poverty\" + 0.019*\"scifi\" + 0.019*\"francis ford coppola\" + 0.016*\"michael caine\" + 0.016*\"tumey's dvds\"\n", - "2021-08-24 02:39:00,192 : INFO : topic #26 (0.020): 0.219*\"classic\" + 0.174*\"oscar (best picture)\" + 0.067*\"oscar (best directing)\" + 0.044*\"oscar (best supporting actor)\" + 0.034*\"afi 100\" + 0.022*\"national film registry\" + 0.019*\"matt damon\" + 0.019*\"afi 100 (cheers)\" + 0.018*\"best picture\" + 0.017*\"oscar (best supporting actress)\"\n", - "2021-08-24 02:39:00,192 : INFO : topic #47 (0.020): 0.274*\"less than 300 ratings\" + 0.143*\"Drama\" + 0.065*\"nudity (rear)\" + 0.029*\"jackie chan\" + 0.027*\"kung fu\" + 0.026*\"oppl\" + 0.019*\"denzel washington\" + 0.019*\"football\" + 0.017*\"aids\" + 0.014*\"bechdel test:pass\"\n", - "2021-08-24 02:39:00,194 : INFO : topic diff=0.155080, rho=0.194844\n", - "2021-08-24 02:39:00,195 : INFO : PROGRESS: pass 20, at document #4000/10681\n", - "2021-08-24 02:39:00,453 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:00,479 : INFO : topic #41 (0.020): 0.249*\"based on a book\" + 0.114*\"adapted from:book\" + 0.069*\"Drama\" + 0.057*\"robin williams\" + 0.056*\"nudity (full frontal)\" + 0.053*\"netflix\" + 0.038*\"vampire\" + 0.028*\"vampires\" + 0.015*\"book\" + 0.013*\"drama\"\n", - "2021-08-24 02:39:00,480 : INFO : topic #28 (0.020): 0.126*\"pixar\" + 0.093*\"oscar (best actress)\" + 0.042*\"mockumentary\" + 0.028*\"medieval\" + 0.028*\"dinosaurs\" + 0.022*\"nostalgia\" + 0.019*\"computer animation\" + 0.019*\"elijah wood\" + 0.017*\"christopher guest\" + 0.016*\"golf\"\n", - "2021-08-24 02:39:00,481 : INFO : topic #19 (0.020): 0.088*\"ghosts\" + 0.063*\"pg-13\" + 0.040*\"julia roberts\" + 0.036*\"jude law\" + 0.032*\"wedding\" + 0.030*\"hollywood\" + 0.028*\"afternoon section\" + 0.028*\"infidelity\" + 0.027*\"want to own\" + 0.023*\"prostitution\"\n", - "2021-08-24 02:39:00,482 : INFO : topic #38 (0.020): 0.085*\"true story\" + 0.085*\"tom hanks\" + 0.059*\"biography\" + 0.059*\"based on a true story\" + 0.047*\"Drama\" + 0.045*\"drama\" + 0.030*\"music\" + 0.030*\"historical\" + 0.028*\"biopic\" + 0.027*\"oscar (best actor)\"\n", - "2021-08-24 02:39:00,482 : INFO : topic #18 (0.020): 0.053*\"franchise\" + 0.047*\"surrealism\" + 0.039*\"creepy\" + 0.039*\"slasher\" + 0.028*\"robert downey jr\" + 0.028*\"united states\" + 0.024*\"lurid\" + 0.023*\"unique\" + 0.021*\"no rec?\" + 0.020*\"goth\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:00,483 : INFO : topic diff=0.027052, rho=0.194844\n", - "2021-08-24 02:39:00,485 : INFO : PROGRESS: pass 20, at document #6000/10681\n", - "2021-08-24 02:39:00,723 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:00,749 : INFO : topic #32 (0.020): 0.088*\"based on a tv show\" + 0.066*\"jim carrey\" + 0.061*\"keanu reeves\" + 0.047*\"video game adaptation\" + 0.046*\"memory\" + 0.045*\"england\" + 0.038*\"star trek\" + 0.037*\"tv\" + 0.034*\"road trip\" + 0.028*\"friday night movie\"\n", - "2021-08-24 02:39:00,750 : INFO : topic #17 (0.020): 0.059*\"will smith\" + 0.058*\"police\" + 0.032*\"corruption\" + 0.032*\"sandra bullock\" + 0.028*\"computers\" + 0.022*\"milla jovovich\" + 0.018*\"torture\" + 0.018*\"no dialogue\" + 0.016*\"action packed\" + 0.015*\"luc besson\"\n", - "2021-08-24 02:39:00,750 : INFO : topic #25 (0.020): 0.198*\"Musical\" + 0.124*\"70mm\" + 0.073*\"musical\" + 0.064*\"religion\" + 0.027*\"rock and roll\" + 0.025*\"friendship\" + 0.022*\"music\" + 0.018*\"monty python\" + 0.018*\"adapted from b'way\" + 0.015*\"trains\"\n", - "2021-08-24 02:39:00,751 : INFO : topic #6 (0.020): 0.084*\"martial arts\" + 0.060*\"cult film\" + 0.044*\"gothic\" + 0.037*\"campy\" + 0.029*\"cult classic\" + 0.028*\"silly\" + 0.027*\"australia\" + 0.026*\"australian\" + 0.020*\"sam raimi\" + 0.018*\"don't remember\"\n", - "2021-08-24 02:39:00,752 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.046*\"directorial debut\" + 0.044*\"surreal\" + 0.025*\"woody allen\" + 0.024*\"bittersweet\" + 0.024*\"humorous\" + 0.022*\"whimsical\" + 0.020*\"coming of age\" + 0.019*\"underrated\" + 0.019*\"erlend's dvds\"\n", - "2021-08-24 02:39:00,754 : INFO : topic diff=0.054220, rho=0.194844\n", - "2021-08-24 02:39:00,756 : INFO : PROGRESS: pass 20, at document #8000/10681\n", - "2021-08-24 02:39:00,988 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:01,014 : INFO : topic #20 (0.020): 0.135*\"politics\" + 0.090*\"documentary\" + 0.065*\"jack nicholson\" + 0.043*\"terrorism\" + 0.037*\"seen 2008\" + 0.033*\"propaganda\" + 0.032*\"Documentary\" + 0.028*\"political\" + 0.027*\"love story\" + 0.027*\"aviation\"\n", - "2021-08-24 02:39:01,015 : INFO : topic #10 (0.020): 0.448*\"Comedy\" + 0.234*\"Romance\" + 0.194*\"Drama\" + 0.013*\"bibliothek\" + 0.013*\"betamax\" + 0.004*\"oppl\" + 0.004*\"divorce\" + 0.004*\"owen wilson\" + 0.004*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:39:01,016 : INFO : topic #8 (0.020): 0.091*\"space\" + 0.040*\"harrison ford\" + 0.031*\"christianity\" + 0.025*\"marx brothers\" + 0.023*\"kevin smith\" + 0.022*\"george lucas\" + 0.022*\"spaghetti western\" + 0.020*\"archaeology\" + 0.019*\"werewolves\" + 0.018*\"sergio leone\"\n", - "2021-08-24 02:39:01,017 : INFO : topic #22 (0.020): 0.081*\"national film registry\" + 0.056*\"oscar (best cinematography)\" + 0.038*\"classic\" + 0.029*\"imdb top 250\" + 0.028*\"tumey's dvds\" + 0.027*\"afi 100 (thrills)\" + 0.026*\"clv\" + 0.026*\"black and white\" + 0.024*\"stanley kubrick\" + 0.024*\"erlend's dvds\"\n", - "2021-08-24 02:39:01,018 : INFO : topic #40 (0.020): 0.123*\"nudity (full frontal - notable)\" + 0.093*\"magic\" + 0.067*\"edward norton\" + 0.058*\"sports\" + 0.049*\"not corv lib\" + 0.045*\"baseball\" + 0.044*\"hw drama\" + 0.035*\"compassionate\" + 0.025*\"christopher walken\" + 0.024*\"inspirational\"\n", - "2021-08-24 02:39:01,019 : INFO : topic diff=0.044141, rho=0.194844\n", - "2021-08-24 02:39:01,020 : INFO : PROGRESS: pass 20, at document #10000/10681\n", - "2021-08-24 02:39:01,268 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:01,295 : INFO : topic #35 (0.020): 0.154*\"comedy\" + 0.134*\"Comedy\" + 0.085*\"funny\" + 0.047*\"parody\" + 0.039*\"hilarious\" + 0.029*\"ummarti2006\" + 0.026*\"can't remember\" + 0.023*\"stupid\" + 0.020*\"spoof\" + 0.018*\"afi 100 (laughs)\"\n", - "2021-08-24 02:39:01,296 : INFO : topic #20 (0.020): 0.142*\"politics\" + 0.097*\"documentary\" + 0.056*\"jack nicholson\" + 0.053*\"terrorism\" + 0.039*\"propaganda\" + 0.038*\"political\" + 0.037*\"love story\" + 0.035*\"seen 2008\" + 0.035*\"Documentary\" + 0.027*\"aviation\"\n", - "2021-08-24 02:39:01,297 : INFO : topic #42 (0.020): 0.167*\"johnny depp\" + 0.072*\"tim burton\" + 0.055*\"martin scorsese\" + 0.039*\"george clooney\" + 0.039*\"narrated\" + 0.024*\"ei muista\" + 0.023*\"monster\" + 0.017*\"brothers\" + 0.017*\"alien invasion\" + 0.016*\"las vegas\"\n", - "2021-08-24 02:39:01,298 : INFO : topic #41 (0.020): 0.242*\"based on a book\" + 0.113*\"adapted from:book\" + 0.071*\"netflix\" + 0.066*\"Drama\" + 0.055*\"nudity (full frontal)\" + 0.045*\"robin williams\" + 0.036*\"vampire\" + 0.034*\"vampires\" + 0.014*\"book\" + 0.010*\"drama\"\n", - "2021-08-24 02:39:01,299 : INFO : topic #11 (0.020): 0.266*\"Documentary\" + 0.148*\"to see\" + 0.083*\"in netflix queue\" + 0.073*\"gay\" + 0.036*\"oscar (best actor)\" + 0.025*\"homosexuality\" + 0.020*\"india\" + 0.019*\"matter-of-fact\" + 0.017*\"IMAX\" + 0.014*\"ian mckellen\"\n", - "2021-08-24 02:39:01,300 : INFO : topic diff=0.030812, rho=0.194844\n", - "2021-08-24 02:39:01,448 : INFO : -20.977 per-word bound, 2064612.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:01,449 : INFO : PROGRESS: pass 20, at document #10681/10681\n", - "2021-08-24 02:39:01,543 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:01,569 : INFO : topic #29 (0.020): 0.119*\"action\" + 0.055*\"Action\" + 0.048*\"adventure\" + 0.039*\"sequel\" + 0.035*\"Adventure\" + 0.033*\"robots\" + 0.033*\"boring\" + 0.031*\"pirates\" + 0.028*\"death\" + 0.026*\"motorcycle\"\n", - "2021-08-24 02:39:01,570 : INFO : topic #19 (0.020): 0.097*\"pg-13\" + 0.065*\"ghosts\" + 0.041*\"jude law\" + 0.039*\"wedding\" + 0.033*\"julia roberts\" + 0.030*\"infidelity\" + 0.025*\"want to own\" + 0.021*\"prostitution\" + 0.021*\"hollywood\" + 0.016*\"afternoon section\"\n", - "2021-08-24 02:39:01,570 : INFO : topic #37 (0.020): 0.079*\"easily confused with other movie(s) (title)\" + 0.041*\"j netflix\" + 0.033*\"food\" + 0.033*\"oscar (best supporting actress)\" + 0.030*\"want to see again\" + 0.027*\"vietnam war\" + 0.026*\"19th century\" + 0.025*\"vietnam\" + 0.024*\"father daughter relationship\" + 0.023*\"slow\"\n", - "2021-08-24 02:39:01,571 : INFO : topic #46 (0.020): 0.084*\"dvd\" + 0.074*\"overrated\" + 0.047*\"seen more than once\" + 0.046*\"owned\" + 0.036*\"imdb top 250\" + 0.032*\"samuel l. jackson\" + 0.027*\"eric's dvds\" + 0.027*\"bibliothek\" + 0.021*\"tumey's dvds\" + 0.020*\"ridley scott\"\n", - "2021-08-24 02:39:01,572 : INFO : topic #36 (0.020): 0.106*\"bechdel test:fail\" + 0.104*\"dark comedy\" + 0.069*\"black comedy\" + 0.043*\"nazis\" + 0.040*\"cold war\" + 0.029*\"funniest movies\" + 0.018*\"existentialism\" + 0.015*\"communism\" + 0.015*\"author:charles dickens\" + 0.014*\"russia\"\n", - "2021-08-24 02:39:01,573 : INFO : topic diff=0.075818, rho=0.194844\n", - "2021-08-24 02:39:01,574 : INFO : PROGRESS: pass 21, at document #2000/10681\n", - "2021-08-24 02:39:01,822 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:01,848 : INFO : topic #39 (0.020): 0.124*\"bruce willis\" + 0.047*\"interesting\" + 0.034*\"love\" + 0.033*\"africa\" + 0.026*\"apocalypse\" + 0.026*\"predictable\" + 0.026*\"end of the world\" + 0.025*\"gerard depardieu\" + 0.023*\"good\" + 0.023*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:39:01,849 : INFO : topic #30 (0.020): 0.644*\"Drama\" + 0.039*\"Film-Noir\" + 0.012*\"tumey's dvds\" + 0.010*\"small town\" + 0.008*\"film noir\" + 0.008*\"witch\" + 0.007*\"erlend's dvds\" + 0.007*\"italy\" + 0.007*\"animal:dog\" + 0.007*\"glbt\"\n", - "2021-08-24 02:39:01,849 : INFO : topic #42 (0.020): 0.155*\"johnny depp\" + 0.078*\"tim burton\" + 0.065*\"martin scorsese\" + 0.036*\"narrated\" + 0.031*\"george clooney\" + 0.026*\"monster\" + 0.022*\"alien invasion\" + 0.021*\"brothers\" + 0.020*\"ei muista\" + 0.018*\"disaster\"\n", - "2021-08-24 02:39:01,850 : INFO : topic #27 (0.020): 0.152*\"aliens\" + 0.091*\"steven spielberg\" + 0.054*\"family\" + 0.036*\"michael crichton\" + 0.025*\"to-rent\" + 0.025*\"gambling\" + 0.025*\"childhood\" + 0.022*\"alien\" + 0.019*\"didn't finish\" + 0.017*\"poker\"\n", - "2021-08-24 02:39:01,850 : INFO : topic #34 (0.020): 0.169*\"War\" + 0.121*\"Drama\" + 0.096*\"world war ii\" + 0.072*\"drugs\" + 0.051*\"war\" + 0.043*\"prison\" + 0.038*\"history\" + 0.028*\"Action\" + 0.022*\"london\" + 0.019*\"Adventure\"\n", - "2021-08-24 02:39:01,852 : INFO : topic diff=0.151509, rho=0.191248\n", - "2021-08-24 02:39:01,853 : INFO : PROGRESS: pass 21, at document #4000/10681\n", - "2021-08-24 02:39:02,111 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:02,137 : INFO : topic #40 (0.020): 0.121*\"nudity (full frontal - notable)\" + 0.082*\"magic\" + 0.071*\"edward norton\" + 0.061*\"sports\" + 0.051*\"not corv lib\" + 0.046*\"baseball\" + 0.037*\"hw drama\" + 0.031*\"inspirational\" + 0.025*\"christopher walken\" + 0.025*\"compassionate\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:02,138 : INFO : topic #47 (0.020): 0.249*\"less than 300 ratings\" + 0.135*\"Drama\" + 0.070*\"nudity (rear)\" + 0.036*\"jackie chan\" + 0.028*\"kung fu\" + 0.026*\"football\" + 0.022*\"oppl\" + 0.018*\"denzel washington\" + 0.015*\"bechdel test:pass\" + 0.015*\"aids\"\n", - "2021-08-24 02:39:02,139 : INFO : topic #20 (0.020): 0.139*\"politics\" + 0.072*\"jack nicholson\" + 0.067*\"documentary\" + 0.044*\"terrorism\" + 0.035*\"love story\" + 0.035*\"seen 2008\" + 0.032*\"aviation\" + 0.031*\"political\" + 0.031*\"propaganda\" + 0.029*\"Documentary\"\n", - "2021-08-24 02:39:02,139 : INFO : topic #10 (0.020): 0.435*\"Comedy\" + 0.236*\"Romance\" + 0.198*\"Drama\" + 0.012*\"bibliothek\" + 0.011*\"betamax\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.005*\"owen wilson\" + 0.004*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:39:02,140 : INFO : topic #32 (0.020): 0.093*\"based on a tv show\" + 0.074*\"jim carrey\" + 0.060*\"keanu reeves\" + 0.048*\"england\" + 0.043*\"video game adaptation\" + 0.041*\"tv\" + 0.040*\"star trek\" + 0.038*\"road trip\" + 0.034*\"memory\" + 0.025*\"seen at the cinema\"\n", - "2021-08-24 02:39:02,142 : INFO : topic diff=0.026180, rho=0.191248\n", - "2021-08-24 02:39:02,143 : INFO : PROGRESS: pass 21, at document #6000/10681\n", - "2021-08-24 02:39:02,360 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:02,387 : INFO : topic #17 (0.020): 0.059*\"will smith\" + 0.058*\"police\" + 0.032*\"corruption\" + 0.032*\"sandra bullock\" + 0.028*\"computers\" + 0.022*\"milla jovovich\" + 0.018*\"torture\" + 0.018*\"no dialogue\" + 0.016*\"action packed\" + 0.015*\"stage\"\n", - "2021-08-24 02:39:02,387 : INFO : topic #11 (0.020): 0.262*\"Documentary\" + 0.151*\"to see\" + 0.073*\"gay\" + 0.069*\"in netflix queue\" + 0.050*\"oscar (best actor)\" + 0.022*\"india\" + 0.021*\"IMAX\" + 0.021*\"homosexuality\" + 0.018*\"matter-of-fact\" + 0.014*\"ian mckellen\"\n", - "2021-08-24 02:39:02,388 : INFO : topic #29 (0.020): 0.140*\"action\" + 0.057*\"Action\" + 0.050*\"adventure\" + 0.038*\"sequel\" + 0.035*\"boring\" + 0.032*\"Adventure\" + 0.029*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.021*\"70mm\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:39:02,389 : INFO : topic #14 (0.020): 0.117*\"Mystery\" + 0.062*\"Horror\" + 0.059*\"Thriller\" + 0.039*\"disturbing\" + 0.032*\"atmospheric\" + 0.026*\"erlend's dvds\" + 0.025*\"ominous\" + 0.025*\"tense\" + 0.022*\"menacing\" + 0.020*\"tumey's dvds\"\n", - "2021-08-24 02:39:02,390 : INFO : topic #15 (0.020): 0.060*\"coen brothers\" + 0.060*\"racism\" + 0.058*\"stephen king\" + 0.039*\"adultery\" + 0.032*\"espionage\" + 0.026*\"great acting\" + 0.024*\"los angeles\" + 0.021*\"multiple storylines\" + 0.020*\"notable nudity\" + 0.018*\"ensemble cast\"\n", - "2021-08-24 02:39:02,391 : INFO : topic diff=0.052999, rho=0.191248\n", - "2021-08-24 02:39:02,392 : INFO : PROGRESS: pass 21, at document #8000/10681\n", - "2021-08-24 02:39:02,648 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:02,674 : INFO : topic #36 (0.020): 0.103*\"dark comedy\" + 0.074*\"black comedy\" + 0.074*\"bechdel test:fail\" + 0.036*\"nazis\" + 0.036*\"funniest movies\" + 0.033*\"cold war\" + 0.014*\"russia\" + 0.014*\"communism\" + 0.014*\"author:charles dickens\" + 0.013*\"existentialism\"\n", - "2021-08-24 02:39:02,675 : INFO : topic #38 (0.020): 0.082*\"true story\" + 0.082*\"tom hanks\" + 0.058*\"biography\" + 0.052*\"based on a true story\" + 0.047*\"Drama\" + 0.046*\"drama\" + 0.031*\"historical\" + 0.028*\"biopic\" + 0.027*\"music\" + 0.024*\"paris\"\n", - "2021-08-24 02:39:02,676 : INFO : topic #13 (0.020): 0.153*\"time travel\" + 0.068*\"twist ending\" + 0.057*\"holocaust\" + 0.035*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.024*\"post apocalyptic\" + 0.020*\"very good\" + 0.020*\"michael moore\" + 0.019*\"mindfuck\" + 0.016*\"liam neeson\"\n", - "2021-08-24 02:39:02,677 : INFO : topic #1 (0.020): 0.121*\"remake\" + 0.049*\"hitchcock\" + 0.045*\"horror\" + 0.037*\"alfred hitchcock\" + 0.029*\"tumey's dvds\" + 0.029*\"moody\" + 0.026*\"Thriller\" + 0.019*\"courtroom drama\" + 0.019*\"scary\" + 0.015*\"3\"\n", - "2021-08-24 02:39:02,678 : INFO : topic #43 (0.020): 0.307*\"Crime\" + 0.159*\"Drama\" + 0.108*\"Thriller\" + 0.048*\"nudity (topless - notable)\" + 0.044*\"Mystery\" + 0.023*\"shakespeare\" + 0.022*\"nicolas cage\" + 0.017*\"remade\" + 0.015*\"sean penn\" + 0.015*\"adapted from:play\"\n", - "2021-08-24 02:39:02,679 : INFO : topic diff=0.043260, rho=0.191248\n", - "2021-08-24 02:39:02,681 : INFO : PROGRESS: pass 21, at document #10000/10681\n", - "2021-08-24 02:39:02,923 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:02,951 : INFO : topic #49 (0.020): 0.128*\"anime\" + 0.060*\"psychology\" + 0.052*\"serial killer\" + 0.051*\"high school\" + 0.045*\"teen\" + 0.022*\"japan\" + 0.017*\"mental illness\" + 0.016*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.013*\"steve martin\"\n", - "2021-08-24 02:39:02,952 : INFO : topic #47 (0.020): 0.329*\"less than 300 ratings\" + 0.152*\"Drama\" + 0.064*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.020*\"kung fu\" + 0.020*\"football\" + 0.016*\"denzel washington\" + 0.015*\"oppl\" + 0.014*\"brian de palma\" + 0.010*\"bechdel test:pass\"\n", - "2021-08-24 02:39:02,952 : INFO : topic #16 (0.020): 0.169*\"sci-fi\" + 0.087*\"dystopia\" + 0.061*\"Sci-Fi\" + 0.060*\"futuristmovies.com\" + 0.032*\"military\" + 0.032*\"future\" + 0.017*\"joaquin phoenix\" + 0.015*\"weird\" + 0.011*\"gfei own it\" + 0.011*\"court\"\n", - "2021-08-24 02:39:02,953 : INFO : topic #31 (0.020): 0.100*\"murder\" + 0.090*\"james bond\" + 0.055*\"007\" + 0.050*\"bond\" + 0.036*\"assassin\" + 0.031*\"claymation\" + 0.022*\"Thriller\" + 0.021*\"aardman\" + 0.021*\"Action\" + 0.019*\"franchise\"\n", - "2021-08-24 02:39:02,954 : INFO : topic #32 (0.020): 0.085*\"based on a tv show\" + 0.066*\"jim carrey\" + 0.065*\"england\" + 0.056*\"keanu reeves\" + 0.047*\"video game adaptation\" + 0.047*\"memory\" + 0.045*\"road trip\" + 0.033*\"not funny\" + 0.028*\"tv\" + 0.027*\"friday night movie\"\n", - "2021-08-24 02:39:02,955 : INFO : topic diff=0.030189, rho=0.191248\n", - "2021-08-24 02:39:03,106 : INFO : -20.977 per-word bound, 2064346.2 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:03,107 : INFO : PROGRESS: pass 21, at document #10681/10681\n", - "2021-08-24 02:39:03,210 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:03,236 : INFO : topic #6 (0.020): 0.091*\"martial arts\" + 0.046*\"cult film\" + 0.033*\"gothic\" + 0.032*\"campy\" + 0.028*\"seen 2006\" + 0.026*\"australia\" + 0.024*\"australian\" + 0.024*\"silly\" + 0.020*\"cult classic\" + 0.018*\"freedom\"\n", - "2021-08-24 02:39:03,237 : INFO : topic #44 (0.020): 0.124*\"Adventure\" + 0.124*\"Fantasy\" + 0.114*\"Children\" + 0.073*\"Animation\" + 0.062*\"Comedy\" + 0.047*\"disney\" + 0.046*\"fantasy\" + 0.042*\"animation\" + 0.019*\"children\" + 0.018*\"movie to see\"\n", - "2021-08-24 02:39:03,238 : INFO : topic #25 (0.020): 0.218*\"Musical\" + 0.100*\"70mm\" + 0.077*\"musical\" + 0.075*\"religion\" + 0.034*\"friendship\" + 0.026*\"rock and roll\" + 0.020*\"trains\" + 0.020*\"music\" + 0.016*\"monty python\" + 0.015*\"adapted from b'way\"\n", - "2021-08-24 02:39:03,239 : INFO : topic #34 (0.020): 0.189*\"War\" + 0.135*\"Drama\" + 0.086*\"world war ii\" + 0.070*\"drugs\" + 0.043*\"war\" + 0.042*\"history\" + 0.032*\"Action\" + 0.029*\"prison\" + 0.026*\"london\" + 0.020*\"Adventure\"\n", - "2021-08-24 02:39:03,240 : INFO : topic #2 (0.020): 0.110*\"Comedy\" + 0.098*\"satire\" + 0.089*\"lesbian\" + 0.062*\"imdb bottom 100\" + 0.026*\"sexy\" + 0.022*\"get\" + 0.020*\"olympics\" + 0.017*\"ben stiller\" + 0.016*\"john turturro\" + 0.016*\"adolf hitler\"\n", - "2021-08-24 02:39:03,241 : INFO : topic diff=0.074160, rho=0.191248\n", - "2021-08-24 02:39:03,243 : INFO : PROGRESS: pass 22, at document #2000/10681\n", - "2021-08-24 02:39:03,497 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:03,524 : INFO : topic #24 (0.020): 0.059*\"tom cruise\" + 0.035*\"golden palm\" + 0.035*\"france\" + 0.033*\"dustin hoffman\" + 0.029*\"french\" + 0.027*\"on dvr\" + 0.025*\"disability\" + 0.025*\"watch\" + 0.025*\"nicole kidman\" + 0.021*\"sweet\"\n", - "2021-08-24 02:39:03,525 : INFO : topic #28 (0.020): 0.123*\"pixar\" + 0.087*\"oscar (best actress)\" + 0.045*\"mockumentary\" + 0.033*\"dinosaurs\" + 0.028*\"medieval\" + 0.024*\"elijah wood\" + 0.018*\"computer animation\" + 0.017*\"christopher guest\" + 0.017*\"cgi\" + 0.015*\"best war films\"\n", - "2021-08-24 02:39:03,526 : INFO : topic #41 (0.020): 0.257*\"based on a book\" + 0.110*\"adapted from:book\" + 0.067*\"Drama\" + 0.062*\"netflix\" + 0.061*\"nudity (full frontal)\" + 0.050*\"robin williams\" + 0.035*\"vampire\" + 0.027*\"vampires\" + 0.014*\"book\" + 0.013*\"drama\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:03,527 : INFO : topic #26 (0.020): 0.222*\"classic\" + 0.171*\"oscar (best picture)\" + 0.066*\"oscar (best directing)\" + 0.044*\"oscar (best supporting actor)\" + 0.034*\"afi 100\" + 0.023*\"national film registry\" + 0.019*\"matt damon\" + 0.019*\"afi 100 (cheers)\" + 0.018*\"best picture\" + 0.017*\"oscar (best supporting actress)\"\n", - "2021-08-24 02:39:03,528 : INFO : topic #10 (0.020): 0.430*\"Comedy\" + 0.242*\"Romance\" + 0.199*\"Drama\" + 0.011*\"bibliothek\" + 0.008*\"betamax\" + 0.007*\"divorce\" + 0.007*\"oppl\" + 0.005*\"owen wilson\" + 0.004*\"world war i\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:39:03,529 : INFO : topic diff=0.148991, rho=0.187844\n", - "2021-08-24 02:39:03,530 : INFO : PROGRESS: pass 22, at document #4000/10681\n", - "2021-08-24 02:39:03,780 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:03,807 : INFO : topic #43 (0.020): 0.287*\"Crime\" + 0.157*\"Drama\" + 0.106*\"Thriller\" + 0.050*\"nudity (topless - notable)\" + 0.039*\"Mystery\" + 0.030*\"shakespeare\" + 0.025*\"nicolas cage\" + 0.018*\"remade\" + 0.017*\"adapted from:play\" + 0.014*\"sean penn\"\n", - "2021-08-24 02:39:03,808 : INFO : topic #22 (0.020): 0.073*\"national film registry\" + 0.056*\"oscar (best cinematography)\" + 0.040*\"classic\" + 0.034*\"imdb top 250\" + 0.031*\"stanley kubrick\" + 0.031*\"afi 100 (thrills)\" + 0.030*\"tumey's dvds\" + 0.029*\"afi 100\" + 0.027*\"black and white\" + 0.026*\"70mm\"\n", - "2021-08-24 02:39:03,809 : INFO : topic #14 (0.020): 0.118*\"Mystery\" + 0.062*\"Horror\" + 0.059*\"Thriller\" + 0.039*\"disturbing\" + 0.033*\"atmospheric\" + 0.026*\"ominous\" + 0.025*\"erlend's dvds\" + 0.025*\"tense\" + 0.022*\"menacing\" + 0.022*\"scary movies to see on halloween\"\n", - "2021-08-24 02:39:03,810 : INFO : topic #13 (0.020): 0.149*\"time travel\" + 0.073*\"twist ending\" + 0.059*\"holocaust\" + 0.034*\"bill murray\" + 0.030*\"post apocalyptic\" + 0.029*\"post-apocalyptic\" + 0.020*\"good dialogue\" + 0.018*\"mindfuck\" + 0.017*\"liam neeson\" + 0.016*\"very good\"\n", - "2021-08-24 02:39:03,811 : INFO : topic #24 (0.020): 0.066*\"tom cruise\" + 0.039*\"golden palm\" + 0.033*\"france\" + 0.032*\"dustin hoffman\" + 0.028*\"on dvr\" + 0.025*\"nicole kidman\" + 0.025*\"disability\" + 0.025*\"french\" + 0.023*\"watch\" + 0.019*\"sweet\"\n", - "2021-08-24 02:39:03,812 : INFO : topic diff=0.025509, rho=0.187844\n", - "2021-08-24 02:39:03,814 : INFO : PROGRESS: pass 22, at document #6000/10681\n", - "2021-08-24 02:39:04,037 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:04,063 : INFO : topic #3 (0.020): 0.300*\"nudity (topless)\" + 0.082*\"zombies\" + 0.053*\"nudity (topless - brief)\" + 0.026*\"virtual reality\" + 0.023*\"zombie\" + 0.018*\"keira knightley\" + 0.015*\"ingmar bergman\" + 0.014*\"robert altman\" + 0.013*\"futuristic\" + 0.012*\"strippers\"\n", - "2021-08-24 02:39:04,064 : INFO : topic #5 (0.020): 0.229*\"Action\" + 0.201*\"Thriller\" + 0.174*\"Horror\" + 0.135*\"Sci-Fi\" + 0.065*\"Adventure\" + 0.040*\"betamax\" + 0.020*\"can't remember\" + 0.009*\"courtroom\" + 0.007*\"dvd-video\" + 0.007*\"vhs\"\n", - "2021-08-24 02:39:04,065 : INFO : topic #10 (0.020): 0.447*\"Comedy\" + 0.232*\"Romance\" + 0.193*\"Drama\" + 0.013*\"bibliothek\" + 0.013*\"betamax\" + 0.005*\"divorce\" + 0.005*\"oppl\" + 0.005*\"owen wilson\" + 0.004*\"library vhs\" + 0.004*\"world war i\"\n", - "2021-08-24 02:39:04,066 : INFO : topic #44 (0.020): 0.114*\"Children\" + 0.110*\"Adventure\" + 0.104*\"Fantasy\" + 0.071*\"disney\" + 0.064*\"Animation\" + 0.054*\"Comedy\" + 0.050*\"fantasy\" + 0.043*\"animation\" + 0.021*\"children\" + 0.017*\"fairy tale\"\n", - "2021-08-24 02:39:04,067 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.083*\"quentin tarantino\" + 0.065*\"heist\" + 0.065*\"nonlinear\" + 0.054*\"tarantino\" + 0.047*\"rape\" + 0.030*\"blindfold\" + 0.024*\"terry gilliam\" + 0.018*\"xmas theme\" + 0.016*\"ireland\"\n", - "2021-08-24 02:39:04,068 : INFO : topic diff=0.052128, rho=0.187844\n", - "2021-08-24 02:39:04,070 : INFO : PROGRESS: pass 22, at document #8000/10681\n", - "2021-08-24 02:39:04,300 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:04,327 : INFO : topic #9 (0.020): 0.201*\"r\" + 0.108*\"Western\" + 0.094*\"clearplay\" + 0.086*\"nudity (topless - brief)\" + 0.057*\"Drama\" + 0.026*\"pg13\" + 0.025*\"british\" + 0.022*\"Thriller\" + 0.021*\"russell crowe\" + 0.018*\"to see\"\n", - "2021-08-24 02:39:04,328 : INFO : topic #5 (0.020): 0.230*\"Action\" + 0.201*\"Thriller\" + 0.172*\"Horror\" + 0.135*\"Sci-Fi\" + 0.066*\"Adventure\" + 0.039*\"betamax\" + 0.018*\"can't remember\" + 0.010*\"courtroom\" + 0.008*\"vhs\" + 0.007*\"dvd-video\"\n", - "2021-08-24 02:39:04,328 : INFO : topic #11 (0.020): 0.272*\"Documentary\" + 0.143*\"to see\" + 0.076*\"in netflix queue\" + 0.068*\"gay\" + 0.045*\"oscar (best actor)\" + 0.024*\"india\" + 0.020*\"homosexuality\" + 0.019*\"matter-of-fact\" + 0.018*\"IMAX\" + 0.015*\"ian mckellen\"\n", - "2021-08-24 02:39:04,329 : INFO : topic #40 (0.020): 0.123*\"nudity (full frontal - notable)\" + 0.093*\"magic\" + 0.067*\"edward norton\" + 0.058*\"sports\" + 0.049*\"not corv lib\" + 0.045*\"baseball\" + 0.044*\"hw drama\" + 0.035*\"compassionate\" + 0.025*\"christopher walken\" + 0.024*\"inspirational\"\n", - "2021-08-24 02:39:04,330 : INFO : topic #7 (0.020): 0.171*\"movie to see\" + 0.060*\"mel gibson\" + 0.047*\"boxing\" + 0.039*\"uma thurman\" + 0.034*\"kidnapping\" + 0.029*\"suburbia\" + 0.026*\"john travolta\" + 0.026*\"sylvester stallone\" + 0.021*\"basketball\" + 0.015*\"tobey maguire\"\n", - "2021-08-24 02:39:04,331 : INFO : topic diff=0.042459, rho=0.187844\n", - "2021-08-24 02:39:04,333 : INFO : PROGRESS: pass 22, at document #10000/10681\n", - "2021-08-24 02:39:04,578 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:04,605 : INFO : topic #23 (0.020): 0.059*\"mafia\" + 0.039*\"al pacino\" + 0.028*\"robert de niro\" + 0.026*\"guns\" + 0.024*\"poverty\" + 0.022*\"michael caine\" + 0.020*\"marlon brando\" + 0.019*\"tumey's dvds\" + 0.018*\"eddie murphy\" + 0.017*\"scifi\"\n", - "2021-08-24 02:39:04,606 : INFO : topic #39 (0.020): 0.107*\"bruce willis\" + 0.057*\"interesting\" + 0.047*\"africa\" + 0.034*\"love\" + 0.031*\"predictable\" + 0.028*\"avi\" + 0.026*\"gerard depardieu\" + 0.024*\"end of the world\" + 0.023*\"golden raspberry (worst actor)\" + 0.022*\"tragedy\"\n", - "2021-08-24 02:39:04,606 : INFO : topic #47 (0.020): 0.328*\"less than 300 ratings\" + 0.152*\"Drama\" + 0.064*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.020*\"kung fu\" + 0.020*\"football\" + 0.016*\"denzel washington\" + 0.015*\"oppl\" + 0.014*\"brian de palma\" + 0.010*\"bechdel test:pass\"\n", - "2021-08-24 02:39:04,607 : INFO : topic #13 (0.020): 0.145*\"time travel\" + 0.077*\"twist ending\" + 0.051*\"holocaust\" + 0.038*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.023*\"post apocalyptic\" + 0.019*\"liam neeson\" + 0.018*\"michael moore\" + 0.018*\"very good\" + 0.018*\"mindfuck\"\n", - "2021-08-24 02:39:04,608 : INFO : topic #21 (0.020): 0.099*\"Drama\" + 0.054*\"criterion\" + 0.028*\"library\" + 0.025*\"sven's to see list\" + 0.024*\"tumey's dvds\" + 0.022*\"dvd-video\" + 0.022*\"atmospheric\" + 0.022*\"reflective\" + 0.021*\"stylized\" + 0.021*\"deliberate\"\n", - "2021-08-24 02:39:04,610 : INFO : topic diff=0.029638, rho=0.187844\n", - "2021-08-24 02:39:04,754 : INFO : -20.976 per-word bound, 2062189.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:04,755 : INFO : PROGRESS: pass 22, at document #10681/10681\n", - "2021-08-24 02:39:04,853 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:04,879 : INFO : topic #14 (0.020): 0.143*\"Mystery\" + 0.064*\"Horror\" + 0.064*\"Thriller\" + 0.041*\"disturbing\" + 0.029*\"atmospheric\" + 0.025*\"erlend's dvds\" + 0.023*\"tense\" + 0.022*\"ominous\" + 0.022*\"dvd-r\" + 0.021*\"menacing\"\n", - "2021-08-24 02:39:04,880 : INFO : topic #16 (0.020): 0.157*\"sci-fi\" + 0.084*\"dystopia\" + 0.061*\"Sci-Fi\" + 0.055*\"futuristmovies.com\" + 0.036*\"military\" + 0.029*\"future\" + 0.015*\"weird\" + 0.015*\"joaquin phoenix\" + 0.014*\"betrayal\" + 0.012*\"imdb top 250\"\n", - "2021-08-24 02:39:04,881 : INFO : topic #31 (0.020): 0.116*\"murder\" + 0.077*\"james bond\" + 0.047*\"007\" + 0.046*\"bond\" + 0.045*\"assassin\" + 0.026*\"claymation\" + 0.025*\"Action\" + 0.025*\"Thriller\" + 0.020*\"killer-as-protagonist\" + 0.020*\"aardman\"\n", - "2021-08-24 02:39:04,882 : INFO : topic #23 (0.020): 0.059*\"mafia\" + 0.035*\"al pacino\" + 0.028*\"poverty\" + 0.025*\"robert de niro\" + 0.024*\"michael caine\" + 0.024*\"guns\" + 0.019*\"virus\" + 0.018*\"marlon brando\" + 0.018*\"nudity (full frontal - brief)\" + 0.017*\"tumey's dvds\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:04,882 : INFO : topic #25 (0.020): 0.218*\"Musical\" + 0.102*\"70mm\" + 0.076*\"musical\" + 0.075*\"religion\" + 0.034*\"friendship\" + 0.026*\"rock and roll\" + 0.020*\"trains\" + 0.019*\"music\" + 0.016*\"monty python\" + 0.015*\"adapted from b'way\"\n", - "2021-08-24 02:39:04,883 : INFO : topic diff=0.072898, rho=0.187844\n", - "2021-08-24 02:39:04,885 : INFO : PROGRESS: pass 23, at document #2000/10681\n", - "2021-08-24 02:39:05,152 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:05,180 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.111*\"Adventure\" + 0.106*\"Fantasy\" + 0.079*\"disney\" + 0.062*\"Animation\" + 0.056*\"Comedy\" + 0.043*\"animation\" + 0.041*\"fantasy\" + 0.020*\"children\" + 0.016*\"fairy tale\"\n", - "2021-08-24 02:39:05,181 : INFO : topic #11 (0.020): 0.270*\"Documentary\" + 0.172*\"to see\" + 0.074*\"gay\" + 0.063*\"in netflix queue\" + 0.051*\"oscar (best actor)\" + 0.028*\"india\" + 0.019*\"homosexuality\" + 0.017*\"IMAX\" + 0.015*\"matter-of-fact\" + 0.011*\"ian mckellen\"\n", - "2021-08-24 02:39:05,182 : INFO : topic #42 (0.020): 0.155*\"johnny depp\" + 0.078*\"tim burton\" + 0.065*\"martin scorsese\" + 0.036*\"narrated\" + 0.031*\"george clooney\" + 0.026*\"monster\" + 0.022*\"alien invasion\" + 0.021*\"brothers\" + 0.020*\"ei muista\" + 0.017*\"disaster\"\n", - "2021-08-24 02:39:05,183 : INFO : topic #2 (0.020): 0.106*\"satire\" + 0.099*\"Comedy\" + 0.095*\"lesbian\" + 0.053*\"imdb bottom 100\" + 0.027*\"sexy\" + 0.022*\"get\" + 0.021*\"olympics\" + 0.018*\"john turturro\" + 0.016*\"1\" + 0.016*\"queer\"\n", - "2021-08-24 02:39:05,184 : INFO : topic #7 (0.020): 0.205*\"movie to see\" + 0.065*\"mel gibson\" + 0.045*\"boxing\" + 0.035*\"uma thurman\" + 0.032*\"kidnapping\" + 0.031*\"john travolta\" + 0.028*\"sylvester stallone\" + 0.024*\"suburbia\" + 0.019*\"basketball\" + 0.014*\"sports\"\n", - "2021-08-24 02:39:05,185 : INFO : topic diff=0.145917, rho=0.184615\n", - "2021-08-24 02:39:05,186 : INFO : PROGRESS: pass 23, at document #4000/10681\n", - "2021-08-24 02:39:05,453 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:05,479 : INFO : topic #46 (0.020): 0.086*\"dvd\" + 0.063*\"overrated\" + 0.054*\"seen more than once\" + 0.043*\"owned\" + 0.034*\"samuel l. jackson\" + 0.033*\"imdb top 250\" + 0.029*\"bibliothek\" + 0.027*\"eric's dvds\" + 0.025*\"kevin spacey\" + 0.023*\"seen at the cinema\"\n", - "2021-08-24 02:39:05,480 : INFO : topic #39 (0.020): 0.131*\"bruce willis\" + 0.052*\"interesting\" + 0.033*\"love\" + 0.028*\"africa\" + 0.025*\"end of the world\" + 0.025*\"golden raspberry (worst actor)\" + 0.024*\"good\" + 0.023*\"apocalypse\" + 0.023*\"predictable\" + 0.022*\"avi\"\n", - "2021-08-24 02:39:05,481 : INFO : topic #2 (0.020): 0.102*\"satire\" + 0.099*\"Comedy\" + 0.096*\"lesbian\" + 0.051*\"imdb bottom 100\" + 0.032*\"sexy\" + 0.027*\"get\" + 0.019*\"olympics\" + 0.018*\"1\" + 0.017*\"john turturro\" + 0.016*\"queer\"\n", - "2021-08-24 02:39:05,482 : INFO : topic #20 (0.020): 0.139*\"politics\" + 0.072*\"jack nicholson\" + 0.068*\"documentary\" + 0.044*\"terrorism\" + 0.035*\"love story\" + 0.035*\"seen 2008\" + 0.032*\"aviation\" + 0.031*\"propaganda\" + 0.031*\"political\" + 0.030*\"Documentary\"\n", - "2021-08-24 02:39:05,482 : INFO : topic #8 (0.020): 0.103*\"space\" + 0.048*\"harrison ford\" + 0.026*\"spaghetti western\" + 0.026*\"christianity\" + 0.025*\"george lucas\" + 0.025*\"kevin smith\" + 0.024*\"archaeology\" + 0.022*\"sergio leone\" + 0.019*\"space opera\" + 0.019*\"fantasy\"\n", - "2021-08-24 02:39:05,484 : INFO : topic diff=0.025060, rho=0.184615\n", - "2021-08-24 02:39:05,485 : INFO : PROGRESS: pass 23, at document #6000/10681\n", - "2021-08-24 02:39:05,717 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:05,743 : INFO : topic #30 (0.020): 0.658*\"Drama\" + 0.035*\"Film-Noir\" + 0.013*\"tumey's dvds\" + 0.010*\"small town\" + 0.008*\"italy\" + 0.007*\"glbt\" + 0.007*\"erlend's dvds\" + 0.007*\"oscar (best foreign language film)\" + 0.007*\"film noir\" + 0.007*\"witch\"\n", - "2021-08-24 02:39:05,745 : INFO : topic #38 (0.020): 0.083*\"tom hanks\" + 0.079*\"true story\" + 0.058*\"biography\" + 0.056*\"based on a true story\" + 0.047*\"Drama\" + 0.045*\"drama\" + 0.031*\"music\" + 0.029*\"historical\" + 0.027*\"biopic\" + 0.024*\"paris\"\n", - "2021-08-24 02:39:05,746 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.058*\"Action\" + 0.050*\"adventure\" + 0.038*\"sequel\" + 0.035*\"boring\" + 0.032*\"Adventure\" + 0.029*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.021*\"70mm\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:39:05,747 : INFO : topic #47 (0.020): 0.245*\"less than 300 ratings\" + 0.137*\"Drama\" + 0.068*\"nudity (rear)\" + 0.040*\"jackie chan\" + 0.028*\"kung fu\" + 0.025*\"football\" + 0.019*\"oppl\" + 0.019*\"denzel washington\" + 0.018*\"brian de palma\" + 0.015*\"bechdel test:pass\"\n", - "2021-08-24 02:39:05,748 : INFO : topic #3 (0.020): 0.300*\"nudity (topless)\" + 0.082*\"zombies\" + 0.055*\"nudity (topless - brief)\" + 0.026*\"virtual reality\" + 0.023*\"zombie\" + 0.018*\"keira knightley\" + 0.015*\"ingmar bergman\" + 0.014*\"robert altman\" + 0.013*\"futuristic\" + 0.012*\"strippers\"\n", - "2021-08-24 02:39:05,749 : INFO : topic diff=0.050976, rho=0.184615\n", - "2021-08-24 02:39:05,751 : INFO : PROGRESS: pass 23, at document #8000/10681\n", - "2021-08-24 02:39:05,990 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:06,017 : INFO : topic #7 (0.020): 0.172*\"movie to see\" + 0.060*\"mel gibson\" + 0.047*\"boxing\" + 0.039*\"uma thurman\" + 0.034*\"kidnapping\" + 0.029*\"suburbia\" + 0.026*\"john travolta\" + 0.026*\"sylvester stallone\" + 0.021*\"basketball\" + 0.015*\"tobey maguire\"\n", - "2021-08-24 02:39:06,018 : INFO : topic #28 (0.020): 0.127*\"pixar\" + 0.092*\"oscar (best actress)\" + 0.038*\"mockumentary\" + 0.026*\"medieval\" + 0.025*\"elijah wood\" + 0.023*\"dinosaurs\" + 0.021*\"nostalgia\" + 0.021*\"computer animation\" + 0.017*\"cgi\" + 0.016*\"high fantasy\"\n", - "2021-08-24 02:39:06,019 : INFO : topic #35 (0.020): 0.163*\"comedy\" + 0.137*\"Comedy\" + 0.082*\"funny\" + 0.050*\"parody\" + 0.038*\"hilarious\" + 0.032*\"can't remember\" + 0.021*\"stupid\" + 0.021*\"spoof\" + 0.020*\"funny as hell\" + 0.018*\"seen more than once\"\n", - "2021-08-24 02:39:06,019 : INFO : topic #25 (0.020): 0.207*\"Musical\" + 0.136*\"70mm\" + 0.067*\"religion\" + 0.062*\"musical\" + 0.027*\"rock and roll\" + 0.024*\"friendship\" + 0.023*\"monty python\" + 0.020*\"music\" + 0.016*\"trains\" + 0.016*\"adapted from b'way\"\n", - "2021-08-24 02:39:06,020 : INFO : topic #46 (0.020): 0.090*\"dvd\" + 0.064*\"overrated\" + 0.062*\"seen more than once\" + 0.042*\"owned\" + 0.032*\"bibliothek\" + 0.030*\"samuel l. jackson\" + 0.029*\"eric's dvds\" + 0.029*\"imdb top 250\" + 0.023*\"tumey's dvds\" + 0.022*\"seen at the cinema\"\n", - "2021-08-24 02:39:06,021 : INFO : topic diff=0.041707, rho=0.184615\n", - "2021-08-24 02:39:06,023 : INFO : PROGRESS: pass 23, at document #10000/10681\n", - "2021-08-24 02:39:06,327 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:06,366 : INFO : topic #18 (0.020): 0.045*\"creepy\" + 0.042*\"surrealism\" + 0.041*\"franchise\" + 0.029*\"lurid\" + 0.029*\"slasher\" + 0.028*\"robert downey jr\" + 0.027*\"united states\" + 0.024*\"unique\" + 0.021*\"macabre\" + 0.020*\"adolescence\"\n", - "2021-08-24 02:39:06,368 : INFO : topic #33 (0.020): 0.188*\"comic book\" + 0.118*\"superhero\" + 0.079*\"super-hero\" + 0.040*\"adapted from:comic\" + 0.034*\"gene hackman\" + 0.027*\"action\" + 0.024*\"alter ego\" + 0.024*\"Action\" + 0.021*\"batman\" + 0.019*\"dark\"\n", - "2021-08-24 02:39:06,369 : INFO : topic #45 (0.020): 0.055*\"japan\" + 0.045*\"tense\" + 0.041*\"revenge\" + 0.036*\"clint eastwood\" + 0.031*\"forceful\" + 0.029*\"gritty\" + 0.027*\"corvallis library\" + 0.026*\"atmospheric\" + 0.025*\"Action\" + 0.022*\"sweeping\"\n", - "2021-08-24 02:39:06,383 : INFO : topic #42 (0.020): 0.166*\"johnny depp\" + 0.072*\"tim burton\" + 0.055*\"martin scorsese\" + 0.039*\"george clooney\" + 0.039*\"narrated\" + 0.024*\"ei muista\" + 0.023*\"monster\" + 0.017*\"brothers\" + 0.017*\"alien invasion\" + 0.016*\"las vegas\"\n", - "2021-08-24 02:39:06,384 : INFO : topic #11 (0.020): 0.267*\"Documentary\" + 0.148*\"to see\" + 0.082*\"in netflix queue\" + 0.073*\"gay\" + 0.036*\"oscar (best actor)\" + 0.025*\"homosexuality\" + 0.020*\"india\" + 0.019*\"matter-of-fact\" + 0.017*\"IMAX\" + 0.014*\"ian mckellen\"\n", - "2021-08-24 02:39:06,390 : INFO : topic diff=0.029126, rho=0.184615\n", - "2021-08-24 02:39:06,562 : INFO : -20.976 per-word bound, 2062457.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:06,563 : INFO : PROGRESS: pass 23, at document #10681/10681\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:06,671 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:06,699 : INFO : topic #28 (0.020): 0.147*\"pixar\" + 0.076*\"oscar (best actress)\" + 0.041*\"mockumentary\" + 0.030*\"elijah wood\" + 0.024*\"medieval\" + 0.023*\"computer animation\" + 0.020*\"cgi\" + 0.020*\"dinosaurs\" + 0.017*\"family drama\" + 0.016*\"social commentary\"\n", - "2021-08-24 02:39:06,700 : INFO : topic #15 (0.020): 0.055*\"racism\" + 0.054*\"coen brothers\" + 0.044*\"stephen king\" + 0.040*\"great acting\" + 0.040*\"adultery\" + 0.037*\"espionage\" + 0.026*\"los angeles\" + 0.020*\"san francisco\" + 0.020*\"ensemble cast\" + 0.019*\"notable nudity\"\n", - "2021-08-24 02:39:06,701 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.043*\"tense\" + 0.040*\"revenge\" + 0.034*\"clint eastwood\" + 0.030*\"forceful\" + 0.029*\"gritty\" + 0.027*\"corvallis library\" + 0.025*\"atmospheric\" + 0.024*\"Action\" + 0.023*\"akira kurosawa\"\n", - "2021-08-24 02:39:06,702 : INFO : topic #31 (0.020): 0.116*\"murder\" + 0.077*\"james bond\" + 0.047*\"007\" + 0.046*\"bond\" + 0.045*\"assassin\" + 0.026*\"claymation\" + 0.025*\"Thriller\" + 0.025*\"Action\" + 0.020*\"killer-as-protagonist\" + 0.020*\"aardman\"\n", - "2021-08-24 02:39:06,703 : INFO : topic #40 (0.020): 0.119*\"nudity (full frontal - notable)\" + 0.115*\"magic\" + 0.058*\"edward norton\" + 0.057*\"not corv lib\" + 0.050*\"sports\" + 0.036*\"baseball\" + 0.035*\"hw drama\" + 0.031*\"inspirational\" + 0.027*\"compassionate\" + 0.026*\"don cheadle\"\n", - "2021-08-24 02:39:06,704 : INFO : topic diff=0.071633, rho=0.184615\n", - "2021-08-24 02:39:06,705 : INFO : PROGRESS: pass 24, at document #2000/10681\n", - "2021-08-24 02:39:06,969 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:06,995 : INFO : topic #6 (0.020): 0.073*\"martial arts\" + 0.062*\"cult film\" + 0.036*\"gothic\" + 0.034*\"campy\" + 0.027*\"australia\" + 0.025*\"silly\" + 0.025*\"cult classic\" + 0.023*\"australian\" + 0.022*\"seen 2006\" + 0.019*\"sam raimi\"\n", - "2021-08-24 02:39:06,995 : INFO : topic #19 (0.020): 0.072*\"pg-13\" + 0.067*\"ghosts\" + 0.039*\"wedding\" + 0.038*\"julia roberts\" + 0.036*\"jude law\" + 0.032*\"hollywood\" + 0.028*\"want to own\" + 0.024*\"infidelity\" + 0.024*\"afternoon section\" + 0.022*\"prostitution\"\n", - "2021-08-24 02:39:06,996 : INFO : topic #8 (0.020): 0.112*\"space\" + 0.049*\"harrison ford\" + 0.027*\"christianity\" + 0.025*\"kevin smith\" + 0.025*\"george lucas\" + 0.024*\"archaeology\" + 0.020*\"space opera\" + 0.019*\"fantasy\" + 0.019*\"marx brothers\" + 0.019*\"sergio leone\"\n", - "2021-08-24 02:39:06,997 : INFO : topic #36 (0.020): 0.123*\"dark comedy\" + 0.088*\"bechdel test:fail\" + 0.078*\"black comedy\" + 0.043*\"nazis\" + 0.036*\"cold war\" + 0.027*\"funniest movies\" + 0.016*\"existentialism\" + 0.015*\"communism\" + 0.015*\"nuclear war\" + 0.014*\"russia\"\n", - "2021-08-24 02:39:06,998 : INFO : topic #30 (0.020): 0.645*\"Drama\" + 0.039*\"Film-Noir\" + 0.012*\"tumey's dvds\" + 0.010*\"small town\" + 0.008*\"witch\" + 0.008*\"film noir\" + 0.007*\"erlend's dvds\" + 0.007*\"italy\" + 0.007*\"animal:dog\" + 0.007*\"glbt\"\n", - "2021-08-24 02:39:07,000 : INFO : topic diff=0.143274, rho=0.181547\n", - "2021-08-24 02:39:07,001 : INFO : PROGRESS: pass 24, at document #4000/10681\n", - "2021-08-24 02:39:07,243 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:07,270 : INFO : topic #11 (0.020): 0.263*\"Documentary\" + 0.161*\"to see\" + 0.074*\"gay\" + 0.069*\"in netflix queue\" + 0.054*\"oscar (best actor)\" + 0.024*\"india\" + 0.021*\"homosexuality\" + 0.015*\"matter-of-fact\" + 0.015*\"IMAX\" + 0.012*\"ian mckellen\"\n", - "2021-08-24 02:39:07,271 : INFO : topic #40 (0.020): 0.121*\"nudity (full frontal - notable)\" + 0.083*\"magic\" + 0.071*\"edward norton\" + 0.061*\"sports\" + 0.051*\"not corv lib\" + 0.046*\"baseball\" + 0.037*\"hw drama\" + 0.031*\"inspirational\" + 0.025*\"christopher walken\" + 0.025*\"compassionate\"\n", - "2021-08-24 02:39:07,272 : INFO : topic #41 (0.020): 0.250*\"based on a book\" + 0.114*\"adapted from:book\" + 0.068*\"Drama\" + 0.057*\"robin williams\" + 0.057*\"nudity (full frontal)\" + 0.054*\"netflix\" + 0.038*\"vampire\" + 0.029*\"vampires\" + 0.015*\"book\" + 0.013*\"drama\"\n", - "2021-08-24 02:39:07,272 : INFO : topic #28 (0.020): 0.127*\"pixar\" + 0.093*\"oscar (best actress)\" + 0.042*\"mockumentary\" + 0.028*\"medieval\" + 0.028*\"dinosaurs\" + 0.022*\"nostalgia\" + 0.020*\"elijah wood\" + 0.019*\"computer animation\" + 0.016*\"christopher guest\" + 0.016*\"cgi\"\n", - "2021-08-24 02:39:07,273 : INFO : topic #37 (0.020): 0.060*\"easily confused with other movie(s) (title)\" + 0.041*\"vietnam war\" + 0.041*\"want to see again\" + 0.041*\"vietnam\" + 0.041*\"oscar (best supporting actress)\" + 0.036*\"food\" + 0.033*\"j netflix\" + 0.022*\"19th century\" + 0.020*\"peter sellers\" + 0.019*\"slow\"\n", - "2021-08-24 02:39:07,275 : INFO : topic diff=0.024666, rho=0.181547\n", - "2021-08-24 02:39:07,276 : INFO : PROGRESS: pass 24, at document #6000/10681\n", - "2021-08-24 02:39:07,498 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:07,525 : INFO : topic #32 (0.020): 0.089*\"based on a tv show\" + 0.067*\"jim carrey\" + 0.061*\"keanu reeves\" + 0.048*\"video game adaptation\" + 0.046*\"memory\" + 0.046*\"england\" + 0.038*\"star trek\" + 0.037*\"tv\" + 0.034*\"road trip\" + 0.028*\"friday night movie\"\n", - "2021-08-24 02:39:07,526 : INFO : topic #17 (0.020): 0.059*\"will smith\" + 0.058*\"police\" + 0.032*\"corruption\" + 0.032*\"sandra bullock\" + 0.028*\"computers\" + 0.022*\"milla jovovich\" + 0.018*\"torture\" + 0.018*\"no dialogue\" + 0.017*\"stage\" + 0.016*\"action packed\"\n", - "2021-08-24 02:39:07,527 : INFO : topic #33 (0.020): 0.157*\"comic book\" + 0.125*\"superhero\" + 0.068*\"super-hero\" + 0.044*\"gene hackman\" + 0.039*\"adapted from:comic\" + 0.028*\"batman\" + 0.024*\"alter ego\" + 0.024*\"Action\" + 0.023*\"action\" + 0.017*\"based on a comic\"\n", - "2021-08-24 02:39:07,528 : INFO : topic #10 (0.020): 0.447*\"Comedy\" + 0.232*\"Romance\" + 0.193*\"Drama\" + 0.013*\"bibliothek\" + 0.013*\"betamax\" + 0.005*\"divorce\" + 0.005*\"oppl\" + 0.005*\"owen wilson\" + 0.004*\"library vhs\" + 0.004*\"world war i\"\n", - "2021-08-24 02:39:07,529 : INFO : topic #11 (0.020): 0.263*\"Documentary\" + 0.150*\"to see\" + 0.073*\"gay\" + 0.069*\"in netflix queue\" + 0.049*\"oscar (best actor)\" + 0.022*\"india\" + 0.021*\"IMAX\" + 0.021*\"homosexuality\" + 0.018*\"matter-of-fact\" + 0.014*\"ian mckellen\"\n", - "2021-08-24 02:39:07,530 : INFO : topic diff=0.049720, rho=0.181547\n", - "2021-08-24 02:39:07,532 : INFO : PROGRESS: pass 24, at document #8000/10681\n", - "2021-08-24 02:39:07,780 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:07,807 : INFO : topic #28 (0.020): 0.127*\"pixar\" + 0.092*\"oscar (best actress)\" + 0.038*\"mockumentary\" + 0.026*\"medieval\" + 0.025*\"elijah wood\" + 0.023*\"dinosaurs\" + 0.021*\"nostalgia\" + 0.021*\"computer animation\" + 0.017*\"cgi\" + 0.016*\"high fantasy\"\n", - "2021-08-24 02:39:07,808 : INFO : topic #30 (0.020): 0.658*\"Drama\" + 0.035*\"Film-Noir\" + 0.013*\"tumey's dvds\" + 0.010*\"small town\" + 0.008*\"oscar (best foreign language film)\" + 0.008*\"italy\" + 0.007*\"erlend's dvds\" + 0.007*\"witch\" + 0.006*\"glbt\" + 0.006*\"animal:dog\"\n", - "2021-08-24 02:39:07,809 : INFO : topic #8 (0.020): 0.092*\"space\" + 0.040*\"harrison ford\" + 0.031*\"christianity\" + 0.025*\"marx brothers\" + 0.023*\"kevin smith\" + 0.022*\"george lucas\" + 0.022*\"spaghetti western\" + 0.020*\"archaeology\" + 0.019*\"werewolves\" + 0.018*\"sergio leone\"\n", - "2021-08-24 02:39:07,810 : INFO : topic #26 (0.020): 0.217*\"classic\" + 0.135*\"oscar (best picture)\" + 0.054*\"oscar (best directing)\" + 0.052*\"oscar (best supporting actor)\" + 0.028*\"afi 100\" + 0.027*\"matt damon\" + 0.024*\"national film registry\" + 0.022*\"afi 100 (cheers)\" + 0.020*\"oscar (best supporting actress)\" + 0.018*\"afi 100 (laughs)\"\n", - "2021-08-24 02:39:07,810 : INFO : topic #9 (0.020): 0.202*\"r\" + 0.108*\"Western\" + 0.095*\"clearplay\" + 0.084*\"nudity (topless - brief)\" + 0.057*\"Drama\" + 0.026*\"pg13\" + 0.025*\"british\" + 0.022*\"Thriller\" + 0.021*\"russell crowe\" + 0.019*\"to see\"\n", - "2021-08-24 02:39:07,812 : INFO : topic diff=0.041102, rho=0.181547\n", - "2021-08-24 02:39:07,813 : INFO : PROGRESS: pass 24, at document #10000/10681\n", - "2021-08-24 02:39:08,124 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:08,151 : INFO : topic #32 (0.020): 0.085*\"based on a tv show\" + 0.067*\"jim carrey\" + 0.064*\"england\" + 0.056*\"keanu reeves\" + 0.047*\"video game adaptation\" + 0.047*\"memory\" + 0.045*\"road trip\" + 0.033*\"not funny\" + 0.028*\"tv\" + 0.027*\"friday night movie\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:08,152 : INFO : topic #4 (0.020): 0.106*\"romance\" + 0.049*\"Romance\" + 0.044*\"chick flick\" + 0.042*\"new york city\" + 0.041*\"watched 2006\" + 0.039*\"cars\" + 0.028*\"new york\" + 0.027*\"sean connery\" + 0.025*\"girlie movie\" + 0.022*\"dance\"\n", - "2021-08-24 02:39:08,153 : INFO : topic #19 (0.020): 0.095*\"pg-13\" + 0.069*\"ghosts\" + 0.043*\"jude law\" + 0.035*\"wedding\" + 0.031*\"julia roberts\" + 0.028*\"want to own\" + 0.027*\"infidelity\" + 0.025*\"hollywood\" + 0.021*\"prostitution\" + 0.019*\"afternoon section\"\n", - "2021-08-24 02:39:08,154 : INFO : topic #49 (0.020): 0.127*\"anime\" + 0.060*\"psychology\" + 0.052*\"serial killer\" + 0.051*\"high school\" + 0.045*\"teen\" + 0.021*\"japan\" + 0.017*\"mental illness\" + 0.016*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.013*\"steve martin\"\n", - "2021-08-24 02:39:08,154 : INFO : topic #11 (0.020): 0.267*\"Documentary\" + 0.148*\"to see\" + 0.082*\"in netflix queue\" + 0.073*\"gay\" + 0.036*\"oscar (best actor)\" + 0.025*\"homosexuality\" + 0.020*\"india\" + 0.019*\"matter-of-fact\" + 0.017*\"IMAX\" + 0.014*\"ian mckellen\"\n", - "2021-08-24 02:39:08,156 : INFO : topic diff=0.028524, rho=0.181547\n", - "2021-08-24 02:39:08,304 : INFO : -20.975 per-word bound, 2061614.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:08,305 : INFO : PROGRESS: pass 24, at document #10681/10681\n", - "2021-08-24 02:39:08,401 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:08,427 : INFO : topic #0 (0.020): 0.091*\"crime\" + 0.057*\"violence\" + 0.055*\"brad pitt\" + 0.047*\"Crime\" + 0.046*\"violent\" + 0.042*\"morgan freeman\" + 0.033*\"organized crime\" + 0.030*\"tommy lee jones\" + 0.029*\"philip seymour hoffman\" + 0.028*\"Thriller\"\n", - "2021-08-24 02:39:08,428 : INFO : topic #15 (0.020): 0.055*\"racism\" + 0.054*\"coen brothers\" + 0.044*\"stephen king\" + 0.040*\"adultery\" + 0.040*\"great acting\" + 0.037*\"espionage\" + 0.026*\"los angeles\" + 0.020*\"san francisco\" + 0.020*\"ensemble cast\" + 0.019*\"notable nudity\"\n", - "2021-08-24 02:39:08,429 : INFO : topic #24 (0.020): 0.049*\"tom cruise\" + 0.038*\"france\" + 0.033*\"golden palm\" + 0.032*\"nicole kidman\" + 0.030*\"disability\" + 0.030*\"on dvr\" + 0.028*\"french\" + 0.026*\"dustin hoffman\" + 0.019*\"mask\" + 0.018*\"bank robbery\"\n", - "2021-08-24 02:39:08,430 : INFO : topic #9 (0.020): 0.249*\"r\" + 0.126*\"clearplay\" + 0.086*\"Western\" + 0.074*\"nudity (topless - brief)\" + 0.066*\"Drama\" + 0.038*\"pg13\" + 0.028*\"to see\" + 0.025*\"movie to see\" + 0.025*\"Thriller\" + 0.017*\"british\"\n", - "2021-08-24 02:39:08,430 : INFO : topic #17 (0.020): 0.068*\"will smith\" + 0.059*\"police\" + 0.047*\"corruption\" + 0.029*\"torture\" + 0.023*\"action packed\" + 0.023*\"milla jovovich\" + 0.021*\"computers\" + 0.021*\"sandra bullock\" + 0.018*\"tattoo\" + 0.017*\"suicide attempt\"\n", - "2021-08-24 02:39:08,432 : INFO : topic diff=0.070556, rho=0.181547\n", - "2021-08-24 02:39:08,434 : INFO : PROGRESS: pass 25, at document #2000/10681\n", - "2021-08-24 02:39:08,691 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:08,717 : INFO : topic #5 (0.020): 0.242*\"Action\" + 0.212*\"Thriller\" + 0.168*\"Horror\" + 0.131*\"Sci-Fi\" + 0.066*\"Adventure\" + 0.026*\"betamax\" + 0.018*\"can't remember\" + 0.008*\"courtroom\" + 0.006*\"dvd-video\" + 0.006*\"vhs\"\n", - "2021-08-24 02:39:08,718 : INFO : topic #21 (0.020): 0.094*\"Drama\" + 0.048*\"criterion\" + 0.027*\"library\" + 0.023*\"tumey's dvds\" + 0.021*\"sven's to see list\" + 0.021*\"irreverent\" + 0.021*\"atmospheric\" + 0.020*\"dvd-video\" + 0.020*\"reflective\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:39:08,718 : INFO : topic #2 (0.020): 0.106*\"satire\" + 0.100*\"Comedy\" + 0.094*\"lesbian\" + 0.053*\"imdb bottom 100\" + 0.027*\"sexy\" + 0.022*\"get\" + 0.021*\"olympics\" + 0.018*\"john turturro\" + 0.016*\"1\" + 0.015*\"queer\"\n", - "2021-08-24 02:39:08,719 : INFO : topic #28 (0.020): 0.124*\"pixar\" + 0.088*\"oscar (best actress)\" + 0.045*\"mockumentary\" + 0.032*\"dinosaurs\" + 0.028*\"medieval\" + 0.024*\"elijah wood\" + 0.019*\"computer animation\" + 0.017*\"christopher guest\" + 0.017*\"cgi\" + 0.015*\"best war films\"\n", - "2021-08-24 02:39:08,720 : INFO : topic #6 (0.020): 0.073*\"martial arts\" + 0.063*\"cult film\" + 0.036*\"gothic\" + 0.034*\"campy\" + 0.027*\"australia\" + 0.025*\"silly\" + 0.025*\"cult classic\" + 0.023*\"australian\" + 0.022*\"seen 2006\" + 0.019*\"sam raimi\"\n", - "2021-08-24 02:39:08,721 : INFO : topic diff=0.140794, rho=0.178627\n", - "2021-08-24 02:39:08,723 : INFO : PROGRESS: pass 25, at document #4000/10681\n", - "2021-08-24 02:39:08,971 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:08,997 : INFO : topic #42 (0.020): 0.153*\"johnny depp\" + 0.090*\"tim burton\" + 0.054*\"martin scorsese\" + 0.039*\"narrated\" + 0.032*\"george clooney\" + 0.026*\"ei muista\" + 0.026*\"monster\" + 0.019*\"alien invasion\" + 0.019*\"brothers\" + 0.016*\"disaster\"\n", - "2021-08-24 02:39:08,998 : INFO : topic #23 (0.020): 0.075*\"mafia\" + 0.043*\"al pacino\" + 0.036*\"robert de niro\" + 0.023*\"guns\" + 0.022*\"marlon brando\" + 0.020*\"poverty\" + 0.017*\"tumey's dvds\" + 0.017*\"eddie murphy\" + 0.017*\"francis ford coppola\" + 0.017*\"michael caine\"\n", - "2021-08-24 02:39:08,998 : INFO : topic #46 (0.020): 0.086*\"dvd\" + 0.064*\"overrated\" + 0.053*\"seen more than once\" + 0.043*\"owned\" + 0.034*\"samuel l. jackson\" + 0.033*\"imdb top 250\" + 0.030*\"bibliothek\" + 0.027*\"eric's dvds\" + 0.025*\"kevin spacey\" + 0.024*\"seen at the cinema\"\n", - "2021-08-24 02:39:08,999 : INFO : topic #20 (0.020): 0.140*\"politics\" + 0.072*\"jack nicholson\" + 0.068*\"documentary\" + 0.044*\"terrorism\" + 0.035*\"love story\" + 0.035*\"seen 2008\" + 0.032*\"propaganda\" + 0.031*\"aviation\" + 0.031*\"political\" + 0.030*\"Documentary\"\n", - "2021-08-24 02:39:09,000 : INFO : topic #13 (0.020): 0.149*\"time travel\" + 0.073*\"twist ending\" + 0.059*\"holocaust\" + 0.034*\"bill murray\" + 0.030*\"post apocalyptic\" + 0.029*\"post-apocalyptic\" + 0.020*\"good dialogue\" + 0.018*\"mindfuck\" + 0.017*\"liam neeson\" + 0.016*\"very good\"\n", - "2021-08-24 02:39:09,001 : INFO : topic diff=0.024025, rho=0.178627\n", - "2021-08-24 02:39:09,003 : INFO : PROGRESS: pass 25, at document #6000/10681\n", - "2021-08-24 02:39:09,232 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:09,258 : INFO : topic #9 (0.020): 0.209*\"r\" + 0.103*\"clearplay\" + 0.094*\"Western\" + 0.088*\"nudity (topless - brief)\" + 0.059*\"Drama\" + 0.027*\"pg13\" + 0.025*\"british\" + 0.023*\"Thriller\" + 0.021*\"russell crowe\" + 0.021*\"to see\"\n", - "2021-08-24 02:39:09,259 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.084*\"quentin tarantino\" + 0.065*\"heist\" + 0.064*\"nonlinear\" + 0.054*\"tarantino\" + 0.046*\"rape\" + 0.030*\"blindfold\" + 0.023*\"terry gilliam\" + 0.018*\"xmas theme\" + 0.016*\"ireland\"\n", - "2021-08-24 02:39:09,260 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.058*\"Action\" + 0.050*\"adventure\" + 0.038*\"sequel\" + 0.035*\"boring\" + 0.032*\"Adventure\" + 0.029*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.022*\"70mm\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:39:09,261 : INFO : topic #27 (0.020): 0.149*\"aliens\" + 0.088*\"steven spielberg\" + 0.057*\"family\" + 0.035*\"michael crichton\" + 0.025*\"alien\" + 0.025*\"gambling\" + 0.022*\"to-rent\" + 0.022*\"didn't finish\" + 0.020*\"childhood\" + 0.019*\"poker\"\n", - "2021-08-24 02:39:09,261 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.046*\"directorial debut\" + 0.045*\"surreal\" + 0.025*\"woody allen\" + 0.024*\"bittersweet\" + 0.024*\"humorous\" + 0.021*\"whimsical\" + 0.020*\"coming of age\" + 0.019*\"underrated\" + 0.019*\"erlend's dvds\"\n", - "2021-08-24 02:39:09,263 : INFO : topic diff=0.048947, rho=0.178627\n", - "2021-08-24 02:39:09,264 : INFO : PROGRESS: pass 25, at document #8000/10681\n", - "2021-08-24 02:39:09,505 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:09,532 : INFO : topic #4 (0.020): 0.110*\"romance\" + 0.050*\"Romance\" + 0.045*\"chick flick\" + 0.038*\"new york city\" + 0.038*\"cars\" + 0.032*\"sean connery\" + 0.027*\"watched 2006\" + 0.026*\"new york\" + 0.026*\"girlie movie\" + 0.022*\"light\"\n", - "2021-08-24 02:39:09,532 : INFO : topic #8 (0.020): 0.092*\"space\" + 0.040*\"harrison ford\" + 0.031*\"christianity\" + 0.025*\"marx brothers\" + 0.023*\"kevin smith\" + 0.023*\"george lucas\" + 0.022*\"spaghetti western\" + 0.020*\"archaeology\" + 0.019*\"werewolves\" + 0.018*\"sergio leone\"\n", - "2021-08-24 02:39:09,533 : INFO : topic #20 (0.020): 0.135*\"politics\" + 0.089*\"documentary\" + 0.065*\"jack nicholson\" + 0.043*\"terrorism\" + 0.037*\"seen 2008\" + 0.034*\"propaganda\" + 0.032*\"Documentary\" + 0.028*\"political\" + 0.028*\"love story\" + 0.027*\"aviation\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:09,534 : INFO : topic #17 (0.020): 0.062*\"police\" + 0.056*\"will smith\" + 0.031*\"computers\" + 0.029*\"corruption\" + 0.027*\"sandra bullock\" + 0.019*\"milla jovovich\" + 0.019*\"stage\" + 0.018*\"no dialogue\" + 0.018*\"torture\" + 0.016*\"action packed\"\n", - "2021-08-24 02:39:09,535 : INFO : topic #6 (0.020): 0.091*\"martial arts\" + 0.058*\"cult film\" + 0.042*\"gothic\" + 0.035*\"campy\" + 0.026*\"cult classic\" + 0.026*\"silly\" + 0.025*\"australia\" + 0.024*\"australian\" + 0.021*\"sam raimi\" + 0.020*\"seen 2006\"\n", - "2021-08-24 02:39:09,536 : INFO : topic diff=0.040500, rho=0.178627\n", - "2021-08-24 02:39:09,538 : INFO : PROGRESS: pass 25, at document #10000/10681\n", - "2021-08-24 02:39:09,785 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:09,812 : INFO : topic #34 (0.020): 0.173*\"War\" + 0.126*\"Drama\" + 0.092*\"world war ii\" + 0.069*\"drugs\" + 0.050*\"war\" + 0.041*\"history\" + 0.033*\"Action\" + 0.029*\"prison\" + 0.025*\"london\" + 0.019*\"Adventure\"\n", - "2021-08-24 02:39:09,813 : INFO : topic #32 (0.020): 0.085*\"based on a tv show\" + 0.067*\"jim carrey\" + 0.064*\"england\" + 0.056*\"keanu reeves\" + 0.047*\"video game adaptation\" + 0.047*\"memory\" + 0.045*\"road trip\" + 0.033*\"not funny\" + 0.029*\"tv\" + 0.027*\"friday night movie\"\n", - "2021-08-24 02:39:09,814 : INFO : topic #19 (0.020): 0.095*\"pg-13\" + 0.069*\"ghosts\" + 0.043*\"jude law\" + 0.035*\"wedding\" + 0.032*\"julia roberts\" + 0.028*\"want to own\" + 0.027*\"infidelity\" + 0.025*\"hollywood\" + 0.021*\"prostitution\" + 0.019*\"afternoon section\"\n", - "2021-08-24 02:39:09,815 : INFO : topic #44 (0.020): 0.116*\"Adventure\" + 0.114*\"Children\" + 0.113*\"Fantasy\" + 0.064*\"Animation\" + 0.056*\"disney\" + 0.055*\"Comedy\" + 0.049*\"fantasy\" + 0.044*\"animation\" + 0.019*\"children\" + 0.018*\"fairy tale\"\n", - "2021-08-24 02:39:09,815 : INFO : topic #46 (0.020): 0.086*\"dvd\" + 0.073*\"overrated\" + 0.053*\"seen more than once\" + 0.043*\"owned\" + 0.033*\"eric's dvds\" + 0.032*\"samuel l. jackson\" + 0.030*\"bibliothek\" + 0.029*\"imdb top 250\" + 0.023*\"tumey's dvds\" + 0.023*\"seen at the cinema\"\n", - "2021-08-24 02:39:09,817 : INFO : topic diff=0.028083, rho=0.178627\n", - "2021-08-24 02:39:09,963 : INFO : -20.976 per-word bound, 2061854.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:09,964 : INFO : PROGRESS: pass 25, at document #10681/10681\n", - "2021-08-24 02:39:10,066 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:10,094 : INFO : topic #39 (0.020): 0.098*\"bruce willis\" + 0.051*\"interesting\" + 0.043*\"love\" + 0.042*\"africa\" + 0.030*\"predictable\" + 0.029*\"apocalypse\" + 0.025*\"avi\" + 0.023*\"gerard depardieu\" + 0.023*\"tragedy\" + 0.022*\"end of the world\"\n", - "2021-08-24 02:39:10,095 : INFO : topic #26 (0.020): 0.196*\"classic\" + 0.126*\"oscar (best picture)\" + 0.050*\"oscar (best directing)\" + 0.047*\"oscar (best supporting actor)\" + 0.032*\"matt damon\" + 0.024*\"afi 100\" + 0.024*\"national film registry\" + 0.022*\"afi 100 (laughs)\" + 0.020*\"afi 100 (cheers)\" + 0.017*\"oscar (best supporting actress)\"\n", - "2021-08-24 02:39:10,096 : INFO : topic #5 (0.020): 0.244*\"Action\" + 0.211*\"Thriller\" + 0.173*\"Horror\" + 0.139*\"Sci-Fi\" + 0.068*\"Adventure\" + 0.027*\"betamax\" + 0.012*\"can't remember\" + 0.007*\"courtroom\" + 0.006*\"vhs\" + 0.006*\"dvd-video\"\n", - "2021-08-24 02:39:10,097 : INFO : topic #11 (0.020): 0.298*\"Documentary\" + 0.188*\"to see\" + 0.063*\"in netflix queue\" + 0.063*\"gay\" + 0.028*\"oscar (best actor)\" + 0.025*\"india\" + 0.019*\"homosexuality\" + 0.019*\"IMAX\" + 0.015*\"matter-of-fact\" + 0.011*\"mexico\"\n", - "2021-08-24 02:39:10,098 : INFO : topic #22 (0.020): 0.081*\"national film registry\" + 0.051*\"oscar (best cinematography)\" + 0.029*\"tumey's dvds\" + 0.029*\"angelina jolie\" + 0.028*\"black and white\" + 0.026*\"classic\" + 0.026*\"imdb top 250\" + 0.025*\"philip k. dick\" + 0.024*\"journalism\" + 0.023*\"erlend's dvds\"\n", - "2021-08-24 02:39:10,099 : INFO : topic diff=0.069263, rho=0.178627\n", - "2021-08-24 02:39:10,101 : INFO : PROGRESS: pass 26, at document #2000/10681\n", - "2021-08-24 02:39:10,393 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:10,421 : INFO : topic #3 (0.020): 0.313*\"nudity (topless)\" + 0.091*\"zombies\" + 0.059*\"nudity (topless - brief)\" + 0.024*\"keira knightley\" + 0.018*\"virtual reality\" + 0.018*\"zombie\" + 0.016*\"ingmar bergman\" + 0.014*\"strippers\" + 0.013*\"futuristic\" + 0.012*\"Horror\"\n", - "2021-08-24 02:39:10,422 : INFO : topic #46 (0.020): 0.080*\"dvd\" + 0.067*\"overrated\" + 0.049*\"seen more than once\" + 0.043*\"owned\" + 0.035*\"imdb top 250\" + 0.034*\"samuel l. jackson\" + 0.029*\"bibliothek\" + 0.028*\"eric's dvds\" + 0.025*\"kevin spacey\" + 0.023*\"seen at the cinema\"\n", - "2021-08-24 02:39:10,423 : INFO : topic #41 (0.020): 0.257*\"based on a book\" + 0.110*\"adapted from:book\" + 0.066*\"Drama\" + 0.062*\"netflix\" + 0.061*\"nudity (full frontal)\" + 0.050*\"robin williams\" + 0.035*\"vampire\" + 0.027*\"vampires\" + 0.014*\"book\" + 0.013*\"drama\"\n", - "2021-08-24 02:39:10,424 : INFO : topic #7 (0.020): 0.206*\"movie to see\" + 0.064*\"mel gibson\" + 0.045*\"boxing\" + 0.034*\"uma thurman\" + 0.032*\"kidnapping\" + 0.031*\"john travolta\" + 0.028*\"sylvester stallone\" + 0.024*\"suburbia\" + 0.019*\"basketball\" + 0.014*\"sports\"\n", - "2021-08-24 02:39:10,424 : INFO : topic #23 (0.020): 0.081*\"mafia\" + 0.039*\"al pacino\" + 0.033*\"robert de niro\" + 0.025*\"marlon brando\" + 0.023*\"guns\" + 0.021*\"poverty\" + 0.018*\"francis ford coppola\" + 0.018*\"scifi\" + 0.017*\"michael caine\" + 0.016*\"tumey's dvds\"\n", - "2021-08-24 02:39:10,426 : INFO : topic diff=0.137888, rho=0.175844\n", - "2021-08-24 02:39:10,427 : INFO : PROGRESS: pass 26, at document #4000/10681\n", - "2021-08-24 02:39:10,696 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:10,724 : INFO : topic #31 (0.020): 0.094*\"murder\" + 0.088*\"james bond\" + 0.061*\"007\" + 0.059*\"bond\" + 0.041*\"assassin\" + 0.031*\"claymation\" + 0.023*\"Action\" + 0.023*\"Thriller\" + 0.023*\"aardman\" + 0.022*\"franchise\"\n", - "2021-08-24 02:39:10,724 : INFO : topic #49 (0.020): 0.087*\"anime\" + 0.057*\"serial killer\" + 0.057*\"teen\" + 0.057*\"psychology\" + 0.057*\"high school\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.016*\"cult film\" + 0.015*\"japan\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:39:10,725 : INFO : topic #10 (0.020): 0.436*\"Comedy\" + 0.236*\"Romance\" + 0.198*\"Drama\" + 0.012*\"bibliothek\" + 0.011*\"betamax\" + 0.006*\"divorce\" + 0.006*\"oppl\" + 0.005*\"owen wilson\" + 0.004*\"library vhs\" + 0.003*\"world war i\"\n", - "2021-08-24 02:39:10,726 : INFO : topic #14 (0.020): 0.118*\"Mystery\" + 0.062*\"Horror\" + 0.058*\"Thriller\" + 0.039*\"disturbing\" + 0.033*\"atmospheric\" + 0.026*\"ominous\" + 0.025*\"tense\" + 0.025*\"erlend's dvds\" + 0.022*\"menacing\" + 0.022*\"scary movies to see on halloween\"\n", - "2021-08-24 02:39:10,727 : INFO : topic #34 (0.020): 0.165*\"War\" + 0.118*\"Drama\" + 0.097*\"world war ii\" + 0.082*\"drugs\" + 0.048*\"war\" + 0.040*\"prison\" + 0.036*\"history\" + 0.030*\"Action\" + 0.020*\"london\" + 0.016*\"Adventure\"\n", - "2021-08-24 02:39:10,728 : INFO : topic diff=0.023529, rho=0.175844\n", - "2021-08-24 02:39:10,730 : INFO : PROGRESS: pass 26, at document #6000/10681\n", - "2021-08-24 02:39:10,960 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:10,987 : INFO : topic #6 (0.020): 0.084*\"martial arts\" + 0.061*\"cult film\" + 0.043*\"gothic\" + 0.037*\"campy\" + 0.028*\"cult classic\" + 0.028*\"silly\" + 0.027*\"australia\" + 0.025*\"australian\" + 0.019*\"sam raimi\" + 0.018*\"seen 2006\"\n", - "2021-08-24 02:39:10,988 : INFO : topic #40 (0.020): 0.120*\"nudity (full frontal - notable)\" + 0.095*\"magic\" + 0.071*\"edward norton\" + 0.057*\"sports\" + 0.050*\"not corv lib\" + 0.049*\"baseball\" + 0.040*\"hw drama\" + 0.029*\"compassionate\" + 0.026*\"inspirational\" + 0.025*\"christopher walken\"\n", - "2021-08-24 02:39:10,989 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.058*\"Action\" + 0.050*\"adventure\" + 0.038*\"sequel\" + 0.035*\"boring\" + 0.032*\"Adventure\" + 0.029*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.022*\"70mm\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:39:10,990 : INFO : topic #49 (0.020): 0.103*\"anime\" + 0.063*\"psychology\" + 0.056*\"serial killer\" + 0.053*\"teen\" + 0.050*\"high school\" + 0.019*\"mental illness\" + 0.018*\"japan\" + 0.017*\"cannibalism\" + 0.016*\"anthony hopkins\" + 0.014*\"cult film\"\n", - "2021-08-24 02:39:10,990 : INFO : topic #22 (0.020): 0.068*\"national film registry\" + 0.057*\"oscar (best cinematography)\" + 0.031*\"classic\" + 0.031*\"imdb top 250\" + 0.030*\"tumey's dvds\" + 0.029*\"stanley kubrick\" + 0.028*\"afi 100 (thrills)\" + 0.027*\"black and white\" + 0.026*\"70mm\" + 0.026*\"clv\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:10,991 : INFO : topic diff=0.048196, rho=0.175844\n", - "2021-08-24 02:39:10,992 : INFO : PROGRESS: pass 26, at document #8000/10681\n", - "2021-08-24 02:39:11,230 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:11,257 : INFO : topic #20 (0.020): 0.135*\"politics\" + 0.089*\"documentary\" + 0.065*\"jack nicholson\" + 0.043*\"terrorism\" + 0.037*\"seen 2008\" + 0.034*\"propaganda\" + 0.032*\"Documentary\" + 0.028*\"political\" + 0.028*\"love story\" + 0.027*\"aviation\"\n", - "2021-08-24 02:39:11,258 : INFO : topic #47 (0.020): 0.292*\"less than 300 ratings\" + 0.147*\"Drama\" + 0.062*\"nudity (rear)\" + 0.037*\"jackie chan\" + 0.025*\"kung fu\" + 0.023*\"football\" + 0.017*\"oppl\" + 0.016*\"brian de palma\" + 0.016*\"denzel washington\" + 0.013*\"bechdel test:pass\"\n", - "2021-08-24 02:39:11,259 : INFO : topic #18 (0.020): 0.050*\"creepy\" + 0.046*\"franchise\" + 0.045*\"surrealism\" + 0.036*\"slasher\" + 0.027*\"lurid\" + 0.024*\"robert downey jr\" + 0.022*\"united states\" + 0.022*\"adolescence\" + 0.022*\"unique\" + 0.021*\"goth\"\n", - "2021-08-24 02:39:11,260 : INFO : topic #42 (0.020): 0.156*\"johnny depp\" + 0.075*\"tim burton\" + 0.052*\"martin scorsese\" + 0.041*\"narrated\" + 0.033*\"george clooney\" + 0.028*\"ei muista\" + 0.025*\"monster\" + 0.018*\"alien invasion\" + 0.018*\"brothers\" + 0.014*\"las vegas\"\n", - "2021-08-24 02:39:11,261 : INFO : topic #8 (0.020): 0.092*\"space\" + 0.040*\"harrison ford\" + 0.031*\"christianity\" + 0.025*\"marx brothers\" + 0.023*\"kevin smith\" + 0.023*\"george lucas\" + 0.022*\"spaghetti western\" + 0.020*\"archaeology\" + 0.019*\"werewolves\" + 0.018*\"sergio leone\"\n", - "2021-08-24 02:39:11,262 : INFO : topic diff=0.039611, rho=0.175844\n", - "2021-08-24 02:39:11,263 : INFO : PROGRESS: pass 26, at document #10000/10681\n", - "2021-08-24 02:39:11,515 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:11,545 : INFO : topic #39 (0.020): 0.108*\"bruce willis\" + 0.056*\"interesting\" + 0.046*\"africa\" + 0.033*\"love\" + 0.031*\"predictable\" + 0.028*\"avi\" + 0.026*\"gerard depardieu\" + 0.024*\"end of the world\" + 0.023*\"golden raspberry (worst actor)\" + 0.022*\"tragedy\"\n", - "2021-08-24 02:39:11,546 : INFO : topic #10 (0.020): 0.443*\"Comedy\" + 0.237*\"Romance\" + 0.196*\"Drama\" + 0.012*\"bibliothek\" + 0.011*\"betamax\" + 0.005*\"divorce\" + 0.005*\"owen wilson\" + 0.004*\"oppl\" + 0.004*\"world war i\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:39:11,547 : INFO : topic #20 (0.020): 0.142*\"politics\" + 0.095*\"documentary\" + 0.057*\"jack nicholson\" + 0.052*\"terrorism\" + 0.039*\"propaganda\" + 0.037*\"political\" + 0.037*\"love story\" + 0.036*\"seen 2008\" + 0.035*\"Documentary\" + 0.027*\"aviation\"\n", - "2021-08-24 02:39:11,547 : INFO : topic #49 (0.020): 0.127*\"anime\" + 0.060*\"psychology\" + 0.052*\"serial killer\" + 0.051*\"high school\" + 0.046*\"teen\" + 0.021*\"japan\" + 0.017*\"mental illness\" + 0.016*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.013*\"steve martin\"\n", - "2021-08-24 02:39:11,549 : INFO : topic #28 (0.020): 0.136*\"pixar\" + 0.085*\"oscar (best actress)\" + 0.040*\"mockumentary\" + 0.028*\"elijah wood\" + 0.027*\"medieval\" + 0.023*\"computer animation\" + 0.022*\"dinosaurs\" + 0.022*\"cgi\" + 0.018*\"nostalgia\" + 0.013*\"family drama\"\n", - "2021-08-24 02:39:11,550 : INFO : topic diff=0.027439, rho=0.175844\n", - "2021-08-24 02:39:11,690 : INFO : -20.974 per-word bound, 2060329.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:11,690 : INFO : PROGRESS: pass 26, at document #10681/10681\n", - "2021-08-24 02:39:11,786 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:11,812 : INFO : topic #20 (0.020): 0.147*\"politics\" + 0.084*\"documentary\" + 0.051*\"jack nicholson\" + 0.049*\"terrorism\" + 0.042*\"propaganda\" + 0.041*\"seen 2008\" + 0.039*\"love story\" + 0.038*\"Documentary\" + 0.033*\"political\" + 0.026*\"aviation\"\n", - "2021-08-24 02:39:11,813 : INFO : topic #39 (0.020): 0.098*\"bruce willis\" + 0.051*\"interesting\" + 0.042*\"love\" + 0.042*\"africa\" + 0.030*\"predictable\" + 0.029*\"apocalypse\" + 0.025*\"avi\" + 0.023*\"gerard depardieu\" + 0.023*\"tragedy\" + 0.022*\"good\"\n", - "2021-08-24 02:39:11,814 : INFO : topic #9 (0.020): 0.248*\"r\" + 0.125*\"clearplay\" + 0.086*\"Western\" + 0.076*\"nudity (topless - brief)\" + 0.066*\"Drama\" + 0.038*\"pg13\" + 0.028*\"to see\" + 0.026*\"movie to see\" + 0.025*\"Thriller\" + 0.017*\"british\"\n", - "2021-08-24 02:39:11,815 : INFO : topic #6 (0.020): 0.091*\"martial arts\" + 0.048*\"cult film\" + 0.034*\"gothic\" + 0.032*\"campy\" + 0.028*\"seen 2006\" + 0.026*\"australia\" + 0.024*\"silly\" + 0.024*\"australian\" + 0.020*\"cult classic\" + 0.017*\"freedom\"\n", - "2021-08-24 02:39:11,815 : INFO : topic #13 (0.020): 0.132*\"time travel\" + 0.076*\"twist ending\" + 0.055*\"holocaust\" + 0.035*\"post-apocalyptic\" + 0.033*\"bill murray\" + 0.026*\"post apocalyptic\" + 0.021*\"good dialogue\" + 0.017*\"liam neeson\" + 0.016*\"michael moore\" + 0.016*\"mindfuck\"\n", - "2021-08-24 02:39:11,817 : INFO : topic diff=0.068205, rho=0.175844\n", - "2021-08-24 02:39:11,818 : INFO : PROGRESS: pass 27, at document #2000/10681\n", - "2021-08-24 02:39:12,095 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:12,120 : INFO : topic #27 (0.020): 0.150*\"aliens\" + 0.090*\"steven spielberg\" + 0.054*\"family\" + 0.035*\"michael crichton\" + 0.025*\"gambling\" + 0.025*\"to-rent\" + 0.024*\"childhood\" + 0.022*\"alien\" + 0.019*\"didn't finish\" + 0.018*\"poker\"\n", - "2021-08-24 02:39:12,121 : INFO : topic #47 (0.020): 0.276*\"less than 300 ratings\" + 0.143*\"Drama\" + 0.065*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.027*\"kung fu\" + 0.025*\"oppl\" + 0.019*\"denzel washington\" + 0.019*\"football\" + 0.017*\"aids\" + 0.014*\"bechdel test:pass\"\n", - "2021-08-24 02:39:12,122 : INFO : topic #3 (0.020): 0.313*\"nudity (topless)\" + 0.091*\"zombies\" + 0.059*\"nudity (topless - brief)\" + 0.024*\"keira knightley\" + 0.018*\"virtual reality\" + 0.018*\"zombie\" + 0.016*\"ingmar bergman\" + 0.014*\"strippers\" + 0.013*\"futuristic\" + 0.012*\"Horror\"\n", - "2021-08-24 02:39:12,122 : INFO : topic #33 (0.020): 0.169*\"comic book\" + 0.132*\"superhero\" + 0.066*\"super-hero\" + 0.039*\"adapted from:comic\" + 0.037*\"gene hackman\" + 0.037*\"batman\" + 0.027*\"action\" + 0.025*\"Action\" + 0.020*\"alter ego\" + 0.019*\"based on a comic\"\n", - "2021-08-24 02:39:12,123 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.112*\"Adventure\" + 0.107*\"Fantasy\" + 0.078*\"disney\" + 0.062*\"Animation\" + 0.056*\"Comedy\" + 0.043*\"animation\" + 0.042*\"fantasy\" + 0.020*\"children\" + 0.016*\"fairy tale\"\n", - "2021-08-24 02:39:12,125 : INFO : topic diff=0.135844, rho=0.173186\n", - "2021-08-24 02:39:12,126 : INFO : PROGRESS: pass 27, at document #4000/10681\n", - "2021-08-24 02:39:12,369 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:12,395 : INFO : topic #43 (0.020): 0.287*\"Crime\" + 0.158*\"Drama\" + 0.107*\"Thriller\" + 0.050*\"nudity (topless - notable)\" + 0.040*\"Mystery\" + 0.030*\"shakespeare\" + 0.025*\"nicolas cage\" + 0.017*\"remade\" + 0.016*\"adapted from:play\" + 0.014*\"sean penn\"\n", - "2021-08-24 02:39:12,396 : INFO : topic #25 (0.020): 0.191*\"Musical\" + 0.119*\"70mm\" + 0.072*\"musical\" + 0.065*\"religion\" + 0.027*\"rock and roll\" + 0.026*\"friendship\" + 0.023*\"music\" + 0.020*\"monty python\" + 0.018*\"adapted from b'way\" + 0.017*\"trains\"\n", - "2021-08-24 02:39:12,396 : INFO : topic #13 (0.020): 0.149*\"time travel\" + 0.073*\"twist ending\" + 0.059*\"holocaust\" + 0.034*\"bill murray\" + 0.030*\"post apocalyptic\" + 0.029*\"post-apocalyptic\" + 0.020*\"good dialogue\" + 0.018*\"mindfuck\" + 0.017*\"liam neeson\" + 0.016*\"very good\"\n", - "2021-08-24 02:39:12,397 : INFO : topic #5 (0.020): 0.230*\"Action\" + 0.205*\"Thriller\" + 0.173*\"Horror\" + 0.135*\"Sci-Fi\" + 0.066*\"Adventure\" + 0.034*\"betamax\" + 0.020*\"can't remember\" + 0.009*\"courtroom\" + 0.007*\"vhs\" + 0.007*\"dvd-video\"\n", - "2021-08-24 02:39:12,398 : INFO : topic #35 (0.020): 0.171*\"comedy\" + 0.129*\"Comedy\" + 0.081*\"funny\" + 0.050*\"parody\" + 0.040*\"hilarious\" + 0.034*\"can't remember\" + 0.025*\"stupid\" + 0.022*\"spoof\" + 0.020*\"funny as hell\" + 0.019*\"seen more than once\"\n", - "2021-08-24 02:39:12,400 : INFO : topic diff=0.023049, rho=0.173186\n", - "2021-08-24 02:39:12,401 : INFO : PROGRESS: pass 27, at document #6000/10681\n", - "2021-08-24 02:39:12,626 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:12,652 : INFO : topic #26 (0.020): 0.216*\"classic\" + 0.141*\"oscar (best picture)\" + 0.056*\"oscar (best directing)\" + 0.048*\"oscar (best supporting actor)\" + 0.032*\"afi 100\" + 0.027*\"afi 100 (laughs)\" + 0.026*\"national film registry\" + 0.025*\"matt damon\" + 0.021*\"afi 100 (cheers)\" + 0.019*\"oscar (best supporting actress)\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:12,652 : INFO : topic #35 (0.020): 0.171*\"comedy\" + 0.132*\"Comedy\" + 0.081*\"funny\" + 0.052*\"parody\" + 0.039*\"hilarious\" + 0.035*\"can't remember\" + 0.023*\"stupid\" + 0.022*\"spoof\" + 0.020*\"funny as hell\" + 0.018*\"seen more than once\"\n", - "2021-08-24 02:39:12,653 : INFO : topic #49 (0.020): 0.103*\"anime\" + 0.063*\"psychology\" + 0.056*\"serial killer\" + 0.053*\"teen\" + 0.050*\"high school\" + 0.019*\"mental illness\" + 0.018*\"japan\" + 0.017*\"cannibalism\" + 0.016*\"anthony hopkins\" + 0.014*\"cult film\"\n", - "2021-08-24 02:39:12,654 : INFO : topic #8 (0.020): 0.102*\"space\" + 0.044*\"harrison ford\" + 0.026*\"christianity\" + 0.024*\"george lucas\" + 0.024*\"kevin smith\" + 0.024*\"spaghetti western\" + 0.023*\"archaeology\" + 0.020*\"sergio leone\" + 0.019*\"fantasy\" + 0.019*\"space opera\"\n", - "2021-08-24 02:39:12,655 : INFO : topic #46 (0.020): 0.091*\"dvd\" + 0.063*\"overrated\" + 0.055*\"seen more than once\" + 0.042*\"owned\" + 0.031*\"samuel l. jackson\" + 0.030*\"bibliothek\" + 0.030*\"imdb top 250\" + 0.029*\"eric's dvds\" + 0.024*\"seen at the cinema\" + 0.023*\"tumey's dvds\"\n", - "2021-08-24 02:39:12,656 : INFO : topic diff=0.047263, rho=0.173186\n", - "2021-08-24 02:39:12,658 : INFO : PROGRESS: pass 27, at document #8000/10681\n", - "2021-08-24 02:39:12,923 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:12,955 : INFO : topic #42 (0.020): 0.156*\"johnny depp\" + 0.075*\"tim burton\" + 0.052*\"martin scorsese\" + 0.041*\"narrated\" + 0.033*\"george clooney\" + 0.028*\"ei muista\" + 0.025*\"monster\" + 0.018*\"alien invasion\" + 0.018*\"brothers\" + 0.014*\"las vegas\"\n", - "2021-08-24 02:39:12,956 : INFO : topic #2 (0.020): 0.102*\"Comedy\" + 0.095*\"lesbian\" + 0.091*\"satire\" + 0.051*\"imdb bottom 100\" + 0.036*\"sexy\" + 0.028*\"get\" + 0.018*\"olympics\" + 0.018*\"ben stiller\" + 0.017*\"john turturro\" + 0.017*\"1\"\n", - "2021-08-24 02:39:12,956 : INFO : topic #29 (0.020): 0.139*\"action\" + 0.058*\"Action\" + 0.049*\"adventure\" + 0.039*\"sequel\" + 0.034*\"boring\" + 0.033*\"Adventure\" + 0.027*\"robots\" + 0.026*\"arnold schwarzenegger\" + 0.025*\"pirates\" + 0.022*\"motorcycle\"\n", - "2021-08-24 02:39:12,957 : INFO : topic #11 (0.020): 0.272*\"Documentary\" + 0.143*\"to see\" + 0.075*\"in netflix queue\" + 0.068*\"gay\" + 0.045*\"oscar (best actor)\" + 0.024*\"india\" + 0.020*\"homosexuality\" + 0.019*\"matter-of-fact\" + 0.018*\"IMAX\" + 0.015*\"ian mckellen\"\n", - "2021-08-24 02:39:12,958 : INFO : topic #48 (0.020): 0.052*\"quirky\" + 0.047*\"surreal\" + 0.043*\"directorial debut\" + 0.027*\"bittersweet\" + 0.026*\"woody allen\" + 0.023*\"humorous\" + 0.020*\"whimsical\" + 0.020*\"erlend's dvds\" + 0.019*\"coming of age\" + 0.019*\"peter jackson\"\n", - "2021-08-24 02:39:12,959 : INFO : topic diff=0.038957, rho=0.173186\n", - "2021-08-24 02:39:12,960 : INFO : PROGRESS: pass 27, at document #10000/10681\n", - "2021-08-24 02:39:13,231 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:13,259 : INFO : topic #17 (0.020): 0.061*\"police\" + 0.050*\"will smith\" + 0.040*\"corruption\" + 0.026*\"computers\" + 0.026*\"sandra bullock\" + 0.021*\"action packed\" + 0.021*\"milla jovovich\" + 0.019*\"torture\" + 0.017*\"snakes\" + 0.017*\"stage\"\n", - "2021-08-24 02:39:13,260 : INFO : topic #45 (0.020): 0.055*\"japan\" + 0.044*\"tense\" + 0.041*\"revenge\" + 0.036*\"clint eastwood\" + 0.031*\"forceful\" + 0.029*\"gritty\" + 0.028*\"atmospheric\" + 0.027*\"corvallis library\" + 0.025*\"Action\" + 0.022*\"sweeping\"\n", - "2021-08-24 02:39:13,261 : INFO : topic #26 (0.020): 0.205*\"classic\" + 0.126*\"oscar (best picture)\" + 0.052*\"oscar (best directing)\" + 0.048*\"oscar (best supporting actor)\" + 0.031*\"matt damon\" + 0.031*\"afi 100 (laughs)\" + 0.026*\"afi 100\" + 0.026*\"national film registry\" + 0.021*\"afi 100 (cheers)\" + 0.018*\"oscar (best supporting actress)\"\n", - "2021-08-24 02:39:13,262 : INFO : topic #48 (0.020): 0.048*\"quirky\" + 0.043*\"surreal\" + 0.041*\"directorial debut\" + 0.027*\"bittersweet\" + 0.024*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"erlend's dvds\" + 0.018*\"coming of age\" + 0.018*\"underrated\"\n", - "2021-08-24 02:39:13,264 : INFO : topic #38 (0.020): 0.083*\"true story\" + 0.068*\"tom hanks\" + 0.060*\"biography\" + 0.053*\"based on a true story\" + 0.050*\"Drama\" + 0.041*\"drama\" + 0.034*\"biopic\" + 0.029*\"historical\" + 0.027*\"pg\" + 0.026*\"music\"\n", - "2021-08-24 02:39:13,265 : INFO : topic diff=0.027214, rho=0.173186\n", - "2021-08-24 02:39:13,420 : INFO : -20.974 per-word bound, 2060319.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:13,421 : INFO : PROGRESS: pass 27, at document #10681/10681\n", - "2021-08-24 02:39:13,530 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:13,557 : INFO : topic #46 (0.020): 0.088*\"dvd\" + 0.072*\"overrated\" + 0.048*\"seen more than once\" + 0.046*\"owned\" + 0.035*\"imdb top 250\" + 0.032*\"samuel l. jackson\" + 0.030*\"eric's dvds\" + 0.027*\"bibliothek\" + 0.021*\"tumey's dvds\" + 0.020*\"seen at the cinema\"\n", - "2021-08-24 02:39:13,558 : INFO : topic #12 (0.020): 0.101*\"christmas\" + 0.085*\"quentin tarantino\" + 0.071*\"heist\" + 0.050*\"tarantino\" + 0.047*\"nonlinear\" + 0.047*\"rape\" + 0.034*\"blindfold\" + 0.019*\"xmas theme\" + 0.018*\"robbery\" + 0.018*\"ireland\"\n", - "2021-08-24 02:39:13,559 : INFO : topic #25 (0.020): 0.217*\"Musical\" + 0.106*\"70mm\" + 0.076*\"musical\" + 0.074*\"religion\" + 0.033*\"friendship\" + 0.026*\"rock and roll\" + 0.020*\"music\" + 0.019*\"trains\" + 0.016*\"monty python\" + 0.015*\"adapted from b'way\"\n", - "2021-08-24 02:39:13,560 : INFO : topic #2 (0.020): 0.110*\"Comedy\" + 0.098*\"satire\" + 0.089*\"lesbian\" + 0.061*\"imdb bottom 100\" + 0.027*\"sexy\" + 0.022*\"get\" + 0.020*\"olympics\" + 0.017*\"ben stiller\" + 0.016*\"john turturro\" + 0.016*\"adolf hitler\"\n", - "2021-08-24 02:39:13,561 : INFO : topic #38 (0.020): 0.085*\"true story\" + 0.077*\"based on a true story\" + 0.065*\"biography\" + 0.063*\"tom hanks\" + 0.052*\"Drama\" + 0.036*\"drama\" + 0.031*\"historical\" + 0.030*\"biopic\" + 0.026*\"paris\" + 0.025*\"music\"\n", - "2021-08-24 02:39:13,562 : INFO : topic diff=0.067028, rho=0.173186\n", - "2021-08-24 02:39:13,564 : INFO : PROGRESS: pass 28, at document #2000/10681\n", - "2021-08-24 02:39:13,847 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:13,873 : INFO : topic #15 (0.020): 0.069*\"coen brothers\" + 0.057*\"stephen king\" + 0.053*\"racism\" + 0.038*\"adultery\" + 0.033*\"great acting\" + 0.030*\"espionage\" + 0.025*\"los angeles\" + 0.017*\"ensemble cast\" + 0.017*\"notable nudity\" + 0.017*\"san francisco\"\n", - "2021-08-24 02:39:13,874 : INFO : topic #49 (0.020): 0.095*\"anime\" + 0.061*\"serial killer\" + 0.054*\"psychology\" + 0.052*\"high school\" + 0.039*\"teen\" + 0.020*\"cannibalism\" + 0.019*\"mental illness\" + 0.018*\"japan\" + 0.016*\"cult film\" + 0.016*\"anthony hopkins\"\n", - "2021-08-24 02:39:13,875 : INFO : topic #8 (0.020): 0.112*\"space\" + 0.049*\"harrison ford\" + 0.028*\"christianity\" + 0.025*\"kevin smith\" + 0.024*\"george lucas\" + 0.024*\"archaeology\" + 0.020*\"space opera\" + 0.020*\"marx brothers\" + 0.019*\"fantasy\" + 0.019*\"spaghetti western\"\n", - "2021-08-24 02:39:13,876 : INFO : topic #33 (0.020): 0.169*\"comic book\" + 0.131*\"superhero\" + 0.066*\"super-hero\" + 0.039*\"adapted from:comic\" + 0.037*\"gene hackman\" + 0.037*\"batman\" + 0.027*\"action\" + 0.025*\"Action\" + 0.020*\"alter ego\" + 0.019*\"based on a comic\"\n", - "2021-08-24 02:39:13,877 : INFO : topic #46 (0.020): 0.081*\"dvd\" + 0.067*\"overrated\" + 0.049*\"seen more than once\" + 0.043*\"owned\" + 0.034*\"samuel l. jackson\" + 0.034*\"imdb top 250\" + 0.029*\"bibliothek\" + 0.028*\"eric's dvds\" + 0.025*\"kevin spacey\" + 0.023*\"seen at the cinema\"\n", - "2021-08-24 02:39:13,878 : INFO : topic diff=0.133390, rho=0.170646\n", - "2021-08-24 02:39:13,880 : INFO : PROGRESS: pass 28, at document #4000/10681\n", - "2021-08-24 02:39:14,137 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:14,164 : INFO : topic #15 (0.020): 0.065*\"stephen king\" + 0.065*\"coen brothers\" + 0.064*\"racism\" + 0.040*\"adultery\" + 0.029*\"great acting\" + 0.028*\"espionage\" + 0.024*\"los angeles\" + 0.019*\"ensemble cast\" + 0.018*\"notable nudity\" + 0.018*\"multiple storylines\"\n", - "2021-08-24 02:39:14,165 : INFO : topic #44 (0.020): 0.118*\"Children\" + 0.112*\"Adventure\" + 0.104*\"Fantasy\" + 0.076*\"disney\" + 0.064*\"Animation\" + 0.056*\"Comedy\" + 0.042*\"animation\" + 0.041*\"fantasy\" + 0.020*\"children\" + 0.015*\"disney animated feature\"\n", - "2021-08-24 02:39:14,165 : INFO : topic #22 (0.020): 0.071*\"national film registry\" + 0.057*\"oscar (best cinematography)\" + 0.033*\"imdb top 250\" + 0.032*\"stanley kubrick\" + 0.031*\"classic\" + 0.030*\"afi 100 (thrills)\" + 0.030*\"tumey's dvds\" + 0.028*\"70mm\" + 0.028*\"black and white\" + 0.026*\"erlend's dvds\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:14,166 : INFO : topic #49 (0.020): 0.088*\"anime\" + 0.057*\"serial killer\" + 0.057*\"psychology\" + 0.057*\"teen\" + 0.057*\"high school\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.016*\"cult film\" + 0.015*\"japan\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:39:14,167 : INFO : topic #48 (0.020): 0.048*\"quirky\" + 0.044*\"directorial debut\" + 0.044*\"surreal\" + 0.026*\"woody allen\" + 0.025*\"bittersweet\" + 0.024*\"humorous\" + 0.021*\"underrated\" + 0.020*\"coming of age\" + 0.019*\"erlend's dvds\" + 0.018*\"whimsical\"\n", - "2021-08-24 02:39:14,168 : INFO : topic diff=0.022702, rho=0.170646\n", - "2021-08-24 02:39:14,169 : INFO : PROGRESS: pass 28, at document #6000/10681\n", - "2021-08-24 02:39:14,396 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:14,422 : INFO : topic #45 (0.020): 0.044*\"tense\" + 0.042*\"japan\" + 0.041*\"clint eastwood\" + 0.032*\"forceful\" + 0.032*\"revenge\" + 0.030*\"gritty\" + 0.029*\"atmospheric\" + 0.024*\"Action\" + 0.023*\"sweeping\" + 0.022*\"samurai\"\n", - "2021-08-24 02:39:14,424 : INFO : topic #17 (0.020): 0.059*\"will smith\" + 0.058*\"police\" + 0.032*\"corruption\" + 0.031*\"sandra bullock\" + 0.028*\"computers\" + 0.022*\"milla jovovich\" + 0.019*\"stage\" + 0.019*\"torture\" + 0.018*\"no dialogue\" + 0.016*\"action packed\"\n", - "2021-08-24 02:39:14,424 : INFO : topic #38 (0.020): 0.082*\"tom hanks\" + 0.079*\"true story\" + 0.058*\"biography\" + 0.056*\"based on a true story\" + 0.048*\"Drama\" + 0.045*\"drama\" + 0.030*\"music\" + 0.029*\"historical\" + 0.027*\"biopic\" + 0.024*\"oscar (best actor)\"\n", - "2021-08-24 02:39:14,426 : INFO : topic #25 (0.020): 0.198*\"Musical\" + 0.131*\"70mm\" + 0.072*\"musical\" + 0.064*\"religion\" + 0.027*\"rock and roll\" + 0.025*\"friendship\" + 0.022*\"music\" + 0.018*\"monty python\" + 0.017*\"adapted from b'way\" + 0.015*\"trains\"\n", - "2021-08-24 02:39:14,427 : INFO : topic #41 (0.020): 0.251*\"based on a book\" + 0.116*\"adapted from:book\" + 0.065*\"Drama\" + 0.057*\"robin williams\" + 0.055*\"nudity (full frontal)\" + 0.049*\"netflix\" + 0.038*\"vampire\" + 0.031*\"vampires\" + 0.016*\"book\" + 0.012*\"drama\"\n", - "2021-08-24 02:39:14,428 : INFO : topic diff=0.046406, rho=0.170646\n", - "2021-08-24 02:39:14,430 : INFO : PROGRESS: pass 28, at document #8000/10681\n", - "2021-08-24 02:39:14,660 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:14,686 : INFO : topic #36 (0.020): 0.104*\"dark comedy\" + 0.075*\"bechdel test:fail\" + 0.074*\"black comedy\" + 0.037*\"nazis\" + 0.036*\"funniest movies\" + 0.033*\"cold war\" + 0.014*\"russia\" + 0.014*\"communism\" + 0.013*\"author:charles dickens\" + 0.013*\"existentialism\"\n", - "2021-08-24 02:39:14,687 : INFO : topic #44 (0.020): 0.116*\"Adventure\" + 0.116*\"Children\" + 0.107*\"Fantasy\" + 0.066*\"disney\" + 0.063*\"Animation\" + 0.054*\"Comedy\" + 0.049*\"fantasy\" + 0.043*\"animation\" + 0.019*\"children\" + 0.017*\"fairy tale\"\n", - "2021-08-24 02:39:14,688 : INFO : topic #19 (0.020): 0.077*\"ghosts\" + 0.063*\"pg-13\" + 0.037*\"jude law\" + 0.036*\"wedding\" + 0.036*\"julia roberts\" + 0.027*\"want to own\" + 0.027*\"hollywood\" + 0.024*\"infidelity\" + 0.023*\"afternoon section\" + 0.023*\"prostitution\"\n", - "2021-08-24 02:39:14,689 : INFO : topic #2 (0.020): 0.102*\"Comedy\" + 0.095*\"lesbian\" + 0.091*\"satire\" + 0.051*\"imdb bottom 100\" + 0.036*\"sexy\" + 0.028*\"get\" + 0.018*\"olympics\" + 0.018*\"ben stiller\" + 0.017*\"john turturro\" + 0.017*\"1\"\n", - "2021-08-24 02:39:14,690 : INFO : topic #37 (0.020): 0.061*\"easily confused with other movie(s) (title)\" + 0.041*\"want to see again\" + 0.040*\"oscar (best supporting actress)\" + 0.037*\"vietnam war\" + 0.035*\"vietnam\" + 0.032*\"food\" + 0.030*\"peter sellers\" + 0.030*\"j netflix\" + 0.023*\"19th century\" + 0.020*\"father daughter relationship\"\n", - "2021-08-24 02:39:14,691 : INFO : topic diff=0.038459, rho=0.170646\n", - "2021-08-24 02:39:14,692 : INFO : PROGRESS: pass 28, at document #10000/10681\n", - "2021-08-24 02:39:14,954 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:14,981 : INFO : topic #48 (0.020): 0.049*\"quirky\" + 0.043*\"surreal\" + 0.041*\"directorial debut\" + 0.027*\"bittersweet\" + 0.024*\"woody allen\" + 0.023*\"humorous\" + 0.021*\"whimsical\" + 0.019*\"erlend's dvds\" + 0.018*\"coming of age\" + 0.018*\"underrated\"\n", - "2021-08-24 02:39:14,982 : INFO : topic #22 (0.020): 0.078*\"national film registry\" + 0.055*\"oscar (best cinematography)\" + 0.029*\"tumey's dvds\" + 0.027*\"imdb top 250\" + 0.026*\"philip k. dick\" + 0.025*\"black and white\" + 0.025*\"erlend's dvds\" + 0.024*\"afi 100 (thrills)\" + 0.024*\"clv\" + 0.024*\"classic\"\n", - "2021-08-24 02:39:14,983 : INFO : topic #13 (0.020): 0.146*\"time travel\" + 0.077*\"twist ending\" + 0.052*\"holocaust\" + 0.037*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.023*\"post apocalyptic\" + 0.019*\"liam neeson\" + 0.018*\"michael moore\" + 0.018*\"mindfuck\" + 0.018*\"very good\"\n", - "2021-08-24 02:39:14,983 : INFO : topic #29 (0.020): 0.132*\"action\" + 0.058*\"Action\" + 0.048*\"adventure\" + 0.039*\"sequel\" + 0.036*\"boring\" + 0.034*\"Adventure\" + 0.032*\"pirates\" + 0.026*\"robots\" + 0.026*\"motorcycle\" + 0.024*\"watched 2007\"\n", - "2021-08-24 02:39:14,984 : INFO : topic #47 (0.020): 0.323*\"less than 300 ratings\" + 0.152*\"Drama\" + 0.064*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.021*\"kung fu\" + 0.020*\"football\" + 0.016*\"denzel washington\" + 0.016*\"oppl\" + 0.014*\"brian de palma\" + 0.010*\"bechdel test:pass\"\n", - "2021-08-24 02:39:14,985 : INFO : topic diff=0.026603, rho=0.170646\n", - "2021-08-24 02:39:15,135 : INFO : -20.974 per-word bound, 2059024.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:15,135 : INFO : PROGRESS: pass 28, at document #10681/10681\n", - "2021-08-24 02:39:15,235 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:15,262 : INFO : topic #45 (0.020): 0.054*\"japan\" + 0.042*\"tense\" + 0.040*\"revenge\" + 0.034*\"clint eastwood\" + 0.030*\"forceful\" + 0.029*\"gritty\" + 0.027*\"corvallis library\" + 0.027*\"atmospheric\" + 0.024*\"Action\" + 0.023*\"akira kurosawa\"\n", - "2021-08-24 02:39:15,263 : INFO : topic #19 (0.020): 0.094*\"pg-13\" + 0.067*\"ghosts\" + 0.041*\"jude law\" + 0.038*\"wedding\" + 0.033*\"julia roberts\" + 0.029*\"infidelity\" + 0.025*\"want to own\" + 0.022*\"hollywood\" + 0.021*\"prostitution\" + 0.017*\"afternoon section\"\n", - "2021-08-24 02:39:15,264 : INFO : topic #31 (0.020): 0.113*\"murder\" + 0.078*\"james bond\" + 0.048*\"007\" + 0.047*\"bond\" + 0.045*\"assassin\" + 0.026*\"claymation\" + 0.025*\"Thriller\" + 0.024*\"Action\" + 0.020*\"killer-as-protagonist\" + 0.020*\"aardman\"\n", - "2021-08-24 02:39:15,265 : INFO : topic #9 (0.020): 0.247*\"r\" + 0.125*\"clearplay\" + 0.086*\"Western\" + 0.076*\"nudity (topless - brief)\" + 0.067*\"Drama\" + 0.038*\"pg13\" + 0.028*\"to see\" + 0.026*\"movie to see\" + 0.025*\"Thriller\" + 0.017*\"british\"\n", - "2021-08-24 02:39:15,266 : INFO : topic #16 (0.020): 0.160*\"sci-fi\" + 0.084*\"dystopia\" + 0.062*\"Sci-Fi\" + 0.056*\"futuristmovies.com\" + 0.035*\"military\" + 0.029*\"future\" + 0.015*\"weird\" + 0.015*\"joaquin phoenix\" + 0.014*\"betrayal\" + 0.012*\"imdb top 250\"\n", - "2021-08-24 02:39:15,267 : INFO : topic diff=0.066220, rho=0.170646\n", - "2021-08-24 02:39:15,268 : INFO : PROGRESS: pass 29, at document #2000/10681\n", - "2021-08-24 02:39:15,526 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:15,551 : INFO : topic #34 (0.020): 0.170*\"War\" + 0.122*\"Drama\" + 0.097*\"world war ii\" + 0.073*\"drugs\" + 0.051*\"war\" + 0.042*\"prison\" + 0.038*\"history\" + 0.028*\"Action\" + 0.022*\"london\" + 0.016*\"Adventure\"\n", - "2021-08-24 02:39:15,552 : INFO : topic #11 (0.020): 0.271*\"Documentary\" + 0.170*\"to see\" + 0.073*\"gay\" + 0.064*\"in netflix queue\" + 0.051*\"oscar (best actor)\" + 0.027*\"india\" + 0.020*\"homosexuality\" + 0.017*\"IMAX\" + 0.015*\"matter-of-fact\" + 0.011*\"ian mckellen\"\n", - "2021-08-24 02:39:15,553 : INFO : topic #7 (0.020): 0.206*\"movie to see\" + 0.064*\"mel gibson\" + 0.045*\"boxing\" + 0.034*\"uma thurman\" + 0.032*\"kidnapping\" + 0.031*\"john travolta\" + 0.028*\"sylvester stallone\" + 0.024*\"suburbia\" + 0.019*\"basketball\" + 0.014*\"sports\"\n", - "2021-08-24 02:39:15,553 : INFO : topic #40 (0.020): 0.121*\"nudity (full frontal - notable)\" + 0.100*\"magic\" + 0.052*\"not corv lib\" + 0.051*\"edward norton\" + 0.051*\"sports\" + 0.041*\"baseball\" + 0.038*\"hw drama\" + 0.034*\"inspirational\" + 0.025*\"compassionate\" + 0.024*\"christopher walken\"\n", - "2021-08-24 02:39:15,554 : INFO : topic #36 (0.020): 0.122*\"dark comedy\" + 0.088*\"bechdel test:fail\" + 0.078*\"black comedy\" + 0.043*\"nazis\" + 0.036*\"cold war\" + 0.027*\"funniest movies\" + 0.016*\"existentialism\" + 0.015*\"communism\" + 0.015*\"nuclear war\" + 0.014*\"author:charles dickens\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:39:15,556 : INFO : topic diff=0.131329, rho=0.168215\n", - "2021-08-24 02:39:15,557 : INFO : PROGRESS: pass 29, at document #4000/10681\n", - "2021-08-24 02:39:15,799 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:15,824 : INFO : topic #0 (0.020): 0.097*\"crime\" + 0.061*\"brad pitt\" + 0.060*\"violence\" + 0.043*\"Crime\" + 0.038*\"morgan freeman\" + 0.037*\"violent\" + 0.032*\"organized crime\" + 0.030*\"thriller\" + 0.028*\"drama\" + 0.027*\"philip seymour hoffman\"\n", - "2021-08-24 02:39:15,825 : INFO : topic #26 (0.020): 0.225*\"classic\" + 0.147*\"oscar (best picture)\" + 0.059*\"oscar (best directing)\" + 0.046*\"oscar (best supporting actor)\" + 0.035*\"afi 100\" + 0.031*\"afi 100 (laughs)\" + 0.028*\"national film registry\" + 0.022*\"afi 100 (cheers)\" + 0.021*\"matt damon\" + 0.018*\"oscar (best supporting actress)\"\n", - "2021-08-24 02:39:15,826 : INFO : topic #7 (0.020): 0.178*\"movie to see\" + 0.071*\"mel gibson\" + 0.053*\"boxing\" + 0.034*\"suburbia\" + 0.031*\"uma thurman\" + 0.029*\"sylvester stallone\" + 0.028*\"kidnapping\" + 0.025*\"john travolta\" + 0.024*\"basketball\" + 0.016*\"sports\"\n", - "2021-08-24 02:39:15,826 : INFO : topic #49 (0.020): 0.088*\"anime\" + 0.057*\"serial killer\" + 0.057*\"psychology\" + 0.057*\"teen\" + 0.057*\"high school\" + 0.020*\"mental illness\" + 0.017*\"cannibalism\" + 0.016*\"cult film\" + 0.015*\"japan\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:39:15,827 : INFO : topic #48 (0.020): 0.048*\"quirky\" + 0.044*\"directorial debut\" + 0.044*\"surreal\" + 0.026*\"woody allen\" + 0.025*\"bittersweet\" + 0.024*\"humorous\" + 0.021*\"underrated\" + 0.020*\"coming of age\" + 0.019*\"erlend's dvds\" + 0.018*\"whimsical\"\n", - "2021-08-24 02:39:15,829 : INFO : topic diff=0.022261, rho=0.168215\n", - "2021-08-24 02:39:15,830 : INFO : PROGRESS: pass 29, at document #6000/10681\n", - "2021-08-24 02:39:16,072 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:16,099 : INFO : topic #29 (0.020): 0.141*\"action\" + 0.058*\"Action\" + 0.049*\"adventure\" + 0.039*\"sequel\" + 0.035*\"boring\" + 0.032*\"Adventure\" + 0.029*\"robots\" + 0.027*\"arnold schwarzenegger\" + 0.022*\"70mm\" + 0.020*\"motorcycle\"\n", - "2021-08-24 02:39:16,100 : INFO : topic #12 (0.020): 0.103*\"christmas\" + 0.085*\"quentin tarantino\" + 0.065*\"heist\" + 0.063*\"nonlinear\" + 0.055*\"tarantino\" + 0.046*\"rape\" + 0.030*\"blindfold\" + 0.023*\"terry gilliam\" + 0.019*\"xmas theme\" + 0.016*\"ireland\"\n", - "2021-08-24 02:39:16,101 : INFO : topic #39 (0.020): 0.120*\"bruce willis\" + 0.055*\"interesting\" + 0.033*\"love\" + 0.032*\"africa\" + 0.028*\"gerard depardieu\" + 0.025*\"golden raspberry (worst actor)\" + 0.024*\"avi\" + 0.024*\"good\" + 0.023*\"predictable\" + 0.022*\"end of the world\"\n", - "2021-08-24 02:39:16,102 : INFO : topic #15 (0.020): 0.060*\"coen brothers\" + 0.060*\"racism\" + 0.058*\"stephen king\" + 0.040*\"adultery\" + 0.032*\"espionage\" + 0.026*\"great acting\" + 0.024*\"los angeles\" + 0.021*\"multiple storylines\" + 0.020*\"notable nudity\" + 0.018*\"ensemble cast\"\n", - "2021-08-24 02:39:16,103 : INFO : topic #16 (0.020): 0.180*\"sci-fi\" + 0.087*\"dystopia\" + 0.069*\"futuristmovies.com\" + 0.063*\"Sci-Fi\" + 0.031*\"military\" + 0.031*\"future\" + 0.014*\"joaquin phoenix\" + 0.014*\"weird\" + 0.013*\"saturn award (best science fiction film)\" + 0.013*\"court\"\n", - "2021-08-24 02:39:16,104 : INFO : topic diff=0.045763, rho=0.168215\n", - "2021-08-24 02:39:16,105 : INFO : PROGRESS: pass 29, at document #8000/10681\n", - "2021-08-24 02:39:16,358 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:16,385 : INFO : topic #45 (0.020): 0.053*\"japan\" + 0.045*\"tense\" + 0.038*\"clint eastwood\" + 0.034*\"forceful\" + 0.031*\"revenge\" + 0.030*\"gritty\" + 0.028*\"atmospheric\" + 0.024*\"akira kurosawa\" + 0.023*\"Action\" + 0.022*\"visceral\"\n", - "2021-08-24 02:39:16,386 : INFO : topic #0 (0.020): 0.101*\"crime\" + 0.060*\"violence\" + 0.058*\"brad pitt\" + 0.041*\"Crime\" + 0.037*\"violent\" + 0.036*\"morgan freeman\" + 0.028*\"organized crime\" + 0.027*\"drama\" + 0.026*\"Thriller\" + 0.026*\"philip seymour hoffman\"\n", - "2021-08-24 02:39:16,386 : INFO : topic #9 (0.020): 0.202*\"r\" + 0.106*\"Western\" + 0.096*\"clearplay\" + 0.086*\"nudity (topless - brief)\" + 0.059*\"Drama\" + 0.027*\"pg13\" + 0.025*\"british\" + 0.022*\"Thriller\" + 0.021*\"russell crowe\" + 0.020*\"to see\"\n", - "2021-08-24 02:39:16,387 : INFO : topic #13 (0.020): 0.153*\"time travel\" + 0.069*\"twist ending\" + 0.057*\"holocaust\" + 0.035*\"bill murray\" + 0.029*\"post-apocalyptic\" + 0.025*\"post apocalyptic\" + 0.020*\"very good\" + 0.019*\"michael moore\" + 0.019*\"mindfuck\" + 0.016*\"liam neeson\"\n", - "2021-08-24 02:39:16,388 : INFO : topic #17 (0.020): 0.061*\"police\" + 0.056*\"will smith\" + 0.031*\"computers\" + 0.030*\"corruption\" + 0.027*\"sandra bullock\" + 0.020*\"stage\" + 0.019*\"milla jovovich\" + 0.018*\"no dialogue\" + 0.018*\"torture\" + 0.016*\"action packed\"\n", - "2021-08-24 02:39:16,390 : INFO : topic diff=0.038014, rho=0.168215\n", - "2021-08-24 02:39:16,391 : INFO : PROGRESS: pass 29, at document #10000/10681\n", - "2021-08-24 02:39:16,647 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:39:16,674 : INFO : topic #47 (0.020): 0.323*\"less than 300 ratings\" + 0.152*\"Drama\" + 0.064*\"nudity (rear)\" + 0.030*\"jackie chan\" + 0.021*\"kung fu\" + 0.020*\"football\" + 0.016*\"denzel washington\" + 0.016*\"oppl\" + 0.014*\"brian de palma\" + 0.010*\"bechdel test:pass\"\n", - "2021-08-24 02:39:16,675 : INFO : topic #49 (0.020): 0.126*\"anime\" + 0.060*\"psychology\" + 0.052*\"serial killer\" + 0.051*\"high school\" + 0.046*\"teen\" + 0.021*\"japan\" + 0.017*\"mental illness\" + 0.016*\"cannibalism\" + 0.014*\"anthony hopkins\" + 0.013*\"steve martin\"\n", - "2021-08-24 02:39:16,675 : INFO : topic #37 (0.020): 0.070*\"easily confused with other movie(s) (title)\" + 0.036*\"oscar (best supporting actress)\" + 0.035*\"j netflix\" + 0.034*\"want to see again\" + 0.031*\"vietnam war\" + 0.030*\"food\" + 0.030*\"vietnam\" + 0.027*\"19th century\" + 0.025*\"peter sellers\" + 0.025*\"slow\"\n", - "2021-08-24 02:39:16,676 : INFO : topic #12 (0.020): 0.096*\"quentin tarantino\" + 0.092*\"christmas\" + 0.069*\"heist\" + 0.056*\"tarantino\" + 0.053*\"nonlinear\" + 0.041*\"rape\" + 0.032*\"blindfold\" + 0.020*\"ireland\" + 0.020*\"terry gilliam\" + 0.019*\"xmas theme\"\n", - "2021-08-24 02:39:16,677 : INFO : topic #30 (0.020): 0.646*\"Drama\" + 0.040*\"Film-Noir\" + 0.014*\"tumey's dvds\" + 0.010*\"small town\" + 0.009*\"witch\" + 0.008*\"glbt\" + 0.007*\"film noir\" + 0.007*\"erlend's dvds\" + 0.007*\"oscar (best foreign language film)\" + 0.007*\"italy\"\n", - "2021-08-24 02:39:16,678 : INFO : topic diff=0.026163, rho=0.168215\n", - "2021-08-24 02:39:16,820 : INFO : -20.974 per-word bound, 2060356.8 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:39:16,821 : INFO : PROGRESS: pass 29, at document #10681/10681\n", - "2021-08-24 02:39:16,919 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:39:16,945 : INFO : topic #21 (0.020): 0.098*\"Drama\" + 0.053*\"criterion\" + 0.029*\"library\" + 0.026*\"sven's to see list\" + 0.023*\"tumey's dvds\" + 0.022*\"dvd-video\" + 0.021*\"atmospheric\" + 0.021*\"reflective\" + 0.020*\"irreverent\" + 0.020*\"deliberate\"\n", - "2021-08-24 02:39:16,946 : INFO : topic #17 (0.020): 0.067*\"will smith\" + 0.059*\"police\" + 0.046*\"corruption\" + 0.029*\"torture\" + 0.023*\"action packed\" + 0.023*\"milla jovovich\" + 0.022*\"computers\" + 0.022*\"sandra bullock\" + 0.018*\"tattoo\" + 0.017*\"suicide attempt\"\n", - "2021-08-24 02:39:16,946 : INFO : topic #13 (0.020): 0.133*\"time travel\" + 0.075*\"twist ending\" + 0.055*\"holocaust\" + 0.034*\"post-apocalyptic\" + 0.033*\"bill murray\" + 0.026*\"post apocalyptic\" + 0.021*\"good dialogue\" + 0.017*\"liam neeson\" + 0.016*\"michael moore\" + 0.016*\"mindfuck\"\n", - "2021-08-24 02:39:16,947 : INFO : topic #8 (0.020): 0.093*\"space\" + 0.043*\"christianity\" + 0.033*\"harrison ford\" + 0.028*\"marx brothers\" + 0.026*\"archaeology\" + 0.022*\"kevin smith\" + 0.019*\"spaghetti western\" + 0.019*\"george lucas\" + 0.019*\"1970s\" + 0.018*\"western\"\n", - "2021-08-24 02:39:16,948 : INFO : topic #39 (0.020): 0.099*\"bruce willis\" + 0.051*\"interesting\" + 0.042*\"love\" + 0.042*\"africa\" + 0.030*\"predictable\" + 0.028*\"apocalypse\" + 0.025*\"avi\" + 0.023*\"gerard depardieu\" + 0.023*\"good\" + 0.022*\"tragedy\"\n", - "2021-08-24 02:39:16,950 : INFO : topic diff=0.065101, rho=0.168215\n", - "2021-08-24 02:39:16,952 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel(num_terms=15261, num_topics=50, decay=0.5, chunksize=2000) in 52.57s', 'datetime': '2021-08-24T02:39:16.952132', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n" - ] - } - ], + "outputs": [], "source": [ "import gensim\n", "import logging\n", @@ -1752,1700 +68,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "30924a14", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:09,772 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n", - "2021-08-24 02:42:09,943 : INFO : adding document #10000 to Dictionary(14749 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...)\n", - "2021-08-24 02:42:09,953 : INFO : built Dictionary(15261 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...) from 10681 documents (total 117144 corpus positions)\n", - "2021-08-24 02:42:09,954 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary(15261 unique tokens: ['3d', 'Adventure', 'Animation', 'Children', 'Comedy']...) from 10681 documents (total 117144 corpus positions)\", 'datetime': '2021-08-24T02:42:09.954077', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n", - "2021-08-24 02:42:10,040 : INFO : using symmetric alpha at 0.02\n", - "2021-08-24 02:42:10,040 : INFO : using symmetric eta at 0.02\n", - "2021-08-24 02:42:10,042 : INFO : using serial LDA version on this node\n", - "2021-08-24 02:42:10,094 : INFO : running online (multi-pass) LDA training, 50 topics, 30 passes over the supplied corpus of 10681 documents, updating model once every 2000 documents, evaluating perplexity every 10681 documents, iterating 50x with a convergence threshold of 0.001000\n", - "2021-08-24 02:42:10,095 : INFO : PROGRESS: pass 0, at document #2000/10681\n", - "2021-08-24 02:42:10,597 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:10,624 : INFO : topic #24 (0.020): 0.043*\"alfred hitchcock\" + 0.036*\"hitchcock\" + 0.018*\"Drama\" + 0.017*\"classic\" + 0.015*\"brad pitt\" + 0.012*\"vampire\" + 0.010*\"tumey's dvds\" + 0.010*\"Thriller\" + 0.009*\"imdb top 250\" + 0.009*\"keanu reeves\"\n", - "2021-08-24 02:42:10,625 : INFO : topic #2 (0.020): 0.033*\"star trek\" + 0.020*\"claymation\" + 0.014*\"politics\" + 0.011*\"quirky\" + 0.010*\"aardman\" + 0.010*\"mockumentary\" + 0.010*\"Comedy\" + 0.009*\"william shatner\" + 0.009*\"stupid\" + 0.008*\"moon\"\n", - "2021-08-24 02:42:10,625 : INFO : topic #21 (0.020): 0.039*\"aliens\" + 0.024*\"action\" + 0.022*\"sci-fi\" + 0.018*\"space\" + 0.016*\"will smith\" + 0.016*\"bruce willis\" + 0.016*\"Action\" + 0.015*\"alien invasion\" + 0.011*\"end of the world\" + 0.010*\"Thriller\"\n", - "2021-08-24 02:42:10,626 : INFO : topic #9 (0.020): 0.028*\"Drama\" + 0.021*\"stanley kubrick\" + 0.013*\"Comedy\" + 0.012*\"aliens\" + 0.011*\"stephen king\" + 0.011*\"horror\" + 0.010*\"based on a book\" + 0.010*\"space\" + 0.009*\"world war ii\" + 0.009*\"Thriller\"\n", - "2021-08-24 02:42:10,627 : INFO : topic #7 (0.020): 0.022*\"action\" + 0.021*\"Comedy\" + 0.021*\"baseball\" + 0.020*\"comedy\" + 0.014*\"Drama\" + 0.012*\"Action\" + 0.012*\"meg ryan\" + 0.010*\"Romance\" + 0.010*\"romance\" + 0.010*\"oscar (best picture)\"\n", - "2021-08-24 02:42:10,628 : INFO : topic diff=46.233425, rho=1.000000\n", - "2021-08-24 02:42:10,629 : INFO : PROGRESS: pass 0, at document #4000/10681\n", - "2021-08-24 02:42:10,979 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:11,005 : INFO : topic #34 (0.020): 0.056*\"ghosts\" + 0.037*\"Thriller\" + 0.035*\"Drama\" + 0.031*\"Comedy\" + 0.021*\"bill murray\" + 0.016*\"Horror\" + 0.016*\"music\" + 0.012*\"70mm\" + 0.012*\"seen more than once\" + 0.011*\"comedy\"\n", - "2021-08-24 02:42:11,006 : INFO : topic #13 (0.020): 0.045*\"disney\" + 0.028*\"Children\" + 0.025*\"Animation\" + 0.022*\"Comedy\" + 0.021*\"Drama\" + 0.019*\"Romance\" + 0.019*\"road trip\" + 0.017*\"Fantasy\" + 0.015*\"fairy tale\" + 0.015*\"can't remember\"\n", - "2021-08-24 02:42:11,007 : INFO : topic #7 (0.020): 0.046*\"Comedy\" + 0.032*\"comedy\" + 0.030*\"baseball\" + 0.019*\"Drama\" + 0.018*\"romance\" + 0.016*\"action\" + 0.016*\"Romance\" + 0.014*\"meg ryan\" + 0.013*\"Action\" + 0.013*\"tom hanks\"\n", - "2021-08-24 02:42:11,008 : INFO : topic #31 (0.020): 0.032*\"mummy\" + 0.026*\"cult classic\" + 0.026*\"Drama\" + 0.022*\"comedy\" + 0.020*\"joaquin phoenix\" + 0.019*\"sam raimi\" + 0.018*\"Comedy\" + 0.015*\"infidelity\" + 0.015*\"pg-13\" + 0.015*\"Horror\"\n", - "2021-08-24 02:42:11,008 : INFO : topic #39 (0.020): 0.062*\"time travel\" + 0.025*\"post apocalyptic\" + 0.021*\"Comedy\" + 0.020*\"post-apocalyptic\" + 0.017*\"Drama\" + 0.015*\"adventure\" + 0.011*\"matt damon\" + 0.011*\"brad pitt\" + 0.010*\"comedy\" + 0.009*\"classic\"\n", - "2021-08-24 02:42:11,010 : INFO : topic diff=0.428098, rho=0.707107\n", - "2021-08-24 02:42:11,011 : INFO : PROGRESS: pass 0, at document #6000/10681\n", - "2021-08-24 02:42:11,303 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:11,332 : INFO : topic #29 (0.020): 0.059*\"Drama\" + 0.050*\"nudity (full frontal - notable)\" + 0.040*\"Comedy\" + 0.037*\"betamax\" + 0.025*\"Romance\" + 0.018*\"nudity (topless)\" + 0.017*\"shakespeare\" + 0.017*\"Action\" + 0.016*\"Horror\" + 0.015*\"satire\"\n", - "2021-08-24 02:42:11,333 : INFO : topic #1 (0.020): 0.028*\"tom hanks\" + 0.024*\"Drama\" + 0.017*\"suburbia\" + 0.016*\"brian de palma\" + 0.016*\"overrated\" + 0.012*\"tobey maguire\" + 0.012*\"based on a book\" + 0.011*\"homosexuality\" + 0.010*\"must see\" + 0.010*\"crime\"\n", - "2021-08-24 02:42:11,334 : INFO : topic #12 (0.020): 0.067*\"comic book\" + 0.049*\"superhero\" + 0.046*\"holocaust\" + 0.039*\"super-hero\" + 0.038*\"dystopia\" + 0.031*\"Sci-Fi\" + 0.028*\"Action\" + 0.019*\"action\" + 0.019*\"Drama\" + 0.018*\"adapted from:comic\"\n", - "2021-08-24 02:42:11,335 : INFO : topic #45 (0.020): 0.077*\"Drama\" + 0.069*\"less than 300 ratings\" + 0.035*\"Comedy\" + 0.018*\"tim burton\" + 0.017*\"Romance\" + 0.014*\"Fantasy\" + 0.013*\"Adventure\" + 0.010*\"depressing\" + 0.010*\"erotic\" + 0.010*\"michael moore\"\n", - "2021-08-24 02:42:11,335 : INFO : topic #34 (0.020): 0.061*\"ghosts\" + 0.037*\"Drama\" + 0.032*\"Thriller\" + 0.032*\"Comedy\" + 0.015*\"roman polanski\" + 0.015*\"bill murray\" + 0.015*\"Horror\" + 0.015*\"music\" + 0.014*\"television\" + 0.012*\"70mm\"\n", - "2021-08-24 02:42:11,337 : INFO : topic diff=0.375383, rho=0.577350\n", - "2021-08-24 02:42:11,338 : INFO : PROGRESS: pass 0, at document #8000/10681\n", - "2021-08-24 02:42:11,622 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:11,650 : INFO : topic #40 (0.020): 0.044*\"r\" + 0.038*\"john wayne\" + 0.025*\"clearplay\" + 0.023*\"prison\" + 0.022*\"Comedy\" + 0.021*\"movie to see\" + 0.019*\"teen\" + 0.019*\"1970s\" + 0.019*\"viggo mortensen\" + 0.016*\"morgan freeman\"\n", - "2021-08-24 02:42:11,651 : INFO : topic #18 (0.020): 0.061*\"Drama\" + 0.028*\"gay\" + 0.026*\"magic\" + 0.022*\"Comedy\" + 0.021*\"Romance\" + 0.020*\"based on a book\" + 0.017*\"classic\" + 0.015*\"biting\" + 0.014*\"adapted from:book\" + 0.012*\"matter-of-fact\"\n", - "2021-08-24 02:42:11,652 : INFO : topic #44 (0.020): 0.034*\"oscar (best supporting actor)\" + 0.028*\"Comedy\" + 0.027*\"mafia\" + 0.025*\"music\" + 0.023*\"martin scorsese\" + 0.023*\"Musical\" + 0.022*\"confrontational\" + 0.022*\"gritty\" + 0.021*\"Drama\" + 0.019*\"rock and roll\"\n", - "2021-08-24 02:42:11,653 : INFO : topic #38 (0.020): 0.097*\"drugs\" + 0.038*\"peter sellers\" + 0.026*\"Drama\" + 0.025*\"ewan mcgregor\" + 0.019*\"poverty\" + 0.018*\"Crime\" + 0.018*\"divx\" + 0.017*\"village\" + 0.017*\"Comedy\" + 0.016*\"samuel l. jackson\"\n", - "2021-08-24 02:42:11,654 : INFO : topic #36 (0.020): 0.043*\"peter jackson\" + 0.035*\"Comedy\" + 0.029*\"cult film\" + 0.029*\"not available from netflix\" + 0.025*\"dvd-video\" + 0.024*\"teen\" + 0.021*\"russell crowe\" + 0.017*\"based on a book\" + 0.017*\"downbeat\" + 0.017*\"Drama\"\n", - "2021-08-24 02:42:11,655 : INFO : topic diff=0.273149, rho=0.500000\n", - "2021-08-24 02:42:11,656 : INFO : PROGRESS: pass 0, at document #10000/10681\n", - "2021-08-24 02:42:11,953 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:11,981 : INFO : topic #6 (0.020): 0.110*\"Musical\" + 0.039*\"terrorism\" + 0.037*\"politics\" + 0.032*\"Drama\" + 0.030*\"political\" + 0.029*\"satire\" + 0.023*\"netwatch\" + 0.020*\"nicolas cage\" + 0.019*\"corruption\" + 0.019*\"Comedy\"\n", - "2021-08-24 02:42:11,982 : INFO : topic #19 (0.020): 0.035*\"ummarti2007\" + 0.033*\"philip k. dick\" + 0.024*\"soccer\" + 0.023*\"Drama\" + 0.023*\"violence\" + 0.018*\"drama\" + 0.016*\"nudity (topless)\" + 0.015*\"r\" + 0.014*\"slow\" + 0.013*\"surreal\"\n", - "2021-08-24 02:42:11,983 : INFO : topic #40 (0.020): 0.168*\"r\" + 0.089*\"clearplay\" + 0.048*\"movie to see\" + 0.023*\"john wayne\" + 0.021*\"seen 2006\" + 0.021*\"don cheadle\" + 0.020*\"Drama\" + 0.019*\"Comedy\" + 0.019*\"friendship\" + 0.018*\"morgan freeman\"\n", - "2021-08-24 02:42:11,984 : INFO : topic #27 (0.020): 0.253*\"Horror\" + 0.117*\"Thriller\" + 0.056*\"Mystery\" + 0.040*\"easily confused with other movie(s) (title)\" + 0.034*\"Drama\" + 0.029*\"Film-Noir\" + 0.029*\"Comedy\" + 0.023*\"Fantasy\" + 0.017*\"eerie\" + 0.013*\"creepy\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:11,985 : INFO : topic #16 (0.020): 0.080*\"nudity (rear)\" + 0.053*\"pg\" + 0.041*\"military\" + 0.035*\"claymation\" + 0.031*\"weird\" + 0.030*\"nudity (topless)\" + 0.024*\"aardman\" + 0.019*\"nudity (topless - brief)\" + 0.019*\"childhood\" + 0.018*\"penguins\"\n", - "2021-08-24 02:42:11,986 : INFO : topic diff=0.241935, rho=0.447214\n", - "2021-08-24 02:42:12,143 : INFO : -21.496 per-word bound, 2957512.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:12,144 : INFO : PROGRESS: pass 0, at document #10681/10681\n", - "2021-08-24 02:42:12,246 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:12,272 : INFO : topic #36 (0.020): 0.035*\"russell crowe\" + 0.031*\"virus\" + 0.029*\"not available from netflix\" + 0.028*\"Comedy\" + 0.026*\"based on a book\" + 0.025*\"cult film\" + 0.025*\"peter jackson\" + 0.024*\"movielens quickpick\" + 0.023*\"1980s\" + 0.021*\"adam sandler\"\n", - "2021-08-24 02:42:12,273 : INFO : topic #6 (0.020): 0.125*\"Musical\" + 0.045*\"politics\" + 0.039*\"satire\" + 0.036*\"terrorism\" + 0.032*\"Drama\" + 0.029*\"corruption\" + 0.024*\"political\" + 0.023*\"police\" + 0.020*\"nicolas cage\" + 0.018*\"good dialogue\"\n", - "2021-08-24 02:42:12,274 : INFO : topic #20 (0.020): 0.056*\"Drama\" + 0.043*\"biography\" + 0.041*\"suicide\" + 0.037*\"corvallis library\" + 0.036*\"movie to see\" + 0.033*\"death\" + 0.027*\"biopic\" + 0.026*\"forest whitaker\" + 0.025*\"history\" + 0.023*\"ensemble cast\"\n", - "2021-08-24 02:42:12,275 : INFO : topic #34 (0.020): 0.041*\"ghosts\" + 0.040*\"secret service\" + 0.038*\"bill murray\" + 0.029*\"television\" + 0.024*\"Drama\" + 0.022*\"Comedy\" + 0.018*\"Thriller\" + 0.017*\"courtroom\" + 0.016*\"nudity (topless - brief)\" + 0.016*\"smoking\"\n", - "2021-08-24 02:42:12,276 : INFO : topic #39 (0.020): 0.093*\"time travel\" + 0.047*\"post-apocalyptic\" + 0.033*\"motorcycle\" + 0.032*\"post apocalyptic\" + 0.031*\"jude law\" + 0.023*\"bechdel test:fail\" + 0.022*\"travel\" + 0.019*\"science\" + 0.017*\"old\" + 0.014*\"jungle\"\n", - "2021-08-24 02:42:12,277 : INFO : topic diff=0.309799, rho=0.408248\n", - "2021-08-24 02:42:12,278 : INFO : PROGRESS: pass 1, at document #2000/10681\n", - "2021-08-24 02:42:12,578 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:12,603 : INFO : topic #37 (0.020): 0.145*\"War\" + 0.101*\"world war ii\" + 0.083*\"Drama\" + 0.043*\"jim carrey\" + 0.035*\"war\" + 0.030*\"history\" + 0.019*\"nazis\" + 0.019*\"wwii\" + 0.015*\"true story\" + 0.013*\"Action\"\n", - "2021-08-24 02:42:12,604 : INFO : topic #5 (0.020): 0.224*\"classic\" + 0.066*\"musical\" + 0.022*\"john travolta\" + 0.021*\"afi 100\" + 0.020*\"los angeles\" + 0.015*\"family\" + 0.014*\"afi 100 (laughs)\" + 0.012*\"national film registry\" + 0.012*\"movie to see\" + 0.010*\"70mm\"\n", - "2021-08-24 02:42:12,605 : INFO : topic #22 (0.020): 0.083*\"betamax\" + 0.042*\"library on hold\" + 0.039*\"clv\" + 0.037*\"aviation\" + 0.031*\"terry gilliam\" + 0.023*\"oscar (best music - original song)\" + 0.022*\"steven seagal\" + 0.020*\"seen 2008\" + 0.019*\"gilliam\" + 0.018*\"family drama\"\n", - "2021-08-24 02:42:12,605 : INFO : topic #6 (0.020): 0.088*\"Musical\" + 0.051*\"politics\" + 0.047*\"satire\" + 0.035*\"nicolas cage\" + 0.029*\"terrorism\" + 0.026*\"sean connery\" + 0.025*\"good dialogue\" + 0.025*\"Drama\" + 0.023*\"political\" + 0.019*\"dvd-r\"\n", - "2021-08-24 02:42:12,606 : INFO : topic #21 (0.020): 0.080*\"aliens\" + 0.042*\"Sci-Fi\" + 0.035*\"space\" + 0.032*\"sci-fi\" + 0.031*\"will smith\" + 0.028*\"Action\" + 0.023*\"tommy lee jones\" + 0.022*\"futuristmovies.com\" + 0.020*\"action\" + 0.017*\"michael crichton\"\n", - "2021-08-24 02:42:12,607 : INFO : topic diff=0.455071, rho=0.369094\n", - "2021-08-24 02:42:12,608 : INFO : PROGRESS: pass 1, at document #4000/10681\n", - "2021-08-24 02:42:12,891 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:12,917 : INFO : topic #5 (0.020): 0.224*\"classic\" + 0.056*\"musical\" + 0.026*\"afi 100 (laughs)\" + 0.020*\"los angeles\" + 0.018*\"afi 100\" + 0.017*\"national film registry\" + 0.015*\"john travolta\" + 0.014*\"delights\" + 0.013*\"family\" + 0.011*\"70mm\"\n", - "2021-08-24 02:42:12,918 : INFO : topic #23 (0.020): 0.079*\"high school\" + 0.040*\"dance\" + 0.040*\"cars\" + 0.030*\"teen\" + 0.023*\"sandra bullock\" + 0.021*\"eddie murphy\" + 0.017*\"Comedy\" + 0.016*\"romance\" + 0.016*\"chick flick\" + 0.014*\"school\"\n", - "2021-08-24 02:42:12,919 : INFO : topic #19 (0.020): 0.033*\"violence\" + 0.030*\"drama\" + 0.027*\"vietnam war\" + 0.026*\"vietnam\" + 0.023*\"philip k. dick\" + 0.019*\"Drama\" + 0.019*\"racism\" + 0.018*\"nudity (topless)\" + 0.018*\"based on a book\" + 0.014*\"kubrick\"\n", - "2021-08-24 02:42:12,919 : INFO : topic #46 (0.020): 0.065*\"action\" + 0.063*\"sci-fi\" + 0.045*\"fantasy\" + 0.027*\"space\" + 0.021*\"harrison ford\" + 0.021*\"seen at the cinema\" + 0.020*\"dvd\" + 0.020*\"seen more than once\" + 0.020*\"arnold schwarzenegger\" + 0.018*\"adventure\"\n", - "2021-08-24 02:42:12,920 : INFO : topic #24 (0.020): 0.085*\"hitchcock\" + 0.062*\"alfred hitchcock\" + 0.057*\"vampire\" + 0.044*\"oppl\" + 0.040*\"vampires\" + 0.039*\"murder\" + 0.031*\"Thriller\" + 0.023*\"tumey's dvds\" + 0.022*\"Mystery\" + 0.020*\"memory\"\n", - "2021-08-24 02:42:12,921 : INFO : topic diff=0.183325, rho=0.369094\n", - "2021-08-24 02:42:12,922 : INFO : PROGRESS: pass 1, at document #6000/10681\n", - "2021-08-24 02:42:13,172 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:13,198 : INFO : topic #25 (0.020): 0.116*\"Western\" + 0.052*\"Drama\" + 0.046*\"Comedy\" + 0.043*\"erlend's dvds\" + 0.036*\"woody allen\" + 0.033*\"gothic\" + 0.026*\"nicole kidman\" + 0.025*\"george clooney\" + 0.024*\"gene hackman\" + 0.021*\"scary movies to see on halloween\"\n", - "2021-08-24 02:42:13,199 : INFO : topic #4 (0.020): 0.056*\"Drama\" + 0.045*\"sports\" + 0.042*\"boxing\" + 0.039*\"nonlinear\" + 0.037*\"hw drama\" + 0.029*\"sven's to see list\" + 0.029*\"british\" + 0.028*\"london\" + 0.027*\"r\" + 0.022*\"underrated\"\n", - "2021-08-24 02:42:13,200 : INFO : topic #23 (0.020): 0.065*\"high school\" + 0.041*\"cars\" + 0.032*\"dance\" + 0.030*\"eddie murphy\" + 0.024*\"teen\" + 0.022*\"sandra bullock\" + 0.019*\"renee zellweger\" + 0.016*\"Comedy\" + 0.015*\"romance\" + 0.014*\"chick flick\"\n", - "2021-08-24 02:42:13,200 : INFO : topic #36 (0.020): 0.058*\"cult film\" + 0.045*\"teen\" + 0.043*\"peter jackson\" + 0.034*\"russell crowe\" + 0.029*\"dvd-video\" + 0.028*\"Comedy\" + 0.021*\"adam sandler\" + 0.021*\"1980s\" + 0.018*\"80s\" + 0.016*\"new zealand\"\n", - "2021-08-24 02:42:13,201 : INFO : topic #18 (0.020): 0.061*\"gay\" + 0.056*\"magic\" + 0.044*\"Drama\" + 0.027*\"pg13\" + 0.026*\"based on a book\" + 0.021*\"racism\" + 0.018*\"biting\" + 0.017*\"food\" + 0.016*\"19th century\" + 0.016*\"denzel washington\"\n", - "2021-08-24 02:42:13,202 : INFO : topic diff=0.227582, rho=0.369094\n", - "2021-08-24 02:42:13,204 : INFO : PROGRESS: pass 1, at document #8000/10681\n", - "2021-08-24 02:42:13,447 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:13,474 : INFO : topic #32 (0.020): 0.117*\"johnny depp\" + 0.061*\"clint eastwood\" + 0.052*\"vhs\" + 0.042*\"jackie chan\" + 0.025*\"kung fu\" + 0.024*\"western\" + 0.024*\"david lynch\" + 0.023*\"Action\" + 0.022*\"india\" + 0.021*\"leonardo dicaprio\"\n", - "2021-08-24 02:42:13,474 : INFO : topic #5 (0.020): 0.199*\"classic\" + 0.057*\"musical\" + 0.029*\"afi 100 (laughs)\" + 0.021*\"national film registry\" + 0.018*\"john travolta\" + 0.017*\"los angeles\" + 0.015*\"warm\" + 0.015*\"70mm\" + 0.012*\"afi 100\" + 0.012*\"adapted from b'way\"\n", - "2021-08-24 02:42:13,475 : INFO : topic #48 (0.020): 0.070*\"bruce willis\" + 0.070*\"twist ending\" + 0.033*\"coming of age\" + 0.030*\"journalism\" + 0.028*\"netflix\" + 0.027*\"sexy\" + 0.026*\"psychology\" + 0.024*\"Drama\" + 0.021*\"avi\" + 0.018*\"mystery\"\n", - "2021-08-24 02:42:13,476 : INFO : topic #31 (0.020): 0.141*\"zombies\" + 0.054*\"pg-13\" + 0.046*\"zombie\" + 0.029*\"campy\" + 0.028*\"cult classic\" + 0.028*\"sam raimi\" + 0.025*\"Horror\" + 0.024*\"horror\" + 0.021*\"books\" + 0.020*\"joaquin phoenix\"\n", - "2021-08-24 02:42:13,477 : INFO : topic #45 (0.020): 0.228*\"less than 300 ratings\" + 0.104*\"Drama\" + 0.040*\"nudity (topless - notable)\" + 0.039*\"Comedy\" + 0.031*\"tim burton\" + 0.020*\"library\" + 0.018*\"michael moore\" + 0.014*\"blindfold\" + 0.013*\"depressing\" + 0.010*\"golden raspberry (worst picture)\"\n", - "2021-08-24 02:42:13,479 : INFO : topic diff=0.192650, rho=0.369094\n", - "2021-08-24 02:42:13,480 : INFO : PROGRESS: pass 1, at document #10000/10681\n", - "2021-08-24 02:42:13,755 : INFO : merging changes from 2000 documents into a model of 10681 documents\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:13,783 : INFO : topic #42 (0.020): 0.086*\"martial arts\" + 0.059*\"robin williams\" + 0.042*\"jonossa\" + 0.041*\"seen 2006\" + 0.031*\"japan\" + 0.029*\"samurai\" + 0.027*\"Drama\" + 0.025*\"orson welles\" + 0.024*\"hugh grant\" + 0.023*\"divorce\"\n", - "2021-08-24 02:42:13,784 : INFO : topic #41 (0.020): 0.141*\"remake\" + 0.058*\"new york city\" + 0.049*\"christmas\" + 0.044*\"family\" + 0.037*\"new york\" + 0.031*\"Drama\" + 0.027*\"seen\" + 0.024*\"kevin spacey\" + 0.023*\"predictable\" + 0.021*\"friday night movie\"\n", - "2021-08-24 02:42:13,784 : INFO : topic #5 (0.020): 0.162*\"classic\" + 0.082*\"musical\" + 0.027*\"afi 100 (laughs)\" + 0.024*\"los angeles\" + 0.018*\"witch\" + 0.018*\"national film registry\" + 0.016*\"john travolta\" + 0.015*\"Musical\" + 0.014*\"adapted from b'way\" + 0.013*\"movie to see\"\n", - "2021-08-24 02:42:13,785 : INFO : topic #49 (0.020): 0.081*\"national film registry\" + 0.026*\"natalie portman\" + 0.025*\"tumey's dvds\" + 0.022*\"Film-Noir\" + 0.020*\"film noir\" + 0.020*\"black and white\" + 0.019*\"imdb top 250\" + 0.015*\"Drama\" + 0.014*\"afi 100\" + 0.014*\"surprise ending\"\n", - "2021-08-24 02:42:13,786 : INFO : topic #9 (0.020): 0.127*\"based on a book\" + 0.070*\"adapted from:book\" + 0.058*\"Drama\" + 0.038*\"jack nicholson\" + 0.036*\"imdb bottom 100\" + 0.030*\"horror\" + 0.026*\"Comedy\" + 0.025*\"stephen king\" + 0.024*\"genocide\" + 0.020*\"stupid\"\n", - "2021-08-24 02:42:13,787 : INFO : topic diff=0.177447, rho=0.369094\n", - "2021-08-24 02:42:13,942 : INFO : -21.161 per-word bound, 2344453.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:13,942 : INFO : PROGRESS: pass 1, at document #10681/10681\n", - "2021-08-24 02:42:14,045 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:14,071 : INFO : topic #47 (0.020): 0.057*\"serial killer\" + 0.048*\"brad pitt\" + 0.033*\"edward norton\" + 0.032*\"matt damon\" + 0.029*\"hayao miyazaki\" + 0.025*\"crime\" + 0.020*\"steven spielberg\" + 0.020*\"psychology\" + 0.014*\"twist\" + 0.013*\"anthony hopkins\"\n", - "2021-08-24 02:42:14,072 : INFO : topic #42 (0.020): 0.076*\"martial arts\" + 0.048*\"robin williams\" + 0.036*\"Drama\" + 0.035*\"divorce\" + 0.035*\"samurai\" + 0.034*\"jonossa\" + 0.034*\"seen 2006\" + 0.028*\"japan\" + 0.026*\"akira kurosawa\" + 0.020*\"beautiful\"\n", - "2021-08-24 02:42:14,073 : INFO : topic #18 (0.020): 0.097*\"pg13\" + 0.081*\"magic\" + 0.061*\"gay\" + 0.042*\"Drama\" + 0.029*\"based on a book\" + 0.023*\"racism\" + 0.022*\"food\" + 0.021*\"denzel washington\" + 0.018*\"19th century\" + 0.016*\"air force\"\n", - "2021-08-24 02:42:14,074 : INFO : topic #41 (0.020): 0.149*\"remake\" + 0.086*\"new york city\" + 0.065*\"christmas\" + 0.034*\"new york\" + 0.031*\"family\" + 0.030*\"Drama\" + 0.029*\"movie to see\" + 0.021*\"kevin spacey\" + 0.020*\"predictable\" + 0.019*\"seen\"\n", - "2021-08-24 02:42:14,075 : INFO : topic #27 (0.020): 0.299*\"Horror\" + 0.154*\"Thriller\" + 0.122*\"Mystery\" + 0.040*\"easily confused with other movie(s) (title)\" + 0.036*\"Film-Noir\" + 0.031*\"Fantasy\" + 0.030*\"Drama\" + 0.016*\"Comedy\" + 0.012*\"eerie\" + 0.011*\"marriage\"\n", - "2021-08-24 02:42:14,076 : INFO : topic diff=0.239336, rho=0.369094\n", - "2021-08-24 02:42:14,077 : INFO : PROGRESS: pass 2, at document #2000/10681\n", - "2021-08-24 02:42:14,406 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:14,432 : INFO : topic #33 (0.020): 0.084*\"comedy\" + 0.070*\"Comedy\" + 0.046*\"dark comedy\" + 0.042*\"coen brothers\" + 0.038*\"funny\" + 0.034*\"quirky\" + 0.032*\"hilarious\" + 0.027*\"black comedy\" + 0.019*\"witty\" + 0.016*\"england\"\n", - "2021-08-24 02:42:14,433 : INFO : topic #9 (0.020): 0.185*\"based on a book\" + 0.078*\"adapted from:book\" + 0.052*\"jack nicholson\" + 0.051*\"stephen king\" + 0.049*\"Drama\" + 0.039*\"horror\" + 0.029*\"imdb bottom 100\" + 0.021*\"Comedy\" + 0.019*\"stupid\" + 0.017*\"pregnancy\"\n", - "2021-08-24 02:42:14,434 : INFO : topic #42 (0.020): 0.094*\"robin williams\" + 0.049*\"martial arts\" + 0.038*\"samurai\" + 0.038*\"orson welles\" + 0.038*\"divorce\" + 0.026*\"kurosawa\" + 0.026*\"Drama\" + 0.025*\"akira kurosawa\" + 0.023*\"japan\" + 0.022*\"jonossa\"\n", - "2021-08-24 02:42:14,434 : INFO : topic #43 (0.020): 0.061*\"surreal\" + 0.053*\"stanley kubrick\" + 0.027*\"satirical\" + 0.027*\"cynical\" + 0.024*\"stylized\" + 0.023*\"hallucinatory\" + 0.022*\"narrated\" + 0.020*\"irreverent\" + 0.018*\"quirky\" + 0.015*\"dreamlike\"\n", - "2021-08-24 02:42:14,435 : INFO : topic #13 (0.020): 0.052*\"jane austen\" + 0.050*\"fairy tale\" + 0.036*\"assassination\" + 0.035*\"historical\" + 0.031*\"cold war\" + 0.028*\"road trip\" + 0.022*\"disney\" + 0.021*\"based on book\" + 0.021*\"gerard depardieu\" + 0.020*\"swashbuckler\"\n", - "2021-08-24 02:42:14,436 : INFO : topic diff=0.371936, rho=0.346261\n", - "2021-08-24 02:42:14,438 : INFO : PROGRESS: pass 2, at document #4000/10681\n", - "2021-08-24 02:42:14,725 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:14,752 : INFO : topic #11 (0.020): 0.129*\"religion\" + 0.118*\"keanu reeves\" + 0.019*\"class issues\" + 0.019*\"dark\" + 0.018*\"courtesan\" + 0.017*\"facebook rec\" + 0.016*\"product placement\" + 0.015*\"reality tv\" + 0.014*\"slash\" + 0.012*\"jesus\"\n", - "2021-08-24 02:42:14,753 : INFO : topic #19 (0.020): 0.042*\"drama\" + 0.035*\"violence\" + 0.028*\"vietnam\" + 0.028*\"vietnam war\" + 0.025*\"philip k. dick\" + 0.019*\"rape\" + 0.018*\"based on a book\" + 0.017*\"very good\" + 0.016*\"Drama\" + 0.016*\"kubrick\"\n", - "2021-08-24 02:42:14,753 : INFO : topic #16 (0.020): 0.254*\"nudity (topless)\" + 0.180*\"nudity (topless - brief)\" + 0.065*\"nudity (rear)\" + 0.035*\"claymation\" + 0.029*\"military\" + 0.024*\"aardman\" + 0.020*\"childhood\" + 0.018*\"pg\" + 0.013*\"weird\" + 0.012*\"freedom\"\n", - "2021-08-24 02:42:14,754 : INFO : topic #3 (0.020): 0.210*\"oscar (best picture)\" + 0.056*\"oscar (best directing)\" + 0.041*\"al pacino\" + 0.023*\"great acting\" + 0.021*\"best picture\" + 0.019*\"john cusack\" + 0.019*\"dvd\" + 0.019*\"civil war\" + 0.018*\"tumey's dvds\" + 0.017*\"Drama\"\n", - "2021-08-24 02:42:14,755 : INFO : topic #26 (0.020): 0.040*\"james bond\" + 0.034*\"Drama\" + 0.029*\"007\" + 0.027*\"bond\" + 0.024*\"mel gibson\" + 0.020*\"atmospheric\" + 0.019*\"reflective\" + 0.017*\"assassin\" + 0.016*\"poignant\" + 0.015*\"lyrical\"\n", - "2021-08-24 02:42:14,756 : INFO : topic diff=0.117084, rho=0.346261\n", - "2021-08-24 02:42:14,757 : INFO : PROGRESS: pass 2, at document #6000/10681\n", - "2021-08-24 02:42:15,005 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:15,032 : INFO : topic #41 (0.020): 0.118*\"remake\" + 0.068*\"christmas\" + 0.058*\"new york city\" + 0.041*\"family\" + 0.038*\"new york\" + 0.033*\"kevin spacey\" + 0.027*\"Drama\" + 0.027*\"robert de niro\" + 0.024*\"bibliothek\" + 0.024*\"friday night movie\"\n", - "2021-08-24 02:42:15,033 : INFO : topic #32 (0.020): 0.118*\"johnny depp\" + 0.067*\"clint eastwood\" + 0.047*\"jackie chan\" + 0.045*\"vhs\" + 0.033*\"western\" + 0.032*\"leonardo dicaprio\" + 0.029*\"david lynch\" + 0.028*\"spaghetti western\" + 0.028*\"kung fu\" + 0.022*\"Action\"\n", - "2021-08-24 02:42:15,033 : INFO : topic #4 (0.020): 0.057*\"sports\" + 0.057*\"Drama\" + 0.045*\"boxing\" + 0.039*\"hw drama\" + 0.036*\"british\" + 0.032*\"sven's to see list\" + 0.032*\"london\" + 0.031*\"underrated\" + 0.028*\"nonlinear\" + 0.023*\"college\"\n", - "2021-08-24 02:42:15,034 : INFO : topic #13 (0.020): 0.056*\"fairy tale\" + 0.032*\"jane austen\" + 0.032*\"historical\" + 0.030*\"road trip\" + 0.029*\"gerard depardieu\" + 0.026*\"sequel\" + 0.025*\"based on book\" + 0.025*\"kids\" + 0.024*\"cold war\" + 0.022*\"assassination\"\n", - "2021-08-24 02:42:15,035 : INFO : topic #15 (0.020): 0.130*\"disney\" + 0.100*\"animation\" + 0.076*\"pixar\" + 0.036*\"children\" + 0.030*\"angelina jolie\" + 0.028*\"Animation\" + 0.022*\"disney animated feature\" + 0.021*\"cartoon\" + 0.020*\"Children\" + 0.017*\"animated\"\n", - "2021-08-24 02:42:15,036 : INFO : topic diff=0.153361, rho=0.346261\n", - "2021-08-24 02:42:15,038 : INFO : PROGRESS: pass 2, at document #8000/10681\n", - "2021-08-24 02:42:15,295 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:15,320 : INFO : topic #43 (0.020): 0.091*\"surreal\" + 0.041*\"satirical\" + 0.036*\"cynical\" + 0.031*\"quirky\" + 0.029*\"hallucinatory\" + 0.029*\"narrated\" + 0.027*\"stylized\" + 0.025*\"wry\" + 0.023*\"dreamlike\" + 0.020*\"stanley kubrick\"\n", - "2021-08-24 02:42:15,321 : INFO : topic #19 (0.020): 0.043*\"drama\" + 0.034*\"philip k. dick\" + 0.033*\"violence\" + 0.024*\"soccer\" + 0.024*\"vietnam war\" + 0.023*\"vietnam\" + 0.023*\"very good\" + 0.020*\"rape\" + 0.018*\"blaxploitation\" + 0.017*\"based on a book\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:15,322 : INFO : topic #45 (0.020): 0.235*\"less than 300 ratings\" + 0.109*\"Drama\" + 0.061*\"nudity (topless - notable)\" + 0.036*\"Comedy\" + 0.035*\"tim burton\" + 0.023*\"library\" + 0.018*\"michael moore\" + 0.015*\"blindfold\" + 0.014*\"depressing\" + 0.011*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:15,323 : INFO : topic #37 (0.020): 0.185*\"War\" + 0.109*\"world war ii\" + 0.103*\"Drama\" + 0.047*\"history\" + 0.047*\"war\" + 0.035*\"jim carrey\" + 0.025*\"Action\" + 0.019*\"70mm\" + 0.014*\"nazis\" + 0.013*\"mental illness\"\n", - "2021-08-24 02:42:15,324 : INFO : topic #38 (0.020): 0.142*\"drugs\" + 0.037*\"samuel l. jackson\" + 0.035*\"peter sellers\" + 0.030*\"ewan mcgregor\" + 0.024*\"divx\" + 0.022*\"poverty\" + 0.020*\"addiction\" + 0.019*\"hulu\" + 0.018*\"michael caine\" + 0.017*\"Drama\"\n", - "2021-08-24 02:42:15,325 : INFO : topic diff=0.119826, rho=0.346261\n", - "2021-08-24 02:42:15,327 : INFO : PROGRESS: pass 2, at document #10000/10681\n", - "2021-08-24 02:42:15,579 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:15,606 : INFO : topic #10 (0.020): 0.214*\"Thriller\" + 0.208*\"Crime\" + 0.178*\"Action\" + 0.161*\"Drama\" + 0.035*\"Comedy\" + 0.032*\"Mystery\" + 0.022*\"Adventure\" + 0.010*\"murder\" + 0.007*\"virtual reality\" + 0.005*\"greg kinnear\"\n", - "2021-08-24 02:42:15,607 : INFO : topic #11 (0.020): 0.120*\"religion\" + 0.097*\"keanu reeves\" + 0.030*\"dark\" + 0.028*\"product placement\" + 0.021*\"facebook rec\" + 0.020*\"courtesan\" + 0.017*\"gender identity\" + 0.017*\"social message\" + 0.017*\"reality tv\" + 0.017*\"cross-dressing women\"\n", - "2021-08-24 02:42:15,608 : INFO : topic #37 (0.020): 0.196*\"War\" + 0.109*\"Drama\" + 0.097*\"world war ii\" + 0.052*\"war\" + 0.050*\"history\" + 0.028*\"jim carrey\" + 0.026*\"Action\" + 0.014*\"nazis\" + 0.013*\"70mm\" + 0.013*\"wwii\"\n", - "2021-08-24 02:42:15,608 : INFO : topic #9 (0.020): 0.186*\"based on a book\" + 0.096*\"adapted from:book\" + 0.064*\"Drama\" + 0.035*\"jack nicholson\" + 0.032*\"imdb bottom 100\" + 0.029*\"horror\" + 0.025*\"stephen king\" + 0.021*\"stupid\" + 0.020*\"genocide\" + 0.018*\"based on book\"\n", - "2021-08-24 02:42:15,609 : INFO : topic #29 (0.020): 0.089*\"nudity (full frontal - notable)\" + 0.057*\"Drama\" + 0.046*\"shakespeare\" + 0.042*\"based on a play\" + 0.037*\"christianity\" + 0.031*\"adapted from:play\" + 0.028*\"religion\" + 0.025*\"fascism\" + 0.025*\"biblical\" + 0.019*\"k movie\"\n", - "2021-08-24 02:42:15,611 : INFO : topic diff=0.108163, rho=0.346261\n", - "2021-08-24 02:42:15,756 : INFO : -21.064 per-word bound, 2191656.4 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:15,757 : INFO : PROGRESS: pass 2, at document #10681/10681\n", - "2021-08-24 02:42:15,858 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:15,883 : INFO : topic #19 (0.020): 0.033*\"nudity (full frontal)\" + 0.031*\"ummarti2007\" + 0.030*\"violence\" + 0.029*\"drama\" + 0.027*\"rape\" + 0.027*\"philip k. dick\" + 0.024*\"to see\" + 0.021*\"slow\" + 0.020*\"male nudity\" + 0.017*\"soccer\"\n", - "2021-08-24 02:42:15,884 : INFO : topic #29 (0.020): 0.095*\"nudity (full frontal - notable)\" + 0.059*\"christianity\" + 0.058*\"Drama\" + 0.042*\"religion\" + 0.038*\"based on a play\" + 0.033*\"shakespeare\" + 0.030*\"adapted from:play\" + 0.023*\"cannibalism\" + 0.019*\"to see\" + 0.018*\"fascism\"\n", - "2021-08-24 02:42:15,885 : INFO : topic #12 (0.020): 0.150*\"comic book\" + 0.137*\"superhero\" + 0.057*\"dystopia\" + 0.049*\"super-hero\" + 0.040*\"batman\" + 0.039*\"holocaust\" + 0.038*\"adapted from:comic\" + 0.024*\"kidnapping\" + 0.020*\"Action\" + 0.015*\"alter ego\"\n", - "2021-08-24 02:42:15,886 : INFO : topic #46 (0.020): 0.068*\"action\" + 0.068*\"fantasy\" + 0.047*\"sci-fi\" + 0.031*\"adventure\" + 0.026*\"watched 2006\" + 0.026*\"dvd\" + 0.026*\"robots\" + 0.021*\"space\" + 0.020*\"Adventure\" + 0.019*\"seen at the cinema\"\n", - "2021-08-24 02:42:15,886 : INFO : topic #18 (0.020): 0.097*\"pg13\" + 0.089*\"magic\" + 0.066*\"gay\" + 0.042*\"Drama\" + 0.031*\"racism\" + 0.025*\"food\" + 0.022*\"denzel washington\" + 0.020*\"19th century\" + 0.016*\"air force\" + 0.015*\"based on a book\"\n", - "2021-08-24 02:42:15,887 : INFO : topic diff=0.169463, rho=0.346261\n", - "2021-08-24 02:42:15,889 : INFO : PROGRESS: pass 3, at document #2000/10681\n", - "2021-08-24 02:42:16,165 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:16,191 : INFO : topic #17 (0.020): 0.279*\"Sci-Fi\" + 0.087*\"Action\" + 0.060*\"Horror\" + 0.040*\"video game adaptation\" + 0.040*\"Adventure\" + 0.026*\"animals\" + 0.025*\"Fantasy\" + 0.024*\"movie to see\" + 0.017*\"futuristic\" + 0.016*\"football\"\n", - "2021-08-24 02:42:16,192 : INFO : topic #9 (0.020): 0.230*\"based on a book\" + 0.095*\"adapted from:book\" + 0.054*\"Drama\" + 0.048*\"stephen king\" + 0.048*\"jack nicholson\" + 0.037*\"horror\" + 0.027*\"imdb bottom 100\" + 0.020*\"stupid\" + 0.019*\"based on book\" + 0.016*\"pregnancy\"\n", - "2021-08-24 02:42:16,193 : INFO : topic #30 (0.020): 0.191*\"Adventure\" + 0.153*\"Children\" + 0.116*\"Comedy\" + 0.102*\"Fantasy\" + 0.063*\"Animation\" + 0.042*\"Action\" + 0.032*\"Drama\" + 0.030*\"70mm\" + 0.027*\"Musical\" + 0.014*\"submarine\"\n", - "2021-08-24 02:42:16,194 : INFO : topic #1 (0.020): 0.057*\"quentin tarantino\" + 0.043*\"overrated\" + 0.038*\"tarantino\" + 0.032*\"violent\" + 0.028*\"crime\" + 0.027*\"imdb top 250\" + 0.023*\"drama\" + 0.022*\"revenge\" + 0.020*\"tom hanks\" + 0.018*\"uma thurman\"\n", - "2021-08-24 02:42:16,194 : INFO : topic #26 (0.020): 0.037*\"Drama\" + 0.023*\"mel gibson\" + 0.020*\"atmospheric\" + 0.019*\"bittersweet\" + 0.018*\"james bond\" + 0.018*\"poignant\" + 0.018*\"assassin\" + 0.017*\"reflective\" + 0.016*\"lyrical\" + 0.015*\"stylized\"\n", - "2021-08-24 02:42:16,196 : INFO : topic diff=0.304291, rho=0.327201\n", - "2021-08-24 02:42:16,197 : INFO : PROGRESS: pass 3, at document #4000/10681\n", - "2021-08-24 02:42:16,445 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:16,470 : INFO : topic #49 (0.020): 0.080*\"national film registry\" + 0.048*\"imdb top 250\" + 0.040*\"afi 100\" + 0.035*\"black and white\" + 0.031*\"afi 100 (thrills)\" + 0.030*\"tumey's dvds\" + 0.026*\"usa film registry\" + 0.025*\"afi 100 (movie quotes)\" + 0.020*\"erlend's dvds\" + 0.019*\"emerson must see\"\n", - "2021-08-24 02:42:16,471 : INFO : topic #31 (0.020): 0.117*\"zombies\" + 0.067*\"pg-13\" + 0.051*\"cult classic\" + 0.030*\"campy\" + 0.027*\"joaquin phoenix\" + 0.026*\"sam raimi\" + 0.023*\"mummy\" + 0.020*\"books\" + 0.019*\"zombie\" + 0.017*\"horror\"\n", - "2021-08-24 02:42:16,472 : INFO : topic #47 (0.020): 0.068*\"serial killer\" + 0.057*\"brad pitt\" + 0.042*\"edward norton\" + 0.037*\"psychology\" + 0.034*\"steven spielberg\" + 0.029*\"crime\" + 0.024*\"matt damon\" + 0.018*\"heist\" + 0.016*\"dinosaurs\" + 0.014*\"hayao miyazaki\"\n", - "2021-08-24 02:42:16,473 : INFO : topic #6 (0.020): 0.094*\"Musical\" + 0.075*\"politics\" + 0.066*\"satire\" + 0.053*\"sean connery\" + 0.048*\"nicolas cage\" + 0.030*\"police\" + 0.029*\"terrorism\" + 0.027*\"dvd-r\" + 0.021*\"political\" + 0.020*\"good dialogue\"\n", - "2021-08-24 02:42:16,474 : INFO : topic #12 (0.020): 0.116*\"comic book\" + 0.109*\"superhero\" + 0.086*\"dystopia\" + 0.054*\"holocaust\" + 0.051*\"super-hero\" + 0.032*\"adapted from:comic\" + 0.024*\"batman\" + 0.020*\"Action\" + 0.018*\"kidnapping\" + 0.017*\"alter ego\"\n", - "2021-08-24 02:42:16,475 : INFO : topic diff=0.073617, rho=0.327201\n", - "2021-08-24 02:42:16,477 : INFO : PROGRESS: pass 3, at document #6000/10681\n", - "2021-08-24 02:42:16,714 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:16,739 : INFO : topic #14 (0.020): 0.055*\"criterion\" + 0.049*\"Drama\" + 0.047*\"tense\" + 0.042*\"disturbing\" + 0.041*\"atmospheric\" + 0.033*\"tumey's dvds\" + 0.026*\"bleak\" + 0.024*\"erlend's dvds\" + 0.023*\"menacing\" + 0.022*\"visceral\"\n", - "2021-08-24 02:42:16,740 : INFO : topic #28 (0.020): 0.075*\"oscar (best cinematography)\" + 0.064*\"Drama\" + 0.050*\"oscar (best supporting actress)\" + 0.049*\"oscar (best actor)\" + 0.044*\"oscar (best actress)\" + 0.037*\"in netflix queue\" + 0.025*\"remade\" + 0.017*\"70mm\" + 0.017*\"Romance\" + 0.017*\"oscar (best supporting actor)\"\n", - "2021-08-24 02:42:16,741 : INFO : topic #24 (0.020): 0.077*\"hitchcock\" + 0.069*\"murder\" + 0.064*\"vampire\" + 0.057*\"alfred hitchcock\" + 0.054*\"vampires\" + 0.047*\"memory\" + 0.042*\"oppl\" + 0.017*\"Thriller\" + 0.017*\"tumey's dvds\" + 0.016*\"cary grant\"\n", - "2021-08-24 02:42:16,742 : INFO : topic #15 (0.020): 0.146*\"disney\" + 0.097*\"animation\" + 0.074*\"pixar\" + 0.042*\"children\" + 0.033*\"Animation\" + 0.029*\"angelina jolie\" + 0.027*\"disney animated feature\" + 0.023*\"Children\" + 0.022*\"cartoon\" + 0.017*\"pirates\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:16,743 : INFO : topic #11 (0.020): 0.127*\"religion\" + 0.115*\"keanu reeves\" + 0.020*\"dark\" + 0.018*\"reality tv\" + 0.018*\"courtesan\" + 0.018*\"slash\" + 0.017*\"slavery\" + 0.017*\"facebook rec\" + 0.014*\"class issues\" + 0.012*\"product placement\"\n", - "2021-08-24 02:42:16,744 : INFO : topic diff=0.113857, rho=0.327201\n", - "2021-08-24 02:42:16,746 : INFO : PROGRESS: pass 3, at document #8000/10681\n", - "2021-08-24 02:42:16,986 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:17,013 : INFO : topic #20 (0.020): 0.086*\"biography\" + 0.060*\"Drama\" + 0.043*\"biopic\" + 0.042*\"ingmar bergman\" + 0.035*\"ensemble cast\" + 0.031*\"julia roberts\" + 0.030*\"suicide\" + 0.028*\"corvallis library\" + 0.027*\"multiple storylines\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:17,015 : INFO : topic #47 (0.020): 0.059*\"serial killer\" + 0.046*\"psychology\" + 0.045*\"brad pitt\" + 0.041*\"edward norton\" + 0.033*\"heist\" + 0.031*\"hayao miyazaki\" + 0.031*\"steven spielberg\" + 0.029*\"crime\" + 0.029*\"matt damon\" + 0.014*\"my dvds\"\n", - "2021-08-24 02:42:17,015 : INFO : topic #30 (0.020): 0.210*\"Adventure\" + 0.128*\"Children\" + 0.107*\"Comedy\" + 0.100*\"Fantasy\" + 0.060*\"70mm\" + 0.056*\"Animation\" + 0.055*\"Action\" + 0.031*\"Drama\" + 0.030*\"Musical\" + 0.009*\"submarine\"\n", - "2021-08-24 02:42:17,016 : INFO : topic #5 (0.020): 0.252*\"classic\" + 0.060*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.030*\"national film registry\" + 0.027*\"Musical\" + 0.017*\"john travolta\" + 0.015*\"afi 100\" + 0.013*\"70mm\" + 0.013*\"warm\" + 0.012*\"adapted from b'way\"\n", - "2021-08-24 02:42:17,017 : INFO : topic #12 (0.020): 0.133*\"comic book\" + 0.092*\"superhero\" + 0.076*\"dystopia\" + 0.069*\"super-hero\" + 0.052*\"holocaust\" + 0.030*\"adapted from:comic\" + 0.026*\"kidnapping\" + 0.020*\"alter ego\" + 0.017*\"Sci-Fi\" + 0.017*\"Action\"\n", - "2021-08-24 02:42:17,018 : INFO : topic diff=0.087537, rho=0.327201\n", - "2021-08-24 02:42:17,019 : INFO : PROGRESS: pass 3, at document #10000/10681\n", - "2021-08-24 02:42:17,284 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:17,312 : INFO : topic #36 (0.020): 0.054*\"cult film\" + 0.045*\"peter jackson\" + 0.039*\"not available from netflix\" + 0.038*\"downbeat\" + 0.037*\"russell crowe\" + 0.030*\"adam sandler\" + 0.023*\"new zealand\" + 0.023*\"1980s\" + 0.021*\"existentialism\" + 0.018*\"must see!\"\n", - "2021-08-24 02:42:17,313 : INFO : topic #32 (0.020): 0.152*\"johnny depp\" + 0.061*\"clint eastwood\" + 0.057*\"watched 2007\" + 0.052*\"vhs\" + 0.039*\"propaganda\" + 0.034*\"jackie chan\" + 0.028*\"leonardo dicaprio\" + 0.026*\"western\" + 0.023*\"david lynch\" + 0.022*\"italian\"\n", - "2021-08-24 02:42:17,314 : INFO : topic #34 (0.020): 0.064*\"ghosts\" + 0.061*\"bill murray\" + 0.037*\"television\" + 0.037*\"courtroom\" + 0.031*\"courtroom drama\" + 0.024*\"roman polanski\" + 0.023*\"ben stiller\" + 0.021*\"las vegas\" + 0.018*\"smoking\" + 0.016*\"18th century\"\n", - "2021-08-24 02:42:17,315 : INFO : topic #40 (0.020): 0.289*\"r\" + 0.122*\"clearplay\" + 0.058*\"movie to see\" + 0.042*\"Drama\" + 0.032*\"prison\" + 0.025*\"morgan freeman\" + 0.024*\"friendship\" + 0.019*\"john wayne\" + 0.017*\"don cheadle\" + 0.016*\"conspiracy\"\n", - "2021-08-24 02:42:17,316 : INFO : topic #1 (0.020): 0.052*\"overrated\" + 0.042*\"revenge\" + 0.042*\"quentin tarantino\" + 0.031*\"violent\" + 0.023*\"crime\" + 0.022*\"tarantino\" + 0.021*\"violence\" + 0.020*\"imdb top 250\" + 0.019*\"uma thurman\" + 0.018*\"must see\"\n", - "2021-08-24 02:42:17,317 : INFO : topic diff=0.079974, rho=0.327201\n", - "2021-08-24 02:42:17,464 : INFO : -21.025 per-word bound, 2133784.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:17,465 : INFO : PROGRESS: pass 3, at document #10681/10681\n", - "2021-08-24 02:42:17,562 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:17,588 : INFO : topic #19 (0.020): 0.070*\"nudity (full frontal)\" + 0.032*\"drama\" + 0.029*\"ummarti2007\" + 0.028*\"to see\" + 0.028*\"violence\" + 0.027*\"rape\" + 0.026*\"philip k. dick\" + 0.021*\"male nudity\" + 0.021*\"slow\" + 0.016*\"unique\"\n", - "2021-08-24 02:42:17,588 : INFO : topic #28 (0.020): 0.071*\"Drama\" + 0.051*\"in netflix queue\" + 0.051*\"oscar (best cinematography)\" + 0.036*\"oscar (best actress)\" + 0.033*\"oscar (best supporting actress)\" + 0.029*\"oscar (best actor)\" + 0.022*\"remade\" + 0.019*\"to see\" + 0.019*\"father daughter relationship\" + 0.019*\"immortality\"\n", - "2021-08-24 02:42:17,589 : INFO : topic #12 (0.020): 0.151*\"comic book\" + 0.137*\"superhero\" + 0.059*\"dystopia\" + 0.050*\"super-hero\" + 0.040*\"holocaust\" + 0.039*\"batman\" + 0.038*\"adapted from:comic\" + 0.025*\"kidnapping\" + 0.018*\"Action\" + 0.015*\"alter ego\"\n", - "2021-08-24 02:42:17,590 : INFO : topic #1 (0.020): 0.052*\"overrated\" + 0.041*\"violent\" + 0.038*\"revenge\" + 0.035*\"quentin tarantino\" + 0.034*\"imdb top 250\" + 0.021*\"crime\" + 0.019*\"tarantino\" + 0.019*\"owned\" + 0.018*\"must see\" + 0.018*\"elijah wood\"\n", - "2021-08-24 02:42:17,591 : INFO : topic #46 (0.020): 0.073*\"action\" + 0.066*\"fantasy\" + 0.048*\"sci-fi\" + 0.034*\"adventure\" + 0.028*\"dvd\" + 0.025*\"watched 2006\" + 0.025*\"robots\" + 0.021*\"Adventure\" + 0.020*\"space\" + 0.020*\"seen at the cinema\"\n", - "2021-08-24 02:42:17,592 : INFO : topic diff=0.143431, rho=0.327201\n", - "2021-08-24 02:42:17,594 : INFO : PROGRESS: pass 4, at document #2000/10681\n", - "2021-08-24 02:42:17,880 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:17,906 : INFO : topic #20 (0.020): 0.107*\"biography\" + 0.056*\"Drama\" + 0.054*\"biopic\" + 0.042*\"suicide\" + 0.036*\"death\" + 0.035*\"julia roberts\" + 0.031*\"corvallis library\" + 0.030*\"ensemble cast\" + 0.028*\"medieval\" + 0.024*\"forest whitaker\"\n", - "2021-08-24 02:42:17,907 : INFO : topic #49 (0.020): 0.081*\"national film registry\" + 0.050*\"imdb top 250\" + 0.041*\"afi 100\" + 0.034*\"black and white\" + 0.032*\"afi 100 (thrills)\" + 0.027*\"usa film registry\" + 0.025*\"tumey's dvds\" + 0.023*\"afi 100 (movie quotes)\" + 0.018*\"emerson must see\" + 0.017*\"france\"\n", - "2021-08-24 02:42:17,908 : INFO : topic #47 (0.020): 0.074*\"serial killer\" + 0.055*\"brad pitt\" + 0.044*\"steven spielberg\" + 0.035*\"psychology\" + 0.033*\"crime\" + 0.027*\"heist\" + 0.021*\"matt damon\" + 0.021*\"dinosaurs\" + 0.021*\"edward norton\" + 0.015*\"anthony hopkins\"\n", - "2021-08-24 02:42:17,909 : INFO : topic #41 (0.020): 0.111*\"remake\" + 0.072*\"new york city\" + 0.069*\"christmas\" + 0.044*\"kevin spacey\" + 0.042*\"family\" + 0.033*\"new york\" + 0.026*\"robert de niro\" + 0.023*\"Drama\" + 0.021*\"movie to see\" + 0.021*\"eric's dvds\"\n", - "2021-08-24 02:42:17,909 : INFO : topic #3 (0.020): 0.251*\"oscar (best picture)\" + 0.090*\"oscar (best directing)\" + 0.030*\"al pacino\" + 0.029*\"oscar (best actor)\" + 0.026*\"best picture\" + 0.025*\"oscar (best supporting actor)\" + 0.025*\"great acting\" + 0.018*\"afi 100 (cheers)\" + 0.018*\"civil war\" + 0.017*\"afi 100\"\n", - "2021-08-24 02:42:17,911 : INFO : topic diff=0.269327, rho=0.310978\n", - "2021-08-24 02:42:17,912 : INFO : PROGRESS: pass 4, at document #4000/10681\n", - "2021-08-24 02:42:18,177 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:18,204 : INFO : topic #30 (0.020): 0.212*\"Adventure\" + 0.142*\"Children\" + 0.108*\"Comedy\" + 0.103*\"Fantasy\" + 0.060*\"Animation\" + 0.060*\"Action\" + 0.047*\"70mm\" + 0.033*\"Drama\" + 0.026*\"Musical\" + 0.013*\"submarine\"\n", - "2021-08-24 02:42:18,204 : INFO : topic #25 (0.020): 0.129*\"Western\" + 0.064*\"erlend's dvds\" + 0.049*\"woody allen\" + 0.048*\"gothic\" + 0.046*\"Drama\" + 0.037*\"gene hackman\" + 0.035*\"scary movies to see on halloween\" + 0.035*\"Comedy\" + 0.024*\"george clooney\" + 0.022*\"sad\"\n", - "2021-08-24 02:42:18,205 : INFO : topic #32 (0.020): 0.136*\"johnny depp\" + 0.064*\"clint eastwood\" + 0.043*\"western\" + 0.042*\"vhs\" + 0.041*\"jackie chan\" + 0.038*\"spaghetti western\" + 0.032*\"sergio leone\" + 0.030*\"kung fu\" + 0.027*\"india\" + 0.026*\"david lynch\"\n", - "2021-08-24 02:42:18,206 : INFO : topic #22 (0.020): 0.236*\"betamax\" + 0.085*\"dvd-video\" + 0.076*\"clv\" + 0.038*\"aviation\" + 0.026*\"library on hold\" + 0.024*\"terry gilliam\" + 0.019*\"dvd collection\" + 0.017*\"gilliam\" + 0.015*\"oscar (best music - original song)\" + 0.012*\"steven seagal\"\n", - "2021-08-24 02:42:18,207 : INFO : topic #12 (0.020): 0.119*\"comic book\" + 0.110*\"superhero\" + 0.087*\"dystopia\" + 0.055*\"holocaust\" + 0.052*\"super-hero\" + 0.033*\"adapted from:comic\" + 0.025*\"batman\" + 0.019*\"kidnapping\" + 0.018*\"Action\" + 0.017*\"alter ego\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:18,208 : INFO : topic diff=0.057320, rho=0.310978\n", - "2021-08-24 02:42:18,209 : INFO : PROGRESS: pass 4, at document #6000/10681\n", - "2021-08-24 02:42:18,450 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:18,476 : INFO : topic #40 (0.020): 0.236*\"r\" + 0.117*\"clearplay\" + 0.053*\"prison\" + 0.051*\"movie to see\" + 0.039*\"Drama\" + 0.033*\"morgan freeman\" + 0.025*\"friendship\" + 0.020*\"john wayne\" + 0.019*\"mel brooks\" + 0.017*\"conspiracy\"\n", - "2021-08-24 02:42:18,477 : INFO : topic #5 (0.020): 0.268*\"classic\" + 0.066*\"musical\" + 0.046*\"afi 100 (laughs)\" + 0.028*\"Musical\" + 0.027*\"national film registry\" + 0.020*\"afi 100\" + 0.019*\"john travolta\" + 0.012*\"delights\" + 0.012*\"adapted from b'way\" + 0.012*\"breakthroughs\"\n", - "2021-08-24 02:42:18,478 : INFO : topic #49 (0.020): 0.076*\"national film registry\" + 0.049*\"imdb top 250\" + 0.035*\"black and white\" + 0.032*\"afi 100\" + 0.030*\"tumey's dvds\" + 0.027*\"afi 100 (thrills)\" + 0.024*\"afi 100 (movie quotes)\" + 0.023*\"france\" + 0.022*\"paris\" + 0.021*\"usa film registry\"\n", - "2021-08-24 02:42:18,478 : INFO : topic #2 (0.020): 0.272*\"Documentary\" + 0.067*\"to see\" + 0.049*\"documentary\" + 0.032*\"sexuality\" + 0.027*\"star trek\" + 0.025*\"kevin smith\" + 0.024*\"mockumentary\" + 0.022*\"dogs\" + 0.017*\"owen wilson\" + 0.016*\"in netflix queue\"\n", - "2021-08-24 02:42:18,479 : INFO : topic #33 (0.020): 0.109*\"comedy\" + 0.108*\"Comedy\" + 0.053*\"funny\" + 0.040*\"quirky\" + 0.038*\"parody\" + 0.033*\"dark comedy\" + 0.029*\"hilarious\" + 0.027*\"coen brothers\" + 0.021*\"black comedy\" + 0.020*\"seen more than once\"\n", - "2021-08-24 02:42:18,480 : INFO : topic diff=0.097891, rho=0.310978\n", - "2021-08-24 02:42:18,482 : INFO : PROGRESS: pass 4, at document #8000/10681\n", - "2021-08-24 02:42:18,731 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:18,757 : INFO : topic #39 (0.020): 0.179*\"time travel\" + 0.042*\"motorcycle\" + 0.040*\"adultery\" + 0.035*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.029*\"jude law\" + 0.020*\"old\" + 0.020*\"ian mckellen\" + 0.018*\"hollywood\" + 0.018*\"want to own\"\n", - "2021-08-24 02:42:18,759 : INFO : topic #40 (0.020): 0.230*\"r\" + 0.103*\"clearplay\" + 0.048*\"prison\" + 0.048*\"movie to see\" + 0.041*\"Drama\" + 0.029*\"morgan freeman\" + 0.026*\"friendship\" + 0.024*\"john wayne\" + 0.018*\"conspiracy\" + 0.018*\"1970s\"\n", - "2021-08-24 02:42:18,760 : INFO : topic #35 (0.020): 0.349*\"Comedy\" + 0.311*\"Drama\" + 0.192*\"Romance\" + 0.015*\"lesbian\" + 0.013*\"bibliothek\" + 0.008*\"oscar (best foreign language film)\" + 0.007*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.004*\"m. night shyamalan\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:18,760 : INFO : topic #34 (0.020): 0.077*\"ghosts\" + 0.054*\"bill murray\" + 0.044*\"courtroom\" + 0.040*\"television\" + 0.026*\"courtroom drama\" + 0.023*\"court\" + 0.023*\"roman polanski\" + 0.021*\"ben stiller\" + 0.017*\"las vegas\" + 0.017*\"2.5\"\n", - "2021-08-24 02:42:18,761 : INFO : topic #14 (0.020): 0.060*\"criterion\" + 0.054*\"Drama\" + 0.049*\"tense\" + 0.044*\"disturbing\" + 0.043*\"atmospheric\" + 0.030*\"tumey's dvds\" + 0.030*\"bleak\" + 0.026*\"stylized\" + 0.026*\"erlend's dvds\" + 0.025*\"menacing\"\n", - "2021-08-24 02:42:18,763 : INFO : topic diff=0.075232, rho=0.310978\n", - "2021-08-24 02:42:18,765 : INFO : PROGRESS: pass 4, at document #10000/10681\n", - "2021-08-24 02:42:19,047 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:19,074 : INFO : topic #12 (0.020): 0.171*\"comic book\" + 0.096*\"superhero\" + 0.075*\"dystopia\" + 0.071*\"super-hero\" + 0.039*\"holocaust\" + 0.035*\"adapted from:comic\" + 0.021*\"alter ego\" + 0.021*\"kidnapping\" + 0.017*\"action\" + 0.016*\"Action\"\n", - "2021-08-24 02:42:19,075 : INFO : topic #27 (0.020): 0.299*\"Horror\" + 0.157*\"Thriller\" + 0.132*\"Mystery\" + 0.038*\"Drama\" + 0.036*\"easily confused with other movie(s) (title)\" + 0.032*\"Fantasy\" + 0.032*\"Film-Noir\" + 0.017*\"eerie\" + 0.014*\"tumey's dvds\" + 0.011*\"claustrophobic\"\n", - "2021-08-24 02:42:19,076 : INFO : topic #25 (0.020): 0.169*\"Western\" + 0.061*\"erlend's dvds\" + 0.054*\"Drama\" + 0.042*\"woody allen\" + 0.031*\"george clooney\" + 0.031*\"gothic\" + 0.030*\"gene hackman\" + 0.026*\"nicole kidman\" + 0.026*\"scary movies to see on halloween\" + 0.026*\"Comedy\"\n", - "2021-08-24 02:42:19,077 : INFO : topic #15 (0.020): 0.101*\"animation\" + 0.101*\"disney\" + 0.089*\"pixar\" + 0.054*\"pirates\" + 0.039*\"children\" + 0.034*\"Animation\" + 0.029*\"angelina jolie\" + 0.025*\"Children\" + 0.023*\"disney animated feature\" + 0.023*\"cartoon\"\n", - "2021-08-24 02:42:19,078 : INFO : topic #48 (0.020): 0.113*\"netflix\" + 0.083*\"twist ending\" + 0.078*\"bruce willis\" + 0.041*\"coming of age\" + 0.039*\"journalism\" + 0.025*\"talking animals\" + 0.022*\"Drama\" + 0.022*\"sexy\" + 0.019*\"avi\" + 0.018*\"dreams\"\n", - "2021-08-24 02:42:19,079 : INFO : topic diff=0.067367, rho=0.310978\n", - "2021-08-24 02:42:19,233 : INFO : -21.006 per-word bound, 2106538.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:19,233 : INFO : PROGRESS: pass 4, at document #10681/10681\n", - "2021-08-24 02:42:19,335 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:19,361 : INFO : topic #23 (0.020): 0.083*\"high school\" + 0.064*\"cars\" + 0.052*\"teen\" + 0.040*\"marx brothers\" + 0.031*\"dance\" + 0.026*\"Comedy\" + 0.021*\"no\" + 0.021*\"kate winslet\" + 0.019*\"robbery\" + 0.018*\"eddie murphy\"\n", - "2021-08-24 02:42:19,362 : INFO : topic #20 (0.020): 0.102*\"biography\" + 0.066*\"Drama\" + 0.050*\"suicide\" + 0.042*\"biopic\" + 0.042*\"death\" + 0.041*\"corvallis library\" + 0.029*\"ensemble cast\" + 0.026*\"forest whitaker\" + 0.025*\"julia roberts\" + 0.025*\"history\"\n", - "2021-08-24 02:42:19,363 : INFO : topic #21 (0.020): 0.068*\"Sci-Fi\" + 0.063*\"aliens\" + 0.048*\"will smith\" + 0.035*\"tommy lee jones\" + 0.032*\"space\" + 0.027*\"ridley scott\" + 0.027*\"Action\" + 0.022*\"futuristmovies.com\" + 0.022*\"apocalypse\" + 0.021*\"sci-fi\"\n", - "2021-08-24 02:42:19,364 : INFO : topic #26 (0.020): 0.041*\"Drama\" + 0.030*\"james bond\" + 0.022*\"assassin\" + 0.020*\"atmospheric\" + 0.019*\"reflective\" + 0.019*\"bittersweet\" + 0.019*\"poignant\" + 0.017*\"bond\" + 0.017*\"007\" + 0.016*\"stylized\"\n", - "2021-08-24 02:42:19,365 : INFO : topic #10 (0.020): 0.245*\"Thriller\" + 0.231*\"Crime\" + 0.180*\"Drama\" + 0.172*\"Action\" + 0.027*\"Mystery\" + 0.015*\"murder\" + 0.008*\"crime\" + 0.006*\"Comedy\" + 0.005*\"virtual reality\" + 0.004*\"greg kinnear\"\n", - "2021-08-24 02:42:19,366 : INFO : topic diff=0.131006, rho=0.310978\n", - "2021-08-24 02:42:19,368 : INFO : PROGRESS: pass 5, at document #2000/10681\n", - "2021-08-24 02:42:19,668 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:19,695 : INFO : topic #10 (0.020): 0.246*\"Thriller\" + 0.223*\"Crime\" + 0.172*\"Action\" + 0.166*\"Drama\" + 0.028*\"Mystery\" + 0.013*\"murder\" + 0.010*\"crime\" + 0.006*\"virtual reality\" + 0.005*\"Comedy\" + 0.004*\"thriller\"\n", - "2021-08-24 02:42:19,696 : INFO : topic #16 (0.020): 0.294*\"nudity (topless)\" + 0.182*\"nudity (topless - brief)\" + 0.058*\"nudity (rear)\" + 0.039*\"claymation\" + 0.025*\"military\" + 0.023*\"aardman\" + 0.022*\"pg\" + 0.020*\"childhood\" + 0.013*\"shark\" + 0.012*\"freedom\"\n", - "2021-08-24 02:42:19,697 : INFO : topic #18 (0.020): 0.081*\"gay\" + 0.068*\"magic\" + 0.057*\"pg13\" + 0.047*\"racism\" + 0.042*\"Drama\" + 0.028*\"food\" + 0.021*\"aids\" + 0.020*\"denzel washington\" + 0.018*\"homosexuality\" + 0.017*\"19th century\"\n", - "2021-08-24 02:42:19,697 : INFO : topic #17 (0.020): 0.302*\"Sci-Fi\" + 0.078*\"Action\" + 0.061*\"Horror\" + 0.043*\"video game adaptation\" + 0.038*\"Adventure\" + 0.028*\"animals\" + 0.025*\"movie to see\" + 0.018*\"football\" + 0.018*\"futuristic\" + 0.014*\"milla jovovich\"\n", - "2021-08-24 02:42:19,698 : INFO : topic #6 (0.020): 0.102*\"Musical\" + 0.101*\"politics\" + 0.070*\"satire\" + 0.047*\"nicolas cage\" + 0.035*\"sean connery\" + 0.033*\"terrorism\" + 0.029*\"dvd-r\" + 0.027*\"good dialogue\" + 0.025*\"political\" + 0.023*\"police\"\n", - "2021-08-24 02:42:19,699 : INFO : topic diff=0.248501, rho=0.296950\n", - "2021-08-24 02:42:19,701 : INFO : PROGRESS: pass 5, at document #4000/10681\n", - "2021-08-24 02:42:19,966 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:19,993 : INFO : topic #3 (0.020): 0.203*\"oscar (best picture)\" + 0.080*\"oscar (best directing)\" + 0.039*\"al pacino\" + 0.036*\"oscar (best actor)\" + 0.027*\"oscar (best supporting actor)\" + 0.022*\"great acting\" + 0.021*\"afi 100 (cheers)\" + 0.020*\"best picture\" + 0.018*\"afi 100\" + 0.018*\"tumey's dvds\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:19,994 : INFO : topic #6 (0.020): 0.098*\"Musical\" + 0.090*\"politics\" + 0.067*\"satire\" + 0.052*\"sean connery\" + 0.048*\"nicolas cage\" + 0.030*\"terrorism\" + 0.030*\"dvd-r\" + 0.029*\"police\" + 0.022*\"political\" + 0.020*\"good dialogue\"\n", - "2021-08-24 02:42:19,995 : INFO : topic #28 (0.020): 0.083*\"oscar (best cinematography)\" + 0.066*\"Drama\" + 0.052*\"oscar (best supporting actress)\" + 0.047*\"oscar (best actress)\" + 0.041*\"oscar (best actor)\" + 0.039*\"in netflix queue\" + 0.027*\"remade\" + 0.019*\"marlon brando\" + 0.017*\"70mm\" + 0.016*\"Romance\"\n", - "2021-08-24 02:42:19,996 : INFO : topic #32 (0.020): 0.137*\"johnny depp\" + 0.064*\"clint eastwood\" + 0.045*\"vhs\" + 0.043*\"western\" + 0.041*\"jackie chan\" + 0.038*\"spaghetti western\" + 0.032*\"sergio leone\" + 0.031*\"kung fu\" + 0.027*\"india\" + 0.026*\"david lynch\"\n", - "2021-08-24 02:42:19,997 : INFO : topic #36 (0.020): 0.091*\"cult film\" + 0.049*\"russell crowe\" + 0.034*\"1980s\" + 0.033*\"downbeat\" + 0.032*\"adam sandler\" + 0.025*\"peter jackson\" + 0.021*\"80s\" + 0.018*\"new zealand\" + 0.016*\"not available from netflix\" + 0.015*\"virus\"\n", - "2021-08-24 02:42:19,998 : INFO : topic diff=0.050091, rho=0.296950\n", - "2021-08-24 02:42:19,999 : INFO : PROGRESS: pass 5, at document #6000/10681\n", - "2021-08-24 02:42:20,238 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:20,265 : INFO : topic #10 (0.020): 0.249*\"Thriller\" + 0.217*\"Crime\" + 0.176*\"Action\" + 0.162*\"Drama\" + 0.027*\"Mystery\" + 0.013*\"murder\" + 0.010*\"virtual reality\" + 0.009*\"crime\" + 0.005*\"well done\" + 0.005*\"greg kinnear\"\n", - "2021-08-24 02:42:20,267 : INFO : topic #39 (0.020): 0.204*\"time travel\" + 0.046*\"adultery\" + 0.040*\"post apocalyptic\" + 0.035*\"motorcycle\" + 0.031*\"jude law\" + 0.030*\"post-apocalyptic\" + 0.019*\"hollywood\" + 0.018*\"want to own\" + 0.018*\"bechdel test:fail\" + 0.017*\"ian mckellen\"\n", - "2021-08-24 02:42:20,267 : INFO : topic #21 (0.020): 0.086*\"aliens\" + 0.071*\"Sci-Fi\" + 0.039*\"space\" + 0.037*\"futuristmovies.com\" + 0.030*\"sci-fi\" + 0.027*\"will smith\" + 0.026*\"ridley scott\" + 0.026*\"Action\" + 0.023*\"tommy lee jones\" + 0.020*\"michael crichton\"\n", - "2021-08-24 02:42:20,268 : INFO : topic #31 (0.020): 0.121*\"zombies\" + 0.061*\"pg-13\" + 0.042*\"cult classic\" + 0.040*\"zombie\" + 0.033*\"campy\" + 0.029*\"sam raimi\" + 0.028*\"books\" + 0.028*\"joaquin phoenix\" + 0.023*\"mummy\" + 0.021*\"horror\"\n", - "2021-08-24 02:42:20,269 : INFO : topic #29 (0.020): 0.103*\"nudity (full frontal - notable)\" + 0.059*\"shakespeare\" + 0.054*\"Drama\" + 0.048*\"based on a play\" + 0.040*\"adapted from:play\" + 0.029*\"christianity\" + 0.027*\"religion\" + 0.022*\"jodie foster\" + 0.019*\"cannibalism\" + 0.016*\"k movie\"\n", - "2021-08-24 02:42:20,270 : INFO : topic diff=0.089472, rho=0.296950\n", - "2021-08-24 02:42:20,271 : INFO : PROGRESS: pass 5, at document #8000/10681\n", - "2021-08-24 02:42:20,505 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:20,533 : INFO : topic #10 (0.020): 0.243*\"Thriller\" + 0.231*\"Crime\" + 0.172*\"Action\" + 0.163*\"Drama\" + 0.030*\"Mystery\" + 0.012*\"virtual reality\" + 0.011*\"murder\" + 0.008*\"crime\" + 0.005*\"well done\" + 0.004*\"greg kinnear\"\n", - "2021-08-24 02:42:20,534 : INFO : topic #25 (0.020): 0.168*\"Western\" + 0.065*\"erlend's dvds\" + 0.052*\"Drama\" + 0.048*\"woody allen\" + 0.041*\"gothic\" + 0.034*\"gene hackman\" + 0.032*\"scary movies to see on halloween\" + 0.030*\"nicole kidman\" + 0.023*\"cute\" + 0.022*\"george clooney\"\n", - "2021-08-24 02:42:20,535 : INFO : topic #6 (0.020): 0.135*\"Musical\" + 0.086*\"politics\" + 0.056*\"satire\" + 0.043*\"sean connery\" + 0.040*\"nicolas cage\" + 0.037*\"dvd-r\" + 0.030*\"police\" + 0.030*\"terrorism\" + 0.024*\"netwatch\" + 0.023*\"dvd-ram\"\n", - "2021-08-24 02:42:20,535 : INFO : topic #7 (0.020): 0.112*\"romance\" + 0.052*\"Romance\" + 0.041*\"chick flick\" + 0.033*\"boring\" + 0.033*\"Comedy\" + 0.032*\"comedy\" + 0.027*\"baseball\" + 0.025*\"girlie movie\" + 0.024*\"whimsical\" + 0.018*\"wedding\"\n", - "2021-08-24 02:42:20,536 : INFO : topic #30 (0.020): 0.225*\"Adventure\" + 0.119*\"Children\" + 0.106*\"Comedy\" + 0.101*\"Fantasy\" + 0.070*\"Action\" + 0.066*\"70mm\" + 0.049*\"Animation\" + 0.037*\"Drama\" + 0.028*\"Musical\" + 0.009*\"submarine\"\n", - "2021-08-24 02:42:20,537 : INFO : topic diff=0.068985, rho=0.296950\n", - "2021-08-24 02:42:20,539 : INFO : PROGRESS: pass 5, at document #10000/10681\n", - "2021-08-24 02:42:20,783 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:20,811 : INFO : topic #28 (0.020): 0.076*\"Drama\" + 0.063*\"in netflix queue\" + 0.063*\"oscar (best cinematography)\" + 0.043*\"oscar (best actress)\" + 0.041*\"oscar (best supporting actress)\" + 0.023*\"oscar (best actor)\" + 0.022*\"remade\" + 0.019*\"father daughter relationship\" + 0.016*\"marlon brando\" + 0.016*\"Romance\"\n", - "2021-08-24 02:42:20,812 : INFO : topic #39 (0.020): 0.167*\"time travel\" + 0.054*\"motorcycle\" + 0.052*\"adultery\" + 0.041*\"jude law\" + 0.035*\"post-apocalyptic\" + 0.032*\"post apocalyptic\" + 0.022*\"old\" + 0.021*\"want to own\" + 0.017*\"ian mckellen\" + 0.017*\"hollywood\"\n", - "2021-08-24 02:42:20,813 : INFO : topic #2 (0.020): 0.289*\"Documentary\" + 0.085*\"to see\" + 0.082*\"documentary\" + 0.027*\"mockumentary\" + 0.024*\"sexuality\" + 0.020*\"movie to see\" + 0.019*\"owen wilson\" + 0.019*\"dogs\" + 0.018*\"kevin smith\" + 0.017*\"in netflix queue\"\n", - "2021-08-24 02:42:20,814 : INFO : topic #23 (0.020): 0.078*\"high school\" + 0.069*\"cars\" + 0.065*\"teen\" + 0.046*\"marx brothers\" + 0.036*\"dance\" + 0.030*\"Comedy\" + 0.024*\"kate winslet\" + 0.021*\"eddie murphy\" + 0.020*\"(s)vcd\" + 0.018*\"car chase\"\n", - "2021-08-24 02:42:20,814 : INFO : topic #25 (0.020): 0.171*\"Western\" + 0.064*\"erlend's dvds\" + 0.052*\"Drama\" + 0.043*\"woody allen\" + 0.032*\"gothic\" + 0.031*\"george clooney\" + 0.031*\"gene hackman\" + 0.027*\"cute\" + 0.026*\"scary movies to see on halloween\" + 0.026*\"nicole kidman\"\n", - "2021-08-24 02:42:20,816 : INFO : topic diff=0.060792, rho=0.296950\n", - "2021-08-24 02:42:20,962 : INFO : -20.996 per-word bound, 2091603.9 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:20,963 : INFO : PROGRESS: pass 5, at document #10681/10681\n", - "2021-08-24 02:42:21,059 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:21,086 : INFO : topic #38 (0.020): 0.147*\"drugs\" + 0.043*\"samuel l. jackson\" + 0.035*\"divx\" + 0.028*\"michael caine\" + 0.028*\"poverty\" + 0.025*\"sex\" + 0.024*\"addiction\" + 0.023*\"ewan mcgregor\" + 0.021*\"sexual\" + 0.019*\"basketball\"\n", - "2021-08-24 02:42:21,087 : INFO : topic #41 (0.020): 0.147*\"remake\" + 0.087*\"new york city\" + 0.066*\"christmas\" + 0.039*\"family\" + 0.035*\"new york\" + 0.034*\"movie to see\" + 0.028*\"eric's dvds\" + 0.025*\"Drama\" + 0.024*\"kevin spacey\" + 0.020*\"predictable\"\n", - "2021-08-24 02:42:21,087 : INFO : topic #5 (0.020): 0.193*\"classic\" + 0.106*\"musical\" + 0.041*\"Musical\" + 0.040*\"afi 100 (laughs)\" + 0.022*\"national film registry\" + 0.015*\"adapted from b'way\" + 0.014*\"witch\" + 0.013*\"john travolta\" + 0.013*\"movie to see\" + 0.012*\"family\"\n", - "2021-08-24 02:42:21,088 : INFO : topic #17 (0.020): 0.355*\"Sci-Fi\" + 0.082*\"Action\" + 0.071*\"Horror\" + 0.039*\"video game adaptation\" + 0.033*\"movie to see\" + 0.030*\"Adventure\" + 0.022*\"football\" + 0.020*\"animals\" + 0.015*\"futuristic\" + 0.014*\"milla jovovich\"\n", - "2021-08-24 02:42:21,089 : INFO : topic #37 (0.020): 0.233*\"War\" + 0.126*\"Drama\" + 0.093*\"world war ii\" + 0.051*\"history\" + 0.046*\"war\" + 0.035*\"Action\" + 0.023*\"jim carrey\" + 0.023*\"nazis\" + 0.011*\"mental illness\" + 0.010*\"wwii\"\n", - "2021-08-24 02:42:21,091 : INFO : topic diff=0.122598, rho=0.296950\n", - "2021-08-24 02:42:21,093 : INFO : PROGRESS: pass 6, at document #2000/10681\n", - "2021-08-24 02:42:21,355 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:21,383 : INFO : topic #0 (0.020): 0.198*\"can't remember\" + 0.104*\"based on a tv show\" + 0.094*\"directorial debut\" + 0.067*\"Comedy\" + 0.037*\"ummarti2006\" + 0.033*\"keira knightley\" + 0.029*\"australia\" + 0.023*\"australian\" + 0.019*\"immigrants\" + 0.019*\"robert downey jr\"\n", - "2021-08-24 02:42:21,384 : INFO : topic #48 (0.020): 0.104*\"bruce willis\" + 0.096*\"netflix\" + 0.080*\"twist ending\" + 0.035*\"coming of age\" + 0.031*\"journalism\" + 0.029*\"Drama\" + 0.027*\"to see\" + 0.022*\"talking animals\" + 0.021*\"relationships\" + 0.019*\"sexy\"\n", - "2021-08-24 02:42:21,385 : INFO : topic #29 (0.020): 0.089*\"nudity (full frontal - notable)\" + 0.067*\"shakespeare\" + 0.052*\"Drama\" + 0.043*\"based on a play\" + 0.035*\"adapted from:play\" + 0.035*\"christianity\" + 0.032*\"religion\" + 0.026*\"jodie foster\" + 0.025*\"cannibalism\" + 0.023*\"president\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:21,386 : INFO : topic #37 (0.020): 0.179*\"War\" + 0.106*\"world war ii\" + 0.101*\"Drama\" + 0.061*\"war\" + 0.045*\"jim carrey\" + 0.044*\"history\" + 0.028*\"Action\" + 0.023*\"nazis\" + 0.018*\"wwii\" + 0.015*\"mental illness\"\n", - "2021-08-24 02:42:21,387 : INFO : topic #43 (0.020): 0.067*\"surreal\" + 0.056*\"stanley kubrick\" + 0.037*\"satirical\" + 0.035*\"cynical\" + 0.032*\"narrated\" + 0.029*\"dreamlike\" + 0.029*\"irreverent\" + 0.026*\"quirky\" + 0.025*\"biting\" + 0.023*\"hallucinatory\"\n", - "2021-08-24 02:42:21,388 : INFO : topic diff=0.233043, rho=0.284665\n", - "2021-08-24 02:42:21,390 : INFO : PROGRESS: pass 6, at document #4000/10681\n", - "2021-08-24 02:42:21,641 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:21,668 : INFO : topic #46 (0.020): 0.089*\"action\" + 0.064*\"sci-fi\" + 0.047*\"fantasy\" + 0.033*\"adventure\" + 0.028*\"dvd\" + 0.024*\"seen at the cinema\" + 0.023*\"seen more than once\" + 0.021*\"Adventure\" + 0.021*\"space\" + 0.020*\"harrison ford\"\n", - "2021-08-24 02:42:21,669 : INFO : topic #39 (0.020): 0.180*\"time travel\" + 0.051*\"adultery\" + 0.048*\"post apocalyptic\" + 0.036*\"motorcycle\" + 0.036*\"post-apocalyptic\" + 0.029*\"jude law\" + 0.020*\"hollywood\" + 0.019*\"want to own\" + 0.018*\"bechdel test:fail\" + 0.015*\"1950s\"\n", - "2021-08-24 02:42:21,670 : INFO : topic #35 (0.020): 0.342*\"Comedy\" + 0.312*\"Drama\" + 0.190*\"Romance\" + 0.017*\"lesbian\" + 0.012*\"bibliothek\" + 0.009*\"philip seymour hoffman\" + 0.007*\"m. night shyamalan\" + 0.006*\"sean penn\" + 0.006*\"oscar (best foreign language film)\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:21,671 : INFO : topic #43 (0.020): 0.080*\"surreal\" + 0.047*\"stanley kubrick\" + 0.040*\"satirical\" + 0.038*\"cynical\" + 0.037*\"narrated\" + 0.028*\"dreamlike\" + 0.028*\"surrealism\" + 0.026*\"biting\" + 0.026*\"irreverent\" + 0.025*\"quirky\"\n", - "2021-08-24 02:42:21,672 : INFO : topic #3 (0.020): 0.195*\"oscar (best picture)\" + 0.078*\"oscar (best directing)\" + 0.054*\"oscar (best actor)\" + 0.038*\"al pacino\" + 0.027*\"oscar (best supporting actor)\" + 0.023*\"afi 100 (cheers)\" + 0.022*\"great acting\" + 0.020*\"afi 100\" + 0.019*\"best picture\" + 0.018*\"tumey's dvds\"\n", - "2021-08-24 02:42:21,673 : INFO : topic diff=0.045326, rho=0.284665\n", - "2021-08-24 02:42:21,675 : INFO : PROGRESS: pass 6, at document #6000/10681\n", - "2021-08-24 02:42:21,906 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:21,934 : INFO : topic #1 (0.020): 0.045*\"overrated\" + 0.039*\"violence\" + 0.034*\"crime\" + 0.031*\"quentin tarantino\" + 0.029*\"nonlinear\" + 0.027*\"imdb top 250\" + 0.025*\"violent\" + 0.023*\"drama\" + 0.023*\"owned\" + 0.022*\"revenge\"\n", - "2021-08-24 02:42:21,935 : INFO : topic #36 (0.020): 0.084*\"cult film\" + 0.049*\"peter jackson\" + 0.044*\"russell crowe\" + 0.035*\"downbeat\" + 0.030*\"adam sandler\" + 0.029*\"1980s\" + 0.023*\"80s\" + 0.021*\"new zealand\" + 0.016*\"not available from netflix\" + 0.013*\"long\"\n", - "2021-08-24 02:42:21,936 : INFO : topic #25 (0.020): 0.150*\"Western\" + 0.066*\"erlend's dvds\" + 0.046*\"Drama\" + 0.045*\"woody allen\" + 0.044*\"gothic\" + 0.040*\"gene hackman\" + 0.030*\"nicole kidman\" + 0.029*\"scary movies to see on halloween\" + 0.029*\"george clooney\" + 0.025*\"sad\"\n", - "2021-08-24 02:42:21,936 : INFO : topic #26 (0.020): 0.039*\"Drama\" + 0.036*\"james bond\" + 0.025*\"007\" + 0.025*\"bond\" + 0.022*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.018*\"poignant\" + 0.018*\"lyrical\" + 0.016*\"bittersweet\"\n", - "2021-08-24 02:42:21,937 : INFO : topic #27 (0.020): 0.320*\"Horror\" + 0.151*\"Thriller\" + 0.124*\"Mystery\" + 0.034*\"Drama\" + 0.028*\"Fantasy\" + 0.026*\"easily confused with other movie(s) (title)\" + 0.025*\"Film-Noir\" + 0.018*\"eerie\" + 0.017*\"franchise\" + 0.015*\"slasher\"\n", - "2021-08-24 02:42:21,938 : INFO : topic diff=0.083762, rho=0.284665\n", - "2021-08-24 02:42:21,940 : INFO : PROGRESS: pass 6, at document #8000/10681\n", - "2021-08-24 02:42:22,164 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:22,191 : INFO : topic #31 (0.020): 0.148*\"zombies\" + 0.063*\"pg-13\" + 0.045*\"zombie\" + 0.034*\"cult classic\" + 0.029*\"sam raimi\" + 0.028*\"campy\" + 0.026*\"horror\" + 0.024*\"books\" + 0.023*\"joaquin phoenix\" + 0.019*\"mummy\"\n", - "2021-08-24 02:42:22,191 : INFO : topic #4 (0.020): 0.070*\"sports\" + 0.062*\"Drama\" + 0.047*\"sven's to see list\" + 0.045*\"hw drama\" + 0.042*\"boxing\" + 0.041*\"london\" + 0.040*\"british\" + 0.037*\"underrated\" + 0.025*\"werewolves\" + 0.023*\"romantic\"\n", - "2021-08-24 02:42:22,192 : INFO : topic #9 (0.020): 0.235*\"based on a book\" + 0.120*\"adapted from:book\" + 0.062*\"Drama\" + 0.037*\"stephen king\" + 0.036*\"jack nicholson\" + 0.029*\"horror\" + 0.027*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.019*\"stupid\" + 0.019*\"los angeles\"\n", - "2021-08-24 02:42:22,193 : INFO : topic #35 (0.020): 0.360*\"Comedy\" + 0.306*\"Drama\" + 0.192*\"Romance\" + 0.015*\"lesbian\" + 0.013*\"bibliothek\" + 0.008*\"oscar (best foreign language film)\" + 0.007*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.004*\"m. night shyamalan\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:22,194 : INFO : topic #25 (0.020): 0.169*\"Western\" + 0.067*\"erlend's dvds\" + 0.051*\"Drama\" + 0.049*\"woody allen\" + 0.041*\"gothic\" + 0.035*\"gene hackman\" + 0.032*\"scary movies to see on halloween\" + 0.030*\"nicole kidman\" + 0.023*\"cute\" + 0.022*\"george clooney\"\n", - "2021-08-24 02:42:22,195 : INFO : topic diff=0.064514, rho=0.284665\n", - "2021-08-24 02:42:22,197 : INFO : PROGRESS: pass 6, at document #10000/10681\n", - "2021-08-24 02:42:22,442 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:22,469 : INFO : topic #36 (0.020): 0.061*\"cult film\" + 0.047*\"downbeat\" + 0.046*\"peter jackson\" + 0.038*\"russell crowe\" + 0.038*\"not available from netflix\" + 0.030*\"adam sandler\" + 0.024*\"1980s\" + 0.024*\"new zealand\" + 0.020*\"existentialism\" + 0.018*\"must see!\"\n", - "2021-08-24 02:42:22,470 : INFO : topic #5 (0.020): 0.231*\"classic\" + 0.075*\"musical\" + 0.047*\"afi 100 (laughs)\" + 0.037*\"Musical\" + 0.027*\"national film registry\" + 0.017*\"witch\" + 0.016*\"john travolta\" + 0.015*\"adapted from b'way\" + 0.014*\"breakthroughs\" + 0.013*\"afi 100\"\n", - "2021-08-24 02:42:22,471 : INFO : topic #17 (0.020): 0.341*\"Sci-Fi\" + 0.078*\"Horror\" + 0.071*\"Action\" + 0.039*\"video game adaptation\" + 0.022*\"football\" + 0.019*\"animals\" + 0.017*\"Adventure\" + 0.016*\"movie to see\" + 0.015*\"futuristic\" + 0.014*\"milla jovovich\"\n", - "2021-08-24 02:42:22,472 : INFO : topic #29 (0.020): 0.093*\"nudity (full frontal - notable)\" + 0.058*\"Drama\" + 0.050*\"shakespeare\" + 0.046*\"based on a play\" + 0.038*\"christianity\" + 0.033*\"adapted from:play\" + 0.030*\"religion\" + 0.024*\"biblical\" + 0.023*\"fascism\" + 0.019*\"k movie\"\n", - "2021-08-24 02:42:22,473 : INFO : topic #2 (0.020): 0.289*\"Documentary\" + 0.091*\"to see\" + 0.081*\"documentary\" + 0.027*\"mockumentary\" + 0.023*\"sexuality\" + 0.021*\"movie to see\" + 0.019*\"dogs\" + 0.019*\"owen wilson\" + 0.018*\"kevin smith\" + 0.017*\"in netflix queue\"\n", - "2021-08-24 02:42:22,475 : INFO : topic diff=0.056593, rho=0.284665\n", - "2021-08-24 02:42:22,613 : INFO : -20.989 per-word bound, 2081582.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:22,614 : INFO : PROGRESS: pass 6, at document #10681/10681\n", - "2021-08-24 02:42:22,707 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:22,733 : INFO : topic #3 (0.020): 0.124*\"oscar (best picture)\" + 0.051*\"great acting\" + 0.049*\"oscar (best directing)\" + 0.037*\"oscar (best actor)\" + 0.033*\"al pacino\" + 0.020*\"afi 100 (cheers)\" + 0.018*\"Drama\" + 0.018*\"oscar (best supporting actor)\" + 0.017*\"civil war\" + 0.015*\"tumey's dvds\"\n", - "2021-08-24 02:42:22,734 : INFO : topic #36 (0.020): 0.055*\"cult film\" + 0.045*\"russell crowe\" + 0.036*\"downbeat\" + 0.036*\"peter jackson\" + 0.033*\"not available from netflix\" + 0.031*\"adam sandler\" + 0.031*\"virus\" + 0.030*\"1980s\" + 0.025*\"movielens quickpick\" + 0.022*\"new zealand\"\n", - "2021-08-24 02:42:22,735 : INFO : topic #32 (0.020): 0.149*\"johnny depp\" + 0.049*\"clint eastwood\" + 0.049*\"kung fu\" + 0.048*\"propaganda\" + 0.046*\"vhs\" + 0.042*\"watched 2007\" + 0.038*\"jackie chan\" + 0.037*\"india\" + 0.028*\"western\" + 0.025*\"brothers\"\n", - "2021-08-24 02:42:22,736 : INFO : topic #31 (0.020): 0.174*\"zombies\" + 0.106*\"pg-13\" + 0.028*\"zombie\" + 0.027*\"movie to see\" + 0.027*\"infidelity\" + 0.027*\"betrayal\" + 0.024*\"books\" + 0.023*\"joaquin phoenix\" + 0.020*\"campy\" + 0.019*\"cult classic\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:22,737 : INFO : topic #0 (0.020): 0.115*\"can't remember\" + 0.110*\"based on a tv show\" + 0.087*\"directorial debut\" + 0.071*\"Comedy\" + 0.056*\"ummarti2006\" + 0.055*\"keira knightley\" + 0.028*\"australia\" + 0.026*\"immigrants\" + 0.026*\"dani2006\" + 0.025*\"australian\"\n", - "2021-08-24 02:42:22,738 : INFO : topic diff=0.116521, rho=0.284665\n", - "2021-08-24 02:42:22,739 : INFO : PROGRESS: pass 7, at document #2000/10681\n", - "2021-08-24 02:42:22,985 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:23,011 : INFO : topic #34 (0.020): 0.069*\"ghosts\" + 0.049*\"bill murray\" + 0.044*\"television\" + 0.028*\"courtroom drama\" + 0.027*\"secret service\" + 0.026*\"courtroom\" + 0.019*\"roman polanski\" + 0.018*\"18th century\" + 0.018*\"opera\" + 0.017*\"smoking\"\n", - "2021-08-24 02:42:23,012 : INFO : topic #13 (0.020): 0.056*\"fairy tale\" + 0.054*\"jane austen\" + 0.037*\"assassination\" + 0.035*\"historical\" + 0.033*\"road trip\" + 0.033*\"cold war\" + 0.029*\"sequel\" + 0.023*\"gerard depardieu\" + 0.022*\"swashbuckler\" + 0.020*\"kids\"\n", - "2021-08-24 02:42:23,013 : INFO : topic #12 (0.020): 0.135*\"comic book\" + 0.106*\"superhero\" + 0.081*\"dystopia\" + 0.054*\"holocaust\" + 0.050*\"super-hero\" + 0.033*\"batman\" + 0.031*\"adapted from:comic\" + 0.024*\"kidnapping\" + 0.016*\"action\" + 0.015*\"alter ego\"\n", - "2021-08-24 02:42:23,014 : INFO : topic #43 (0.020): 0.067*\"surreal\" + 0.055*\"stanley kubrick\" + 0.038*\"satirical\" + 0.035*\"cynical\" + 0.032*\"narrated\" + 0.029*\"irreverent\" + 0.029*\"dreamlike\" + 0.027*\"biting\" + 0.026*\"quirky\" + 0.023*\"hallucinatory\"\n", - "2021-08-24 02:42:23,015 : INFO : topic #3 (0.020): 0.226*\"oscar (best picture)\" + 0.086*\"oscar (best directing)\" + 0.061*\"oscar (best actor)\" + 0.029*\"al pacino\" + 0.027*\"oscar (best supporting actor)\" + 0.023*\"best picture\" + 0.023*\"great acting\" + 0.023*\"afi 100 (cheers)\" + 0.022*\"afi 100\" + 0.017*\"civil war\"\n", - "2021-08-24 02:42:23,016 : INFO : topic diff=0.221249, rho=0.273788\n", - "2021-08-24 02:42:23,018 : INFO : PROGRESS: pass 7, at document #4000/10681\n", - "2021-08-24 02:42:23,261 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:23,287 : INFO : topic #16 (0.020): 0.291*\"nudity (topless)\" + 0.199*\"nudity (topless - brief)\" + 0.062*\"nudity (rear)\" + 0.033*\"claymation\" + 0.022*\"military\" + 0.022*\"aardman\" + 0.022*\"pg\" + 0.018*\"childhood\" + 0.012*\"weird\" + 0.011*\"freedom\"\n", - "2021-08-24 02:42:23,288 : INFO : topic #47 (0.020): 0.070*\"serial killer\" + 0.058*\"brad pitt\" + 0.051*\"psychology\" + 0.043*\"edward norton\" + 0.034*\"steven spielberg\" + 0.025*\"matt damon\" + 0.025*\"heist\" + 0.023*\"crime\" + 0.016*\"dinosaurs\" + 0.015*\"hayao miyazaki\"\n", - "2021-08-24 02:42:23,289 : INFO : topic #48 (0.020): 0.108*\"bruce willis\" + 0.085*\"twist ending\" + 0.075*\"netflix\" + 0.050*\"coming of age\" + 0.033*\"journalism\" + 0.028*\"Drama\" + 0.026*\"sexy\" + 0.024*\"talking animals\" + 0.022*\"to see\" + 0.018*\"relationships\"\n", - "2021-08-24 02:42:23,289 : INFO : topic #29 (0.020): 0.093*\"nudity (full frontal - notable)\" + 0.072*\"shakespeare\" + 0.051*\"Drama\" + 0.046*\"based on a play\" + 0.041*\"adapted from:play\" + 0.031*\"christianity\" + 0.029*\"religion\" + 0.021*\"jodie foster\" + 0.021*\"cannibalism\" + 0.019*\"playwright:shakespeare\"\n", - "2021-08-24 02:42:23,290 : INFO : topic #21 (0.020): 0.084*\"aliens\" + 0.072*\"Sci-Fi\" + 0.043*\"space\" + 0.037*\"futuristmovies.com\" + 0.034*\"sci-fi\" + 0.028*\"will smith\" + 0.028*\"Action\" + 0.023*\"tommy lee jones\" + 0.021*\"70mm\" + 0.021*\"ridley scott\"\n", - "2021-08-24 02:42:23,291 : INFO : topic diff=0.042538, rho=0.273788\n", - "2021-08-24 02:42:23,293 : INFO : PROGRESS: pass 7, at document #6000/10681\n", - "2021-08-24 02:42:23,508 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:23,540 : INFO : topic #37 (0.020): 0.180*\"War\" + 0.115*\"world war ii\" + 0.098*\"Drama\" + 0.059*\"war\" + 0.041*\"history\" + 0.035*\"Action\" + 0.031*\"jim carrey\" + 0.020*\"nazis\" + 0.018*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:42:23,542 : INFO : topic #11 (0.020): 0.131*\"religion\" + 0.118*\"keanu reeves\" + 0.021*\"slavery\" + 0.019*\"courtesan\" + 0.018*\"reality tv\" + 0.018*\"slash\" + 0.017*\"facebook rec\" + 0.016*\"class issues\" + 0.014*\"product placement\" + 0.012*\"cross-dressing women\"\n", - "2021-08-24 02:42:23,544 : INFO : topic #26 (0.020): 0.039*\"Drama\" + 0.036*\"james bond\" + 0.025*\"007\" + 0.025*\"bond\" + 0.022*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.019*\"poignant\" + 0.018*\"lyrical\" + 0.016*\"bittersweet\"\n", - "2021-08-24 02:42:23,545 : INFO : topic #49 (0.020): 0.077*\"national film registry\" + 0.056*\"imdb top 250\" + 0.040*\"black and white\" + 0.032*\"tumey's dvds\" + 0.030*\"afi 100\" + 0.030*\"afi 100 (thrills)\" + 0.024*\"afi 100 (movie quotes)\" + 0.024*\"france\" + 0.023*\"paris\" + 0.022*\"usa film registry\"\n", - "2021-08-24 02:42:23,548 : INFO : topic #43 (0.020): 0.084*\"surreal\" + 0.046*\"satirical\" + 0.040*\"cynical\" + 0.036*\"stanley kubrick\" + 0.036*\"narrated\" + 0.032*\"biting\" + 0.031*\"dreamlike\" + 0.029*\"quirky\" + 0.026*\"irreverent\" + 0.025*\"mindfuck\"\n", - "2021-08-24 02:42:23,551 : INFO : topic diff=0.079000, rho=0.273788\n", - "2021-08-24 02:42:23,552 : INFO : PROGRESS: pass 7, at document #8000/10681\n", - "2021-08-24 02:42:23,787 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:23,814 : INFO : topic #14 (0.020): 0.060*\"criterion\" + 0.051*\"tense\" + 0.051*\"Drama\" + 0.049*\"disturbing\" + 0.047*\"atmospheric\" + 0.031*\"tumey's dvds\" + 0.030*\"bleak\" + 0.029*\"stylized\" + 0.026*\"erlend's dvds\" + 0.025*\"menacing\"\n", - "2021-08-24 02:42:23,815 : INFO : topic #25 (0.020): 0.169*\"Western\" + 0.068*\"erlend's dvds\" + 0.051*\"Drama\" + 0.049*\"woody allen\" + 0.042*\"gothic\" + 0.035*\"gene hackman\" + 0.032*\"scary movies to see on halloween\" + 0.030*\"nicole kidman\" + 0.023*\"cute\" + 0.022*\"george clooney\"\n", - "2021-08-24 02:42:23,816 : INFO : topic #43 (0.020): 0.088*\"surreal\" + 0.046*\"satirical\" + 0.041*\"cynical\" + 0.035*\"quirky\" + 0.034*\"dreamlike\" + 0.033*\"narrated\" + 0.030*\"biting\" + 0.027*\"hallucinatory\" + 0.026*\"stanley kubrick\" + 0.026*\"irreverent\"\n", - "2021-08-24 02:42:23,817 : INFO : topic #3 (0.020): 0.151*\"oscar (best picture)\" + 0.060*\"oscar (best directing)\" + 0.057*\"oscar (best actor)\" + 0.035*\"al pacino\" + 0.027*\"afi 100 (cheers)\" + 0.025*\"oscar (best supporting actor)\" + 0.020*\"mst3k\" + 0.019*\"tumey's dvds\" + 0.019*\"Drama\" + 0.018*\"civil war\"\n", - "2021-08-24 02:42:23,817 : INFO : topic #23 (0.020): 0.080*\"high school\" + 0.080*\"teen\" + 0.065*\"cars\" + 0.033*\"dance\" + 0.031*\"marx brothers\" + 0.029*\"eddie murphy\" + 0.025*\"(s)vcd\" + 0.023*\"Comedy\" + 0.023*\"car chase\" + 0.018*\"sandra bullock\"\n", - "2021-08-24 02:42:23,818 : INFO : topic diff=0.061580, rho=0.273788\n", - "2021-08-24 02:42:23,820 : INFO : PROGRESS: pass 7, at document #10000/10681\n", - "2021-08-24 02:42:24,082 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:24,110 : INFO : topic #36 (0.020): 0.062*\"cult film\" + 0.048*\"downbeat\" + 0.046*\"peter jackson\" + 0.039*\"russell crowe\" + 0.037*\"not available from netflix\" + 0.031*\"adam sandler\" + 0.024*\"1980s\" + 0.024*\"new zealand\" + 0.020*\"existentialism\" + 0.018*\"must see!\"\n", - "2021-08-24 02:42:24,111 : INFO : topic #11 (0.020): 0.130*\"religion\" + 0.105*\"keanu reeves\" + 0.026*\"product placement\" + 0.022*\"slavery\" + 0.021*\"facebook rec\" + 0.021*\"courtesan\" + 0.018*\"reality tv\" + 0.017*\"social message\" + 0.016*\"cross-dressing women\" + 0.016*\"gender identity\"\n", - "2021-08-24 02:42:24,112 : INFO : topic #12 (0.020): 0.168*\"comic book\" + 0.097*\"superhero\" + 0.073*\"dystopia\" + 0.070*\"super-hero\" + 0.041*\"holocaust\" + 0.034*\"adapted from:comic\" + 0.021*\"kidnapping\" + 0.021*\"alter ego\" + 0.016*\"Action\" + 0.016*\"dark\"\n", - "2021-08-24 02:42:24,113 : INFO : topic #6 (0.020): 0.129*\"Musical\" + 0.103*\"politics\" + 0.060*\"satire\" + 0.040*\"nicolas cage\" + 0.040*\"terrorism\" + 0.037*\"dvd-r\" + 0.029*\"political\" + 0.029*\"sean connery\" + 0.028*\"netwatch\" + 0.025*\"police\"\n", - "2021-08-24 02:42:24,114 : INFO : topic #24 (0.020): 0.069*\"vampire\" + 0.068*\"murder\" + 0.067*\"vampires\" + 0.056*\"memory\" + 0.050*\"oppl\" + 0.049*\"hitchcock\" + 0.037*\"alfred hitchcock\" + 0.022*\"cary grant\" + 0.021*\"kirsten dunst\" + 0.021*\"incest\"\n", - "2021-08-24 02:42:24,115 : INFO : topic diff=0.053372, rho=0.273788\n", - "2021-08-24 02:42:24,321 : INFO : -20.983 per-word bound, 2072879.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:24,322 : INFO : PROGRESS: pass 7, at document #10681/10681\n", - "2021-08-24 02:42:24,415 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:24,442 : INFO : topic #21 (0.020): 0.074*\"Sci-Fi\" + 0.064*\"aliens\" + 0.044*\"will smith\" + 0.034*\"space\" + 0.033*\"tommy lee jones\" + 0.030*\"Action\" + 0.026*\"ridley scott\" + 0.025*\"sci-fi\" + 0.025*\"futuristmovies.com\" + 0.021*\"monster\"\n", - "2021-08-24 02:42:24,443 : INFO : topic #37 (0.020): 0.229*\"War\" + 0.125*\"Drama\" + 0.095*\"world war ii\" + 0.051*\"history\" + 0.048*\"war\" + 0.039*\"Action\" + 0.023*\"jim carrey\" + 0.022*\"nazis\" + 0.011*\"mental illness\" + 0.010*\"wwii\"\n", - "2021-08-24 02:42:24,444 : INFO : topic #26 (0.020): 0.043*\"Drama\" + 0.030*\"james bond\" + 0.021*\"assassin\" + 0.020*\"poignant\" + 0.020*\"reflective\" + 0.019*\"atmospheric\" + 0.019*\"bittersweet\" + 0.018*\"bond\" + 0.018*\"007\" + 0.017*\"lyrical\"\n", - "2021-08-24 02:42:24,445 : INFO : topic #46 (0.020): 0.077*\"action\" + 0.065*\"fantasy\" + 0.049*\"sci-fi\" + 0.036*\"adventure\" + 0.031*\"dvd\" + 0.024*\"Adventure\" + 0.023*\"watched 2006\" + 0.023*\"robots\" + 0.019*\"space\" + 0.019*\"seen at the cinema\"\n", - "2021-08-24 02:42:24,446 : INFO : topic #45 (0.020): 0.274*\"less than 300 ratings\" + 0.116*\"Drama\" + 0.059*\"nudity (topless - notable)\" + 0.037*\"tim burton\" + 0.030*\"not corv lib\" + 0.029*\"library\" + 0.016*\"blindfold\" + 0.015*\"depressing\" + 0.014*\"san francisco\" + 0.014*\"seen 2007\"\n", - "2021-08-24 02:42:24,447 : INFO : topic diff=0.111718, rho=0.273788\n", - "2021-08-24 02:42:24,449 : INFO : PROGRESS: pass 8, at document #2000/10681\n", - "2021-08-24 02:42:24,716 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:24,744 : INFO : topic #5 (0.020): 0.313*\"classic\" + 0.066*\"musical\" + 0.034*\"afi 100 (laughs)\" + 0.030*\"Musical\" + 0.027*\"national film registry\" + 0.023*\"afi 100\" + 0.018*\"john travolta\" + 0.013*\"breakthroughs\" + 0.011*\"family\" + 0.011*\"erlend's dvds\"\n", - "2021-08-24 02:42:24,745 : INFO : topic #1 (0.020): 0.049*\"quentin tarantino\" + 0.047*\"overrated\" + 0.037*\"crime\" + 0.036*\"violence\" + 0.032*\"tarantino\" + 0.032*\"imdb top 250\" + 0.029*\"violent\" + 0.026*\"drama\" + 0.025*\"owned\" + 0.023*\"revenge\"\n", - "2021-08-24 02:42:24,746 : INFO : topic #2 (0.020): 0.273*\"Documentary\" + 0.117*\"to see\" + 0.053*\"documentary\" + 0.031*\"star trek\" + 0.031*\"mockumentary\" + 0.027*\"kevin smith\" + 0.022*\"movie to see\" + 0.020*\"dogs\" + 0.018*\"sexuality\" + 0.015*\"owen wilson\"\n", - "2021-08-24 02:42:24,747 : INFO : topic #44 (0.020): 0.119*\"mafia\" + 0.082*\"music\" + 0.064*\"martin scorsese\" + 0.055*\"organized crime\" + 0.035*\"oscar (best supporting actor)\" + 0.034*\"rock and roll\" + 0.031*\"Musical\" + 0.022*\"francis ford coppola\" + 0.021*\"guns\" + 0.019*\"wired 50 greatest soundtracks\"\n", - "2021-08-24 02:42:24,747 : INFO : topic #13 (0.020): 0.056*\"fairy tale\" + 0.053*\"jane austen\" + 0.037*\"assassination\" + 0.034*\"road trip\" + 0.033*\"historical\" + 0.033*\"cold war\" + 0.029*\"sequel\" + 0.023*\"gerard depardieu\" + 0.022*\"swashbuckler\" + 0.020*\"kids\"\n", - "2021-08-24 02:42:24,749 : INFO : topic diff=0.211074, rho=0.264069\n", - "2021-08-24 02:42:24,751 : INFO : PROGRESS: pass 8, at document #4000/10681\n", - "2021-08-24 02:42:25,004 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:25,030 : INFO : topic #7 (0.020): 0.111*\"romance\" + 0.061*\"Romance\" + 0.054*\"chick flick\" + 0.036*\"boring\" + 0.031*\"girlie movie\" + 0.030*\"Comedy\" + 0.027*\"baseball\" + 0.027*\"comedy\" + 0.019*\"love story\" + 0.019*\"whimsical\"\n", - "2021-08-24 02:42:25,031 : INFO : topic #13 (0.020): 0.052*\"fairy tale\" + 0.045*\"jane austen\" + 0.040*\"road trip\" + 0.035*\"sequel\" + 0.032*\"historical\" + 0.030*\"assassination\" + 0.028*\"cold war\" + 0.022*\"kids\" + 0.021*\"heartwarming\" + 0.020*\"italy\"\n", - "2021-08-24 02:42:25,032 : INFO : topic #21 (0.020): 0.084*\"aliens\" + 0.071*\"Sci-Fi\" + 0.044*\"space\" + 0.038*\"futuristmovies.com\" + 0.036*\"sci-fi\" + 0.028*\"will smith\" + 0.028*\"Action\" + 0.023*\"tommy lee jones\" + 0.021*\"70mm\" + 0.021*\"ridley scott\"\n", - "2021-08-24 02:42:25,033 : INFO : topic #1 (0.020): 0.047*\"overrated\" + 0.043*\"violence\" + 0.039*\"quentin tarantino\" + 0.033*\"crime\" + 0.030*\"imdb top 250\" + 0.026*\"owned\" + 0.026*\"drama\" + 0.025*\"tarantino\" + 0.025*\"violent\" + 0.021*\"revenge\"\n", - "2021-08-24 02:42:25,034 : INFO : topic #19 (0.020): 0.063*\"nudity (full frontal)\" + 0.061*\"drama\" + 0.032*\"Drama\" + 0.028*\"vietnam\" + 0.028*\"vietnam war\" + 0.027*\"philip k. dick\" + 0.027*\"rape\" + 0.021*\"very good\" + 0.020*\"to see\" + 0.016*\"slow\"\n", - "2021-08-24 02:42:25,035 : INFO : topic diff=0.040053, rho=0.264069\n", - "2021-08-24 02:42:25,036 : INFO : PROGRESS: pass 8, at document #6000/10681\n", - "2021-08-24 02:42:25,283 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:25,311 : INFO : topic #43 (0.020): 0.083*\"surreal\" + 0.046*\"satirical\" + 0.040*\"cynical\" + 0.036*\"stanley kubrick\" + 0.035*\"narrated\" + 0.033*\"biting\" + 0.031*\"dreamlike\" + 0.030*\"quirky\" + 0.026*\"irreverent\" + 0.025*\"mindfuck\"\n", - "2021-08-24 02:42:25,312 : INFO : topic #1 (0.020): 0.047*\"overrated\" + 0.042*\"violence\" + 0.035*\"crime\" + 0.031*\"quentin tarantino\" + 0.028*\"nonlinear\" + 0.027*\"imdb top 250\" + 0.024*\"owned\" + 0.024*\"drama\" + 0.024*\"violent\" + 0.022*\"revenge\"\n", - "2021-08-24 02:42:25,314 : INFO : topic #47 (0.020): 0.065*\"serial killer\" + 0.057*\"psychology\" + 0.052*\"brad pitt\" + 0.043*\"edward norton\" + 0.035*\"steven spielberg\" + 0.032*\"heist\" + 0.028*\"matt damon\" + 0.026*\"crime\" + 0.021*\"hayao miyazaki\" + 0.016*\"scary\"\n", - "2021-08-24 02:42:25,315 : INFO : topic #39 (0.020): 0.202*\"time travel\" + 0.054*\"adultery\" + 0.040*\"post apocalyptic\" + 0.036*\"motorcycle\" + 0.031*\"jude law\" + 0.031*\"post-apocalyptic\" + 0.020*\"want to own\" + 0.019*\"hollywood\" + 0.019*\"bechdel test:fail\" + 0.017*\"ian mckellen\"\n", - "2021-08-24 02:42:25,315 : INFO : topic #48 (0.020): 0.098*\"bruce willis\" + 0.084*\"twist ending\" + 0.063*\"netflix\" + 0.049*\"coming of age\" + 0.036*\"sexy\" + 0.027*\"journalism\" + 0.026*\"Drama\" + 0.021*\"to see\" + 0.021*\"talking animals\" + 0.019*\"relationships\"\n", - "2021-08-24 02:42:25,317 : INFO : topic diff=0.075289, rho=0.264069\n", - "2021-08-24 02:42:25,319 : INFO : PROGRESS: pass 8, at document #8000/10681\n", - "2021-08-24 02:42:25,597 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:25,626 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.057*\"Romance\" + 0.043*\"chick flick\" + 0.037*\"boring\" + 0.029*\"Comedy\" + 0.027*\"girlie movie\" + 0.027*\"baseball\" + 0.027*\"comedy\" + 0.023*\"whimsical\" + 0.019*\"wedding\"\n", - "2021-08-24 02:42:25,627 : INFO : topic #9 (0.020): 0.237*\"based on a book\" + 0.119*\"adapted from:book\" + 0.060*\"Drama\" + 0.037*\"stephen king\" + 0.036*\"jack nicholson\" + 0.031*\"based on book\" + 0.028*\"horror\" + 0.027*\"imdb bottom 100\" + 0.021*\"literary adaptation\" + 0.019*\"stupid\"\n", - "2021-08-24 02:42:25,627 : INFO : topic #18 (0.020): 0.082*\"magic\" + 0.074*\"gay\" + 0.055*\"racism\" + 0.040*\"Drama\" + 0.037*\"pg13\" + 0.028*\"food\" + 0.023*\"homosexuality\" + 0.023*\"social commentary\" + 0.020*\"19th century\" + 0.017*\"robert altman\"\n", - "2021-08-24 02:42:25,629 : INFO : topic #42 (0.020): 0.102*\"japan\" + 0.093*\"martial arts\" + 0.071*\"robin williams\" + 0.044*\"akira kurosawa\" + 0.033*\"samurai\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"christopher walken\" + 0.020*\"hugh grant\"\n", - "2021-08-24 02:42:25,631 : INFO : topic #35 (0.020): 0.361*\"Comedy\" + 0.305*\"Drama\" + 0.192*\"Romance\" + 0.015*\"lesbian\" + 0.013*\"bibliothek\" + 0.007*\"oscar (best foreign language film)\" + 0.007*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:25,633 : INFO : topic diff=0.058712, rho=0.264069\n", - "2021-08-24 02:42:25,643 : INFO : PROGRESS: pass 8, at document #10000/10681\n", - "2021-08-24 02:42:25,932 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:25,959 : INFO : topic #6 (0.020): 0.129*\"Musical\" + 0.103*\"politics\" + 0.060*\"satire\" + 0.040*\"nicolas cage\" + 0.040*\"terrorism\" + 0.038*\"dvd-r\" + 0.029*\"sean connery\" + 0.029*\"political\" + 0.028*\"netwatch\" + 0.025*\"police\"\n", - "2021-08-24 02:42:25,960 : INFO : topic #23 (0.020): 0.079*\"high school\" + 0.069*\"cars\" + 0.068*\"teen\" + 0.044*\"marx brothers\" + 0.036*\"dance\" + 0.028*\"Comedy\" + 0.024*\"kate winslet\" + 0.022*\"eddie murphy\" + 0.021*\"(s)vcd\" + 0.020*\"car chase\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:25,961 : INFO : topic #3 (0.020): 0.131*\"oscar (best picture)\" + 0.056*\"oscar (best directing)\" + 0.053*\"oscar (best actor)\" + 0.037*\"al pacino\" + 0.027*\"oscar (best supporting actor)\" + 0.024*\"afi 100 (cheers)\" + 0.022*\"Drama\" + 0.020*\"great acting\" + 0.019*\"tumey's dvds\" + 0.019*\"civil war\"\n", - "2021-08-24 02:42:25,962 : INFO : topic #15 (0.020): 0.109*\"disney\" + 0.100*\"animation\" + 0.087*\"pixar\" + 0.050*\"pirates\" + 0.040*\"Animation\" + 0.039*\"children\" + 0.030*\"Children\" + 0.028*\"angelina jolie\" + 0.024*\"disney animated feature\" + 0.022*\"cartoon\"\n", - "2021-08-24 02:42:25,963 : INFO : topic #14 (0.020): 0.060*\"criterion\" + 0.055*\"disturbing\" + 0.053*\"Drama\" + 0.051*\"tense\" + 0.046*\"atmospheric\" + 0.031*\"tumey's dvds\" + 0.030*\"stylized\" + 0.029*\"bleak\" + 0.024*\"erlend's dvds\" + 0.023*\"menacing\"\n", - "2021-08-24 02:42:25,964 : INFO : topic diff=0.050891, rho=0.264069\n", - "2021-08-24 02:42:26,115 : INFO : -20.979 per-word bound, 2066694.4 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:26,116 : INFO : PROGRESS: pass 8, at document #10681/10681\n", - "2021-08-24 02:42:26,220 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:26,247 : INFO : topic #41 (0.020): 0.142*\"remake\" + 0.083*\"new york city\" + 0.065*\"christmas\" + 0.039*\"family\" + 0.034*\"new york\" + 0.034*\"movie to see\" + 0.029*\"eric's dvds\" + 0.028*\"Drama\" + 0.025*\"kevin spacey\" + 0.025*\"bibliothek\"\n", - "2021-08-24 02:42:26,247 : INFO : topic #37 (0.020): 0.228*\"War\" + 0.125*\"Drama\" + 0.095*\"world war ii\" + 0.050*\"history\" + 0.048*\"war\" + 0.038*\"Action\" + 0.024*\"jim carrey\" + 0.022*\"nazis\" + 0.011*\"mental illness\" + 0.011*\"wwii\"\n", - "2021-08-24 02:42:26,248 : INFO : topic #15 (0.020): 0.102*\"animation\" + 0.096*\"pixar\" + 0.091*\"disney\" + 0.045*\"Animation\" + 0.042*\"children\" + 0.041*\"pirates\" + 0.038*\"angelina jolie\" + 0.028*\"Children\" + 0.020*\"disney animated feature\" + 0.019*\"gambling\"\n", - "2021-08-24 02:42:26,249 : INFO : topic #24 (0.020): 0.109*\"oppl\" + 0.079*\"murder\" + 0.072*\"vampire\" + 0.051*\"vampires\" + 0.043*\"memory\" + 0.038*\"hitchcock\" + 0.029*\"alfred hitchcock\" + 0.017*\"unintentional comedy\" + 0.016*\"cary grant\" + 0.016*\"kirsten dunst\"\n", - "2021-08-24 02:42:26,250 : INFO : topic #21 (0.020): 0.074*\"Sci-Fi\" + 0.065*\"aliens\" + 0.044*\"will smith\" + 0.035*\"space\" + 0.032*\"tommy lee jones\" + 0.031*\"Action\" + 0.027*\"sci-fi\" + 0.026*\"ridley scott\" + 0.026*\"futuristmovies.com\" + 0.021*\"monster\"\n", - "2021-08-24 02:42:26,251 : INFO : topic diff=0.107135, rho=0.264069\n", - "2021-08-24 02:42:26,253 : INFO : PROGRESS: pass 9, at document #2000/10681\n", - "2021-08-24 02:42:26,526 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:26,554 : INFO : topic #44 (0.020): 0.120*\"mafia\" + 0.082*\"music\" + 0.064*\"martin scorsese\" + 0.055*\"organized crime\" + 0.034*\"rock and roll\" + 0.031*\"Musical\" + 0.026*\"oscar (best supporting actor)\" + 0.022*\"francis ford coppola\" + 0.021*\"guns\" + 0.020*\"wired 50 greatest soundtracks\"\n", - "2021-08-24 02:42:26,554 : INFO : topic #24 (0.020): 0.086*\"hitchcock\" + 0.082*\"alfred hitchcock\" + 0.074*\"oppl\" + 0.064*\"murder\" + 0.062*\"vampire\" + 0.046*\"vampires\" + 0.034*\"memory\" + 0.024*\"incest\" + 0.021*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:26,555 : INFO : topic #41 (0.020): 0.112*\"remake\" + 0.071*\"new york city\" + 0.067*\"christmas\" + 0.045*\"family\" + 0.041*\"kevin spacey\" + 0.033*\"new york\" + 0.027*\"Drama\" + 0.026*\"bibliothek\" + 0.025*\"eric's dvds\" + 0.024*\"robert de niro\"\n", - "2021-08-24 02:42:26,556 : INFO : topic #3 (0.020): 0.216*\"oscar (best picture)\" + 0.082*\"oscar (best directing)\" + 0.066*\"oscar (best actor)\" + 0.032*\"oscar (best supporting actor)\" + 0.029*\"al pacino\" + 0.024*\"afi 100\" + 0.023*\"afi 100 (cheers)\" + 0.022*\"great acting\" + 0.022*\"best picture\" + 0.018*\"Drama\"\n", - "2021-08-24 02:42:26,556 : INFO : topic #0 (0.020): 0.207*\"can't remember\" + 0.101*\"based on a tv show\" + 0.092*\"directorial debut\" + 0.072*\"Comedy\" + 0.038*\"ummarti2006\" + 0.033*\"keira knightley\" + 0.028*\"australia\" + 0.023*\"australian\" + 0.019*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:26,558 : INFO : topic diff=0.202025, rho=0.255317\n", - "2021-08-24 02:42:26,559 : INFO : PROGRESS: pass 9, at document #4000/10681\n", - "2021-08-24 02:42:26,803 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:26,829 : INFO : topic #44 (0.020): 0.107*\"mafia\" + 0.085*\"music\" + 0.051*\"martin scorsese\" + 0.049*\"organized crime\" + 0.041*\"rock and roll\" + 0.030*\"Musical\" + 0.029*\"wired 50 greatest soundtracks\" + 0.022*\"guns\" + 0.021*\"oscar (best supporting actor)\" + 0.020*\"mob\"\n", - "2021-08-24 02:42:26,830 : INFO : topic #34 (0.020): 0.097*\"ghosts\" + 0.051*\"bill murray\" + 0.043*\"television\" + 0.035*\"courtroom\" + 0.033*\"courtroom drama\" + 0.023*\"court\" + 0.021*\"roman polanski\" + 0.019*\"secret service\" + 0.018*\"opera\" + 0.017*\"2.5\"\n", - "2021-08-24 02:42:26,831 : INFO : topic #4 (0.020): 0.073*\"sports\" + 0.061*\"Drama\" + 0.055*\"boxing\" + 0.046*\"underrated\" + 0.045*\"british\" + 0.038*\"london\" + 0.034*\"sven's to see list\" + 0.032*\"hw drama\" + 0.030*\"sylvester stallone\" + 0.028*\"inspirational\"\n", - "2021-08-24 02:42:26,832 : INFO : topic #31 (0.020): 0.126*\"zombies\" + 0.073*\"pg-13\" + 0.047*\"cult classic\" + 0.028*\"campy\" + 0.028*\"infidelity\" + 0.026*\"joaquin phoenix\" + 0.025*\"sam raimi\" + 0.022*\"zombie\" + 0.021*\"books\" + 0.021*\"mummy\"\n", - "2021-08-24 02:42:26,832 : INFO : topic #25 (0.020): 0.142*\"Western\" + 0.069*\"erlend's dvds\" + 0.054*\"gothic\" + 0.051*\"woody allen\" + 0.043*\"Drama\" + 0.039*\"gene hackman\" + 0.035*\"scary movies to see on halloween\" + 0.025*\"george clooney\" + 0.025*\"sad\" + 0.020*\"cute\"\n", - "2021-08-24 02:42:26,833 : INFO : topic diff=0.037856, rho=0.255317\n", - "2021-08-24 02:42:26,835 : INFO : PROGRESS: pass 9, at document #6000/10681\n", - "2021-08-24 02:42:27,066 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:27,094 : INFO : topic #30 (0.020): 0.236*\"Adventure\" + 0.121*\"Children\" + 0.106*\"Comedy\" + 0.103*\"Fantasy\" + 0.076*\"Action\" + 0.061*\"70mm\" + 0.051*\"Animation\" + 0.039*\"Drama\" + 0.023*\"Musical\" + 0.010*\"submarine\"\n", - "2021-08-24 02:42:27,095 : INFO : topic #42 (0.020): 0.095*\"robin williams\" + 0.087*\"martial arts\" + 0.062*\"japan\" + 0.037*\"samurai\" + 0.035*\"akira kurosawa\" + 0.031*\"Drama\" + 0.026*\"orson welles\" + 0.026*\"hugh grant\" + 0.023*\"julianne moore\" + 0.021*\"christopher walken\"\n", - "2021-08-24 02:42:27,096 : INFO : topic #37 (0.020): 0.181*\"War\" + 0.114*\"world war ii\" + 0.098*\"Drama\" + 0.059*\"war\" + 0.041*\"history\" + 0.036*\"Action\" + 0.031*\"jim carrey\" + 0.020*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:42:27,097 : INFO : topic #35 (0.020): 0.357*\"Comedy\" + 0.309*\"Drama\" + 0.187*\"Romance\" + 0.015*\"lesbian\" + 0.012*\"bibliothek\" + 0.008*\"philip seymour hoffman\" + 0.006*\"m. night shyamalan\" + 0.006*\"sean penn\" + 0.006*\"oscar (best foreign language film)\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:27,098 : INFO : topic #38 (0.020): 0.180*\"drugs\" + 0.046*\"samuel l. jackson\" + 0.026*\"ewan mcgregor\" + 0.026*\"divx\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.022*\"poverty\" + 0.021*\"basketball\" + 0.021*\"michael caine\" + 0.020*\"liam neeson\"\n", - "2021-08-24 02:42:27,099 : INFO : topic diff=0.072192, rho=0.255317\n", - "2021-08-24 02:42:27,100 : INFO : PROGRESS: pass 9, at document #8000/10681\n", - "2021-08-24 02:42:27,330 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:27,357 : INFO : topic #30 (0.020): 0.236*\"Adventure\" + 0.115*\"Children\" + 0.104*\"Comedy\" + 0.100*\"Fantasy\" + 0.077*\"Action\" + 0.070*\"70mm\" + 0.047*\"Animation\" + 0.043*\"Drama\" + 0.025*\"Musical\" + 0.009*\"submarine\"\n", - "2021-08-24 02:42:27,359 : INFO : topic #29 (0.020): 0.104*\"nudity (full frontal - notable)\" + 0.058*\"Drama\" + 0.053*\"shakespeare\" + 0.044*\"based on a play\" + 0.038*\"adapted from:play\" + 0.036*\"christianity\" + 0.029*\"religion\" + 0.019*\"disability\" + 0.018*\"takeshi kitano\" + 0.018*\"jodie foster\"\n", - "2021-08-24 02:42:27,360 : INFO : topic #26 (0.020): 0.044*\"Drama\" + 0.032*\"james bond\" + 0.022*\"reflective\" + 0.022*\"007\" + 0.021*\"poignant\" + 0.021*\"atmospheric\" + 0.021*\"bond\" + 0.019*\"bittersweet\" + 0.019*\"deliberate\" + 0.019*\"lyrical\"\n", - "2021-08-24 02:42:27,361 : INFO : topic #46 (0.020): 0.087*\"action\" + 0.065*\"fantasy\" + 0.057*\"sci-fi\" + 0.036*\"adventure\" + 0.033*\"dvd\" + 0.024*\"Adventure\" + 0.022*\"seen more than once\" + 0.022*\"seen at the cinema\" + 0.018*\"tom cruise\" + 0.016*\"Action\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:27,362 : INFO : topic #27 (0.020): 0.319*\"Horror\" + 0.150*\"Thriller\" + 0.127*\"Mystery\" + 0.040*\"Drama\" + 0.030*\"Fantasy\" + 0.030*\"easily confused with other movie(s) (title)\" + 0.025*\"Film-Noir\" + 0.019*\"eerie\" + 0.015*\"tumey's dvds\" + 0.013*\"franchise\"\n", - "2021-08-24 02:42:27,363 : INFO : topic diff=0.056291, rho=0.255317\n", - "2021-08-24 02:42:27,364 : INFO : PROGRESS: pass 9, at document #10000/10681\n", - "2021-08-24 02:42:27,614 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:27,641 : INFO : topic #7 (0.020): 0.106*\"romance\" + 0.059*\"Romance\" + 0.043*\"chick flick\" + 0.040*\"boring\" + 0.027*\"Comedy\" + 0.025*\"girlie movie\" + 0.025*\"love story\" + 0.024*\"baseball\" + 0.024*\"whimsical\" + 0.023*\"comedy\"\n", - "2021-08-24 02:42:27,643 : INFO : topic #11 (0.020): 0.130*\"religion\" + 0.106*\"keanu reeves\" + 0.026*\"product placement\" + 0.022*\"slavery\" + 0.021*\"facebook rec\" + 0.021*\"courtesan\" + 0.018*\"reality tv\" + 0.017*\"social message\" + 0.016*\"cross-dressing women\" + 0.016*\"gender identity\"\n", - "2021-08-24 02:42:27,644 : INFO : topic #45 (0.020): 0.265*\"less than 300 ratings\" + 0.118*\"Drama\" + 0.056*\"nudity (topless - notable)\" + 0.036*\"tim burton\" + 0.031*\"not corv lib\" + 0.025*\"library\" + 0.017*\"seen 2007\" + 0.017*\"depressing\" + 0.015*\"blindfold\" + 0.013*\"michael moore\"\n", - "2021-08-24 02:42:27,644 : INFO : topic #13 (0.020): 0.064*\"fairy tale\" + 0.051*\"road trip\" + 0.041*\"sequel\" + 0.040*\"jane austen\" + 0.027*\"historical\" + 0.025*\"swashbuckler\" + 0.025*\"gerard depardieu\" + 0.025*\"kids\" + 0.023*\"notable nudity\" + 0.022*\"assassination\"\n", - "2021-08-24 02:42:27,645 : INFO : topic #15 (0.020): 0.110*\"disney\" + 0.100*\"animation\" + 0.087*\"pixar\" + 0.049*\"pirates\" + 0.041*\"Animation\" + 0.039*\"children\" + 0.031*\"Children\" + 0.028*\"angelina jolie\" + 0.024*\"disney animated feature\" + 0.022*\"cartoon\"\n", - "2021-08-24 02:42:27,646 : INFO : topic diff=0.048824, rho=0.255317\n", - "2021-08-24 02:42:27,797 : INFO : -20.978 per-word bound, 2065515.2 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:27,798 : INFO : PROGRESS: pass 9, at document #10681/10681\n", - "2021-08-24 02:42:27,898 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:27,924 : INFO : topic #48 (0.020): 0.137*\"netflix\" + 0.078*\"twist ending\" + 0.065*\"bruce willis\" + 0.045*\"coming of age\" + 0.036*\"journalism\" + 0.034*\"to see\" + 0.033*\"Drama\" + 0.025*\"talking animals\" + 0.025*\"relationships\" + 0.021*\"father-son relationship\"\n", - "2021-08-24 02:42:27,925 : INFO : topic #18 (0.020): 0.093*\"magic\" + 0.086*\"pg13\" + 0.071*\"gay\" + 0.050*\"racism\" + 0.039*\"Drama\" + 0.026*\"food\" + 0.025*\"social commentary\" + 0.022*\"homosexuality\" + 0.021*\"19th century\" + 0.021*\"denzel washington\"\n", - "2021-08-24 02:42:27,926 : INFO : topic #6 (0.020): 0.135*\"Musical\" + 0.116*\"politics\" + 0.062*\"satire\" + 0.039*\"terrorism\" + 0.037*\"nicolas cage\" + 0.031*\"dvd-r\" + 0.029*\"corruption\" + 0.026*\"police\" + 0.025*\"political\" + 0.024*\"sean connery\"\n", - "2021-08-24 02:42:27,927 : INFO : topic #11 (0.020): 0.155*\"religion\" + 0.101*\"keanu reeves\" + 0.026*\"product placement\" + 0.025*\"class issues\" + 0.019*\"social message\" + 0.017*\"slavery\" + 0.016*\"facebook rec\" + 0.016*\"courtesan\" + 0.014*\"reality tv\" + 0.013*\"cross-dressing women\"\n", - "2021-08-24 02:42:27,927 : INFO : topic #35 (0.020): 0.355*\"Comedy\" + 0.315*\"Drama\" + 0.202*\"Romance\" + 0.013*\"lesbian\" + 0.008*\"sean penn\" + 0.008*\"bibliothek\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.005*\"oscar (best foreign language film)\" + 0.003*\"great cinematography\"\n", - "2021-08-24 02:42:27,928 : INFO : topic diff=0.103233, rho=0.255317\n", - "2021-08-24 02:42:27,930 : INFO : PROGRESS: pass 10, at document #2000/10681\n", - "2021-08-24 02:42:28,180 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:28,207 : INFO : topic #21 (0.020): 0.083*\"aliens\" + 0.064*\"Sci-Fi\" + 0.050*\"space\" + 0.042*\"sci-fi\" + 0.033*\"futuristmovies.com\" + 0.032*\"will smith\" + 0.027*\"Action\" + 0.026*\"tommy lee jones\" + 0.022*\"ridley scott\" + 0.019*\"michael crichton\"\n", - "2021-08-24 02:42:28,208 : INFO : topic #0 (0.020): 0.207*\"can't remember\" + 0.101*\"based on a tv show\" + 0.093*\"directorial debut\" + 0.071*\"Comedy\" + 0.038*\"ummarti2006\" + 0.033*\"keira knightley\" + 0.028*\"australia\" + 0.023*\"australian\" + 0.019*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:28,209 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.067*\"Romance\" + 0.052*\"chick flick\" + 0.035*\"boring\" + 0.028*\"girlie movie\" + 0.027*\"Comedy\" + 0.024*\"comedy\" + 0.024*\"love story\" + 0.022*\"baseball\" + 0.020*\"wedding\"\n", - "2021-08-24 02:42:28,209 : INFO : topic #9 (0.020): 0.261*\"based on a book\" + 0.108*\"adapted from:book\" + 0.052*\"Drama\" + 0.042*\"stephen king\" + 0.042*\"jack nicholson\" + 0.032*\"based on book\" + 0.026*\"imdb bottom 100\" + 0.021*\"stupid\" + 0.021*\"los angeles\" + 0.021*\"literary adaptation\"\n", - "2021-08-24 02:42:28,210 : INFO : topic #44 (0.020): 0.121*\"mafia\" + 0.084*\"music\" + 0.065*\"martin scorsese\" + 0.056*\"organized crime\" + 0.035*\"rock and roll\" + 0.031*\"Musical\" + 0.022*\"francis ford coppola\" + 0.022*\"guns\" + 0.021*\"wired 50 greatest soundtracks\" + 0.019*\"confrontational\"\n", - "2021-08-24 02:42:28,211 : INFO : topic diff=0.194247, rho=0.247382\n", - "2021-08-24 02:42:28,212 : INFO : PROGRESS: pass 10, at document #4000/10681\n", - "2021-08-24 02:42:28,468 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:28,494 : INFO : topic #7 (0.020): 0.112*\"romance\" + 0.063*\"Romance\" + 0.055*\"chick flick\" + 0.037*\"boring\" + 0.031*\"girlie movie\" + 0.028*\"Comedy\" + 0.027*\"baseball\" + 0.024*\"comedy\" + 0.019*\"love story\" + 0.019*\"whimsical\"\n", - "2021-08-24 02:42:28,495 : INFO : topic #48 (0.020): 0.107*\"bruce willis\" + 0.085*\"twist ending\" + 0.077*\"netflix\" + 0.050*\"coming of age\" + 0.033*\"journalism\" + 0.027*\"Drama\" + 0.026*\"talking animals\" + 0.026*\"sexy\" + 0.023*\"to see\" + 0.018*\"relationships\"\n", - "2021-08-24 02:42:28,496 : INFO : topic #14 (0.020): 0.053*\"criterion\" + 0.051*\"atmospheric\" + 0.050*\"tense\" + 0.050*\"disturbing\" + 0.044*\"Drama\" + 0.033*\"tumey's dvds\" + 0.027*\"bleak\" + 0.027*\"stylized\" + 0.025*\"erlend's dvds\" + 0.025*\"menacing\"\n", - "2021-08-24 02:42:28,497 : INFO : topic #6 (0.020): 0.104*\"Musical\" + 0.100*\"politics\" + 0.067*\"satire\" + 0.050*\"sean connery\" + 0.047*\"nicolas cage\" + 0.033*\"dvd-r\" + 0.031*\"terrorism\" + 0.029*\"police\" + 0.022*\"political\" + 0.021*\"dvd-ram\"\n", - "2021-08-24 02:42:28,498 : INFO : topic #39 (0.020): 0.179*\"time travel\" + 0.056*\"adultery\" + 0.047*\"post apocalyptic\" + 0.036*\"motorcycle\" + 0.035*\"post-apocalyptic\" + 0.029*\"jude law\" + 0.022*\"dystopia\" + 0.020*\"hollywood\" + 0.019*\"want to own\" + 0.018*\"bechdel test:fail\"\n", - "2021-08-24 02:42:28,499 : INFO : topic diff=0.036165, rho=0.247382\n", - "2021-08-24 02:42:28,501 : INFO : PROGRESS: pass 10, at document #6000/10681\n", - "2021-08-24 02:42:28,732 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:28,763 : INFO : topic #45 (0.020): 0.181*\"less than 300 ratings\" + 0.108*\"Drama\" + 0.083*\"nudity (topless - notable)\" + 0.050*\"tim burton\" + 0.032*\"not corv lib\" + 0.021*\"library\" + 0.017*\"depressing\" + 0.017*\"blindfold\" + 0.015*\"golden raspberry (worst actor)\" + 0.014*\"san francisco\"\n", - "2021-08-24 02:42:28,766 : INFO : topic #2 (0.020): 0.271*\"Documentary\" + 0.104*\"to see\" + 0.050*\"documentary\" + 0.028*\"star trek\" + 0.028*\"sexuality\" + 0.025*\"mockumentary\" + 0.024*\"kevin smith\" + 0.023*\"dogs\" + 0.017*\"owen wilson\" + 0.017*\"movie to see\"\n", - "2021-08-24 02:42:28,768 : INFO : topic #22 (0.020): 0.272*\"betamax\" + 0.102*\"dvd-video\" + 0.069*\"clv\" + 0.030*\"aviation\" + 0.023*\"terry gilliam\" + 0.021*\"library on hold\" + 0.018*\"dvd collection\" + 0.014*\"gilliam\" + 0.014*\"steven seagal\" + 0.013*\"seen 2008\"\n", - "2021-08-24 02:42:28,769 : INFO : topic #34 (0.020): 0.095*\"ghosts\" + 0.042*\"bill murray\" + 0.041*\"television\" + 0.037*\"courtroom\" + 0.034*\"courtroom drama\" + 0.023*\"court\" + 0.023*\"roman polanski\" + 0.017*\"2.5\" + 0.017*\"secret service\" + 0.016*\"opera\"\n", - "2021-08-24 02:42:28,771 : INFO : topic #30 (0.020): 0.236*\"Adventure\" + 0.120*\"Children\" + 0.106*\"Comedy\" + 0.103*\"Fantasy\" + 0.077*\"Action\" + 0.062*\"70mm\" + 0.050*\"Animation\" + 0.042*\"Drama\" + 0.022*\"Musical\" + 0.010*\"submarine\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:28,773 : INFO : topic diff=0.069619, rho=0.247382\n", - "2021-08-24 02:42:28,775 : INFO : PROGRESS: pass 10, at document #8000/10681\n", - "2021-08-24 02:42:29,049 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:29,076 : INFO : topic #16 (0.020): 0.269*\"nudity (topless)\" + 0.184*\"nudity (topless - brief)\" + 0.067*\"nudity (rear)\" + 0.028*\"pg\" + 0.026*\"claymation\" + 0.022*\"military\" + 0.018*\"childhood\" + 0.018*\"weird\" + 0.016*\"aardman\" + 0.010*\"shark\"\n", - "2021-08-24 02:42:29,077 : INFO : topic #23 (0.020): 0.081*\"high school\" + 0.081*\"teen\" + 0.065*\"cars\" + 0.034*\"dance\" + 0.031*\"marx brothers\" + 0.029*\"eddie murphy\" + 0.024*\"(s)vcd\" + 0.023*\"car chase\" + 0.023*\"Comedy\" + 0.019*\"sandra bullock\"\n", - "2021-08-24 02:42:29,078 : INFO : topic #30 (0.020): 0.237*\"Adventure\" + 0.115*\"Children\" + 0.104*\"Comedy\" + 0.100*\"Fantasy\" + 0.077*\"Action\" + 0.070*\"70mm\" + 0.046*\"Animation\" + 0.045*\"Drama\" + 0.024*\"Musical\" + 0.009*\"submarine\"\n", - "2021-08-24 02:42:29,079 : INFO : topic #9 (0.020): 0.241*\"based on a book\" + 0.121*\"adapted from:book\" + 0.060*\"Drama\" + 0.038*\"stephen king\" + 0.037*\"jack nicholson\" + 0.033*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.021*\"literary adaptation\" + 0.020*\"stupid\" + 0.019*\"los angeles\"\n", - "2021-08-24 02:42:29,080 : INFO : topic #27 (0.020): 0.319*\"Horror\" + 0.150*\"Thriller\" + 0.128*\"Mystery\" + 0.040*\"Drama\" + 0.030*\"easily confused with other movie(s) (title)\" + 0.029*\"Fantasy\" + 0.026*\"Film-Noir\" + 0.019*\"eerie\" + 0.016*\"tumey's dvds\" + 0.013*\"franchise\"\n", - "2021-08-24 02:42:29,081 : INFO : topic diff=0.054776, rho=0.247382\n", - "2021-08-24 02:42:29,082 : INFO : PROGRESS: pass 10, at document #10000/10681\n", - "2021-08-24 02:42:29,338 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:29,365 : INFO : topic #45 (0.020): 0.264*\"less than 300 ratings\" + 0.118*\"Drama\" + 0.057*\"nudity (topless - notable)\" + 0.036*\"tim burton\" + 0.031*\"not corv lib\" + 0.025*\"library\" + 0.017*\"depressing\" + 0.017*\"seen 2007\" + 0.015*\"blindfold\" + 0.013*\"michael moore\"\n", - "2021-08-24 02:42:29,366 : INFO : topic #42 (0.020): 0.102*\"japan\" + 0.092*\"martial arts\" + 0.064*\"robin williams\" + 0.036*\"akira kurosawa\" + 0.032*\"samurai\" + 0.032*\"Drama\" + 0.032*\"seen 2006\" + 0.032*\"jonossa\" + 0.025*\"orson welles\" + 0.022*\"hugh grant\"\n", - "2021-08-24 02:42:29,367 : INFO : topic #48 (0.020): 0.107*\"netflix\" + 0.084*\"twist ending\" + 0.082*\"bruce willis\" + 0.044*\"coming of age\" + 0.038*\"journalism\" + 0.025*\"talking animals\" + 0.024*\"Drama\" + 0.023*\"sexy\" + 0.021*\"avi\" + 0.019*\"to see\"\n", - "2021-08-24 02:42:29,367 : INFO : topic #47 (0.020): 0.061*\"serial killer\" + 0.055*\"psychology\" + 0.049*\"brad pitt\" + 0.042*\"edward norton\" + 0.037*\"heist\" + 0.033*\"matt damon\" + 0.032*\"hayao miyazaki\" + 0.027*\"steven spielberg\" + 0.018*\"crime\" + 0.016*\"brilliant\"\n", - "2021-08-24 02:42:29,368 : INFO : topic #35 (0.020): 0.356*\"Comedy\" + 0.305*\"Drama\" + 0.198*\"Romance\" + 0.016*\"lesbian\" + 0.010*\"bibliothek\" + 0.007*\"sean penn\" + 0.007*\"m. night shyamalan\" + 0.006*\"oscar (best foreign language film)\" + 0.006*\"philip seymour hoffman\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:29,369 : INFO : topic diff=0.046688, rho=0.247382\n", - "2021-08-24 02:42:29,513 : INFO : -20.975 per-word bound, 2061095.9 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:29,513 : INFO : PROGRESS: pass 10, at document #10681/10681\n", - "2021-08-24 02:42:29,608 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:29,635 : INFO : topic #9 (0.020): 0.275*\"based on a book\" + 0.109*\"adapted from:book\" + 0.061*\"Drama\" + 0.034*\"imdb bottom 100\" + 0.030*\"jack nicholson\" + 0.028*\"based on book\" + 0.027*\"stephen king\" + 0.024*\"los angeles\" + 0.020*\"pregnancy\" + 0.018*\"stupid\"\n", - "2021-08-24 02:42:29,636 : INFO : topic #19 (0.020): 0.086*\"nudity (full frontal)\" + 0.043*\"drama\" + 0.038*\"Drama\" + 0.035*\"to see\" + 0.028*\"philip k. dick\" + 0.028*\"rape\" + 0.026*\"ummarti2007\" + 0.023*\"male nudity\" + 0.021*\"slow\" + 0.017*\"very good\"\n", - "2021-08-24 02:42:29,637 : INFO : topic #8 (0.020): 0.133*\"anime\" + 0.094*\"based on a true story\" + 0.082*\"true story\" + 0.062*\"tom hanks\" + 0.023*\"good\" + 0.023*\"to-rent\" + 0.023*\"archaeology\" + 0.023*\"japan\" + 0.022*\"interesting\" + 0.021*\"not funny\"\n", - "2021-08-24 02:42:29,638 : INFO : topic #3 (0.020): 0.124*\"oscar (best picture)\" + 0.049*\"oscar (best directing)\" + 0.048*\"oscar (best actor)\" + 0.043*\"oscar (best supporting actor)\" + 0.043*\"great acting\" + 0.032*\"al pacino\" + 0.023*\"Drama\" + 0.022*\"afi 100 (cheers)\" + 0.017*\"tumey's dvds\" + 0.016*\"civil war\"\n", - "2021-08-24 02:42:29,639 : INFO : topic #46 (0.020): 0.079*\"action\" + 0.065*\"fantasy\" + 0.048*\"sci-fi\" + 0.037*\"adventure\" + 0.031*\"dvd\" + 0.025*\"Adventure\" + 0.022*\"watched 2006\" + 0.022*\"robots\" + 0.019*\"seen at the cinema\" + 0.019*\"space\"\n", - "2021-08-24 02:42:29,640 : INFO : topic diff=0.099707, rho=0.247382\n", - "2021-08-24 02:42:29,642 : INFO : PROGRESS: pass 11, at document #2000/10681\n", - "2021-08-24 02:42:29,921 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:29,948 : INFO : topic #3 (0.020): 0.208*\"oscar (best picture)\" + 0.080*\"oscar (best directing)\" + 0.065*\"oscar (best actor)\" + 0.046*\"oscar (best supporting actor)\" + 0.028*\"al pacino\" + 0.023*\"afi 100 (cheers)\" + 0.023*\"afi 100\" + 0.022*\"great acting\" + 0.021*\"best picture\" + 0.019*\"Drama\"\n", - "2021-08-24 02:42:29,949 : INFO : topic #17 (0.020): 0.324*\"Sci-Fi\" + 0.069*\"Action\" + 0.067*\"Horror\" + 0.046*\"video game adaptation\" + 0.028*\"animals\" + 0.026*\"movie to see\" + 0.020*\"football\" + 0.019*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"luc besson\"\n", - "2021-08-24 02:42:29,949 : INFO : topic #21 (0.020): 0.082*\"aliens\" + 0.065*\"Sci-Fi\" + 0.050*\"space\" + 0.046*\"sci-fi\" + 0.033*\"futuristmovies.com\" + 0.032*\"will smith\" + 0.026*\"Action\" + 0.025*\"tommy lee jones\" + 0.021*\"ridley scott\" + 0.019*\"michael crichton\"\n", - "2021-08-24 02:42:29,950 : INFO : topic #48 (0.020): 0.102*\"bruce willis\" + 0.097*\"netflix\" + 0.080*\"twist ending\" + 0.038*\"coming of age\" + 0.032*\"journalism\" + 0.028*\"Drama\" + 0.028*\"to see\" + 0.025*\"talking animals\" + 0.021*\"relationships\" + 0.020*\"sexy\"\n", - "2021-08-24 02:42:29,951 : INFO : topic #46 (0.020): 0.089*\"action\" + 0.059*\"sci-fi\" + 0.049*\"fantasy\" + 0.033*\"adventure\" + 0.027*\"dvd\" + 0.023*\"Adventure\" + 0.023*\"space\" + 0.022*\"seen at the cinema\" + 0.021*\"harrison ford\" + 0.021*\"seen more than once\"\n", - "2021-08-24 02:42:29,952 : INFO : topic diff=0.187209, rho=0.240143\n", - "2021-08-24 02:42:29,953 : INFO : PROGRESS: pass 11, at document #4000/10681\n", - "2021-08-24 02:42:30,185 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:30,212 : INFO : topic #24 (0.020): 0.097*\"hitchcock\" + 0.077*\"murder\" + 0.074*\"alfred hitchcock\" + 0.067*\"vampire\" + 0.059*\"oppl\" + 0.050*\"vampires\" + 0.033*\"memory\" + 0.021*\"incest\" + 0.018*\"cary grant\" + 0.015*\"kirsten dunst\"\n", - "2021-08-24 02:42:30,213 : INFO : topic #39 (0.020): 0.180*\"time travel\" + 0.057*\"adultery\" + 0.047*\"post apocalyptic\" + 0.036*\"motorcycle\" + 0.035*\"post-apocalyptic\" + 0.029*\"jude law\" + 0.023*\"dystopia\" + 0.020*\"hollywood\" + 0.020*\"want to own\" + 0.018*\"bechdel test:fail\"\n", - "2021-08-24 02:42:30,213 : INFO : topic #31 (0.020): 0.122*\"zombies\" + 0.070*\"pg-13\" + 0.059*\"horror\" + 0.044*\"cult classic\" + 0.027*\"campy\" + 0.027*\"infidelity\" + 0.025*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.022*\"zombie\" + 0.020*\"books\"\n", - "2021-08-24 02:42:30,214 : INFO : topic #23 (0.020): 0.096*\"teen\" + 0.091*\"high school\" + 0.050*\"cars\" + 0.043*\"dance\" + 0.025*\"sandra bullock\" + 0.023*\"eddie murphy\" + 0.022*\"Comedy\" + 0.022*\"car chase\" + 0.021*\"(s)vcd\" + 0.019*\"marx brothers\"\n", - "2021-08-24 02:42:30,215 : INFO : topic #0 (0.020): 0.234*\"can't remember\" + 0.100*\"directorial debut\" + 0.092*\"based on a tv show\" + 0.080*\"Comedy\" + 0.029*\"ummarti2006\" + 0.029*\"australia\" + 0.024*\"keira knightley\" + 0.020*\"robert downey jr\" + 0.019*\"australian\" + 0.016*\"immigrants\"\n", - "2021-08-24 02:42:30,217 : INFO : topic diff=0.034716, rho=0.240143\n", - "2021-08-24 02:42:30,218 : INFO : PROGRESS: pass 11, at document #6000/10681\n", - "2021-08-24 02:42:30,447 : INFO : merging changes from 2000 documents into a model of 10681 documents\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:30,473 : INFO : topic #28 (0.020): 0.087*\"oscar (best cinematography)\" + 0.073*\"Drama\" + 0.057*\"oscar (best supporting actress)\" + 0.049*\"in netflix queue\" + 0.044*\"oscar (best actress)\" + 0.029*\"remade\" + 0.019*\"marlon brando\" + 0.014*\"exceptional acting\" + 0.013*\"china\" + 0.013*\"alcoholism\"\n", - "2021-08-24 02:42:30,474 : INFO : topic #12 (0.020): 0.126*\"comic book\" + 0.101*\"superhero\" + 0.074*\"dystopia\" + 0.061*\"holocaust\" + 0.055*\"super-hero\" + 0.032*\"adapted from:comic\" + 0.024*\"kidnapping\" + 0.022*\"batman\" + 0.020*\"alter ego\" + 0.016*\"Action\"\n", - "2021-08-24 02:42:30,475 : INFO : topic #27 (0.020): 0.321*\"Horror\" + 0.154*\"Thriller\" + 0.127*\"Mystery\" + 0.039*\"Drama\" + 0.028*\"Fantasy\" + 0.027*\"easily confused with other movie(s) (title)\" + 0.026*\"Film-Noir\" + 0.017*\"eerie\" + 0.015*\"slasher\" + 0.015*\"franchise\"\n", - "2021-08-24 02:42:30,475 : INFO : topic #41 (0.020): 0.116*\"remake\" + 0.068*\"christmas\" + 0.063*\"new york city\" + 0.044*\"family\" + 0.036*\"new york\" + 0.036*\"bibliothek\" + 0.034*\"kevin spacey\" + 0.030*\"Drama\" + 0.029*\"eric's dvds\" + 0.027*\"friday night movie\"\n", - "2021-08-24 02:42:30,476 : INFO : topic #18 (0.020): 0.083*\"magic\" + 0.080*\"gay\" + 0.064*\"racism\" + 0.040*\"Drama\" + 0.040*\"pg13\" + 0.027*\"food\" + 0.027*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.019*\"denzel washington\"\n", - "2021-08-24 02:42:30,477 : INFO : topic diff=0.066696, rho=0.240143\n", - "2021-08-24 02:42:30,479 : INFO : PROGRESS: pass 11, at document #8000/10681\n", - "2021-08-24 02:42:30,715 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:30,743 : INFO : topic #2 (0.020): 0.288*\"Documentary\" + 0.099*\"to see\" + 0.066*\"documentary\" + 0.026*\"sexuality\" + 0.025*\"mockumentary\" + 0.022*\"star trek\" + 0.021*\"dogs\" + 0.020*\"kevin smith\" + 0.019*\"in netflix queue\" + 0.017*\"movie to see\"\n", - "2021-08-24 02:42:30,744 : INFO : topic #42 (0.020): 0.101*\"japan\" + 0.093*\"martial arts\" + 0.073*\"robin williams\" + 0.044*\"akira kurosawa\" + 0.034*\"samurai\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"christopher walken\" + 0.020*\"hugh grant\"\n", - "2021-08-24 02:42:30,745 : INFO : topic #39 (0.020): 0.181*\"time travel\" + 0.053*\"adultery\" + 0.042*\"motorcycle\" + 0.038*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.030*\"jude law\" + 0.023*\"dystopia\" + 0.019*\"want to own\" + 0.019*\"ian mckellen\" + 0.018*\"old\"\n", - "2021-08-24 02:42:30,746 : INFO : topic #28 (0.020): 0.081*\"oscar (best cinematography)\" + 0.079*\"Drama\" + 0.055*\"in netflix queue\" + 0.054*\"oscar (best supporting actress)\" + 0.046*\"oscar (best actress)\" + 0.028*\"remade\" + 0.016*\"marlon brando\" + 0.014*\"father daughter relationship\" + 0.014*\"exceptional acting\" + 0.013*\"blindness\"\n", - "2021-08-24 02:42:30,747 : INFO : topic #21 (0.020): 0.077*\"Sci-Fi\" + 0.075*\"aliens\" + 0.040*\"sci-fi\" + 0.039*\"futuristmovies.com\" + 0.038*\"space\" + 0.027*\"will smith\" + 0.025*\"Action\" + 0.023*\"tommy lee jones\" + 0.021*\"ridley scott\" + 0.021*\"michael crichton\"\n", - "2021-08-24 02:42:30,748 : INFO : topic diff=0.053036, rho=0.240143\n", - "2021-08-24 02:42:30,750 : INFO : PROGRESS: pass 11, at document #10000/10681\n", - "2021-08-24 02:42:31,017 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:31,044 : INFO : topic #44 (0.020): 0.080*\"mafia\" + 0.076*\"music\" + 0.059*\"martin scorsese\" + 0.048*\"rock and roll\" + 0.037*\"Musical\" + 0.036*\"organized crime\" + 0.030*\"wired 50 greatest soundtracks\" + 0.027*\"guns\" + 0.024*\"confrontational\" + 0.022*\"dysfunctional family\"\n", - "2021-08-24 02:42:31,045 : INFO : topic #24 (0.020): 0.072*\"murder\" + 0.069*\"vampire\" + 0.065*\"vampires\" + 0.055*\"memory\" + 0.053*\"hitchcock\" + 0.051*\"oppl\" + 0.040*\"alfred hitchcock\" + 0.021*\"cary grant\" + 0.021*\"kirsten dunst\" + 0.021*\"incest\"\n", - "2021-08-24 02:42:31,046 : INFO : topic #29 (0.020): 0.095*\"nudity (full frontal - notable)\" + 0.056*\"Drama\" + 0.051*\"shakespeare\" + 0.045*\"based on a play\" + 0.037*\"christianity\" + 0.034*\"adapted from:play\" + 0.029*\"religion\" + 0.023*\"biblical\" + 0.021*\"fascism\" + 0.020*\"disability\"\n", - "2021-08-24 02:42:31,047 : INFO : topic #48 (0.020): 0.106*\"netflix\" + 0.084*\"twist ending\" + 0.082*\"bruce willis\" + 0.044*\"coming of age\" + 0.038*\"journalism\" + 0.026*\"talking animals\" + 0.024*\"Drama\" + 0.023*\"sexy\" + 0.021*\"avi\" + 0.020*\"to see\"\n", - "2021-08-24 02:42:31,048 : INFO : topic #35 (0.020): 0.355*\"Comedy\" + 0.305*\"Drama\" + 0.198*\"Romance\" + 0.016*\"lesbian\" + 0.010*\"bibliothek\" + 0.007*\"sean penn\" + 0.007*\"m. night shyamalan\" + 0.006*\"oscar (best foreign language film)\" + 0.006*\"philip seymour hoffman\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:31,049 : INFO : topic diff=0.045196, rho=0.240143\n", - "2021-08-24 02:42:31,193 : INFO : -20.972 per-word bound, 2056460.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:31,194 : INFO : PROGRESS: pass 11, at document #10681/10681\n", - "2021-08-24 02:42:31,296 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:31,323 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.053*\"Drama\" + 0.052*\"disturbing\" + 0.049*\"tense\" + 0.043*\"atmospheric\" + 0.028*\"tumey's dvds\" + 0.028*\"stylized\" + 0.027*\"bleak\" + 0.023*\"erlend's dvds\" + 0.022*\"menacing\"\n", - "2021-08-24 02:42:31,324 : INFO : topic #44 (0.020): 0.076*\"mafia\" + 0.076*\"music\" + 0.053*\"organized crime\" + 0.048*\"martin scorsese\" + 0.046*\"rock and roll\" + 0.043*\"Musical\" + 0.025*\"wired 50 greatest soundtracks\" + 0.022*\"guns\" + 0.019*\"confrontational\" + 0.018*\"dysfunctional family\"\n", - "2021-08-24 02:42:31,325 : INFO : topic #13 (0.020): 0.053*\"fairy tale\" + 0.052*\"road trip\" + 0.039*\"assassination\" + 0.035*\"cold war\" + 0.035*\"historical\" + 0.034*\"sequel\" + 0.033*\"jane austen\" + 0.024*\"kids\" + 0.022*\"notable nudity\" + 0.021*\"swashbuckler\"\n", - "2021-08-24 02:42:31,326 : INFO : topic #46 (0.020): 0.079*\"action\" + 0.065*\"fantasy\" + 0.045*\"sci-fi\" + 0.037*\"adventure\" + 0.032*\"dvd\" + 0.026*\"Adventure\" + 0.022*\"watched 2006\" + 0.022*\"robots\" + 0.019*\"seen at the cinema\" + 0.018*\"space\"\n", - "2021-08-24 02:42:31,327 : INFO : topic #1 (0.020): 0.056*\"overrated\" + 0.039*\"violence\" + 0.033*\"revenge\" + 0.033*\"quentin tarantino\" + 0.032*\"violent\" + 0.032*\"imdb top 250\" + 0.028*\"crime\" + 0.025*\"owned\" + 0.020*\"drama\" + 0.019*\"tarantino\"\n", - "2021-08-24 02:42:31,328 : INFO : topic diff=0.096439, rho=0.240143\n", - "2021-08-24 02:42:31,329 : INFO : PROGRESS: pass 12, at document #2000/10681\n", - "2021-08-24 02:42:31,602 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:31,628 : INFO : topic #18 (0.020): 0.082*\"gay\" + 0.074*\"magic\" + 0.059*\"pg13\" + 0.053*\"racism\" + 0.039*\"Drama\" + 0.028*\"food\" + 0.024*\"social commentary\" + 0.021*\"homosexuality\" + 0.020*\"denzel washington\" + 0.020*\"aids\"\n", - "2021-08-24 02:42:31,629 : INFO : topic #6 (0.020): 0.112*\"Musical\" + 0.111*\"politics\" + 0.069*\"satire\" + 0.046*\"nicolas cage\" + 0.035*\"sean connery\" + 0.034*\"terrorism\" + 0.033*\"dvd-r\" + 0.025*\"political\" + 0.025*\"good dialogue\" + 0.024*\"police\"\n", - "2021-08-24 02:42:31,630 : INFO : topic #39 (0.020): 0.194*\"time travel\" + 0.050*\"adultery\" + 0.040*\"post apocalyptic\" + 0.034*\"motorcycle\" + 0.032*\"post-apocalyptic\" + 0.028*\"jude law\" + 0.022*\"dystopia\" + 0.021*\"bechdel test:fail\" + 0.020*\"want to own\" + 0.020*\"hollywood\"\n", - "2021-08-24 02:42:31,631 : INFO : topic #49 (0.020): 0.081*\"national film registry\" + 0.058*\"imdb top 250\" + 0.042*\"black and white\" + 0.033*\"afi 100\" + 0.032*\"afi 100 (thrills)\" + 0.030*\"tumey's dvds\" + 0.025*\"usa film registry\" + 0.020*\"erlend's dvds\" + 0.019*\"paris\" + 0.019*\"afi 100 (movie quotes)\"\n", - "2021-08-24 02:42:31,632 : INFO : topic #12 (0.020): 0.138*\"comic book\" + 0.108*\"superhero\" + 0.073*\"dystopia\" + 0.054*\"holocaust\" + 0.052*\"super-hero\" + 0.032*\"batman\" + 0.032*\"adapted from:comic\" + 0.024*\"kidnapping\" + 0.016*\"Action\" + 0.016*\"alter ego\"\n", - "2021-08-24 02:42:31,633 : INFO : topic diff=0.181122, rho=0.233504\n", - "2021-08-24 02:42:31,635 : INFO : PROGRESS: pass 12, at document #4000/10681\n", - "2021-08-24 02:42:31,875 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:31,902 : INFO : topic #45 (0.020): 0.188*\"less than 300 ratings\" + 0.107*\"Drama\" + 0.081*\"nudity (topless - notable)\" + 0.061*\"tim burton\" + 0.030*\"not corv lib\" + 0.023*\"library\" + 0.019*\"depressing\" + 0.015*\"golden raspberry (worst actor)\" + 0.015*\"blindfold\" + 0.013*\"san francisco\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:31,903 : INFO : topic #15 (0.020): 0.166*\"disney\" + 0.091*\"animation\" + 0.076*\"pixar\" + 0.047*\"Animation\" + 0.043*\"children\" + 0.039*\"Children\" + 0.034*\"disney animated feature\" + 0.025*\"angelina jolie\" + 0.024*\"pirates\" + 0.020*\"cartoon\"\n", - "2021-08-24 02:42:31,904 : INFO : topic #33 (0.020): 0.112*\"comedy\" + 0.108*\"Comedy\" + 0.056*\"funny\" + 0.036*\"parody\" + 0.036*\"dark comedy\" + 0.033*\"quirky\" + 0.032*\"seen more than once\" + 0.029*\"coen brothers\" + 0.028*\"hilarious\" + 0.022*\"black comedy\"\n", - "2021-08-24 02:42:31,904 : INFO : topic #27 (0.020): 0.309*\"Horror\" + 0.157*\"Thriller\" + 0.127*\"Mystery\" + 0.040*\"Drama\" + 0.029*\"Film-Noir\" + 0.029*\"easily confused with other movie(s) (title)\" + 0.029*\"Fantasy\" + 0.016*\"eerie\" + 0.015*\"franchise\" + 0.015*\"tumey's dvds\"\n", - "2021-08-24 02:42:31,906 : INFO : topic #44 (0.020): 0.109*\"mafia\" + 0.087*\"music\" + 0.053*\"martin scorsese\" + 0.050*\"organized crime\" + 0.042*\"rock and roll\" + 0.031*\"Musical\" + 0.030*\"wired 50 greatest soundtracks\" + 0.023*\"guns\" + 0.020*\"mob\" + 0.020*\"confrontational\"\n", - "2021-08-24 02:42:31,907 : INFO : topic diff=0.033378, rho=0.233504\n", - "2021-08-24 02:42:31,908 : INFO : PROGRESS: pass 12, at document #6000/10681\n", - "2021-08-24 02:42:32,142 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:32,169 : INFO : topic #7 (0.020): 0.111*\"romance\" + 0.059*\"Romance\" + 0.052*\"chick flick\" + 0.039*\"boring\" + 0.032*\"girlie movie\" + 0.029*\"baseball\" + 0.027*\"Comedy\" + 0.023*\"whimsical\" + 0.020*\"comedy\" + 0.018*\"wedding\"\n", - "2021-08-24 02:42:32,169 : INFO : topic #36 (0.020): 0.085*\"cult film\" + 0.047*\"peter jackson\" + 0.044*\"russell crowe\" + 0.038*\"downbeat\" + 0.031*\"adam sandler\" + 0.029*\"1980s\" + 0.022*\"80s\" + 0.021*\"new zealand\" + 0.018*\"not available from netflix\" + 0.013*\"must see!\"\n", - "2021-08-24 02:42:32,170 : INFO : topic #4 (0.020): 0.069*\"sports\" + 0.061*\"Drama\" + 0.048*\"boxing\" + 0.043*\"british\" + 0.041*\"underrated\" + 0.039*\"sven's to see list\" + 0.039*\"hw drama\" + 0.038*\"london\" + 0.026*\"sylvester stallone\" + 0.024*\"college\"\n", - "2021-08-24 02:42:32,171 : INFO : topic #22 (0.020): 0.268*\"betamax\" + 0.102*\"dvd-video\" + 0.069*\"clv\" + 0.030*\"aviation\" + 0.023*\"terry gilliam\" + 0.022*\"library on hold\" + 0.018*\"dvd collection\" + 0.015*\"70mm\" + 0.014*\"gilliam\" + 0.014*\"steven seagal\"\n", - "2021-08-24 02:42:32,172 : INFO : topic #27 (0.020): 0.321*\"Horror\" + 0.155*\"Thriller\" + 0.126*\"Mystery\" + 0.039*\"Drama\" + 0.028*\"Fantasy\" + 0.027*\"easily confused with other movie(s) (title)\" + 0.027*\"Film-Noir\" + 0.017*\"eerie\" + 0.015*\"slasher\" + 0.015*\"franchise\"\n", - "2021-08-24 02:42:32,173 : INFO : topic diff=0.064371, rho=0.233504\n", - "2021-08-24 02:42:32,175 : INFO : PROGRESS: pass 12, at document #8000/10681\n", - "2021-08-24 02:42:32,398 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:32,425 : INFO : topic #5 (0.020): 0.269*\"classic\" + 0.060*\"musical\" + 0.046*\"afi 100 (laughs)\" + 0.035*\"Musical\" + 0.031*\"national film registry\" + 0.018*\"afi 100\" + 0.017*\"adapted from b'way\" + 0.016*\"john travolta\" + 0.015*\"70mm\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:32,426 : INFO : topic #42 (0.020): 0.100*\"japan\" + 0.093*\"martial arts\" + 0.073*\"robin williams\" + 0.044*\"akira kurosawa\" + 0.034*\"samurai\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"christopher walken\" + 0.021*\"hugh grant\"\n", - "2021-08-24 02:42:32,427 : INFO : topic #45 (0.020): 0.234*\"less than 300 ratings\" + 0.117*\"Drama\" + 0.074*\"nudity (topless - notable)\" + 0.041*\"tim burton\" + 0.028*\"not corv lib\" + 0.025*\"library\" + 0.016*\"michael moore\" + 0.016*\"blindfold\" + 0.015*\"depressing\" + 0.012*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:32,428 : INFO : topic #37 (0.020): 0.189*\"War\" + 0.110*\"world war ii\" + 0.109*\"Drama\" + 0.055*\"war\" + 0.048*\"history\" + 0.037*\"Action\" + 0.036*\"jim carrey\" + 0.017*\"nazis\" + 0.015*\"mental illness\" + 0.015*\"70mm\"\n", - "2021-08-24 02:42:32,429 : INFO : topic #8 (0.020): 0.145*\"anime\" + 0.091*\"tom hanks\" + 0.077*\"true story\" + 0.055*\"based on a true story\" + 0.026*\"drama\" + 0.026*\"good\" + 0.026*\"japan\" + 0.023*\"interesting\" + 0.022*\"not funny\" + 0.017*\"archaeology\"\n", - "2021-08-24 02:42:32,430 : INFO : topic diff=0.051567, rho=0.233504\n", - "2021-08-24 02:42:32,431 : INFO : PROGRESS: pass 12, at document #10000/10681\n", - "2021-08-24 02:42:32,673 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:32,701 : INFO : topic #14 (0.020): 0.060*\"criterion\" + 0.055*\"disturbing\" + 0.052*\"Drama\" + 0.051*\"tense\" + 0.047*\"atmospheric\" + 0.031*\"stylized\" + 0.031*\"tumey's dvds\" + 0.030*\"bleak\" + 0.024*\"erlend's dvds\" + 0.024*\"menacing\"\n", - "2021-08-24 02:42:32,702 : INFO : topic #7 (0.020): 0.107*\"romance\" + 0.060*\"Romance\" + 0.044*\"chick flick\" + 0.041*\"boring\" + 0.026*\"girlie movie\" + 0.025*\"Comedy\" + 0.024*\"baseball\" + 0.024*\"love story\" + 0.024*\"whimsical\" + 0.019*\"wedding\"\n", - "2021-08-24 02:42:32,703 : INFO : topic #6 (0.020): 0.134*\"Musical\" + 0.104*\"politics\" + 0.062*\"satire\" + 0.041*\"nicolas cage\" + 0.040*\"terrorism\" + 0.039*\"dvd-r\" + 0.031*\"sean connery\" + 0.029*\"political\" + 0.028*\"netwatch\" + 0.025*\"police\"\n", - "2021-08-24 02:42:32,704 : INFO : topic #22 (0.020): 0.230*\"betamax\" + 0.100*\"dvd-video\" + 0.057*\"clv\" + 0.034*\"aviation\" + 0.023*\"library on hold\" + 0.020*\"terry gilliam\" + 0.019*\"seen 2008\" + 0.016*\"family drama\" + 0.015*\"surveillance\" + 0.015*\"gilliam\"\n", - "2021-08-24 02:42:32,705 : INFO : topic #44 (0.020): 0.081*\"mafia\" + 0.076*\"music\" + 0.059*\"martin scorsese\" + 0.048*\"rock and roll\" + 0.038*\"Musical\" + 0.036*\"organized crime\" + 0.030*\"wired 50 greatest soundtracks\" + 0.027*\"guns\" + 0.024*\"confrontational\" + 0.022*\"dysfunctional family\"\n", - "2021-08-24 02:42:32,706 : INFO : topic diff=0.043541, rho=0.233504\n", - "2021-08-24 02:42:32,850 : INFO : -20.970 per-word bound, 2054162.9 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:32,851 : INFO : PROGRESS: pass 12, at document #10681/10681\n", - "2021-08-24 02:42:32,944 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:32,970 : INFO : topic #29 (0.020): 0.097*\"nudity (full frontal - notable)\" + 0.056*\"Drama\" + 0.051*\"christianity\" + 0.042*\"based on a play\" + 0.041*\"shakespeare\" + 0.038*\"religion\" + 0.032*\"adapted from:play\" + 0.027*\"disability\" + 0.023*\"to see\" + 0.023*\"cannibalism\"\n", - "2021-08-24 02:42:32,971 : INFO : topic #23 (0.020): 0.084*\"high school\" + 0.063*\"cars\" + 0.062*\"teen\" + 0.037*\"marx brothers\" + 0.033*\"dance\" + 0.025*\"Comedy\" + 0.021*\"kate winslet\" + 0.020*\"eddie murphy\" + 0.019*\"no\" + 0.018*\"(s)vcd\"\n", - "2021-08-24 02:42:32,971 : INFO : topic #49 (0.020): 0.089*\"national film registry\" + 0.047*\"imdb top 250\" + 0.041*\"black and white\" + 0.030*\"tumey's dvds\" + 0.028*\"paris\" + 0.026*\"france\" + 0.022*\"natalie portman\" + 0.020*\"afi 100 (thrills)\" + 0.019*\"erlend's dvds\" + 0.018*\"afi 100\"\n", - "2021-08-24 02:42:32,972 : INFO : topic #17 (0.020): 0.365*\"Sci-Fi\" + 0.075*\"Horror\" + 0.073*\"Action\" + 0.041*\"video game adaptation\" + 0.032*\"movie to see\" + 0.024*\"football\" + 0.021*\"animals\" + 0.016*\"futuristic\" + 0.015*\"milla jovovich\" + 0.015*\"g\"\n", - "2021-08-24 02:42:32,973 : INFO : topic #18 (0.020): 0.093*\"magic\" + 0.084*\"pg13\" + 0.072*\"gay\" + 0.050*\"racism\" + 0.039*\"Drama\" + 0.026*\"food\" + 0.025*\"social commentary\" + 0.022*\"homosexuality\" + 0.021*\"19th century\" + 0.021*\"denzel washington\"\n", - "2021-08-24 02:42:32,974 : INFO : topic diff=0.093856, rho=0.233504\n", - "2021-08-24 02:42:32,976 : INFO : PROGRESS: pass 13, at document #2000/10681\n", - "2021-08-24 02:42:33,242 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:33,269 : INFO : topic #43 (0.020): 0.069*\"surreal\" + 0.051*\"stanley kubrick\" + 0.038*\"satirical\" + 0.035*\"cynical\" + 0.033*\"narrated\" + 0.032*\"biting\" + 0.031*\"dreamlike\" + 0.030*\"irreverent\" + 0.027*\"quirky\" + 0.023*\"hallucinatory\"\n", - "2021-08-24 02:42:33,270 : INFO : topic #0 (0.020): 0.206*\"can't remember\" + 0.100*\"based on a tv show\" + 0.093*\"directorial debut\" + 0.071*\"Comedy\" + 0.038*\"ummarti2006\" + 0.033*\"keira knightley\" + 0.028*\"australia\" + 0.024*\"australian\" + 0.019*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:33,270 : INFO : topic #24 (0.020): 0.084*\"hitchcock\" + 0.079*\"alfred hitchcock\" + 0.073*\"oppl\" + 0.067*\"murder\" + 0.062*\"vampire\" + 0.047*\"vampires\" + 0.036*\"memory\" + 0.023*\"incest\" + 0.021*\"cary grant\" + 0.017*\"kirsten dunst\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:33,271 : INFO : topic #42 (0.020): 0.084*\"robin williams\" + 0.077*\"japan\" + 0.067*\"martial arts\" + 0.037*\"samurai\" + 0.035*\"akira kurosawa\" + 0.033*\"orson welles\" + 0.032*\"divorce\" + 0.030*\"Drama\" + 0.025*\"kurosawa\" + 0.022*\"jonossa\"\n", - "2021-08-24 02:42:33,272 : INFO : topic #30 (0.020): 0.236*\"Adventure\" + 0.129*\"Children\" + 0.112*\"Comedy\" + 0.106*\"Fantasy\" + 0.078*\"Action\" + 0.049*\"Animation\" + 0.046*\"Drama\" + 0.044*\"70mm\" + 0.020*\"Musical\" + 0.012*\"submarine\"\n", - "2021-08-24 02:42:33,273 : INFO : topic diff=0.175800, rho=0.227387\n", - "2021-08-24 02:42:33,275 : INFO : PROGRESS: pass 13, at document #4000/10681\n", - "2021-08-24 02:42:33,527 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:33,555 : INFO : topic #4 (0.020): 0.072*\"sports\" + 0.062*\"Drama\" + 0.055*\"boxing\" + 0.045*\"underrated\" + 0.045*\"british\" + 0.039*\"london\" + 0.034*\"sven's to see list\" + 0.033*\"hw drama\" + 0.030*\"sylvester stallone\" + 0.028*\"inspirational\"\n", - "2021-08-24 02:42:33,555 : INFO : topic #49 (0.020): 0.083*\"national film registry\" + 0.061*\"imdb top 250\" + 0.044*\"black and white\" + 0.034*\"tumey's dvds\" + 0.033*\"afi 100 (thrills)\" + 0.032*\"afi 100\" + 0.024*\"usa film registry\" + 0.024*\"erlend's dvds\" + 0.022*\"afi 100 (movie quotes)\" + 0.019*\"emerson must see\"\n", - "2021-08-24 02:42:33,556 : INFO : topic #43 (0.020): 0.079*\"surreal\" + 0.045*\"stanley kubrick\" + 0.041*\"satirical\" + 0.037*\"cynical\" + 0.037*\"narrated\" + 0.031*\"biting\" + 0.030*\"dreamlike\" + 0.027*\"irreverent\" + 0.027*\"quirky\" + 0.026*\"surrealism\"\n", - "2021-08-24 02:42:33,557 : INFO : topic #27 (0.020): 0.309*\"Horror\" + 0.158*\"Thriller\" + 0.127*\"Mystery\" + 0.040*\"Drama\" + 0.029*\"Film-Noir\" + 0.029*\"easily confused with other movie(s) (title)\" + 0.029*\"Fantasy\" + 0.015*\"franchise\" + 0.015*\"tumey's dvds\" + 0.015*\"eerie\"\n", - "2021-08-24 02:42:33,558 : INFO : topic #19 (0.020): 0.064*\"nudity (full frontal)\" + 0.063*\"drama\" + 0.036*\"Drama\" + 0.028*\"vietnam\" + 0.028*\"vietnam war\" + 0.028*\"philip k. dick\" + 0.027*\"rape\" + 0.022*\"to see\" + 0.021*\"very good\" + 0.016*\"slow\"\n", - "2021-08-24 02:42:33,559 : INFO : topic diff=0.032253, rho=0.227387\n", - "2021-08-24 02:42:33,561 : INFO : PROGRESS: pass 13, at document #6000/10681\n", - "2021-08-24 02:42:33,801 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:33,828 : INFO : topic #1 (0.020): 0.049*\"overrated\" + 0.042*\"violence\" + 0.035*\"crime\" + 0.031*\"quentin tarantino\" + 0.026*\"nonlinear\" + 0.026*\"imdb top 250\" + 0.025*\"owned\" + 0.024*\"drama\" + 0.023*\"violent\" + 0.023*\"revenge\"\n", - "2021-08-24 02:42:33,829 : INFO : topic #16 (0.020): 0.286*\"nudity (topless)\" + 0.193*\"nudity (topless - brief)\" + 0.064*\"nudity (rear)\" + 0.029*\"claymation\" + 0.027*\"pg\" + 0.021*\"military\" + 0.019*\"aardman\" + 0.017*\"childhood\" + 0.016*\"weird\" + 0.010*\"shark\"\n", - "2021-08-24 02:42:33,830 : INFO : topic #13 (0.020): 0.060*\"fairy tale\" + 0.037*\"jane austen\" + 0.037*\"sequel\" + 0.035*\"road trip\" + 0.027*\"gerard depardieu\" + 0.027*\"historical\" + 0.027*\"cold war\" + 0.026*\"assassination\" + 0.025*\"kids\" + 0.022*\"notable nudity\"\n", - "2021-08-24 02:42:33,831 : INFO : topic #21 (0.020): 0.081*\"aliens\" + 0.075*\"Sci-Fi\" + 0.049*\"sci-fi\" + 0.043*\"space\" + 0.039*\"futuristmovies.com\" + 0.027*\"will smith\" + 0.026*\"Action\" + 0.024*\"ridley scott\" + 0.022*\"tommy lee jones\" + 0.019*\"70mm\"\n", - "2021-08-24 02:42:33,832 : INFO : topic #5 (0.020): 0.278*\"classic\" + 0.065*\"musical\" + 0.046*\"afi 100 (laughs)\" + 0.032*\"Musical\" + 0.029*\"national film registry\" + 0.021*\"afi 100\" + 0.018*\"john travolta\" + 0.016*\"adapted from b'way\" + 0.015*\"breakthroughs\" + 0.014*\"70mm\"\n", - "2021-08-24 02:42:33,833 : INFO : topic diff=0.062374, rho=0.227387\n", - "2021-08-24 02:42:33,835 : INFO : PROGRESS: pass 13, at document #8000/10681\n", - "2021-08-24 02:42:34,085 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:34,113 : INFO : topic #23 (0.020): 0.081*\"high school\" + 0.080*\"teen\" + 0.063*\"cars\" + 0.034*\"dance\" + 0.030*\"marx brothers\" + 0.028*\"eddie murphy\" + 0.024*\"(s)vcd\" + 0.023*\"Comedy\" + 0.023*\"car chase\" + 0.019*\"sandra bullock\"\n", - "2021-08-24 02:42:34,113 : INFO : topic #36 (0.020): 0.078*\"cult film\" + 0.050*\"peter jackson\" + 0.043*\"downbeat\" + 0.041*\"russell crowe\" + 0.031*\"adam sandler\" + 0.030*\"not available from netflix\" + 0.025*\"1980s\" + 0.024*\"new zealand\" + 0.021*\"80s\" + 0.016*\"drew barrymore\"\n", - "2021-08-24 02:42:34,114 : INFO : topic #42 (0.020): 0.099*\"japan\" + 0.093*\"martial arts\" + 0.073*\"robin williams\" + 0.043*\"akira kurosawa\" + 0.034*\"samurai\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"hugh grant\" + 0.021*\"christopher walken\"\n", - "2021-08-24 02:42:34,115 : INFO : topic #11 (0.020): 0.143*\"religion\" + 0.111*\"keanu reeves\" + 0.027*\"island\" + 0.020*\"courtesan\" + 0.017*\"slavery\" + 0.016*\"class issues\" + 0.016*\"reality tv\" + 0.015*\"facebook rec\" + 0.013*\"slash\" + 0.012*\"social message\"\n", - "2021-08-24 02:42:34,116 : INFO : topic #33 (0.020): 0.118*\"Comedy\" + 0.107*\"comedy\" + 0.058*\"funny\" + 0.036*\"parody\" + 0.035*\"seen more than once\" + 0.035*\"quirky\" + 0.028*\"dark comedy\" + 0.027*\"hilarious\" + 0.023*\"coen brothers\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:34,117 : INFO : topic diff=0.050171, rho=0.227387\n", - "2021-08-24 02:42:34,118 : INFO : PROGRESS: pass 13, at document #10000/10681\n", - "2021-08-24 02:42:34,353 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:34,381 : INFO : topic #18 (0.020): 0.102*\"magic\" + 0.077*\"gay\" + 0.060*\"pg13\" + 0.048*\"racism\" + 0.038*\"Drama\" + 0.027*\"homosexuality\" + 0.025*\"social commentary\" + 0.024*\"food\" + 0.023*\"19th century\" + 0.017*\"denzel washington\"\n", - "2021-08-24 02:42:34,382 : INFO : topic #20 (0.020): 0.095*\"biography\" + 0.067*\"Drama\" + 0.055*\"biopic\" + 0.046*\"corvallis library\" + 0.043*\"death\" + 0.037*\"suicide\" + 0.034*\"ensemble cast\" + 0.027*\"ingmar bergman\" + 0.027*\"julia roberts\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:34,383 : INFO : topic #26 (0.020): 0.046*\"Drama\" + 0.033*\"james bond\" + 0.021*\"reflective\" + 0.021*\"poignant\" + 0.020*\"atmospheric\" + 0.020*\"007\" + 0.019*\"bittersweet\" + 0.019*\"lyrical\" + 0.018*\"deliberate\" + 0.018*\"bond\"\n", - "2021-08-24 02:42:34,383 : INFO : topic #19 (0.020): 0.063*\"nudity (full frontal)\" + 0.053*\"drama\" + 0.035*\"Drama\" + 0.034*\"philip k. dick\" + 0.026*\"ummarti2007\" + 0.024*\"slow\" + 0.023*\"rape\" + 0.022*\"male nudity\" + 0.021*\"very good\" + 0.021*\"vietnam war\"\n", - "2021-08-24 02:42:34,384 : INFO : topic #10 (0.020): 0.247*\"Thriller\" + 0.238*\"Crime\" + 0.167*\"Action\" + 0.162*\"Drama\" + 0.031*\"Mystery\" + 0.017*\"murder\" + 0.009*\"crime\" + 0.008*\"virtual reality\" + 0.005*\"greg kinnear\" + 0.005*\"thriller\"\n", - "2021-08-24 02:42:34,386 : INFO : topic diff=0.042069, rho=0.227387\n", - "2021-08-24 02:42:34,521 : INFO : -20.969 per-word bound, 2051875.8 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:34,522 : INFO : PROGRESS: pass 13, at document #10681/10681\n", - "2021-08-24 02:42:34,615 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:34,642 : INFO : topic #9 (0.020): 0.273*\"based on a book\" + 0.111*\"adapted from:book\" + 0.061*\"Drama\" + 0.034*\"imdb bottom 100\" + 0.030*\"jack nicholson\" + 0.030*\"based on book\" + 0.028*\"stephen king\" + 0.024*\"los angeles\" + 0.019*\"pregnancy\" + 0.018*\"stupid\"\n", - "2021-08-24 02:42:34,642 : INFO : topic #21 (0.020): 0.076*\"Sci-Fi\" + 0.065*\"aliens\" + 0.041*\"sci-fi\" + 0.040*\"will smith\" + 0.037*\"space\" + 0.030*\"tommy lee jones\" + 0.028*\"futuristmovies.com\" + 0.028*\"Action\" + 0.025*\"ridley scott\" + 0.019*\"monster\"\n", - "2021-08-24 02:42:34,643 : INFO : topic #46 (0.020): 0.080*\"action\" + 0.064*\"fantasy\" + 0.042*\"sci-fi\" + 0.037*\"adventure\" + 0.032*\"dvd\" + 0.026*\"Adventure\" + 0.022*\"watched 2006\" + 0.021*\"robots\" + 0.019*\"seen at the cinema\" + 0.018*\"space\"\n", - "2021-08-24 02:42:34,644 : INFO : topic #31 (0.020): 0.161*\"zombies\" + 0.098*\"pg-13\" + 0.054*\"horror\" + 0.028*\"zombie\" + 0.026*\"infidelity\" + 0.024*\"movie to see\" + 0.023*\"betrayal\" + 0.023*\"books\" + 0.022*\"joaquin phoenix\" + 0.021*\"cult classic\"\n", - "2021-08-24 02:42:34,645 : INFO : topic #48 (0.020): 0.131*\"netflix\" + 0.078*\"twist ending\" + 0.067*\"bruce willis\" + 0.045*\"coming of age\" + 0.035*\"journalism\" + 0.035*\"to see\" + 0.032*\"Drama\" + 0.026*\"talking animals\" + 0.024*\"relationships\" + 0.020*\"father-son relationship\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:34,646 : INFO : topic diff=0.091201, rho=0.227387\n", - "2021-08-24 02:42:34,647 : INFO : PROGRESS: pass 14, at document #2000/10681\n", - "2021-08-24 02:42:34,905 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:34,932 : INFO : topic #22 (0.020): 0.172*\"betamax\" + 0.091*\"dvd-video\" + 0.082*\"clv\" + 0.038*\"library on hold\" + 0.034*\"aviation\" + 0.026*\"terry gilliam\" + 0.019*\"dvd collection\" + 0.017*\"steven seagal\" + 0.016*\"gilliam\" + 0.016*\"family drama\"\n", - "2021-08-24 02:42:34,933 : INFO : topic #40 (0.020): 0.270*\"r\" + 0.138*\"clearplay\" + 0.064*\"movie to see\" + 0.056*\"prison\" + 0.044*\"Drama\" + 0.035*\"morgan freeman\" + 0.028*\"friendship\" + 0.021*\"conspiracy\" + 0.015*\"1970s\" + 0.012*\"mel brooks\"\n", - "2021-08-24 02:42:34,934 : INFO : topic #9 (0.020): 0.264*\"based on a book\" + 0.112*\"adapted from:book\" + 0.053*\"Drama\" + 0.042*\"stephen king\" + 0.042*\"jack nicholson\" + 0.035*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.022*\"stupid\" + 0.021*\"los angeles\"\n", - "2021-08-24 02:42:34,934 : INFO : topic #14 (0.020): 0.053*\"criterion\" + 0.052*\"tense\" + 0.052*\"disturbing\" + 0.048*\"atmospheric\" + 0.046*\"Drama\" + 0.030*\"stylized\" + 0.030*\"tumey's dvds\" + 0.029*\"bleak\" + 0.025*\"visceral\" + 0.024*\"menacing\"\n", - "2021-08-24 02:42:34,935 : INFO : topic #4 (0.020): 0.059*\"sports\" + 0.058*\"Drama\" + 0.048*\"london\" + 0.046*\"boxing\" + 0.045*\"underrated\" + 0.044*\"british\" + 0.037*\"sven's to see list\" + 0.034*\"hw drama\" + 0.032*\"inspirational\" + 0.030*\"notable soundtrack\"\n", - "2021-08-24 02:42:34,936 : INFO : topic diff=0.170525, rho=0.221727\n", - "2021-08-24 02:42:34,937 : INFO : PROGRESS: pass 14, at document #4000/10681\n", - "2021-08-24 02:42:35,163 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:35,191 : INFO : topic #20 (0.020): 0.098*\"biography\" + 0.066*\"Drama\" + 0.048*\"death\" + 0.047*\"biopic\" + 0.039*\"suicide\" + 0.038*\"julia roberts\" + 0.035*\"ensemble cast\" + 0.034*\"corvallis library\" + 0.029*\"medieval\" + 0.025*\"multiple storylines\"\n", - "2021-08-24 02:42:35,191 : INFO : topic #24 (0.020): 0.096*\"hitchcock\" + 0.076*\"murder\" + 0.073*\"alfred hitchcock\" + 0.067*\"vampire\" + 0.059*\"oppl\" + 0.050*\"vampires\" + 0.034*\"memory\" + 0.021*\"incest\" + 0.018*\"cary grant\" + 0.015*\"kirsten dunst\"\n", - "2021-08-24 02:42:35,192 : INFO : topic #16 (0.020): 0.293*\"nudity (topless)\" + 0.197*\"nudity (topless - brief)\" + 0.062*\"nudity (rear)\" + 0.033*\"claymation\" + 0.023*\"pg\" + 0.022*\"military\" + 0.022*\"aardman\" + 0.018*\"childhood\" + 0.013*\"weird\" + 0.011*\"freedom\"\n", - "2021-08-24 02:42:35,193 : INFO : topic #10 (0.020): 0.254*\"Thriller\" + 0.225*\"Crime\" + 0.166*\"Action\" + 0.159*\"Drama\" + 0.026*\"Mystery\" + 0.017*\"murder\" + 0.012*\"virtual reality\" + 0.012*\"crime\" + 0.007*\"thriller\" + 0.005*\"greg kinnear\"\n", - "2021-08-24 02:42:35,194 : INFO : topic #31 (0.020): 0.122*\"zombies\" + 0.074*\"horror\" + 0.070*\"pg-13\" + 0.042*\"cult classic\" + 0.026*\"campy\" + 0.026*\"infidelity\" + 0.024*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.022*\"zombie\" + 0.020*\"books\"\n", - "2021-08-24 02:42:35,195 : INFO : topic diff=0.031046, rho=0.221727\n", - "2021-08-24 02:42:35,197 : INFO : PROGRESS: pass 14, at document #6000/10681\n", - "2021-08-24 02:42:35,436 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:35,463 : INFO : topic #33 (0.020): 0.114*\"Comedy\" + 0.113*\"comedy\" + 0.057*\"funny\" + 0.038*\"parody\" + 0.036*\"quirky\" + 0.034*\"seen more than once\" + 0.032*\"dark comedy\" + 0.028*\"hilarious\" + 0.026*\"coen brothers\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:35,464 : INFO : topic #46 (0.020): 0.087*\"action\" + 0.064*\"fantasy\" + 0.046*\"sci-fi\" + 0.036*\"adventure\" + 0.033*\"dvd\" + 0.023*\"Adventure\" + 0.023*\"seen at the cinema\" + 0.022*\"seen more than once\" + 0.019*\"tom cruise\" + 0.018*\"space\"\n", - "2021-08-24 02:42:35,465 : INFO : topic #37 (0.020): 0.182*\"War\" + 0.113*\"world war ii\" + 0.100*\"Drama\" + 0.058*\"war\" + 0.042*\"history\" + 0.035*\"Action\" + 0.032*\"jim carrey\" + 0.020*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:42:35,465 : INFO : topic #26 (0.020): 0.041*\"Drama\" + 0.035*\"james bond\" + 0.024*\"007\" + 0.024*\"bond\" + 0.022*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.019*\"poignant\" + 0.018*\"lyrical\" + 0.017*\"bittersweet\"\n", - "2021-08-24 02:42:35,466 : INFO : topic #7 (0.020): 0.111*\"romance\" + 0.060*\"Romance\" + 0.052*\"chick flick\" + 0.038*\"boring\" + 0.032*\"girlie movie\" + 0.029*\"baseball\" + 0.026*\"Comedy\" + 0.023*\"whimsical\" + 0.018*\"comedy\" + 0.018*\"wedding\"\n", - "2021-08-24 02:42:35,468 : INFO : topic diff=0.060258, rho=0.221727\n", - "2021-08-24 02:42:35,470 : INFO : PROGRESS: pass 14, at document #8000/10681\n", - "2021-08-24 02:42:35,715 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:35,743 : INFO : topic #42 (0.020): 0.099*\"japan\" + 0.092*\"martial arts\" + 0.074*\"robin williams\" + 0.043*\"akira kurosawa\" + 0.034*\"samurai\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"hugh grant\" + 0.021*\"christopher walken\"\n", - "2021-08-24 02:42:35,744 : INFO : topic #48 (0.020): 0.089*\"bruce willis\" + 0.080*\"twist ending\" + 0.061*\"netflix\" + 0.047*\"coming of age\" + 0.033*\"journalism\" + 0.031*\"sexy\" + 0.026*\"Drama\" + 0.025*\"avi\" + 0.022*\"talking animals\" + 0.021*\"to see\"\n", - "2021-08-24 02:42:35,745 : INFO : topic #10 (0.020): 0.247*\"Thriller\" + 0.238*\"Crime\" + 0.166*\"Action\" + 0.160*\"Drama\" + 0.030*\"Mystery\" + 0.015*\"murder\" + 0.011*\"virtual reality\" + 0.010*\"crime\" + 0.005*\"thriller\" + 0.004*\"well done\"\n", - "2021-08-24 02:42:35,745 : INFO : topic #46 (0.020): 0.088*\"action\" + 0.064*\"fantasy\" + 0.048*\"sci-fi\" + 0.037*\"adventure\" + 0.034*\"dvd\" + 0.025*\"Adventure\" + 0.023*\"seen more than once\" + 0.021*\"seen at the cinema\" + 0.018*\"tom cruise\" + 0.017*\"Action\"\n", - "2021-08-24 02:42:35,746 : INFO : topic #17 (0.020): 0.356*\"Sci-Fi\" + 0.084*\"Horror\" + 0.065*\"Action\" + 0.036*\"video game adaptation\" + 0.025*\"football\" + 0.018*\"animals\" + 0.018*\"futuristic\" + 0.016*\"movie to see\" + 0.014*\"g\" + 0.012*\"milla jovovich\"\n", - "2021-08-24 02:42:35,747 : INFO : topic diff=0.049040, rho=0.221727\n", - "2021-08-24 02:42:35,749 : INFO : PROGRESS: pass 14, at document #10000/10681\n", - "2021-08-24 02:42:35,996 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:36,024 : INFO : topic #31 (0.020): 0.153*\"zombies\" + 0.102*\"pg-13\" + 0.060*\"horror\" + 0.034*\"zombie\" + 0.027*\"joaquin phoenix\" + 0.025*\"cult classic\" + 0.024*\"infidelity\" + 0.022*\"sam raimi\" + 0.021*\"campy\" + 0.020*\"books\"\n", - "2021-08-24 02:42:36,025 : INFO : topic #8 (0.020): 0.154*\"anime\" + 0.081*\"true story\" + 0.074*\"tom hanks\" + 0.058*\"based on a true story\" + 0.028*\"to-rent\" + 0.028*\"interesting\" + 0.026*\"japan\" + 0.026*\"not funny\" + 0.024*\"good\" + 0.021*\"drama\"\n", - "2021-08-24 02:42:36,025 : INFO : topic #42 (0.020): 0.101*\"japan\" + 0.091*\"martial arts\" + 0.066*\"robin williams\" + 0.036*\"akira kurosawa\" + 0.033*\"samurai\" + 0.032*\"seen 2006\" + 0.031*\"Drama\" + 0.031*\"jonossa\" + 0.025*\"orson welles\" + 0.022*\"hugh grant\"\n", - "2021-08-24 02:42:36,026 : INFO : topic #39 (0.020): 0.171*\"time travel\" + 0.057*\"adultery\" + 0.050*\"motorcycle\" + 0.038*\"jude law\" + 0.034*\"post apocalyptic\" + 0.034*\"post-apocalyptic\" + 0.021*\"want to own\" + 0.021*\"dystopia\" + 0.020*\"old\" + 0.017*\"hollywood\"\n", - "2021-08-24 02:42:36,027 : INFO : topic #21 (0.020): 0.077*\"Sci-Fi\" + 0.068*\"aliens\" + 0.050*\"sci-fi\" + 0.039*\"space\" + 0.034*\"futuristmovies.com\" + 0.024*\"will smith\" + 0.024*\"Action\" + 0.022*\"tommy lee jones\" + 0.020*\"ridley scott\" + 0.018*\"michael crichton\"\n", - "2021-08-24 02:42:36,030 : INFO : topic diff=0.040909, rho=0.221727\n", - "2021-08-24 02:42:36,189 : INFO : -20.968 per-word bound, 2050907.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:36,189 : INFO : PROGRESS: pass 14, at document #10681/10681\n", - "2021-08-24 02:42:36,325 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:36,374 : INFO : topic #44 (0.020): 0.078*\"mafia\" + 0.076*\"music\" + 0.053*\"organized crime\" + 0.049*\"martin scorsese\" + 0.046*\"rock and roll\" + 0.042*\"Musical\" + 0.025*\"wired 50 greatest soundtracks\" + 0.024*\"guns\" + 0.020*\"confrontational\" + 0.018*\"dysfunctional family\"\n", - "2021-08-24 02:42:36,375 : INFO : topic #19 (0.020): 0.084*\"nudity (full frontal)\" + 0.045*\"drama\" + 0.039*\"Drama\" + 0.034*\"to see\" + 0.029*\"philip k. dick\" + 0.027*\"rape\" + 0.025*\"ummarti2007\" + 0.022*\"male nudity\" + 0.020*\"slow\" + 0.018*\"very good\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:36,377 : INFO : topic #43 (0.020): 0.077*\"surreal\" + 0.040*\"narrated\" + 0.038*\"satirical\" + 0.032*\"cynical\" + 0.032*\"irreverent\" + 0.031*\"quirky\" + 0.030*\"dreamlike\" + 0.027*\"biting\" + 0.023*\"hallucinatory\" + 0.021*\"stanley kubrick\"\n", - "2021-08-24 02:42:36,378 : INFO : topic #35 (0.020): 0.355*\"Comedy\" + 0.314*\"Drama\" + 0.201*\"Romance\" + 0.013*\"lesbian\" + 0.008*\"sean penn\" + 0.008*\"bibliothek\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.005*\"oscar (best foreign language film)\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:42:36,381 : INFO : topic #31 (0.020): 0.160*\"zombies\" + 0.097*\"pg-13\" + 0.056*\"horror\" + 0.028*\"zombie\" + 0.026*\"infidelity\" + 0.024*\"movie to see\" + 0.023*\"betrayal\" + 0.022*\"books\" + 0.022*\"joaquin phoenix\" + 0.021*\"cult classic\"\n", - "2021-08-24 02:42:36,384 : INFO : topic diff=0.089153, rho=0.221727\n", - "2021-08-24 02:42:36,386 : INFO : PROGRESS: pass 15, at document #2000/10681\n", - "2021-08-24 02:42:36,688 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:36,715 : INFO : topic #24 (0.020): 0.084*\"hitchcock\" + 0.078*\"alfred hitchcock\" + 0.073*\"oppl\" + 0.067*\"murder\" + 0.063*\"vampire\" + 0.047*\"vampires\" + 0.036*\"memory\" + 0.023*\"incest\" + 0.021*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:36,716 : INFO : topic #48 (0.020): 0.100*\"bruce willis\" + 0.097*\"netflix\" + 0.081*\"twist ending\" + 0.038*\"coming of age\" + 0.032*\"journalism\" + 0.030*\"Drama\" + 0.029*\"to see\" + 0.025*\"talking animals\" + 0.021*\"relationships\" + 0.020*\"sexy\"\n", - "2021-08-24 02:42:36,716 : INFO : topic #29 (0.020): 0.091*\"nudity (full frontal - notable)\" + 0.064*\"shakespeare\" + 0.051*\"Drama\" + 0.043*\"based on a play\" + 0.035*\"christianity\" + 0.035*\"adapted from:play\" + 0.031*\"religion\" + 0.024*\"cannibalism\" + 0.024*\"jodie foster\" + 0.024*\"president\"\n", - "2021-08-24 02:42:36,717 : INFO : topic #36 (0.020): 0.085*\"cult film\" + 0.042*\"russell crowe\" + 0.035*\"peter jackson\" + 0.034*\"downbeat\" + 0.032*\"adam sandler\" + 0.027*\"1980s\" + 0.024*\"new zealand\" + 0.023*\"not available from netflix\" + 0.020*\"virus\" + 0.017*\"must see!\"\n", - "2021-08-24 02:42:36,717 : INFO : topic #49 (0.020): 0.081*\"national film registry\" + 0.059*\"imdb top 250\" + 0.043*\"black and white\" + 0.032*\"afi 100 (thrills)\" + 0.031*\"afi 100\" + 0.030*\"tumey's dvds\" + 0.025*\"usa film registry\" + 0.021*\"erlend's dvds\" + 0.019*\"paris\" + 0.019*\"france\"\n", - "2021-08-24 02:42:36,719 : INFO : topic diff=0.166177, rho=0.216470\n", - "2021-08-24 02:42:36,720 : INFO : PROGRESS: pass 15, at document #4000/10681\n", - "2021-08-24 02:42:36,968 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:36,995 : INFO : topic #1 (0.020): 0.049*\"overrated\" + 0.042*\"violence\" + 0.038*\"quentin tarantino\" + 0.033*\"crime\" + 0.028*\"imdb top 250\" + 0.026*\"owned\" + 0.026*\"drama\" + 0.025*\"tarantino\" + 0.024*\"violent\" + 0.022*\"revenge\"\n", - "2021-08-24 02:42:36,996 : INFO : topic #22 (0.020): 0.228*\"betamax\" + 0.099*\"dvd-video\" + 0.075*\"clv\" + 0.037*\"aviation\" + 0.028*\"library on hold\" + 0.023*\"terry gilliam\" + 0.019*\"dvd collection\" + 0.016*\"gilliam\" + 0.013*\"70mm\" + 0.013*\"seen 2008\"\n", - "2021-08-24 02:42:36,996 : INFO : topic #42 (0.020): 0.094*\"robin williams\" + 0.089*\"martial arts\" + 0.069*\"japan\" + 0.040*\"samurai\" + 0.036*\"akira kurosawa\" + 0.029*\"Drama\" + 0.027*\"orson welles\" + 0.025*\"divorce\" + 0.022*\"kurosawa\" + 0.020*\"christopher walken\"\n", - "2021-08-24 02:42:36,997 : INFO : topic #48 (0.020): 0.105*\"bruce willis\" + 0.085*\"twist ending\" + 0.079*\"netflix\" + 0.049*\"coming of age\" + 0.033*\"journalism\" + 0.028*\"Drama\" + 0.026*\"talking animals\" + 0.026*\"sexy\" + 0.024*\"to see\" + 0.019*\"relationships\"\n", - "2021-08-24 02:42:36,998 : INFO : topic #39 (0.020): 0.179*\"time travel\" + 0.057*\"adultery\" + 0.046*\"post apocalyptic\" + 0.037*\"motorcycle\" + 0.035*\"post-apocalyptic\" + 0.030*\"jude law\" + 0.024*\"dystopia\" + 0.019*\"want to own\" + 0.019*\"hollywood\" + 0.018*\"bechdel test:fail\"\n", - "2021-08-24 02:42:37,000 : INFO : topic diff=0.030138, rho=0.216470\n", - "2021-08-24 02:42:37,001 : INFO : PROGRESS: pass 15, at document #6000/10681\n", - "2021-08-24 02:42:37,216 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:37,243 : INFO : topic #11 (0.020): 0.129*\"religion\" + 0.115*\"keanu reeves\" + 0.032*\"island\" + 0.021*\"slavery\" + 0.018*\"courtesan\" + 0.018*\"reality tv\" + 0.017*\"facebook rec\" + 0.016*\"slash\" + 0.016*\"class issues\" + 0.015*\"product placement\"\n", - "2021-08-24 02:42:37,244 : INFO : topic #41 (0.020): 0.115*\"remake\" + 0.067*\"christmas\" + 0.063*\"new york city\" + 0.044*\"family\" + 0.041*\"bibliothek\" + 0.036*\"new york\" + 0.033*\"kevin spacey\" + 0.032*\"Drama\" + 0.029*\"eric's dvds\" + 0.027*\"friday night movie\"\n", - "2021-08-24 02:42:37,245 : INFO : topic #33 (0.020): 0.114*\"Comedy\" + 0.114*\"comedy\" + 0.057*\"funny\" + 0.038*\"parody\" + 0.035*\"quirky\" + 0.035*\"seen more than once\" + 0.032*\"dark comedy\" + 0.028*\"hilarious\" + 0.026*\"coen brothers\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:37,246 : INFO : topic #19 (0.020): 0.062*\"drama\" + 0.060*\"nudity (full frontal)\" + 0.035*\"philip k. dick\" + 0.033*\"Drama\" + 0.029*\"vietnam war\" + 0.027*\"rape\" + 0.025*\"vietnam\" + 0.023*\"very good\" + 0.019*\"to see\" + 0.016*\"brazil\"\n", - "2021-08-24 02:42:37,247 : INFO : topic #43 (0.020): 0.082*\"surreal\" + 0.046*\"satirical\" + 0.039*\"cynical\" + 0.036*\"stanley kubrick\" + 0.035*\"narrated\" + 0.035*\"biting\" + 0.032*\"dreamlike\" + 0.031*\"quirky\" + 0.026*\"irreverent\" + 0.025*\"mindfuck\"\n", - "2021-08-24 02:42:37,248 : INFO : topic diff=0.058712, rho=0.216470\n", - "2021-08-24 02:42:37,249 : INFO : PROGRESS: pass 15, at document #8000/10681\n", - "2021-08-24 02:42:37,480 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:37,508 : INFO : topic #43 (0.020): 0.085*\"surreal\" + 0.046*\"satirical\" + 0.040*\"cynical\" + 0.036*\"quirky\" + 0.034*\"dreamlike\" + 0.034*\"biting\" + 0.034*\"narrated\" + 0.029*\"stanley kubrick\" + 0.026*\"irreverent\" + 0.026*\"hallucinatory\"\n", - "2021-08-24 02:42:37,509 : INFO : topic #36 (0.020): 0.078*\"cult film\" + 0.049*\"peter jackson\" + 0.043*\"downbeat\" + 0.041*\"russell crowe\" + 0.031*\"adam sandler\" + 0.030*\"not available from netflix\" + 0.025*\"1980s\" + 0.024*\"new zealand\" + 0.021*\"80s\" + 0.016*\"drew barrymore\"\n", - "2021-08-24 02:42:37,509 : INFO : topic #6 (0.020): 0.143*\"Musical\" + 0.096*\"politics\" + 0.059*\"satire\" + 0.043*\"sean connery\" + 0.042*\"nicolas cage\" + 0.040*\"dvd-r\" + 0.031*\"terrorism\" + 0.029*\"police\" + 0.025*\"dvd-ram\" + 0.024*\"netwatch\"\n", - "2021-08-24 02:42:37,510 : INFO : topic #3 (0.020): 0.147*\"oscar (best picture)\" + 0.062*\"oscar (best actor)\" + 0.059*\"oscar (best supporting actor)\" + 0.058*\"oscar (best directing)\" + 0.033*\"al pacino\" + 0.026*\"afi 100 (cheers)\" + 0.024*\"Drama\" + 0.019*\"tumey's dvds\" + 0.018*\"afi 100\" + 0.017*\"great acting\"\n", - "2021-08-24 02:42:37,511 : INFO : topic #26 (0.020): 0.045*\"Drama\" + 0.032*\"james bond\" + 0.022*\"reflective\" + 0.022*\"007\" + 0.021*\"atmospheric\" + 0.021*\"poignant\" + 0.021*\"bond\" + 0.019*\"bittersweet\" + 0.019*\"lyrical\" + 0.018*\"deliberate\"\n", - "2021-08-24 02:42:37,513 : INFO : topic diff=0.047805, rho=0.216470\n", - "2021-08-24 02:42:37,514 : INFO : PROGRESS: pass 15, at document #10000/10681\n", - "2021-08-24 02:42:37,755 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:37,783 : INFO : topic #19 (0.020): 0.063*\"nudity (full frontal)\" + 0.054*\"drama\" + 0.034*\"Drama\" + 0.034*\"philip k. dick\" + 0.025*\"ummarti2007\" + 0.024*\"slow\" + 0.024*\"rape\" + 0.022*\"male nudity\" + 0.021*\"very good\" + 0.021*\"vietnam war\"\n", - "2021-08-24 02:42:37,784 : INFO : topic #8 (0.020): 0.153*\"anime\" + 0.081*\"true story\" + 0.074*\"tom hanks\" + 0.059*\"based on a true story\" + 0.028*\"to-rent\" + 0.028*\"interesting\" + 0.026*\"japan\" + 0.026*\"not funny\" + 0.024*\"good\" + 0.021*\"drama\"\n", - "2021-08-24 02:42:37,785 : INFO : topic #0 (0.020): 0.164*\"can't remember\" + 0.098*\"directorial debut\" + 0.087*\"based on a tv show\" + 0.073*\"Comedy\" + 0.061*\"ummarti2006\" + 0.036*\"keira knightley\" + 0.029*\"australian\" + 0.027*\"dani2006\" + 0.026*\"australia\" + 0.021*\"robert downey jr\"\n", - "2021-08-24 02:42:37,785 : INFO : topic #1 (0.020): 0.056*\"overrated\" + 0.040*\"violence\" + 0.037*\"quentin tarantino\" + 0.034*\"revenge\" + 0.030*\"crime\" + 0.026*\"violent\" + 0.025*\"owned\" + 0.023*\"imdb top 250\" + 0.021*\"tarantino\" + 0.021*\"drama\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:37,786 : INFO : topic #37 (0.020): 0.194*\"War\" + 0.112*\"Drama\" + 0.102*\"world war ii\" + 0.057*\"war\" + 0.049*\"history\" + 0.036*\"Action\" + 0.031*\"jim carrey\" + 0.018*\"africa\" + 0.017*\"nazis\" + 0.014*\"wwii\"\n", - "2021-08-24 02:42:37,787 : INFO : topic diff=0.039867, rho=0.216470\n", - "2021-08-24 02:42:37,937 : INFO : -20.968 per-word bound, 2051264.9 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:37,937 : INFO : PROGRESS: pass 15, at document #10681/10681\n", - "2021-08-24 02:42:38,034 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:38,063 : INFO : topic #40 (0.020): 0.310*\"r\" + 0.159*\"clearplay\" + 0.074*\"movie to see\" + 0.049*\"Drama\" + 0.032*\"prison\" + 0.029*\"friendship\" + 0.027*\"morgan freeman\" + 0.016*\"conspiracy\" + 0.013*\"1970s\" + 0.013*\"don cheadle\"\n", - "2021-08-24 02:42:38,064 : INFO : topic #39 (0.020): 0.150*\"time travel\" + 0.054*\"adultery\" + 0.046*\"motorcycle\" + 0.046*\"post apocalyptic\" + 0.043*\"post-apocalyptic\" + 0.035*\"jude law\" + 0.025*\"bechdel test:fail\" + 0.023*\"dystopia\" + 0.018*\"want to own\" + 0.017*\"old\"\n", - "2021-08-24 02:42:38,065 : INFO : topic #25 (0.020): 0.188*\"Western\" + 0.067*\"erlend's dvds\" + 0.047*\"Drama\" + 0.041*\"woody allen\" + 0.038*\"gothic\" + 0.033*\"george clooney\" + 0.028*\"gene hackman\" + 0.027*\"sad\" + 0.025*\"nicole kidman\" + 0.024*\"scary movies to see on halloween\"\n", - "2021-08-24 02:42:38,066 : INFO : topic #19 (0.020): 0.083*\"nudity (full frontal)\" + 0.045*\"drama\" + 0.038*\"Drama\" + 0.034*\"to see\" + 0.029*\"philip k. dick\" + 0.027*\"rape\" + 0.025*\"ummarti2007\" + 0.022*\"male nudity\" + 0.020*\"slow\" + 0.018*\"very good\"\n", - "2021-08-24 02:42:38,066 : INFO : topic #4 (0.020): 0.066*\"Drama\" + 0.062*\"london\" + 0.061*\"sports\" + 0.047*\"sven's to see list\" + 0.044*\"boxing\" + 0.040*\"underrated\" + 0.038*\"british\" + 0.033*\"hw drama\" + 0.033*\"notable soundtrack\" + 0.031*\"inspirational\"\n", - "2021-08-24 02:42:38,068 : INFO : topic diff=0.086955, rho=0.216470\n", - "2021-08-24 02:42:38,069 : INFO : PROGRESS: pass 16, at document #2000/10681\n", - "2021-08-24 02:42:38,345 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:38,374 : INFO : topic #44 (0.020): 0.120*\"mafia\" + 0.085*\"music\" + 0.065*\"martin scorsese\" + 0.056*\"organized crime\" + 0.037*\"rock and roll\" + 0.033*\"Musical\" + 0.024*\"guns\" + 0.022*\"wired 50 greatest soundtracks\" + 0.022*\"francis ford coppola\" + 0.020*\"confrontational\"\n", - "2021-08-24 02:42:38,375 : INFO : topic #20 (0.020): 0.105*\"biography\" + 0.064*\"Drama\" + 0.052*\"death\" + 0.052*\"biopic\" + 0.041*\"suicide\" + 0.034*\"julia roberts\" + 0.032*\"corvallis library\" + 0.031*\"ensemble cast\" + 0.027*\"medieval\" + 0.023*\"forest whitaker\"\n", - "2021-08-24 02:42:38,376 : INFO : topic #40 (0.020): 0.270*\"r\" + 0.138*\"clearplay\" + 0.064*\"movie to see\" + 0.055*\"prison\" + 0.045*\"Drama\" + 0.034*\"morgan freeman\" + 0.028*\"friendship\" + 0.021*\"conspiracy\" + 0.015*\"1970s\" + 0.012*\"mel brooks\"\n", - "2021-08-24 02:42:38,377 : INFO : topic #28 (0.020): 0.087*\"oscar (best cinematography)\" + 0.065*\"Drama\" + 0.050*\"in netflix queue\" + 0.049*\"oscar (best supporting actress)\" + 0.038*\"oscar (best actress)\" + 0.027*\"remade\" + 0.026*\"marlon brando\" + 0.017*\"exceptional acting\" + 0.014*\"audrey hepburn\" + 0.013*\"father daughter relationship\"\n", - "2021-08-24 02:42:38,377 : INFO : topic #35 (0.020): 0.344*\"Comedy\" + 0.315*\"Drama\" + 0.199*\"Romance\" + 0.016*\"lesbian\" + 0.009*\"bibliothek\" + 0.007*\"sean penn\" + 0.006*\"philip seymour hoffman\" + 0.005*\"oscar (best foreign language film)\" + 0.004*\"m. night shyamalan\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:38,379 : INFO : topic diff=0.161909, rho=0.211570\n", - "2021-08-24 02:42:38,380 : INFO : PROGRESS: pass 16, at document #4000/10681\n", - "2021-08-24 02:42:38,625 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:38,652 : INFO : topic #49 (0.020): 0.082*\"national film registry\" + 0.062*\"imdb top 250\" + 0.044*\"black and white\" + 0.034*\"tumey's dvds\" + 0.034*\"afi 100 (thrills)\" + 0.030*\"afi 100\" + 0.025*\"erlend's dvds\" + 0.024*\"usa film registry\" + 0.022*\"afi 100 (movie quotes)\" + 0.019*\"emerson must see\"\n", - "2021-08-24 02:42:38,653 : INFO : topic #32 (0.020): 0.141*\"johnny depp\" + 0.064*\"clint eastwood\" + 0.053*\"vhs\" + 0.042*\"western\" + 0.041*\"jackie chan\" + 0.035*\"spaghetti western\" + 0.032*\"kung fu\" + 0.029*\"sergio leone\" + 0.029*\"india\" + 0.028*\"propaganda\"\n", - "2021-08-24 02:42:38,653 : INFO : topic #10 (0.020): 0.254*\"Thriller\" + 0.226*\"Crime\" + 0.166*\"Action\" + 0.159*\"Drama\" + 0.026*\"Mystery\" + 0.018*\"murder\" + 0.012*\"virtual reality\" + 0.012*\"crime\" + 0.007*\"thriller\" + 0.005*\"greg kinnear\"\n", - "2021-08-24 02:42:38,655 : INFO : topic #17 (0.020): 0.335*\"Sci-Fi\" + 0.077*\"Horror\" + 0.065*\"Action\" + 0.038*\"video game adaptation\" + 0.028*\"football\" + 0.026*\"animals\" + 0.022*\"movie to see\" + 0.017*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:42:38,655 : INFO : topic #29 (0.020): 0.094*\"nudity (full frontal - notable)\" + 0.068*\"shakespeare\" + 0.051*\"Drama\" + 0.045*\"based on a play\" + 0.040*\"adapted from:play\" + 0.032*\"christianity\" + 0.029*\"religion\" + 0.021*\"disability\" + 0.021*\"cannibalism\" + 0.021*\"jodie foster\"\n", - "2021-08-24 02:42:38,657 : INFO : topic diff=0.029288, rho=0.211570\n", - "2021-08-24 02:42:38,658 : INFO : PROGRESS: pass 16, at document #6000/10681\n", - "2021-08-24 02:42:38,890 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:38,917 : INFO : topic #33 (0.020): 0.114*\"Comedy\" + 0.114*\"comedy\" + 0.057*\"funny\" + 0.038*\"parody\" + 0.035*\"seen more than once\" + 0.034*\"quirky\" + 0.032*\"dark comedy\" + 0.028*\"hilarious\" + 0.026*\"coen brothers\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:38,918 : INFO : topic #25 (0.020): 0.154*\"Western\" + 0.076*\"erlend's dvds\" + 0.049*\"gothic\" + 0.046*\"woody allen\" + 0.045*\"Drama\" + 0.040*\"gene hackman\" + 0.029*\"scary movies to see on halloween\" + 0.028*\"george clooney\" + 0.028*\"nicole kidman\" + 0.025*\"sad\"\n", - "2021-08-24 02:42:38,918 : INFO : topic #19 (0.020): 0.061*\"drama\" + 0.060*\"nudity (full frontal)\" + 0.035*\"philip k. dick\" + 0.034*\"Drama\" + 0.029*\"vietnam war\" + 0.027*\"rape\" + 0.025*\"vietnam\" + 0.023*\"very good\" + 0.019*\"to see\" + 0.016*\"brazil\"\n", - "2021-08-24 02:42:38,919 : INFO : topic #14 (0.020): 0.056*\"criterion\" + 0.052*\"tense\" + 0.052*\"disturbing\" + 0.051*\"atmospheric\" + 0.047*\"Drama\" + 0.033*\"tumey's dvds\" + 0.029*\"stylized\" + 0.028*\"bleak\" + 0.024*\"erlend's dvds\" + 0.024*\"menacing\"\n", - "2021-08-24 02:42:38,920 : INFO : topic #23 (0.020): 0.088*\"teen\" + 0.080*\"high school\" + 0.050*\"cars\" + 0.037*\"dance\" + 0.028*\"eddie murphy\" + 0.024*\"sandra bullock\" + 0.022*\"Comedy\" + 0.021*\"(s)vcd\" + 0.019*\"car chase\" + 0.017*\"marx brothers\"\n", - "2021-08-24 02:42:38,922 : INFO : topic diff=0.057386, rho=0.211570\n", - "2021-08-24 02:42:38,923 : INFO : PROGRESS: pass 16, at document #8000/10681\n", - "2021-08-24 02:42:39,159 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:39,188 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.052*\"tense\" + 0.051*\"disturbing\" + 0.050*\"atmospheric\" + 0.049*\"Drama\" + 0.031*\"tumey's dvds\" + 0.031*\"stylized\" + 0.030*\"bleak\" + 0.025*\"menacing\" + 0.025*\"erlend's dvds\"\n", - "2021-08-24 02:42:39,189 : INFO : topic #30 (0.020): 0.245*\"Adventure\" + 0.116*\"Children\" + 0.106*\"Comedy\" + 0.099*\"Fantasy\" + 0.082*\"Action\" + 0.067*\"70mm\" + 0.047*\"Drama\" + 0.046*\"Animation\" + 0.018*\"Musical\" + 0.009*\"submarine\"\n", - "2021-08-24 02:42:39,189 : INFO : topic #47 (0.020): 0.063*\"serial killer\" + 0.058*\"psychology\" + 0.049*\"brad pitt\" + 0.041*\"edward norton\" + 0.035*\"heist\" + 0.029*\"steven spielberg\" + 0.029*\"matt damon\" + 0.028*\"hayao miyazaki\" + 0.023*\"crime\" + 0.016*\"scary\"\n", - "2021-08-24 02:42:39,190 : INFO : topic #33 (0.020): 0.117*\"Comedy\" + 0.108*\"comedy\" + 0.058*\"funny\" + 0.036*\"parody\" + 0.036*\"seen more than once\" + 0.033*\"quirky\" + 0.028*\"dark comedy\" + 0.027*\"hilarious\" + 0.023*\"coen brothers\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:39,191 : INFO : topic #24 (0.020): 0.069*\"vampire\" + 0.069*\"murder\" + 0.068*\"hitchcock\" + 0.065*\"vampires\" + 0.056*\"memory\" + 0.051*\"alfred hitchcock\" + 0.050*\"oppl\" + 0.020*\"kirsten dunst\" + 0.019*\"cary grant\" + 0.018*\"incest\"\n", - "2021-08-24 02:42:39,192 : INFO : topic diff=0.046593, rho=0.211570\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:39,194 : INFO : PROGRESS: pass 16, at document #10000/10681\n", - "2021-08-24 02:42:39,438 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:39,466 : INFO : topic #40 (0.020): 0.282*\"r\" + 0.124*\"clearplay\" + 0.057*\"movie to see\" + 0.046*\"Drama\" + 0.039*\"prison\" + 0.029*\"morgan freeman\" + 0.025*\"friendship\" + 0.018*\"john wayne\" + 0.018*\"conspiracy\" + 0.015*\"don cheadle\"\n", - "2021-08-24 02:42:39,466 : INFO : topic #7 (0.020): 0.108*\"romance\" + 0.062*\"Romance\" + 0.045*\"chick flick\" + 0.041*\"boring\" + 0.026*\"girlie movie\" + 0.025*\"baseball\" + 0.024*\"love story\" + 0.024*\"whimsical\" + 0.024*\"Comedy\" + 0.019*\"wedding\"\n", - "2021-08-24 02:42:39,467 : INFO : topic #45 (0.020): 0.258*\"less than 300 ratings\" + 0.117*\"Drama\" + 0.059*\"nudity (topless - notable)\" + 0.038*\"tim burton\" + 0.031*\"not corv lib\" + 0.026*\"library\" + 0.017*\"depressing\" + 0.016*\"seen 2007\" + 0.016*\"blindfold\" + 0.013*\"michael moore\"\n", - "2021-08-24 02:42:39,468 : INFO : topic #41 (0.020): 0.130*\"remake\" + 0.064*\"new york city\" + 0.057*\"christmas\" + 0.048*\"family\" + 0.039*\"bibliothek\" + 0.035*\"new york\" + 0.035*\"eric's dvds\" + 0.030*\"Drama\" + 0.028*\"kevin spacey\" + 0.026*\"friday night movie\"\n", - "2021-08-24 02:42:39,469 : INFO : topic #6 (0.020): 0.141*\"Musical\" + 0.104*\"politics\" + 0.062*\"satire\" + 0.042*\"nicolas cage\" + 0.039*\"terrorism\" + 0.039*\"dvd-r\" + 0.032*\"sean connery\" + 0.028*\"political\" + 0.028*\"netwatch\" + 0.026*\"police\"\n", - "2021-08-24 02:42:39,470 : INFO : topic diff=0.038947, rho=0.211570\n", - "2021-08-24 02:42:39,614 : INFO : -20.966 per-word bound, 2048027.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:39,615 : INFO : PROGRESS: pass 16, at document #10681/10681\n", - "2021-08-24 02:42:39,709 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:39,735 : INFO : topic #47 (0.020): 0.061*\"serial killer\" + 0.053*\"psychology\" + 0.050*\"brad pitt\" + 0.040*\"heist\" + 0.038*\"edward norton\" + 0.031*\"matt damon\" + 0.027*\"hayao miyazaki\" + 0.024*\"steven spielberg\" + 0.019*\"crime\" + 0.015*\"anthony hopkins\"\n", - "2021-08-24 02:42:39,736 : INFO : topic #13 (0.020): 0.054*\"fairy tale\" + 0.051*\"road trip\" + 0.037*\"assassination\" + 0.034*\"sequel\" + 0.034*\"cold war\" + 0.034*\"jane austen\" + 0.033*\"historical\" + 0.024*\"kids\" + 0.022*\"notable nudity\" + 0.021*\"gerard depardieu\"\n", - "2021-08-24 02:42:39,736 : INFO : topic #20 (0.020): 0.100*\"biography\" + 0.070*\"Drama\" + 0.056*\"death\" + 0.046*\"suicide\" + 0.044*\"biopic\" + 0.039*\"corvallis library\" + 0.030*\"ensemble cast\" + 0.027*\"julia roberts\" + 0.024*\"forest whitaker\" + 0.024*\"history\"\n", - "2021-08-24 02:42:39,737 : INFO : topic #33 (0.020): 0.118*\"Comedy\" + 0.101*\"comedy\" + 0.062*\"funny\" + 0.038*\"parody\" + 0.034*\"dark comedy\" + 0.029*\"seen more than once\" + 0.028*\"hilarious\" + 0.028*\"quirky\" + 0.027*\"coen brothers\" + 0.022*\"england\"\n", - "2021-08-24 02:42:39,738 : INFO : topic #40 (0.020): 0.309*\"r\" + 0.158*\"clearplay\" + 0.074*\"movie to see\" + 0.049*\"Drama\" + 0.032*\"prison\" + 0.029*\"friendship\" + 0.027*\"morgan freeman\" + 0.016*\"conspiracy\" + 0.013*\"1970s\" + 0.013*\"don cheadle\"\n", - "2021-08-24 02:42:39,739 : INFO : topic diff=0.084956, rho=0.211570\n", - "2021-08-24 02:42:39,741 : INFO : PROGRESS: pass 17, at document #2000/10681\n", - "2021-08-24 02:42:39,999 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:40,025 : INFO : topic #25 (0.020): 0.158*\"Western\" + 0.068*\"erlend's dvds\" + 0.055*\"woody allen\" + 0.045*\"Drama\" + 0.043*\"gothic\" + 0.038*\"gene hackman\" + 0.026*\"george clooney\" + 0.025*\"sad\" + 0.020*\"nicole kidman\" + 0.020*\"cute\"\n", - "2021-08-24 02:42:40,026 : INFO : topic #15 (0.020): 0.170*\"disney\" + 0.094*\"animation\" + 0.074*\"pixar\" + 0.045*\"Animation\" + 0.043*\"children\" + 0.038*\"Children\" + 0.030*\"disney animated feature\" + 0.029*\"pirates\" + 0.024*\"angelina jolie\" + 0.021*\"cartoon\"\n", - "2021-08-24 02:42:40,027 : INFO : topic #17 (0.020): 0.326*\"Sci-Fi\" + 0.068*\"Horror\" + 0.068*\"Action\" + 0.045*\"video game adaptation\" + 0.028*\"animals\" + 0.026*\"movie to see\" + 0.021*\"football\" + 0.019*\"futuristic\" + 0.015*\"milla jovovich\" + 0.012*\"g\"\n", - "2021-08-24 02:42:40,028 : INFO : topic #13 (0.020): 0.057*\"fairy tale\" + 0.051*\"jane austen\" + 0.036*\"road trip\" + 0.036*\"assassination\" + 0.032*\"cold war\" + 0.031*\"sequel\" + 0.029*\"historical\" + 0.023*\"gerard depardieu\" + 0.023*\"swashbuckler\" + 0.021*\"kids\"\n", - "2021-08-24 02:42:40,028 : INFO : topic #5 (0.020): 0.303*\"classic\" + 0.066*\"musical\" + 0.036*\"afi 100 (laughs)\" + 0.032*\"Musical\" + 0.027*\"national film registry\" + 0.023*\"afi 100\" + 0.018*\"john travolta\" + 0.016*\"70mm\" + 0.013*\"breakthroughs\" + 0.012*\"adapted from b'way\"\n", - "2021-08-24 02:42:40,030 : INFO : topic diff=0.157369, rho=0.206988\n", - "2021-08-24 02:42:40,031 : INFO : PROGRESS: pass 17, at document #4000/10681\n", - "2021-08-24 02:42:40,275 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:40,301 : INFO : topic #11 (0.020): 0.130*\"religion\" + 0.116*\"keanu reeves\" + 0.032*\"island\" + 0.025*\"slavery\" + 0.019*\"class issues\" + 0.018*\"courtesan\" + 0.018*\"product placement\" + 0.017*\"facebook rec\" + 0.015*\"reality tv\" + 0.014*\"slash\"\n", - "2021-08-24 02:42:40,302 : INFO : topic #25 (0.020): 0.146*\"Western\" + 0.075*\"erlend's dvds\" + 0.053*\"gothic\" + 0.050*\"woody allen\" + 0.045*\"Drama\" + 0.038*\"gene hackman\" + 0.033*\"scary movies to see on halloween\" + 0.026*\"george clooney\" + 0.025*\"sad\" + 0.020*\"cute\"\n", - "2021-08-24 02:42:40,302 : INFO : topic #13 (0.020): 0.054*\"fairy tale\" + 0.045*\"jane austen\" + 0.041*\"road trip\" + 0.035*\"sequel\" + 0.030*\"assassination\" + 0.029*\"cold war\" + 0.028*\"historical\" + 0.022*\"kids\" + 0.021*\"heartwarming\" + 0.020*\"gerard depardieu\"\n", - "2021-08-24 02:42:40,303 : INFO : topic #18 (0.020): 0.079*\"gay\" + 0.070*\"racism\" + 0.065*\"magic\" + 0.049*\"pg13\" + 0.039*\"Drama\" + 0.030*\"food\" + 0.028*\"social commentary\" + 0.022*\"homosexuality\" + 0.018*\"denzel washington\" + 0.018*\"19th century\"\n", - "2021-08-24 02:42:40,304 : INFO : topic #30 (0.020): 0.245*\"Adventure\" + 0.126*\"Children\" + 0.108*\"Comedy\" + 0.103*\"Fantasy\" + 0.081*\"Action\" + 0.054*\"70mm\" + 0.049*\"Animation\" + 0.046*\"Drama\" + 0.016*\"Musical\" + 0.011*\"submarine\"\n", - "2021-08-24 02:42:40,305 : INFO : topic diff=0.028605, rho=0.206988\n", - "2021-08-24 02:42:40,307 : INFO : PROGRESS: pass 17, at document #6000/10681\n", - "2021-08-24 02:42:40,540 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:40,566 : INFO : topic #0 (0.020): 0.225*\"can't remember\" + 0.104*\"directorial debut\" + 0.085*\"based on a tv show\" + 0.082*\"Comedy\" + 0.029*\"ummarti2006\" + 0.029*\"australia\" + 0.028*\"australian\" + 0.021*\"keira knightley\" + 0.018*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:40,567 : INFO : topic #2 (0.020): 0.275*\"Documentary\" + 0.107*\"to see\" + 0.052*\"documentary\" + 0.028*\"star trek\" + 0.027*\"sexuality\" + 0.026*\"mockumentary\" + 0.024*\"kevin smith\" + 0.023*\"dogs\" + 0.018*\"movie to see\" + 0.017*\"owen wilson\"\n", - "2021-08-24 02:42:40,568 : INFO : topic #34 (0.020): 0.093*\"ghosts\" + 0.044*\"bill murray\" + 0.041*\"television\" + 0.037*\"courtroom\" + 0.033*\"courtroom drama\" + 0.023*\"roman polanski\" + 0.022*\"court\" + 0.018*\"secret service\" + 0.017*\"2.5\" + 0.016*\"opera\"\n", - "2021-08-24 02:42:40,568 : INFO : topic #44 (0.020): 0.100*\"mafia\" + 0.085*\"music\" + 0.055*\"martin scorsese\" + 0.048*\"organized crime\" + 0.043*\"rock and roll\" + 0.032*\"Musical\" + 0.029*\"wired 50 greatest soundtracks\" + 0.027*\"guns\" + 0.020*\"francis ford coppola\" + 0.019*\"confrontational\"\n", - "2021-08-24 02:42:40,569 : INFO : topic #18 (0.020): 0.082*\"magic\" + 0.079*\"gay\" + 0.063*\"racism\" + 0.042*\"pg13\" + 0.040*\"Drama\" + 0.027*\"food\" + 0.027*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.019*\"denzel washington\"\n", - "2021-08-24 02:42:40,571 : INFO : topic diff=0.055706, rho=0.206988\n", - "2021-08-24 02:42:40,572 : INFO : PROGRESS: pass 17, at document #8000/10681\n", - "2021-08-24 02:42:40,840 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:40,867 : INFO : topic #32 (0.020): 0.135*\"johnny depp\" + 0.069*\"vhs\" + 0.067*\"clint eastwood\" + 0.045*\"jackie chan\" + 0.033*\"western\" + 0.030*\"kung fu\" + 0.030*\"propaganda\" + 0.029*\"india\" + 0.027*\"david lynch\" + 0.026*\"italian\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:40,868 : INFO : topic #39 (0.020): 0.182*\"time travel\" + 0.054*\"adultery\" + 0.041*\"motorcycle\" + 0.038*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.030*\"jude law\" + 0.024*\"dystopia\" + 0.019*\"want to own\" + 0.019*\"ian mckellen\" + 0.018*\"hollywood\"\n", - "2021-08-24 02:42:40,868 : INFO : topic #48 (0.020): 0.089*\"bruce willis\" + 0.081*\"twist ending\" + 0.063*\"netflix\" + 0.047*\"coming of age\" + 0.033*\"journalism\" + 0.031*\"sexy\" + 0.026*\"Drama\" + 0.025*\"avi\" + 0.022*\"to see\" + 0.022*\"talking animals\"\n", - "2021-08-24 02:42:40,869 : INFO : topic #8 (0.020): 0.142*\"anime\" + 0.090*\"tom hanks\" + 0.077*\"true story\" + 0.056*\"based on a true story\" + 0.027*\"drama\" + 0.026*\"good\" + 0.025*\"japan\" + 0.023*\"interesting\" + 0.021*\"not funny\" + 0.018*\"archaeology\"\n", - "2021-08-24 02:42:40,870 : INFO : topic #15 (0.020): 0.140*\"disney\" + 0.096*\"animation\" + 0.079*\"pixar\" + 0.046*\"Animation\" + 0.040*\"children\" + 0.037*\"Children\" + 0.033*\"pirates\" + 0.029*\"disney animated feature\" + 0.025*\"angelina jolie\" + 0.021*\"cartoon\"\n", - "2021-08-24 02:42:40,871 : INFO : topic diff=0.045531, rho=0.206988\n", - "2021-08-24 02:42:40,873 : INFO : PROGRESS: pass 17, at document #10000/10681\n", - "2021-08-24 02:42:41,142 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:41,171 : INFO : topic #33 (0.020): 0.119*\"Comedy\" + 0.106*\"comedy\" + 0.063*\"funny\" + 0.036*\"parody\" + 0.031*\"seen more than once\" + 0.030*\"quirky\" + 0.029*\"hilarious\" + 0.027*\"dark comedy\" + 0.023*\"england\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:41,172 : INFO : topic #10 (0.020): 0.247*\"Thriller\" + 0.239*\"Crime\" + 0.167*\"Action\" + 0.162*\"Drama\" + 0.031*\"Mystery\" + 0.017*\"murder\" + 0.010*\"crime\" + 0.008*\"virtual reality\" + 0.005*\"greg kinnear\" + 0.005*\"thriller\"\n", - "2021-08-24 02:42:41,173 : INFO : topic #9 (0.020): 0.244*\"based on a book\" + 0.114*\"adapted from:book\" + 0.059*\"Drama\" + 0.037*\"based on book\" + 0.035*\"jack nicholson\" + 0.031*\"stephen king\" + 0.028*\"imdb bottom 100\" + 0.023*\"stupid\" + 0.022*\"literary adaptation\" + 0.022*\"los angeles\"\n", - "2021-08-24 02:42:41,174 : INFO : topic #16 (0.020): 0.289*\"nudity (topless)\" + 0.179*\"nudity (topless - brief)\" + 0.069*\"nudity (rear)\" + 0.038*\"pg\" + 0.029*\"claymation\" + 0.022*\"military\" + 0.018*\"aardman\" + 0.016*\"childhood\" + 0.016*\"weird\" + 0.009*\"freedom\"\n", - "2021-08-24 02:42:41,175 : INFO : topic #0 (0.020): 0.166*\"can't remember\" + 0.097*\"directorial debut\" + 0.087*\"based on a tv show\" + 0.073*\"Comedy\" + 0.060*\"ummarti2006\" + 0.036*\"keira knightley\" + 0.029*\"australian\" + 0.027*\"dani2006\" + 0.026*\"australia\" + 0.021*\"robert downey jr\"\n", - "2021-08-24 02:42:41,176 : INFO : topic diff=0.037784, rho=0.206988\n", - "2021-08-24 02:42:41,329 : INFO : -20.966 per-word bound, 2048261.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:41,330 : INFO : PROGRESS: pass 17, at document #10681/10681\n", - "2021-08-24 02:42:41,435 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:41,461 : INFO : topic #31 (0.020): 0.159*\"zombies\" + 0.096*\"pg-13\" + 0.058*\"horror\" + 0.029*\"zombie\" + 0.025*\"infidelity\" + 0.024*\"movie to see\" + 0.022*\"books\" + 0.022*\"joaquin phoenix\" + 0.022*\"betrayal\" + 0.022*\"cult classic\"\n", - "2021-08-24 02:42:41,462 : INFO : topic #1 (0.020): 0.054*\"overrated\" + 0.039*\"violence\" + 0.034*\"quentin tarantino\" + 0.032*\"revenge\" + 0.031*\"violent\" + 0.031*\"imdb top 250\" + 0.028*\"crime\" + 0.025*\"owned\" + 0.020*\"drama\" + 0.019*\"tarantino\"\n", - "2021-08-24 02:42:41,463 : INFO : topic #13 (0.020): 0.054*\"fairy tale\" + 0.051*\"road trip\" + 0.037*\"assassination\" + 0.034*\"jane austen\" + 0.034*\"cold war\" + 0.033*\"sequel\" + 0.033*\"historical\" + 0.024*\"kids\" + 0.022*\"notable nudity\" + 0.022*\"gerard depardieu\"\n", - "2021-08-24 02:42:41,464 : INFO : topic #21 (0.020): 0.077*\"Sci-Fi\" + 0.065*\"aliens\" + 0.050*\"sci-fi\" + 0.038*\"will smith\" + 0.037*\"space\" + 0.030*\"futuristmovies.com\" + 0.029*\"tommy lee jones\" + 0.026*\"Action\" + 0.024*\"ridley scott\" + 0.018*\"monster\"\n", - "2021-08-24 02:42:41,465 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.053*\"disturbing\" + 0.052*\"Drama\" + 0.050*\"tense\" + 0.046*\"atmospheric\" + 0.030*\"stylized\" + 0.028*\"tumey's dvds\" + 0.028*\"bleak\" + 0.022*\"menacing\" + 0.022*\"erlend's dvds\"\n", - "2021-08-24 02:42:41,467 : INFO : topic diff=0.083209, rho=0.206988\n", - "2021-08-24 02:42:41,469 : INFO : PROGRESS: pass 18, at document #2000/10681\n", - "2021-08-24 02:42:41,725 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:41,751 : INFO : topic #24 (0.020): 0.082*\"hitchcock\" + 0.076*\"alfred hitchcock\" + 0.072*\"oppl\" + 0.069*\"murder\" + 0.063*\"vampire\" + 0.048*\"vampires\" + 0.037*\"memory\" + 0.023*\"incest\" + 0.021*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:41,752 : INFO : topic #23 (0.020): 0.084*\"high school\" + 0.060*\"teen\" + 0.051*\"cars\" + 0.035*\"dance\" + 0.028*\"sandra bullock\" + 0.028*\"marx brothers\" + 0.023*\"Comedy\" + 0.021*\"kate winslet\" + 0.020*\"(s)vcd\" + 0.020*\"eddie murphy\"\n", - "2021-08-24 02:42:41,753 : INFO : topic #17 (0.020): 0.326*\"Sci-Fi\" + 0.069*\"Horror\" + 0.068*\"Action\" + 0.045*\"video game adaptation\" + 0.028*\"animals\" + 0.026*\"movie to see\" + 0.021*\"football\" + 0.019*\"futuristic\" + 0.015*\"milla jovovich\" + 0.012*\"g\"\n", - "2021-08-24 02:42:41,753 : INFO : topic #37 (0.020): 0.185*\"War\" + 0.106*\"world war ii\" + 0.104*\"Drama\" + 0.060*\"war\" + 0.045*\"history\" + 0.041*\"jim carrey\" + 0.031*\"Action\" + 0.023*\"nazis\" + 0.017*\"wwii\" + 0.015*\"mental illness\"\n", - "2021-08-24 02:42:41,754 : INFO : topic #31 (0.020): 0.143*\"zombies\" + 0.076*\"pg-13\" + 0.074*\"horror\" + 0.031*\"cult classic\" + 0.027*\"zombie\" + 0.024*\"sam raimi\" + 0.023*\"campy\" + 0.021*\"infidelity\" + 0.021*\"books\" + 0.020*\"joaquin phoenix\"\n", - "2021-08-24 02:42:41,755 : INFO : topic diff=0.153429, rho=0.202691\n", - "2021-08-24 02:42:41,756 : INFO : PROGRESS: pass 18, at document #4000/10681\n", - "2021-08-24 02:42:42,035 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:42,062 : INFO : topic #6 (0.020): 0.118*\"Musical\" + 0.103*\"politics\" + 0.068*\"satire\" + 0.048*\"sean connery\" + 0.047*\"nicolas cage\" + 0.034*\"dvd-r\" + 0.032*\"terrorism\" + 0.029*\"police\" + 0.023*\"political\" + 0.022*\"dvd-ram\"\n", - "2021-08-24 02:42:42,063 : INFO : topic #31 (0.020): 0.123*\"zombies\" + 0.075*\"horror\" + 0.071*\"pg-13\" + 0.041*\"cult classic\" + 0.026*\"campy\" + 0.025*\"infidelity\" + 0.024*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.023*\"zombie\" + 0.020*\"books\"\n", - "2021-08-24 02:42:42,064 : INFO : topic #17 (0.020): 0.336*\"Sci-Fi\" + 0.077*\"Horror\" + 0.065*\"Action\" + 0.038*\"video game adaptation\" + 0.028*\"football\" + 0.026*\"animals\" + 0.022*\"movie to see\" + 0.017*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:42:42,065 : INFO : topic #22 (0.020): 0.228*\"betamax\" + 0.099*\"dvd-video\" + 0.075*\"clv\" + 0.036*\"aviation\" + 0.028*\"library on hold\" + 0.023*\"terry gilliam\" + 0.018*\"dvd collection\" + 0.016*\"gilliam\" + 0.013*\"seen 2008\" + 0.013*\"70mm\"\n", - "2021-08-24 02:42:42,066 : INFO : topic #44 (0.020): 0.109*\"mafia\" + 0.088*\"music\" + 0.054*\"martin scorsese\" + 0.051*\"organized crime\" + 0.043*\"rock and roll\" + 0.032*\"Musical\" + 0.030*\"wired 50 greatest soundtracks\" + 0.025*\"guns\" + 0.020*\"confrontational\" + 0.020*\"mob\"\n", - "2021-08-24 02:42:42,067 : INFO : topic diff=0.027774, rho=0.202691\n", - "2021-08-24 02:42:42,068 : INFO : PROGRESS: pass 18, at document #6000/10681\n", - "2021-08-24 02:42:42,310 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:42,337 : INFO : topic #35 (0.020): 0.356*\"Comedy\" + 0.310*\"Drama\" + 0.189*\"Romance\" + 0.015*\"lesbian\" + 0.011*\"bibliothek\" + 0.008*\"philip seymour hoffman\" + 0.006*\"sean penn\" + 0.006*\"m. night shyamalan\" + 0.006*\"oscar (best foreign language film)\" + 0.004*\"library vhs\"\n", - "2021-08-24 02:42:42,337 : INFO : topic #29 (0.020): 0.101*\"nudity (full frontal - notable)\" + 0.059*\"shakespeare\" + 0.052*\"Drama\" + 0.046*\"based on a play\" + 0.039*\"adapted from:play\" + 0.031*\"christianity\" + 0.027*\"religion\" + 0.021*\"jodie foster\" + 0.021*\"disability\" + 0.020*\"cannibalism\"\n", - "2021-08-24 02:42:42,338 : INFO : topic #46 (0.020): 0.088*\"action\" + 0.064*\"fantasy\" + 0.043*\"sci-fi\" + 0.036*\"adventure\" + 0.034*\"dvd\" + 0.024*\"Adventure\" + 0.023*\"seen at the cinema\" + 0.022*\"seen more than once\" + 0.019*\"tom cruise\" + 0.018*\"space\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:42,339 : INFO : topic #47 (0.020): 0.065*\"serial killer\" + 0.056*\"psychology\" + 0.053*\"brad pitt\" + 0.042*\"edward norton\" + 0.032*\"steven spielberg\" + 0.032*\"heist\" + 0.028*\"matt damon\" + 0.024*\"crime\" + 0.021*\"hayao miyazaki\" + 0.017*\"scary\"\n", - "2021-08-24 02:42:42,341 : INFO : topic #34 (0.020): 0.093*\"ghosts\" + 0.044*\"bill murray\" + 0.041*\"television\" + 0.037*\"courtroom\" + 0.033*\"courtroom drama\" + 0.023*\"roman polanski\" + 0.022*\"court\" + 0.018*\"secret service\" + 0.017*\"2.5\" + 0.016*\"opera\"\n", - "2021-08-24 02:42:42,343 : INFO : topic diff=0.054279, rho=0.202691\n", - "2021-08-24 02:42:42,345 : INFO : PROGRESS: pass 18, at document #8000/10681\n", - "2021-08-24 02:42:42,592 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:42,620 : INFO : topic #16 (0.020): 0.275*\"nudity (topless)\" + 0.186*\"nudity (topless - brief)\" + 0.067*\"nudity (rear)\" + 0.028*\"pg\" + 0.027*\"claymation\" + 0.022*\"military\" + 0.018*\"childhood\" + 0.017*\"weird\" + 0.017*\"aardman\" + 0.010*\"shark\"\n", - "2021-08-24 02:42:42,621 : INFO : topic #9 (0.020): 0.245*\"based on a book\" + 0.122*\"adapted from:book\" + 0.060*\"Drama\" + 0.039*\"stephen king\" + 0.038*\"jack nicholson\" + 0.036*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.020*\"stupid\" + 0.020*\"los angeles\"\n", - "2021-08-24 02:42:42,622 : INFO : topic #8 (0.020): 0.141*\"anime\" + 0.090*\"tom hanks\" + 0.077*\"true story\" + 0.056*\"based on a true story\" + 0.027*\"drama\" + 0.026*\"good\" + 0.025*\"japan\" + 0.023*\"interesting\" + 0.021*\"not funny\" + 0.018*\"archaeology\"\n", - "2021-08-24 02:42:42,622 : INFO : topic #26 (0.020): 0.045*\"Drama\" + 0.032*\"james bond\" + 0.022*\"007\" + 0.022*\"reflective\" + 0.021*\"atmospheric\" + 0.021*\"poignant\" + 0.021*\"bond\" + 0.019*\"bittersweet\" + 0.019*\"lyrical\" + 0.018*\"deliberate\"\n", - "2021-08-24 02:42:42,623 : INFO : topic #24 (0.020): 0.070*\"murder\" + 0.069*\"vampire\" + 0.068*\"hitchcock\" + 0.065*\"vampires\" + 0.055*\"memory\" + 0.052*\"alfred hitchcock\" + 0.051*\"oppl\" + 0.019*\"kirsten dunst\" + 0.019*\"cary grant\" + 0.018*\"incest\"\n", - "2021-08-24 02:42:42,625 : INFO : topic diff=0.044541, rho=0.202691\n", - "2021-08-24 02:42:42,626 : INFO : PROGRESS: pass 18, at document #10000/10681\n", - "2021-08-24 02:42:42,899 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:42,926 : INFO : topic #1 (0.020): 0.054*\"overrated\" + 0.040*\"violence\" + 0.037*\"quentin tarantino\" + 0.034*\"revenge\" + 0.030*\"crime\" + 0.026*\"violent\" + 0.025*\"owned\" + 0.023*\"imdb top 250\" + 0.021*\"tarantino\" + 0.021*\"drama\"\n", - "2021-08-24 02:42:42,927 : INFO : topic #17 (0.020): 0.347*\"Sci-Fi\" + 0.079*\"Horror\" + 0.067*\"Action\" + 0.040*\"video game adaptation\" + 0.023*\"football\" + 0.020*\"animals\" + 0.019*\"movie to see\" + 0.016*\"futuristic\" + 0.014*\"milla jovovich\" + 0.014*\"g\"\n", - "2021-08-24 02:42:42,928 : INFO : topic #30 (0.020): 0.247*\"Adventure\" + 0.115*\"Children\" + 0.110*\"Comedy\" + 0.106*\"Fantasy\" + 0.085*\"Action\" + 0.058*\"70mm\" + 0.049*\"Drama\" + 0.048*\"Animation\" + 0.017*\"Musical\" + 0.008*\"submarine\"\n", - "2021-08-24 02:42:42,928 : INFO : topic #7 (0.020): 0.108*\"romance\" + 0.062*\"Romance\" + 0.045*\"chick flick\" + 0.040*\"boring\" + 0.026*\"girlie movie\" + 0.025*\"baseball\" + 0.024*\"Comedy\" + 0.024*\"love story\" + 0.023*\"whimsical\" + 0.019*\"wedding\"\n", - "2021-08-24 02:42:42,929 : INFO : topic #20 (0.020): 0.095*\"biography\" + 0.068*\"Drama\" + 0.054*\"biopic\" + 0.045*\"death\" + 0.044*\"corvallis library\" + 0.037*\"suicide\" + 0.034*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.027*\"ingmar bergman\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:42,930 : INFO : topic diff=0.037005, rho=0.202691\n", - "2021-08-24 02:42:43,076 : INFO : -20.966 per-word bound, 2048239.5 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:43,077 : INFO : PROGRESS: pass 18, at document #10681/10681\n", - "2021-08-24 02:42:43,176 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:43,202 : INFO : topic #6 (0.020): 0.144*\"Musical\" + 0.115*\"politics\" + 0.063*\"satire\" + 0.039*\"nicolas cage\" + 0.038*\"terrorism\" + 0.033*\"dvd-r\" + 0.028*\"corruption\" + 0.027*\"sean connery\" + 0.027*\"police\" + 0.025*\"political\"\n", - "2021-08-24 02:42:43,203 : INFO : topic #41 (0.020): 0.135*\"remake\" + 0.078*\"new york city\" + 0.064*\"christmas\" + 0.041*\"family\" + 0.034*\"new york\" + 0.034*\"bibliothek\" + 0.031*\"movie to see\" + 0.030*\"Drama\" + 0.030*\"eric's dvds\" + 0.026*\"kevin spacey\"\n", - "2021-08-24 02:42:43,204 : INFO : topic #26 (0.020): 0.044*\"Drama\" + 0.031*\"james bond\" + 0.020*\"reflective\" + 0.020*\"poignant\" + 0.019*\"atmospheric\" + 0.019*\"007\" + 0.019*\"assassin\" + 0.019*\"bond\" + 0.019*\"bittersweet\" + 0.018*\"lyrical\"\n", - "2021-08-24 02:42:43,205 : INFO : topic #2 (0.020): 0.317*\"Documentary\" + 0.148*\"to see\" + 0.060*\"documentary\" + 0.026*\"movie to see\" + 0.023*\"mockumentary\" + 0.020*\"owen wilson\" + 0.018*\"dogs\" + 0.017*\"sexuality\" + 0.016*\"kevin smith\" + 0.014*\"bechdel test:fail\"\n", - "2021-08-24 02:42:43,205 : INFO : topic #15 (0.020): 0.103*\"disney\" + 0.100*\"animation\" + 0.091*\"pixar\" + 0.049*\"Animation\" + 0.042*\"children\" + 0.039*\"pirates\" + 0.035*\"angelina jolie\" + 0.033*\"Children\" + 0.022*\"disney animated feature\" + 0.019*\"cartoon\"\n", - "2021-08-24 02:42:43,207 : INFO : topic diff=0.081275, rho=0.202691\n", - "2021-08-24 02:42:43,209 : INFO : PROGRESS: pass 19, at document #2000/10681\n", - "2021-08-24 02:42:43,476 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:43,502 : INFO : topic #26 (0.020): 0.043*\"Drama\" + 0.024*\"james bond\" + 0.022*\"mel gibson\" + 0.021*\"poignant\" + 0.020*\"atmospheric\" + 0.019*\"bittersweet\" + 0.019*\"reflective\" + 0.018*\"lyrical\" + 0.017*\"assassin\" + 0.017*\"bond\"\n", - "2021-08-24 02:42:43,503 : INFO : topic #41 (0.020): 0.113*\"remake\" + 0.069*\"new york city\" + 0.066*\"christmas\" + 0.044*\"family\" + 0.039*\"kevin spacey\" + 0.035*\"bibliothek\" + 0.033*\"new york\" + 0.029*\"Drama\" + 0.027*\"eric's dvds\" + 0.023*\"movie to see\"\n", - "2021-08-24 02:42:43,504 : INFO : topic #45 (0.020): 0.219*\"less than 300 ratings\" + 0.111*\"Drama\" + 0.073*\"nudity (topless - notable)\" + 0.048*\"tim burton\" + 0.030*\"not corv lib\" + 0.025*\"library\" + 0.016*\"blindfold\" + 0.014*\"depressing\" + 0.013*\"san francisco\" + 0.013*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:43,504 : INFO : topic #9 (0.020): 0.263*\"based on a book\" + 0.112*\"adapted from:book\" + 0.054*\"Drama\" + 0.042*\"stephen king\" + 0.042*\"jack nicholson\" + 0.035*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.021*\"los angeles\" + 0.021*\"stupid\"\n", - "2021-08-24 02:42:43,505 : INFO : topic #15 (0.020): 0.169*\"disney\" + 0.094*\"animation\" + 0.074*\"pixar\" + 0.045*\"Animation\" + 0.043*\"children\" + 0.038*\"Children\" + 0.030*\"disney animated feature\" + 0.029*\"pirates\" + 0.024*\"angelina jolie\" + 0.021*\"cartoon\"\n", - "2021-08-24 02:42:43,507 : INFO : topic diff=0.149984, rho=0.198652\n", - "2021-08-24 02:42:43,509 : INFO : PROGRESS: pass 19, at document #4000/10681\n", - "2021-08-24 02:42:43,757 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:43,784 : INFO : topic #26 (0.020): 0.041*\"Drama\" + 0.038*\"james bond\" + 0.027*\"007\" + 0.026*\"bond\" + 0.024*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.020*\"poignant\" + 0.018*\"lyrical\" + 0.018*\"assassin\"\n", - "2021-08-24 02:42:43,785 : INFO : topic #3 (0.020): 0.174*\"oscar (best picture)\" + 0.070*\"oscar (best directing)\" + 0.064*\"oscar (best actor)\" + 0.053*\"oscar (best supporting actor)\" + 0.034*\"al pacino\" + 0.025*\"afi 100 (cheers)\" + 0.023*\"Drama\" + 0.022*\"afi 100\" + 0.020*\"great acting\" + 0.018*\"tumey's dvds\"\n", - "2021-08-24 02:42:43,785 : INFO : topic #6 (0.020): 0.119*\"Musical\" + 0.103*\"politics\" + 0.068*\"satire\" + 0.048*\"sean connery\" + 0.047*\"nicolas cage\" + 0.034*\"dvd-r\" + 0.032*\"terrorism\" + 0.029*\"police\" + 0.023*\"political\" + 0.022*\"dvd-ram\"\n", - "2021-08-24 02:42:43,787 : INFO : topic #24 (0.020): 0.093*\"hitchcock\" + 0.078*\"murder\" + 0.071*\"alfred hitchcock\" + 0.067*\"vampire\" + 0.060*\"oppl\" + 0.050*\"vampires\" + 0.035*\"memory\" + 0.021*\"incest\" + 0.018*\"cary grant\" + 0.016*\"kirsten dunst\"\n", - "2021-08-24 02:42:43,787 : INFO : topic #38 (0.020): 0.169*\"drugs\" + 0.050*\"samuel l. jackson\" + 0.025*\"divx\" + 0.025*\"basketball\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.023*\"ewan mcgregor\" + 0.021*\"michael caine\" + 0.020*\"liam neeson\" + 0.020*\"poverty\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:43,789 : INFO : topic diff=0.027116, rho=0.198652\n", - "2021-08-24 02:42:43,790 : INFO : PROGRESS: pass 19, at document #6000/10681\n", - "2021-08-24 02:42:44,024 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:44,050 : INFO : topic #5 (0.020): 0.277*\"classic\" + 0.065*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.033*\"Musical\" + 0.029*\"national film registry\" + 0.021*\"afi 100\" + 0.018*\"john travolta\" + 0.017*\"70mm\" + 0.016*\"adapted from b'way\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:44,051 : INFO : topic #36 (0.020): 0.084*\"cult film\" + 0.046*\"peter jackson\" + 0.044*\"russell crowe\" + 0.038*\"downbeat\" + 0.031*\"adam sandler\" + 0.029*\"1980s\" + 0.022*\"80s\" + 0.021*\"new zealand\" + 0.019*\"not available from netflix\" + 0.014*\"must see!\"\n", - "2021-08-24 02:42:44,052 : INFO : topic #11 (0.020): 0.129*\"religion\" + 0.115*\"keanu reeves\" + 0.035*\"island\" + 0.021*\"slavery\" + 0.018*\"courtesan\" + 0.017*\"reality tv\" + 0.016*\"class issues\" + 0.016*\"slash\" + 0.016*\"facebook rec\" + 0.015*\"product placement\"\n", - "2021-08-24 02:42:44,052 : INFO : topic #24 (0.020): 0.080*\"hitchcock\" + 0.078*\"murder\" + 0.068*\"vampire\" + 0.061*\"alfred hitchcock\" + 0.056*\"vampires\" + 0.053*\"oppl\" + 0.048*\"memory\" + 0.019*\"incest\" + 0.018*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:44,053 : INFO : topic #45 (0.020): 0.191*\"less than 300 ratings\" + 0.109*\"Drama\" + 0.081*\"nudity (topless - notable)\" + 0.050*\"tim burton\" + 0.031*\"not corv lib\" + 0.022*\"library\" + 0.017*\"depressing\" + 0.017*\"blindfold\" + 0.015*\"golden raspberry (worst actor)\" + 0.014*\"san francisco\"\n", - "2021-08-24 02:42:44,054 : INFO : topic diff=0.053205, rho=0.198652\n", - "2021-08-24 02:42:44,055 : INFO : PROGRESS: pass 19, at document #8000/10681\n", - "2021-08-24 02:42:44,298 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:44,325 : INFO : topic #36 (0.020): 0.078*\"cult film\" + 0.049*\"peter jackson\" + 0.042*\"downbeat\" + 0.041*\"russell crowe\" + 0.031*\"adam sandler\" + 0.029*\"not available from netflix\" + 0.026*\"1980s\" + 0.024*\"new zealand\" + 0.021*\"80s\" + 0.016*\"drew barrymore\"\n", - "2021-08-24 02:42:44,326 : INFO : topic #31 (0.020): 0.139*\"zombies\" + 0.073*\"horror\" + 0.065*\"pg-13\" + 0.039*\"zombie\" + 0.033*\"cult classic\" + 0.026*\"sam raimi\" + 0.026*\"campy\" + 0.023*\"books\" + 0.022*\"joaquin phoenix\" + 0.020*\"infidelity\"\n", - "2021-08-24 02:42:44,327 : INFO : topic #40 (0.020): 0.246*\"r\" + 0.115*\"clearplay\" + 0.054*\"movie to see\" + 0.050*\"prison\" + 0.045*\"Drama\" + 0.031*\"morgan freeman\" + 0.027*\"friendship\" + 0.020*\"john wayne\" + 0.019*\"conspiracy\" + 0.017*\"1970s\"\n", - "2021-08-24 02:42:44,328 : INFO : topic #32 (0.020): 0.136*\"johnny depp\" + 0.069*\"vhs\" + 0.067*\"clint eastwood\" + 0.045*\"jackie chan\" + 0.033*\"western\" + 0.030*\"kung fu\" + 0.030*\"propaganda\" + 0.029*\"india\" + 0.027*\"david lynch\" + 0.026*\"italian\"\n", - "2021-08-24 02:42:44,329 : INFO : topic #19 (0.020): 0.060*\"drama\" + 0.053*\"nudity (full frontal)\" + 0.034*\"philip k. dick\" + 0.033*\"Drama\" + 0.026*\"rape\" + 0.026*\"vietnam war\" + 0.025*\"vietnam\" + 0.024*\"very good\" + 0.020*\"soccer\" + 0.017*\"to see\"\n", - "2021-08-24 02:42:44,330 : INFO : topic diff=0.043673, rho=0.198652\n", - "2021-08-24 02:42:44,332 : INFO : PROGRESS: pass 19, at document #10000/10681\n", - "2021-08-24 02:42:44,584 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:44,612 : INFO : topic #19 (0.020): 0.063*\"nudity (full frontal)\" + 0.054*\"drama\" + 0.035*\"Drama\" + 0.034*\"philip k. dick\" + 0.025*\"ummarti2007\" + 0.024*\"rape\" + 0.024*\"slow\" + 0.021*\"male nudity\" + 0.021*\"vietnam war\" + 0.021*\"very good\"\n", - "2021-08-24 02:42:44,613 : INFO : topic #15 (0.020): 0.118*\"disney\" + 0.098*\"animation\" + 0.085*\"pixar\" + 0.045*\"Animation\" + 0.044*\"pirates\" + 0.040*\"children\" + 0.035*\"Children\" + 0.028*\"angelina jolie\" + 0.025*\"disney animated feature\" + 0.022*\"cartoon\"\n", - "2021-08-24 02:42:44,614 : INFO : topic #17 (0.020): 0.347*\"Sci-Fi\" + 0.079*\"Horror\" + 0.067*\"Action\" + 0.040*\"video game adaptation\" + 0.023*\"football\" + 0.020*\"animals\" + 0.019*\"movie to see\" + 0.016*\"futuristic\" + 0.014*\"milla jovovich\" + 0.014*\"g\"\n", - "2021-08-24 02:42:44,615 : INFO : topic #36 (0.020): 0.068*\"cult film\" + 0.046*\"downbeat\" + 0.045*\"peter jackson\" + 0.040*\"russell crowe\" + 0.034*\"not available from netflix\" + 0.031*\"adam sandler\" + 0.025*\"1980s\" + 0.023*\"new zealand\" + 0.018*\"existentialism\" + 0.017*\"must see!\"\n", - "2021-08-24 02:42:44,616 : INFO : topic #47 (0.020): 0.062*\"serial killer\" + 0.055*\"psychology\" + 0.050*\"brad pitt\" + 0.041*\"edward norton\" + 0.036*\"heist\" + 0.032*\"matt damon\" + 0.030*\"hayao miyazaki\" + 0.026*\"steven spielberg\" + 0.019*\"crime\" + 0.016*\"brilliant\"\n", - "2021-08-24 02:42:44,617 : INFO : topic diff=0.036102, rho=0.198652\n", - "2021-08-24 02:42:44,771 : INFO : -20.966 per-word bound, 2047618.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:44,772 : INFO : PROGRESS: pass 19, at document #10681/10681\n", - "2021-08-24 02:42:44,871 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:44,898 : INFO : topic #0 (0.020): 0.146*\"can't remember\" + 0.102*\"based on a tv show\" + 0.089*\"directorial debut\" + 0.074*\"Comedy\" + 0.052*\"ummarti2006\" + 0.046*\"keira knightley\" + 0.028*\"australia\" + 0.025*\"australian\" + 0.023*\"immigrants\" + 0.023*\"dani2006\"\n", - "2021-08-24 02:42:44,899 : INFO : topic #44 (0.020): 0.080*\"mafia\" + 0.077*\"music\" + 0.052*\"organized crime\" + 0.050*\"martin scorsese\" + 0.046*\"rock and roll\" + 0.042*\"Musical\" + 0.026*\"wired 50 greatest soundtracks\" + 0.025*\"guns\" + 0.020*\"confrontational\" + 0.018*\"dysfunctional family\"\n", - "2021-08-24 02:42:44,900 : INFO : topic #48 (0.020): 0.126*\"netflix\" + 0.079*\"twist ending\" + 0.071*\"bruce willis\" + 0.045*\"coming of age\" + 0.035*\"journalism\" + 0.034*\"to see\" + 0.032*\"Drama\" + 0.025*\"talking animals\" + 0.024*\"relationships\" + 0.020*\"sexy\"\n", - "2021-08-24 02:42:44,900 : INFO : topic #26 (0.020): 0.044*\"Drama\" + 0.031*\"james bond\" + 0.020*\"reflective\" + 0.020*\"poignant\" + 0.019*\"atmospheric\" + 0.019*\"007\" + 0.019*\"bond\" + 0.019*\"assassin\" + 0.019*\"bittersweet\" + 0.018*\"lyrical\"\n", - "2021-08-24 02:42:44,901 : INFO : topic #45 (0.020): 0.264*\"less than 300 ratings\" + 0.115*\"Drama\" + 0.062*\"nudity (topless - notable)\" + 0.039*\"tim burton\" + 0.030*\"not corv lib\" + 0.028*\"library\" + 0.016*\"blindfold\" + 0.016*\"depressing\" + 0.014*\"san francisco\" + 0.013*\"seen 2007\"\n", - "2021-08-24 02:42:44,902 : INFO : topic diff=0.079569, rho=0.198652\n", - "2021-08-24 02:42:44,904 : INFO : PROGRESS: pass 20, at document #2000/10681\n", - "2021-08-24 02:42:45,189 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:45,217 : INFO : topic #31 (0.020): 0.143*\"zombies\" + 0.076*\"pg-13\" + 0.074*\"horror\" + 0.031*\"cult classic\" + 0.027*\"zombie\" + 0.024*\"sam raimi\" + 0.023*\"campy\" + 0.021*\"infidelity\" + 0.021*\"books\" + 0.020*\"joaquin phoenix\"\n", - "2021-08-24 02:42:45,217 : INFO : topic #8 (0.020): 0.105*\"anime\" + 0.085*\"true story\" + 0.082*\"tom hanks\" + 0.071*\"based on a true story\" + 0.026*\"good\" + 0.026*\"drama\" + 0.025*\"archaeology\" + 0.021*\"japan\" + 0.020*\"to-rent\" + 0.020*\"interesting\"\n", - "2021-08-24 02:42:45,218 : INFO : topic #23 (0.020): 0.084*\"high school\" + 0.061*\"teen\" + 0.051*\"cars\" + 0.035*\"dance\" + 0.028*\"marx brothers\" + 0.027*\"sandra bullock\" + 0.023*\"Comedy\" + 0.021*\"kate winslet\" + 0.020*\"(s)vcd\" + 0.020*\"eddie murphy\"\n", - "2021-08-24 02:42:45,219 : INFO : topic #46 (0.020): 0.090*\"action\" + 0.052*\"fantasy\" + 0.047*\"sci-fi\" + 0.035*\"adventure\" + 0.029*\"dvd\" + 0.024*\"Adventure\" + 0.022*\"seen at the cinema\" + 0.021*\"seen more than once\" + 0.021*\"space\" + 0.020*\"harrison ford\"\n", - "2021-08-24 02:42:45,220 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.068*\"Romance\" + 0.052*\"chick flick\" + 0.036*\"boring\" + 0.028*\"girlie movie\" + 0.024*\"Comedy\" + 0.024*\"love story\" + 0.023*\"baseball\" + 0.020*\"wedding\" + 0.020*\"whimsical\"\n", - "2021-08-24 02:42:45,221 : INFO : topic diff=0.146576, rho=0.194844\n", - "2021-08-24 02:42:45,223 : INFO : PROGRESS: pass 20, at document #4000/10681\n", - "2021-08-24 02:42:45,491 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:45,517 : INFO : topic #33 (0.020): 0.114*\"comedy\" + 0.109*\"Comedy\" + 0.057*\"funny\" + 0.036*\"parody\" + 0.036*\"dark comedy\" + 0.034*\"seen more than once\" + 0.029*\"coen brothers\" + 0.029*\"quirky\" + 0.029*\"hilarious\" + 0.022*\"black comedy\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:45,518 : INFO : topic #9 (0.020): 0.245*\"based on a book\" + 0.112*\"adapted from:book\" + 0.054*\"Drama\" + 0.049*\"stephen king\" + 0.041*\"jack nicholson\" + 0.037*\"based on book\" + 0.026*\"imdb bottom 100\" + 0.024*\"stupid\" + 0.021*\"literary adaptation\" + 0.021*\"los angeles\"\n", - "2021-08-24 02:42:45,519 : INFO : topic #36 (0.020): 0.089*\"cult film\" + 0.047*\"russell crowe\" + 0.040*\"downbeat\" + 0.032*\"adam sandler\" + 0.032*\"1980s\" + 0.030*\"peter jackson\" + 0.020*\"80s\" + 0.020*\"not available from netflix\" + 0.020*\"new zealand\" + 0.016*\"virus\"\n", - "2021-08-24 02:42:45,520 : INFO : topic #15 (0.020): 0.162*\"disney\" + 0.091*\"animation\" + 0.076*\"pixar\" + 0.049*\"Animation\" + 0.043*\"children\" + 0.040*\"Children\" + 0.033*\"disney animated feature\" + 0.026*\"angelina jolie\" + 0.025*\"pirates\" + 0.020*\"cartoon\"\n", - "2021-08-24 02:42:45,521 : INFO : topic #42 (0.020): 0.092*\"robin williams\" + 0.089*\"martial arts\" + 0.070*\"japan\" + 0.039*\"samurai\" + 0.036*\"akira kurosawa\" + 0.029*\"Drama\" + 0.027*\"orson welles\" + 0.025*\"divorce\" + 0.022*\"kurosawa\" + 0.020*\"christopher walken\"\n", - "2021-08-24 02:42:45,522 : INFO : topic diff=0.026657, rho=0.194844\n", - "2021-08-24 02:42:45,524 : INFO : PROGRESS: pass 20, at document #6000/10681\n", - "2021-08-24 02:42:45,768 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:45,796 : INFO : topic #27 (0.020): 0.319*\"Horror\" + 0.157*\"Thriller\" + 0.127*\"Mystery\" + 0.042*\"Drama\" + 0.028*\"easily confused with other movie(s) (title)\" + 0.028*\"Fantasy\" + 0.027*\"Film-Noir\" + 0.015*\"eerie\" + 0.015*\"slasher\" + 0.014*\"tumey's dvds\"\n", - "2021-08-24 02:42:45,797 : INFO : topic #7 (0.020): 0.111*\"romance\" + 0.061*\"Romance\" + 0.052*\"chick flick\" + 0.038*\"boring\" + 0.032*\"girlie movie\" + 0.029*\"baseball\" + 0.025*\"Comedy\" + 0.023*\"whimsical\" + 0.018*\"wedding\" + 0.017*\"comedy\"\n", - "2021-08-24 02:42:45,797 : INFO : topic #42 (0.020): 0.092*\"robin williams\" + 0.087*\"martial arts\" + 0.067*\"japan\" + 0.037*\"samurai\" + 0.035*\"akira kurosawa\" + 0.031*\"Drama\" + 0.027*\"orson welles\" + 0.025*\"hugh grant\" + 0.022*\"julianne moore\" + 0.022*\"divorce\"\n", - "2021-08-24 02:42:45,798 : INFO : topic #21 (0.020): 0.078*\"aliens\" + 0.075*\"Sci-Fi\" + 0.060*\"sci-fi\" + 0.043*\"space\" + 0.040*\"futuristmovies.com\" + 0.026*\"will smith\" + 0.024*\"Action\" + 0.023*\"ridley scott\" + 0.022*\"tommy lee jones\" + 0.019*\"70mm\"\n", - "2021-08-24 02:42:45,799 : INFO : topic #38 (0.020): 0.177*\"drugs\" + 0.044*\"samuel l. jackson\" + 0.027*\"divx\" + 0.026*\"ewan mcgregor\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.022*\"poverty\" + 0.021*\"michael caine\" + 0.021*\"basketball\" + 0.020*\"liam neeson\"\n", - "2021-08-24 02:42:45,801 : INFO : topic diff=0.051831, rho=0.194844\n", - "2021-08-24 02:42:45,803 : INFO : PROGRESS: pass 20, at document #8000/10681\n", - "2021-08-24 02:42:46,047 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:46,074 : INFO : topic #45 (0.020): 0.231*\"less than 300 ratings\" + 0.116*\"Drama\" + 0.074*\"nudity (topless - notable)\" + 0.042*\"tim burton\" + 0.028*\"not corv lib\" + 0.025*\"library\" + 0.016*\"blindfold\" + 0.016*\"depressing\" + 0.015*\"michael moore\" + 0.013*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:46,075 : INFO : topic #2 (0.020): 0.288*\"Documentary\" + 0.103*\"to see\" + 0.064*\"documentary\" + 0.026*\"mockumentary\" + 0.026*\"sexuality\" + 0.023*\"star trek\" + 0.021*\"dogs\" + 0.021*\"kevin smith\" + 0.018*\"in netflix queue\" + 0.018*\"movie to see\"\n", - "2021-08-24 02:42:46,076 : INFO : topic #19 (0.020): 0.061*\"drama\" + 0.053*\"nudity (full frontal)\" + 0.034*\"philip k. dick\" + 0.033*\"Drama\" + 0.026*\"rape\" + 0.026*\"vietnam war\" + 0.025*\"vietnam\" + 0.024*\"very good\" + 0.019*\"soccer\" + 0.017*\"to see\"\n", - "2021-08-24 02:42:46,076 : INFO : topic #25 (0.020): 0.167*\"Western\" + 0.077*\"erlend's dvds\" + 0.049*\"woody allen\" + 0.048*\"Drama\" + 0.046*\"gothic\" + 0.036*\"gene hackman\" + 0.032*\"scary movies to see on halloween\" + 0.029*\"nicole kidman\" + 0.024*\"george clooney\" + 0.024*\"sad\"\n", - "2021-08-24 02:42:46,077 : INFO : topic #43 (0.020): 0.083*\"surreal\" + 0.046*\"satirical\" + 0.042*\"quirky\" + 0.039*\"cynical\" + 0.033*\"dreamlike\" + 0.033*\"biting\" + 0.033*\"narrated\" + 0.029*\"stanley kubrick\" + 0.026*\"irreverent\" + 0.025*\"hallucinatory\"\n", - "2021-08-24 02:42:46,078 : INFO : topic diff=0.042951, rho=0.194844\n", - "2021-08-24 02:42:46,079 : INFO : PROGRESS: pass 20, at document #10000/10681\n", - "2021-08-24 02:42:46,322 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:46,350 : INFO : topic #7 (0.020): 0.108*\"romance\" + 0.062*\"Romance\" + 0.045*\"chick flick\" + 0.040*\"boring\" + 0.027*\"girlie movie\" + 0.025*\"baseball\" + 0.023*\"Comedy\" + 0.023*\"love story\" + 0.023*\"whimsical\" + 0.019*\"wedding\"\n", - "2021-08-24 02:42:46,351 : INFO : topic #27 (0.020): 0.307*\"Horror\" + 0.161*\"Thriller\" + 0.133*\"Mystery\" + 0.049*\"Drama\" + 0.034*\"easily confused with other movie(s) (title)\" + 0.031*\"Fantasy\" + 0.031*\"Film-Noir\" + 0.015*\"eerie\" + 0.014*\"tumey's dvds\" + 0.010*\"slasher\"\n", - "2021-08-24 02:42:46,352 : INFO : topic #32 (0.020): 0.150*\"johnny depp\" + 0.064*\"clint eastwood\" + 0.061*\"vhs\" + 0.046*\"watched 2007\" + 0.039*\"jackie chan\" + 0.038*\"propaganda\" + 0.030*\"western\" + 0.029*\"leonardo dicaprio\" + 0.027*\"italian\" + 0.026*\"kung fu\"\n", - "2021-08-24 02:42:46,353 : INFO : topic #12 (0.020): 0.160*\"comic book\" + 0.100*\"superhero\" + 0.068*\"dystopia\" + 0.068*\"super-hero\" + 0.045*\"holocaust\" + 0.034*\"adapted from:comic\" + 0.022*\"kidnapping\" + 0.021*\"alter ego\" + 0.018*\"batman\" + 0.017*\"dark\"\n", - "2021-08-24 02:42:46,354 : INFO : topic #48 (0.020): 0.102*\"netflix\" + 0.084*\"bruce willis\" + 0.084*\"twist ending\" + 0.044*\"coming of age\" + 0.037*\"journalism\" + 0.025*\"talking animals\" + 0.025*\"Drama\" + 0.024*\"sexy\" + 0.022*\"to see\" + 0.021*\"avi\"\n", - "2021-08-24 02:42:46,355 : INFO : topic diff=0.035285, rho=0.194844\n", - "2021-08-24 02:42:46,501 : INFO : -20.965 per-word bound, 2047510.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:46,501 : INFO : PROGRESS: pass 20, at document #10681/10681\n", - "2021-08-24 02:42:46,599 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:46,626 : INFO : topic #0 (0.020): 0.147*\"can't remember\" + 0.102*\"based on a tv show\" + 0.090*\"directorial debut\" + 0.074*\"Comedy\" + 0.051*\"ummarti2006\" + 0.046*\"keira knightley\" + 0.028*\"australia\" + 0.025*\"australian\" + 0.023*\"immigrants\" + 0.023*\"dani2006\"\n", - "2021-08-24 02:42:46,627 : INFO : topic #4 (0.020): 0.066*\"Drama\" + 0.062*\"sports\" + 0.060*\"london\" + 0.047*\"sven's to see list\" + 0.045*\"boxing\" + 0.040*\"underrated\" + 0.038*\"british\" + 0.033*\"hw drama\" + 0.032*\"notable soundtrack\" + 0.030*\"inspirational\"\n", - "2021-08-24 02:42:46,628 : INFO : topic #35 (0.020): 0.355*\"Comedy\" + 0.314*\"Drama\" + 0.200*\"Romance\" + 0.014*\"lesbian\" + 0.008*\"bibliothek\" + 0.008*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.005*\"oscar (best foreign language film)\" + 0.003*\"great cinematography\"\n", - "2021-08-24 02:42:46,628 : INFO : topic #6 (0.020): 0.144*\"Musical\" + 0.114*\"politics\" + 0.063*\"satire\" + 0.039*\"nicolas cage\" + 0.038*\"terrorism\" + 0.033*\"dvd-r\" + 0.028*\"corruption\" + 0.028*\"sean connery\" + 0.027*\"police\" + 0.025*\"political\"\n", - "2021-08-24 02:42:46,629 : INFO : topic #32 (0.020): 0.148*\"johnny depp\" + 0.054*\"clint eastwood\" + 0.054*\"vhs\" + 0.043*\"propaganda\" + 0.042*\"kung fu\" + 0.040*\"jackie chan\" + 0.039*\"watched 2007\" + 0.034*\"india\" + 0.030*\"western\" + 0.024*\"leonardo dicaprio\"\n", - "2021-08-24 02:42:46,630 : INFO : topic diff=0.078000, rho=0.194844\n", - "2021-08-24 02:42:46,632 : INFO : PROGRESS: pass 21, at document #2000/10681\n", - "2021-08-24 02:42:46,911 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:46,938 : INFO : topic #46 (0.020): 0.090*\"action\" + 0.052*\"fantasy\" + 0.047*\"sci-fi\" + 0.035*\"adventure\" + 0.029*\"dvd\" + 0.024*\"Adventure\" + 0.022*\"seen at the cinema\" + 0.021*\"seen more than once\" + 0.021*\"space\" + 0.020*\"harrison ford\"\n", - "2021-08-24 02:42:46,939 : INFO : topic #12 (0.020): 0.139*\"comic book\" + 0.108*\"superhero\" + 0.073*\"dystopia\" + 0.054*\"holocaust\" + 0.054*\"super-hero\" + 0.032*\"adapted from:comic\" + 0.031*\"batman\" + 0.024*\"kidnapping\" + 0.017*\"action\" + 0.016*\"alter ego\"\n", - "2021-08-24 02:42:46,940 : INFO : topic #26 (0.020): 0.043*\"Drama\" + 0.025*\"james bond\" + 0.022*\"mel gibson\" + 0.021*\"poignant\" + 0.020*\"atmospheric\" + 0.019*\"bittersweet\" + 0.019*\"reflective\" + 0.018*\"lyrical\" + 0.017*\"assassin\" + 0.017*\"bond\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:46,941 : INFO : topic #15 (0.020): 0.167*\"disney\" + 0.094*\"animation\" + 0.074*\"pixar\" + 0.047*\"Animation\" + 0.043*\"children\" + 0.038*\"Children\" + 0.030*\"disney animated feature\" + 0.029*\"pirates\" + 0.024*\"angelina jolie\" + 0.021*\"cartoon\"\n", - "2021-08-24 02:42:46,942 : INFO : topic #23 (0.020): 0.084*\"high school\" + 0.061*\"teen\" + 0.051*\"cars\" + 0.035*\"dance\" + 0.028*\"marx brothers\" + 0.027*\"sandra bullock\" + 0.023*\"Comedy\" + 0.021*\"kate winslet\" + 0.020*\"(s)vcd\" + 0.020*\"eddie murphy\"\n", - "2021-08-24 02:42:46,943 : INFO : topic diff=0.143643, rho=0.191248\n", - "2021-08-24 02:42:46,944 : INFO : PROGRESS: pass 21, at document #4000/10681\n", - "2021-08-24 02:42:47,195 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:47,223 : INFO : topic #13 (0.020): 0.054*\"fairy tale\" + 0.044*\"jane austen\" + 0.041*\"road trip\" + 0.036*\"sequel\" + 0.030*\"assassination\" + 0.029*\"cold war\" + 0.027*\"historical\" + 0.022*\"kids\" + 0.021*\"heartwarming\" + 0.020*\"gerard depardieu\"\n", - "2021-08-24 02:42:47,224 : INFO : topic #31 (0.020): 0.125*\"zombies\" + 0.075*\"horror\" + 0.071*\"pg-13\" + 0.040*\"cult classic\" + 0.026*\"campy\" + 0.025*\"infidelity\" + 0.024*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.023*\"zombie\" + 0.020*\"books\"\n", - "2021-08-24 02:42:47,225 : INFO : topic #19 (0.020): 0.065*\"nudity (full frontal)\" + 0.062*\"drama\" + 0.036*\"Drama\" + 0.028*\"philip k. dick\" + 0.028*\"vietnam war\" + 0.028*\"vietnam\" + 0.027*\"rape\" + 0.022*\"to see\" + 0.021*\"very good\" + 0.017*\"slow\"\n", - "2021-08-24 02:42:47,225 : INFO : topic #12 (0.020): 0.131*\"comic book\" + 0.112*\"superhero\" + 0.075*\"dystopia\" + 0.056*\"super-hero\" + 0.055*\"holocaust\" + 0.034*\"adapted from:comic\" + 0.026*\"batman\" + 0.021*\"kidnapping\" + 0.018*\"alter ego\" + 0.017*\"Action\"\n", - "2021-08-24 02:42:47,226 : INFO : topic #37 (0.020): 0.181*\"War\" + 0.107*\"world war ii\" + 0.102*\"Drama\" + 0.056*\"war\" + 0.041*\"history\" + 0.037*\"jim carrey\" + 0.035*\"Action\" + 0.022*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:42:47,228 : INFO : topic diff=0.025834, rho=0.191248\n", - "2021-08-24 02:42:47,229 : INFO : PROGRESS: pass 21, at document #6000/10681\n", - "2021-08-24 02:42:47,602 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:47,635 : INFO : topic #10 (0.020): 0.252*\"Thriller\" + 0.231*\"Crime\" + 0.169*\"Action\" + 0.159*\"Drama\" + 0.028*\"Mystery\" + 0.016*\"murder\" + 0.011*\"crime\" + 0.010*\"virtual reality\" + 0.006*\"thriller\" + 0.005*\"well done\"\n", - "2021-08-24 02:42:47,635 : INFO : topic #45 (0.020): 0.192*\"less than 300 ratings\" + 0.110*\"Drama\" + 0.080*\"nudity (topless - notable)\" + 0.050*\"tim burton\" + 0.031*\"not corv lib\" + 0.022*\"library\" + 0.017*\"depressing\" + 0.017*\"blindfold\" + 0.015*\"golden raspberry (worst actor)\" + 0.014*\"san francisco\"\n", - "2021-08-24 02:42:47,636 : INFO : topic #37 (0.020): 0.183*\"War\" + 0.112*\"world war ii\" + 0.101*\"Drama\" + 0.058*\"war\" + 0.042*\"history\" + 0.035*\"Action\" + 0.032*\"jim carrey\" + 0.021*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:42:47,637 : INFO : topic #22 (0.020): 0.260*\"betamax\" + 0.100*\"dvd-video\" + 0.069*\"clv\" + 0.031*\"aviation\" + 0.023*\"library on hold\" + 0.023*\"terry gilliam\" + 0.017*\"dvd collection\" + 0.015*\"gilliam\" + 0.014*\"70mm\" + 0.014*\"steven seagal\"\n", - "2021-08-24 02:42:47,638 : INFO : topic #6 (0.020): 0.134*\"Musical\" + 0.095*\"politics\" + 0.068*\"satire\" + 0.045*\"nicolas cage\" + 0.043*\"sean connery\" + 0.034*\"dvd-r\" + 0.029*\"terrorism\" + 0.028*\"police\" + 0.023*\"netwatch\" + 0.022*\"dvd-ram\"\n", - "2021-08-24 02:42:47,639 : INFO : topic diff=0.050826, rho=0.191248\n", - "2021-08-24 02:42:47,641 : INFO : PROGRESS: pass 21, at document #8000/10681\n", - "2021-08-24 02:42:47,942 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:47,970 : INFO : topic #22 (0.020): 0.260*\"betamax\" + 0.103*\"dvd-video\" + 0.068*\"clv\" + 0.029*\"aviation\" + 0.019*\"library on hold\" + 0.019*\"terry gilliam\" + 0.017*\"dvd collection\" + 0.015*\"seen 2008\" + 0.014*\"70mm\" + 0.014*\"gilliam\"\n", - "2021-08-24 02:42:47,971 : INFO : topic #3 (0.020): 0.148*\"oscar (best picture)\" + 0.061*\"oscar (best actor)\" + 0.059*\"oscar (best directing)\" + 0.058*\"oscar (best supporting actor)\" + 0.033*\"al pacino\" + 0.026*\"afi 100 (cheers)\" + 0.025*\"Drama\" + 0.019*\"tumey's dvds\" + 0.018*\"afi 100\" + 0.017*\"great acting\"\n", - "2021-08-24 02:42:47,972 : INFO : topic #2 (0.020): 0.288*\"Documentary\" + 0.103*\"to see\" + 0.064*\"documentary\" + 0.026*\"mockumentary\" + 0.025*\"sexuality\" + 0.023*\"star trek\" + 0.021*\"dogs\" + 0.021*\"kevin smith\" + 0.018*\"in netflix queue\" + 0.018*\"movie to see\"\n", - "2021-08-24 02:42:47,974 : INFO : topic #18 (0.020): 0.081*\"magic\" + 0.076*\"gay\" + 0.056*\"racism\" + 0.042*\"pg13\" + 0.039*\"Drama\" + 0.028*\"food\" + 0.024*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.017*\"denzel washington\"\n", - "2021-08-24 02:42:47,975 : INFO : topic #46 (0.020): 0.089*\"action\" + 0.064*\"fantasy\" + 0.044*\"sci-fi\" + 0.037*\"adventure\" + 0.034*\"dvd\" + 0.025*\"Adventure\" + 0.023*\"seen more than once\" + 0.022*\"seen at the cinema\" + 0.018*\"tom cruise\" + 0.017*\"Action\"\n", - "2021-08-24 02:42:47,976 : INFO : topic diff=0.041875, rho=0.191248\n", - "2021-08-24 02:42:47,978 : INFO : PROGRESS: pass 21, at document #10000/10681\n", - "2021-08-24 02:42:48,234 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:48,264 : INFO : topic #20 (0.020): 0.095*\"biography\" + 0.068*\"Drama\" + 0.054*\"biopic\" + 0.045*\"death\" + 0.044*\"corvallis library\" + 0.037*\"suicide\" + 0.034*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.027*\"ingmar bergman\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:48,265 : INFO : topic #23 (0.020): 0.080*\"high school\" + 0.070*\"teen\" + 0.064*\"cars\" + 0.038*\"marx brothers\" + 0.036*\"dance\" + 0.027*\"Comedy\" + 0.022*\"eddie murphy\" + 0.022*\"kate winslet\" + 0.021*\"(s)vcd\" + 0.020*\"car chase\"\n", - "2021-08-24 02:42:48,265 : INFO : topic #37 (0.020): 0.194*\"War\" + 0.112*\"Drama\" + 0.103*\"world war ii\" + 0.057*\"war\" + 0.048*\"history\" + 0.036*\"Action\" + 0.032*\"jim carrey\" + 0.018*\"africa\" + 0.017*\"nazis\" + 0.014*\"wwii\"\n", - "2021-08-24 02:42:48,266 : INFO : topic #21 (0.020): 0.077*\"Sci-Fi\" + 0.068*\"aliens\" + 0.061*\"sci-fi\" + 0.039*\"space\" + 0.036*\"futuristmovies.com\" + 0.024*\"will smith\" + 0.023*\"Action\" + 0.022*\"future\" + 0.021*\"tommy lee jones\" + 0.020*\"ridley scott\"\n", - "2021-08-24 02:42:48,267 : INFO : topic #36 (0.020): 0.069*\"cult film\" + 0.046*\"downbeat\" + 0.045*\"peter jackson\" + 0.040*\"russell crowe\" + 0.034*\"not available from netflix\" + 0.031*\"adam sandler\" + 0.026*\"1980s\" + 0.023*\"new zealand\" + 0.018*\"existentialism\" + 0.017*\"must see!\"\n", - "2021-08-24 02:42:48,268 : INFO : topic diff=0.034685, rho=0.191248\n", - "2021-08-24 02:42:48,425 : INFO : -20.964 per-word bound, 2045562.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:48,426 : INFO : PROGRESS: pass 21, at document #10681/10681\n", - "2021-08-24 02:42:48,530 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:48,556 : INFO : topic #46 (0.020): 0.083*\"action\" + 0.064*\"fantasy\" + 0.038*\"sci-fi\" + 0.037*\"adventure\" + 0.033*\"dvd\" + 0.027*\"Adventure\" + 0.021*\"watched 2006\" + 0.021*\"robots\" + 0.020*\"seen at the cinema\" + 0.018*\"seen more than once\"\n", - "2021-08-24 02:42:48,557 : INFO : topic #33 (0.020): 0.118*\"Comedy\" + 0.103*\"comedy\" + 0.063*\"funny\" + 0.038*\"parody\" + 0.034*\"dark comedy\" + 0.031*\"seen more than once\" + 0.028*\"hilarious\" + 0.027*\"coen brothers\" + 0.024*\"quirky\" + 0.022*\"black comedy\"\n", - "2021-08-24 02:42:48,558 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.053*\"disturbing\" + 0.051*\"Drama\" + 0.050*\"tense\" + 0.046*\"atmospheric\" + 0.030*\"stylized\" + 0.028*\"bleak\" + 0.027*\"tumey's dvds\" + 0.023*\"menacing\" + 0.023*\"erlend's dvds\"\n", - "2021-08-24 02:42:48,559 : INFO : topic #10 (0.020): 0.250*\"Thriller\" + 0.240*\"Crime\" + 0.170*\"Drama\" + 0.167*\"Action\" + 0.027*\"Mystery\" + 0.018*\"murder\" + 0.010*\"crime\" + 0.007*\"virtual reality\" + 0.004*\"thriller\" + 0.004*\"greg kinnear\"\n", - "2021-08-24 02:42:48,560 : INFO : topic #19 (0.020): 0.081*\"nudity (full frontal)\" + 0.047*\"drama\" + 0.038*\"Drama\" + 0.033*\"to see\" + 0.029*\"philip k. dick\" + 0.027*\"rape\" + 0.024*\"ummarti2007\" + 0.022*\"male nudity\" + 0.020*\"slow\" + 0.019*\"vietnam war\"\n", - "2021-08-24 02:42:48,561 : INFO : topic diff=0.076797, rho=0.191248\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:48,563 : INFO : PROGRESS: pass 22, at document #2000/10681\n", - "2021-08-24 02:42:48,808 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:48,834 : INFO : topic #2 (0.020): 0.278*\"Documentary\" + 0.121*\"to see\" + 0.055*\"documentary\" + 0.030*\"mockumentary\" + 0.029*\"star trek\" + 0.025*\"kevin smith\" + 0.022*\"movie to see\" + 0.020*\"dogs\" + 0.019*\"sexuality\" + 0.016*\"owen wilson\"\n", - "2021-08-24 02:42:48,835 : INFO : topic #34 (0.020): 0.073*\"ghosts\" + 0.050*\"bill murray\" + 0.043*\"television\" + 0.030*\"courtroom\" + 0.029*\"courtroom drama\" + 0.026*\"secret service\" + 0.020*\"roman polanski\" + 0.017*\"18th century\" + 0.017*\"opera\" + 0.016*\"court\"\n", - "2021-08-24 02:42:48,836 : INFO : topic #31 (0.020): 0.143*\"zombies\" + 0.076*\"pg-13\" + 0.074*\"horror\" + 0.031*\"cult classic\" + 0.027*\"zombie\" + 0.024*\"sam raimi\" + 0.023*\"campy\" + 0.021*\"infidelity\" + 0.021*\"books\" + 0.020*\"joaquin phoenix\"\n", - "2021-08-24 02:42:48,836 : INFO : topic #33 (0.020): 0.109*\"Comedy\" + 0.106*\"comedy\" + 0.056*\"funny\" + 0.038*\"dark comedy\" + 0.034*\"parody\" + 0.032*\"seen more than once\" + 0.032*\"coen brothers\" + 0.028*\"hilarious\" + 0.027*\"quirky\" + 0.024*\"black comedy\"\n", - "2021-08-24 02:42:48,837 : INFO : topic #38 (0.020): 0.156*\"drugs\" + 0.049*\"samuel l. jackson\" + 0.027*\"divx\" + 0.025*\"addiction\" + 0.024*\"ewan mcgregor\" + 0.023*\"peter sellers\" + 0.022*\"hulu\" + 0.022*\"michael caine\" + 0.021*\"poverty\" + 0.020*\"liam neeson\"\n", - "2021-08-24 02:42:48,838 : INFO : topic diff=0.140542, rho=0.187844\n", - "2021-08-24 02:42:48,840 : INFO : PROGRESS: pass 22, at document #4000/10681\n", - "2021-08-24 02:42:49,079 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:49,106 : INFO : topic #38 (0.020): 0.169*\"drugs\" + 0.046*\"samuel l. jackson\" + 0.025*\"divx\" + 0.024*\"basketball\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.023*\"ewan mcgregor\" + 0.021*\"michael caine\" + 0.020*\"liam neeson\" + 0.020*\"poverty\"\n", - "2021-08-24 02:42:49,107 : INFO : topic #26 (0.020): 0.041*\"Drama\" + 0.037*\"james bond\" + 0.026*\"007\" + 0.025*\"bond\" + 0.023*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.020*\"poignant\" + 0.018*\"lyrical\" + 0.018*\"assassin\"\n", - "2021-08-24 02:42:49,108 : INFO : topic #17 (0.020): 0.337*\"Sci-Fi\" + 0.076*\"Horror\" + 0.065*\"Action\" + 0.038*\"video game adaptation\" + 0.028*\"football\" + 0.026*\"animals\" + 0.022*\"movie to see\" + 0.017*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:42:49,109 : INFO : topic #29 (0.020): 0.095*\"nudity (full frontal - notable)\" + 0.067*\"shakespeare\" + 0.051*\"Drama\" + 0.045*\"based on a play\" + 0.039*\"adapted from:play\" + 0.033*\"christianity\" + 0.029*\"religion\" + 0.022*\"cannibalism\" + 0.021*\"disability\" + 0.021*\"jodie foster\"\n", - "2021-08-24 02:42:49,110 : INFO : topic #46 (0.020): 0.091*\"action\" + 0.052*\"fantasy\" + 0.045*\"sci-fi\" + 0.035*\"adventure\" + 0.031*\"dvd\" + 0.025*\"Adventure\" + 0.023*\"seen at the cinema\" + 0.022*\"seen more than once\" + 0.020*\"harrison ford\" + 0.019*\"space\"\n", - "2021-08-24 02:42:49,111 : INFO : topic diff=0.025264, rho=0.187844\n", - "2021-08-24 02:42:49,113 : INFO : PROGRESS: pass 22, at document #6000/10681\n", - "2021-08-24 02:42:49,342 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:49,369 : INFO : topic #13 (0.020): 0.059*\"fairy tale\" + 0.038*\"jane austen\" + 0.038*\"sequel\" + 0.036*\"road trip\" + 0.027*\"cold war\" + 0.027*\"assassination\" + 0.027*\"historical\" + 0.027*\"gerard depardieu\" + 0.024*\"kids\" + 0.022*\"notable nudity\"\n", - "2021-08-24 02:42:49,370 : INFO : topic #22 (0.020): 0.260*\"betamax\" + 0.100*\"dvd-video\" + 0.069*\"clv\" + 0.031*\"aviation\" + 0.023*\"library on hold\" + 0.023*\"terry gilliam\" + 0.017*\"dvd collection\" + 0.015*\"gilliam\" + 0.014*\"steven seagal\" + 0.013*\"seen 2008\"\n", - "2021-08-24 02:42:49,371 : INFO : topic #32 (0.020): 0.135*\"johnny depp\" + 0.068*\"clint eastwood\" + 0.059*\"vhs\" + 0.046*\"jackie chan\" + 0.037*\"western\" + 0.031*\"kung fu\" + 0.031*\"leonardo dicaprio\" + 0.030*\"spaghetti western\" + 0.029*\"propaganda\" + 0.029*\"david lynch\"\n", - "2021-08-24 02:42:49,372 : INFO : topic #40 (0.020): 0.252*\"r\" + 0.124*\"clearplay\" + 0.057*\"movie to see\" + 0.052*\"prison\" + 0.044*\"Drama\" + 0.033*\"morgan freeman\" + 0.026*\"friendship\" + 0.018*\"conspiracy\" + 0.018*\"mel brooks\" + 0.017*\"john wayne\"\n", - "2021-08-24 02:42:49,373 : INFO : topic #3 (0.020): 0.162*\"oscar (best picture)\" + 0.064*\"oscar (best directing)\" + 0.063*\"oscar (best actor)\" + 0.056*\"oscar (best supporting actor)\" + 0.037*\"al pacino\" + 0.024*\"afi 100 (cheers)\" + 0.024*\"Drama\" + 0.020*\"afi 100\" + 0.019*\"great acting\" + 0.017*\"tumey's dvds\"\n", - "2021-08-24 02:42:49,375 : INFO : topic diff=0.049694, rho=0.187844\n", - "2021-08-24 02:42:49,376 : INFO : PROGRESS: pass 22, at document #8000/10681\n", - "2021-08-24 02:42:49,615 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:49,642 : INFO : topic #44 (0.020): 0.091*\"mafia\" + 0.080*\"music\" + 0.053*\"martin scorsese\" + 0.046*\"rock and roll\" + 0.043*\"organized crime\" + 0.037*\"Musical\" + 0.030*\"wired 50 greatest soundtracks\" + 0.025*\"confrontational\" + 0.025*\"guns\" + 0.020*\"francis ford coppola\"\n", - "2021-08-24 02:42:49,643 : INFO : topic #16 (0.020): 0.277*\"nudity (topless)\" + 0.187*\"nudity (topless - brief)\" + 0.066*\"nudity (rear)\" + 0.028*\"pg\" + 0.028*\"claymation\" + 0.022*\"military\" + 0.018*\"childhood\" + 0.017*\"aardman\" + 0.017*\"weird\" + 0.010*\"shark\"\n", - "2021-08-24 02:42:49,644 : INFO : topic #17 (0.020): 0.354*\"Sci-Fi\" + 0.083*\"Horror\" + 0.065*\"Action\" + 0.037*\"video game adaptation\" + 0.025*\"football\" + 0.019*\"animals\" + 0.018*\"futuristic\" + 0.017*\"movie to see\" + 0.014*\"g\" + 0.013*\"milla jovovich\"\n", - "2021-08-24 02:42:49,645 : INFO : topic #9 (0.020): 0.245*\"based on a book\" + 0.121*\"adapted from:book\" + 0.060*\"Drama\" + 0.039*\"stephen king\" + 0.038*\"jack nicholson\" + 0.037*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.020*\"stupid\" + 0.020*\"los angeles\"\n", - "2021-08-24 02:42:49,646 : INFO : topic #0 (0.020): 0.204*\"can't remember\" + 0.103*\"directorial debut\" + 0.083*\"based on a tv show\" + 0.081*\"Comedy\" + 0.036*\"ummarti2006\" + 0.027*\"australia\" + 0.026*\"australian\" + 0.025*\"keira knightley\" + 0.020*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:49,647 : INFO : topic diff=0.041137, rho=0.187844\n", - "2021-08-24 02:42:49,648 : INFO : PROGRESS: pass 22, at document #10000/10681\n", - "2021-08-24 02:42:49,880 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:49,907 : INFO : topic #42 (0.020): 0.098*\"japan\" + 0.091*\"martial arts\" + 0.068*\"robin williams\" + 0.037*\"akira kurosawa\" + 0.033*\"samurai\" + 0.031*\"Drama\" + 0.030*\"seen 2006\" + 0.029*\"jonossa\" + 0.025*\"orson welles\" + 0.022*\"divorce\"\n", - "2021-08-24 02:42:49,908 : INFO : topic #20 (0.020): 0.095*\"biography\" + 0.068*\"Drama\" + 0.054*\"biopic\" + 0.045*\"death\" + 0.043*\"corvallis library\" + 0.037*\"suicide\" + 0.034*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.027*\"ingmar bergman\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:49,909 : INFO : topic #39 (0.020): 0.175*\"time travel\" + 0.057*\"adultery\" + 0.049*\"motorcycle\" + 0.037*\"jude law\" + 0.036*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.021*\"dystopia\" + 0.021*\"want to own\" + 0.019*\"old\" + 0.018*\"hollywood\"\n", - "2021-08-24 02:42:49,910 : INFO : topic #45 (0.020): 0.255*\"less than 300 ratings\" + 0.117*\"Drama\" + 0.061*\"nudity (topless - notable)\" + 0.039*\"tim burton\" + 0.031*\"not corv lib\" + 0.025*\"library\" + 0.017*\"depressing\" + 0.016*\"blindfold\" + 0.015*\"seen 2007\" + 0.013*\"michael moore\"\n", - "2021-08-24 02:42:49,911 : INFO : topic #24 (0.020): 0.074*\"murder\" + 0.069*\"vampire\" + 0.063*\"vampires\" + 0.058*\"hitchcock\" + 0.053*\"oppl\" + 0.053*\"memory\" + 0.044*\"alfred hitchcock\" + 0.021*\"cary grant\" + 0.020*\"incest\" + 0.020*\"kirsten dunst\"\n", - "2021-08-24 02:42:49,912 : INFO : topic diff=0.033872, rho=0.187844\n", - "2021-08-24 02:42:50,056 : INFO : -20.964 per-word bound, 2045059.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:50,056 : INFO : PROGRESS: pass 22, at document #10681/10681\n", - "2021-08-24 02:42:50,158 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:50,184 : INFO : topic #41 (0.020): 0.134*\"remake\" + 0.077*\"new york city\" + 0.064*\"christmas\" + 0.041*\"family\" + 0.034*\"new york\" + 0.034*\"bibliothek\" + 0.030*\"Drama\" + 0.030*\"movie to see\" + 0.029*\"eric's dvds\" + 0.027*\"kevin spacey\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:50,185 : INFO : topic #22 (0.020): 0.209*\"betamax\" + 0.086*\"dvd-video\" + 0.053*\"clv\" + 0.051*\"library on hold\" + 0.036*\"aviation\" + 0.019*\"family drama\" + 0.019*\"seen 2008\" + 0.018*\"terry gilliam\" + 0.013*\"gilliam\" + 0.013*\"dvd collection\"\n", - "2021-08-24 02:42:50,186 : INFO : topic #20 (0.020): 0.100*\"biography\" + 0.071*\"Drama\" + 0.055*\"death\" + 0.045*\"suicide\" + 0.045*\"biopic\" + 0.038*\"corvallis library\" + 0.031*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.023*\"forest whitaker\" + 0.023*\"history\"\n", - "2021-08-24 02:42:50,187 : INFO : topic #39 (0.020): 0.156*\"time travel\" + 0.055*\"adultery\" + 0.045*\"motorcycle\" + 0.045*\"post apocalyptic\" + 0.042*\"post-apocalyptic\" + 0.035*\"jude law\" + 0.024*\"bechdel test:fail\" + 0.023*\"dystopia\" + 0.019*\"want to own\" + 0.017*\"old\"\n", - "2021-08-24 02:42:50,188 : INFO : topic #42 (0.020): 0.092*\"japan\" + 0.088*\"martial arts\" + 0.063*\"robin williams\" + 0.038*\"akira kurosawa\" + 0.035*\"samurai\" + 0.035*\"Drama\" + 0.028*\"seen 2006\" + 0.027*\"divorce\" + 0.027*\"jonossa\" + 0.023*\"orson welles\"\n", - "2021-08-24 02:42:50,190 : INFO : topic diff=0.075117, rho=0.187844\n", - "2021-08-24 02:42:50,192 : INFO : PROGRESS: pass 23, at document #2000/10681\n", - "2021-08-24 02:42:50,459 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:50,485 : INFO : topic #17 (0.020): 0.329*\"Sci-Fi\" + 0.069*\"Horror\" + 0.068*\"Action\" + 0.044*\"video game adaptation\" + 0.027*\"animals\" + 0.026*\"movie to see\" + 0.021*\"football\" + 0.019*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:42:50,487 : INFO : topic #34 (0.020): 0.073*\"ghosts\" + 0.051*\"bill murray\" + 0.044*\"television\" + 0.030*\"courtroom\" + 0.029*\"courtroom drama\" + 0.026*\"secret service\" + 0.020*\"roman polanski\" + 0.017*\"18th century\" + 0.017*\"opera\" + 0.017*\"court\"\n", - "2021-08-24 02:42:50,487 : INFO : topic #31 (0.020): 0.143*\"zombies\" + 0.076*\"pg-13\" + 0.074*\"horror\" + 0.031*\"cult classic\" + 0.027*\"zombie\" + 0.024*\"sam raimi\" + 0.022*\"campy\" + 0.021*\"infidelity\" + 0.021*\"books\" + 0.020*\"joaquin phoenix\"\n", - "2021-08-24 02:42:50,488 : INFO : topic #49 (0.020): 0.080*\"national film registry\" + 0.060*\"imdb top 250\" + 0.044*\"black and white\" + 0.034*\"tumey's dvds\" + 0.033*\"afi 100 (thrills)\" + 0.030*\"afi 100\" + 0.024*\"usa film registry\" + 0.021*\"erlend's dvds\" + 0.021*\"afi 100 (movie quotes)\" + 0.020*\"paris\"\n", - "2021-08-24 02:42:50,489 : INFO : topic #45 (0.020): 0.220*\"less than 300 ratings\" + 0.112*\"Drama\" + 0.073*\"nudity (topless - notable)\" + 0.048*\"tim burton\" + 0.030*\"not corv lib\" + 0.025*\"library\" + 0.016*\"blindfold\" + 0.014*\"depressing\" + 0.013*\"san francisco\" + 0.013*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:50,490 : INFO : topic diff=0.137872, rho=0.184615\n", - "2021-08-24 02:42:50,492 : INFO : PROGRESS: pass 23, at document #4000/10681\n", - "2021-08-24 02:42:50,727 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:50,753 : INFO : topic #34 (0.020): 0.092*\"ghosts\" + 0.051*\"bill murray\" + 0.043*\"television\" + 0.035*\"courtroom\" + 0.033*\"courtroom drama\" + 0.022*\"court\" + 0.022*\"roman polanski\" + 0.020*\"secret service\" + 0.018*\"opera\" + 0.016*\"2.5\"\n", - "2021-08-24 02:42:50,754 : INFO : topic #28 (0.020): 0.089*\"oscar (best cinematography)\" + 0.071*\"Drama\" + 0.055*\"oscar (best supporting actress)\" + 0.049*\"in netflix queue\" + 0.042*\"oscar (best actress)\" + 0.029*\"remade\" + 0.022*\"marlon brando\" + 0.016*\"exceptional acting\" + 0.014*\"china\" + 0.013*\"alcoholism\"\n", - "2021-08-24 02:42:50,755 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.065*\"Romance\" + 0.054*\"chick flick\" + 0.037*\"boring\" + 0.030*\"girlie movie\" + 0.027*\"baseball\" + 0.024*\"Comedy\" + 0.020*\"love story\" + 0.019*\"whimsical\" + 0.017*\"wedding\"\n", - "2021-08-24 02:42:50,756 : INFO : topic #35 (0.020): 0.347*\"Comedy\" + 0.311*\"Drama\" + 0.193*\"Romance\" + 0.016*\"lesbian\" + 0.011*\"bibliothek\" + 0.008*\"philip seymour hoffman\" + 0.007*\"sean penn\" + 0.006*\"m. night shyamalan\" + 0.006*\"oscar (best foreign language film)\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:42:50,757 : INFO : topic #4 (0.020): 0.070*\"sports\" + 0.063*\"Drama\" + 0.053*\"boxing\" + 0.044*\"underrated\" + 0.044*\"british\" + 0.041*\"london\" + 0.036*\"sven's to see list\" + 0.033*\"hw drama\" + 0.029*\"sylvester stallone\" + 0.028*\"inspirational\"\n", - "2021-08-24 02:42:50,758 : INFO : topic diff=0.025036, rho=0.184615\n", - "2021-08-24 02:42:50,759 : INFO : PROGRESS: pass 23, at document #6000/10681\n", - "2021-08-24 02:42:50,981 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:51,007 : INFO : topic #39 (0.020): 0.196*\"time travel\" + 0.055*\"adultery\" + 0.041*\"post apocalyptic\" + 0.037*\"motorcycle\" + 0.032*\"post-apocalyptic\" + 0.031*\"jude law\" + 0.026*\"dystopia\" + 0.020*\"want to own\" + 0.019*\"bechdel test:fail\" + 0.019*\"hollywood\"\n", - "2021-08-24 02:42:51,008 : INFO : topic #7 (0.020): 0.112*\"romance\" + 0.061*\"Romance\" + 0.052*\"chick flick\" + 0.038*\"boring\" + 0.031*\"girlie movie\" + 0.029*\"baseball\" + 0.024*\"Comedy\" + 0.022*\"whimsical\" + 0.018*\"wedding\" + 0.017*\"drama\"\n", - "2021-08-24 02:42:51,009 : INFO : topic #5 (0.020): 0.277*\"classic\" + 0.065*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.034*\"Musical\" + 0.029*\"national film registry\" + 0.021*\"afi 100\" + 0.018*\"70mm\" + 0.017*\"john travolta\" + 0.016*\"adapted from b'way\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:51,010 : INFO : topic #0 (0.020): 0.223*\"can't remember\" + 0.103*\"directorial debut\" + 0.086*\"based on a tv show\" + 0.082*\"Comedy\" + 0.031*\"ummarti2006\" + 0.028*\"australia\" + 0.027*\"australian\" + 0.022*\"keira knightley\" + 0.018*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:42:51,010 : INFO : topic #28 (0.020): 0.087*\"oscar (best cinematography)\" + 0.073*\"Drama\" + 0.056*\"oscar (best supporting actress)\" + 0.050*\"in netflix queue\" + 0.043*\"oscar (best actress)\" + 0.029*\"remade\" + 0.020*\"marlon brando\" + 0.015*\"exceptional acting\" + 0.014*\"alcoholism\" + 0.013*\"china\"\n", - "2021-08-24 02:42:51,012 : INFO : topic diff=0.048747, rho=0.184615\n", - "2021-08-24 02:42:51,013 : INFO : PROGRESS: pass 23, at document #8000/10681\n", - "2021-08-24 02:42:51,246 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:51,273 : INFO : topic #6 (0.020): 0.143*\"Musical\" + 0.097*\"politics\" + 0.060*\"satire\" + 0.042*\"sean connery\" + 0.042*\"nicolas cage\" + 0.039*\"dvd-r\" + 0.031*\"terrorism\" + 0.029*\"police\" + 0.025*\"dvd-ram\" + 0.024*\"netwatch\"\n", - "2021-08-24 02:42:51,274 : INFO : topic #20 (0.020): 0.094*\"biography\" + 0.068*\"Drama\" + 0.045*\"biopic\" + 0.044*\"death\" + 0.035*\"suicide\" + 0.034*\"ensemble cast\" + 0.033*\"julia roberts\" + 0.033*\"ingmar bergman\" + 0.030*\"corvallis library\" + 0.027*\"multiple storylines\"\n", - "2021-08-24 02:42:51,274 : INFO : topic #40 (0.020): 0.247*\"r\" + 0.116*\"clearplay\" + 0.055*\"movie to see\" + 0.049*\"prison\" + 0.045*\"Drama\" + 0.031*\"morgan freeman\" + 0.027*\"friendship\" + 0.020*\"john wayne\" + 0.019*\"conspiracy\" + 0.017*\"1970s\"\n", - "2021-08-24 02:42:51,275 : INFO : topic #4 (0.020): 0.069*\"sports\" + 0.063*\"Drama\" + 0.045*\"sven's to see list\" + 0.045*\"boxing\" + 0.043*\"london\" + 0.042*\"hw drama\" + 0.041*\"british\" + 0.039*\"underrated\" + 0.024*\"sylvester stallone\" + 0.023*\"inspirational\"\n", - "2021-08-24 02:42:51,276 : INFO : topic #34 (0.020): 0.082*\"ghosts\" + 0.053*\"bill murray\" + 0.042*\"television\" + 0.041*\"courtroom\" + 0.029*\"courtroom drama\" + 0.023*\"roman polanski\" + 0.023*\"court\" + 0.019*\"ben stiller\" + 0.016*\"secret service\" + 0.016*\"opera\"\n", - "2021-08-24 02:42:51,277 : INFO : topic diff=0.040507, rho=0.184615\n", - "2021-08-24 02:42:51,279 : INFO : PROGRESS: pass 23, at document #10000/10681\n", - "2021-08-24 02:42:51,514 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:51,543 : INFO : topic #31 (0.020): 0.150*\"zombies\" + 0.098*\"pg-13\" + 0.063*\"horror\" + 0.034*\"zombie\" + 0.027*\"cult classic\" + 0.026*\"joaquin phoenix\" + 0.024*\"infidelity\" + 0.022*\"sam raimi\" + 0.021*\"campy\" + 0.020*\"books\"\n", - "2021-08-24 02:42:51,543 : INFO : topic #47 (0.020): 0.062*\"serial killer\" + 0.055*\"psychology\" + 0.050*\"brad pitt\" + 0.041*\"edward norton\" + 0.036*\"heist\" + 0.031*\"matt damon\" + 0.029*\"hayao miyazaki\" + 0.026*\"steven spielberg\" + 0.019*\"crime\" + 0.016*\"brilliant\"\n", - "2021-08-24 02:42:51,544 : INFO : topic #12 (0.020): 0.159*\"comic book\" + 0.101*\"superhero\" + 0.068*\"dystopia\" + 0.067*\"super-hero\" + 0.045*\"holocaust\" + 0.034*\"adapted from:comic\" + 0.022*\"kidnapping\" + 0.021*\"alter ego\" + 0.018*\"batman\" + 0.017*\"Action\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:51,545 : INFO : topic #34 (0.020): 0.075*\"ghosts\" + 0.058*\"bill murray\" + 0.040*\"television\" + 0.038*\"courtroom\" + 0.032*\"courtroom drama\" + 0.024*\"roman polanski\" + 0.021*\"ben stiller\" + 0.019*\"las vegas\" + 0.018*\"court\" + 0.016*\"secret service\"\n", - "2021-08-24 02:42:51,546 : INFO : topic #40 (0.020): 0.278*\"r\" + 0.124*\"clearplay\" + 0.058*\"movie to see\" + 0.047*\"Drama\" + 0.040*\"prison\" + 0.029*\"morgan freeman\" + 0.025*\"friendship\" + 0.018*\"conspiracy\" + 0.018*\"john wayne\" + 0.015*\"1970s\"\n", - "2021-08-24 02:42:51,547 : INFO : topic diff=0.033403, rho=0.184615\n", - "2021-08-24 02:42:51,688 : INFO : -20.964 per-word bound, 2045057.6 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:51,688 : INFO : PROGRESS: pass 23, at document #10681/10681\n", - "2021-08-24 02:42:51,780 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:51,805 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.053*\"disturbing\" + 0.051*\"Drama\" + 0.050*\"tense\" + 0.046*\"atmospheric\" + 0.030*\"stylized\" + 0.028*\"bleak\" + 0.027*\"tumey's dvds\" + 0.023*\"menacing\" + 0.022*\"erlend's dvds\"\n", - "2021-08-24 02:42:51,806 : INFO : topic #25 (0.020): 0.184*\"Western\" + 0.068*\"erlend's dvds\" + 0.047*\"Drama\" + 0.042*\"woody allen\" + 0.039*\"gothic\" + 0.032*\"george clooney\" + 0.029*\"gene hackman\" + 0.027*\"sad\" + 0.025*\"nicole kidman\" + 0.025*\"scary movies to see on halloween\"\n", - "2021-08-24 02:42:51,807 : INFO : topic #2 (0.020): 0.314*\"Documentary\" + 0.144*\"to see\" + 0.060*\"documentary\" + 0.026*\"movie to see\" + 0.024*\"mockumentary\" + 0.020*\"owen wilson\" + 0.018*\"dogs\" + 0.018*\"sexuality\" + 0.016*\"kevin smith\" + 0.014*\"star trek\"\n", - "2021-08-24 02:42:51,808 : INFO : topic #46 (0.020): 0.083*\"action\" + 0.064*\"fantasy\" + 0.038*\"sci-fi\" + 0.037*\"adventure\" + 0.033*\"dvd\" + 0.027*\"Adventure\" + 0.021*\"watched 2006\" + 0.021*\"robots\" + 0.020*\"seen at the cinema\" + 0.019*\"seen more than once\"\n", - "2021-08-24 02:42:51,808 : INFO : topic #34 (0.020): 0.073*\"ghosts\" + 0.052*\"bill murray\" + 0.042*\"television\" + 0.034*\"courtroom\" + 0.032*\"secret service\" + 0.028*\"courtroom drama\" + 0.022*\"roman polanski\" + 0.019*\"ben stiller\" + 0.017*\"smoking\" + 0.017*\"las vegas\"\n", - "2021-08-24 02:42:51,809 : INFO : topic diff=0.073837, rho=0.184615\n", - "2021-08-24 02:42:51,811 : INFO : PROGRESS: pass 24, at document #2000/10681\n", - "2021-08-24 02:42:52,052 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:52,078 : INFO : topic #9 (0.020): 0.262*\"based on a book\" + 0.112*\"adapted from:book\" + 0.054*\"Drama\" + 0.042*\"stephen king\" + 0.041*\"jack nicholson\" + 0.036*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.021*\"stupid\" + 0.021*\"los angeles\"\n", - "2021-08-24 02:42:52,079 : INFO : topic #48 (0.020): 0.099*\"bruce willis\" + 0.097*\"netflix\" + 0.081*\"twist ending\" + 0.039*\"coming of age\" + 0.032*\"journalism\" + 0.029*\"to see\" + 0.028*\"Drama\" + 0.025*\"talking animals\" + 0.021*\"sexy\" + 0.021*\"relationships\"\n", - "2021-08-24 02:42:52,080 : INFO : topic #14 (0.020): 0.053*\"criterion\" + 0.053*\"tense\" + 0.052*\"disturbing\" + 0.049*\"atmospheric\" + 0.046*\"Drama\" + 0.032*\"stylized\" + 0.029*\"bleak\" + 0.027*\"tumey's dvds\" + 0.026*\"visceral\" + 0.025*\"menacing\"\n", - "2021-08-24 02:42:52,081 : INFO : topic #34 (0.020): 0.073*\"ghosts\" + 0.051*\"bill murray\" + 0.044*\"television\" + 0.030*\"courtroom\" + 0.029*\"courtroom drama\" + 0.026*\"secret service\" + 0.020*\"roman polanski\" + 0.017*\"18th century\" + 0.017*\"opera\" + 0.017*\"court\"\n", - "2021-08-24 02:42:52,082 : INFO : topic #41 (0.020): 0.114*\"remake\" + 0.069*\"new york city\" + 0.066*\"christmas\" + 0.045*\"family\" + 0.038*\"kevin spacey\" + 0.035*\"bibliothek\" + 0.033*\"new york\" + 0.029*\"Drama\" + 0.027*\"eric's dvds\" + 0.023*\"movie to see\"\n", - "2021-08-24 02:42:52,083 : INFO : topic diff=0.135277, rho=0.181547\n", - "2021-08-24 02:42:52,084 : INFO : PROGRESS: pass 24, at document #4000/10681\n", - "2021-08-24 02:42:52,313 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:52,339 : INFO : topic #38 (0.020): 0.168*\"drugs\" + 0.046*\"samuel l. jackson\" + 0.025*\"divx\" + 0.024*\"basketball\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.023*\"ewan mcgregor\" + 0.021*\"michael caine\" + 0.020*\"liam neeson\" + 0.020*\"poverty\"\n", - "2021-08-24 02:42:52,340 : INFO : topic #17 (0.020): 0.337*\"Sci-Fi\" + 0.077*\"Horror\" + 0.065*\"Action\" + 0.038*\"video game adaptation\" + 0.028*\"football\" + 0.025*\"animals\" + 0.022*\"movie to see\" + 0.017*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:42:52,341 : INFO : topic #26 (0.020): 0.041*\"Drama\" + 0.037*\"james bond\" + 0.026*\"007\" + 0.025*\"bond\" + 0.023*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.020*\"poignant\" + 0.018*\"lyrical\" + 0.018*\"assassin\"\n", - "2021-08-24 02:42:52,342 : INFO : topic #22 (0.020): 0.228*\"betamax\" + 0.097*\"dvd-video\" + 0.074*\"clv\" + 0.036*\"aviation\" + 0.029*\"library on hold\" + 0.023*\"terry gilliam\" + 0.018*\"dvd collection\" + 0.016*\"gilliam\" + 0.013*\"seen 2008\" + 0.013*\"steven seagal\"\n", - "2021-08-24 02:42:52,343 : INFO : topic #14 (0.020): 0.053*\"criterion\" + 0.052*\"atmospheric\" + 0.052*\"tense\" + 0.052*\"disturbing\" + 0.045*\"Drama\" + 0.030*\"tumey's dvds\" + 0.030*\"stylized\" + 0.028*\"bleak\" + 0.025*\"menacing\" + 0.024*\"erlend's dvds\"\n", - "2021-08-24 02:42:52,344 : INFO : topic diff=0.024440, rho=0.181547\n", - "2021-08-24 02:42:52,345 : INFO : PROGRESS: pass 24, at document #6000/10681\n", - "2021-08-24 02:42:52,585 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:52,613 : INFO : topic #4 (0.020): 0.068*\"sports\" + 0.062*\"Drama\" + 0.048*\"boxing\" + 0.042*\"british\" + 0.041*\"underrated\" + 0.040*\"london\" + 0.040*\"sven's to see list\" + 0.038*\"hw drama\" + 0.026*\"sylvester stallone\" + 0.024*\"notable soundtrack\"\n", - "2021-08-24 02:42:52,614 : INFO : topic #45 (0.020): 0.193*\"less than 300 ratings\" + 0.110*\"Drama\" + 0.080*\"nudity (topless - notable)\" + 0.050*\"tim burton\" + 0.031*\"not corv lib\" + 0.023*\"library\" + 0.017*\"depressing\" + 0.017*\"blindfold\" + 0.014*\"golden raspberry (worst actor)\" + 0.014*\"san francisco\"\n", - "2021-08-24 02:42:52,615 : INFO : topic #30 (0.020): 0.244*\"Adventure\" + 0.120*\"Children\" + 0.109*\"Comedy\" + 0.103*\"Fantasy\" + 0.083*\"Action\" + 0.062*\"70mm\" + 0.048*\"Animation\" + 0.045*\"Drama\" + 0.017*\"Musical\" + 0.010*\"submarine\"\n", - "2021-08-24 02:42:52,617 : INFO : topic #29 (0.020): 0.100*\"nudity (full frontal - notable)\" + 0.059*\"shakespeare\" + 0.052*\"Drama\" + 0.046*\"based on a play\" + 0.039*\"adapted from:play\" + 0.032*\"christianity\" + 0.028*\"religion\" + 0.021*\"jodie foster\" + 0.021*\"disability\" + 0.021*\"cannibalism\"\n", - "2021-08-24 02:42:52,617 : INFO : topic #40 (0.020): 0.252*\"r\" + 0.125*\"clearplay\" + 0.058*\"movie to see\" + 0.052*\"prison\" + 0.044*\"Drama\" + 0.033*\"morgan freeman\" + 0.026*\"friendship\" + 0.018*\"conspiracy\" + 0.018*\"mel brooks\" + 0.017*\"john wayne\"\n", - "2021-08-24 02:42:52,619 : INFO : topic diff=0.047856, rho=0.181547\n", - "2021-08-24 02:42:52,620 : INFO : PROGRESS: pass 24, at document #8000/10681\n", - "2021-08-24 02:42:52,874 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:52,901 : INFO : topic #42 (0.020): 0.096*\"japan\" + 0.091*\"martial arts\" + 0.076*\"robin williams\" + 0.042*\"akira kurosawa\" + 0.035*\"samurai\" + 0.030*\"Drama\" + 0.027*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"hugh grant\" + 0.020*\"christopher walken\"\n", - "2021-08-24 02:42:52,902 : INFO : topic #9 (0.020): 0.245*\"based on a book\" + 0.121*\"adapted from:book\" + 0.059*\"Drama\" + 0.039*\"stephen king\" + 0.038*\"jack nicholson\" + 0.037*\"based on book\" + 0.027*\"imdb bottom 100\" + 0.022*\"literary adaptation\" + 0.020*\"stupid\" + 0.020*\"los angeles\"\n", - "2021-08-24 02:42:52,903 : INFO : topic #32 (0.020): 0.136*\"johnny depp\" + 0.068*\"vhs\" + 0.066*\"clint eastwood\" + 0.044*\"jackie chan\" + 0.034*\"western\" + 0.030*\"kung fu\" + 0.030*\"propaganda\" + 0.029*\"india\" + 0.027*\"david lynch\" + 0.026*\"leonardo dicaprio\"\n", - "2021-08-24 02:42:52,904 : INFO : topic #21 (0.020): 0.077*\"Sci-Fi\" + 0.072*\"aliens\" + 0.065*\"sci-fi\" + 0.041*\"futuristmovies.com\" + 0.039*\"space\" + 0.026*\"will smith\" + 0.023*\"future\" + 0.023*\"Action\" + 0.022*\"tommy lee jones\" + 0.020*\"ridley scott\"\n", - "2021-08-24 02:42:52,905 : INFO : topic #15 (0.020): 0.141*\"disney\" + 0.096*\"animation\" + 0.079*\"pixar\" + 0.047*\"Animation\" + 0.041*\"children\" + 0.037*\"Children\" + 0.032*\"pirates\" + 0.029*\"disney animated feature\" + 0.026*\"angelina jolie\" + 0.021*\"cartoon\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:52,907 : INFO : topic diff=0.039945, rho=0.181547\n", - "2021-08-24 02:42:52,908 : INFO : PROGRESS: pass 24, at document #10000/10681\n", - "2021-08-24 02:42:53,173 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:53,201 : INFO : topic #29 (0.020): 0.095*\"nudity (full frontal - notable)\" + 0.054*\"Drama\" + 0.052*\"shakespeare\" + 0.045*\"based on a play\" + 0.036*\"christianity\" + 0.034*\"adapted from:play\" + 0.029*\"religion\" + 0.021*\"biblical\" + 0.021*\"disability\" + 0.019*\"fascism\"\n", - "2021-08-24 02:42:53,202 : INFO : topic #26 (0.020): 0.045*\"Drama\" + 0.033*\"james bond\" + 0.021*\"reflective\" + 0.021*\"poignant\" + 0.020*\"007\" + 0.020*\"atmospheric\" + 0.019*\"bittersweet\" + 0.019*\"lyrical\" + 0.019*\"bond\" + 0.018*\"deliberate\"\n", - "2021-08-24 02:42:53,203 : INFO : topic #13 (0.020): 0.062*\"fairy tale\" + 0.048*\"road trip\" + 0.041*\"sequel\" + 0.040*\"jane austen\" + 0.026*\"historical\" + 0.025*\"gerard depardieu\" + 0.024*\"swashbuckler\" + 0.024*\"assassination\" + 0.024*\"kids\" + 0.024*\"cold war\"\n", - "2021-08-24 02:42:53,204 : INFO : topic #17 (0.020): 0.347*\"Sci-Fi\" + 0.079*\"Horror\" + 0.067*\"Action\" + 0.040*\"video game adaptation\" + 0.023*\"football\" + 0.021*\"animals\" + 0.019*\"movie to see\" + 0.016*\"futuristic\" + 0.014*\"milla jovovich\" + 0.014*\"g\"\n", - "2021-08-24 02:42:53,205 : INFO : topic #9 (0.020): 0.245*\"based on a book\" + 0.114*\"adapted from:book\" + 0.059*\"Drama\" + 0.038*\"based on book\" + 0.035*\"jack nicholson\" + 0.032*\"stephen king\" + 0.028*\"imdb bottom 100\" + 0.023*\"stupid\" + 0.022*\"literary adaptation\" + 0.021*\"los angeles\"\n", - "2021-08-24 02:42:53,206 : INFO : topic diff=0.032591, rho=0.181547\n", - "2021-08-24 02:42:53,348 : INFO : -20.964 per-word bound, 2046008.4 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:53,349 : INFO : PROGRESS: pass 24, at document #10681/10681\n", - "2021-08-24 02:42:53,442 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:53,469 : INFO : topic #40 (0.020): 0.304*\"r\" + 0.154*\"clearplay\" + 0.072*\"movie to see\" + 0.050*\"Drama\" + 0.034*\"prison\" + 0.029*\"friendship\" + 0.027*\"morgan freeman\" + 0.016*\"conspiracy\" + 0.014*\"1970s\" + 0.013*\"don cheadle\"\n", - "2021-08-24 02:42:53,470 : INFO : topic #41 (0.020): 0.133*\"remake\" + 0.076*\"new york city\" + 0.064*\"christmas\" + 0.041*\"family\" + 0.034*\"bibliothek\" + 0.034*\"new york\" + 0.030*\"Drama\" + 0.030*\"movie to see\" + 0.029*\"eric's dvds\" + 0.027*\"kevin spacey\"\n", - "2021-08-24 02:42:53,471 : INFO : topic #2 (0.020): 0.314*\"Documentary\" + 0.144*\"to see\" + 0.060*\"documentary\" + 0.025*\"movie to see\" + 0.024*\"mockumentary\" + 0.020*\"owen wilson\" + 0.018*\"dogs\" + 0.018*\"sexuality\" + 0.016*\"kevin smith\" + 0.015*\"star trek\"\n", - "2021-08-24 02:42:53,472 : INFO : topic #35 (0.020): 0.355*\"Comedy\" + 0.314*\"Drama\" + 0.200*\"Romance\" + 0.014*\"lesbian\" + 0.008*\"bibliothek\" + 0.008*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.005*\"oscar (best foreign language film)\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:42:53,473 : INFO : topic #7 (0.020): 0.108*\"romance\" + 0.063*\"Romance\" + 0.044*\"chick flick\" + 0.037*\"boring\" + 0.027*\"love story\" + 0.024*\"girlie movie\" + 0.024*\"love\" + 0.023*\"Comedy\" + 0.023*\"baseball\" + 0.022*\"wedding\"\n", - "2021-08-24 02:42:53,474 : INFO : topic diff=0.072728, rho=0.181547\n", - "2021-08-24 02:42:53,476 : INFO : PROGRESS: pass 25, at document #2000/10681\n", - "2021-08-24 02:42:53,752 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:53,779 : INFO : topic #27 (0.020): 0.292*\"Horror\" + 0.160*\"Thriller\" + 0.133*\"Mystery\" + 0.045*\"Drama\" + 0.033*\"easily confused with other movie(s) (title)\" + 0.032*\"Film-Noir\" + 0.031*\"Fantasy\" + 0.016*\"slasher\" + 0.015*\"franchise\" + 0.014*\"eerie\"\n", - "2021-08-24 02:42:53,781 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.068*\"Romance\" + 0.051*\"chick flick\" + 0.037*\"boring\" + 0.028*\"girlie movie\" + 0.023*\"Comedy\" + 0.023*\"love story\" + 0.023*\"baseball\" + 0.020*\"wedding\" + 0.019*\"whimsical\"\n", - "2021-08-24 02:42:53,781 : INFO : topic #30 (0.020): 0.242*\"Adventure\" + 0.127*\"Children\" + 0.113*\"Comedy\" + 0.106*\"Fantasy\" + 0.081*\"Action\" + 0.049*\"Animation\" + 0.048*\"70mm\" + 0.047*\"Drama\" + 0.017*\"Musical\" + 0.012*\"submarine\"\n", - "2021-08-24 02:42:53,782 : INFO : topic #42 (0.020): 0.083*\"robin williams\" + 0.080*\"japan\" + 0.071*\"martial arts\" + 0.037*\"samurai\" + 0.036*\"akira kurosawa\" + 0.032*\"orson welles\" + 0.031*\"divorce\" + 0.030*\"Drama\" + 0.024*\"kurosawa\" + 0.022*\"jonossa\"\n", - "2021-08-24 02:42:53,783 : INFO : topic #23 (0.020): 0.084*\"high school\" + 0.062*\"teen\" + 0.051*\"cars\" + 0.035*\"dance\" + 0.028*\"marx brothers\" + 0.027*\"sandra bullock\" + 0.023*\"Comedy\" + 0.020*\"(s)vcd\" + 0.020*\"kate winslet\" + 0.020*\"eddie murphy\"\n", - "2021-08-24 02:42:53,784 : INFO : topic diff=0.132767, rho=0.178627\n", - "2021-08-24 02:42:53,785 : INFO : PROGRESS: pass 25, at document #4000/10681\n", - "2021-08-24 02:42:54,021 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:54,049 : INFO : topic #29 (0.020): 0.095*\"nudity (full frontal - notable)\" + 0.066*\"shakespeare\" + 0.050*\"Drama\" + 0.045*\"based on a play\" + 0.039*\"adapted from:play\" + 0.033*\"christianity\" + 0.029*\"religion\" + 0.022*\"cannibalism\" + 0.021*\"disability\" + 0.021*\"jodie foster\"\n", - "2021-08-24 02:42:54,050 : INFO : topic #45 (0.020): 0.198*\"less than 300 ratings\" + 0.109*\"Drama\" + 0.078*\"nudity (topless - notable)\" + 0.057*\"tim burton\" + 0.030*\"not corv lib\" + 0.023*\"library\" + 0.019*\"depressing\" + 0.015*\"blindfold\" + 0.014*\"golden raspberry (worst actor)\" + 0.013*\"san francisco\"\n", - "2021-08-24 02:42:54,050 : INFO : topic #43 (0.020): 0.076*\"surreal\" + 0.043*\"quirky\" + 0.042*\"stanley kubrick\" + 0.041*\"satirical\" + 0.036*\"cynical\" + 0.035*\"narrated\" + 0.030*\"biting\" + 0.029*\"dreamlike\" + 0.028*\"irreverent\" + 0.025*\"humorous\"\n", - "2021-08-24 02:42:54,051 : INFO : topic #16 (0.020): 0.294*\"nudity (topless)\" + 0.196*\"nudity (topless - brief)\" + 0.062*\"nudity (rear)\" + 0.032*\"claymation\" + 0.024*\"pg\" + 0.022*\"military\" + 0.021*\"aardman\" + 0.019*\"childhood\" + 0.013*\"weird\" + 0.011*\"freedom\"\n", - "2021-08-24 02:42:54,052 : INFO : topic #9 (0.020): 0.246*\"based on a book\" + 0.112*\"adapted from:book\" + 0.055*\"Drama\" + 0.048*\"stephen king\" + 0.041*\"jack nicholson\" + 0.037*\"based on book\" + 0.026*\"imdb bottom 100\" + 0.024*\"stupid\" + 0.021*\"literary adaptation\" + 0.021*\"los angeles\"\n", - "2021-08-24 02:42:54,053 : INFO : topic diff=0.023974, rho=0.178627\n", - "2021-08-24 02:42:54,054 : INFO : PROGRESS: pass 25, at document #6000/10681\n", - "2021-08-24 02:42:54,295 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:54,322 : INFO : topic #38 (0.020): 0.176*\"drugs\" + 0.042*\"samuel l. jackson\" + 0.027*\"divx\" + 0.026*\"ewan mcgregor\" + 0.024*\"addiction\" + 0.023*\"hulu\" + 0.022*\"poverty\" + 0.021*\"michael caine\" + 0.021*\"basketball\" + 0.020*\"liam neeson\"\n", - "2021-08-24 02:42:54,323 : INFO : topic #31 (0.020): 0.124*\"zombies\" + 0.078*\"horror\" + 0.066*\"pg-13\" + 0.037*\"cult classic\" + 0.034*\"zombie\" + 0.027*\"campy\" + 0.025*\"sam raimi\" + 0.025*\"joaquin phoenix\" + 0.024*\"books\" + 0.021*\"infidelity\"\n", - "2021-08-24 02:42:54,324 : INFO : topic #24 (0.020): 0.080*\"hitchcock\" + 0.077*\"murder\" + 0.068*\"vampire\" + 0.061*\"alfred hitchcock\" + 0.056*\"vampires\" + 0.054*\"oppl\" + 0.048*\"memory\" + 0.019*\"incest\" + 0.018*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:54,325 : INFO : topic #9 (0.020): 0.246*\"based on a book\" + 0.113*\"adapted from:book\" + 0.056*\"Drama\" + 0.042*\"stephen king\" + 0.039*\"based on book\" + 0.039*\"jack nicholson\" + 0.026*\"imdb bottom 100\" + 0.022*\"stupid\" + 0.022*\"literary adaptation\" + 0.020*\"los angeles\"\n", - "2021-08-24 02:42:54,326 : INFO : topic #40 (0.020): 0.253*\"r\" + 0.125*\"clearplay\" + 0.057*\"movie to see\" + 0.052*\"prison\" + 0.044*\"Drama\" + 0.033*\"morgan freeman\" + 0.026*\"friendship\" + 0.018*\"conspiracy\" + 0.018*\"mel brooks\" + 0.017*\"john wayne\"\n", - "2021-08-24 02:42:54,327 : INFO : topic diff=0.047043, rho=0.178627\n", - "2021-08-24 02:42:54,328 : INFO : PROGRESS: pass 25, at document #8000/10681\n", - "2021-08-24 02:42:54,554 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:54,580 : INFO : topic #33 (0.020): 0.116*\"Comedy\" + 0.111*\"comedy\" + 0.059*\"funny\" + 0.037*\"seen more than once\" + 0.037*\"parody\" + 0.030*\"dark comedy\" + 0.028*\"hilarious\" + 0.024*\"coen brothers\" + 0.024*\"quirky\" + 0.021*\"black comedy\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:54,581 : INFO : topic #47 (0.020): 0.063*\"serial killer\" + 0.057*\"psychology\" + 0.050*\"brad pitt\" + 0.041*\"edward norton\" + 0.034*\"heist\" + 0.028*\"matt damon\" + 0.028*\"steven spielberg\" + 0.027*\"hayao miyazaki\" + 0.023*\"crime\" + 0.016*\"scary\"\n", - "2021-08-24 02:42:54,582 : INFO : topic #22 (0.020): 0.259*\"betamax\" + 0.101*\"dvd-video\" + 0.068*\"clv\" + 0.030*\"aviation\" + 0.020*\"library on hold\" + 0.019*\"terry gilliam\" + 0.017*\"dvd collection\" + 0.015*\"seen 2008\" + 0.014*\"gilliam\" + 0.012*\"steven seagal\"\n", - "2021-08-24 02:42:54,583 : INFO : topic #5 (0.020): 0.270*\"classic\" + 0.062*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.036*\"Musical\" + 0.030*\"national film registry\" + 0.019*\"70mm\" + 0.018*\"afi 100\" + 0.016*\"john travolta\" + 0.016*\"adapted from b'way\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:54,584 : INFO : topic #8 (0.020): 0.138*\"anime\" + 0.090*\"tom hanks\" + 0.078*\"true story\" + 0.058*\"based on a true story\" + 0.027*\"drama\" + 0.025*\"good\" + 0.025*\"japan\" + 0.023*\"interesting\" + 0.021*\"not funny\" + 0.018*\"archaeology\"\n", - "2021-08-24 02:42:54,585 : INFO : topic diff=0.039034, rho=0.178627\n", - "2021-08-24 02:42:54,587 : INFO : PROGRESS: pass 25, at document #10000/10681\n", - "2021-08-24 02:42:54,822 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:54,849 : INFO : topic #15 (0.020): 0.121*\"disney\" + 0.098*\"animation\" + 0.084*\"pixar\" + 0.047*\"Animation\" + 0.043*\"pirates\" + 0.040*\"children\" + 0.035*\"Children\" + 0.028*\"angelina jolie\" + 0.026*\"disney animated feature\" + 0.022*\"cartoon\"\n", - "2021-08-24 02:42:54,850 : INFO : topic #33 (0.020): 0.117*\"Comedy\" + 0.109*\"comedy\" + 0.063*\"funny\" + 0.036*\"parody\" + 0.032*\"seen more than once\" + 0.030*\"hilarious\" + 0.028*\"dark comedy\" + 0.023*\"england\" + 0.022*\"quirky\" + 0.021*\"black comedy\"\n", - "2021-08-24 02:42:54,851 : INFO : topic #36 (0.020): 0.070*\"cult film\" + 0.046*\"downbeat\" + 0.045*\"peter jackson\" + 0.040*\"russell crowe\" + 0.033*\"not available from netflix\" + 0.031*\"adam sandler\" + 0.026*\"1980s\" + 0.023*\"new zealand\" + 0.017*\"existentialism\" + 0.017*\"must see!\"\n", - "2021-08-24 02:42:54,852 : INFO : topic #20 (0.020): 0.095*\"biography\" + 0.068*\"Drama\" + 0.053*\"biopic\" + 0.045*\"death\" + 0.043*\"corvallis library\" + 0.037*\"suicide\" + 0.034*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.026*\"ingmar bergman\" + 0.025*\"medieval\"\n", - "2021-08-24 02:42:54,853 : INFO : topic #49 (0.020): 0.086*\"national film registry\" + 0.053*\"imdb top 250\" + 0.040*\"black and white\" + 0.039*\"tumey's dvds\" + 0.026*\"afi 100 (thrills)\" + 0.025*\"paris\" + 0.023*\"france\" + 0.022*\"erlend's dvds\" + 0.021*\"afi 100\" + 0.020*\"afi 100 (movie quotes)\"\n", - "2021-08-24 02:42:54,854 : INFO : topic diff=0.031886, rho=0.178627\n", - "2021-08-24 02:42:55,003 : INFO : -20.964 per-word bound, 2045800.0 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:55,003 : INFO : PROGRESS: pass 25, at document #10681/10681\n", - "2021-08-24 02:42:55,101 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:55,127 : INFO : topic #18 (0.020): 0.090*\"magic\" + 0.076*\"pg13\" + 0.073*\"gay\" + 0.052*\"racism\" + 0.040*\"Drama\" + 0.026*\"food\" + 0.025*\"social commentary\" + 0.023*\"homosexuality\" + 0.021*\"19th century\" + 0.020*\"denzel washington\"\n", - "2021-08-24 02:42:55,128 : INFO : topic #39 (0.020): 0.158*\"time travel\" + 0.055*\"adultery\" + 0.045*\"motorcycle\" + 0.045*\"post apocalyptic\" + 0.042*\"post-apocalyptic\" + 0.035*\"jude law\" + 0.024*\"bechdel test:fail\" + 0.023*\"dystopia\" + 0.019*\"want to own\" + 0.017*\"old\"\n", - "2021-08-24 02:42:55,128 : INFO : topic #6 (0.020): 0.143*\"Musical\" + 0.113*\"politics\" + 0.063*\"satire\" + 0.040*\"nicolas cage\" + 0.038*\"terrorism\" + 0.033*\"dvd-r\" + 0.029*\"sean connery\" + 0.027*\"corruption\" + 0.027*\"police\" + 0.025*\"political\"\n", - "2021-08-24 02:42:55,129 : INFO : topic #46 (0.020): 0.084*\"action\" + 0.064*\"fantasy\" + 0.037*\"adventure\" + 0.037*\"sci-fi\" + 0.032*\"dvd\" + 0.027*\"Adventure\" + 0.021*\"watched 2006\" + 0.021*\"robots\" + 0.020*\"seen at the cinema\" + 0.019*\"seen more than once\"\n", - "2021-08-24 02:42:55,130 : INFO : topic #10 (0.020): 0.251*\"Thriller\" + 0.239*\"Crime\" + 0.169*\"Drama\" + 0.166*\"Action\" + 0.028*\"Mystery\" + 0.017*\"murder\" + 0.011*\"crime\" + 0.007*\"virtual reality\" + 0.004*\"thriller\" + 0.004*\"greg kinnear\"\n", - "2021-08-24 02:42:55,131 : INFO : topic diff=0.071425, rho=0.178627\n", - "2021-08-24 02:42:55,133 : INFO : PROGRESS: pass 26, at document #2000/10681\n", - "2021-08-24 02:42:55,385 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:55,411 : INFO : topic #3 (0.020): 0.194*\"oscar (best picture)\" + 0.075*\"oscar (best directing)\" + 0.063*\"oscar (best actor)\" + 0.050*\"oscar (best supporting actor)\" + 0.029*\"al pacino\" + 0.023*\"afi 100 (cheers)\" + 0.022*\"afi 100\" + 0.022*\"Drama\" + 0.022*\"great acting\" + 0.020*\"best picture\"\n", - "2021-08-24 02:42:55,412 : INFO : topic #24 (0.020): 0.081*\"hitchcock\" + 0.074*\"alfred hitchcock\" + 0.071*\"oppl\" + 0.071*\"murder\" + 0.064*\"vampire\" + 0.049*\"vampires\" + 0.038*\"memory\" + 0.022*\"incest\" + 0.020*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:55,413 : INFO : topic #25 (0.020): 0.159*\"Western\" + 0.069*\"erlend's dvds\" + 0.054*\"woody allen\" + 0.045*\"Drama\" + 0.044*\"gothic\" + 0.038*\"gene hackman\" + 0.026*\"george clooney\" + 0.026*\"sad\" + 0.021*\"scary movies to see on halloween\" + 0.021*\"nicole kidman\"\n", - "2021-08-24 02:42:55,414 : INFO : topic #5 (0.020): 0.299*\"classic\" + 0.067*\"musical\" + 0.037*\"afi 100 (laughs)\" + 0.033*\"Musical\" + 0.027*\"national film registry\" + 0.022*\"afi 100\" + 0.018*\"john travolta\" + 0.016*\"70mm\" + 0.014*\"breakthroughs\" + 0.013*\"adapted from b'way\"\n", - "2021-08-24 02:42:55,414 : INFO : topic #19 (0.020): 0.070*\"nudity (full frontal)\" + 0.059*\"drama\" + 0.036*\"Drama\" + 0.030*\"philip k. dick\" + 0.029*\"vietnam\" + 0.028*\"vietnam war\" + 0.025*\"to see\" + 0.024*\"rape\" + 0.019*\"slow\" + 0.018*\"very good\"\n", - "2021-08-24 02:42:55,416 : INFO : topic diff=0.130237, rho=0.175844\n", - "2021-08-24 02:42:55,418 : INFO : PROGRESS: pass 26, at document #4000/10681\n", - "2021-08-24 02:42:55,659 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:55,685 : INFO : topic #48 (0.020): 0.103*\"bruce willis\" + 0.085*\"twist ending\" + 0.083*\"netflix\" + 0.048*\"coming of age\" + 0.034*\"journalism\" + 0.027*\"Drama\" + 0.026*\"talking animals\" + 0.026*\"sexy\" + 0.025*\"to see\" + 0.019*\"relationships\"\n", - "2021-08-24 02:42:55,686 : INFO : topic #13 (0.020): 0.054*\"fairy tale\" + 0.044*\"jane austen\" + 0.041*\"road trip\" + 0.037*\"sequel\" + 0.030*\"assassination\" + 0.029*\"cold war\" + 0.027*\"historical\" + 0.022*\"kids\" + 0.021*\"heartwarming\" + 0.020*\"gerard depardieu\"\n", - "2021-08-24 02:42:55,686 : INFO : topic #16 (0.020): 0.294*\"nudity (topless)\" + 0.196*\"nudity (topless - brief)\" + 0.062*\"nudity (rear)\" + 0.032*\"claymation\" + 0.024*\"pg\" + 0.022*\"military\" + 0.021*\"aardman\" + 0.019*\"childhood\" + 0.013*\"weird\" + 0.011*\"freedom\"\n", - "2021-08-24 02:42:55,687 : INFO : topic #43 (0.020): 0.076*\"surreal\" + 0.044*\"quirky\" + 0.041*\"stanley kubrick\" + 0.041*\"satirical\" + 0.036*\"cynical\" + 0.035*\"narrated\" + 0.030*\"biting\" + 0.029*\"dreamlike\" + 0.028*\"irreverent\" + 0.026*\"humorous\"\n", - "2021-08-24 02:42:55,688 : INFO : topic #32 (0.020): 0.141*\"johnny depp\" + 0.063*\"clint eastwood\" + 0.054*\"vhs\" + 0.041*\"jackie chan\" + 0.041*\"western\" + 0.034*\"spaghetti western\" + 0.032*\"kung fu\" + 0.029*\"india\" + 0.029*\"propaganda\" + 0.028*\"sergio leone\"\n", - "2021-08-24 02:42:55,689 : INFO : topic diff=0.023494, rho=0.175844\n", - "2021-08-24 02:42:55,690 : INFO : PROGRESS: pass 26, at document #6000/10681\n", - "2021-08-24 02:42:55,910 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:55,937 : INFO : topic #9 (0.020): 0.246*\"based on a book\" + 0.113*\"adapted from:book\" + 0.056*\"Drama\" + 0.042*\"stephen king\" + 0.039*\"based on book\" + 0.039*\"jack nicholson\" + 0.026*\"imdb bottom 100\" + 0.022*\"stupid\" + 0.022*\"literary adaptation\" + 0.020*\"los angeles\"\n", - "2021-08-24 02:42:55,938 : INFO : topic #8 (0.020): 0.125*\"anime\" + 0.094*\"tom hanks\" + 0.078*\"true story\" + 0.064*\"based on a true story\" + 0.026*\"drama\" + 0.025*\"japan\" + 0.025*\"good\" + 0.023*\"interesting\" + 0.021*\"archaeology\" + 0.019*\"not funny\"\n", - "2021-08-24 02:42:55,938 : INFO : topic #3 (0.020): 0.162*\"oscar (best picture)\" + 0.064*\"oscar (best directing)\" + 0.063*\"oscar (best actor)\" + 0.056*\"oscar (best supporting actor)\" + 0.037*\"al pacino\" + 0.024*\"Drama\" + 0.024*\"afi 100 (cheers)\" + 0.019*\"afi 100\" + 0.019*\"great acting\" + 0.017*\"tumey's dvds\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:55,939 : INFO : topic #18 (0.020): 0.082*\"magic\" + 0.079*\"gay\" + 0.062*\"racism\" + 0.044*\"pg13\" + 0.040*\"Drama\" + 0.027*\"food\" + 0.027*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.019*\"denzel washington\"\n", - "2021-08-24 02:42:55,940 : INFO : topic #19 (0.020): 0.062*\"nudity (full frontal)\" + 0.061*\"drama\" + 0.035*\"Drama\" + 0.034*\"philip k. dick\" + 0.028*\"vietnam war\" + 0.027*\"rape\" + 0.025*\"vietnam\" + 0.022*\"very good\" + 0.020*\"to see\" + 0.016*\"slow\"\n", - "2021-08-24 02:42:55,942 : INFO : topic diff=0.046241, rho=0.175844\n", - "2021-08-24 02:42:55,943 : INFO : PROGRESS: pass 26, at document #8000/10681\n", - "2021-08-24 02:42:56,180 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:56,207 : INFO : topic #2 (0.020): 0.287*\"Documentary\" + 0.104*\"to see\" + 0.064*\"documentary\" + 0.026*\"mockumentary\" + 0.025*\"sexuality\" + 0.023*\"star trek\" + 0.021*\"dogs\" + 0.021*\"kevin smith\" + 0.018*\"movie to see\" + 0.017*\"in netflix queue\"\n", - "2021-08-24 02:42:56,208 : INFO : topic #45 (0.020): 0.230*\"less than 300 ratings\" + 0.116*\"Drama\" + 0.074*\"nudity (topless - notable)\" + 0.043*\"tim burton\" + 0.028*\"not corv lib\" + 0.025*\"library\" + 0.016*\"blindfold\" + 0.016*\"depressing\" + 0.015*\"michael moore\" + 0.013*\"golden raspberry (worst actor)\"\n", - "2021-08-24 02:42:56,209 : INFO : topic #21 (0.020): 0.078*\"Sci-Fi\" + 0.072*\"aliens\" + 0.067*\"sci-fi\" + 0.041*\"futuristmovies.com\" + 0.039*\"space\" + 0.026*\"will smith\" + 0.024*\"future\" + 0.022*\"Action\" + 0.022*\"tommy lee jones\" + 0.020*\"ridley scott\"\n", - "2021-08-24 02:42:56,210 : INFO : topic #38 (0.020): 0.160*\"drugs\" + 0.039*\"samuel l. jackson\" + 0.029*\"divx\" + 0.029*\"peter sellers\" + 0.029*\"ewan mcgregor\" + 0.024*\"hulu\" + 0.023*\"addiction\" + 0.022*\"poverty\" + 0.021*\"michael caine\" + 0.020*\"basketball\"\n", - "2021-08-24 02:42:56,210 : INFO : topic #19 (0.020): 0.061*\"drama\" + 0.055*\"nudity (full frontal)\" + 0.034*\"philip k. dick\" + 0.033*\"Drama\" + 0.026*\"rape\" + 0.026*\"vietnam war\" + 0.025*\"vietnam\" + 0.024*\"very good\" + 0.019*\"soccer\" + 0.018*\"to see\"\n", - "2021-08-24 02:42:56,211 : INFO : topic diff=0.038566, rho=0.175844\n", - "2021-08-24 02:42:56,213 : INFO : PROGRESS: pass 26, at document #10000/10681\n", - "2021-08-24 02:42:56,456 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:56,484 : INFO : topic #8 (0.020): 0.148*\"anime\" + 0.081*\"true story\" + 0.077*\"tom hanks\" + 0.060*\"based on a true story\" + 0.027*\"interesting\" + 0.026*\"to-rent\" + 0.025*\"japan\" + 0.024*\"not funny\" + 0.024*\"good\" + 0.023*\"drama\"\n", - "2021-08-24 02:42:56,485 : INFO : topic #13 (0.020): 0.062*\"fairy tale\" + 0.048*\"road trip\" + 0.040*\"sequel\" + 0.040*\"jane austen\" + 0.026*\"historical\" + 0.025*\"gerard depardieu\" + 0.024*\"assassination\" + 0.024*\"swashbuckler\" + 0.024*\"cold war\" + 0.024*\"kids\"\n", - "2021-08-24 02:42:56,486 : INFO : topic #1 (0.020): 0.054*\"overrated\" + 0.039*\"violence\" + 0.037*\"quentin tarantino\" + 0.032*\"revenge\" + 0.030*\"crime\" + 0.025*\"violent\" + 0.025*\"owned\" + 0.024*\"imdb top 250\" + 0.022*\"tarantino\" + 0.021*\"drama\"\n", - "2021-08-24 02:42:56,487 : INFO : topic #41 (0.020): 0.127*\"remake\" + 0.064*\"new york city\" + 0.059*\"christmas\" + 0.047*\"family\" + 0.040*\"bibliothek\" + 0.035*\"new york\" + 0.034*\"eric's dvds\" + 0.030*\"Drama\" + 0.029*\"kevin spacey\" + 0.026*\"friday night movie\"\n", - "2021-08-24 02:42:56,488 : INFO : topic #19 (0.020): 0.063*\"nudity (full frontal)\" + 0.055*\"drama\" + 0.035*\"Drama\" + 0.034*\"philip k. dick\" + 0.024*\"rape\" + 0.024*\"ummarti2007\" + 0.023*\"slow\" + 0.022*\"vietnam war\" + 0.021*\"very good\" + 0.021*\"to see\"\n", - "2021-08-24 02:42:56,489 : INFO : topic diff=0.031511, rho=0.175844\n", - "2021-08-24 02:42:56,631 : INFO : -20.962 per-word bound, 2043163.3 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:56,632 : INFO : PROGRESS: pass 26, at document #10681/10681\n", - "2021-08-24 02:42:56,724 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:56,750 : INFO : topic #33 (0.020): 0.117*\"Comedy\" + 0.105*\"comedy\" + 0.063*\"funny\" + 0.038*\"parody\" + 0.034*\"dark comedy\" + 0.031*\"seen more than once\" + 0.029*\"hilarious\" + 0.027*\"coen brothers\" + 0.023*\"black comedy\" + 0.022*\"england\"\n", - "2021-08-24 02:42:56,751 : INFO : topic #5 (0.020): 0.232*\"classic\" + 0.085*\"musical\" + 0.042*\"afi 100 (laughs)\" + 0.042*\"Musical\" + 0.026*\"national film registry\" + 0.017*\"adapted from b'way\" + 0.016*\"70mm\" + 0.015*\"afi 100\" + 0.015*\"john travolta\" + 0.014*\"breakthroughs\"\n", - "2021-08-24 02:42:56,751 : INFO : topic #20 (0.020): 0.099*\"biography\" + 0.071*\"Drama\" + 0.055*\"death\" + 0.045*\"biopic\" + 0.044*\"suicide\" + 0.038*\"corvallis library\" + 0.031*\"ensemble cast\" + 0.028*\"julia roberts\" + 0.023*\"forest whitaker\" + 0.023*\"multiple storylines\"\n", - "2021-08-24 02:42:56,752 : INFO : topic #31 (0.020): 0.156*\"zombies\" + 0.093*\"pg-13\" + 0.060*\"horror\" + 0.029*\"zombie\" + 0.025*\"infidelity\" + 0.023*\"cult classic\" + 0.023*\"movie to see\" + 0.023*\"joaquin phoenix\" + 0.022*\"books\" + 0.021*\"betrayal\"\n", - "2021-08-24 02:42:56,753 : INFO : topic #47 (0.020): 0.061*\"serial killer\" + 0.053*\"psychology\" + 0.050*\"brad pitt\" + 0.038*\"heist\" + 0.038*\"edward norton\" + 0.030*\"matt damon\" + 0.027*\"hayao miyazaki\" + 0.024*\"steven spielberg\" + 0.019*\"crime\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:42:56,754 : INFO : topic diff=0.070429, rho=0.175844\n", - "2021-08-24 02:42:56,756 : INFO : PROGRESS: pass 27, at document #2000/10681\n", - "2021-08-24 02:42:57,007 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:57,034 : INFO : topic #35 (0.020): 0.346*\"Comedy\" + 0.315*\"Drama\" + 0.198*\"Romance\" + 0.016*\"lesbian\" + 0.009*\"bibliothek\" + 0.007*\"sean penn\" + 0.006*\"philip seymour hoffman\" + 0.005*\"oscar (best foreign language film)\" + 0.004*\"m. night shyamalan\" + 0.003*\"library vhs\"\n", - "2021-08-24 02:42:57,035 : INFO : topic #26 (0.020): 0.043*\"Drama\" + 0.025*\"james bond\" + 0.021*\"mel gibson\" + 0.021*\"poignant\" + 0.020*\"atmospheric\" + 0.019*\"bittersweet\" + 0.019*\"reflective\" + 0.018*\"lyrical\" + 0.017*\"bond\" + 0.017*\"assassin\"\n", - "2021-08-24 02:42:57,036 : INFO : topic #44 (0.020): 0.117*\"mafia\" + 0.085*\"music\" + 0.064*\"martin scorsese\" + 0.055*\"organized crime\" + 0.039*\"rock and roll\" + 0.033*\"Musical\" + 0.025*\"guns\" + 0.024*\"wired 50 greatest soundtracks\" + 0.022*\"francis ford coppola\" + 0.020*\"confrontational\"\n", - "2021-08-24 02:42:57,037 : INFO : topic #8 (0.020): 0.108*\"anime\" + 0.085*\"true story\" + 0.082*\"tom hanks\" + 0.071*\"based on a true story\" + 0.026*\"drama\" + 0.026*\"good\" + 0.024*\"archaeology\" + 0.021*\"japan\" + 0.020*\"interesting\" + 0.020*\"to-rent\"\n", - "2021-08-24 02:42:57,038 : INFO : topic #41 (0.020): 0.114*\"remake\" + 0.069*\"new york city\" + 0.066*\"christmas\" + 0.045*\"family\" + 0.038*\"kevin spacey\" + 0.036*\"bibliothek\" + 0.033*\"new york\" + 0.029*\"Drama\" + 0.027*\"eric's dvds\" + 0.023*\"movie to see\"\n", - "2021-08-24 02:42:57,039 : INFO : topic diff=0.128704, rho=0.173186\n", - "2021-08-24 02:42:57,041 : INFO : PROGRESS: pass 27, at document #4000/10681\n", - "2021-08-24 02:42:57,308 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:57,336 : INFO : topic #40 (0.020): 0.258*\"r\" + 0.130*\"clearplay\" + 0.058*\"movie to see\" + 0.052*\"prison\" + 0.044*\"Drama\" + 0.033*\"morgan freeman\" + 0.027*\"friendship\" + 0.020*\"mel brooks\" + 0.020*\"conspiracy\" + 0.016*\"1970s\"\n", - "2021-08-24 02:42:57,337 : INFO : topic #28 (0.020): 0.088*\"oscar (best cinematography)\" + 0.072*\"Drama\" + 0.055*\"oscar (best supporting actress)\" + 0.049*\"in netflix queue\" + 0.042*\"oscar (best actress)\" + 0.029*\"remade\" + 0.022*\"marlon brando\" + 0.016*\"exceptional acting\" + 0.014*\"china\" + 0.013*\"alcoholism\"\n", - "2021-08-24 02:42:57,338 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.065*\"Romance\" + 0.054*\"chick flick\" + 0.038*\"boring\" + 0.030*\"girlie movie\" + 0.027*\"baseball\" + 0.023*\"Comedy\" + 0.020*\"love story\" + 0.019*\"whimsical\" + 0.017*\"wedding\"\n", - "2021-08-24 02:42:57,339 : INFO : topic #0 (0.020): 0.224*\"can't remember\" + 0.098*\"directorial debut\" + 0.092*\"based on a tv show\" + 0.079*\"Comedy\" + 0.032*\"ummarti2006\" + 0.028*\"australia\" + 0.026*\"keira knightley\" + 0.021*\"australian\" + 0.020*\"robert downey jr\" + 0.017*\"immigrants\"\n", - "2021-08-24 02:42:57,340 : INFO : topic #32 (0.020): 0.141*\"johnny depp\" + 0.063*\"clint eastwood\" + 0.054*\"vhs\" + 0.041*\"jackie chan\" + 0.041*\"western\" + 0.034*\"spaghetti western\" + 0.032*\"kung fu\" + 0.029*\"india\" + 0.029*\"propaganda\" + 0.028*\"sergio leone\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:57,341 : INFO : topic diff=0.023122, rho=0.173186\n", - "2021-08-24 02:42:57,343 : INFO : PROGRESS: pass 27, at document #6000/10681\n", - "2021-08-24 02:42:57,586 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:57,613 : INFO : topic #18 (0.020): 0.081*\"magic\" + 0.079*\"gay\" + 0.062*\"racism\" + 0.044*\"pg13\" + 0.040*\"Drama\" + 0.027*\"food\" + 0.027*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.019*\"denzel washington\"\n", - "2021-08-24 02:42:57,614 : INFO : topic #32 (0.020): 0.135*\"johnny depp\" + 0.067*\"clint eastwood\" + 0.059*\"vhs\" + 0.045*\"jackie chan\" + 0.037*\"western\" + 0.031*\"kung fu\" + 0.031*\"leonardo dicaprio\" + 0.030*\"spaghetti western\" + 0.029*\"propaganda\" + 0.029*\"david lynch\"\n", - "2021-08-24 02:42:57,615 : INFO : topic #26 (0.020): 0.042*\"Drama\" + 0.035*\"james bond\" + 0.024*\"007\" + 0.023*\"bond\" + 0.021*\"mel gibson\" + 0.021*\"reflective\" + 0.020*\"atmospheric\" + 0.019*\"poignant\" + 0.018*\"lyrical\" + 0.017*\"bittersweet\"\n", - "2021-08-24 02:42:57,616 : INFO : topic #24 (0.020): 0.080*\"hitchcock\" + 0.077*\"murder\" + 0.068*\"vampire\" + 0.061*\"alfred hitchcock\" + 0.056*\"vampires\" + 0.055*\"oppl\" + 0.048*\"memory\" + 0.019*\"incest\" + 0.018*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:42:57,617 : INFO : topic #6 (0.020): 0.133*\"Musical\" + 0.096*\"politics\" + 0.067*\"satire\" + 0.045*\"nicolas cage\" + 0.043*\"sean connery\" + 0.034*\"dvd-r\" + 0.030*\"terrorism\" + 0.028*\"police\" + 0.023*\"netwatch\" + 0.022*\"dvd-ram\"\n", - "2021-08-24 02:42:57,618 : INFO : topic diff=0.045444, rho=0.173186\n", - "2021-08-24 02:42:57,620 : INFO : PROGRESS: pass 27, at document #8000/10681\n", - "2021-08-24 02:42:57,859 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:57,887 : INFO : topic #1 (0.020): 0.048*\"overrated\" + 0.039*\"violence\" + 0.038*\"quentin tarantino\" + 0.033*\"crime\" + 0.025*\"tarantino\" + 0.024*\"owned\" + 0.024*\"imdb top 250\" + 0.023*\"violent\" + 0.023*\"revenge\" + 0.023*\"drama\"\n", - "2021-08-24 02:42:57,888 : INFO : topic #14 (0.020): 0.058*\"criterion\" + 0.053*\"tense\" + 0.051*\"disturbing\" + 0.051*\"atmospheric\" + 0.049*\"Drama\" + 0.032*\"stylized\" + 0.030*\"bleak\" + 0.028*\"tumey's dvds\" + 0.025*\"menacing\" + 0.025*\"erlend's dvds\"\n", - "2021-08-24 02:42:57,889 : INFO : topic #39 (0.020): 0.184*\"time travel\" + 0.054*\"adultery\" + 0.041*\"motorcycle\" + 0.039*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.030*\"jude law\" + 0.024*\"dystopia\" + 0.020*\"want to own\" + 0.018*\"ian mckellen\" + 0.018*\"bechdel test:fail\"\n", - "2021-08-24 02:42:57,890 : INFO : topic #48 (0.020): 0.091*\"bruce willis\" + 0.082*\"twist ending\" + 0.069*\"netflix\" + 0.047*\"coming of age\" + 0.034*\"journalism\" + 0.030*\"sexy\" + 0.025*\"Drama\" + 0.024*\"avi\" + 0.022*\"to see\" + 0.022*\"talking animals\"\n", - "2021-08-24 02:42:57,891 : INFO : topic #36 (0.020): 0.079*\"cult film\" + 0.048*\"peter jackson\" + 0.042*\"downbeat\" + 0.042*\"russell crowe\" + 0.031*\"adam sandler\" + 0.029*\"not available from netflix\" + 0.027*\"1980s\" + 0.024*\"new zealand\" + 0.020*\"80s\" + 0.015*\"drew barrymore\"\n", - "2021-08-24 02:42:57,892 : INFO : topic diff=0.037896, rho=0.173186\n", - "2021-08-24 02:42:57,894 : INFO : PROGRESS: pass 27, at document #10000/10681\n", - "2021-08-24 02:42:58,152 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:58,180 : INFO : topic #1 (0.020): 0.054*\"overrated\" + 0.040*\"violence\" + 0.037*\"quentin tarantino\" + 0.032*\"revenge\" + 0.031*\"crime\" + 0.025*\"violent\" + 0.024*\"owned\" + 0.024*\"imdb top 250\" + 0.022*\"tarantino\" + 0.021*\"nonlinear\"\n", - "2021-08-24 02:42:58,181 : INFO : topic #31 (0.020): 0.149*\"zombies\" + 0.096*\"pg-13\" + 0.064*\"horror\" + 0.034*\"zombie\" + 0.027*\"cult classic\" + 0.026*\"joaquin phoenix\" + 0.024*\"infidelity\" + 0.022*\"sam raimi\" + 0.021*\"campy\" + 0.020*\"books\"\n", - "2021-08-24 02:42:58,182 : INFO : topic #16 (0.020): 0.290*\"nudity (topless)\" + 0.181*\"nudity (topless - brief)\" + 0.068*\"nudity (rear)\" + 0.036*\"pg\" + 0.029*\"claymation\" + 0.022*\"military\" + 0.018*\"aardman\" + 0.017*\"childhood\" + 0.016*\"weird\" + 0.010*\"freedom\"\n", - "2021-08-24 02:42:58,183 : INFO : topic #4 (0.020): 0.070*\"sports\" + 0.064*\"Drama\" + 0.056*\"london\" + 0.049*\"boxing\" + 0.046*\"sven's to see list\" + 0.040*\"british\" + 0.039*\"underrated\" + 0.039*\"hw drama\" + 0.025*\"inspirational\" + 0.024*\"sylvester stallone\"\n", - "2021-08-24 02:42:58,183 : INFO : topic #48 (0.020): 0.102*\"netflix\" + 0.086*\"bruce willis\" + 0.085*\"twist ending\" + 0.044*\"coming of age\" + 0.037*\"journalism\" + 0.025*\"talking animals\" + 0.025*\"sexy\" + 0.025*\"Drama\" + 0.022*\"to see\" + 0.021*\"avi\"\n", - "2021-08-24 02:42:58,184 : INFO : topic diff=0.030878, rho=0.173186\n", - "2021-08-24 02:42:58,326 : INFO : -20.963 per-word bound, 2043730.1 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:42:58,327 : INFO : PROGRESS: pass 27, at document #10681/10681\n", - "2021-08-24 02:42:58,429 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:42:58,455 : INFO : topic #43 (0.020): 0.074*\"surreal\" + 0.049*\"quirky\" + 0.039*\"satirical\" + 0.037*\"narrated\" + 0.032*\"cynical\" + 0.031*\"irreverent\" + 0.030*\"dreamlike\" + 0.027*\"biting\" + 0.025*\"humorous\" + 0.023*\"stanley kubrick\"\n", - "2021-08-24 02:42:58,456 : INFO : topic #16 (0.020): 0.327*\"nudity (topless)\" + 0.200*\"nudity (topless - brief)\" + 0.061*\"nudity (rear)\" + 0.028*\"pg\" + 0.023*\"claymation\" + 0.020*\"childhood\" + 0.020*\"military\" + 0.014*\"aardman\" + 0.014*\"weird\" + 0.012*\"mother-son relationship\"\n", - "2021-08-24 02:42:58,457 : INFO : topic #33 (0.020): 0.117*\"Comedy\" + 0.106*\"comedy\" + 0.063*\"funny\" + 0.038*\"parody\" + 0.034*\"dark comedy\" + 0.031*\"seen more than once\" + 0.029*\"hilarious\" + 0.027*\"coen brothers\" + 0.023*\"black comedy\" + 0.022*\"england\"\n", - "2021-08-24 02:42:58,458 : INFO : topic #40 (0.020): 0.302*\"r\" + 0.153*\"clearplay\" + 0.071*\"movie to see\" + 0.050*\"Drama\" + 0.034*\"prison\" + 0.028*\"friendship\" + 0.027*\"morgan freeman\" + 0.016*\"conspiracy\" + 0.014*\"1970s\" + 0.013*\"don cheadle\"\n", - "2021-08-24 02:42:58,459 : INFO : topic #17 (0.020): 0.360*\"Sci-Fi\" + 0.077*\"Horror\" + 0.071*\"Action\" + 0.040*\"video game adaptation\" + 0.030*\"movie to see\" + 0.024*\"football\" + 0.022*\"animals\" + 0.017*\"futuristic\" + 0.015*\"milla jovovich\" + 0.014*\"g\"\n", - "2021-08-24 02:42:58,460 : INFO : topic diff=0.069196, rho=0.173186\n", - "2021-08-24 02:42:58,462 : INFO : PROGRESS: pass 28, at document #2000/10681\n", - "2021-08-24 02:42:58,708 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:58,735 : INFO : topic #22 (0.020): 0.188*\"betamax\" + 0.089*\"dvd-video\" + 0.079*\"clv\" + 0.037*\"library on hold\" + 0.034*\"aviation\" + 0.025*\"terry gilliam\" + 0.018*\"dvd collection\" + 0.016*\"steven seagal\" + 0.016*\"gilliam\" + 0.015*\"family drama\"\n", - "2021-08-24 02:42:58,736 : INFO : topic #36 (0.020): 0.084*\"cult film\" + 0.042*\"russell crowe\" + 0.037*\"peter jackson\" + 0.036*\"downbeat\" + 0.032*\"adam sandler\" + 0.028*\"1980s\" + 0.024*\"not available from netflix\" + 0.023*\"new zealand\" + 0.019*\"virus\" + 0.017*\"must see!\"\n", - "2021-08-24 02:42:58,736 : INFO : topic #43 (0.020): 0.069*\"surreal\" + 0.044*\"stanley kubrick\" + 0.044*\"quirky\" + 0.039*\"satirical\" + 0.034*\"cynical\" + 0.032*\"narrated\" + 0.030*\"biting\" + 0.030*\"irreverent\" + 0.030*\"dreamlike\" + 0.024*\"humorous\"\n", - "2021-08-24 02:42:58,737 : INFO : topic #6 (0.020): 0.123*\"Musical\" + 0.111*\"politics\" + 0.068*\"satire\" + 0.046*\"nicolas cage\" + 0.036*\"sean connery\" + 0.034*\"terrorism\" + 0.034*\"dvd-r\" + 0.025*\"political\" + 0.025*\"police\" + 0.023*\"good dialogue\"\n", - "2021-08-24 02:42:58,738 : INFO : topic #19 (0.020): 0.070*\"nudity (full frontal)\" + 0.059*\"drama\" + 0.036*\"Drama\" + 0.030*\"philip k. dick\" + 0.029*\"vietnam\" + 0.028*\"vietnam war\" + 0.025*\"to see\" + 0.024*\"rape\" + 0.019*\"slow\" + 0.018*\"very good\"\n", - "2021-08-24 02:42:58,739 : INFO : topic diff=0.126077, rho=0.170646\n", - "2021-08-24 02:42:58,741 : INFO : PROGRESS: pass 28, at document #4000/10681\n", - "2021-08-24 02:42:58,998 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:59,025 : INFO : topic #22 (0.020): 0.229*\"betamax\" + 0.096*\"dvd-video\" + 0.074*\"clv\" + 0.036*\"aviation\" + 0.029*\"library on hold\" + 0.023*\"terry gilliam\" + 0.018*\"dvd collection\" + 0.016*\"gilliam\" + 0.014*\"seen 2008\" + 0.013*\"steven seagal\"\n", - "2021-08-24 02:42:59,027 : INFO : topic #37 (0.020): 0.182*\"War\" + 0.107*\"world war ii\" + 0.103*\"Drama\" + 0.056*\"war\" + 0.041*\"history\" + 0.037*\"jim carrey\" + 0.036*\"Action\" + 0.022*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:42:59,028 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.065*\"Romance\" + 0.054*\"chick flick\" + 0.038*\"boring\" + 0.030*\"girlie movie\" + 0.027*\"baseball\" + 0.023*\"Comedy\" + 0.020*\"love story\" + 0.019*\"whimsical\" + 0.017*\"wedding\"\n", - "2021-08-24 02:42:59,029 : INFO : topic #5 (0.020): 0.292*\"classic\" + 0.062*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.034*\"Musical\" + 0.030*\"national film registry\" + 0.021*\"afi 100\" + 0.016*\"70mm\" + 0.015*\"adapted from b'way\" + 0.015*\"john travolta\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:59,029 : INFO : topic #31 (0.020): 0.127*\"zombies\" + 0.074*\"horror\" + 0.072*\"pg-13\" + 0.039*\"cult classic\" + 0.025*\"infidelity\" + 0.025*\"campy\" + 0.024*\"zombie\" + 0.024*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.020*\"books\"\n", - "2021-08-24 02:42:59,030 : INFO : topic diff=0.022572, rho=0.170646\n", - "2021-08-24 02:42:59,032 : INFO : PROGRESS: pass 28, at document #6000/10681\n", - "2021-08-24 02:42:59,270 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:59,297 : INFO : topic #48 (0.020): 0.098*\"bruce willis\" + 0.085*\"twist ending\" + 0.075*\"netflix\" + 0.048*\"coming of age\" + 0.033*\"sexy\" + 0.030*\"journalism\" + 0.025*\"Drama\" + 0.024*\"to see\" + 0.023*\"talking animals\" + 0.020*\"relationships\"\n", - "2021-08-24 02:42:59,297 : INFO : topic #18 (0.020): 0.081*\"magic\" + 0.079*\"gay\" + 0.062*\"racism\" + 0.045*\"pg13\" + 0.040*\"Drama\" + 0.027*\"food\" + 0.027*\"social commentary\" + 0.023*\"homosexuality\" + 0.020*\"19th century\" + 0.019*\"denzel washington\"\n", - "2021-08-24 02:42:59,298 : INFO : topic #8 (0.020): 0.125*\"anime\" + 0.094*\"tom hanks\" + 0.078*\"true story\" + 0.064*\"based on a true story\" + 0.026*\"drama\" + 0.025*\"japan\" + 0.025*\"good\" + 0.023*\"interesting\" + 0.021*\"archaeology\" + 0.019*\"not funny\"\n", - "2021-08-24 02:42:59,299 : INFO : topic #32 (0.020): 0.135*\"johnny depp\" + 0.067*\"clint eastwood\" + 0.059*\"vhs\" + 0.045*\"jackie chan\" + 0.037*\"western\" + 0.031*\"kung fu\" + 0.031*\"leonardo dicaprio\" + 0.030*\"spaghetti western\" + 0.029*\"propaganda\" + 0.028*\"david lynch\"\n", - "2021-08-24 02:42:59,300 : INFO : topic #5 (0.020): 0.277*\"classic\" + 0.066*\"musical\" + 0.045*\"afi 100 (laughs)\" + 0.034*\"Musical\" + 0.029*\"national film registry\" + 0.020*\"afi 100\" + 0.017*\"john travolta\" + 0.017*\"70mm\" + 0.016*\"adapted from b'way\" + 0.015*\"breakthroughs\"\n", - "2021-08-24 02:42:59,301 : INFO : topic diff=0.044435, rho=0.170646\n", - "2021-08-24 02:42:59,303 : INFO : PROGRESS: pass 28, at document #8000/10681\n", - "2021-08-24 02:42:59,553 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:59,581 : INFO : topic #41 (0.020): 0.118*\"remake\" + 0.066*\"christmas\" + 0.061*\"new york city\" + 0.046*\"family\" + 0.042*\"bibliothek\" + 0.034*\"new york\" + 0.032*\"kevin spacey\" + 0.031*\"Drama\" + 0.031*\"eric's dvds\" + 0.027*\"friday night movie\"\n", - "2021-08-24 02:42:59,582 : INFO : topic #36 (0.020): 0.079*\"cult film\" + 0.047*\"peter jackson\" + 0.042*\"downbeat\" + 0.042*\"russell crowe\" + 0.031*\"adam sandler\" + 0.029*\"not available from netflix\" + 0.027*\"1980s\" + 0.024*\"new zealand\" + 0.020*\"80s\" + 0.015*\"drew barrymore\"\n", - "2021-08-24 02:42:59,583 : INFO : topic #7 (0.020): 0.114*\"romance\" + 0.062*\"Romance\" + 0.047*\"chick flick\" + 0.038*\"boring\" + 0.028*\"girlie movie\" + 0.027*\"baseball\" + 0.022*\"Comedy\" + 0.022*\"whimsical\" + 0.019*\"wedding\" + 0.018*\"love\"\n", - "2021-08-24 02:42:59,584 : INFO : topic #28 (0.020): 0.082*\"oscar (best cinematography)\" + 0.078*\"Drama\" + 0.055*\"in netflix queue\" + 0.054*\"oscar (best supporting actress)\" + 0.044*\"oscar (best actress)\" + 0.028*\"remade\" + 0.018*\"marlon brando\" + 0.015*\"exceptional acting\" + 0.014*\"father daughter relationship\" + 0.012*\"china\"\n", - "2021-08-24 02:42:59,585 : INFO : topic #47 (0.020): 0.063*\"serial killer\" + 0.057*\"psychology\" + 0.050*\"brad pitt\" + 0.040*\"edward norton\" + 0.034*\"heist\" + 0.028*\"matt damon\" + 0.028*\"steven spielberg\" + 0.026*\"hayao miyazaki\" + 0.023*\"crime\" + 0.016*\"mystery\"\n", - "2021-08-24 02:42:59,586 : INFO : topic diff=0.037378, rho=0.170646\n", - "2021-08-24 02:42:59,587 : INFO : PROGRESS: pass 28, at document #10000/10681\n", - "2021-08-24 02:42:59,849 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:42:59,878 : INFO : topic #2 (0.020): 0.288*\"Documentary\" + 0.107*\"to see\" + 0.072*\"documentary\" + 0.027*\"mockumentary\" + 0.023*\"sexuality\" + 0.020*\"dogs\" + 0.020*\"movie to see\" + 0.020*\"kevin smith\" + 0.020*\"star trek\" + 0.018*\"owen wilson\"\n", - "2021-08-24 02:42:59,879 : INFO : topic #31 (0.020): 0.149*\"zombies\" + 0.096*\"pg-13\" + 0.064*\"horror\" + 0.034*\"zombie\" + 0.027*\"cult classic\" + 0.026*\"joaquin phoenix\" + 0.024*\"infidelity\" + 0.022*\"sam raimi\" + 0.021*\"campy\" + 0.020*\"books\"\n", - "2021-08-24 02:42:59,879 : INFO : topic #40 (0.020): 0.277*\"r\" + 0.125*\"clearplay\" + 0.058*\"movie to see\" + 0.047*\"Drama\" + 0.041*\"prison\" + 0.030*\"morgan freeman\" + 0.025*\"friendship\" + 0.018*\"conspiracy\" + 0.017*\"john wayne\" + 0.015*\"1970s\"\n", - "2021-08-24 02:42:59,880 : INFO : topic #18 (0.020): 0.096*\"magic\" + 0.077*\"gay\" + 0.058*\"pg13\" + 0.050*\"racism\" + 0.039*\"Drama\" + 0.026*\"homosexuality\" + 0.025*\"social commentary\" + 0.025*\"food\" + 0.022*\"19th century\" + 0.017*\"denzel washington\"\n", - "2021-08-24 02:42:59,881 : INFO : topic #39 (0.020): 0.177*\"time travel\" + 0.058*\"adultery\" + 0.049*\"motorcycle\" + 0.037*\"jude law\" + 0.037*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.022*\"dystopia\" + 0.021*\"want to own\" + 0.019*\"old\" + 0.018*\"hollywood\"\n", - "2021-08-24 02:42:59,882 : INFO : topic diff=0.030540, rho=0.170646\n", - "2021-08-24 02:43:00,033 : INFO : -20.962 per-word bound, 2043089.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:43:00,033 : INFO : PROGRESS: pass 28, at document #10681/10681\n", - "2021-08-24 02:43:00,134 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:43:00,161 : INFO : topic #34 (0.020): 0.074*\"ghosts\" + 0.052*\"bill murray\" + 0.042*\"television\" + 0.034*\"courtroom\" + 0.031*\"secret service\" + 0.029*\"courtroom drama\" + 0.022*\"roman polanski\" + 0.019*\"ben stiller\" + 0.017*\"court\" + 0.017*\"las vegas\"\n", - "2021-08-24 02:43:00,162 : INFO : topic #16 (0.020): 0.327*\"nudity (topless)\" + 0.200*\"nudity (topless - brief)\" + 0.061*\"nudity (rear)\" + 0.028*\"pg\" + 0.023*\"claymation\" + 0.020*\"childhood\" + 0.020*\"military\" + 0.014*\"aardman\" + 0.014*\"weird\" + 0.012*\"mother-son relationship\"\n", - "2021-08-24 02:43:00,163 : INFO : topic #47 (0.020): 0.061*\"serial killer\" + 0.053*\"psychology\" + 0.050*\"brad pitt\" + 0.038*\"heist\" + 0.038*\"edward norton\" + 0.030*\"matt damon\" + 0.027*\"hayao miyazaki\" + 0.024*\"steven spielberg\" + 0.019*\"crime\" + 0.014*\"anthony hopkins\"\n", - "2021-08-24 02:43:00,164 : INFO : topic #35 (0.020): 0.356*\"Comedy\" + 0.313*\"Drama\" + 0.199*\"Romance\" + 0.014*\"lesbian\" + 0.008*\"bibliothek\" + 0.008*\"sean penn\" + 0.007*\"philip seymour hoffman\" + 0.005*\"m. night shyamalan\" + 0.005*\"oscar (best foreign language film)\" + 0.003*\"great cinematography\"\n", - "2021-08-24 02:43:00,165 : INFO : topic #8 (0.020): 0.132*\"anime\" + 0.084*\"based on a true story\" + 0.082*\"true story\" + 0.069*\"tom hanks\" + 0.023*\"good\" + 0.023*\"japan\" + 0.023*\"interesting\" + 0.022*\"to-rent\" + 0.022*\"drama\" + 0.022*\"archaeology\"\n", - "2021-08-24 02:43:00,166 : INFO : topic diff=0.068102, rho=0.170646\n", - "2021-08-24 02:43:00,168 : INFO : PROGRESS: pass 29, at document #2000/10681\n", - "2021-08-24 02:43:00,417 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:43:00,443 : INFO : topic #41 (0.020): 0.114*\"remake\" + 0.069*\"new york city\" + 0.066*\"christmas\" + 0.045*\"family\" + 0.038*\"kevin spacey\" + 0.037*\"bibliothek\" + 0.033*\"new york\" + 0.030*\"Drama\" + 0.027*\"eric's dvds\" + 0.023*\"movie to see\"\n", - "2021-08-24 02:43:00,444 : INFO : topic #35 (0.020): 0.347*\"Comedy\" + 0.315*\"Drama\" + 0.198*\"Romance\" + 0.016*\"lesbian\" + 0.009*\"bibliothek\" + 0.007*\"sean penn\" + 0.006*\"philip seymour hoffman\" + 0.005*\"oscar (best foreign language film)\" + 0.004*\"m. night shyamalan\" + 0.003*\"great cinematography\"\n", - "2021-08-24 02:43:00,445 : INFO : topic #39 (0.020): 0.193*\"time travel\" + 0.052*\"adultery\" + 0.041*\"post apocalyptic\" + 0.037*\"motorcycle\" + 0.033*\"post-apocalyptic\" + 0.030*\"jude law\" + 0.023*\"dystopia\" + 0.021*\"bechdel test:fail\" + 0.020*\"want to own\" + 0.019*\"hollywood\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:43:00,446 : INFO : topic #17 (0.020): 0.330*\"Sci-Fi\" + 0.071*\"Horror\" + 0.068*\"Action\" + 0.044*\"video game adaptation\" + 0.027*\"animals\" + 0.025*\"movie to see\" + 0.022*\"football\" + 0.018*\"futuristic\" + 0.015*\"milla jovovich\" + 0.013*\"g\"\n", - "2021-08-24 02:43:00,447 : INFO : topic #34 (0.020): 0.074*\"ghosts\" + 0.051*\"bill murray\" + 0.044*\"television\" + 0.030*\"courtroom\" + 0.030*\"courtroom drama\" + 0.026*\"secret service\" + 0.021*\"roman polanski\" + 0.017*\"18th century\" + 0.017*\"opera\" + 0.017*\"court\"\n", - "2021-08-24 02:43:00,448 : INFO : topic diff=0.124178, rho=0.168215\n", - "2021-08-24 02:43:00,449 : INFO : PROGRESS: pass 29, at document #4000/10681\n", - "2021-08-24 02:43:00,687 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:43:00,714 : INFO : topic #31 (0.020): 0.127*\"zombies\" + 0.074*\"horror\" + 0.072*\"pg-13\" + 0.039*\"cult classic\" + 0.025*\"infidelity\" + 0.025*\"campy\" + 0.024*\"zombie\" + 0.024*\"joaquin phoenix\" + 0.024*\"sam raimi\" + 0.020*\"books\"\n", - "2021-08-24 02:43:00,715 : INFO : topic #7 (0.020): 0.113*\"romance\" + 0.065*\"Romance\" + 0.054*\"chick flick\" + 0.038*\"boring\" + 0.030*\"girlie movie\" + 0.027*\"baseball\" + 0.023*\"Comedy\" + 0.020*\"love story\" + 0.019*\"whimsical\" + 0.017*\"wedding\"\n", - "2021-08-24 02:43:00,716 : INFO : topic #24 (0.020): 0.090*\"hitchcock\" + 0.079*\"murder\" + 0.069*\"alfred hitchcock\" + 0.067*\"vampire\" + 0.061*\"oppl\" + 0.051*\"vampires\" + 0.037*\"memory\" + 0.021*\"incest\" + 0.018*\"cary grant\" + 0.016*\"tumey's dvds\"\n", - "2021-08-24 02:43:00,717 : INFO : topic #20 (0.020): 0.098*\"biography\" + 0.068*\"Drama\" + 0.049*\"death\" + 0.047*\"biopic\" + 0.039*\"suicide\" + 0.036*\"julia roberts\" + 0.035*\"ensemble cast\" + 0.034*\"corvallis library\" + 0.028*\"medieval\" + 0.025*\"multiple storylines\"\n", - "2021-08-24 02:43:00,717 : INFO : topic #38 (0.020): 0.168*\"drugs\" + 0.045*\"samuel l. jackson\" + 0.026*\"divx\" + 0.024*\"basketball\" + 0.024*\"addiction\" + 0.023*\"ewan mcgregor\" + 0.023*\"hulu\" + 0.022*\"michael caine\" + 0.020*\"liam neeson\" + 0.020*\"poverty\"\n", - "2021-08-24 02:43:00,719 : INFO : topic diff=0.022241, rho=0.168215\n", - "2021-08-24 02:43:00,720 : INFO : PROGRESS: pass 29, at document #6000/10681\n", - "2021-08-24 02:43:00,950 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:43:00,976 : INFO : topic #27 (0.020): 0.317*\"Horror\" + 0.157*\"Thriller\" + 0.127*\"Mystery\" + 0.043*\"Drama\" + 0.029*\"easily confused with other movie(s) (title)\" + 0.028*\"Fantasy\" + 0.028*\"Film-Noir\" + 0.016*\"eerie\" + 0.015*\"slasher\" + 0.014*\"tumey's dvds\"\n", - "2021-08-24 02:43:00,977 : INFO : topic #37 (0.020): 0.184*\"War\" + 0.111*\"world war ii\" + 0.102*\"Drama\" + 0.058*\"war\" + 0.043*\"history\" + 0.035*\"Action\" + 0.033*\"jim carrey\" + 0.021*\"nazis\" + 0.017*\"mental illness\" + 0.016*\"wwii\"\n", - "2021-08-24 02:43:00,978 : INFO : topic #17 (0.020): 0.344*\"Sci-Fi\" + 0.080*\"Horror\" + 0.067*\"Action\" + 0.039*\"video game adaptation\" + 0.025*\"football\" + 0.022*\"animals\" + 0.019*\"movie to see\" + 0.017*\"futuristic\" + 0.014*\"milla jovovich\" + 0.012*\"g\"\n", - "2021-08-24 02:43:00,979 : INFO : topic #24 (0.020): 0.080*\"hitchcock\" + 0.078*\"murder\" + 0.068*\"vampire\" + 0.061*\"alfred hitchcock\" + 0.056*\"vampires\" + 0.055*\"oppl\" + 0.048*\"memory\" + 0.019*\"incest\" + 0.018*\"cary grant\" + 0.017*\"kirsten dunst\"\n", - "2021-08-24 02:43:00,980 : INFO : topic #13 (0.020): 0.059*\"fairy tale\" + 0.039*\"jane austen\" + 0.038*\"sequel\" + 0.037*\"road trip\" + 0.027*\"cold war\" + 0.027*\"assassination\" + 0.027*\"historical\" + 0.026*\"gerard depardieu\" + 0.024*\"kids\" + 0.022*\"notable nudity\"\n", - "2021-08-24 02:43:00,981 : INFO : topic diff=0.043852, rho=0.168215\n", - "2021-08-24 02:43:00,982 : INFO : PROGRESS: pass 29, at document #8000/10681\n", - "2021-08-24 02:43:01,220 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:43:01,248 : INFO : topic #0 (0.020): 0.204*\"can't remember\" + 0.103*\"directorial debut\" + 0.084*\"based on a tv show\" + 0.081*\"Comedy\" + 0.036*\"ummarti2006\" + 0.027*\"australia\" + 0.026*\"australian\" + 0.025*\"keira knightley\" + 0.020*\"immigrants\" + 0.018*\"robert downey jr\"\n", - "2021-08-24 02:43:01,249 : INFO : topic #42 (0.020): 0.095*\"japan\" + 0.091*\"martial arts\" + 0.076*\"robin williams\" + 0.042*\"akira kurosawa\" + 0.035*\"samurai\" + 0.031*\"Drama\" + 0.028*\"orson welles\" + 0.024*\"kurosawa\" + 0.021*\"hugh grant\" + 0.020*\"christopher walken\"\n", - "2021-08-24 02:43:01,250 : INFO : topic #36 (0.020): 0.079*\"cult film\" + 0.047*\"peter jackson\" + 0.042*\"downbeat\" + 0.042*\"russell crowe\" + 0.031*\"adam sandler\" + 0.029*\"not available from netflix\" + 0.027*\"1980s\" + 0.024*\"new zealand\" + 0.020*\"80s\" + 0.015*\"drew barrymore\"\n", - "2021-08-24 02:43:01,251 : INFO : topic #38 (0.020): 0.160*\"drugs\" + 0.039*\"samuel l. jackson\" + 0.029*\"divx\" + 0.028*\"peter sellers\" + 0.028*\"ewan mcgregor\" + 0.024*\"hulu\" + 0.023*\"addiction\" + 0.022*\"poverty\" + 0.021*\"michael caine\" + 0.020*\"basketball\"\n", - "2021-08-24 02:43:01,251 : INFO : topic #26 (0.020): 0.044*\"Drama\" + 0.032*\"james bond\" + 0.022*\"007\" + 0.022*\"reflective\" + 0.021*\"bond\" + 0.021*\"poignant\" + 0.021*\"atmospheric\" + 0.019*\"bittersweet\" + 0.018*\"lyrical\" + 0.018*\"deliberate\"\n", - "2021-08-24 02:43:01,253 : INFO : topic diff=0.036880, rho=0.168215\n", - "2021-08-24 02:43:01,254 : INFO : PROGRESS: pass 29, at document #10000/10681\n", - "2021-08-24 02:43:01,516 : INFO : merging changes from 2000 documents into a model of 10681 documents\n", - "2021-08-24 02:43:01,544 : INFO : topic #30 (0.020): 0.246*\"Adventure\" + 0.116*\"Children\" + 0.110*\"Comedy\" + 0.106*\"Fantasy\" + 0.084*\"Action\" + 0.063*\"70mm\" + 0.049*\"Drama\" + 0.045*\"Animation\" + 0.017*\"Musical\" + 0.008*\"submarine\"\n", - "2021-08-24 02:43:01,544 : INFO : topic #18 (0.020): 0.096*\"magic\" + 0.077*\"gay\" + 0.058*\"pg13\" + 0.051*\"racism\" + 0.039*\"Drama\" + 0.026*\"homosexuality\" + 0.025*\"social commentary\" + 0.025*\"food\" + 0.022*\"19th century\" + 0.017*\"denzel washington\"\n", - "2021-08-24 02:43:01,545 : INFO : topic #39 (0.020): 0.177*\"time travel\" + 0.058*\"adultery\" + 0.049*\"motorcycle\" + 0.037*\"jude law\" + 0.037*\"post apocalyptic\" + 0.035*\"post-apocalyptic\" + 0.022*\"dystopia\" + 0.021*\"want to own\" + 0.019*\"old\" + 0.018*\"hollywood\"\n", - "2021-08-24 02:43:01,546 : INFO : topic #14 (0.020): 0.059*\"criterion\" + 0.055*\"disturbing\" + 0.052*\"tense\" + 0.051*\"Drama\" + 0.049*\"atmospheric\" + 0.032*\"stylized\" + 0.030*\"bleak\" + 0.028*\"tumey's dvds\" + 0.024*\"menacing\" + 0.024*\"erlend's dvds\"\n", - "2021-08-24 02:43:01,547 : INFO : topic #43 (0.020): 0.077*\"surreal\" + 0.052*\"quirky\" + 0.041*\"satirical\" + 0.034*\"cynical\" + 0.033*\"narrated\" + 0.031*\"dreamlike\" + 0.030*\"irreverent\" + 0.029*\"biting\" + 0.026*\"humorous\" + 0.025*\"stanley kubrick\"\n", - "2021-08-24 02:43:01,548 : INFO : topic diff=0.030108, rho=0.168215\n", - "2021-08-24 02:43:01,687 : INFO : -20.963 per-word bound, 2043979.7 perplexity estimate based on a held-out corpus of 681 documents with 5199 words\n", - "2021-08-24 02:43:01,688 : INFO : PROGRESS: pass 29, at document #10681/10681\n", - "2021-08-24 02:43:01,782 : INFO : merging changes from 681 documents into a model of 10681 documents\n", - "2021-08-24 02:43:01,808 : INFO : topic #39 (0.020): 0.160*\"time travel\" + 0.055*\"adultery\" + 0.045*\"motorcycle\" + 0.045*\"post apocalyptic\" + 0.041*\"post-apocalyptic\" + 0.035*\"jude law\" + 0.023*\"dystopia\" + 0.023*\"bechdel test:fail\" + 0.019*\"want to own\" + 0.017*\"old\"\n", - "2021-08-24 02:43:01,809 : INFO : topic #23 (0.020): 0.082*\"high school\" + 0.066*\"teen\" + 0.061*\"cars\" + 0.034*\"marx brothers\" + 0.033*\"dance\" + 0.026*\"Comedy\" + 0.021*\"eddie murphy\" + 0.020*\"kate winslet\" + 0.019*\"(s)vcd\" + 0.019*\"car chase\"\n", - "2021-08-24 02:43:01,810 : INFO : topic #42 (0.020): 0.092*\"japan\" + 0.088*\"martial arts\" + 0.065*\"robin williams\" + 0.038*\"akira kurosawa\" + 0.036*\"samurai\" + 0.035*\"Drama\" + 0.028*\"seen 2006\" + 0.027*\"divorce\" + 0.026*\"jonossa\" + 0.024*\"orson welles\"\n", - "2021-08-24 02:43:01,811 : INFO : topic #30 (0.020): 0.248*\"Adventure\" + 0.114*\"Comedy\" + 0.113*\"Children\" + 0.112*\"Fantasy\" + 0.089*\"Action\" + 0.051*\"70mm\" + 0.051*\"Animation\" + 0.050*\"Drama\" + 0.016*\"Musical\" + 0.008*\"dvd\"\n", - "2021-08-24 02:43:01,812 : INFO : topic #4 (0.020): 0.066*\"Drama\" + 0.063*\"sports\" + 0.058*\"london\" + 0.046*\"sven's to see list\" + 0.045*\"boxing\" + 0.040*\"underrated\" + 0.039*\"british\" + 0.034*\"hw drama\" + 0.031*\"notable soundtrack\" + 0.029*\"inspirational\"\n", - "2021-08-24 02:43:01,813 : INFO : topic diff=0.067227, rho=0.168215\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-08-24 02:43:01,815 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel(num_terms=15261, num_topics=50, decay=0.5, chunksize=2000) in 51.72s', 'datetime': '2021-08-24T02:43:01.815087', 'gensim': '4.0.1', 'python': '3.7.8 (default, May 2 2021, 19:43:55) \\n[Clang 10.0.1 (clang-1001.0.46.4)]', 'platform': 'Darwin-18.7.0-x86_64-i386-64bit', 'event': 'created'}\n" - ] - } - ], + "outputs": [], "source": [ "# LDAContent추천\n", "from src.lda_content import LDAContentRecommender\n", @@ -3455,18 +81,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "25fadb68", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.004, Recall@K=0.012\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/MF.ipynb b/chapter5/notebook/MF.ipynb index d408ae1..b55d1eb 100644 --- a/chapter5/notebook/MF.ipynb +++ b/chapter5/notebook/MF.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "99e09acf", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "179ce864", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "48aed5ee", "metadata": {}, "outputs": [], @@ -49,18 +49,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "ab4ed572", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.051, Precision@K=0.010, Recall@K=0.035\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -72,21 +64,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "ed2edb2d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.934, Precision@K=0.005, Recall@K=0.016\n", - "rmse=0.953, Precision@K=0.007, Recall@K=0.026\n", - "rmse=1.050, Precision@K=0.011, Recall@K=0.040\n", - "rmse=1.140, Precision@K=0.016, Recall@K=0.054\n" - ] - } - ], + "outputs": [], "source": [ "# 평가 수의 임곗값과 정밀도의 관계\n", "for minimum_num_rating in [0, 10, 100, 300]:\n", @@ -99,20 +80,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6cb9724e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.047, Precision@K=0.010, Recall@K=0.032\n", - "rmse=1.047, Precision@K=0.012, Recall@K=0.043\n", - "rmse=1.048, Precision@K=0.013, Recall@K=0.045\n" - ] - } - ], + "outputs": [], "source": [ "# 인자 수 k와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", diff --git a/chapter5/notebook/NMF.ipynb b/chapter5/notebook/NMF.ipynb index 0b4962c..6949cb4 100644 --- a/chapter5/notebook/NMF.ipynb +++ b/chapter5/notebook/NMF.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "9cb2f773", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ed3c6a08", "metadata": {}, "outputs": [], @@ -36,27 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "afd52c43", - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# NMF 추천\n", "from src.nmf import NMFRecommender\n", @@ -66,18 +49,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "48b2ed3a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=3.340, Precision@K=0.010, Recall@K=0.032\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -89,34 +64,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "1683599e", - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:315: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", - " \"'nndsvda' in 1.1 (renaming of 0.26).\"), FutureWarning)\n", - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/sklearn/decomposition/_nmf.py:1091: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", - " \" improve convergence.\" % max_iter, ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.045, Precision@K=0.012, Recall@K=0.040\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "# 결손값을 평균값으로 채운다\n", "recommend_result = recommender.recommend(movielens, fillna_with_zero=False)\n", diff --git a/chapter5/notebook/Popularity.ipynb b/chapter5/notebook/Popularity.ipynb index 5812150..51d0080 100644 --- a/chapter5/notebook/Popularity.ipynb +++ b/chapter5/notebook/Popularity.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "13333f1c", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f0b3a8f5", "metadata": {}, "outputs": [], @@ -39,102 +39,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a4e56039", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rating
sizemean
movie_idtitle
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27255Wind Will Carry Us, The (Bad ma ra khahad bord) (1999)15.0
4453Michael Jordan to the Max (2000)25.0
3415Mirror, The (Zerkalo) (1975)15.0
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rating
sizemean
movie_idtitle
318Shawshank Redemption, The (1994)4244.491745
50Usual Suspects, The (1995)3344.459581
912Casablanca (1942)1634.444785
904Rear Window (1954)1294.441860
2019Seven Samurai (Shichinin no samurai) (1954)1044.408654
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" - ], - "text/plain": [ - " rating \n", - " size mean\n", - "movie_id title \n", - "318 Shawshank Redemption, The (1994) 424 4.491745\n", - "50 Usual Suspects, The (1995) 334 4.459581\n", - "912 Casablanca (1942) 163 4.444785\n", - "904 Rear Window (1954) 129 4.441860\n", - "2019 Seven Samurai (Shichinin no samurai) (1954) 104 4.408654" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# 임곗값을 도입\n", "movie_stats = movielens.train.groupby(['movie_id', 'title']).agg({'rating': [np.size, np.mean]})\n", @@ -251,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6c559fdc", "metadata": {}, "outputs": [], @@ -264,18 +80,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "ca8d383f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.082, Precision@K=0.008, Recall@K=0.027\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -287,19 +95,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "fd52c464", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.082, Precision@K=0.000, Recall@K=0.000\n", - "rmse=1.082, Precision@K=0.013, Recall@K=0.042\n" - ] - } - ], + "outputs": [], "source": [ "# 임곗값을 변경했을 때의 동작\n", "for minimum_num_rating in [1, 200]:\n", diff --git a/chapter5/notebook/RF.ipynb b/chapter5/notebook/RF.ipynb index c812dfe..8ad826f 100644 --- a/chapter5/notebook/RF.ipynb +++ b/chapter5/notebook/RF.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "f5b2cf43-9653-4899-950f-b00443bd4020", "metadata": {}, "outputs": [], @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "52eedd0b", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d3c02fa7", "metadata": {}, "outputs": [], @@ -39,23 +39,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c8b4d83e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yuya/Documents/projects/recommender-systems-at-work/chapter5/notebook/../src/rf.py:54: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " test_x = test_x.merge(movie_genres, on=\"movie_id\")\n" - ] - } - ], + "outputs": [], "source": [ "# Random Forest를 사용한 회귀 추천\n", "from src.rf import RFRecommender\n", @@ -65,18 +52,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "253e5f67", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.988, Precision@K=0.000, Recall@K=0.001\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/Random.ipynb b/chapter5/notebook/Random.ipynb index 4487c74..f2b6fd6 100644 --- a/chapter5/notebook/Random.ipynb +++ b/chapter5/notebook/Random.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "2e403fbc", "metadata": {}, "outputs": [], @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9827acbb", "metadata": {}, "outputs": [], @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bdfd3743", "metadata": {}, "outputs": [], @@ -50,18 +50,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "39dc3945", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.883, Precision@K=0.000, Recall@K=0.001\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/SVD.ipynb b/chapter5/notebook/SVD.ipynb index 9227231..d55536e 100644 --- a/chapter5/notebook/SVD.ipynb +++ b/chapter5/notebook/SVD.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "5e2540ed", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a8895ba7", "metadata": {}, "outputs": [], @@ -36,399 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "ebe3329b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1000 rows × 6673 columns

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" - ], - "text/plain": [ - "movie_id 1 2 3 4 5 6 7 8 9 \\\n", - "user_id \n", - "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 1.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... ... \n", - "1048 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1050 0.0 3.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 \n", - "1051 5.0 0.0 3.0 0.0 3.0 0.0 4.0 0.0 0.0 \n", - "1052 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1053 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - "movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n", - "user_id ... \n", - "1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... ... ... \n", - "1048 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1050 3.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1051 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1052 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1053 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - "movie_id 63992 64716 \n", - "user_id \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "... ... ... \n", - "1048 0.0 0.0 \n", - "1050 0.0 0.0 \n", - "1051 0.0 0.0 \n", - "1052 0.0 0.0 \n", - "1053 0.0 0.0 \n", - "\n", - "[1000 rows x 6673 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "user_movie_matrix.fillna(0)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "75ee52d8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "P: (1000, 5), S: (5,), Qt: (5, 6673), pred_matrix: (1000, 6673)\n" - ] - } - ], + "outputs": [], "source": [ "import scipy\n", "import numpy as np\n", @@ -893,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "2a761397", "metadata": {}, "outputs": [], @@ -906,18 +112,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c895f553", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=3.335, Precision@K=0.009, Recall@K=0.029\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -929,18 +127,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d4e03ece", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.046, Precision@K=0.013, Recall@K=0.043\n" - ] - } - ], + "outputs": [], "source": [ "# 결손값을 평균값으로 채운다\n", "recommend_result = recommender.recommend(movielens, fillna_with_zero=False)\n", @@ -952,20 +142,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "1f95570c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=1.046, Precision@K=0.013, Recall@K=0.043\n", - "rmse=1.042, Precision@K=0.011, Recall@K=0.039\n", - "rmse=1.038, Precision@K=0.011, Recall@K=0.036\n" - ] - } - ], + "outputs": [], "source": [ "# 인자 수와 정밀도의 관계\n", "for factors in [5, 10, 30]:\n", diff --git a/chapter5/notebook/Template.ipynb b/chapter5/notebook/Template.ipynb index f9842c6..0ef4af4 100644 --- a/chapter5/notebook/Template.ipynb +++ b/chapter5/notebook/Template.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "52eedd0b", "metadata": {}, "outputs": [], @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d3c02fa7", "metadata": {}, "outputs": [], diff --git a/chapter5/notebook/UMCF.ipynb b/chapter5/notebook/UMCF.ipynb index bb41031..fff77cc 100644 --- a/chapter5/notebook/UMCF.ipynb +++ b/chapter5/notebook/UMCF.ipynb @@ -10,19 +10,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "ac7fbf6b-c4d2-471c-8cd9-4ba3097f4d99", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -30,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "9cb2f773", "metadata": {}, "outputs": [], @@ -44,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "ed3c6a08", "metadata": {}, "outputs": [], @@ -56,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "40bfe18c-e5b2-45fe-870a-e2769ffdfcb4", "metadata": {}, "outputs": [], @@ -66,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "afd52c43", "metadata": { "tags": [] @@ -80,18 +71,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "48b2ed3a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.956, Precision@K=0.000, Recall@K=0.000\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", @@ -111,19 +94,10 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "5701ea33-b2b1-4e26-af9e-41fc1453522c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing the pearson similarity matrix...\n", - "Done computing similarity matrix.\n" - ] - } - ], + "outputs": [], "source": [ "recommender = UMCFRecommender()\n", "recommend_result = recommender.recommend(movielens, is_naive=False)" @@ -131,18 +105,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "504aa258-feca-4fea-b509-5e3199e4914d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.962, Precision@K=0.002, Recall@K=0.005\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/Word2vec.ipynb b/chapter5/notebook/Word2vec.ipynb index e182dc2..5084de7 100644 --- a/chapter5/notebook/Word2vec.ipynb +++ b/chapter5/notebook/Word2vec.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "20b107d9", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9f40fb34", "metadata": {}, "outputs": [], @@ -36,19 +36,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "07907088", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mk/.pyenv/versions/3.7.8/envs/recommender-systems-at-work3.7.8/lib/python3.7/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "import gensim\n", "import logging\n", @@ -68,30 +59,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "cad9e002", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('studio ghibli', 0.8409439921379089),\n", - " ('zibri studio', 0.8167261481285095),\n", - " ('pelicula anime', 0.797528862953186),\n", - " ('miyazaki', 0.786034107208252),\n", - " ('hayao miyazaki', 0.7857083678245544),\n", - " ('japan', 0.6094456911087036),\n", - " ('Animation', 0.5489083528518677),\n", - " ('wilderness', 0.5156647562980652),\n", - " ('steampunk', 0.49222904443740845),\n", - " ('environmental', 0.4773883521556854)]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# anime 태그와 비슷한 태그를 확인한다\n", "model.wv.most_similar('anime')" @@ -99,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a157be67", "metadata": {}, "outputs": [], @@ -112,18 +83,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "f1e57317", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rmse=0.000, Precision@K=0.010, Recall@K=0.033\n" - ] - } - ], + "outputs": [], "source": [ "# 평가\n", "metric_calculator = MetricCalculator()\n", diff --git a/chapter5/notebook/data_download.ipynb b/chapter5/notebook/data_download.ipynb index e3fe52b..af59fad 100644 --- a/chapter5/notebook/data_download.ipynb +++ b/chapter5/notebook/data_download.ipynb @@ -9,18 +9,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/bin/bash: wget: command not found\n", - "unzip: cannot find or open ../data/ml-10m.zip, ../data/ml-10m.zip.zip or ../data/ml-10m.zip.ZIP.\n" - ] - } - ], + "outputs": [], "source": [ "# MovieLens의 데이터셋을 data 디렉터리에 다운로드 한 뒤 압축을 푼다\n", "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", @@ -36,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -125,7 +116,7 @@ "4 [Comedy] " ] }, - "execution_count": 13, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -143,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -222,7 +213,7 @@ "4 20 2424 hanks 1188263835" ] }, - "execution_count": 14, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -240,16 +231,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "タグ種類=15241\n", - "タグレコード数=95580\n", - "タグが付いている映画数=7601\n" + "태그 종류=15241\n", + "태그 레코드 수=95580\n", + "태그가 붙어있는 영화 수=7601\n" ] } ], @@ -261,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -354,7 +345,7 @@ "4 [steve martin, pregnancy, remake, steve martin... " ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -450,7 +441,7 @@ "4 1 316 5.0 838983392" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -543,7 +534,7 @@ "4 1 316 5.0 838983392" ] }, - "execution_count": 22, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -557,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -661,7 +652,7 @@ "4 [dating, nudity (topless - brief), can't remem... " ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -691,7 +682,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -747,7 +738,7 @@ "len 1000.00" ] }, - "execution_count": 27, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -760,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -816,7 +807,7 @@ "len 6736.000000" ] }, - "execution_count": 29, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -828,14 +819,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "評価値数=132830\n" + "평갓값 수=132830\n" ] } ], @@ -845,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -936,7 +927,7 @@ "5.0 23631" ] }, - "execution_count": 35, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -947,29 +938,27 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -986,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ From f5632a8ad23438bf64973103a7d4fd1cf88807e3 Mon Sep 17 00:00:00 2001 From: Moses Kim Date: Tue, 27 Dec 2022 16:13:55 +0900 Subject: [PATCH 07/17] updated *.py --- chapter5/util/data_loader.py | 30 +++++++++++++++--------------- chapter5/util/metric_calculator.py | 2 +- chapter5/util/models.py | 20 ++++++++++---------- 3 files changed, 26 insertions(+), 26 deletions(-) diff --git a/chapter5/util/data_loader.py b/chapter5/util/data_loader.py index fbbbfd7..dcfe91b 100644 --- a/chapter5/util/data_loader.py +++ b/chapter5/util/data_loader.py @@ -12,54 +12,54 @@ def __init__(self, num_users: int = 1000, num_test_items: int = 5, data_path: st def load(self) -> Dataset: ratings, movie_content = self._load() movielens_train, movielens_test = self._split_data(ratings) - # ranking用の評価データは、各ユーザーの評価値が4以上の映画だけを正解とする - # キーはユーザーID、バリューはユーザーが高評価したアイテムIDのリスト + # ranking 용 평가 데이터는 각 사용자의 평갓값이 4 이상인 영화만을 정답으로 한다 + # 키는 사용자 ID, 값은 사용자가 높이 평가한 아이템의 ID 리스트 movielens_test_user2items = ( movielens_test[movielens_test.rating >= 4].groupby("user_id").agg({"movie_id": list})["movie_id"].to_dict() ) return Dataset(movielens_train, movielens_test, movielens_test_user2items, movie_content) def _split_data(self, movielens: pd.DataFrame) -> (pd.DataFrame, pd.DataFrame): - # 学習用とテスト用にデータを分割する - # 各ユーザの直近の５件の映画を評価用に使い、それ以外を学習用とする - # まずは、それぞれのユーザが評価した映画の順序を計算する - # 直近付与した映画から順番を付与していく(0始まり) + # 학습용과 테스트용으로 데이터를 분할한다 + # 각 사용자의 직전 5개 영화를 평가용으로 사용하고, 그 이외는 학습용으로 한다 + # 먼저, 각 사용자가 평가한 영화의 순서를 계산한다 + # 최근 부여한 영화부터 순서를 부여한다(0부터 시작) movielens["rating_order"] = movielens.groupby("user_id")["timestamp"].rank(ascending=False, method="first") movielens_train = movielens[movielens["rating_order"] > self.num_test_items] movielens_test = movielens[movielens["rating_order"] <= self.num_test_items] return movielens_train, movielens_test def _load(self) -> (pd.DataFrame, pd.DataFrame): - # 映画の情報の読み込み(10197作品) - # movie_idとタイトル名のみ使用 + # 영화 정보 로딩(10197 작품) + # movie_id와 제목만 사용 m_cols = ["movie_id", "title", "genre"] movies = pd.read_csv( os.path.join(self.data_path, "movies.dat"), names=m_cols, sep="::", encoding="latin-1", engine="python" ) - # genreをlist形式で保持する + # genre를 list 형식으로 저장한다 movies["genre"] = movies.genre.apply(lambda x: x.split("|")) - # ユーザが付与した映画のタグ情報の読み込み + # 사용자가 부여한 영화의 태그 정보를 로딩한다 t_cols = ["user_id", "movie_id", "tag", "timestamp"] user_tagged_movies = pd.read_csv( os.path.join(self.data_path, "tags.dat"), names=t_cols, sep="::", engine="python" ) - # tagを小文字にする + # tag를 소문자로 한다 user_tagged_movies["tag"] = user_tagged_movies["tag"].str.lower() movie_tags = user_tagged_movies.groupby("movie_id").agg({"tag": list}) - # タグ情報を結合する + # 태그 정보를 결합한다 movies = movies.merge(movie_tags, on="movie_id", how="left") - # 評価データの読み込み + # 평가 데이터를 로딩한다 r_cols = ["user_id", "movie_id", "rating", "timestamp"] ratings = pd.read_csv(os.path.join(self.data_path, "ratings.dat"), names=r_cols, sep="::", engine="python") - # user数をnum_usersに絞る + # user 수를 num_users로 줄인다 valid_user_ids = sorted(ratings.user_id.unique())[: self.num_users] ratings = ratings[ratings.user_id <= max(valid_user_ids)] - # 上記のデータを結合する + # 위 데이터를 결합한다 movielens_ratings = ratings.merge(movies, on="movie_id") return movielens_ratings, movies diff --git a/chapter5/util/metric_calculator.py b/chapter5/util/metric_calculator.py index 3b2283c..d0cb294 100644 --- a/chapter5/util/metric_calculator.py +++ b/chapter5/util/metric_calculator.py @@ -49,7 +49,7 @@ def _calc_precision_at_k( self, true_user2items: Dict[int, List[int]], pred_user2items: Dict[int, List[int]], k: int ) -> float: scores = [] - # テストデータに存在する各ユーザーのprecision@kを計算 + # 테스트 데이터에 존재하는 각 사용자의 precision@k를 계산한다 for user_id in true_user2items.keys(): p_at_k = self._precision_at_k(true_user2items[user_id], pred_user2items[user_id], k) scores.append(p_at_k) diff --git a/chapter5/util/models.py b/chapter5/util/models.py index 3a4ae12..898f12c 100644 --- a/chapter5/util/models.py +++ b/chapter5/util/models.py @@ -4,34 +4,34 @@ @dataclasses.dataclass(frozen=True) -# 推薦システムの学習と評価に使うデータセット +# 추천 시스템의 학습과 평가에 사용하는 데이터셋 class Dataset: - # 学習用の評価値データセット + # 학습용 평갓값 데이터셋 train: pd.DataFrame - # テスト用の評価値データセット + # 테스트용 평갓값 데이터셋 test: pd.DataFrame - # ランキング指標のテストデータセット。キーはユーザーID、バリューはユーザーが高評価したアイテムIDのリスト。 + # 순위 지표의 테스트 데이터셋. 키는 사용자 ID, 값은 사용자가 높이 평가한 아이템의 ID 리스트 test_user2items: Dict[int, List[int]] - # アイテムのコンテンツ情報 + # 아이템 콘텐츠 정보 item_content: pd.DataFrame @dataclasses.dataclass(frozen=True) -# 推薦システムの予測結果 +# 추천 시스템 예측 결과 class RecommendResult: - # テストデータセットの予測評価値。RMSEの評価 + # 테스트 데이터셋의 예측 평갓값. RMSE 평가 rating: pd.DataFrame - # キーはユーザーID、バリューはおすすめアイテムIDのリスト。ランキング指標の評価。 + # 키는 사용자 ID, 값은 추천 아이템 ID 리스트. 순위 지표 평가. user2items: Dict[int, List[int]] @dataclasses.dataclass(frozen=True) -# 推薦システムの評価 +# 추천 시스템 평가 class Metrics: rmse: float precision_at_k: float recall_at_k: float - # 評価結果を出力する時に少数は第３桁までにする + # 평가 결과는 소수 셋째 자리까지만 출력한다 def __repr__(self): return f"rmse={self.rmse:.3f}, Precision@K={self.precision_at_k:.3f}, Recall@K={self.recall_at_k:.3f}" From c520ba2a1dc564e0708380c6119a3bbe6b83ffd6 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 27 Apr 2023 15:37:56 +0900 Subject: [PATCH 08/17] uploaded cover.jpg --- cover.jpg | Bin 20360 -> 126524 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/cover.jpg b/cover.jpg index f4c4bf75bd53ed9be78ffef3e0e432ef6c05d764..16bdd136994715b13cbbfd584f768796ccfe8c92 100644 GIT binary patch literal 126524 zcmeFYdo+}9*fu;$32CwqF=ZDaDf?u8B?%$>y-AYIBncTAGnMQ&Q-q>S2-#;}l8G54 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Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 27 Apr 2023 16:42:01 +0900 Subject: [PATCH 10/17] updated README.md --- README.md | 38 +++++----------- chapter5/README.md | 105 +++++++++++++++++++++++++-------------------- 2 files changed, 69 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index 5f6d869..36105f7 100644 --- a/README.md +++ b/README.md @@ -1,46 +1,30 @@ -# 추천 시스템 실전 입문 +# 추천 시스템 입문 ~Recommender Systems~ --- - + --- - +이 저장소는 한빛미디어에서 발행한 [추천 시스템 입문(Recommender Ssystem)]()의 샘플 코드를 제공하기 위한 저장소입니다. -이 저장소는 한빛미디어 발행 [추천 시스템 실전 입문]()의 지원 사이트입니다. +## 샘플 코드에 관하여 - +이 책은 독자의 업무를 돕기 위해 쓰여진 책이며, 책에서 제공하는 코드는 일반적으로 독자가 작성하는 프로그램에 문서에 사용해도 좋습니다. 코드의 대부분을 게재하는 경우를 제외하고, 특별한 거리를 받을 필요는 없습니다. 예를 들어, 이 책의 코드의 일부를 사용하는 프로그램을 작성하는 데 별도의 허가는 필요하지 않습니다. 다만, 이 책의 코드 예제를 판매/배포하는 경우에는 허가를 받아야 합니다. -## 샘플 코드 +이 책 또는 이 책의 코드 예제를 인용해 질문 등에 답하는 경우에는 별도의 허가가 필요하지 않습니다. 그러나, 이 책의 코드 예제의 상당한 부분을 제품 매뉴얼에 전재하는 등의 경우에는 허가를 받아야 합니다. - +출처를 반드시 명기해야 하는 것은 아니지만, 표기해 주신다면 감사하겠습니다. 출처에는 제목, 저자, 출판사, ISBN을 기재해 주십시오. 예를 들어, 「風間正弘、飯塚洸二郎、松村優也著『推薦システム実践入門』（オライリー・ジャパン、ISBN978-4-87311-966-3）」과 같이 기재할 수 있습니다. -이 책의 목적은 독자의 업무를 돕는 것으로, 일반적으로 이 책에 실린 코드는 독자의 프로그램이나 문서에 사용해도 좋습니다. 코드의 대부분을 전재하는 경우를 제외하고는 특별한 허가를 받을 필요는 없습니다. 예를 들어, 이 책의 코드의 일부를 사용하는 프로그램을 작성하기 위한 허가는 필요하지 않습니다. 이 책의 코드 예를 판매/배포하는 경우에는 허가를 받아야 합니다. - - - -이 책이나 이 책의 코드 예를 인용해서 질문 등에 답하는 경우에는 허가가 필요하지 않습니다. 이 책의 코드 예의 상당한 부분을 제품 매뉴얼에 전재쟈하는 등의 경우에는 허가를 받아야 합니다. - - - -출처를 명기해야 하는 것은 아니지만, 표기한다면 감사하겠습니다. 출처에는 일반적으로 제목, 저자, 출판사, ISBN을 기재해 주십시오. 예를 들어, 「風間正弘、飯塚洸二郎、松村優也著『推薦システム実践入門』（オライリー・ジャパン、ISBN978-4-87311-966-3）」과 같습니다. - - - -### 파일 구성 +### 파일의 구성 |파일명|설명| |:---|:---| -|chapter5|5장에서 사용하는 코드와 데이터| -|chapter7|7장에서 사용하는 코드| +|`chapter5`|5장에서 사용하는 코드와 데이터| +|`chapter7`|7장에서 사용하는 코드| - - -코드나 데이터 설명은 책의 본문을 참조하기 바랍니다. +코드 또는 데이터에 관한 설명은 책 본문을 참조하기 바랍니다. ## 정오표 - - 아직 없습니다. 오기 등 잘못을 발견했을 때는 japan@oreilly.co.jp로 알려주십시오. diff --git a/chapter5/README.md b/chapter5/README.md index e77e43c..eedb9e7 100644 --- a/chapter5/README.md +++ b/chapter5/README.md @@ -1,13 +1,10 @@ -# ５章 推薦アルゴリズムの詳細 -書籍の第５章では、各種推薦アルゴリズムを紹介しました。 -そこで紹介した各種アルゴリズムをjupyter notebookで実行する方法を説明します。 +# ５장 추천 알고리즘 상세 -ご自身のPCで実行する方法と、Google Colabのnotebookで実行する方法があります。 -環境構築をせずに手っ取り早くアルゴリズムをお試ししたい方は、Google Colabをご利用ください。 -ご自身のPCで、環境構築する場合は、poetryを利用して構築する方法と、Dockerを利用して構築する方法の２通りを紹介しますので、ご参考ください。 +이 책의 5장에서는 각종 추천 알고리즘을 소개합니다. 5장에서 소개한 각종 알고리즘을 jupyter notebook으로 실행하는 방법을 설명합니다. +로컬 PC에서 실행하는 방법과 Google Colab의 notebook에서 실행하는 방법이 있습니다. 별도의 환경을 구축하지 않고 빠르게 알고리즘을 실행하고 싶다면 Google Colab을 사용하기 바랍니다. 로컬 PC에서 환경을 구축할 때는 `poetry`를 사용하는 방법과 도커(Docker)를 사용하는 방법을 설명했으므로 참고하기 바랍니다. -## フォルダ構成 +## 폴더 구성 ``` chapter5 ├─── src @@ -28,43 +25,43 @@ chapter5 ├── BPR.ipynb └── etc... ``` -`chapter5`フォルダ配下には、`src`, `notebook`, `util`,`colab`の４つのフォルダがあります。 -`src`, `notebook`, `util`は、統一モジュールを利用して、アルゴリズムを実行するコードが格納されています。`src`には、`base_recommender.py`のクラス設計に沿って、各種アルゴリズムが実装されています。`util`には、データの読み込みや評価の統一モジュールが実装されています。`notebook`には、`src`で実装したアルゴリズムを利用して、アルゴリズムの動作確認をするコードが記述されています。一部のnotebookには、アルゴリズムのパラメーターを変えて、予測精度の変化を確認しているものもありますので、ご参考ください。 +`chapter5` 폴더 아래에는 `src`, `notebook`, `util`,`colab`라는 4개의 폴더가 있습니다. -`colab`には、Google Colabで動くnotebookが格納されています。 -書籍の中では、データの読み込みや評価をするのに統一モジュールを利用したり、各種推薦アルゴリズムをクラス設計したりしていました。統一フォーマットがあることで、推薦アルゴリズムをシステムに組み込むときに便利です。しかし、初学者にとっては、とっつきにくいところもあります。 -そこで、統一モジュールを利用せずに、１枚のnotebookにデータの読み込みとアルゴリズムのコードを記述し、その中だけで完結するnotebookを用意しました。notebook内では、各種ステップごとに、データを表示するようにしてますので、推薦アルゴリズムの理解の手助けになるかと思います。 +`src`, `notebook`, `util`은 공통의 모듈을 사용해서 알고리즘을 실행하는 코드가 저장되어 있습니다. `src`에는 `base_recommender.py` 클래스 설계에 맞춰 각종 알고리즘이 구현되어 있습니다. `util`에는 데이터 읽기와 평가 공통 모듈이 구현되어 있습니다. `notebook`에는 `src`에 구현한 알고리즘을 사용해서, 알고리즘의 동작을 확인하는 코드가 기술되어 있습니다. 일부 notebook에는 알고리즘의 파라미터를 바꾸어, 예측 정확도의 변화를 확인할 수 있도록 되어 있으므로 참고하기 바랍니다. -## Google Colabで動かす -`chapter5/colab`というフォルダに入っています。 -github上から、notebookを開くと、Colabへのリンクが表示されるので、それをクリックするとcolab上で実行することができます。（Colabではなく、ご自身のPCで実行も可能です。その際には、ご自身のPCで環境構築する必要があります。） -まずは、`Association.ipynb`や`Item2vec.ipynb`などを見ていただくと、レコメンドアルゴリズムのイメージが掴みやすいです。 +`colab`에는 Google Colab에서 동작하는 notebook이 저장되어 있습니다. 책에서는 데이터 읽기 또는 평가를 할 때 공통 모듈을 사용하거나, 각종 추천 알고리즘을 클래스로 설계했습니다. 공통 포맷을 사용하면 추천 알고리즘을 시스템에 조합할 때 편리합니다. 하지만 초보자에게는 다소 어려운 부분도 있습니다. 그래서 공통 모듈을 사용하지 않고 하나의 notebook에 데이터 읽기와 알고리즘 코드를 기술해, 그 자체로 완결되도록 구성했습니다. notebook 안에서는 각 단계 별로 데이터를 표시하도록 했으므로, 추천 알고리즘을 이해하는 데 도움이 될 것입니다. + +## Google Colab에서 동작시키기 + +`chapter5/colab` 폴더에 들어있습니다. github에서 notebook을 열면 Colab 링크가 표시됩니다. 해당 링크를 클릭하면 colab 상에서 실행할 수 있습니다.(Colab이 아닌 로컬 PC에서도 실행할 수 있습니다. 이 때는 로컬 PC에 환경을 구축해야 합니다.) 먼저 `Association.ipynb`, `Item2vec.ipynb` 등을 보면 추천 알고리즘에 관한 이미지를 잡기 쉬울 것입니다. + +## 로컬 PC에서 환경 구축하기 + +### 데이터 다운로드 + +알고리즘 학습에 사용하는 MovieLens 데이터를 `https://files.grouplens.org/datasets/movielens/ml-10m.zip`에서 다운로드한 뒤, 압축을 풀어서 `chapter5/data`에 저장합니다. 또는 다음 코드를 실행해서 다운로드한 뒤, 압축을 풉니다. -## ご自身のPCで環境構築 -### データのダウンロード -アルゴリズムの学習に使用するMovielensのデータを`https://files.grouplens.org/datasets/movielens/ml-10m.zip`から手動でダウンロードして、解凍したものを`chapter5/data`に格納してください。 -または、下記のコードでダウンロードして解凍ください。 ``` -# MovieLensのデータセットをdataディレクトリにダウンロードして展開 +# MovieLens 데이터셋을 data 디렉터리에 다운로드한 뒤, 압축을 푼다 $ wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P chapter5/data $ unzip -n chapter5/data/ml-10m.zip -d chapter5/data ``` -### Poetryを利用した環境構築 -pythonのバージョンは、python3.7.8を利用します。 -python3.7.8のインストールには、[pyenv](https://github.com/pyenv/pyenv)などのバージョン管理ツールをご利用ください。 -また、今回、[poetry](https://python-poetry.org/)をパッケージ管理ツールとして利用しますので、インストールください。 -ご参考に、macOS Montereyに、`pyenv`と`poetry`をインストールした手順を示します。windowsやlinuxの方は、ご自身のOSのコマンドに置き換えいただくか、Dockerを利用した環境構築をご参考ください。 +### Poetry를 사용해서 환경 구축하기 + +python 버전은 python3.7.8을 사용합니다. python3.7.8 설치에는 [pyenv](https://github.com/pyenv/pyenv) 등의 버전 관리 도구를 사용하십시오. 또한, 여기에서는 [poetry](https://python-poetry.org/)를 패키지 관리 도구로 사용하므로 설치하기 바랍니다. 참골, MacOS Monterey에 `pyenv`와 `poetry`를 설치하는 순서를 설명합니다. Windows나 Linux의 경우에는 여러분이 사용하는 OS에 해다아는 명령어로 변경하거나, 도커를 사용한 환경 구축하기를 참조하기 바랍니다. + +#### python3.7.8 설치 + +pyenv를 설치합니다. -#### python3.7.8のインストール -pyenvをインストールします。 ``` $ brew install pyenv ``` -pyenvの設定を普段使用しているシェルの設定に書き込みます。 -`bash`をお使いの方は、`~/.bash_profile`に、`zsh`をお使いの方は、`~/.zshrc`に以下のコードを追加します。 +`pyenv` 설정을 일반적으로 사용하는 셸의 설정에 추가합니다. `bash`를 사용한다면 `~/.bash_profile`, `zsh`를 사용한다면 `~/.zshrc`에 다음 코드를 추가합니다. + ``` export PYENV_ROOT="$HOME/.pyenv" export PATH="$PYENV_ROOT/bin:$PATH" @@ -72,55 +69,69 @@ eval "$(pyenv init --path)" eval "$(pyenv init -)" ``` -シェルの設定を読み込みます。 +셸 설정을 읽습니다. + ``` -# bashの場合 +# bash의 경우 $ source ~/.bash_profile -# zshの場合 +# zsh의 경우 $ source ~/.zshrc ``` -python3.7.8をインストールします。 + +python3.7.8을 설치합니다. + ``` $ pyenv install 3.7.8 ``` -python3.7.8をローカルで使用するように設定します。 + +python3.7.8을 로컬에서 사용할 수 있도록 설정합니다 ``` $ pyenv local 3.7.8 ``` -#### poetryとライブラリのインストール -poetryをインストールします。 +#### poetry와 라이브러리 설치하기 + +poetry를 설치합니다. + ``` $ brew install poetry ``` -ライブラリをインストールします。 +라이브러리를 설치합니다. + ``` $ poetry install ``` -※ macでxlearnライブラリのインストールに失敗し、`Exception: Please install CMake first`と表示された場合は、`xcode-select --install`や`cmake`のインストールをしてから、再度実行します。 + +※ mac에서 xlearn 라이브러리 설치 시 실패하면서 `Exception: Please install CMake first` 메시지가 표시될 때는, `xcode-select --install`이나 `cmake`를 설치한 뒤 재 실행합니다. + ``` $ brew install cmake ``` -#### jupyter notebookの起動 -jupyter notebookの起動は以下になります。 +#### jupyter notebook 기동하기 + +jupyter notebook을 다음과 같이 기동합니다. + ``` $ poetry run jupyter notebook -$ poetry run jupyter lab # jupyter labが好みの方はこちらを +$ poetry run jupyter lab # jupyter lab을 선호한다면 이 명령어를 실행 ``` -### Dockerを利用した環境構築 -dockerを利用したjupyter notebookの起動は以下になります。 -(※ macOS: Monterey, docker: 1.29.2, docker-compose: 1.29.2で動作を確認しています。) +### Docker를 사용해 환경 구축하기 + +docker를 사용한 jupyter notebook은 다음과 같이 기동합니다(※ MacOS: Monterey, docker: 1.29.2, docker-compose: 1.29.2에서 동작을 확인했습니다). + ``` $ docker-compose up -d $ docker-compose exec app poetry run jupyter notebook --allow-root --ip=0.0.0.0 -$ docker-compose exec app poetry run jupyter lab --allow-root --ip=0.0.0.0 # jupyter labが好みの方はこちらを +$ docker-compose exec app poetry run jupyter lab --allow-root --ip=0.0.0.0 # jupyter lab을 선호한다면 이 명령어를 실행 ``` -dockerのプロセスは次のコマンドで停止できます。 + +docker 프로세스는 다음 명령어로 정지할 수 있습니다. + ``` $ docker-compose stop ``` From 9ee5903d0bd85dab575a0c94f404e82028ba7465 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 27 Apr 2023 16:44:01 +0900 Subject: [PATCH 11/17] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 36105f7..317881a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# 추천 시스템 입문 ~Recommender Systems~ +# 추천 시스템 입문 --- From dab8d4048e77e4d86e5573875ade6c7dd75aeb52 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 27 Apr 2023 16:44:17 +0900 Subject: [PATCH 12/17] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 317881a..acebca3 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ --- - + --- From 8bcad5dc5a66cb9eefb8d92450f3b669b87683d7 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 4 May 2023 13:51:23 +0900 Subject: [PATCH 13/17] update README.md --- README.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 36105f7..ed84e1f 100644 --- a/README.md +++ b/README.md @@ -10,11 +10,12 @@ ## 샘플 코드에 관하여 -이 책은 독자의 업무를 돕기 위해 쓰여진 책이며, 책에서 제공하는 코드는 일반적으로 독자가 작성하는 프로그램에 문서에 사용해도 좋습니다. 코드의 대부분을 게재하는 경우를 제외하고, 특별한 거리를 받을 필요는 없습니다. 예를 들어, 이 책의 코드의 일부를 사용하는 프로그램을 작성하는 데 별도의 허가는 필요하지 않습니다. 다만, 이 책의 코드 예제를 판매/배포하는 경우에는 허가를 받아야 합니다. +이 책은 독자의 업무를 돕기 위해 쓰여진 책이며 책에서 제공하는 코드의 일부를 사용해 프로그래밍에 활용해도 좋습니다. -이 책 또는 이 책의 코드 예제를 인용해 질문 등에 답하는 경우에는 별도의 허가가 필요하지 않습니다. 그러나, 이 책의 코드 예제의 상당한 부분을 제품 매뉴얼에 전재하는 등의 경우에는 허가를 받아야 합니다. +블로그에 책으로 공부하신 과정을 남기실 수 있으며 예제도 쓰셔도 됩니다. +다만 저작권 이슈가 있을 수 있으니 요약의 경우 책 내용의 30% 이상이 넘지 않도록 부탁드립니다(비공개 포스팅은 제약이 없습니다). -출처를 반드시 명기해야 하는 것은 아니지만, 표기해 주신다면 감사하겠습니다. 출처에는 제목, 저자, 출판사, ISBN을 기재해 주십시오. 예를 들어, 「風間正弘、飯塚洸二郎、松村優也著『推薦システム実践入門』（オライリー・ジャパン、ISBN978-4-87311-966-3）」과 같이 기재할 수 있습니다. +더불어 <추천 시스템 입문> 도서의 내용을 정리했다는 내용과 함께 출처(책 제목, 한빛미디어 홈페이지 도서 소개 페이지, 표지 이미지)를 표기해 주시면 감사하겠습니다. ### 파일의 구성 @@ -27,4 +28,6 @@ ## 정오표 -아직 없습니다. 오기 등 잘못을 발견했을 때는 japan@oreilly.co.jp로 알려주십시오. +정보표는 아래 URL에서 제공됩니다. + +![https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713](https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713) From 7cd8a7f8bf3b7c747d6a080174a58fea6b1fc661 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 4 May 2023 13:53:52 +0900 Subject: [PATCH 14/17] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4467c5a..d61394d 100644 --- a/README.md +++ b/README.md @@ -30,4 +30,4 @@ 정보표는 아래 URL에서 제공됩니다. -![https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713](https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713) +[https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713](https://www.hanbit.co.kr/store/books/look.php?p_code=B7471666713) From 9591118ad958dc4fcd5aa0543c2c3fef6885a8fc Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 7 Nov 2024 06:23:51 +0900 Subject: [PATCH 15/17] Update README.md --- chapter5/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapter5/README.md b/chapter5/README.md index eedb9e7..0687577 100644 --- a/chapter5/README.md +++ b/chapter5/README.md @@ -50,7 +50,7 @@ $ unzip -n chapter5/data/ml-10m.zip -d chapter5/data ### Poetry를 사용해서 환경 구축하기 -python 버전은 python3.7.8을 사용합니다. python3.7.8 설치에는 [pyenv](https://github.com/pyenv/pyenv) 등의 버전 관리 도구를 사용하십시오. 또한, 여기에서는 [poetry](https://python-poetry.org/)를 패키지 관리 도구로 사용하므로 설치하기 바랍니다. 참골, MacOS Monterey에 `pyenv`와 `poetry`를 설치하는 순서를 설명합니다. Windows나 Linux의 경우에는 여러분이 사용하는 OS에 해다아는 명령어로 변경하거나, 도커를 사용한 환경 구축하기를 참조하기 바랍니다. +python 버전은 python3.7.8을 사용합니다. python3.7.8 설치에는 [pyenv](https://github.com/pyenv/pyenv) 등의 버전 관리 도구를 사용하십시오. 또한, 여기에서는 [poetry](https://python-poetry.org/)를 패키지 관리 도구로 사용하므로 설치하기 바랍니다. 참고로 MacOS Monterey에 `pyenv`와 `poetry`를 설치하는 순서를 설명합니다. Windows나 Linux의 경우에는 여러분이 사용하는 OS에 해당하는 명령어로 변경하거나, 도커를 사용한 환경 구축하기를 참조하기 바랍니다. #### python3.7.8 설치 From ffbed4a2dc3edc2ccfae3a4bf474bddceeba826e Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 7 Nov 2024 08:09:27 +0900 Subject: [PATCH 16/17] update and titles of sample files --- README.md | 2 +- chapter5/README.md | 24 + chapter5/colab/Association.ipynb | 3020 +++++++++++- chapter5/colab/BPR.ipynb | 2862 ++++++++++- chapter5/colab/IMF.ipynb | 2701 ++++++++++- chapter5/colab/LDA_collaboration.ipynb | 2095 +++++++- chapter5/colab/LDA_content.ipynb | 4866 ++++++++++++++++++- chapter5/colab/MF.ipynb | 1981 +++++++- chapter5/colab/NMF.ipynb | 2287 ++++++++- chapter5/colab/RF.ipynb | 5238 ++++++++++++++++++++- chapter5/colab/SVD.ipynb | 2382 +++++++++- chapter5/colab/UMCF.ipynb | 4928 ++++++++++++++++++- chapter5/notebook/Association.ipynb | 2 +- chapter5/notebook/BPR.ipynb | 2 +- chapter5/notebook/IMF.ipynb | 2 +- chapter5/notebook/LDA_collaboration.ipynb | 2 +- chapter5/notebook/LDA_content.ipynb | 2 +- chapter5/notebook/MF.ipynb | 2 +- chapter5/notebook/NMF.ipynb | 2 +- chapter5/notebook/SVD.ipynb | 2 +- chapter5/notebook/UMCF.ipynb | 2 +- 21 files changed, 32384 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index d61394d..e85238d 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ |파일명|설명| |:---|:---| -|`chapter5`|5장에서 사용하는 코드와 데이터| +|`chapter5`|5장에서 사용하는 코드와 데이터[[설명](./chapter5/README.md)]| |`chapter7`|7장에서 사용하는 코드| 코드 또는 데이터에 관한 설명은 책 본문을 참조하기 바랍니다. diff --git a/chapter5/README.md b/chapter5/README.md index 0687577..1dfcd20 100644 --- a/chapter5/README.md +++ b/chapter5/README.md @@ -36,6 +36,30 @@ chapter5 `chapter5/colab` 폴더에 들어있습니다. github에서 notebook을 열면 Colab 링크가 표시됩니다. 해당 링크를 클릭하면 colab 상에서 실행할 수 있습니다.(Colab이 아닌 로컬 PC에서도 실행할 수 있습니다. 이 때는 로컬 PC에 환경을 구축해야 합니다.) 먼저 `Association.ipynb`, `Item2vec.ipynb` 등을 보면 추천 알고리즘에 관한 이미지를 잡기 쉬울 것입니다. +## 5장 샘플 파일 설명(장/절 순) + +5장의 `colab`, `notebook` 디렉터리 안의 노트북(`.ipynb`) 파일은 다음과 같이 구성되어 있습니다. + +|파일명|분석/알고리즘|관련 장/절|관련 페이지| +|:--|:--|:--|:--| +|`data_download.ipynb`|MovieLens 데이터셋 다운로드|5.2|p.103| +|`Random.ipynb`|무작위 추천Random Recommendation|5.3|p.118| +|`Popularity.ipynb`|인기도순 추천|5.4|p.120| +|`Association.ipynb`|연관 규칙(어소시에이션 분석) - Apriori 알고리즘|5.5|p.127| +|`UMCF.ipynb`|사용자-사용자 메모리 기반 방법 협조 필터링User-User Memory Based Collaborative Filtering, UMCF|5.6|p.131| +|`RF.ipynb`|회귀 모델, 랜덤 포레스트Random Forest, RF|5.7|p.137| +|`SVD.ipynb`|특잇값 분해Singular Value Decomposition, SVD|5.8.2|p.144| +|`NMF.ipynb`|비음수 행렬 분해Non-negative Matrix Factorization, NMF|5.8.3|p.148| +|`MF.ipynb`|행렬 분해Matrix Factorization, MF|5.8.4|p.150| +|`IMF.ipynb`|암묵적 행렬 분해Implicit Matrix Factorization, IMF|5.8.5|p.154| +|`BPR.ipynb`|개인화 된 랭킹 문제Bayesian Personalized Ranking, BPR|5.8.6|p.158| +|`FM.ipynb`|Factorization Machines, FM|5.8.7|p.160| +|`LDA_content.ipynb`|잠재 디리클레 할당Latent Dirichlet Allocation, LDA|5.9.1|p.165| +|`LDA_collaboration.ipynb`|LDA를 행동 데이터에 적용|5.9.3|p.169| +|`Word2vec.ipynb`|`word2vec`|5.9.5|p.172| +|`Item2vec.ipynb`|`word2vec`을 사용한 협조 필터링 추천(`item2vec`)|5.9.6|p.176| + + ## 로컬 PC에서 환경 구축하기 ### 데이터 다운로드 diff --git a/chapter5/colab/Association.ipynb b/chapter5/colab/Association.ipynb index 0a2ff61..02f2503 100644 --- a/chapter5/colab/Association.ipynb +++ b/chapter5/colab/Association.ipynb @@ -1 +1,3019 @@ -{"cells":[{"cell_type":"markdown","id":"d9777c7d","metadata":{"id":"d9777c7d"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/Association.ipynb)"]},{"cell_type":"markdown","id":"c7dc31f1","metadata":{"id":"c7dc31f1"},"source":["# 어소시에이션 분석"]},{"cell_type":"code","execution_count":1,"id":"1b316a16","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"1b316a16","executionInfo":{"status":"ok","timestamp":1672116308177,"user_tz":-540,"elapsed":3911,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5beace71-80d1-430b-f7a5-ab9f156eb138"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:45:01-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.8MB/s in 1.0s \n","\n","2022-12-27 04:45:02 (63.8 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"c1b8db75","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c1b8db75","executionInfo":{"status":"ok","timestamp":1672116375742,"user_tz":-540,"elapsed":67569,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e9660e0b-1ab9-4f2f-c69b-890b464bfe3b"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"f29c7aac","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":267},"id":"f29c7aac","executionInfo":{"status":"ok","timestamp":1672116375743,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c6b3c719-f6d4-46dd-b765-6a569ec2f3d6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["movie_id 1 2 3 4 5 6 7 8 9 \\\n","user_id \n","1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","\n","movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n","user_id ... \n","1 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","2 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","3 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","4 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","5 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n","\n","movie_id 63992 64716 \n","user_id \n","1 0.0 0.0 \n","2 0.0 0.0 \n","3 0.0 0.0 \n","4 0.0 0.0 \n","5 0.0 0.0 \n","\n","[5 rows x 6673 columns]"],"text/html":["\n","
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supportitemsets
420.415(593)
230.379(318)
210.369(296)
190.361(260)
250.319(356)
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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antecedentsconsequentslift
649(4993)(5952)5.459770
648(5952)(4993)5.459770
1462(1196, 1198)(1291, 260)4.669188
1463(1291, 260)(1196, 1198)4.669188
1460(1291, 1196)(260, 1198)4.171359
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\n"," "]},"metadata":{},"execution_count":7}],"source":["from mlxtend.frequent_patterns import association_rules\n","\n","# 어소시에이션 규칙 계산(리프트 값이 높은 순으로 표시)\n","rules = association_rules(freq_movies, metric='lift', min_threshold=1)\n","rules.sort_values('lift', ascending=False).head()[['antecedents', 'consequents', 'lift']]"]},{"cell_type":"code","execution_count":8,"id":"781b86a4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"781b86a4","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"efb286e7-011c-48c5-bca0-fc8d74077602"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","5852 5952 Lord of the Rings: The Two Towers, The (2002) \n","\n"," genre \\\n","4899 [Action, Adventure, Fantasy] \n","5852 [Action, Adventure, Fantasy] \n","\n"," tag \n","4899 [based on a book, big budget, new zealand, sce... \n","5852 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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\n"," "]},"metadata":{},"execution_count":8}],"source":["# movie_id=4993, 5952의 제목 확인(반지의 제왕)\n","movies[movies.movie_id.isin([4993, 5952])]"]},{"cell_type":"code","execution_count":9,"id":"7495518b","metadata":{"id":"7495518b","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용 데이터 평갓값이 4 이상인 것만 얻는다.\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]"]},{"cell_type":"code","execution_count":10,"id":"782e5a9d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"782e5a9d","executionInfo":{"status":"ok","timestamp":1672116376970,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c6e2b6b2-6336-45c4-8bb8-df6bf91d99db"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2인 사용자가 4 이상의 평가를 남긴 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":11,"id":"0599276e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"0599276e","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"118190a5-3aa0-40a2-c32c-e80489f5c618"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" antecedents consequents antecedent support \\\n","3 (110) (1) 0.291 \n","5 (260) (1) 0.361 \n","25 (1210) (1) 0.273 \n","31 (110) (32) 0.291 \n","33 (260) (32) 0.361 \n","... ... ... ... \n","1476 (1210, 1196) (2571, 260) 0.197 \n","1477 (2571, 260) (1210, 1196) 0.161 \n","1479 (1196, 260) (1210, 2571) 0.224 \n","1480 (1210) (1196, 2571, 260) 0.273 \n","1482 (260) (1210, 2571, 1196) 0.361 \n","\n"," consequent support support confidence lift leverage conviction \n","3 0.263 0.105 0.360825 1.371957 0.028467 1.153048 \n","5 0.263 0.153 0.423823 1.611493 0.058057 1.279120 \n","25 0.263 0.116 0.424908 1.615621 0.044201 1.281535 \n","31 0.255 0.104 0.357388 1.401523 0.029795 1.159332 \n","33 0.255 0.137 0.379501 1.488241 0.044945 1.200647 \n","... ... ... ... ... ... ... \n","1476 0.161 0.108 0.548223 3.405114 0.076283 1.857112 \n","1477 0.197 0.108 0.670807 3.405114 0.076283 2.439302 \n","1479 0.139 0.108 0.482143 3.468654 0.076864 1.662621 \n","1480 0.141 0.108 0.395604 2.805705 0.069507 1.421255 \n","1482 0.124 0.108 0.299169 2.412653 0.063236 1.249945 \n","\n","[326 rows x 9 columns]"],"text/html":["\n","
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antecedentsconsequentsantecedent supportconsequent supportsupportconfidenceliftleverageconviction
3(110)(1)0.2910.2630.1050.3608251.3719570.0284671.153048
5(260)(1)0.3610.2630.1530.4238231.6114930.0580571.279120
25(1210)(1)0.2730.2630.1160.4249081.6156210.0442011.281535
31(110)(32)0.2910.2550.1040.3573881.4015230.0297951.159332
33(260)(32)0.3610.2550.1370.3795011.4882410.0449451.200647
..............................
1476(1210, 1196)(2571, 260)0.1970.1610.1080.5482233.4051140.0762831.857112
1477(2571, 260)(1210, 1196)0.1610.1970.1080.6708073.4051140.0762832.439302
1479(1196, 260)(1210, 2571)0.2240.1390.1080.4821433.4686540.0768641.662621
1480(1210)(1196, 2571, 260)0.2730.1410.1080.3956042.8057050.0695071.421255
1482(260)(1210, 2571, 1196)0.3610.1240.1080.2991692.4126530.0632361.249945
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326 rows × 9 columns

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\n","
\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2의 사용자가 4 이상의 평가를 남긴 영화 목록\n","user2_data = movielens_train_high_rating[movielens_train_high_rating.user_id==2]\n","\n","# 사용자가 최근 평가한 4개의 영화 얻기\n","input_data = user2_data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n","\n","# 그 영화들이 조건부로 포함된 어오시에이션 규칙을 추출\n","matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n","rules[matched_flags]"]},{"cell_type":"code","execution_count":12,"id":"689b0be8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"689b0be8","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e06efaee-cb09-405b-f283-9252ea6a3e63"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(1196, 92),\n"," (593, 41),\n"," (1198, 34),\n"," (260, 34),\n"," (1210, 34),\n"," (318, 20),\n"," (296, 19),\n"," (2571, 18),\n"," (356, 17),\n"," (589, 16)]"]},"metadata":{},"execution_count":12}],"source":["from collections import defaultdict, Counter\n","\n","# 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장한다\n","# 같은 영화가 여러 차례 귀결부에 나타날 수 있다\n","\n","consequent_movies = []\n","for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows(): # lift値でソートして、上位１０個のルールだけを使うようにするなどの工夫も可能です\n"," consequent_movies.extend(row[\"consequents\"])\n"," \n","# 귀결부에서의 출현 빈도 카운트\n","counter = Counter(consequent_movies)\n","counter.most_common(10)"]},{"cell_type":"code","execution_count":13,"id":"e1f61bb6","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"e1f61bb6","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"4137dad7-e0b6-4859-aad3-8abb4ed70219"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","\n"," genre \\\n","1171 [Action, Adventure, Sci-Fi] \n","\n"," tag \n","1171 [lucas, george lucas, george lucas, gfei own i... "],"text/html":["\n","
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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\n"," "]},"metadata":{},"execution_count":13}],"source":["# movie_id=1196가 92번 귀결부에 출현하므로, user_id=2에는 movie_id=1196(Star Wars: Episode V)가 추천 후보가 된다\n","# (user_id=2의 학습 데이터에서는 Star Wars 에피소드 4, 6의 평가가 높다)\n","movies[movies.movie_id == 1196]"]},{"cell_type":"code","execution_count":14,"id":"7540d95c","metadata":{"id":"7540d95c","executionInfo":{"status":"ok","timestamp":1672116376971,"user_tz":-540,"elapsed":14,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 추천 방법에는 lift 값이 높은 것을 추출하는 방법 등이 있다. 몇 가지 방법을 시도해 보고 자사의 데이터에 맞는 방법을 선택한다."]},{"cell_type":"code","execution_count":15,"id":"53918e5b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53918e5b","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":6233,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"21d86b30-94bb-4d15-efb7-8d68fefe6448"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {2: [1196, 593, 1198, 318, 296, 2571, 356, 589, 1240, 1291],\n"," 6: [593, 296, 318, 541, 47, 608, 50, 589, 527, 1],\n"," 9: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 10: [858, 1196, 260, 318],\n"," 11: [2858, 50, 296, 593],\n"," 12: [260],\n"," 13: [593, 318, 527, 356, 260, 47, 110, 2858, 589, 457],\n"," 17: [1196, 296, 1200, 1240, 541, 2571, 1198, 1210],\n"," 18: [1200, 1197, 50, 858, 1193],\n"," 22: [318, 1196, 260, 457, 608, 2571, 1210, 1240, 1198, 541],\n"," 23: [1196, 1210, 1198, 2571, 318, 1291, 1240, 356, 858, 110],\n"," 24: [1198, 1196, 296, 593, 1221, 1213, 1193, 1214, 541, 2028],\n"," 26: [593, 318, 296, 1196, 260, 50, 356, 527, 1210, 1240],\n"," 27: [296, 593, 50, 318, 541, 858, 2858, 1, 260, 1198],\n"," 33: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 37: [527, 356, 1196, 260, 608, 2858, 457, 858, 480, 1198],\n"," 40: [],\n"," 41: [],\n"," 42: [1196, 260, 1198, 296, 318, 593],\n"," 44: [1196, 260, 1198, 593],\n"," 45: [296, 318, 47, 527, 541, 32],\n"," 46: [593, 1196, 260, 1240, 1210, 296, 318, 2571, 457, 356],\n"," 50: [50, 1196, 260, 1193, 2028, 47, 2858, 1198, 858, 32],\n"," 52: [2858, 296, 593],\n"," 53: [2571, 1196, 318, 296, 593],\n"," 54: [1196, 260, 527, 1240, 1210, 608, 2571, 1198, 32, 1200],\n"," 56: [],\n"," 59: [356, 50, 1196, 527, 260, 110, 589, 1198, 480, 47],\n"," 60: [1196, 1200, 1240, 2571, 1198, 589, 1210],\n"," 61: [1196, 260, 457, 50, 110, 589, 1198, 1210, 150, 480],\n"," 62: [1196, 260, 457, 50, 356, 589, 527, 1198, 110, 1210],\n"," 63: [296, 318, 593, 1196, 260, 2959, 2762, 2028, 2571, 50],\n"," 64: [1210, 1198, 480, 1196, 356, 589, 110, 296, 318, 593],\n"," 71: [593, 318, 50, 356, 527, 457, 110, 1196, 47, 260],\n"," 72: [296, 318, 589, 110, 150, 1198, 1210, 1, 2571, 1196],\n"," 75: [593, 318, 356, 1196, 527, 1210, 260, 457, 50, 590],\n"," 76: [1196, 260, 1240, 1210, 2571, 356, 527, 1036, 1200, 1214],\n"," 77: [260],\n"," 80: [356, 480, 589, 527, 588, 590, 1198, 1196, 260],\n"," 83: [1200, 2571, 589, 593, 296],\n"," 84: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 85: [1196, 260, 1210, 1198, 589, 318, 3578, 4993, 5952, 2959],\n"," 86: [593, 296, 318, 356, 260, 150, 1, 527],\n"," 87: [2571, 318, 296, 593],\n"," 95: [1196, 50, 527, 260, 457, 1210, 47, 2571, 1240, 608],\n"," 99: [2858, 2571, 1196, 593],\n"," 100: [],\n"," 102: [593, 50, 110, 527, 1196, 589, 260, 608, 32, 2571],\n"," 104: [589, 1210, 1200, 296],\n"," 105: [2571, 593, 356, 1240, 589, 296, 1291, 318, 1, 1200],\n"," 106: [364, 480, 457, 110, 593, 296, 318],\n"," 111: [527, 1196, 1210, 260, 589, 50, 1198, 1, 2571, 590],\n"," 113: [2858, 2571, 1196, 318, 296, 593],\n"," 116: [1196, 260, 2571, 1240, 1, 1036, 1200, 1214, 541, 2028],\n"," 119: [1196, 260, 50, 1210, 2571],\n"," 121: [260, 1036, 589],\n"," 129: [2571, 1196, 318, 296, 593],\n"," 132: [50, 1196, 260, 527, 110, 589, 1198, 47, 150, 1210],\n"," 137: [],\n"," 138: [457, 527, 480, 150, 608, 858, 1213, 111, 1097, 590],\n"," 141: [318, 296, 50, 110, 1196, 260, 1193, 2028, 47, 2858],\n"," 142: [296, 318, 1196, 260, 356, 50, 589, 527, 110, 1198],\n"," 145: [318, 1196, 260, 527, 589, 110, 1198, 47, 608, 480],\n"," 148: [2571, 1198, 858, 593],\n"," 149: [],\n"," 150: [593, 318, 296, 110, 1196, 260, 457, 50, 150, 589],\n"," 151: [593, 541, 858, 32, 2858, 1, 1198, 1210],\n"," 152: [296, 32, 527],\n"," 153: [593, 318, 50, 356, 1196, 47, 608, 1210, 2858, 457],\n"," 154: [318],\n"," 155: [],\n"," 161: [1036],\n"," 162: [593, 457, 318, 380, 150],\n"," 164: [50, 110, 1196, 260, 1193, 2028, 2858, 1198, 858, 2571],\n"," 166: [318, 356, 296, 150, 480, 589, 110, 50, 527, 380],\n"," 168: [593, 318, 296, 110, 527, 1196, 1210, 260, 150, 457],\n"," 171: [593, 858],\n"," 172: [593, 318, 1196, 260, 296, 356, 1210, 527, 1240, 2571],\n"," 174: [50, 356, 110, 1196, 47, 260, 457, 589, 1210, 150],\n"," 176: [296, 318, 1196, 260, 356, 50, 589, 527, 110, 1198],\n"," 177: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 178: [296, 50, 590, 480, 2028, 47, 2858, 1198, 858, 32],\n"," 179: [593, 318, 527, 47, 457, 2858, 356],\n"," 180: [2858, 2571, 1196, 318, 296, 593],\n"," 183: [296, 50, 356, 47, 457, 150, 608, 589, 2858, 32],\n"," 184: [593, 318, 356, 527, 1196, 260, 47, 457, 589, 2858],\n"," 188: [318, 1196, 260, 50, 1240, 1210, 2571, 47, 1198, 2028],\n"," 189: [1196, 318, 1210, 50, 356, 527, 2571, 1291, 1240, 480],\n"," 190: [],\n"," 193: [1196, 1214, 2571, 1198, 858, 32, 1210, 608],\n"," 199: [318, 1196, 260, 356, 50, 589, 527, 110, 1198, 480],\n"," 202: [593, 318, 296, 356, 50, 110, 1196, 260, 589, 457],\n"," 204: [1196, 1210, 1198, 593, 296, 2571, 318, 1291, 1240, 589],\n"," 206: [1198, 2858, 858, 110, 589, 260, 356, 318, 296],\n"," 210: [5952],\n"," 211: [318, 1210, 296, 356, 527, 2571, 1036, 590, 480, 588],\n"," 213: [318, 356, 110, 260, 1198, 858, 457, 2959, 1193, 590],\n"," 216: [],\n"," 221: [],\n"," 226: [],\n"," 228: [260],\n"," 229: [593, 296, 318, 541, 47, 50, 589, 2571, 527, 1196],\n"," 230: [2571],\n"," 231: [593, 318, 296, 47, 527, 608, 1196, 260, 110, 457],\n"," 232: [2571],\n"," 234: [593, 318, 296, 356, 110, 1196, 260, 589, 1193, 590],\n"," 235: [1240, 589, 1196],\n"," 236: [541, 858, 32, 2858, 1, 260, 1198, 1196, 1210],\n"," 237: [356, 589, 110, 318, 1, 1270, 541, 2028, 858, 480],\n"," 238: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n"," 239: [260, 1210, 593, 2571, 1291, 1240, 589, 318, 296, 110],\n"," 243: [296, 593, 50, 318, 541, 32, 1, 527],\n"," 245: [2571, 1198, 2858, 1196, 1210, 110, 589, 527, 260, 356],\n"," 246: [260, 1198, 1196, 296, 593, 1221, 1213, 1193, 541, 2028],\n"," 248: [296, 593],\n"," 251: [318],\n"," 252: [1196, 1198, 296, 593, 318, 2571, 858, 110, 589, 356],\n"," 255: [296, 110, 527, 1196, 1210, 260, 457, 480, 589, 1270],\n"," 256: [296, 318, 50, 1196, 260, 457, 589, 527, 110, 1198],\n"," 257: [1196, 593, 1291, 1210, 296, 318, 589, 1197, 1136, 1270],\n"," 260: [2571, 1291, 50, 318],\n"," 261: [1196, 260, 593, 1210, 296, 318, 1198, 589, 541, 47],\n"," 267: [588, 1704, 1213],\n"," 268: [260],\n"," 270: [1196, 1198, 593, 2571, 296, 1240, 589, 1291, 356, 318],\n"," 272: [593, 318, 110, 1196, 260, 457, 1193, 590, 2028, 47],\n"," 273: [1196, 1198, 1214, 296],\n"," 276: [],\n"," 277: [318, 50, 1196, 260, 457, 527, 110, 589, 1198, 1210],\n"," 278: [593, 457, 356, 296, 318, 380, 588, 589, 110, 150],\n"," 281: [1291, 1240, 1198, 2571, 1196, 260],\n"," 287: [593, 318, 296, 527, 1196, 260, 589, 1210, 50, 1198],\n"," 290: [593, 318, 260, 296, 356, 110, 527, 1240, 1210, 150],\n"," 291: [296, 260, 318, 593],\n"," 293: [50, 356, 1196, 260, 527, 110, 589, 1198, 47, 1210],\n"," 296: [110, 1196, 260, 608, 1210, 1198, 1704, 1136, 588],\n"," 300: [457, 593],\n"," 301: [457, 590, 480, 110, 589],\n"," 307: [593, 318, 296, 527, 1291, 858, 457, 150, 50, 1197],\n"," 308: [593, 356, 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724: [296, 593, 50, 318, 541, 858, 2858, 1198, 527, 1196],\n"," 726: [296, 318, 260, 50, 589, 150, 480, 47, 2571, 608],\n"," 727: [608, 1196, 260, 2571, 589, 588, 1089, 1617, 541, 2858],\n"," 728: [260, 593, 1198, 318, 1291, 356, 110, 527, 1200, 1214],\n"," 729: [480, 589, 457, 296, 593, 318],\n"," 730: [593, 50, 356, 110, 527, 1196, 260, 47, 457, 1210],\n"," 733: [593, 1196, 260, 1240, 1210, 296, 318, 457, 356, 110],\n"," 735: [318, 527, 608, 1196, 260, 541, 589, 2571, 1089, 1617],\n"," 737: [593, 296, 318, 541, 47, 608, 50, 589, 2571, 527],\n"," 739: [296, 318, 1196, 260, 527, 1198, 1210, 150, 1240, 32],\n"," 740: [296, 50, 356, 527, 47, 457, 589, 150, 2858, 32],\n"," 741: [260, 1210, 1198, 3578, 4993, 5952, 2959, 1200, 1214, 1240],\n"," 742: [593, 527],\n"," 743: [593, 457, 318, 589, 110, 150, 1198, 1210, 2571, 1196],\n"," 749: [1210, 260, 1198, 608, 480, 32, 1196, 356, 589, 110],\n"," 750: [296, 1196, 260, 318, 593, 1198, 2762, 1617, 2028, 2571],\n"," 751: [296, 318, 1196, 260, 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356, 296, 318, 588, 110, 150, 1198, 1210],\n"," 857: [260, 296, 318, 1210, 1198, 50, 457, 527, 1291, 150],\n"," 859: [318, 2858],\n"," 861: [1196, 260, 1198, 318, 593],\n"," 863: [1196, 260, 457, 541, 858, 1198],\n"," 864: [296, 318, 593, 2571, 1196, 1617, 2028, 858, 50, 1198],\n"," 865: [1198, 1291, 589, 1240, 527, 1270, 1214, 1200, 541, 858],\n"," 873: [593, 296, 318, 541, 47, 608, 50, 527, 1, 260],\n"," 878: [1210, 2571, 1291, 356, 110, 1270, 2028, 2858, 1, 2762],\n"," 879: [296, 318, 1196, 260, 50, 457, 356, 527, 589, 110],\n"," 888: [4993, 2571],\n"," 891: [593],\n"," 901: [1196, 260, 1210, 593, 1198, 1240, 296, 1291, 541, 3578],\n"," 903: [1196, 1210, 1198, 593, 2571, 1291, 1240, 589, 296, 858],\n"," 904: [593, 110, 1196, 260, 150, 588, 1270, 47, 2028, 1198],\n"," 909: [1196, 260, 1210, 593, 589, 296, 318, 3578, 1200, 1214],\n"," 910: [260, 1198, 1196, 1221, 1193, 541, 2028, 608, 2571, 1210],\n"," 916: [2571, 5952, 2858, 296, 593],\n"," 920: [1196, 1210, 1198, 589, 2571, 1291, 296, 318, 1214, 356],\n"," 925: [50, 527, 1196, 47, 457, 589, 150, 608, 1210, 2571],\n"," 927: [593, 318, 296, 110, 527, 1196, 260, 150, 457, 589],\n"," 932: [1291, 1240, 1198, 2571, 1196],\n"," 935: [1210, 593],\n"," 938: [50, 1196, 260, 356, 589, 527, 110, 1198, 480, 1210],\n"," 939: [296, 356, 50, 1196, 527, 110, 589, 1198, 47, 480],\n"," 940: [593, 318, 296, 150, 480, 589, 527, 5952, 380, 588],\n"," 942: [1196, 260, 356, 50, 589, 527, 110, 1198, 150, 480],\n"," 946: [296, 318],\n"," 947: [593, 296, 318, 3578, 4993, 5952, 2959, 2762, 1200, 1214],\n"," 948: [],\n"," 951: [296, 593, 50, 318, 541, 858, 2858, 260, 1198, 527],\n"," 955: [593, 318, 296, 356, 50, 110, 1196, 260, 589, 457],\n"," 958: [2858, 1196, 296, 593],\n"," 960: [50, 318, 32, 2571, 110, 1196],\n"," 961: [296, 593, 858, 50, 318],\n"," 966: [],\n"," 967: [1196, 527, 260, 858, 541, 608, 50, 589, 1, 1210],\n"," 968: [356, 50, 527, 1196, 260, 589, 47, 1210, 1198, 608],\n"," 970: [1196, 260, 457, 356, 589, 527, 1198, 1210, 480, 47],\n"," 972: [],\n"," 975: [593, 318, 527, 47, 1196, 260, 110, 541, 457, 858],\n"," 976: [593, 541, 47, 608, 50, 589, 2571, 527, 260, 1210],\n"," 980: [1196, 1198, 318, 2571, 589, 1240, 110, 1291, 527, 1],\n"," 982: [1196, 1210, 1198, 593, 2571, 296, 1291, 1240, 589, 356],\n"," 985: [2571],\n"," 988: [],\n"," 989: [593, 318, 356, 47, 110, 1210, 589, 457, 480, 150],\n"," 990: [47, 110, 1196, 260, 608, 2858, 32, 858, 1198, 2571],\n"," 992: [2858, 296],\n"," 994: [593, 296, 50, 356, 110, 527, 1196, 47, 457, 260],\n"," 1001: [593, 318, 296, 527, 1196, 1210, 260, 50, 1198, 1],\n"," 1004: [527],\n"," 1005: [],\n"," 1008: [],\n"," 1009: [1196, 260, 1210, 593, 1198, 589, 296, 3578, 4993, 5952],\n"," 1011: [318, 2571, 47, 527, 110],\n"," 1012: [296, 318, 2959, 2762, 1617, 2028, 2571, 858, 50, 1198],\n"," 1013: [1240, 32, 47, 608, 150, 1036, 1200, 380, 1214, 1291],\n"," 1014: [50, 318, 32, 1, 1198, 1196, 1210],\n"," 1015: [50, 296, 593],\n"," 1016: [593, 541, 47, 50, 589, 2571, 1, 1210, 1196, 110],\n"," 1017: [260, 2571, 1198, 593, 296, 356, 1240, 589, 1291, 110],\n"," 1021: [588],\n"," 1022: [1196, 260, 593],\n"," 1023: [318, 356, 296, 1210, 457, 50, 589, 590, 588, 150],\n"," 1025: [593, 296, 50, 356, 527, 47, 150, 32, 1704, 590],\n"," 1030: [1196, 260, 296, 318],\n"," 1031: [318, 356, 296, 1196, 457, 527, 1210, 260, 589, 50],\n"," 1034: [2571, 1198, 2858, 858, 1196, 1210, 110, 589, 527, 260],\n"," 1037: [318, 527, 608, 1196, 260, 110, 457, 1089, 1617, 541],\n"," 1039: [2858, 32, 2571, 589, 110, 1196],\n"," 1041: [608, 480, 32, 356],\n"," 1044: [1196, 2571, 1198, 593, 356, 1240, 589, 296, 1291, 110],\n"," 1046: [318, 1196, 260, 527, 110, 50, 589, 1198, 1210, 2571],\n"," 1048: [1617, 858, 608, 527, 260, 356],\n"," 1053: [593, 318, 296, 527, 457, 50, 590, 480, 588, 150]})"]},"metadata":{},"execution_count":15}],"source":["# 어소시에이션 규칙을 사용해 각 사용자가 아직 평가하지 않은 영화 10편을 추천한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," # 사용자가 최근 5편의 영화를 얻는다\n"," input_data = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n"," # 그 영화들이 조건부에 1편이라도 포함되어 있는 어소시에이션 규칙을 추출한다\n"," matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n","\n"," # 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장하고, 출편 빈도 순으로 배열하고, 사용자가 아직 평가하지 않았다면 추천 리스트에 추가한다\n"," consequent_movies = []\n"," for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows():\n"," consequent_movies.extend(row[\"consequents\"])\n"," # 출현 빈도를 센다\n"," counter = Counter(consequent_movies)\n"," for movie_id, movie_cnt in counter.most_common():\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," # 추천 리스트가 10편이 되면 종료한다\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","# 각 사용자에 대한 추천 리스트\n","pred_user2items"]},{"cell_type":"code","execution_count":16,"id":"7342fdc7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"7342fdc7","executionInfo":{"status":"ok","timestamp":1672116383190,"user_tz":-540,"elapsed":15,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c194befc-f9ec-4450-c916-02791f71155a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
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‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c1b8db75", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 67569, + "status": "ok", + "timestamp": 1672116375742, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "c1b8db75", + "outputId": "e9660e0b-1ab9-4f2f-c69b-890b464bfe3b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f29c7aac", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 267 + }, + "executionInfo": { + "elapsed": 24, + "status": "ok", + "timestamp": 1672116375743, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "f29c7aac", + "outputId": "c6b3c719-f6d4-46dd-b765-6a569ec2f3d6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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antecedentsconsequentslift
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movie_idtitlegenretag
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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antecedentsconsequentsantecedent supportconsequent supportsupportconfidenceliftleverageconviction
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# movie_id=1196가 92번 귀결부에 출현하므로, user_id=2에는 movie_id=1196(Star Wars: Episode V)가 추천 후보가 된다\n", + "# (user_id=2의 학습 데이터에서는 Star Wars 에피소드 4, 6의 평가가 높다)\n", + "movies[movies.movie_id == 1196]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7540d95c", + "metadata": { + "executionInfo": { + "elapsed": 14, + "status": "ok", + "timestamp": 1672116376971, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7540d95c" + }, + "outputs": [], + "source": [ + "# 추천 방법에는 lift 값이 높은 것을 추출하는 방법 등이 있다. 몇 가지 방법을 시도해 보고 자사의 데이터에 맞는 방법을 선택한다." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "53918e5b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 6233, + "status": "ok", + "timestamp": 1672116383190, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "53918e5b", + "outputId": "21d86b30-94bb-4d15-efb7-8d68fefe6448" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {2: [1196, 593, 1198, 318, 296, 2571, 356, 589, 1240, 1291],\n", + " 6: [593, 296, 318, 541, 47, 608, 50, 589, 527, 1],\n", + " 9: [296, 318, 593, 2959, 2762, 1617, 2028, 2571, 858, 50],\n", + " 10: [858, 1196, 260, 318],\n", + " 11: [2858, 50, 296, 593],\n", + " 12: [260],\n", + " 13: [593, 318, 527, 356, 260, 47, 110, 2858, 589, 457],\n", + " 17: [1196, 296, 1200, 1240, 541, 2571, 1198, 1210],\n", + " 18: [1200, 1197, 50, 858, 1193],\n", + " 22: [318, 1196, 260, 457, 608, 2571, 1210, 1240, 1198, 541],\n", + " 23: [1196, 1210, 1198, 2571, 318, 1291, 1240, 356, 858, 110],\n", + " 24: [1198, 1196, 296, 593, 1221, 1213, 1193, 1214, 541, 2028],\n", + " 26: [593, 318, 296, 1196, 260, 50, 356, 527, 1210, 1240],\n", + " 27: [296, 593, 50, 318, 541, 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1044: [1196, 2571, 1198, 593, 356, 1240, 589, 296, 1291, 110],\n", + " 1046: [318, 1196, 260, 527, 110, 50, 589, 1198, 1210, 2571],\n", + " 1048: [1617, 858, 608, 527, 260, 356],\n", + " 1053: [593, 318, 296, 527, 457, 50, 590, 480, 588, 150]})" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 어소시에이션 규칙을 사용해 각 사용자가 아직 평가하지 않은 영화 10편을 추천한다\n", + "pred_user2items = defaultdict(list)\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "\n", + "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", + " # 사용자가 최근 5편의 영화를 얻는다\n", + " input_data = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-5:]\n", + " # 그 영화들이 조건부에 1편이라도 포함되어 있는 어소시에이션 규칙을 추출한다\n", + " matched_flags = rules.antecedents.apply(lambda x: len(set(input_data) & x)) >= 1\n", + "\n", + " # 어소시에이션 규칙의 귀결부의 영화를 리스트로 저장하고, 출편 빈도 순으로 배열하고, 사용자가 아직 평가하지 않았다면 추천 리스트에 추가한다\n", + " consequent_movies = []\n", + " for i, row in rules[matched_flags].sort_values(\"lift\", ascending=False).iterrows():\n", + " consequent_movies.extend(row[\"consequents\"])\n", + " # 출현 빈도를 센다\n", + " counter = Counter(consequent_movies)\n", + " for movie_id, movie_cnt in counter.most_common():\n", + " if movie_id not in user_evaluated_movies[user_id]:\n", + " pred_user2items[user_id].append(movie_id)\n", + " # 추천 리스트가 10편이 되면 종료한다\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "\n", + "# 각 사용자에 대한 추천 리스트\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7342fdc7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "executionInfo": { + "elapsed": 15, + "status": "ok", + "timestamp": 1672116383190, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7342fdc7", + "outputId": "c194befc-f9ec-4450-c916-02791f71155a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
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" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(1196, 593, 1198)\n", + "movies[movies.movie_id.isin([1196, 593, 1198])]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9b22d1f6", + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1672116383190, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "9b22d1f6" + }, + "outputs": [], + "source": [ + "# apriori(user_movie_matrix, min_support=0.1, use_colnames=True)\n", + "# association_rules(freq_movies, metric='lift', min_threshold=1)\n", + "# min_support와 min_threshold가 중요한 파라미터가 되므로 바꾸어 가면서 시험해봅니다." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "64ae2120", + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1672116383190, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "64ae2120" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/BPR.ipynb b/chapter5/colab/BPR.ipynb index d673ac6..468e916 100644 --- a/chapter5/colab/BPR.ipynb +++ b/chapter5/colab/BPR.ipynb @@ -1 +1,2861 @@ -{"cells":[{"cell_type":"markdown","id":"4b7f67c6","metadata":{"id":"4b7f67c6"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/BPR.ipynb)"]},{"cell_type":"markdown","id":"1dd49045","metadata":{"id":"1dd49045"},"source":["# Bayesian Personalized Ranking(BPR)"]},{"cell_type":"code","execution_count":1,"id":"e483d5c3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e483d5c3","executionInfo":{"status":"ok","timestamp":1672116735554,"user_tz":-540,"elapsed":6236,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"21abcbdb-9e9a-4754-b011-d451a8a5c819"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 04:52:06-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 21.2MB/s in 3.0s \n","\n","2022-12-27 04:52:10 (21.2 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"9e2d716c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9e2d716c","executionInfo":{"status":"ok","timestamp":1672116794339,"user_tz":-540,"elapsed":58791,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"af9f8155-e2a1-407d-830c-5efe4794f0a2"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"65728cf5","metadata":{"id":"65728cf5","executionInfo":{"status":"ok","timestamp":1672116794339,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 10\n","# 평가 수의 임곗값\n","minimum_num_rating = 0\n","# 에폭 수\n","n_epochs = 50"]},{"cell_type":"code","execution_count":4,"id":"959a4009","metadata":{"id":"959a4009","executionInfo":{"status":"ok","timestamp":1672116794861,"user_tz":-540,"elapsed":539,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 행렬 분석용으로 행렬을 작성한다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n","\n","# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n","unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n","unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))"]},{"cell_type":"code","execution_count":5,"id":"072a29db","metadata":{"id":"072a29db","executionInfo":{"status":"ok","timestamp":1672116798146,"user_tz":-540,"elapsed":3287,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse import lil_matrix\n","# 희소 행렬을 초기화하고 각 셀에 값을 넣는다\n","movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n","for i, row in movielens_train_high_rating.iterrows():\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," movielens_matrix[movie_index, user_index] = 1.0\n"]},{"cell_type":"code","execution_count":6,"id":"b6c90bc6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b6c90bc6","executionInfo":{"status":"ok","timestamp":1672116874351,"user_tz":-540,"elapsed":76226,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c94b6b49-5769-465c-ea87-fc27280963d5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting implicit==0.4.4\n"," Downloading implicit-0.4.4.tar.gz (1.1 MB)\n","\u001b[K |████████████████████████████████| 1.1 MB 11.7 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n","Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n","Building wheels for collected packages: implicit\n"," Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825514 sha256=7d630d694700acd323d4158a90d015d03f6613575de9846072360a19481a67e8\n"," Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n","Successfully built implicit\n","Installing collected packages: implicit\n","Successfully installed implicit-0.4.4\n"]}],"source":["!pip install implicit==0.4.4"]},{"cell_type":"code","execution_count":7,"id":"25e71312","metadata":{"id":"25e71312","executionInfo":{"status":"ok","timestamp":1672116874352,"user_tz":-540,"elapsed":7,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다"]},{"cell_type":"code","execution_count":8,"id":"6c0cf1a4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6c0cf1a4","executionInfo":{"status":"ok","timestamp":1672116874906,"user_tz":-540,"elapsed":6,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"bf46043c-1d89-4e29-aa41-b9429e68b845"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:implicit:GPU training requires factor size to be a multiple of 32 - 1. Increasing factors from 10 to 31.\n"]}],"source":["import implicit\n","\n","# 모델 초기화\n","model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs)"]},{"cell_type":"code","execution_count":9,"id":"cee6bdcb","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["0da9107a2135419b80d81eeef564292f","ddb3a89fe8634c0fa69705137e88db1d","b5c338dd0da64c47b4184538c7780b19","2700ce5424694283b893b686f7467a85","9e92efe91d6a41c4b20c220c43f1ae26","f9a7d7da750a411fa3b1c90b31a0197f","e97a61c4370c435ea93ae76ad616b02d","9a4aa4972d13480f8734832345fdea59","a78a46d01434404288168d8402a317d5","ebdc7cd012c14d028350d7a39daa0bc2","0318cc47c7ff4fad82354bce4fedb82f"]},"id":"cee6bdcb","executionInfo":{"status":"ok","timestamp":1672116876849,"user_tz":-540,"elapsed":1947,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e7ac20c7-47a4-4941-e069-6d76794e9dcb"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/50 [00:00\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n"," \n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"4457d3f0","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"4457d3f0","executionInfo":{"status":"ok","timestamp":1672116876850,"user_tz":-540,"elapsed":10,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5d397af9-532d-4731-90d0-61de1fcba17c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","293 296 Pulp Fiction (1994) \n","352 356 Forrest Gump (1994) \n","587 593 Silence of the Lambs, The (1991) \n","\n"," genre \\\n","293 [Comedy, Crime, Drama] \n","352 [Comedy, Drama, Romance, War] \n","587 [Crime, Horror, Thriller] \n","\n"," tag \n","293 [quotable, samuel l. jackson, quentin tarantin... \n","352 [psychology, tom hanks, seen more than once, l... \n","587 [based on a book, anthony hopkins, demme, psyc... "],"text/html":["\n","
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movie_idtitlegenretag
293296Pulp Fiction (1994)[Comedy, Crime, Drama][quotable, samuel l. jackson, quentin tarantin...
352356Forrest Gump (1994)[Comedy, Drama, Romance, War][psychology, tom hanks, seen more than once, l...
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(593, 356, 296)\n","movies[movies.movie_id.isin([593, 356, 296])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 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\ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4b7f67c6", + "metadata": { + "id": "4b7f67c6" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/BPR.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "1dd49045", + "metadata": { + "id": "1dd49045" + }, + "source": [ + "# 개인화 된 랭킹 문제(Bayesian Personalized Ranking, BPR)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e483d5c3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 6236, + "status": "ok", + "timestamp": 1672116735554, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "e483d5c3", + "outputId": "21abcbdb-9e9a-4754-b011-d451a8a5c819" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 04:52:06-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 21.2MB/s in 3.0s \n", + "\n", + "2022-12-27 04:52:10 (21.2 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e2d716c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 58791, + "status": "ok", + "timestamp": 1672116794339, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "9e2d716c", + "outputId": "af9f8155-e2a1-407d-830c-5efe4794f0a2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자 수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "65728cf5", + "metadata": { + "executionInfo": { + "elapsed": 18, + "status": "ok", + "timestamp": 1672116794339, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "65728cf5" + }, + "outputs": [], + "source": [ + "# 인자 수\n", + "factors = 10\n", + "# 평가 수의 임곗값\n", + "minimum_num_rating = 0\n", + "# 에폭 수\n", + "n_epochs = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "959a4009", + "metadata": { + "executionInfo": { + "elapsed": 539, + "status": "ok", + "timestamp": 1672116794861, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "959a4009" + }, + "outputs": [], + "source": [ + "# 행렬 분석용으로 행렬을 작성한다\n", + "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", + " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", + ")\n", + "\n", + "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", + "\n", + "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", + "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", + "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", + "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", + "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "072a29db", + "metadata": { + "executionInfo": { + "elapsed": 3287, + "status": "ok", + "timestamp": 1672116798146, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "072a29db" + }, + "outputs": [], + "source": [ + "from scipy.sparse import lil_matrix\n", + "# 희소 행렬을 초기화하고 각 셀에 값을 넣는다\n", + "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", + "for i, row in movielens_train_high_rating.iterrows():\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_index = movie_id2index[row[\"movie_id\"]]\n", + " movielens_matrix[movie_index, user_index] = 1.0\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b6c90bc6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 76226, + "status": "ok", + "timestamp": 1672116874351, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "b6c90bc6", + "outputId": "c94b6b49-5769-465c-ea87-fc27280963d5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting implicit==0.4.4\n", + " Downloading implicit-0.4.4.tar.gz (1.1 MB)\n", + "\u001b[K |████████████████████████████████| 1.1 MB 11.7 MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n", + "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n", + "Building wheels for collected packages: implicit\n", + " Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825514 sha256=7d630d694700acd323d4158a90d015d03f6613575de9846072360a19481a67e8\n", + " Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n", + "Successfully built implicit\n", + "Installing collected packages: implicit\n", + "Successfully installed implicit-0.4.4\n" + ] + } + ], + "source": [ + "!pip install implicit==0.4.4" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "25e71312", + "metadata": { + "executionInfo": { + "elapsed": 7, + "status": "ok", + "timestamp": 1672116874352, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "25e71312" + }, + "outputs": [], + "source": [ + "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6c0cf1a4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 6, + "status": "ok", + "timestamp": 1672116874906, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "6c0cf1a4", + "outputId": "bf46043c-1d89-4e29-aa41-b9429e68b845" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:implicit:GPU training requires factor size to be a multiple of 32 - 1. Increasing factors from 10 to 31.\n" + ] + } + ], + "source": [ + "import implicit\n", + "\n", + "# 모델 초기화\n", + "model = implicit.bpr.BayesianPersonalizedRanking(factors=factors, iterations=n_epochs)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cee6bdcb", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "0da9107a2135419b80d81eeef564292f", + "ddb3a89fe8634c0fa69705137e88db1d", + "b5c338dd0da64c47b4184538c7780b19", + "2700ce5424694283b893b686f7467a85", + "9e92efe91d6a41c4b20c220c43f1ae26", + "f9a7d7da750a411fa3b1c90b31a0197f", + "e97a61c4370c435ea93ae76ad616b02d", + "9a4aa4972d13480f8734832345fdea59", + "a78a46d01434404288168d8402a317d5", + "ebdc7cd012c14d028350d7a39daa0bc2", + "0318cc47c7ff4fad82354bce4fedb82f" + ] + }, + "executionInfo": { + "elapsed": 1947, + "status": "ok", + "timestamp": 1672116876849, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "cee6bdcb", + "outputId": "e7ac20c7-47a4-4941-e069-6d76794e9dcb" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0da9107a2135419b80d81eeef564292f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00\n", + "
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473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
293296Pulp Fiction (1994)[Comedy, Crime, Drama][quotable, samuel l. jackson, quentin tarantin...
352356Forrest Gump (1994)[Comedy, Drama, Romance, War][psychology, tom hanks, seen more than once, l...
587593Silence of the Lambs, The (1991)[Crime, Horror, Thriller][based on a book, anthony hopkins, demme, psyc...
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"_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/IMF.ipynb b/chapter5/colab/IMF.ipynb index 1744802..79fa0d1 100644 --- a/chapter5/colab/IMF.ipynb +++ b/chapter5/colab/IMF.ipynb @@ -1 +1,2700 @@ -{"cells":[{"cell_type":"markdown","id":"31a1f64c","metadata":{"id":"31a1f64c"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/IMF.ipynb)"]},{"cell_type":"markdown","id":"c0dccd9c","metadata":{"id":"c0dccd9c"},"source":["# Implicit Matrix Factorization"]},{"cell_type":"code","execution_count":1,"id":"c302ab23","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c302ab23","executionInfo":{"status":"ok","timestamp":1672118046019,"user_tz":-540,"elapsed":10729,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"99be911a-fa2c-4848-b9fe-de4ba2570907"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:12:10-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 13.6MB/s in 6.1s \n","\n","2022-12-27 05:12:18 (10.3 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"4f1322e1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4f1322e1","executionInfo":{"status":"ok","timestamp":1672118103815,"user_tz":-540,"elapsed":57828,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5aef6143-782d-4602-9b40-0fc6cb3f0475"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"79120322","metadata":{"id":"79120322","executionInfo":{"status":"ok","timestamp":1672118103816,"user_tz":-540,"elapsed":32,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 10\n","# 평가 수의 임곗값\n","minimum_num_rating = 0\n","# 에폭 수\n","n_epochs = 50\n","# alpha\n","alpha = 1.0"]},{"cell_type":"code","execution_count":4,"id":"df43e6c5","metadata":{"id":"df43e6c5","executionInfo":{"status":"ok","timestamp":1672118103817,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 행력 분석용으로 행렬을 작성한다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n","\n","# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n","unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n","unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n","user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n","movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))"]},{"cell_type":"code","execution_count":5,"id":"4c2f401f","metadata":{"id":"4c2f401f","executionInfo":{"status":"ok","timestamp":1672118107348,"user_tz":-540,"elapsed":3561,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse import lil_matrix\n","# 희소 행렬을 초기화하고, 각 셀에 값을 넣는다\n","movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n","for i, row in movielens_train_high_rating.iterrows():\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," movielens_matrix[movie_index, user_index] = 1.0 * alpha # 이후, 영화의 평갓값을 0/1로 이진화한다. 클릭 수 등의 데이터인 경우에는 log(click 수) 등으로 변형하는 것도 효과적이다."]},{"cell_type":"code","execution_count":6,"id":"7ee004fe","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7ee004fe","executionInfo":{"status":"ok","timestamp":1672118180966,"user_tz":-540,"elapsed":73628,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"7561d4a0-8562-4190-93ec-2966e0cf9e32"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting implicit==0.4.4\n"," Downloading implicit-0.4.4.tar.gz (1.1 MB)\n","\u001b[K |████████████████████████████████| 1.1 MB 30.9 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n","Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n","Building wheels for collected packages: implicit\n"," Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825530 sha256=4f9058d1d351d60c74e7cfac5a34e01c831ecc5683e4cb2c3b0bc2816f422259\n"," Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n","Successfully built implicit\n","Installing collected packages: implicit\n","Successfully installed implicit-0.4.4\n"]}],"source":["!pip install implicit==0.4.4"]},{"cell_type":"code","execution_count":7,"id":"e76c9ca3","metadata":{"id":"e76c9ca3","executionInfo":{"status":"ok","timestamp":1672118180966,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다"]},{"cell_type":"code","execution_count":8,"id":"c5a6a5bc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c5a6a5bc","executionInfo":{"status":"ok","timestamp":1672118181566,"user_tz":-540,"elapsed":607,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"2bf44fbc-c3cb-4966-854d-99bc40b41415"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:implicit:GPU training requires factor size to be a multiple of 32. Increasing factors from 10 to 32.\n","WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n"]}],"source":["import implicit\n","\n","# 모델 초기화\n","model = implicit.als.AlternatingLeastSquares(\n"," factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1\n",")"]},{"cell_type":"code","execution_count":9,"id":"e812e181","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["fb6ab01e37634a9abeec2b179da03960","e1607d7715e04820885029cdf3a74c6f","2fdc799239424e849ffee921e6ee603f","6092009309584cdf9c993516020fd83d","5f77d52aa8c24e999f128edf4bba9ba1","5a37456cfd97490b82dc40007c2e2df1","4a54ee19a6434221b42e0604706c3e98","c2f622744a7b4e9e93eea9d685587d1a","cbb30e2efa2b43189dc50c6c9aebb349","1866fe3ab47444b2b106afb7f42b56ad","2292d6ee2146480c929ca724004f0eb0"]},"id":"e812e181","executionInfo":{"status":"ok","timestamp":1672118185699,"user_tz":-540,"elapsed":4134,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"35f00436-113b-421c-da6c-4b24030ef824"},"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/50 [00:00\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n"," \n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"04b38bdb","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":239},"id":"04b38bdb","executionInfo":{"status":"ok","timestamp":1672118185701,"user_tz":-540,"elapsed":21,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3deca087-a3c9-4705-b213-014e3e68b09a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","583 589 Terminator 2: Judgment Day (1991) \n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","\n"," genre \\\n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","583 [Action, Sci-Fi] \n","1171 [Action, Adventure, Sci-Fi] \n","\n"," tag \n","0 [pixar, pixar, pixar, animation, pixar, animat... \n","583 [action, sci-fi, dvd, seen more than once, tim... \n","1171 [lucas, george lucas, george lucas, gfei own i... "],"text/html":["\n","
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(1196, 1, 589)\n","movies[movies.movie_id.isin([1196, 1, 589])]"]},{"cell_type":"code","execution_count":13,"id":"3c7399b0","metadata":{"id":"3c7399b0","executionInfo":{"status":"ok","timestamp":1672118185702,"user_tz":-540,"elapsed":20,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# IMF에서는 factors나 alpha 설정이 예측 정밀도에 중요하므로, 값을 바꾸어가며 시도해봅니다."]}],"metadata":{"kernelspec":{"display_name":"Python 3 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\ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "31a1f64c", + "metadata": { + "id": "31a1f64c" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/IMF.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "c0dccd9c", + "metadata": { + "id": "c0dccd9c" + }, + "source": [ + "# 암묵적 행렬 분해(Implicit Matrix Factorization, IMF)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c302ab23", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 10729, + "status": "ok", + "timestamp": 1672118046019, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "c302ab23", + "outputId": "99be911a-fa2c-4848-b9fe-de4ba2570907" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:12:10-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 13.6MB/s in 6.1s \n", + "\n", + "2022-12-27 05:12:18 (10.3 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4f1322e1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 57828, + "status": "ok", + "timestamp": 1672118103815, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "4f1322e1", + "outputId": "5aef6143-782d-4602-9b40-0fc6cb3f0475" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "79120322", + "metadata": { + "executionInfo": { + "elapsed": 32, + "status": "ok", + "timestamp": 1672118103816, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "79120322" + }, + "outputs": [], + "source": [ + "# 인자 수\n", + "factors = 10\n", + "# 평가 수의 임곗값\n", + "minimum_num_rating = 0\n", + "# 에폭 수\n", + "n_epochs = 50\n", + "# alpha\n", + "alpha = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "df43e6c5", + "metadata": { + "executionInfo": { + "elapsed": 31, + "status": "ok", + "timestamp": 1672118103817, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "df43e6c5" + }, + "outputs": [], + "source": [ + "# 행력 분석용으로 행렬을 작성한다\n", + "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", + " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", + ")\n", + "\n", + "movielens_train_high_rating = filtered_movielens_train[filtered_movielens_train.rating >= 4]\n", + "\n", + "# 행렬의 인덱스와 영화/사용자를 대응시킨 딕셔너리를 작성한다\n", + "unique_user_ids = sorted(movielens_train_high_rating.user_id.unique())\n", + "unique_movie_ids = sorted(movielens_train_high_rating.movie_id.unique())\n", + "user_id2index = dict(zip(unique_user_ids, range(len(unique_user_ids))))\n", + "movie_id2index = dict(zip(unique_movie_ids, range(len(unique_movie_ids))))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c2f401f", + "metadata": { + "executionInfo": { + "elapsed": 3561, + "status": "ok", + "timestamp": 1672118107348, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "4c2f401f" + }, + "outputs": [], + "source": [ + "from scipy.sparse import lil_matrix\n", + "# 희소 행렬을 초기화하고, 각 셀에 값을 넣는다\n", + "movielens_matrix = lil_matrix((len(unique_movie_ids), len(unique_user_ids)))\n", + "for i, row in movielens_train_high_rating.iterrows():\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_index = movie_id2index[row[\"movie_id\"]]\n", + " movielens_matrix[movie_index, user_index] = 1.0 * alpha # 이후, 영화의 평갓값을 0/1로 이진화한다. 클릭 수 등의 데이터인 경우에는 log(click 수) 등으로 변형하는 것도 효과적이다." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7ee004fe", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 73628, + "status": "ok", + "timestamp": 1672118180966, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7ee004fe", + "outputId": "7561d4a0-8562-4190-93ec-2966e0cf9e32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting implicit==0.4.4\n", + " Downloading implicit-0.4.4.tar.gz (1.1 MB)\n", + "\u001b[K |████████████████████████████████| 1.1 MB 30.9 MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.21.6)\n", + "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (1.7.3)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from implicit==0.4.4) (4.64.1)\n", + "Building wheels for collected packages: implicit\n", + " Building wheel for implicit (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for implicit: filename=implicit-0.4.4-cp38-cp38-linux_x86_64.whl size=3825530 sha256=4f9058d1d351d60c74e7cfac5a34e01c831ecc5683e4cb2c3b0bc2816f422259\n", + " Stored in directory: /root/.cache/pip/wheels/00/ac/67/6f4536c819ed560c2c7e17c0f7a920e3e50c26108616087d05\n", + "Successfully built implicit\n", + "Installing collected packages: implicit\n", + "Successfully installed implicit-0.4.4\n" + ] + } + ], + "source": [ + "!pip install implicit==0.4.4" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e76c9ca3", + "metadata": { + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1672118180966, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "e76c9ca3" + }, + "outputs": [], + "source": [ + "# colab 상에서 implicit 라이브러리 실행 후에 에러가 발생할 때는, '런타임 → 런타임 유형 변경'에서 하드웨어 엑셀러레이터로 GPU를 선택합니다" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c5a6a5bc", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 607, + "status": "ok", + "timestamp": 1672118181566, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "c5a6a5bc", + "outputId": "2bf44fbc-c3cb-4966-854d-99bc40b41415" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:implicit:GPU training requires factor size to be a multiple of 32. Increasing factors from 10 to 32.\n", + "WARNING:root:OpenBLAS detected. Its highly recommend to set the environment variable 'export OPENBLAS_NUM_THREADS=1' to disable its internal multithreading\n" + ] + } + ], + "source": [ + "import implicit\n", + "\n", + "# 모델 초기화\n", + "model = implicit.als.AlternatingLeastSquares(\n", + " factors=factors, iterations=n_epochs, calculate_training_loss=True, random_state=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e812e181", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "fb6ab01e37634a9abeec2b179da03960", + "e1607d7715e04820885029cdf3a74c6f", + "2fdc799239424e849ffee921e6ee603f", + "6092009309584cdf9c993516020fd83d", + "5f77d52aa8c24e999f128edf4bba9ba1", + "5a37456cfd97490b82dc40007c2e2df1", + "4a54ee19a6434221b42e0604706c3e98", + "c2f622744a7b4e9e93eea9d685587d1a", + "cbb30e2efa2b43189dc50c6c9aebb349", + "1866fe3ab47444b2b106afb7f42b56ad", + "2292d6ee2146480c929ca724004f0eb0" + ] + }, + "executionInfo": { + "elapsed": 4134, + "status": "ok", + "timestamp": 1672118185699, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "e812e181", + "outputId": "35f00436-113b-421c-da6c-4b24030ef824" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fb6ab01e37634a9abeec2b179da03960", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/50 [00:00\n", + "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...
583589Terminator 2: Judgment Day (1991)[Action, Sci-Fi][action, sci-fi, dvd, seen more than once, tim...
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
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\n", + " " + ], + "text/plain": [ + " movie_id title \\\n", + "0 1 Toy Story (1995) \n", + "583 589 Terminator 2: Judgment Day (1991) \n", + "1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n", + "\n", + " genre \\\n", + "0 [Adventure, Animation, Children, Comedy, Fantasy] \n", + "583 [Action, Sci-Fi] \n", + "1171 [Action, Adventure, Sci-Fi] \n", + "\n", + " tag \n", + "0 [pixar, pixar, pixar, animation, pixar, animat... \n", + "583 [action, sci-fi, dvd, seen more than once, tim... \n", + "1171 [lucas, george lucas, george lucas, gfei own i... " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(1196, 1, 589)\n", + "movies[movies.movie_id.isin([1196, 1, 589])]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3c7399b0", + "metadata": { + "executionInfo": { + "elapsed": 20, + "status": "ok", + "timestamp": 1672118185702, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "3c7399b0" + }, + "outputs": [], + "source": [ + "# IMF에서는 factors나 alpha 설정이 예측 정밀도에 중요하므로, 값을 바꾸어가며 시도해봅니다." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_collaboration.ipynb)"]},{"cell_type":"markdown","id":"942a9897","metadata":{"id":"942a9897"},"source":["# Latent Dirichlet Allocation (LDA)를 행동 데이터에 적용"]},{"cell_type":"code","execution_count":1,"id":"b8cb8f0b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b8cb8f0b","executionInfo":{"status":"ok","timestamp":1672118267114,"user_tz":-540,"elapsed":4818,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"ca848f2f-0377-4da9-e770-5b4f7e92d5b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:15:58-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.6MB/s in 1.0s \n","\n","2022-12-27 05:15:59 (63.6 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"6769fb75","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6769fb75","executionInfo":{"status":"ok","timestamp":1672118333071,"user_tz":-540,"elapsed":65965,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5ab1c72d-1ab3-46bb-ce4e-9463a2c6e6a2"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"f650f9d6","metadata":{"id":"f650f9d6","executionInfo":{"status":"ok","timestamp":1672118333072,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 50\n","# 에폭 수\n","n_epochs = 30"]},{"cell_type":"code","execution_count":4,"id":"f703bf73","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f703bf73","executionInfo":{"status":"ok","timestamp":1672118340381,"user_tz":-540,"elapsed":7337,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"47c9ad1a-7786-4285-ecbb-b86ca5cf30de"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.6 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":5,"id":"d5348432","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d5348432","executionInfo":{"status":"ok","timestamp":1672118342033,"user_tz":-540,"elapsed":1656,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3c8d0dc9-8126-4750-d870-08019b81e513"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["from gensim.corpora.dictionary import Dictionary\n","\n","# LDA 입력으로 사용할 데이터를 작성한다\n","lda_data = []\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," lda_data.append(data[\"movie_id\"].apply(str).tolist())\n","\n","common_dictionary = Dictionary(lda_data)\n","common_corpus = [common_dictionary.doc2bow(text) for text in lda_data]\n"]},{"cell_type":"code","execution_count":6,"id":"d0f0e231","metadata":{"id":"d0f0e231","executionInfo":{"status":"ok","timestamp":1672118435326,"user_tz":-540,"elapsed":93308,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import gensim\n","lda_model = gensim.models.LdaModel(\n"," common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n",")"]},{"cell_type":"code","execution_count":7,"id":"d568841a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d568841a","executionInfo":{"status":"ok","timestamp":1672118435327,"user_tz":-540,"elapsed":41,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"cfc9c430-1bea-4f52-d193-73e15a1ac543"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(2, 0.13143347), (42, 0.81523174)]"]},"metadata":{},"execution_count":7}],"source":["# 각 사용자의 소속 토픽이 저장된다\n","lda_topics = lda_model[common_corpus]\n","\n","# 예: 어떤 사용자의 소속 토픽\n","lda_topics[0]"]},{"cell_type":"code","execution_count":8,"id":"aa768039","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aa768039","executionInfo":{"status":"ok","timestamp":1672118435327,"user_tz":-540,"elapsed":33,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"00099179-91eb-4fd9-80b6-33b281b38e6c"},"outputs":[{"output_type":"stream","name":"stdout","text":["movie_id=2858, title=American Beauty (1999), score=0.014902905561029911\n","movie_id=4022, title=Cast Away (2000), score=0.013141673058271408\n","movie_id=356, title=Forrest Gump (1994), score=0.009344562888145447\n","movie_id=1625, title=Game, The (1997), score=0.009039949625730515\n","movie_id=3408, title=Erin Brockovich (2000), score=0.008023954927921295\n","movie_id=1610, title=Hunt for Red October, The (1990), score=0.007280151825398207\n","movie_id=2762, title=Sixth Sense, The (1999), score=0.007244058884680271\n","movie_id=1833, title=Mercury Rising (1998), score=0.007027590647339821\n","movie_id=50, title=Usual Suspects, The (1995), score=0.00636146729812026\n","movie_id=2706, title=American Pie (1999), score=0.006130032241344452\n"]}],"source":["# topic0인 영화 목록\n","for token_id, score in lda_model.get_topic_terms(0, topn=10):\n"," movie_id = int(common_dictionary.id2token[token_id])\n"," title = movies[movies.movie_id == movie_id].title.tolist()[0]\n"," print(f'movie_id={movie_id}, title={title}, score={score}')"]},{"cell_type":"code","execution_count":9,"id":"9eb2910f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9eb2910f","executionInfo":{"status":"ok","timestamp":1672118435328,"user_tz":-540,"elapsed":29,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"5ecbcb33-daaa-422c-a155-abdcc16844d3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, 0.010000083),\n"," (1, 0.010000083),\n"," (2, 0.010000083),\n"," (3, 0.010000083),\n"," (4, 0.010000083),\n"," (5, 0.010000083),\n"," (6, 0.010000083),\n"," (7, 0.010000083),\n"," (8, 0.010000083),\n"," (9, 0.010000083),\n"," (10, 0.010000083),\n"," (11, 0.010000083),\n"," (12, 0.010000083),\n"," (13, 0.010000083),\n"," (14, 0.010000083),\n"," (15, 0.010000083),\n"," (16, 0.010000083),\n"," (17, 0.010000083),\n"," (18, 0.010000083),\n"," (19, 0.010000083),\n"," (20, 0.010000083),\n"," (21, 0.010000083),\n"," (22, 0.010000083),\n"," (23, 0.010000083),\n"," (24, 0.010000083),\n"," (25, 0.010000083),\n"," (26, 0.010000083),\n"," (27, 0.010000083),\n"," (28, 0.010000083),\n"," (29, 0.010000083),\n"," (30, 0.010000083),\n"," (31, 0.010000083),\n"," (32, 0.010000083),\n"," (33, 0.010000083),\n"," (34, 0.010000083),\n"," (35, 0.010000083),\n"," (36, 0.010000083),\n"," (37, 0.010000083),\n"," (38, 0.010000083),\n"," (39, 0.010000083),\n"," (40, 0.010000083),\n"," (41, 0.50999594),\n"," (42, 0.010000083),\n"," (43, 0.010000083),\n"," (44, 0.010000083),\n"," (45, 0.010000083),\n"," (46, 0.010000083),\n"," (47, 0.010000083),\n"," (48, 0.010000083),\n"," (49, 0.010000083)]"]},"metadata":{},"execution_count":9}],"source":["# 스타워즈/에피소드 5(movie_id=1196)의 토픽(각 토픽에 소속될 확률)\n","lda_model[common_dictionary.doc2bow([\"1196\"])]"]},{"cell_type":"code","execution_count":10,"id":"8de0ac42","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8de0ac42","executionInfo":{"status":"ok","timestamp":1672118441267,"user_tz":-540,"elapsed":5964,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"325da666-9b8b-43b7-ce14-dc81474631fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [457, 380, 593, 150, 349, 590, 318, 165, 110, 296],\n"," 2: [457, 380, 593, 150, 480, 349, 589, 318, 165, 588],\n"," 3: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 356, 2762, 2273],\n"," 4: [457, 593, 318, 296, 356, 10, 47, 50, 1, 474],\n"," 5: [1193, 904, 1252, 750, 1208, 908, 913, 1136, 260, 1304],\n"," 6: [1240, 1210, 589, 1200, 1214, 541, 593, 1374, 1356, 1617],\n"," 7: [1193, 858, 750, 1208, 527, 111, 1247, 1221, 1136, 1206],\n"," 8: [3481, 4034, 4973, 1258, 2692, 7361, 2502, 5902, 296, 4262],\n"," 9: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 10: [1465, 3082, 1610, 474, 2804, 4709, 3469, 2197, 1792, 2989],\n"," 11: [1307, 1197, 1079, 1234, 1278, 1220, 2797, 1968, 1259, 2791],\n"," 12: [720, 1265, 852, 2891, 3254, 4448, 4002, 608, 4223, 4963],\n"," 13: [2571, 4963, 3147, 5349, 6539, 356, 2762, 2273, 6377, 4701],\n"," 14: [2571, 4993, 4963, 3147, 3578, 1580, 356, 2762, 2273, 4701],\n"," 16: [589, 2571, 1198, 2028, 593, 1374, 1356, 1617, 1291, 858],\n"," 17: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 18: [5952, 4993, 7153, 4226, 50, 858, 2329, 1200, 6874, 4886],\n"," 19: [1028, 2797, 2406, 1968, 2791, 2100, 3039, 1784, 1663, 2918],\n"," 22: [457, 590, 318, 10, 434, 1, 474, 204, 442, 733],\n"," 23: [597, 150, 356, 587, 34, 508, 357, 11, 440, 590],\n"," 24: [141, 36, 95, 25, 653, 1393, 58, 52, 14, 783],\n"," 26: [593, 480, 318, 296, 356, 595, 364, 253, 231, 50],\n"," 27: [1, 260, 62, 141, 832, 36, 25, 6, 17, 104],\n"," 28: [1307, 1079, 1234, 1220, 1028, 2797, 1259, 2791, 1380, 914],\n"," 29: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 30: [587, 508, 457, 350, 236, 474, 497, 39, 349, 21],\n"," 33: [260, 733, 494, 832, 25, 802, 104, 805, 653, 1210],\n"," 34: [2572, 3081, 2716, 2396, 1586, 2336, 2858, 1580, 2353, 2541],\n"," 35: [5952, 296, 7153, 3578, 318, 356, 593, 260, 1036, 1198],\n"," 36: [2901, 3005, 2320, 1620, 2997, 3708, 1608, 1258, 1298, 4661],\n"," 37: [1196, 2858, 2762, 356, 260, 1198, 858, 2329, 527, 1270],\n"," 38: [6539, 2273, 4701, 3793, 4886, 780, 4306, 3977, 2028, 3052],\n"," 40: [904, 541, 858, 1252, 923, 912, 750, 1208, 903, 908],\n"," 41: [1234, 1278, 1097, 2791, 1136, 1394, 1663, 1297, 1304, 342],\n"," 42: [150, 356, 587, 34, 508, 593, 339, 11, 440, 457],\n"," 43: [150, 508, 593, 357, 457, 527, 377, 590, 110, 364],\n"," 44: [110, 1968, 2718, 5563, 6281, 5400, 4447, 5418, 2114, 2607],\n"," 45: [5952, 2959, 296, 4993, 7153, 3578, 318, 1210, 4226, 4306],\n"," 46: [2571, 5952, 2959, 296, 4993, 1196, 3578, 318, 1210, 4226],\n"," 47: [356, 47, 527, 1704, 480, 1222, 4886, 1089, 1213, 6377],\n"," 48: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 50: [349, 161, 316, 292, 434, 329, 47, 50, 1, 474],\n"," 51: [3468, 3983, 1084, 694, 4234, 3456, 762, 1250, 1964, 2677],\n"," 52: [541, 858, 923, 912, 608, 750, 903, 908, 527, 111],\n"," 53: [1097, 1193, 356, 919, 3545, 2971, 32, 5504, 1968, 8917],\n"," 54: [595, 153, 253, 1, 527, 32, 204, 442, 225, 208],\n"," 55: [904, 541, 858, 1252, 923, 912, 608, 750, 1208, 903],\n"," 56: [5072, 2905, 1554, 34542, 4144, 4422, 7044, 4085, 1132, 3552],\n"," 57: [466, 543, 520, 2717, 2735, 1982, 5254, 2953, 2002, 2402],\n"," 58: [608, 858, 1094, 2194, 3104, 2289, 457, 1954, 1674, 1299],\n"," 59: [380, 480, 589, 110, 588, 161, 356, 10, 377, 292],\n"," 60: [904, 912, 750, 903, 908, 111, 913, 1207, 1206, 924],\n"," 61: [1307, 2997, 3148, 2804, 2918, 1259, 778, 1704, 1220, 2028],\n"," 62: [457, 480, 589, 110, 356, 377, 364, 47, 50, 1],\n"," 63: [541, 1252, 923, 608, 750, 1208, 903, 908, 111, 913],\n"," 64: [62, 141, 648, 95, 25, 17, 104, 1210, 112, 1356],\n"," 65: [1358, 2028, 2324, 608, 858, 1617, 593, 1094, 2762, 2194],\n"," 66: [55820, 1234, 858, 5147, 923, 4873, 44195, 33880, 111, 4973],\n"," 67: [150, 480, 589, 356, 377, 1, 500, 474, 527, 32],\n"," 68: [858, 912, 750, 527, 111, 1221, 1136, 1304, 910, 1213],\n"," 69: [904, 541, 858, 1252, 923, 608, 750, 1208, 903, 111],\n"," 70: [923, 608, 1208, 903, 111, 1247, 1136, 260, 1206, 1219],\n"," 71: [457, 593, 480, 589, 590, 318, 110, 588, 161, 356],\n"," 72: [150, 589, 318, 110, 296, 161, 316, 377, 592, 364],\n"," 73: [1358, 1704, 590, 2324, 1961, 1617, 1242, 2289, 2243, 36],\n"," 74: [371, 318, 544, 68, 514, 270, 71, 1302, 248, 440],\n"," 75: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 76: [588, 356, 364, 253, 50, 500, 527, 454, 32, 288],\n"," 77: [150, 318, 110, 161, 329, 50, 500, 474, 32, 34],\n"," 78: [4993, 2329, 5952, 5349, 293, 4886, 4878, 4011, 47, 8636],\n"," 79: [527, 111, 353, 112, 232, 307, 290, 6, 306, 16],\n"," 80: [480, 589, 590, 588, 356, 595, 10, 377, 592, 292],\n"," 81: [1307, 1197, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097],\n"," 82: [2959, 1196, 7153, 3578, 318, 1210, 2858, 2762, 356, 2028],\n"," 83: [904, 1252, 1208, 903, 527, 111, 1247, 1206, 1267, 1230],\n"," 84: [1073, 832, 6, 802, 104, 805, 653, 1210, 112, 1356],\n"," 85: [2901, 1732, 1573, 2976, 1617, 2959, 1552, 1917, 3753, 3005],\n"," 86: [260, 541, 32, 593, 1617, 924, 1199, 296, 608, 50],\n"," 87: [2706, 1777, 2485, 2125, 2571, 2394, 1586, 2355, 780, 2424],\n"," 88: [3147, 4246, 4896, 3753, 4306, 4995, 6378, 1704, 2028, 2763],\n"," 89: [457, 593, 480, 349, 589, 590, 356, 595, 316, 10],\n"," 90: [95, 25, 17, 1210, 112, 1356, 1393, 58, 7, 52],\n"," 91: [858, 1252, 908, 111, 1221, 1304, 1225, 910, 1213, 593],\n"," 92: [2959, 296, 4306, 593, 50, 858, 2329, 1214, 1200, 3996],\n"," 93: [5464, 610, 2987, 1089, 5679, 7445, 2140, 1968, 1172, 7254],\n"," 94: [597, 34, 357, 339, 11, 364, 17, 282, 236, 497],\n"," 95: [457, 364, 47, 50, 474, 527, 34, 204, 442, 225],\n"," 96: [1704, 2324, 318, 1094, 2289, 2243, 36, 1641, 1299, 2355],\n"," 97: [446, 317, 500, 2141, 2046, 3409, 92, 708, 919, 2803],\n"," 98: [733, 62, 736, 141, 1073, 32, 494, 832, 95, 17],\n"," 99: [904, 1252, 923, 912, 608, 1208, 903, 908, 913, 1247],\n"," 100: [2959, 4993, 7153, 3578, 4226, 2762, 4306, 1036, 2329, 1214],\n"," 101: [10, 527, 288, 442, 21, 733, 586, 786, 420, 6],\n"," 102: [380, 593, 349, 589, 590, 165, 110, 595, 377, 364],\n"," 103: [1193, 904, 858, 1252, 912, 903, 908, 913, 1221, 1207],\n"," 104: [1358, 1704, 2028, 590, 608, 2396, 1094, 2762, 2194, 3104],\n"," 105: [1240, 589, 1200, 2571, 541, 2028, 593, 1374, 1356, 1617],\n"," 106: [2997, 2683, 858, 1252, 1230, 1221, 1213, 2810, 52, 2706],\n"," 107: [1198, 1214, 593, 1291, 1610, 1036, 2916, 296, 608, 457],\n"," 108: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 2858, 919, 1617],\n"," 110: [150, 349, 165, 110, 588, 296, 161, 595, 316, 10],\n"," 111: [589, 590, 595, 377, 364, 329, 47, 231, 50, 1],\n"," 112: [1148, 1617, 2858, 1952, 1284, 2396, 2395, 720, 1953, 3435],\n"," 113: [2572, 2683, 2706, 3081, 1210, 1777, 2125, 3578, 2571, 2394],\n"," 114: [3949, 55820, 1213, 1222, 7361, 5147, 6874, 2858, 2959, 923],\n"," 115: [2571, 5952, 2959, 4993, 1196, 7153, 1210, 4226, 2028, 4306],\n"," 116: [508, 457, 527, 17, 474, 497, 39, 266, 261, 509],\n"," 117: [1307, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097, 1259],\n"," 118: [588, 595, 364, 1, 500, 34, 339, 733, 367, 586],\n"," 119: [50, 36, 527, 32, 608, 246, 300, 112, 232, 150],\n"," 120: [733, 608, 141, 32, 648, 494, 832, 36, 25, 6],\n"," 121: [260, 1206, 1225, 1212, 1234, 1148, 2019, 1276, 1214, 1254],\n"," 122: [1028, 914, 2100, 342, 1580, 1282, 2407, 1777, 902, 2081],\n"," 123: [370, 318, 1380, 1207, 3053, 188, 2599, 1203, 432, 2081],\n"," 124: [4226, 2959, 3481, 2858, 2542, 2997, 4034, 4973, 2329, 1258],\n"," 125: [2329, 7361, 4878, 778, 8636, 4262, 1748, 2395, 8874, 8464],\n"," 126: [1080, 4973, 4878, 7361, 750, 778, 3949, 1206, 1274, 5618],\n"," 127: [539, 597, 150, 356, 587, 34, 508, 593, 357, 339],\n"," 128: [508, 440, 377, 364, 236, 474, 588, 454, 62, 39],\n"," 129: [1625, 3408, 1610, 1833, 50, 1249, 593, 3101, 1552, 479],\n"," 130: [904, 923, 912, 750, 903, 908, 111, 913, 1247, 1221],\n"," 131: [318, 32, 608, 293, 111, 555, 337, 300, 232, 307],\n"," 132: [150, 480, 589, 110, 588, 161, 10, 377, 292, 364],\n"," 134: [260, 733, 608, 494, 832, 36, 25, 17, 802, 104],\n"," 135: [2571, 2028, 1617, 1610, 2916, 1199, 608, 50, 1676, 318],\n"," 136: [541, 750, 903, 908, 527, 111, 913, 1247, 1221, 260],\n"," 137: [3147, 6539, 356, 2273, 6377, 4246, 2353, 4896, 4886, 5299],\n"," 138: [1291, 858, 527, 3996, 1270, 480, 4886, 1, 1213, 1221],\n"," 139: [4226, 4027, 6711, 2542, 2997, 4993, 4034, 4973, 5952, 1732],\n"," 140: [2571, 5952, 2959, 296, 4993, 1196, 3578, 1210, 2762, 356],\n"," 141: [539, 597, 150, 587, 34, 508, 339, 11, 440, 110],\n"," 142: [296, 50, 318, 223, 527, 293, 47, 246, 555, 337],\n"," 143: [4027, 6711, 2692, 7361, 293, 8636, 6502, 5902, 541, 50],\n"," 144: [2502, 4886, 4979, 3897, 923, 1921, 3911, 1246, 2761, 1784],\n"," 145: [380, 480, 349, 589, 318, 165, 110, 161, 595, 316],\n"," 148: [2571, 1198, 2028, 593, 1356, 1617, 924, 1291, 858, 1610],\n"," 149: [4226, 3481, 4027, 6711, 2542, 4034, 4973, 2329, 1258, 2692],\n"," 150: [296, 50, 318, 36, 25, 223, 32, 593, 608, 293],\n"," 151: [356, 1271, 5418, 2797, 1307, 1721, 2858, 4995, 1246, 3083],\n"," 152: [1704, 590, 2324, 608, 1242, 1094, 3104, 2289, 2243, 1954],\n"," 153: [2571, 5952, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 154: [5952, 4993, 7153, 3578, 318, 4226, 2858, 2028, 4306, 858],\n"," 155: [7153, 4226, 1291, 527, 1200, 6874, 6539, 1222, 4886, 6377],\n"," 156: [1, 260, 733, 494, 832, 36, 95, 17, 802, 805],\n"," 157: [608, 62, 141, 25, 6, 17, 1210, 112, 1356, 58],\n"," 158: [539, 597, 150, 356, 587, 508, 357, 339, 11, 527],\n"," 159: [2140, 2959, 7254, 2143, 1006, 1354, 1967, 3020, 2161, 2683],\n"," 160: [466, 104, 543, 88, 520, 2717, 743, 1982, 724, 1562],\n"," 161: [356, 2028, 4306, 1036, 858, 2329, 1704, 480, 32, 1222],\n"," 162: [318, 4226, 593, 1036, 50, 1291, 2329, 1200, 1704, 1270],\n"," 163: [318, 608, 293, 555, 337, 353, 300, 112, 232, 307],\n"," 164: [110, 161, 364, 329, 253, 50, 1, 34, 185, 204],\n"," 165: [1584,\n"," 431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 1213,\n"," 49530,\n"," 306,\n"," 31696],\n"," 166: [589, 2571, 1198, 541, 32, 2028, 1374, 1356, 1617, 1291],\n"," 167: [260, 733, 1073, 494, 832, 17, 802, 805, 1210, 1356],\n"," 168: [19, 2028, 1784, 1004, 274, 1092, 575, 1610, 2403, 3578],\n"," 169: [1625, 3408, 1610, 2762, 1833, 50, 1249, 593, 3101, 1552],\n"," 170: [5952, 4993, 1196, 7153, 3578, 1210, 2028, 593, 589, 260],\n"," 171: [2571, 5952, 4993, 1196, 7153, 3578, 1210, 4226, 4306, 593],\n"," 172: [380, 593, 349, 590, 318, 296, 161, 356, 595, 316],\n"," 173: [110, 17, 282, 497, 39, 266, 261, 509, 224, 367],\n"," 174: [342, 261, 372, 537, 531, 147, 427, 481, 45, 452],\n"," 175: [908, 1247, 1207, 1304, 1267, 1230, 910, 1148, 1276, 1254],\n"," 176: [480, 349, 589, 318, 110, 588, 296, 356, 595, 10],\n"," 177: [260, 733, 832, 17, 802, 104, 805, 653, 1210, 1356],\n"," 178: [1265, 2567, 1215, 1060, 1343, 2571, 368, 2001, 1500, 2000],\n"," 179: [4226, 2959, 3481, 6711, 2858, 2997, 4034, 4973, 2329, 5952],\n"," 180: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210],\n"," 182: [457, 1674, 260, 1610, 1584, 1198, 1210, 2871, 1036, 1200],\n"," 183: [2324, 608, 1961, 858, 1617, 1242, 2762, 2194, 3104, 2289],\n"," 184: [1466, 1673, 25, 1120, 290, 1912, 1041, 3125, 2718, 5563],\n"," 185: [1230, 912, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358],\n"," 186: [1193, 858, 923, 912, 750, 1208, 908, 527, 913, 1247],\n"," 187: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 188: [318, 165, 588, 161, 595, 10, 377, 292, 364, 47],\n"," 189: [141, 1073, 648, 832, 95, 25, 802, 104, 653, 1210],\n"," 190: [4226, 3481, 4027, 6711, 2858, 2542, 2997, 4034, 4973, 2329],\n"," 191: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 318],\n"," 192: [1234, 5147, 4873, 33880, 1080, 555, 16, 1, 2585, 55805],\n"," 193: [364, 17, 62, 595, 141, 261, 509, 277, 224, 105],\n"," 194: [733, 608, 494, 36, 6, 805, 653, 112, 3, 58],\n"," 195: [1683, 694, 762, 40583, 3730, 1964, 3006, 3363, 2973, 667],\n"," 196: [2863, 2804, 2160, 1256, 346, 2363, 2186, 2273, 861, 1278],\n"," 197: [2683,\n"," 2268,\n"," 648,\n"," 1213,\n"," 2810,\n"," 3000,\n"," 2598,\n"," 8157,\n"," 56286,\n"," 55814],\n"," 198: [3481, 2542, 2997, 4034, 4973, 4011, 778, 4848, 1219, 5618],\n"," 199: [480, 349, 589, 318, 110, 161, 356, 10, 364, 47],\n"," 200: [1, 608, 494, 832, 95, 25, 6, 805, 653, 1210],\n"," 201: [1961, 318, 1242, 2762, 2194, 3104, 2289, 2243, 1954, 1962],\n"," 202: [3578, 2959, 2858, 2707, 589, 2571, 4993, 3157, 1176, 4022],\n"," 203: [2355, 2567, 1968, 2571, 2001, 2396, 2406, 2000, 2706, 1367],\n"," 204: [1073, 832, 36, 802, 104, 805, 653, 1210, 112, 1356],\n"," 205: [904, 923, 750, 903, 527, 1247, 1207, 1219, 1304, 1267],\n"," 206: [1358, 1704, 590, 2324, 1961, 858, 318, 2396, 1242, 1094],\n"," 207: [356, 587, 357, 339, 11, 457, 110, 17, 350, 236],\n"," 208: [457, 593, 480, 589, 590, 356, 595, 377, 292, 364],\n"," 209: [539, 597, 587, 357, 339, 11, 440, 527, 590, 110],\n"," 210: [5952, 2959, 1196, 356, 2028, 260, 1036, 1198, 47, 50],\n"," 211: [1193, 541, 923, 750, 1208, 527, 913, 1247, 1136, 1207],\n"," 212: [1203,\n"," 4406,\n"," 5072,\n"," 7116,\n"," 3310,\n"," 1253,\n"," 39292,\n"," 2726,\n"," 7132,\n"," 3730],\n"," 213: [4993, 3578, 5349, 1580, 6539, 356, 6377, 4701, 3793, 4246],\n"," 214: [1234, 1278, 919, 1394, 1663, 1304, 1246, 2109, 1035, 1081],\n"," 215: [6711, 2329, 6502, 5669, 5377, 4308, 1246, 3471, 3421, 5679],\n"," 216: [3147, 3578, 6539, 356, 6377, 4701, 4025, 2353, 3753, 4995],\n"," 217: [356, 34, 508, 357, 527, 377, 110, 364, 318, 500],\n"," 218: [1265, 852, 3624, 3254, 4448, 3578, 4002, 608, 3745, 4223],\n"," 219: [1079, 1220, 2797, 2406, 1968, 1259, 2791, 1285, 1136, 1101],\n"," 220: [904, 912, 1219, 913, 1193, 928, 903, 1198, 920, 1207],\n"," 221: [2959, 4993, 7153, 3578, 4226, 2762, 4306, 1291, 2329, 1214],\n"," 222: [1210, 97, 2858, 714, 2571, 1094, 1147, 1304, 924, 1090],\n"," 223: [324, 534, 2359, 2858, 2336, 1183, 1252, 2997, 532, 1564],\n"," 224: [260, 733, 832, 802, 805, 653, 1210, 1356, 1393, 58],\n"," 225: [912, 1219, 1193, 928, 1198, 920, 1207, 1258, 4784, 953],\n"," 226: [2959, 6711, 2858, 2997, 4034, 4973, 1258, 1732, 2692, 7361],\n"," 227: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 228: [590, 588, 296, 161, 592, 47, 153, 50, 1, 474],\n"," 229: [62, 494, 832, 36, 95, 6, 17, 802, 104, 653],\n"," 230: [1193, 904, 858, 903, 908, 111, 1207, 1206, 1225, 924],\n"," 231: [6985, 7234, 7072, 3742, 1209, 1260, 3307, 1348, 8125, 6666],\n"," 232: [2571, 2028, 1374, 1356, 1617, 924, 1610, 2916, 1199, 1197],\n"," 234: [5952, 296, 4993, 1196, 7153, 3578, 318, 1210, 2762, 356],\n"," 235: [5952, 1196, 1210, 2762, 356, 2028, 4306, 589, 110, 2329],\n"," 236: [316, 434, 329, 153, 253, 1, 32, 34, 733, 587],\n"," 237: [589, 541, 32, 2028, 1356, 924, 858, 1610, 1036, 2916],\n"," 238: [410, 193, 324, 534, 2359, 1183, 1252, 532, 1564, 3112],\n"," 239: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 2762, 2273],\n"," 241: [4027, 2054, 318, 2858, 1019, 1380, 2761, 1193, 1207, 912],\n"," 242: [608, 1073, 832, 36, 1210, 1393, 3, 58, 7, 52],\n"," 243: [1721, 3148, 1617, 1961, 778, 1704, 1183, 4350, 15, 62],\n"," 244: [923, 903, 908, 111, 1221, 1207, 260, 1206, 1219, 1213],\n"," 245: [3949,\n"," 55820,\n"," 1213,\n"," 1222,\n"," 7361,\n"," 1234,\n"," 5147,\n"," 6874,\n"," 2858,\n"," 2959],\n"," 246: [4226, 2959, 4027, 6711, 2542, 2997, 4993, 4034, 4973, 5952],\n"," 247: [349, 318, 110, 50, 1, 474, 527, 32, 185, 204],\n"," 248: [3578, 2959, 2858, 527, 2707, 589, 2571, 4993, 3157, 1176],\n"," 249: [3481, 2858, 2997, 4973, 1732, 2692, 3996, 3052, 778, 8636],\n"," 250: [296, 50, 36, 25, 223, 527, 32, 593, 608, 293],\n"," 251: [2571, 2028, 1617, 924, 1610, 2916, 1199, 50, 1197, 1676],\n"," 252: [541, 858, 1252, 923, 750, 1208, 903, 111, 913, 1247],\n"," 253: [5952, 2959, 296, 4993, 1196, 7153, 318, 1210, 4226, 2762],\n"," 254: [4993, 7153, 4226, 1036, 1198, 110, 1291, 2329, 1704, 1270],\n"," 255: [457, 380, 480, 349, 589, 165, 110, 296, 595, 316],\n"," 256: [1197, 1079, 1234, 1278, 1220, 1270, 1028, 2797, 1097, 1968],\n"," 257: [5952, 2959, 296, 4993, 1196, 7153, 318, 1210, 4226, 2858],\n"," 258: [1358, 1704, 2324, 858, 318, 593, 2396, 1242, 1094, 2762],\n"," 259: [3481, 4027, 2542, 4034, 1732, 1089, 293, 4011, 223, 8636],\n"," 260: [2571, 2028, 1374, 1617, 1291, 1610, 2916, 1199, 50, 1197],\n"," 261: [527, 356, 2762, 1080, 919, 2028, 2858, 3534, 5504, 1968],\n"," 262: [2863, 2948, 2947, 1136, 2160, 2951, 1997, 1256, 5060, 346],\n"," 263: [7153, 356, 2028, 4306, 6874, 1222, 1089, 4011, 1527, 1580],\n"," 264: [150, 349, 590, 318, 161, 292, 364, 434, 329, 253],\n"," 265: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 266: [97, 714, 1147, 924, 1090, 4002, 1238, 1235, 1288, 2064],\n"," 267: [3949, 1213, 4873, 44195, 33880, 3089, 56367, 555, 16, 4878],\n"," 268: [34, 508, 357, 11, 440, 17, 282, 236, 588, 497],\n"," 269: [260, 733, 608, 62, 32, 494, 832, 36, 6, 17],\n"," 270: [1, 608, 62, 736, 832, 36, 95, 6, 802, 104],\n"," 271: [1200, 2571, 1198, 2028, 593, 1356, 1617, 1291, 858, 1610],\n"," 272: [6711, 2542, 4973, 1732, 2692, 1206, 32, 1089, 2571, 3052],\n"," 273: [2959, 296, 1196, 4226, 2762, 356, 4306, 1198, 47, 1291],\n"," 274: [1968, 1285, 1101, 1297, 2396, 342, 1198, 440, 1291, 1777],\n"," 275: [2324, 858, 1242, 2194, 3104, 2243, 36, 260, 527, 1271],\n"," 276: [4027, 3052, 4262, 5618, 2918, 5377, 8464, 5995, 6539, 1246],\n"," 277: [2571, 2959, 1196, 3578, 318, 1210, 4226, 2858, 2028, 4306],\n"," 278: [356, 296, 1271, 5418, 2797, 1307, 1721, 318, 2858, 4995],\n"," 279: [4226, 3481, 6711, 2858, 4993, 4034, 4973, 2329, 1732, 1206],\n"," 280: [2959, 296, 1196, 3578, 318, 1210, 4226, 2762, 593, 589],\n"," 281: [253, 1, 733, 196, 48, 111, 160, 555, 198, 1092],\n"," 282: [141, 832, 6, 104, 1210, 1356, 1393, 58, 52, 788],\n"," 285: [1196, 1240, 589, 1200, 2571, 1198, 1214, 541, 32, 2028],\n"," 286: [32, 1356, 1617, 924, 858, 1610, 1199, 296, 608, 1197],\n"," 287: [593, 349, 589, 318, 165, 296, 161, 10, 377, 292],\n"," 288: [1193, 541, 858, 1252, 923, 912, 608, 750, 1208, 527],\n"," 289: [2406, 1380, 1285, 1297, 1282, 2371, 1198, 1291, 2081, 1073],\n"," 290: [239, 256, 2142, 1373, 1036, 4799, 1206, 107, 1348, 146],\n"," 291: [4027, 2054, 370, 318, 2858, 1380, 2761, 3053, 912, 188],\n"," 292: [3481, 2542, 293, 778, 4262, 1748, 4848, 923, 1921, 1219],\n"," 293: [380, 480, 589, 110, 356, 377, 292, 364, 47, 50],\n"," 294: [593, 480, 589, 590, 10, 329, 1, 500, 527, 34],\n"," 295: [1259, 914, 1285, 1101, 1297, 342, 440, 2174, 902, 2081],\n"," 296: [3481, 1258, 3996, 8636, 4979, 1748, 2395, 1219, 5618, 8874],\n"," 297: [733, 736, 494, 832, 95, 6, 802, 653, 3, 58],\n"," 298: [2567, 1136, 1215, 1196, 1220, 1060, 1968, 2571, 368, 2001],\n"," 300: [2324, 593, 2396, 3104, 457, 2243, 36, 1962, 1674, 34],\n"," 301: [1193, 904, 903, 908, 111, 913, 1207, 1206, 1219, 1304],\n"," 302: [2572, 2485, 2716, 2125, 2355, 2710, 2336, 2006, 2700, 1597],\n"," 303: [1036, 50, 2329, 1704, 1270, 6539, 1089, 1, 6377, 4963],\n"," 304: [1358, 1704, 2028, 590, 2324, 608, 858, 1617, 318, 593],\n"," 305: [34, 339, 11, 440, 364, 318, 350, 474, 454, 595],\n"," 306: [1721, 2997, 3148, 1617, 608, 778, 2028, 1221, 1183, 858],\n"," 307: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 308: [539, 597, 150, 356, 587, 508, 593, 357, 339, 11],\n"," 309: [733, 608, 1073, 32, 494, 832, 36, 6, 104, 653],\n"," 310: [1242, 2289, 2243, 2355, 1271, 1090, 1095, 3114, 2916, 2890],\n"," 311: [457, 593, 589, 318, 110, 296, 161, 10, 377, 292],\n"," 312: [1617, 1219, 1193, 903, 1198, 920, 1207, 1258, 4784, 953],\n"," 313: [965, 2186, 1617, 2858, 2935, 931, 1066, 2324, 2208, 929],\n"," 314: [608, 1208, 913, 1206, 1230, 910, 1213, 924, 1212, 50],\n"," 315: [17, 236, 474, 497, 141, 266, 21, 509, 22, 277],\n"," 316: [608, 62, 648, 494, 832, 36, 6, 17, 805, 1210],\n"," 317: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 318, 593],\n"," 318: [4226, 2329, 2692, 32, 293, 4878, 223, 6502, 4262, 4848],\n"," 319: [786, 708, 362, 1028, 1029, 79, 1035, 1012, 1022, 661],\n"," 320: [318, 1198, 50, 1291, 858, 2329, 1200, 1704, 6874, 1222],\n"," 321: [150, 590, 318, 165, 110, 161, 10, 377, 434, 47],\n"," 322: [1197, 1079, 1234, 1278, 1220, 1028, 2797, 1097, 2406, 1968],\n"," 323: [2804, 2951, 1997, 1256, 346, 2363, 3508, 2273, 2571, 861],\n"," 324: [296, 1196, 318, 2858, 2762, 356, 2028, 4306, 593, 589],\n"," 325: [3394, 3861, 2447, 1268, 4571, 2622, 1248, 2248, 2021, 4896],\n"," 326: [34, 508, 339, 527, 318, 500, 282, 350, 62, 141],\n"," 327: [141, 832, 36, 6, 17, 104, 805, 653, 112, 3],\n"," 328: [1210, 1198, 2028, 593, 1356, 1617, 1291, 858, 1610, 1036],\n"," 329: [1466, 1673, 608, 25, 50, 296, 1120, 290, 1912, 1041],\n"," 330: [4226, 3481, 4027, 6711, 2997, 4034, 4973, 1258, 1732, 2692],\n"," 331: [5952, 296, 1196, 3578, 1210, 2762, 356, 2028, 4306, 593],\n"," 332: [589, 165, 10, 377, 329, 344, 500, 474, 527, 204],\n"," 333: [2997, 3148, 1617, 2804, 1961, 2918, 1259, 1220, 2028, 1221],\n"," 334: [1079, 1097, 2791, 914, 1285, 1101, 1265, 1394, 2918, 2396],\n"," 335: [1193, 541, 608, 1208, 903, 527, 111, 913, 1221, 1136],\n"," 336: [342, 261, 265, 372, 537, 247, 531, 147, 427, 318],\n"," 337: [1219, 2268, 648, 858, 1252, 2764, 1221, 2810, 3000, 924],\n"," 338: [2571, 2959, 4993, 1196, 7153, 3578, 1210, 4226, 2858, 2762],\n"," 339: [480, 589, 110, 588, 356, 595, 316, 10, 377, 292],\n"," 340: [1028, 2797, 2406, 1968, 2791, 1380, 1641, 2100, 3039, 1784],\n"," 341: [2028, 965, 2186, 919, 2935, 931, 1066, 2208, 929, 947],\n"," 342: [2396, 1699, 1704, 593, 1429, 3671, 923, 1148, 2791, 3897],\n"," 343: [318, 165, 296, 595, 377, 292, 364, 329, 47, 50],\n"," 344: [1200, 2571, 1214, 541, 2028, 593, 1374, 1356, 1617, 924],\n"," 345: [1307, 1079, 1220, 1259, 2791, 1380, 914, 1101, 1663, 342],\n"," 346: [3081, 1210, 2125, 2571, 2394, 1586, 2336, 780, 2424, 2006],\n"," 347: [4558, 7004, 3394, 2410, 3861, 3524, 2447, 2118, 4489, 1268],\n"," 348: [3481, 1732, 1206, 3052, 223, 5902, 2395, 5618, 5669, 2918],\n"," 349: [34, 357, 11, 440, 500, 350, 474, 497, 62, 39],\n"," 350: [427, 1059, 2245, 1131, 1132, 1658, 724, 2657, 2291, 327],\n"," 351: [541, 1252, 1206, 1219, 1267, 910, 1212, 1228, 2019, 1254],\n"," 352: [786, 708, 1210, 362, 1196, 1028, 1097, 260, 1029, 79],\n"," 353: [260, 1240, 1210, 1198, 1374, 1356, 1617, 924, 1291, 858],\n"," 354: [3147, 2273, 4701, 4025, 3793, 2353, 4896, 4886, 3753, 4018],\n"," 355: [380, 480, 589, 588, 161, 10, 377, 592, 292, 364],\n"," 356: [1, 62, 736, 141, 1073, 494, 802, 104, 805, 653],\n"," 357: [1197, 1270, 2797, 1097, 1259, 914, 919, 1641, 2100, 3039],\n"," 358: [5952, 2959, 4993, 7153, 3578, 4226, 2762, 2028, 1036, 1198],\n"," 359: [527, 2707, 589, 1176, 593, 2712, 16, 2700, 1046, 3623],\n"," 360: [260, 733, 608, 62, 1073, 494, 832, 6, 802, 104],\n"," 361: [1193, 904, 541, 923, 912, 750, 903, 908, 527, 111],\n"," 362: [296, 3578, 1210, 2858, 356, 2028, 4306, 589, 1036, 47],\n"," 363: [2571, 3578, 318, 1210, 4226, 2858, 2762, 356, 2028, 4306],\n"," 364: [480, 589, 356, 377, 364, 47, 500, 474, 527, 454],\n"," 365: [539, 440, 377, 318, 282, 350, 474, 588, 454, 62],\n"," 366: [1307, 1270, 1028, 2797, 1097, 1968, 1259, 2791, 1380, 914],\n"," 367: [1704, 590, 2324, 1961, 1617, 593, 2396, 2762, 3104, 2289],\n"," 368: [6711, 2858, 2542, 4034, 2329, 4878, 6502, 4262, 2762, 4848],\n"," 369: [480, 589, 110, 356, 377, 364, 47, 50, 1, 500],\n"," 370: [260, 733, 608, 32, 648, 494, 832, 36, 95, 25],\n"," 371: [2571, 5952, 2959, 7153, 3578, 318, 1210, 4226, 2858, 2762],\n"," 372: [296, 7153, 318, 4226, 2858, 356, 2028, 4306, 593, 260],\n"," 373: [4027, 2054, 370, 2858, 1019, 1380, 2761, 1193, 1207, 3053],\n"," 375: [1307, 1197, 1079, 1278, 1220, 1270, 1028, 1097, 2406, 1968],\n"," 376: [1196, 1198, 1374, 924, 1291, 858, 1610, 1036, 2916, 1199],\n"," 377: [2028, 608, 1961, 1617, 593, 1094, 2762, 2194, 3104, 2289],\n"," 378: [1240, 589, 1200, 2571, 1198, 1214, 541, 32, 2028, 593],\n"," 379: [5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 380: [97, 2858, 714, 2571, 1094, 1147, 1304, 924, 1090, 4002],\n"," 381: [282, 236, 497, 141, 509, 224, 367, 265, 736, 230],\n"," 382: [457, 593, 150, 480, 589, 318, 110, 161, 356, 377],\n"," 383: [2959, 1210, 356, 4306, 1036, 110, 47, 1291, 2329, 527],\n"," 384: [380, 593, 349, 589, 296, 595, 316, 10, 434, 329],\n"," 385: [260, 733, 1073, 832, 36, 802, 805, 653, 1210, 1356],\n"," 386: [1094, 3104, 2289, 1225, 1641, 1299, 2355, 2336, 1207, 1090],\n"," 387: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 388: [5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 2858],\n"," 389: [3481, 2542, 4973, 1258, 1732, 1206, 1089, 1214, 6502, 5902],\n"," 390: [1036, 47, 527, 1214, 1270, 32, 6539, 1222, 1089, 541],\n"," 391: [349, 161, 292, 434, 47, 231, 50, 474, 527, 454],\n"," 392: [161, 316, 329, 1, 500, 474, 185, 204, 442, 225],\n"," 393: [1, 25, 17, 802, 653, 1210, 112, 1356, 58, 7],\n"," 394: [648, 1230, 2764, 2810, 52, 2706, 16, 1080, 3000, 1234],\n"," 395: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226],\n"," 396: [5952, 296, 4993, 7153, 3578, 318, 4226, 2762, 356, 593],\n"," 397: [5952, 4993, 1196, 7153, 1210, 4306, 589, 260, 1036, 1198],\n"," 398: [508, 318, 17, 236, 474, 39, 161, 141, 21, 296],\n"," 399: [1210, 58, 7, 52, 14, 858, 783, 1183, 41, 140],\n"," 400: [965, 2186, 919, 2935, 931, 1066, 2208, 929, 1207, 947],\n"," 401: [1193, 904, 1252, 923, 912, 750, 1208, 903, 908, 111],\n"," 402: [720, 2858, 1265, 852, 2891, 3254, 4002, 608, 4223, 736],\n"," 403: [5952, 2959, 4993, 1196, 3578, 318, 1210, 2858, 2028, 4306],\n"," 404: [593, 595, 434, 231, 50, 500, 474, 527, 32, 288],\n"," 405: [1, 608, 494, 832, 95, 6, 17, 805, 653, 1210],\n"," 406: [457, 380, 593, 480, 589, 590, 318, 110, 588, 356],\n"," 407: [1196, 1210, 1374, 1617, 2916, 1197, 318, 1376, 110, 1213],\n"," 408: [912, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358, 3347],\n"," 409: [508, 339, 457, 527, 110, 364, 318, 500, 17, 282],\n"," 410: [708, 362, 260, 531, 1015, 1367, 837, 1042, 1032, 1282],\n"," 411: [260, 32, 494, 36, 95, 25, 6, 17, 802, 104],\n"," 412: [1252, 1429, 3671, 7153, 1148, 2791, 3897, 1587, 627, 837],\n"," 413: [1252, 912, 608, 750, 1208, 903, 908, 527, 913, 1247],\n"," 414: [2901, 2976, 2028, 1552, 1917, 3753, 3005, 1387, 2320, 1620],\n"," 415: [457, 380, 593, 150, 480, 589, 318, 110, 161, 356],\n"," 416: [589, 318, 165, 110, 296, 595, 316, 329, 253, 50],\n"," 417: [1193, 904, 541, 858, 1252, 923, 912, 608, 750, 1208],\n"," 418: [527, 593, 353, 232, 307, 290, 306, 778, 509, 356],\n"," 419: [923, 1206, 1267, 910, 922, 1228, 2019, 1250, 235, 1952],\n"," 420: [50, 318, 25, 223, 32, 593, 608, 293, 47, 555],\n"," 421: [380, 593, 349, 318, 165, 110, 296, 316, 377, 292],\n"," 422: [1, 608, 141, 1073, 32, 832, 36, 95, 6, 17],\n"," 424: [858, 111, 1221, 1207, 1219, 1225, 1213, 593, 50, 296],\n"," 425: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210],\n"," 426: [2502, 5902, 4848, 1921, 2395, 8464, 5995, 3911, 3175, 3000],\n"," 427: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 428: [256, 1073, 4799, 1206, 1031, 1348, 5373, 8117, 1792, 1333],\n"," 429: [1358, 1704, 2028, 590, 2324, 1961, 858, 318, 593, 2396],\n"," 430: [410, 193, 324, 534, 2359, 2858, 2336, 1183, 1252, 2997],\n"," 431: [2571, 2959, 296, 1196, 3578, 1210, 4226, 2858, 2762, 593],\n"," 432: [539, 587, 357, 39, 161, 141, 266, 252, 367, 380],\n"," 433: [19, 274, 2195, 1092, 575, 2403, 3578, 590, 2329, 3623],\n"," 434: [296, 318, 4226, 47, 50, 2329, 32, 1136, 1089, 1213],\n"," 435: [3481, 4027, 6711, 2858, 2997, 4993, 4034, 4973, 5952, 1258],\n"," 436: [349, 590, 165, 161, 10, 292, 1, 527, 204, 225],\n"," 437: [1193, 858, 923, 912, 1207, 1206, 1304, 1230, 1225, 924],\n"," 438: [904, 1252, 923, 912, 608, 1208, 903, 908, 527, 111],\n"," 439: [1097, 2762, 1080, 919, 2858, 3545, 3534, 2971, 8917, 3101],\n"," 440: [1196, 3578, 1210, 4306, 589, 260, 3996, 1704, 6874, 1136],\n"," 441: [590, 318, 588, 356, 595, 364, 253, 1, 500, 474],\n"," 443: [965, 2186, 919, 1617, 2935, 931, 1066, 2324, 2208, 929],\n"," 444: [1079, 1234, 1220, 1968, 1380, 1641, 1285, 1136, 1784, 1101],\n"," 445: [2028, 965, 2186, 919, 1617, 2858, 2935, 931, 1066, 2324],\n"," 446: [590, 165, 110, 377, 364, 47, 153, 50, 1, 500],\n"," 447: [1193, 904, 541, 858, 1252, 750, 1208, 903, 908, 111],\n"," 448: [720, 1265, 852, 3624, 2891, 3254, 4448, 3578, 4002, 608],\n"," 449: [4993, 1176, 750, 4226, 1046, 6296, 5992, 4848, 3911, 1721],\n"," 450: [2959, 6711, 1258, 2692, 1206, 7361, 4878, 4011, 778, 8636],\n"," 451: [97, 2858, 714, 1094, 1147, 1304, 3753, 5378, 1394, 800],\n"," 452: [5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226, 2858],\n"," 453: [457, 593, 150, 480, 589, 590, 318, 110, 588, 161],\n"," 454: [380, 150, 480, 349, 318, 165, 161, 356, 316, 10],\n"," 455: [141, 1073, 494, 95, 17, 104, 653, 112, 1356, 1393],\n"," 456: [539, 597, 356, 587, 508, 357, 527, 377, 500, 480],\n"," 458: [2605, 2706, 2581, 1210, 1777, 2485, 2125, 2571, 2394, 1586],\n"," 459: [318, 1198, 50, 858, 4886, 1213, 1221, 4995, 8961, 33794],\n"," 460: [480, 589, 318, 356, 377, 364, 47, 50, 1, 500],\n"," 461: [62, 141, 494, 36, 95, 25, 6, 802, 104, 653],\n"," 462: [597, 150, 356, 587, 34, 508, 357, 339, 440, 527],\n"," 463: [590, 1961, 2396, 1094, 3104, 2243, 1962, 2355, 1247, 1271],\n"," 464: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 465: [2542, 5952, 3793, 2502, 32, 2571, 3052, 4886, 223, 1214],\n"," 466: [2571, 2959, 296, 4993, 1196, 7153, 3578, 318, 4226, 2858],\n"," 467: [2571, 5952, 2959, 296, 4993, 1196, 7153, 1210, 4226, 2858],\n"," 468: [50, 318, 36, 25, 223, 608, 293, 47, 246, 111],\n"," 469: [2959, 3481, 2542, 2329, 1258, 1732, 1206, 7361, 2502, 1089],\n"," 470: [2542, 4993, 3793, 2502, 5349, 293, 4886, 4011, 223, 6502],\n"," 471: [539, 597, 150, 587, 508, 593, 357, 339, 11, 440],\n"," 472: [508, 440, 527, 377, 110, 500, 17, 236, 474, 497],\n"," 473: [1, 36, 802, 104, 805, 653, 1210, 112, 1393, 3],\n"," 474: [1198, 32, 2028, 1374, 1617, 1291, 858, 1610, 1036, 1199],\n"," 475: [1732, 2858, 296, 1573, 593, 2762, 1617, 2908, 2028, 1639],\n"," 476: [4034, 2502, 3052, 4886, 223, 6502, 1219, 5618, 2918, 3499],\n"," 477: [3578, 2858, 356, 4306, 589, 110, 1704, 1270, 480, 6874],\n"," 479: [904, 541, 858, 923, 912, 903, 908, 913, 1221, 260],\n"," 480: [6711, 2542, 4034, 2329, 1258, 1732, 7361, 1089, 3052, 293],\n"," 481: [4027, 2997, 4973, 1258, 1732, 2692, 1206, 778, 4979, 5902],\n"," 482: [4027, 6711, 2858, 2997, 4034, 4973, 1258, 2692, 1206, 3996],\n"," 483: [508, 11, 440, 377, 282, 236, 588, 497, 62, 39],\n"," 484: [62, 1073, 36, 104, 1210, 3, 58, 7, 52, 14],\n"," 485: [1358, 1704, 2028, 590, 2324, 1961, 1242, 2194, 3104, 2243],\n"," 486: [2571, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 487: [2355, 1265, 2567, 1136, 1215, 1196, 1220, 1343, 1968, 368],\n"," 488: [318, 165, 110, 588, 296, 356, 595, 316, 377, 292],\n"," 489: [541, 1252, 923, 912, 903, 908, 913, 1219, 1304, 1230],\n"," 490: [260, 2571, 1214, 541, 32, 593, 1374, 1356, 1617, 924],\n"," 491: [3481, 4027, 6711, 2997, 4993, 4034, 2329, 3793, 1732, 2692],\n"," 492: [431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 49530,\n"," 306,\n"," 31696,\n"," 2858,\n"," 8644],\n"," 493: [4993, 5952, 1258, 1206, 2502, 223, 8636, 1214, 4979, 6502],\n"," 494: [588, 595, 10, 364, 434, 329, 153, 1, 474, 32],\n"," 495: [539, 597, 356, 587, 34, 508, 593, 357, 11, 457],\n"," 496: [2858, 1617, 2908, 2959, 1639, 39, 431, 1206, 1923, 4720],\n"," 497: [5952, 2959, 296, 3578, 2858, 356, 2028, 589, 260, 1036],\n"," 498: [6985, 7234, 7072, 3742, 1209, 1260, 3307, 1348, 8125, 6666],\n"," 499: [780, 733, 736, 141, 1073, 648, 494, 832, 95, 17],\n"," 500: [590, 2324, 1961, 858, 1242, 1094, 2762, 2194, 3104, 457],\n"," 501: [1307, 1259, 914, 1641, 3039, 1285, 1101, 1663, 1297, 342],\n"," 502: [1777, 2125, 3578, 2571, 1586, 2355, 780, 2424, 1197, 2006],\n"," 503: [1196, 1210, 260, 1036, 110, 47, 50, 1291, 1214, 1200],\n"," 504: [508, 110, 17, 480, 474, 588, 497, 62, 595, 39],\n"," 505: [508, 593, 357, 527, 500, 282, 350, 236, 474, 497],\n"," 506: [2571, 2028, 1617, 1610, 2916, 1199, 608, 1197, 1676, 1376],\n"," 507: [904, 912, 1219, 913, 1193, 928, 950, 903, 1198, 920],\n"," 508: [2571, 5952, 2959, 296, 4993, 7153, 3578, 318, 1210, 4226],\n"," 509: [4027, 2542, 2997, 4993, 4973, 3052, 4878, 1214, 4979, 3897],\n"," 510: [380, 349, 589, 165, 110, 316, 10, 377, 592, 292],\n"," 511: [457, 593, 480, 589, 318, 588, 356, 10, 377, 364],\n"," 512: [1584, 431, 4308, 11, 45186, 53322, 214, 1213, 49530, 31696],\n"," 513: [1358, 1962, 110, 3114, 1678, 1956, 2890, 1213, 1293, 1250],\n"," 515: [904, 1219, 913, 1193, 950, 903, 1198, 920, 1258, 953],\n"," 516: [1036, 50, 6377, 1080, 33794, 608, 7361, 2918, 1193, 16],\n"," 517: [36, 25, 527, 593, 608, 246, 111, 337, 353, 112],\n"," 518: [1200, 1198, 541, 32, 1374, 1356, 1617, 924, 1291, 858],\n"," 520: [371, 544, 68, 514, 270, 71, 315, 1302, 248, 440],\n"," 521: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226],\n"," 522: [904, 1252, 923, 912, 750, 903, 908, 527, 913, 1247],\n"," 523: [904, 1252, 923, 912, 750, 903, 908, 111, 913, 1136],\n"," 524: [3481, 1258, 3793, 1732, 7361, 32, 3996, 5349, 293, 4886],\n"," 525: [380, 480, 589, 161, 356, 377, 292, 364, 434, 47],\n"," 526: [62, 141, 17, 653, 1210, 112, 1393, 3, 58, 52],\n"," 527: [539, 597, 150, 587, 508, 593, 357, 11, 440, 457],\n"," 529: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 2858, 2762],\n"," 530: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 531: [457, 593, 480, 349, 589, 165, 356, 377, 592, 364],\n"," 532: [1307, 1721, 608, 2804, 2918, 1259, 1704, 1220, 2301, 4350],\n"," 533: [2329, 1732, 293, 4886, 4878, 4011, 778, 4262, 5669, 3081],\n"," 534: [5464, 610, 2987, 1089, 5679, 2140, 1968, 1172, 593, 1193],\n"," 535: [7153, 4226, 2858, 2762, 2329, 1214, 1200, 3996, 1704, 1270],\n"," 536: [597, 356, 587, 11, 440, 527, 590, 110, 364, 318],\n"," 537: [1, 141, 1073, 32, 648, 832, 95, 802, 104, 653],\n"," 539: [457, 110, 161, 356, 595, 10, 344, 253, 231, 1],\n"," 540: [26, 637, 1042, 711, 708, 49278, 786, 719, 1752, 609],\n"," 541: [1234, 914, 919, 1285, 1663, 1297, 1304, 342, 2302, 2144],\n"," 542: [950, 1258, 1271, 2028, 1345, 969, 47, 1023, 2206, 1580],\n"," 543: [3481, 2542, 4034, 2329, 1732, 3052, 4011, 223, 4979, 3897],\n"," 544: [3578, 318, 1210, 4226, 2858, 2762, 356, 2028, 4306, 593],\n"," 545: [3578, 4306, 6539, 4963, 5445, 1527, 1784, 5418, 6333, 2628],\n"," 546: [26, 637, 1042, 711, 724, 708, 786, 12, 719, 1752],\n"," 547: [318, 165, 296, 10, 292, 47, 253, 50, 474, 288],\n"," 548: [371, 544, 68, 514, 270, 71, 315, 248, 342, 105],\n"," 549: [480, 589, 110, 356, 377, 364, 47, 50, 1, 500],\n"," 550: [111, 1206, 1213, 593, 1228, 235, 1199, 1080, 1172, 1175],\n"," 551: [2324, 608, 1961, 1617, 318, 2396, 1242, 1094, 2762, 2194],\n"," 552: [4027, 6711, 4993, 5952, 3793, 1732, 2692, 7361, 32, 3996],\n"," 553: [150, 590, 588, 595, 1, 527, 34, 204, 225, 21],\n"," 554: [541, 858, 1252, 923, 912, 750, 1208, 908, 1247, 1221],\n"," 555: [3481, 4034, 4973, 2692, 7361, 1214, 6502, 5902, 541, 5445],\n"," 556: [590, 380, 1, 592, 253, 555, 1020, 1036, 647, 852],\n"," 557: [733, 32, 648, 832, 36, 95, 25, 17, 104, 805],\n"," 558: [2542, 2997, 4973, 2329, 1258, 1732, 2692, 1206, 7361, 3052],\n"," 559: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1777, 2485, 2716],\n"," 560: [3481, 4027, 6711, 4993, 4973, 5952, 3793, 7361, 2502, 3996],\n"," 561: [318, 1198, 50, 858, 527, 1704, 1136, 1222, 1089, 1],\n"," 562: [376, 1, 524, 596, 281, 257, 708, 7, 289, 262],\n"," 563: [62, 736, 494, 832, 95, 6, 802, 104, 805, 653],\n"," 564: [1358, 1704, 2028, 590, 2324, 593, 1242, 1094, 2762, 2289],\n"," 565: [2571, 5952, 2959, 4993, 1196, 7153, 318, 1210, 4226, 2028],\n"," 566: [19, 21, 2028, 1784, 1004, 274, 2195, 1092, 575, 1610],\n"," 567: [17, 282, 509, 22, 224, 50, 265, 376, 151, 230],\n"," 568: [260, 1073, 832, 802, 805, 1210, 1356, 1393, 52, 788],\n"," 569: [150, 480, 589, 318, 110, 161, 356, 595, 316, 377],\n"," 570: [1358, 1704, 2028, 590, 2324, 1961, 858, 1617, 593, 2396],\n"," 571: [2959, 3481, 4027, 6711, 4993, 4034, 5952, 1258, 1732, 1206],\n"," 572: [5147,\n"," 33880,\n"," 4973,\n"," 3089,\n"," 2585,\n"," 3507,\n"," 6339,\n"," 6235,\n"," 27773,\n"," 2019],\n"," 573: [1961, 858, 1242, 2194, 3104, 2243, 1954, 1962, 1674, 1097],\n"," 574: [150, 590, 318, 110, 161, 356, 595, 316, 10, 592],\n"," 575: [1, 733, 32, 648, 36, 25, 6, 17, 104, 805],\n"," 576: [19, 1004, 274, 2195, 1092, 575, 2403, 3578, 1527, 3623],\n"," 577: [410, 193, 324, 2858, 2997, 532, 1564, 1148, 212, 327],\n"," 578: [2028, 593, 1617, 457, 50, 1197, 318, 47, 1213, 1527],\n"," 579: [1240, 1200, 2028, 1374, 1356, 1617, 924, 858, 1610, 2916],\n"," 580: [4027, 318, 2858, 1019, 2761, 1193, 1207, 3053, 912, 188],\n"," 581: [150, 34, 593, 339, 11, 440, 377, 590, 500, 17],\n"," 582: [541, 923, 750, 1221, 1136, 1206, 1213, 924, 922, 1148],\n"," 583: [4226, 2959, 3481, 4027, 6711, 2858, 2542, 2997, 4993, 4034],\n"," 584: [593, 150, 590, 318, 165, 110, 588, 296, 161, 595],\n"," 585: [1208, 1221, 1212, 1250, 1233, 1952, 1204, 1263, 3435, 3683],\n"," 586: [410, 193, 324, 2359, 2336, 1252, 532, 3112, 2341, 212],\n"," 587: [2571, 5952, 2959, 4993, 7153, 3578, 4226, 2858, 2762, 2028],\n"," 588: [150, 11, 110, 17, 282, 350, 236, 161, 141, 349],\n"," 589: [1252, 923, 912, 903, 908, 111, 913, 1247, 1221, 1136],\n"," 590: [1214, 541, 32, 1374, 1356, 1617, 924, 858, 1199, 1197],\n"," 591: [720, 3624, 2891, 608, 736, 2108, 6016, 2976, 1537, 2609],\n"," 592: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 593: [377, 329, 1, 500, 474, 527, 32, 204, 442, 733],\n"," 594: [736, 141, 32, 494, 36, 95, 25, 6, 17, 802],\n"," 595: [2028, 2324, 1617, 318, 2396, 1094, 3104, 457, 1641, 2355],\n"," 596: [4027, 1732, 1639, 2997, 1500, 2890, 2692, 2108, 5004, 4086],\n"," 597: [720, 852, 3624, 2891, 3254, 4448, 3578, 4002, 3745, 4223],\n"," 598: [1617, 904, 912, 913, 1193, 928, 950, 903, 1198, 920],\n"," 599: [2028, 2858, 2324, 1207, 364, 593, 318, 2731, 3114, 2700],\n"," 600: [2571, 5952, 296, 4993, 1196, 7153, 318, 2762, 356, 2028],\n"," 601: [590, 350, 497, 161, 509, 224, 50, 105, 265, 376],\n"," 602: [1196, 1240, 589, 1200, 2571, 1214, 541, 32, 2028, 1374],\n"," 603: [260, 3083, 2080, 912, 899, 2137, 3448, 1094, 3189, 3174],\n"," 604: [1, 141, 494, 17, 802, 653, 1356, 3, 58, 7],\n"," 605: [593, 246, 111, 300, 232, 6, 16, 509, 31, 175],\n"," 606: [1240, 541, 32, 593, 1374, 1356, 1617, 924, 1291, 1036],\n"," 607: [2571, 2959, 296, 4993, 1196, 7153, 3578, 1210, 2858, 2762],\n"," 608: [2571, 5952, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 609: [1, 474, 204, 442, 733, 586, 786, 196, 553, 6],\n"," 610: [541, 1252, 912, 608, 750, 1208, 908, 111, 913, 1221],\n"," 612: [260, 608, 832, 25, 17, 802, 104, 805, 653, 1210],\n"," 613: [2572, 2683, 2762, 2605, 2706, 2581, 3081, 1210, 1777, 2485],\n"," 614: [1210, 589, 2571, 1214, 541, 32, 593, 1374, 1356, 1617],\n"," 616: [1196, 4306, 858, 2329, 1214, 1200, 3996, 1704, 1270, 6874],\n"," 617: [318, 282, 266, 509, 22, 277, 224, 367, 105, 376],\n"," 618: [2324, 1961, 858, 1242, 2194, 3104, 2289, 2243, 36, 1962],\n"," 619: [1196, 2571, 541, 32, 593, 1374, 1356, 1617, 924, 1291],\n"," 620: [3578, 2959, 2858, 527, 2707, 589, 2571, 4993, 3157, 1176],\n"," 621: [2542, 2329, 5952, 2692, 4878, 4011, 3897, 1748, 4848, 7153],\n"," 622: [1200, 1214, 1374, 1356, 1617, 1291, 858, 1036, 2916, 1199],\n"," 623: [508, 357, 527, 282, 236, 497, 586, 292, 22, 224],\n"," 624: [2762, 3081, 1210, 1777, 2716, 2125, 3578, 2571, 2394, 2396],\n"," 625: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 318],\n"," 627: [6539, 6377, 3793, 4886, 780, 4995, 5989, 6378, 2959, 3408],\n"," 628: [539, 597, 356, 587, 34, 508, 593, 357, 339, 11],\n"," 629: [539, 597, 356, 587, 508, 593, 357, 440, 457, 527],\n"," 630: [260, 608, 62, 141, 1073, 832, 36, 6, 17, 1210],\n"," 631: [904, 541, 858, 1252, 923, 912, 608, 1208, 903, 908],\n"," 632: [4022, 356, 1625, 1610, 1833, 2706, 3101, 1552, 479, 260],\n"," 633: [1247, 910, 924, 1212, 1148, 235, 1952, 969, 1199, 899],\n"," 634: [1234, 2797, 2791, 1380, 914, 919, 3039, 1663, 1297, 2918],\n"," 635: [5418, 1307, 318, 4995, 260, 3083, 608, 2080, 912, 899],\n"," 636: [4226, 2959, 2858, 2997, 1258, 1732, 7361, 2502, 32, 3996],\n"," 637: [2572, 2683, 2605, 2706, 2581, 3081, 1210, 1777, 2485, 2716],\n"," 638: [593, 457, 377, 364, 480, 474, 588, 497, 595, 161],\n"," 639: [2571, 1196, 3578, 1210, 4226, 2858, 2762, 2028, 589, 260],\n"," 640: [296, 2762, 593, 260, 1036, 50, 2329, 527, 1200, 3996],\n"," 641: [150, 165, 110, 588, 356, 292, 364, 434, 47, 253],\n"," 642: [1358, 2324, 1962, 1641, 527, 110, 1299, 1610, 1584, 1207],\n"," 643: [2948, 2947, 2951, 5060, 3508, 2186, 2949, 861, 1198, 2406],\n"," 644: [32, 1374, 1356, 1617, 924, 858, 1610, 2916, 1199, 296],\n"," 645: [593, 349, 329, 344, 50, 1, 288, 225, 339, 733],\n"," 646: [150, 349, 589, 318, 165, 110, 161, 316, 10, 377],\n"," 647: [708, 362, 1196, 1028, 1097, 260, 1029, 79, 1035, 1012],\n"," 648: [750, 1208, 908, 527, 1221, 1136, 260, 1206, 1219, 1304],\n"," 649: [2571, 5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226],\n"," 650: [2959, 4027, 6711, 4973, 5952, 1732, 2692, 1206, 7361, 32],\n"," 651: [904, 1208, 111, 913, 1304, 1230, 924, 593, 922, 1234],\n"," 652: [296, 1271, 2797, 1721, 318, 2858, 260, 1246, 3083, 608],\n"," 653: [431,\n"," 4308,\n"," 45186,\n"," 53322,\n"," 214,\n"," 49530,\n"," 306,\n"," 31696,\n"," 2858,\n"," 8644],\n"," 654: [589, 377, 364, 47, 50, 1, 500, 474, 527, 454],\n"," 655: [1079, 1234, 1278, 1220, 2797, 2406, 1968, 2791, 914, 1641],\n"," 656: [357, 457, 527, 590, 17, 282, 497, 454, 62, 586],\n"," 657: [3793, 2692, 1206, 2502, 293, 4886, 4011, 1748, 4848, 5618],\n"," 658: [3481, 4027, 6711, 2997, 4993, 4034, 4973, 5952, 1258, 3793],\n"," 659: [2959, 3578, 2028, 110, 50, 1291, 2329, 1270, 480, 6539],\n"," 660: [5952, 4993, 1196, 7153, 1210, 4226, 589, 260, 1036, 1198],\n"," 661: [539, 357, 339, 11, 17, 350, 236, 474, 497, 62],\n"," 662: [1252, 923, 1208, 903, 908, 527, 913, 1247, 1136, 1207],\n"," 663: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 664: [7445, 7254, 3897, 1831, 3701, 7022, 180, 5952, 4993, 2424],\n"," 665: [904, 858, 527, 913, 1247, 1221, 1136, 1207, 260, 1219],\n"," 666: [3578, 318, 1210, 2762, 356, 2028, 4306, 593, 589, 260],\n"," 667: [762, 662, 280, 1476, 828, 467, 8327, 836, 2019, 733],\n"," 668: [1193, 904, 541, 858, 1252, 912, 608, 1208, 903, 908],\n"," 669: [595, 10, 377, 364, 434, 47, 50, 1, 500, 474],\n"," 670: [1, 260, 608, 736, 32, 648, 494, 832, 95, 25],\n"," 671: [904, 913, 1207, 910, 1234, 1148, 1276, 1250, 969, 1179],\n"," 672: [5952, 4993, 7153, 318, 4226, 4306, 1036, 1214, 1200, 3996],\n"," 674: [1198, 32, 2028, 1617, 1291, 858, 1610, 2916, 1199, 296],\n"," 675: [2858, 3408, 2762, 1833, 50, 2706, 1249, 2683, 1127, 3101],\n"," 676: [539, 597, 356, 587, 34, 508, 593, 357, 440, 457],\n"," 677: [349, 165, 595, 329, 47, 153, 50, 1, 32, 288],\n"," 678: [904, 111, 1207, 1230, 910, 922, 1212, 1148, 1617, 1288],\n"," 679: [1307, 1197, 1079, 1278, 1220, 1028, 2797, 1097, 2406, 1968],\n"," 680: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 919, 1617, 903],\n"," 681: [4022, 356, 1625, 1610, 2762, 1833, 50, 2706, 1249, 593],\n"," 683: [4963, 3147, 2762, 2353, 4896, 4995, 597, 5989, 2959, 3408],\n"," 684: [527, 1097, 1193, 2762, 1080, 919, 2028, 2858, 3545, 3534],\n"," 685: [62, 494, 832, 95, 802, 104, 112, 1393, 58, 7],\n"," 686: [1356, 1610, 1199, 296, 1676, 318, 1148, 1127, 47, 110],\n"," 688: [2542, 4973, 7361, 2502, 3996, 293, 4878, 223, 8636, 3897],\n"," 689: [589, 165, 10, 377, 364, 329, 47, 253, 50, 1],\n"," 690: [608, 62, 1073, 32, 494, 832, 36, 25, 17, 805],\n"," 691: [2567, 1215, 1343, 1500, 1457, 1079, 373, 1515, 1270, 3114],\n"," 693: [539, 34, 508, 11, 440, 377, 590, 500, 282, 236],\n"," 694: [904, 541, 858, 1252, 923, 608, 750, 1208, 903, 908],\n"," 695: [2572, 2762, 2605, 2706, 3081, 1210, 1777, 2716, 3578, 2355],\n"," 696: [2605, 1210, 1777, 2485, 2716, 2125, 3578, 2571, 2394, 1586],\n"," 697: [3468, 1683, 4034, 5013, 3022, 3983, 4226, 2858, 919, 1617],\n"," 698: [3481, 4027, 2997, 4034, 4973, 2692, 2502, 5349, 3052, 293],\n"," 699: [2087, 2143, 1006, 1354, 3020, 2161, 2683, 2023, 1221, 858],\n"," 700: [110, 296, 595, 364, 434, 329, 253, 231, 50, 500],\n"," 701: [380, 593, 150, 480, 349, 589, 590, 318, 165, 110],\n"," 702: [2571, 1198, 2028, 1356, 1617, 1291, 858, 1610, 1036, 2916],\n"," 703: [1307, 1197, 1079, 1234, 1220, 1028, 2797, 1097, 2406, 1259],\n"," 704: [3147, 6539, 356, 6377, 4018, 5299, 597, 6378, 2763, 588],\n"," 705: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 1210],\n"," 706: [260, 608, 62, 1073, 832, 36, 95, 6, 17, 802],\n"," 707: [1219, 2683, 2268, 648, 858, 1252, 1230, 2764, 1221, 1213],\n"," 708: [1196, 1210, 1198, 593, 1374, 1356, 1617, 1291, 1610, 1036],\n"," 709: [1240, 1210, 1214, 541, 32, 593, 1356, 924, 1291, 1036],\n"," 710: [904, 541, 923, 912, 1208, 903, 111, 913, 1247, 1136],\n"," 711: [2858, 4022, 356, 1625, 3408, 1610, 2762, 1833, 50, 2706],\n"," 712: [539, 597, 587, 34, 508, 593, 339, 11, 440, 457],\n"," 714: [1196, 1210, 593, 589, 1036, 47, 1214, 1200, 1222, 1089],\n"," 715: [457, 593, 480, 349, 589, 590, 318, 110, 356, 377],\n"," 716: [858, 1036, 1676, 110, 1387, 1089, 1201, 2288, 1275, 223],\n"," 718: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 318],\n"," 719: [1020, 1036, 647, 852, 1569, 1035, 541, 1042, 1021, 1566],\n"," 720: [589, 165, 110, 595, 316, 10, 592, 434, 329, 153],\n"," 721: [597, 150, 356, 508, 593, 357, 339, 440, 457, 527],\n"," 723: [296, 1196, 1210, 2858, 2028, 593, 260, 1036, 1198, 50],\n"," 724: [780, 62, 736, 494, 832, 36, 95, 25, 6, 802],\n"," 725: [2863, 2804, 2948, 2947, 1136, 2160, 2951, 1197, 2918, 1997],\n"," 726: [260, 1240, 589, 1200, 2571, 1214, 541, 32, 2028, 1374],\n"," 727: [349, 589, 588, 161, 316, 10, 592, 292, 434, 329],\n"," 728: [260, 1200, 1198, 1214, 541, 32, 593, 1374, 1356, 1617],\n"," 729: [539, 597, 356, 587, 508, 593, 357, 440, 457, 527],\n"," 730: [457, 593, 480, 589, 110, 161, 356, 377, 292, 364],\n"," 731: [1234, 1278, 1220, 1028, 2791, 1380, 1641, 3039, 1297, 2396],\n"," 732: [1465, 1231, 457, 2943, 531, 1056, 593, 3082, 1277, 474],\n"," 733: [2959, 2858, 527, 3157, 1176, 4022, 750, 593, 4226, 2712],\n"," 734: [762, 662, 608, 1476, 828, 467, 8327, 836, 2797, 2028],\n"," 735: [150, 349, 589, 318, 588, 595, 10, 329, 344, 1],\n"," 736: [356, 296, 1271, 5418, 2797, 1307, 1721, 318, 2858, 4995],\n"," 737: [260, 608, 62, 141, 1073, 494, 832, 36, 95, 25],\n"," 738: [608, 62, 736, 36, 25, 6, 17, 802, 104, 805],\n"," 739: [2858, 4022, 3408, 2706, 1127, 3101, 260, 3751, 1210, 1961],\n"," 740: [1240, 589, 1200, 1214, 541, 32, 1374, 1356, 1617, 924],\n"," 741: [260, 1240, 1210, 1200, 1198, 1214, 541, 32, 2028, 1374],\n"," 742: [3753, 2763, 3510, 3623, 1917, 2396, 4367, 8368, 3255, 858],\n"," 743: [457, 593, 150, 349, 589, 590, 318, 165, 110, 161],\n"," 744: [590, 2324, 608, 1961, 1617, 2396, 1242, 1094, 2762, 3104],\n"," 745: [4973, 2329, 5952, 2692, 1206, 7361, 3052, 4878, 8636, 6502],\n"," 746: [2054, 370, 1019, 1380, 1193, 912, 188, 2599, 1203, 432],\n"," 748: [296, 36, 223, 32, 593, 608, 293, 111, 337, 353],\n"," 749: [260, 733, 608, 62, 736, 141, 32, 494, 832, 36],\n"," 750: [55820, 1213, 1222, 7361, 1234, 858, 5147, 923, 4873, 44195],\n"," 751: 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1982, 724, 1562, 2953, 1210, 575, 1573],\n"," 768: [5464, 610, 2987, 1089, 5679, 7445, 2140, 1968, 1172, 7254],\n"," 769: [34, 377, 364, 595, 161, 380, 589, 105, 736, 733],\n"," 770: [608, 62, 32, 36, 25, 6, 104, 1210, 112, 1393],\n"," 771: [1197, 1079, 1234, 1278, 1220, 1259, 1380, 2100, 1285, 1136],\n"," 772: [4226, 2959, 4027, 2858, 2329, 5952, 1258, 2692, 7361, 2502],\n"," 773: [786, 362, 1028, 1029, 79, 1035, 1012, 1022, 661, 1198],\n"," 774: [2571, 5952, 2959, 296, 4993, 1196, 3578, 1210, 4226, 2858],\n"," 775: [508, 593, 357, 527, 110, 282, 62, 21, 509, 292],\n"," 776: [2324, 608, 1961, 1617, 318, 2396, 1242, 2762, 2194, 3104],\n"," 777: [1210, 97, 2571, 1304, 3753, 5378, 1287, 3947, 4643, 920],\n"," 780: [1704, 2289, 2243, 2858, 1299, 2355, 1247, 1207, 1960, 1272],\n"," 781: [260, 1196, 1240, 1210, 589, 1200, 2571, 1198, 1214, 541],\n"," 782: [608, 25, 50, 296, 1120, 110, 1968, 1912, 1041, 3125],\n"," 783: [2959, 296, 3578, 1210, 4226, 2858, 2762, 356, 2028, 589],\n"," 784: 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253, 50, 1, 32, 34, 204, 442, 21],\n"," 800: [733, 608, 494, 832, 36, 95, 25, 17, 805, 653],\n"," 801: [318, 2858, 2762, 356, 1036, 50, 858, 1240, 1222, 1213],\n"," 802: [150, 34, 357, 339, 11, 110, 364, 17, 282, 236],\n"," 803: [1358, 1617, 3104, 527, 2355, 2336, 1095, 3114, 2916, 2571],\n"," 804: [457, 380, 150, 480, 349, 589, 590, 318, 165, 588],\n"," 805: [2959, 296, 318, 4226, 2858, 2762, 356, 2028, 589, 1036],\n"," 806: [539, 597, 587, 508, 17, 350, 474, 497, 62, 141],\n"," 807: [457, 593, 480, 589, 110, 356, 595, 316, 377, 364],\n"," 808: [2571, 4993, 4963, 3147, 3578, 5349, 1580, 6539, 356, 2762],\n"," 809: [1833, 2706, 593, 2683, 3101, 1552, 479, 608, 1210, 3675],\n"," 810: [597, 150, 587, 34, 508, 593, 357, 339, 11, 440],\n"," 811: [260, 608, 32, 832, 36, 6, 802, 104, 805, 1210],\n"," 812: [34, 357, 590, 110, 39, 161, 266, 21, 296, 509],\n"," 813: [1358, 1704, 590, 608, 1961, 1617, 318, 593, 1242, 1094],\n"," 814: [1197, 1079, 1270, 1028, 2797, 1097, 1968, 1259, 1380, 914],\n"," 815: [2959, 3578, 318, 4226, 2858, 1036, 47, 50, 1291, 2329],\n"," 816: [380, 480, 589, 165, 110, 356, 377, 364, 434, 329],\n"," 817: [2028, 590, 608, 858, 1617, 593, 2396, 1242, 2762, 3104],\n"," 818: [371, 544, 68, 514, 270, 71, 315, 1302, 248, 1196],\n"," 819: [720, 2858, 1265, 852, 3624, 2891, 4448, 3578, 4002, 608],\n"," 820: [3481, 2542, 4034, 2329, 4011, 223, 778, 3897, 6502, 4262],\n"," 821: [2571, 4993, 4963, 3578, 5349, 1580, 6539, 2273, 6377, 4701],\n"," 822: [36, 32, 593, 608, 47, 555, 337, 353, 290, 6],\n"," 823: [786, 708, 362, 79, 531, 852, 919, 1270, 1015, 837],\n"," 824: [923, 527, 111, 913, 1267, 1230, 1213, 50, 296, 1148],\n"," 825: [1617, 904, 912, 1219, 913, 928, 950, 903, 1207, 1258],\n"," 826: [2329, 2692, 2502, 5349, 293, 4886, 8636, 2762, 2395, 5669],\n"," 827: [593, 589, 318, 110, 296, 434, 329, 47, 253, 50],\n"," 828: [593, 150, 590, 318, 588, 161, 356, 595, 329, 344],\n"," 829: [608, 25, 104, 112, 1356, 1393, 58, 52, 788, 14],\n"," 830: [1307, 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2943, 531, 1056, 509, 2804, 613, 3469],\n"," 846: [36, 608, 111, 337, 232, 290, 778, 52, 288, 468],\n"," 847: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 588],\n"," 848: [50, 318, 36, 223, 527, 32, 593, 608, 293, 47],\n"," 849: [371, 318, 544, 68, 514, 270, 71, 315, 1302, 248],\n"," 851: [5952, 2959, 4993, 7153, 3578, 318, 4226, 2858, 2028, 593],\n"," 852: [150, 165, 110, 316, 10, 292, 329, 153, 50, 1],\n"," 853: [260, 832, 25, 17, 802, 805, 1210, 1356, 1393, 858],\n"," 854: [1358, 1704, 590, 1617, 318, 2396, 1242, 1094, 3104, 2289],\n"," 855: [1358, 1704, 2028, 590, 1961, 858, 2396, 1242, 1094, 2762],\n"," 856: [260, 1196, 1240, 1210, 1200, 1198, 1214, 541, 32, 593],\n"," 857: [2959, 296, 318, 1210, 4226, 2858, 2762, 4306, 260, 1198],\n"," 859: [318, 2858, 4306, 47, 50, 2329, 527, 1214, 6874, 32],\n"," 860: [410, 193, 324, 534, 2858, 2336, 1183, 1252, 2997, 532],\n"," 861: [5952, 2959, 4993, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 862: [508, 593, 11, 590, 110, 17, 497, 62, 161, 141],\n"," 863: [260, 1196, 1240, 1200, 1198, 1214, 541, 1374, 924, 1291],\n"," 864: [4014,\n"," 8645,\n"," 3148,\n"," 4296,\n"," 6870,\n"," 2023,\n"," 40819,\n"," 3252,\n"," 5364,\n"," 30707],\n"," 865: [5952, 4993, 2762, 2028, 589, 1036, 1198, 1291, 858, 2329],\n"," 866: [1240, 589, 2571, 541, 2028, 1356, 1617, 924, 1291, 1610],\n"," 867: [6711, 2542, 1258, 1206, 7361, 2502, 32, 1089, 3052, 293],\n"," 868: [260, 1196, 1240, 1200, 2571, 1198, 1214, 541, 2028, 1374],\n"," 869: [1639, 2997, 3996, 1500, 2890, 1747, 5004, 4086, 1213, 912],\n"," 870: [50, 36, 32, 608, 47, 555, 353, 235, 307, 290],\n"," 871: [910, 1212, 1148, 2019, 1617, 1254, 1250, 2858, 1233, 1952],\n"," 872: [904, 541, 858, 923, 912, 608, 750, 903, 908, 527],\n"," 873: [912, 527, 3244, 260, 2918, 1240, 3347, 1197, 8405, 3189],\n"," 874: [2273, 4701, 4025, 2353, 4018, 5299, 6378, 3977, 3052, 3510],\n"," 875: [539, 597, 34, 11, 440, 457, 377, 110, 364, 500],\n"," 876: [150, 587, 34, 508, 357, 11, 110, 364, 17, 350],\n"," 878: [1304, 910, 1212, 1234, 1148, 1276, 1617, 1254, 2858, 1233],\n"," 879: [858, 1699, 1429, 7153, 923, 2541, 1587, 627, 837, 1208],\n"," 881: [349, 434, 1, 204, 21, 733, 300, 786, 196, 1036],\n"," 882: [733, 608, 1073, 494, 832, 36, 6, 17, 802, 104],\n"," 883: [539, 597, 150, 356, 587, 34, 508, 593, 357, 339],\n"," 884: [260, 780, 733, 608, 494, 832, 36, 95, 25, 6],\n"," 885: [1234, 1278, 1394, 1663, 1297, 2144, 2371, 440, 2150, 902],\n"," 886: [2324, 1954, 110, 2355, 1247, 1207, 1272, 3114, 2890, 1183],\n"," 887: [1358, 2324, 608, 1961, 858, 2396, 1242, 1094, 2762, 2194],\n"," 888: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 889: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 890: [2901, 1732, 2976, 593, 1617, 2908, 1639, 2396, 2320, 1620],\n"," 891: [593, 858, 2329, 6874, 6539, 1136, 1222, 1089, 1213, 6377],\n"," 892: [337, 112, 232, 235, 307, 290, 306, 509, 31, 288],\n"," 893: [2959, 296, 4993, 7153, 1210, 4226, 2858, 2762, 356, 4306],\n"," 894: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 895: [457, 593, 480, 349, 318, 165, 110, 292, 47, 50],\n"," 896: [318, 246, 111, 555, 337, 300, 232, 6, 16, 509],\n"," 897: [457, 150, 480, 349, 589, 318, 588, 161, 356, 377],\n"," 898: [1240, 1200, 1198, 2028, 593, 1374, 1617, 924, 1291, 858],\n"," 899: [539, 597, 356, 587, 508, 593, 357, 11, 440, 457],\n"," 900: [3, 58, 628, 140, 135, 1405, 76, 748, 743, 671],\n"," 901: [5952, 2959, 296, 4993, 1196, 7153, 3578, 1210, 4226, 2858],\n"," 902: [110, 527, 32, 541, 6377, 1221, 1961, 4973, 4878, 2502],\n"," 903: [608, 1073, 832, 36, 25, 6, 17, 104, 805, 653],\n"," 904: [2028, 1961, 858, 593, 2396, 1242, 2762, 2194, 3104, 2289],\n"," 905: [762, 662, 280, 828, 467, 8327, 836, 2019, 1291, 441],\n"," 906: [1258, 3793, 5349, 3052, 4886, 47, 8636, 1214, 4979, 6502],\n"," 907: [588, 161, 595, 316, 292, 434, 329, 1, 34, 185],\n"," 908: [32, 593, 1617, 1199, 296, 608, 50, 1197, 318, 1148],\n"," 909: [4226, 3481, 4027, 6711, 2542, 4034, 4973, 1732, 2692, 1206],\n"," 910: [2571, 1196, 3578, 1210, 2762, 356, 2028, 589, 260, 1036],\n"," 911: [786, 708, 1097, 1029, 79, 1012, 1022, 661, 531, 852],\n"," 912: [2028, 965, 2186, 919, 2858, 2935, 931, 1066, 2324, 2208],\n"," 913: [1465, 2943, 531, 1056, 3082, 1277, 590, 613, 4709, 2397],\n"," 914: [1270, 1246, 280, 608, 828, 8327, 2019, 1291, 2797, 2028],\n"," 915: [1220, 2406, 1380, 914, 919, 1641, 1285, 1136, 1101, 1663],\n"," 916: [2571, 5952, 296, 1196, 7153, 3578, 318, 1210, 4226, 2858],\n"," 917: [260, 32, 494, 6, 802, 104, 805, 1210, 112, 1356],\n"," 918: [608, 141, 32, 36, 25, 6, 17, 805, 112, 58],\n"," 919: [26, 637, 1042, 711, 724, 708, 49278, 12, 719, 609],\n"," 920: [527, 3707, 1233, 2918, 1358, 3347, 2396, 1197, 8405, 3189],\n"," 921: [1230, 527, 3244, 3707, 260, 1233, 2918, 1240, 1358, 3347],\n"," 922: [4025, 3793, 2353, 5989, 6378, 3408, 1704, 2028, 3052, 2763],\n"," 923: [260, 62, 736, 32, 648, 832, 36, 25, 17, 802],\n"," 924: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 593],\n"," 925: [2863, 2948, 2918, 1256, 346, 1580, 2363, 2186, 2949, 2273],\n"," 926: [858, 2396, 1252, 1699, 1704, 593, 1429, 3671, 1148, 2541],\n"," 927: [2571, 5952, 2959, 296, 4993, 1196, 7153, 3578, 318, 4226],\n"," 928: [7153, 2028, 1036, 110, 1291, 858, 527, 1214, 1200, 1704],\n"," 930: [34, 357, 11, 440, 110, 364, 17, 497, 454, 62],\n"," 931: [1, 260, 608, 62, 141, 648, 832, 36, 95, 25],\n"," 932: [150, 356, 508, 339, 440, 527, 590, 110, 318, 500],\n"," 933: [1374, 924, 858, 1199, 608, 1197, 1376, 1148, 110, 1213],\n"," 934: [2959, 7254, 1006, 1354, 1967, 3020, 2683, 318, 2023, 1221],\n"," 935: [19, 21, 1784, 274, 2195, 1092, 575, 1527, 590, 2329],\n"," 936: [318, 1270, 1, 1213, 1527, 2716, 1580, 2918, 2628, 3114],\n"," 937: [2571, 296, 4993, 1196, 3578, 1210, 4226, 2858, 356, 2028],\n"," 938: [480, 589, 165, 110, 588, 356, 595, 10, 377, 592],\n"," 939: [62, 494, 25, 17, 802, 104, 805, 653, 1210, 112],\n"," 940: [431,\n"," 11,\n"," 45186,\n"," 53322,\n"," 1213,\n"," 49530,\n"," 31696,\n"," 8644,\n"," 3298,\n"," 1573],\n"," 941: [370, 256, 19, 410, 420, 333, 248, 355, 455, 466],\n"," 942: [150, 480, 589, 590, 110, 161, 356, 377, 364, 47],\n"," 943: [466, 104, 543, 88, 520, 1552, 2717, 2735, 743, 1982],\n"," 945: [780, 608, 648, 832, 36, 95, 6, 104, 805, 1210],\n"," 946: [1200, 541, 2028, 1036, 1199, 296, 318, 1148, 1270, 47],\n"," 947: [5952, 2959, 296, 4993, 7153, 3578, 318, 4226, 2858, 2762],\n"," 948: [1358, 590, 1961, 1242, 2194, 3104, 2289, 2243, 1954, 1962],\n"," 949: [1732, 2890, 1747, 4086, 4242, 246, 5303, 1468, 608, 4034],\n"," 950: [4226, 2959, 3481, 4027, 2542, 4993, 4034, 4973, 5952, 3793],\n"," 951: [260, 25, 802, 805, 1210, 1356, 1393, 3, 58, 52],\n"," 952: [1, 608, 141, 32, 36, 25, 6, 104, 653, 112],\n"," 953: [1343, 2396, 1457, 373, 1515, 1967, 73, 919, 1197, 892],\n"," 954: [5952, 296, 4993, 7153, 318, 4226, 2858, 356, 4306, 593],\n"," 955: [1230, 912, 3244, 3707, 260, 1233, 1240, 1358, 3347, 8405],\n"," 956: [316, 1, 204, 733, 786, 196, 173, 6, 1036, 780],\n"," 957: [1704, 2324, 2396, 1094, 2762, 2194, 3104, 2289, 2243, 1962],\n"," 958: [4993, 4963, 3147, 3578, 1580, 6539, 356, 2273, 6377, 4701],\n"," 960: [50, 318, 36, 25, 223, 32, 293, 246, 111, 555],\n"," 961: [904, 541, 858, 1252, 923, 912, 608, 750, 1208, 903],\n"," 962: [356, 1625, 1610, 2762, 1833, 50, 1249, 593, 1127, 3101],\n"," 963: [296, 50, 318, 36, 25, 223, 527, 32, 593, 608],\n"," 964: [5952, 4993, 7153, 2858, 2762, 4306, 1198, 50, 1291, 1214],\n"," 965: [25, 527, 293, 555, 337, 353, 112, 235, 307, 6],\n"," 966: [2329, 1258, 7361, 3996, 3052, 4011, 778, 47, 4262, 2762],\n"," 967: [5952, 4993, 1196, 7153, 3578, 1210, 4226, 2028, 4306, 589],\n"," 968: [380, 589, 588, 161, 356, 595, 10, 377, 592, 364],\n"," 969: [260, 608, 648, 494, 832, 95, 6, 802, 104, 805],\n"," 970: [457, 480, 349, 589, 165, 588, 161, 356, 595, 316],\n"," 971: [3481, 6711, 2542, 7361, 2502, 32, 1089, 4878, 4011, 778],\n"," 972: [1961, 3104, 2289, 2243, 36, 1674, 260, 1299, 2355, 1960],\n"," 973: [3949, 55820, 1222, 7361, 1234, 858, 5147, 6874, 2858, 2959],\n"," 974: [371, 318, 544, 68, 514, 270, 71, 315, 248, 440],\n"," 975: [318, 36, 25, 223, 527, 593, 293, 47, 246, 555],\n"," 976: [457, 593, 480, 589, 110, 356, 316, 10, 377, 292],\n"," 977: [1219, 1304, 1212, 2019, 1254, 1233, 1204, 1394, 1284, 2396],\n"," 978: [527, 1097, 1193, 356, 2762, 1080, 2028, 2858, 3534, 32],\n"," 979: [610, 5679, 7445, 2140, 1172, 7254, 593, 3897, 1193, 1831],\n"," 980: [2571, 5952, 1196, 3578, 318, 2028, 4306, 589, 1036, 1198],\n"," 982: [733, 62, 494, 832, 36, 6, 17, 802, 104, 805],\n"," 983: [4993, 4963, 3578, 2273, 4025, 2353, 3753, 780, 5989, 6378],\n"," 984: [2571, 5952, 4993, 7153, 3578, 4226, 2858, 4306, 589, 260],\n"," 985: [4226, 2959, 3481, 6711, 2858, 2542, 2997, 4034, 2329, 1258],\n"," 986: [34, 508, 364, 17, 497, 62, 595, 39, 141, 21],\n"," 987: [539, 597, 356, 587, 34, 508, 593, 357, 11, 440],\n"," 988: [4027, 6711, 4034, 2329, 1732, 7361, 8636, 4979, 6502, 5902],\n"," 989: [923, 750, 1208, 903, 111, 913, 1247, 1221, 1207, 1206],\n"," 990: [349, 110, 161, 595, 316, 292, 329, 47, 153, 253],\n"," 991: [3949, 55820, 1213, 7361, 858, 5147, 6874, 2858, 2959, 923],\n"," 992: [786, 708, 1210, 362, 1028, 1029, 79, 1035, 1012, 1022],\n"," 993: [2571, 2959, 296, 7153, 318, 2858, 2762, 356, 593, 589],\n"," 994: [261, 265, 372, 537, 247, 531, 147, 427, 481, 45],\n"," 995: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 593],\n"," 996: [587, 440, 364, 17, 282, 480, 588, 497, 595, 141],\n"," 997: [260, 608, 832, 802, 104, 805, 1210, 1356, 1393, 58],\n"," 998: [1732, 318, 1500, 2692, 5004, 4086, 1682, 4242, 5303, 1],\n"," 999: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n"," 1001: [380, 593, 349, 590, 318, 165, 296, 292, 434, 47],\n"," 1002: [1, 733, 608, 62, 648, 494, 832, 36, 95, 25],\n"," 1003: [2581,\n"," 3081,\n"," 1210,\n"," 1777,\n"," 2485,\n"," 2716,\n"," 2125,\n"," 3578,\n"," 2394,\n"," 2396],\n"," 1004: [527, 1080, 919, 3545, 3534, 2971, 5504, 1968, 8917, 3101],\n"," 1005: [34, 508, 11, 590, 17, 282, 497, 266, 261, 509],\n"," 1006: [296, 50, 36, 25, 223, 593, 293, 47, 246, 111],\n"," 1007: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n"," 1008: [3481, 4027, 2542, 4034, 1258, 1206, 2502, 1089, 4011, 223],\n"," 1009: [4993,\n"," 4963,\n"," 3147,\n"," 3578,\n"," 5349,\n"," 6539,\n"," 2762,\n"," 2273,\n"," 4701,\n"," 3793],\n"," 1011: [762, 662, 280, 1476, 828, 467, 8327, 836, 2797, 1242],\n"," 1012: [356, 1625, 3408, 1610, 2762, 1833, 50, 2706, 1249, 2683],\n"," 1013: [2959, 3578, 4226, 2858, 2762, 1036, 47, 1291, 2329, 1214],\n"," 1014: [1193, 923, 912, 750, 1208, 903, 111, 913, 1221, 1136],\n"," 1015: [3468, 4034, 5013, 3022, 3983, 4226, 919, 1084, 3307, 694],\n"," 1016: [904, 541, 858, 1252, 912, 750, 1208, 903, 908, 913],\n"," 1017: [2571, 2959, 296, 3578, 318, 4226, 2858, 2762, 356, 2028],\n"," 1018: [4027, 2692, 2502, 8636, 4979, 5902, 4262, 50, 1748, 923],\n"," 1019: [62, 1073, 32, 494, 832, 36, 25, 6, 17, 802],\n"," 1020: [1079, 1270, 2797, 1097, 1259, 2791, 919, 1641, 3039, 1285],\n"," 1021: [1193, 904, 541, 1252, 912, 608, 750, 1208, 903, 908],\n"," 1022: [1193, 356, 2762, 1080, 2028, 2858, 3545, 3534, 2971, 32],\n"," 1023: [296, 318, 1210, 4226, 2762, 356, 2028, 4306, 589, 1036],\n"," 1025: [1193, 904, 541, 1252, 750, 1208, 903, 527, 913, 1206],\n"," 1026: [6711, 2858, 2542, 2997, 4034, 1732, 2692, 1206, 32, 3996],\n"," 1027: [2571, 5952, 4993, 1196, 7153, 318, 1210, 2858, 2762, 356],\n"," 1028: [589, 318, 110, 588, 595, 377, 364, 344, 47, 253],\n"," 1029: [36, 25, 223, 527, 32, 608, 293, 246, 111, 300],\n"," 1030: [4963,\n"," 3147,\n"," 3578,\n"," 5349,\n"," 6377,\n"," 4701,\n"," 4025,\n"," 4246,\n"," 4896,\n"," 4886],\n"," 1031: [457, 380, 150, 349, 589, 318, 588, 296, 161, 356],\n"," 1032: [1, 260, 733, 608, 62, 141, 32, 832, 36, 25],\n"," 1033: [2571, 5952, 2959, 4993, 7153, 3578, 4226, 2858, 1036, 858],\n"," 1034: [2571, 2959, 296, 4993, 1196, 318, 1210, 4226, 2858, 2762],\n"," 1035: [2140, 2087, 2143, 1006, 1354, 1967, 3020, 2161, 4878, 912],\n"," 1037: [508, 11, 440, 457, 527, 590, 110, 364, 318, 17],\n"," 1038: [786, 362, 79, 1198, 852, 1291, 1367, 837, 653, 745],\n"," 1039: [1196, 1240, 589, 1200, 2571, 1198, 1214, 32, 1374, 1356],\n"," 1040: [466, 104, 543, 88, 520, 1552, 2717, 2735, 743, 1982],\n"," 1041: [356, 4306, 1270, 480, 32, 1136, 1240, 1222, 541, 1213],\n"," 1043: [260, 733, 608, 1073, 832, 802, 104, 805, 653, 1210],\n"," 1044: [608, 62, 141, 494, 832, 36, 6, 17, 104, 805],\n"," 1045: [5952,\n"," 1214,\n"," 1200,\n"," 3996,\n"," 480,\n"," 1240,\n"," 1080,\n"," 33794,\n"," 2716,\n"," 4973],\n"," 1046: [589, 318, 110, 588, 161, 10, 377, 292, 364, 329],\n"," 1047: [1222,\n"," 1234,\n"," 5147,\n"," 4873,\n"," 1080,\n"," 2585,\n"," 55805,\n"," 3507,\n"," 6339,\n"," 5792],\n"," 1048: [3578, 356, 260, 110, 858, 527, 1214, 3996, 480, 1136],\n"," 1050: [36, 608, 246, 111, 112, 232, 235, 307, 290, 306],\n"," 1051: [608, 32, 494, 832, 36, 95, 25, 6, 805, 653],\n"," 1052: [21, 2028, 1004, 274, 2195, 1092, 575, 1610, 2403, 3623],\n"," 1053: [2028, 593, 1374, 1356, 1617, 858, 1199, 296, 608, 457]})"]},"metadata":{},"execution_count":10}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 리스트를 작성한다\n","# 각 사용자의 소속 확률이 가장 높은 토픽을 얻고, 해당 토픽 안에서 확률이 높은 아이템을 저장해 나간다\n","\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","pred_user2items = defaultdict(list)\n","for i, (user_id, data) in enumerate(movielens_train_high_rating.groupby(\"user_id\")):\n"," evaluated_movie_ids = user_evaluated_movies[user_id]\n"," # 사용자의 소속 확률이 가장 높은 토픽을 얻는다\n"," user_topic = sorted(lda_topics[i], key=lambda x: -x[1])[0][0]\n"," # 해당 토픽 안에서 확률이 높은 아이템을 얻는다\n"," topic_movies = lda_model.get_topic_terms(user_topic, topn=len(movies))\n","\n"," for token_id, score in topic_movies:\n"," movie_id = int(common_dictionary.id2token[token_id])\n"," if movie_id not in evaluated_movie_ids:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":11,"id":"1701bd43","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":288},"id":"1701bd43","executionInfo":{"status":"ok","timestamp":1672118441268,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"ee59bd31-4381-4245-f7ae-752409de578c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"16eddf46","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"16eddf46","executionInfo":{"status":"ok","timestamp":1672118441268,"user_tz":-540,"elapsed":18,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"da0309a9-ae28-406a-bcf1-f5d45c559abc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","1171 1196 Star Wars: Episode V - The Empire Strikes Back... \n","1173 1198 Raiders of the Lost Ark (Indiana Jones and the... \n","1212 1240 Terminator, The (1984) \n","\n"," genre \\\n","1171 [Action, Adventure, Sci-Fi] \n","1173 [Action, Adventure] \n","1212 [Action, Sci-Fi, Thriller] \n","\n"," tag \n","1171 [lucas, george lucas, george lucas, gfei own i... \n","1173 [egypt, lucas, seen more than once, dvd collec... \n","1212 [arnold schwarzenegger, sci-fi, time travel, d... "],"text/html":["\n","
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
12121240Terminator, The (1984)[Action, Sci-Fi, Thriller][arnold schwarzenegger, sci-fi, time travel, d...
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(1198, 1196, 1240)\n","movies[movies.movie_id.isin([1198, 1196, 1240])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d45967ea", + "metadata": { + "id": "d45967ea" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_collaboration.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "942a9897", + "metadata": { + "id": "942a9897" + }, + "source": [ + "# LDA를 행동 데이터에 적용" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b8cb8f0b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4818, + "status": "ok", + "timestamp": 1672118267114, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "b8cb8f0b", + "outputId": "ca848f2f-0377-4da9-e770-5b4f7e92d5b6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:15:58-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 63.6MB/s in 1.0s \n", + "\n", + "2022-12-27 05:15:59 (63.6 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6769fb75", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 65965, + "status": "ok", + "timestamp": 1672118333071, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "6769fb75", + "outputId": "5ab1c72d-1ab3-46bb-ce4e-9463a2c6e6a2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f650f9d6", + "metadata": { + "executionInfo": { + "elapsed": 31, + "status": "ok", + "timestamp": 1672118333072, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "f650f9d6" + }, + "outputs": [], + "source": [ + "# 인자 수\n", + "factors = 50\n", + "# 에폭 수\n", + "n_epochs = 30" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f703bf73", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 7337, + "status": "ok", + "timestamp": 1672118340381, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "f703bf73", + "outputId": "47c9ad1a-7786-4285-ecbb-b86ca5cf30de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting gensim==4.0.1\n", + " Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n", + "\u001b[K |████████████████████████████████| 23.9 MB 1.6 MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n", + "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n", + "Installing collected packages: gensim\n", + " Attempting uninstall: gensim\n", + " Found existing installation: gensim 3.6.0\n", + " Uninstalling gensim-3.6.0:\n", + " Successfully uninstalled gensim-3.6.0\n", + "Successfully installed gensim-4.0.1\n" + ] + } + ], + "source": [ + "!pip install gensim==4.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d5348432", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1656, + "status": "ok", + "timestamp": 1672118342033, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "d5348432", + "outputId": "3c8d0dc9-8126-4750-d870-08019b81e513" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "from gensim.corpora.dictionary import Dictionary\n", + "\n", + "# LDA 입력으로 사용할 데이터를 작성한다\n", + "lda_data = []\n", + "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", + "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", + " lda_data.append(data[\"movie_id\"].apply(str).tolist())\n", + "\n", + "common_dictionary = Dictionary(lda_data)\n", + "common_corpus = [common_dictionary.doc2bow(text) for text in lda_data]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0f0e231", + "metadata": { + "executionInfo": { + "elapsed": 93308, + "status": "ok", + "timestamp": 1672118435326, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "d0f0e231" + }, + "outputs": [], + "source": [ + "import gensim\n", + "lda_model = gensim.models.LdaModel(\n", + " common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d568841a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 41, + "status": "ok", + "timestamp": 1672118435327, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "d568841a", + "outputId": "cfc9c430-1bea-4f52-d193-73e15a1ac543" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(2, 0.13143347), (42, 0.81523174)]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 각 사용자의 소속 토픽이 저장된다\n", + "lda_topics = lda_model[common_corpus]\n", + "\n", + "# 예: 어떤 사용자의 소속 토픽\n", + "lda_topics[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "aa768039", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 33, + "status": "ok", + "timestamp": 1672118435327, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "aa768039", + "outputId": "00099179-91eb-4fd9-80b6-33b281b38e6c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "movie_id=2858, title=American Beauty (1999), score=0.014902905561029911\n", + "movie_id=4022, title=Cast Away (2000), score=0.013141673058271408\n", + "movie_id=356, title=Forrest Gump (1994), score=0.009344562888145447\n", + "movie_id=1625, title=Game, The (1997), score=0.009039949625730515\n", + "movie_id=3408, title=Erin Brockovich (2000), score=0.008023954927921295\n", + "movie_id=1610, title=Hunt for Red October, The (1990), score=0.007280151825398207\n", + "movie_id=2762, title=Sixth Sense, The (1999), score=0.007244058884680271\n", + "movie_id=1833, title=Mercury Rising (1998), score=0.007027590647339821\n", + "movie_id=50, title=Usual Suspects, The (1995), score=0.00636146729812026\n", + "movie_id=2706, title=American Pie (1999), score=0.006130032241344452\n" + ] + } + ], + "source": [ + "# topic0인 영화 목록\n", + "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", + " movie_id = int(common_dictionary.id2token[token_id])\n", + " title = movies[movies.movie_id == movie_id].title.tolist()[0]\n", + " print(f'movie_id={movie_id}, title={title}, score={score}')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9eb2910f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 29, + "status": "ok", + "timestamp": 1672118435328, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "9eb2910f", + "outputId": "5ecbcb33-daaa-422c-a155-abdcc16844d3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0.010000083),\n", + " (1, 0.010000083),\n", + " (2, 0.010000083),\n", + " (3, 0.010000083),\n", + " (4, 0.010000083),\n", + " (5, 0.010000083),\n", + " (6, 0.010000083),\n", + " (7, 0.010000083),\n", + " (8, 0.010000083),\n", + " (9, 0.010000083),\n", + " (10, 0.010000083),\n", + " (11, 0.010000083),\n", + " (12, 0.010000083),\n", + " (13, 0.010000083),\n", + " (14, 0.010000083),\n", + " (15, 0.010000083),\n", + " (16, 0.010000083),\n", + " (17, 0.010000083),\n", + " (18, 0.010000083),\n", + " (19, 0.010000083),\n", + " (20, 0.010000083),\n", + " (21, 0.010000083),\n", + " (22, 0.010000083),\n", + " (23, 0.010000083),\n", + " (24, 0.010000083),\n", + " (25, 0.010000083),\n", + " (26, 0.010000083),\n", + " (27, 0.010000083),\n", + " (28, 0.010000083),\n", + " (29, 0.010000083),\n", + " (30, 0.010000083),\n", + " (31, 0.010000083),\n", + " (32, 0.010000083),\n", + " (33, 0.010000083),\n", + " (34, 0.010000083),\n", + " (35, 0.010000083),\n", + " (36, 0.010000083),\n", + " (37, 0.010000083),\n", + " (38, 0.010000083),\n", + " (39, 0.010000083),\n", + " (40, 0.010000083),\n", + " (41, 0.50999594),\n", + " (42, 0.010000083),\n", + " (43, 0.010000083),\n", + " (44, 0.010000083),\n", + " (45, 0.010000083),\n", + " (46, 0.010000083),\n", + " (47, 0.010000083),\n", + " (48, 0.010000083),\n", + " (49, 0.010000083)]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 스타워즈/에피소드 5(movie_id=1196)의 토픽(각 토픽에 소속될 확률)\n", + "lda_model[common_dictionary.doc2bow([\"1196\"])]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8de0ac42", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 5964, + "status": "ok", + "timestamp": 1672118441267, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "8de0ac42", + "outputId": "325da666-9b8b-43b7-ce14-dc81474631fd" + }, + "outputs": [ + { + "data": { + "text/plain": [ 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427, 481, 45],\n", + " 995: [1358, 1704, 2028, 590, 2324, 608, 1961, 858, 1617, 593],\n", + " 996: [587, 440, 364, 17, 282, 480, 588, 497, 595, 141],\n", + " 997: [260, 608, 832, 802, 104, 805, 1210, 1356, 1393, 58],\n", + " 998: [1732, 318, 1500, 2692, 5004, 4086, 1682, 4242, 5303, 1],\n", + " 999: [1702, 158, 1713, 239, 256, 2142, 2083, 2089, 1196, 1373],\n", + " 1001: [380, 593, 349, 590, 318, 165, 296, 292, 434, 47],\n", + " 1002: [1, 733, 608, 62, 648, 494, 832, 36, 95, 25],\n", + " 1003: [2581,\n", + " 3081,\n", + " 1210,\n", + " 1777,\n", + " 2485,\n", + " 2716,\n", + " 2125,\n", + " 3578,\n", + " 2394,\n", + " 2396],\n", + " 1004: [527, 1080, 919, 3545, 3534, 2971, 5504, 1968, 8917, 3101],\n", + " 1005: [34, 508, 11, 590, 17, 282, 497, 266, 261, 509],\n", + " 1006: [296, 50, 36, 25, 223, 593, 293, 47, 246, 111],\n", + " 1007: [356, 2355, 1265, 2567, 1136, 1215, 1196, 1220, 1060, 1343],\n", + " 1008: [3481, 4027, 2542, 4034, 1258, 1206, 2502, 1089, 4011, 223],\n", + " 1009: [4993,\n", + " 4963,\n", + " 3147,\n", + " 3578,\n", + " 5349,\n", + " 6539,\n", + " 2762,\n", + " 2273,\n", + " 4701,\n", + " 3793],\n", + " 1011: [762, 662, 280, 1476, 828, 467, 8327, 836, 2797, 1242],\n", + " 1012: [356, 1625, 3408, 1610, 2762, 1833, 50, 2706, 1249, 2683],\n", + " 1013: [2959, 3578, 4226, 2858, 2762, 1036, 47, 1291, 2329, 1214],\n", + " 1014: [1193, 923, 912, 750, 1208, 903, 111, 913, 1221, 1136],\n", + " 1015: [3468, 4034, 5013, 3022, 3983, 4226, 919, 1084, 3307, 694],\n", + " 1016: [904, 541, 858, 1252, 912, 750, 1208, 903, 908, 913],\n", + " 1017: [2571, 2959, 296, 3578, 318, 4226, 2858, 2762, 356, 2028],\n", + " 1018: [4027, 2692, 2502, 8636, 4979, 5902, 4262, 50, 1748, 923],\n", + " 1019: [62, 1073, 32, 494, 832, 36, 25, 6, 17, 802],\n", + " 1020: [1079, 1270, 2797, 1097, 1259, 2791, 919, 1641, 3039, 1285],\n", + " 1021: [1193, 904, 541, 1252, 912, 608, 750, 1208, 903, 908],\n", + " 1022: [1193, 356, 2762, 1080, 2028, 2858, 3545, 3534, 2971, 32],\n", + " 1023: [296, 318, 1210, 4226, 2762, 356, 2028, 4306, 589, 1036],\n", + " 1025: [1193, 904, 541, 1252, 750, 1208, 903, 527, 913, 1206],\n", + " 1026: [6711, 2858, 2542, 2997, 4034, 1732, 2692, 1206, 32, 3996],\n", + " 1027: [2571, 5952, 4993, 1196, 7153, 318, 1210, 2858, 2762, 356],\n", + " 1028: [589, 318, 110, 588, 595, 377, 364, 344, 47, 253],\n", + " 1029: [36, 25, 223, 527, 32, 608, 293, 246, 111, 300],\n", + " 1030: [4963,\n", + " 3147,\n", + " 3578,\n", + " 5349,\n", + " 6377,\n", + " 4701,\n", + " 4025,\n", + " 4246,\n", + " 4896,\n", + " 4886],\n", + " 1031: [457, 380, 150, 349, 589, 318, 588, 296, 161, 356],\n", + " 1032: [1, 260, 733, 608, 62, 141, 32, 832, 36, 25],\n", + " 1033: [2571, 5952, 2959, 4993, 7153, 3578, 4226, 2858, 1036, 858],\n", + " 1034: [2571, 2959, 296, 4993, 1196, 318, 1210, 4226, 2858, 2762],\n", + " 1035: [2140, 2087, 2143, 1006, 1354, 1967, 3020, 2161, 4878, 912],\n", + " 1037: [508, 11, 440, 457, 527, 590, 110, 364, 318, 17],\n", + " 1038: [786, 362, 79, 1198, 852, 1291, 1367, 837, 653, 745],\n", + " 1039: [1196, 1240, 589, 1200, 2571, 1198, 1214, 32, 1374, 1356],\n", + " 1040: [466, 104, 543, 88, 520, 1552, 2717, 2735, 743, 1982],\n", + " 1041: [356, 4306, 1270, 480, 32, 1136, 1240, 1222, 541, 1213],\n", + " 1043: [260, 733, 608, 1073, 832, 802, 104, 805, 653, 1210],\n", + " 1044: [608, 62, 141, 494, 832, 36, 6, 17, 104, 805],\n", + " 1045: [5952,\n", + " 1214,\n", + " 1200,\n", + " 3996,\n", + " 480,\n", + " 1240,\n", + " 1080,\n", + " 33794,\n", + " 2716,\n", + " 4973],\n", + " 1046: [589, 318, 110, 588, 161, 10, 377, 292, 364, 329],\n", + " 1047: [1222,\n", + " 1234,\n", + " 5147,\n", + " 4873,\n", + " 1080,\n", + " 2585,\n", + " 55805,\n", + " 3507,\n", + " 6339,\n", + " 5792],\n", + " 1048: [3578, 356, 260, 110, 858, 527, 1214, 3996, 480, 1136],\n", + " 1050: [36, 608, 246, 111, 112, 232, 235, 307, 290, 306],\n", + " 1051: [608, 32, 494, 832, 36, 95, 25, 6, 805, 653],\n", + " 1052: [21, 2028, 1004, 274, 2195, 1092, 575, 1610, 2403, 3623],\n", + " 1053: [2028, 593, 1374, 1356, 1617, 858, 1199, 296, 608, 457]})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", + "# 각 사용자의 소속 확률이 가장 높은 토픽을 얻고, 해당 토픽 안에서 확률이 높은 아이템을 저장해 나간다\n", + "\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "\n", + "pred_user2items = defaultdict(list)\n", + "for i, (user_id, data) in enumerate(movielens_train_high_rating.groupby(\"user_id\")):\n", + " evaluated_movie_ids = user_evaluated_movies[user_id]\n", + " # 사용자의 소속 확률이 가장 높은 토픽을 얻는다\n", + " user_topic = sorted(lda_topics[i], key=lambda x: -x[1])[0][0]\n", + " # 해당 토픽 안에서 확률이 높은 아이템을 얻는다\n", + " topic_movies = lda_model.get_topic_terms(user_topic, topn=len(movies))\n", + "\n", + " for token_id, score in topic_movies:\n", + " movie_id = int(common_dictionary.id2token[token_id])\n", + " if movie_id not in evaluated_movie_ids:\n", + " pred_user2items[user_id].append(movie_id)\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1701bd43", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 288 + }, + "executionInfo": { + "elapsed": 24, + "status": "ok", + "timestamp": 1672118441268, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "1701bd43", + "outputId": "ee59bd31-4381-4245-f7ae-752409de578c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
11711196Star Wars: Episode V - The Empire Strikes Back...[Action, Adventure, Sci-Fi][lucas, george lucas, george lucas, gfei own i...
11731198Raiders of the Lost Ark (Indiana Jones and the...[Action, Adventure][egypt, lucas, seen more than once, dvd collec...
12121240Terminator, The (1984)[Action, Sci-Fi, Thriller][arnold schwarzenegger, sci-fi, time travel, d...
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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(1198, 1196, 1240)\n", + "movies[movies.movie_id.isin([1198, 1196, 1240])]" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/LDA_content.ipynb b/chapter5/colab/LDA_content.ipynb index 11cf8eb..9b86a34 100644 --- a/chapter5/colab/LDA_content.ipynb +++ b/chapter5/colab/LDA_content.ipynb @@ -1 +1,4865 @@ -{"cells":[{"cell_type":"markdown","id":"0a902087","metadata":{"id":"0a902087"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_content.ipynb)"]},{"cell_type":"markdown","id":"98853cdc","metadata":{"id":"98853cdc"},"source":["# Latent Dirichlet Allocation (LDA)"]},{"cell_type":"code","execution_count":1,"id":"dd0a4718","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dd0a4718","executionInfo":{"status":"ok","timestamp":1672118379265,"user_tz":-540,"elapsed":3985,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c3c7a4e6-690d-431c-a767-3ff7cd0aea7a"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:19:32-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 106MB/s in 0.6s \n","\n","2022-12-27 05:19:33 (106 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"38f1eabd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"38f1eabd","executionInfo":{"status":"ok","timestamp":1672118460251,"user_tz":-540,"elapsed":80989,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"7dbb3693-16e5-4e38-fd8e-c20f1595a08f"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"ea40c411","metadata":{"id":"ea40c411","executionInfo":{"status":"ok","timestamp":1672118460251,"user_tz":-540,"elapsed":11,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 50\n","# 에폭 수\n","n_epochs = 30"]},{"cell_type":"code","execution_count":4,"id":"7aa2bed1","metadata":{"id":"7aa2bed1","executionInfo":{"status":"ok","timestamp":1672118460252,"user_tz":-540,"elapsed":12,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["movie_content = movies.copy()\n","# tag가 부여되어 있지 않은 영화가 있지만, genre는 모든 영화에 부여되어 있다\n","# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 하여 비슷한 영화를 찾아서 추천한다\n","# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n","movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n","movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: list(map(str, x)))\n","\n","# 태그와 장르 데이터를 사용해 lda를 학습한다\n","tag_genre_data = movie_content.tag_genre.tolist()"]},{"cell_type":"code","execution_count":5,"id":"d5804af1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d5804af1","executionInfo":{"status":"ok","timestamp":1672118468216,"user_tz":-540,"elapsed":7975,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"04a68cd3-8b0e-45ee-a1ee-d1a952a63437"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gensim==4.0.1\n"," Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n","\u001b[K |████████████████████████████████| 23.9 MB 1.4 MB/s \n","\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n","Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n","Installing collected packages: gensim\n"," Attempting uninstall: gensim\n"," Found existing installation: gensim 3.6.0\n"," Uninstalling gensim-3.6.0:\n"," Successfully uninstalled gensim-3.6.0\n","Successfully installed gensim-4.0.1\n"]}],"source":["!pip install gensim==4.0.1"]},{"cell_type":"code","execution_count":6,"id":"c448ef53","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c448ef53","executionInfo":{"status":"ok","timestamp":1672118469654,"user_tz":-540,"elapsed":1442,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"040b678f-bb33-4b0e-eea1-9c147aa589c6"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n"," warnings.warn(msg)\n"]}],"source":["from gensim.corpora.dictionary import Dictionary\n","\n","# LDA의 입력으로 사용할 데이터를 작성한다\n","common_dictionary = Dictionary(tag_genre_data)\n","common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n"]},{"cell_type":"code","execution_count":7,"id":"a4449eb1","metadata":{"id":"a4449eb1","executionInfo":{"status":"ok","timestamp":1672118564370,"user_tz":-540,"elapsed":94718,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import gensim\n","\n","# LDA 학습\n","lda_model = gensim.models.LdaModel(\n"," common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n",")"]},{"cell_type":"code","execution_count":8,"id":"a5911f4e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a5911f4e","executionInfo":{"status":"ok","timestamp":1672118564371,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"851e44c7-0605-4c9b-cd83-701340697d26"},"outputs":[{"output_type":"stream","name":"stdout","text":["word=coen brothers, score=0.06292896717786789\n","word=bechdel test:fail, score=0.04510311409831047\n","word=based on book, score=0.04402820020914078\n","word=philip k. dick, score=0.04182061925530434\n","word=kidnapping, score=0.03534778952598572\n","word=quirky, score=0.02876952663064003\n","word=david lynch, score=0.022975284606218338\n","word=fascism, score=0.01680714637041092\n","word=sure thing, score=0.015899477526545525\n","word=robert altman, score=0.015737345442175865\n"]}],"source":["# topic0인 단어 목록\n","for token_id, score in lda_model.get_topic_terms(0, topn=10):\n"," word = common_dictionary.id2token[token_id]\n"," print(f'word={word}, score={score}')"]},{"cell_type":"code","execution_count":9,"id":"858bf7ea","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"858bf7ea","executionInfo":{"status":"ok","timestamp":1672118564371,"user_tz":-540,"elapsed":11,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"df48bb09-d2ad-4570-fa8f-e5b38679a98c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0, 0.01000004),\n"," (1, 0.01000004),\n"," (2, 0.01000004),\n"," (3, 0.01000004),\n"," (4, 0.01000004),\n"," (5, 0.01000004),\n"," (6, 0.01000004),\n"," (7, 0.01000004),\n"," (8, 0.01000004),\n"," (9, 0.01000004),\n"," (10, 0.01000004),\n"," (11, 0.01000004),\n"," (12, 0.01000004),\n"," (13, 0.01000004),\n"," (14, 0.01000004),\n"," (15, 0.01000004),\n"," (16, 0.01000004),\n"," (17, 0.01000004),\n"," (18, 0.01000004),\n"," (19, 0.01000004),\n"," (20, 0.01000004),\n"," (21, 0.01000004),\n"," (22, 0.01000004),\n"," (23, 0.01000004),\n"," (24, 0.01000004),\n"," (25, 0.01000004),\n"," (26, 0.01000004),\n"," (27, 0.01000004),\n"," (28, 0.01000004),\n"," (29, 0.01000004),\n"," (30, 0.01000004),\n"," (31, 0.01000004),\n"," (32, 0.509998),\n"," (33, 0.01000004),\n"," (34, 0.01000004),\n"," (35, 0.01000004),\n"," (36, 0.01000004),\n"," (37, 0.01000004),\n"," (38, 0.01000004),\n"," (39, 0.01000004),\n"," (40, 0.01000004),\n"," (41, 0.01000004),\n"," (42, 0.01000004),\n"," (43, 0.01000004),\n"," (44, 0.01000004),\n"," (45, 0.01000004),\n"," (46, 0.01000004),\n"," (47, 0.01000004),\n"," (48, 0.01000004),\n"," (49, 0.01000004)]"]},"metadata":{},"execution_count":9}],"source":["# samurai라는 단어의 토픽(각 토픽에 소속될 확률)\n","lda_model[common_dictionary.doc2bow(['samurai'])]"]},{"cell_type":"code","execution_count":10,"id":"e03ab49d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":554},"id":"e03ab49d","executionInfo":{"status":"ok","timestamp":1672118571397,"user_tz":-540,"elapsed":7036,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"04f31642-f84a-4a5b-a7a7-17f5e79ad431"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","0 1 Toy Story (1995) \n","1 2 Jumanji (1995) \n","2 3 Grumpier Old Men (1995) \n","3 4 Waiting to Exhale (1995) \n","4 5 Father of the Bride Part II (1995) \n","... ... ... \n","10676 65088 Bedtime Stories (2008) \n","10677 65091 Manhattan Melodrama (1934) \n","10678 65126 Choke (2008) \n","10679 65130 Revolutionary Road (2008) \n","10680 65133 Blackadder Back & Forth (1999) \n","\n"," genre \\\n","0 [Adventure, Animation, Children, Comedy, Fantasy] \n","1 [Adventure, Children, Fantasy] \n","2 [Comedy, Romance] \n","3 [Comedy, Drama, Romance] \n","4 [Comedy] \n","... ... \n","10676 [Adventure, Children, Comedy] \n","10677 [Crime, Drama, Romance] \n","10678 [Comedy, Drama] \n","10679 [Drama, Romance] \n","10680 [Comedy] \n","\n"," tag \\\n","0 [pixar, pixar, pixar, animation, pixar, animat... \n","1 [for children, game, animals, joe johnston, ro... \n","2 [funniest movies, comedinha de velhinhos engra... \n","3 [girl movie] \n","4 [steve martin, pregnancy, remake, steve martin... \n","... ... \n","10676 NaN \n","10677 NaN \n","10678 [chuck palahniuk, based on book] \n","10679 [toplist08] \n","10680 NaN \n","\n"," tag_genre topic topic_score \n","0 [pixar, pixar, pixar, animation, pixar, animat... 34 0.777594 \n","1 [for children, game, animals, joe johnston, ro... 40 0.275520 \n","2 [funniest movies, comedinha de velhinhos engra... 7 0.501525 \n","3 [girl movie, Comedy, Drama, Romance] 49 0.755000 \n","4 [steve martin, pregnancy, remake, steve martin... 34 0.692084 \n","... ... ... ... \n","10676 [Adventure, Children, Comedy] 34 0.755000 \n","10677 [Crime, Drama, Romance] 49 0.456076 \n","10678 [chuck palahniuk, based on book, Comedy, Drama] 49 0.505002 \n","10679 [toplist08, Drama, Romance] 49 0.673333 \n","10680 [Comedy] 17 0.510000 \n","\n","[10681 rows x 7 columns]"],"text/html":["\n","
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movie_idtitlegenretagtag_genretopictopic_score
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...[pixar, pixar, pixar, animation, pixar, animat...340.777594
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...[for children, game, animals, joe johnston, ro...400.275520
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...[funniest movies, comedinha de velhinhos engra...70.501525
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie][girl movie, Comedy, Drama, Romance]490.755000
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...[steve martin, pregnancy, remake, steve martin...340.692084
........................
1067665088Bedtime Stories (2008)[Adventure, Children, Comedy]NaN[Adventure, Children, Comedy]340.755000
1067765091Manhattan Melodrama (1934)[Crime, Drama, Romance]NaN[Crime, Drama, Romance]490.456076
1067865126Choke (2008)[Comedy, Drama][chuck palahniuk, based on book][chuck palahniuk, based on book, Comedy, Drama]490.505002
1067965130Revolutionary Road (2008)[Drama, Romance][toplist08][toplist08, Drama, Romance]490.673333
1068065133Blackadder Back & Forth (1999)[Comedy]NaN[Comedy]170.510000
\n","

10681 rows × 7 columns

\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":10}],"source":["# 각 영화의 토픽을 저장한다\n","lda_topics = lda_model[common_corpus]\n","\n","# 각 영화에 가장 확률이 높은 토픽을 1개 추출해서 저장한다\n","movie_topics = []\n","movie_topic_scores = []\n","for movie_index, lda_topic in enumerate(lda_topics):\n"," sorted_topic = sorted(lda_topics[movie_index], key=lambda x: -x[1])\n"," # 가장 확률이 높은 토픽\n"," movie_topic, topic_score = sorted_topic[0]\n"," movie_topics.append(movie_topic)\n"," movie_topic_scores.append(topic_score)\n","movie_content[\"topic\"] = movie_topics\n","movie_content[\"topic_score\"] = movie_topic_scores\n","movie_content"]},{"cell_type":"code","execution_count":11,"id":"961bb095","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"961bb095","executionInfo":{"status":"ok","timestamp":1672118572985,"user_tz":-540,"elapsed":1592,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e05ef7b3-811d-4c1e-a76b-32c2721be3ad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 2: [1196, 5378, 33493, 2628, 1200, 1580, 377, 1584, 1438, 788],\n"," 3: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 4: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 5: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n"," 6: [6152, 7236, 3702, 2528, 4460, 6678, 4166, 2117, 1527, 1682],\n"," 7: [25766, 910, 116, 5231, 2136, 5682, 2937, 464, 7080, 2494],\n"," 8: [3683, 4881, 1394, 5756, 33823, 2583, 76, 663, 1464, 45028],\n"," 9: [1513, 45221, 4452, 5802, 1285, 6807, 1602, 5439, 6816, 4102],\n"," 10: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n"," 11: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 12: [1210, 1196, 260, 5378, 33493, 1200, 1580, 377, 1584, 1438],\n"," 13: [260, 5378, 33493, 780, 2628, 1200, 377, 1584, 1438, 788],\n"," 14: [780, 2628, 1200, 1580, 377, 1584, 1438, 788, 8644, 736],\n"," 16: [5378, 33493, 780, 2628, 377, 1584, 1438, 788, 8644, 736],\n"," 17: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 18: [5378, 33493, 2628, 1200, 1584, 8644, 1690, 4812, 3699, 2311],\n"," 19: [104,\n"," 59900,\n"," 1777,\n"," 2014,\n"," 27830,\n"," 60487,\n"," 3979,\n"," 1241,\n"," 6593,\n"," 27873],\n"," 22: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 23: [1210, 1196, 5378, 33493, 2628, 1200, 1580, 1584, 1438, 788],\n"," 24: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 26: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n"," 27: [1210, 1196, 260, 5378, 33493, 2628, 1200, 1580, 377, 1584],\n"," 28: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 29: [597, 34, 1059, 3619, 605, 1552, 5942, 6996, 5245, 457],\n"," 30: [135, 5146, 2810, 27731, 42217, 1537, 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4585, 542, 4619],\n"," 990: [1266,\n"," 2401,\n"," 44168,\n"," 5959,\n"," 2921,\n"," 3508,\n"," 4945,\n"," 54787,\n"," 4785,\n"," 7889],\n"," 991: [1210, 5378, 33493, 780, 2628, 1580, 377, 1584, 1438, 788],\n"," 992: [1210, 5378, 33493, 1200, 377, 1438, 788, 8644, 736, 1690],\n"," 993: [1603,\n"," 2364,\n"," 45981,\n"," 26603,\n"," 5556,\n"," 5700,\n"," 57473,\n"," 5746,\n"," 2338,\n"," 2107],\n"," 994: [26782,\n"," 5017,\n"," 1416,\n"," 7456,\n"," 25951,\n"," 3915,\n"," 918,\n"," 25999,\n"," 7990,\n"," 3425],\n"," 995: [350, 6296, 49282, 2546, 448, 50, 1288, 6187, 6323, 7770],\n"," 996: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 997: [597, 34, 1059, 3619, 605, 1552, 5942, 6996, 5245, 457],\n"," 998: [8749,\n"," 6921,\n"," 3134,\n"," 7176,\n"," 6649,\n"," 8126,\n"," 4046,\n"," 7238,\n"," 26151,\n"," 6985],\n"," 999: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1001: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1002: [7378, 6475, 4676, 4520, 4502, 6464, 4585, 542, 4619, 445],\n"," 1003: [1513,\n"," 45221,\n"," 4452,\n"," 5802,\n"," 1285,\n"," 6807,\n"," 1602,\n"," 5439,\n"," 6816,\n"," 4102],\n"," 1004: [5378,\n"," 33493,\n"," 1200,\n"," 1584,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699],\n"," 1005: [1196,\n"," 5378,\n"," 33493,\n"," 2628,\n"," 1200,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812],\n"," 1006: [8749,\n"," 6921,\n"," 3134,\n"," 7176,\n"," 6649,\n"," 8126,\n"," 4046,\n"," 7238,\n"," 26151,\n"," 6985],\n"," 1007: [1302,\n"," 5644,\n"," 1961,\n"," 1940,\n"," 3100,\n"," 1393,\n"," 7771,\n"," 1943,\n"," 1929,\n"," 1225],\n"," 1008: [5378,\n"," 33493,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699,\n"," 2311,\n"," 6882],\n"," 1009: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 1011: [5378,\n"," 33493,\n"," 2628,\n"," 1438,\n"," 788,\n"," 8644,\n"," 1690,\n"," 4812,\n"," 3699,\n"," 2311],\n"," 1012: [1046,\n"," 2550,\n"," 1789,\n"," 1103,\n"," 1340,\n"," 4722,\n"," 4942,\n"," 2665,\n"," 47836,\n"," 4518],\n"," 1013: [5378, 33493, 2628, 1200, 1580, 1584, 1438, 788, 8644, 736],\n"," 1014: [1348,\n"," 27850,\n"," 2783,\n"," 1694,\n"," 3872,\n"," 8194,\n"," 4113,\n"," 2454,\n"," 2064,\n"," 26306],\n"," 1015: [48161, 993, 930, 2178, 524, 2375, 2186, 290, 43710, 2236],\n"," 1016: [1266,\n"," 2401,\n"," 44168,\n"," 5959,\n"," 2921,\n"," 3508,\n"," 4945,\n"," 54787,\n"," 4785,\n"," 7889],\n"," 1017: [260, 780, 1200, 1580, 377, 1584, 1438, 788, 8644, 736],\n"," 1018: [1603,\n"," 2364,\n"," 45981,\n"," 26603,\n"," 5556,\n"," 5700,\n"," 57473,\n"," 5746,\n"," 1969,\n"," 5691],\n"," 1019: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 1020: [1046,\n"," 2550,\n"," 1789,\n"," 1340,\n"," 4722,\n"," 4942,\n"," 2665,\n"," 47836,\n"," 4518,\n"," 2650],\n"," 1021: [1030,\n"," 3759,\n"," 5109,\n"," 3775,\n"," 3964,\n"," 3611,\n"," 1025,\n"," 1881,\n"," 1848,\n"," 63237],\n"," 1022: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1023: [1210, 5378, 33493, 780, 2628, 1200, 1580, 377, 1584, 1438],\n"," 1025: [45221,\n"," 4452,\n"," 5802,\n"," 1285,\n"," 6807,\n"," 1602,\n"," 5439,\n"," 6816,\n"," 4102,\n"," 6212],\n"," 1026: [3061,\n"," 345,\n"," 1734,\n"," 7091,\n"," 7058,\n"," 34002,\n"," 8734,\n"," 34018,\n"," 4763,\n"," 2432],\n"," 1027: [1210, 1196, 260, 5378, 33493, 780, 2628, 1580, 377, 1584],\n"," 1028: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1029: [1385,\n"," 4466,\n"," 5252,\n"," 64231,\n"," 2540,\n"," 27828,\n"," 8593,\n"," 6280,\n"," 6757,\n"," 60237],\n"," 1030: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 377, 1584],\n"," 1031: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1032: [8749,\n"," 6921,\n"," 3134,\n"," 7176,\n"," 6649,\n"," 8126,\n"," 4046,\n"," 7238,\n"," 26151,\n"," 6985],\n"," 1033: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n"," 1034: [1210, 1196, 260, 5378, 780, 2628, 1200, 1580, 377, 1584],\n"," 1035: [1210, 1196, 260, 5378, 33493, 2628, 1200, 377, 1584, 1438],\n"," 1037: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1038: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1039: [4740,\n"," 7211,\n"," 5077,\n"," 7221,\n"," 838,\n"," 3454,\n"," 6501,\n"," 3501,\n"," 27509,\n"," 1075],\n"," 1040: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1041: [596, 1030, 3759, 5109, 3775, 3964, 3611, 1025, 1881, 1848],\n"," 1043: [1210, 1196, 260, 5378, 33493, 2628, 1200, 1580, 377, 1584],\n"," 1044: [1196,\n"," 5378,\n"," 33493,\n"," 2628,\n"," 1200,\n"," 1580,\n"," 377,\n"," 1584,\n"," 1438,\n"," 8644],\n"," 1045: [262,\n"," 180,\n"," 1130,\n"," 5335,\n"," 60530,\n"," 59290,\n"," 27513,\n"," 3012,\n"," 4514,\n"," 8025],\n"," 1046: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 377],\n"," 1047: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1048: [555, 1089, 2249, 3833, 32019, 1785, 18, 3744, 27734, 1729],\n"," 1050: [104,\n"," 59900,\n"," 1777,\n"," 2014,\n"," 27830,\n"," 60487,\n"," 3979,\n"," 1241,\n"," 1022,\n"," 6593],\n"," 1051: [4740, 7211, 5077, 7221, 838, 745, 1148, 3454, 6501, 3501],\n"," 1052: [1302,\n"," 5644,\n"," 1961,\n"," 1940,\n"," 3100,\n"," 1393,\n"," 7771,\n"," 1943,\n"," 1929,\n"," 1225],\n"," 1053: [5378, 780, 2628, 377, 1438, 788, 736, 1690, 4812, 3699]})"]},"metadata":{},"execution_count":11}],"source":["from collections import defaultdict\n","from collections import Counter\n","\n","# 각 사용자에 대한 추천 리스트를 작성한다\n","# 사용자가 높게 평가한 영화가, 어떤 토픽에 많이 소속되어 있는지 카운트한다\n","# 가장 많은 토픽을 사용자가 좋아하는 토픽으로 간주하고, 해당 토픽의 영화를 추천한다\n","\n","movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","\n","movie_id2index = dict(zip(movie_content.movie_id.tolist(), range(len(movie_content))))\n","pred_user2items = defaultdict(list)\n","for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n"," # 사용자가 높이 평가한 영화\n"," evaluated_movie_ids = user_evaluated_movies[user_id]\n"," # 최근 열람한 영화를 얻는다\n"," movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-10:]\n","\n"," movie_indexes = [movie_id2index[id] for id in movie_ids]\n"," \n"," # 최근 열람한 영화의 토픽을 얻고, 풀현 횟수를 카운트한다\n"," topic_counter = Counter([movie_topics[i] for i in movie_indexes])\n"," # 가장 출현 횟수가 많았던 토픽을 얻는다\n"," frequent_topic = topic_counter.most_common(1)[0][0]\n"," # 해당 토픽의 영화 중에서도 점수가 높은 것을 추천한다\n"," topic_movies = (\n"," movie_content[movie_content.topic == frequent_topic]\n"," .sort_values(\"topic_score\", ascending=False)\n"," .movie_id.tolist()\n"," )\n","\n"," for movie_id in topic_movies:\n"," if movie_id not in evaluated_movie_ids:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":12,"id":"8d238322","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"8d238322","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":10,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"68a428f8-984a-4fab-b1e3-8d24e4c5369a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","8381 2 1210 4.0 868245644 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","8381 [Action, Adventure, Sci-Fi] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2인 사용자가 학습 데이터에서 4 이상의 평가를 부여한 영화 목록\n","movielens_train_high_rating[movielens_train_high_rating.user_id==2]"]},{"cell_type":"code","execution_count":13,"id":"d6877799","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"d6877799","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6892e806-a4b2-4a5b-dfd9-da01db2f2d4a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","75 76 Screamers (1995) \n","1922 2006 Mask of Zorro, The (1998) \n","2031 2115 Indiana Jones and the Temple of Doom (1984) \n","\n"," genre \\\n","75 [Action, Sci-Fi, Thriller] \n","1922 [Action, Adventure, Romance] \n","2031 [Action, Adventure] \n","\n"," tag \n","75 [philip k. dick, artificial intelligence, post... \n","1922 [california, mexico, funny, banderas, anthony ... \n","2031 [lucas, want it, dvd collection, harrison ford... "],"text/html":["\n","
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movie_idtitlegenretag
7576Screamers (1995)[Action, Sci-Fi, Thriller][philip k. dick, artificial intelligence, post...
19222006Mask of Zorro, The (1998)[Action, Adventure, Romance][california, mexico, funny, banderas, anthony ...
20312115Indiana Jones and the Temple of Doom (1984)[Action, Adventure][lucas, want it, dvd collection, harrison ford...
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\n"," "]},"metadata":{},"execution_count":13}],"source":["# user_id=2에 대한 추천(2115, 76, 2006)\n","movies[movies.movie_id.isin([2115, 76, 2006])]"]},{"cell_type":"code","execution_count":14,"id":"129f2877","metadata":{"id":"129f2877","executionInfo":{"status":"ok","timestamp":1672118572986,"user_tz":-540,"elapsed":8,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 여기에서는 각 사용자의 평가 이력으로부터 1개의 토픽을 픽업했지만, 토픽의 확률값을 사용해서 가중치를 주면서 아이템을 추출할 수도 있습니다."]},{"cell_type":"code","execution_count":14,"id":"6a30f15c","metadata":{"id":"6a30f15c","executionInfo":{"status":"ok","timestamp":1672118572987,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a902087", + "metadata": { + "id": "0a902087" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/LDA_content.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "98853cdc", + "metadata": { + "id": "98853cdc" + }, + "source": [ + "# 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dd0a4718", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3985, + "status": "ok", + "timestamp": 1672118379265, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "dd0a4718", + "outputId": "c3c7a4e6-690d-431c-a767-3ff7cd0aea7a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:19:32-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 106MB/s in 0.6s \n", + "\n", + "2022-12-27 05:19:33 (106 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38f1eabd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 80989, + "status": "ok", + "timestamp": 1672118460251, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "38f1eabd", + "outputId": "7dbb3693-16e5-4e38-fd8e-c20f1595a08f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ea40c411", + "metadata": { + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1672118460251, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "ea40c411" + }, + "outputs": [], + "source": [ + "# 인자 수\n", + "factors = 50\n", + "# 에폭 수\n", + "n_epochs = 30" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7aa2bed1", + "metadata": { + "executionInfo": { + "elapsed": 12, + "status": "ok", + "timestamp": 1672118460252, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7aa2bed1" + }, + "outputs": [], + "source": [ + "movie_content = movies.copy()\n", + "# tag가 부여되어 있지 않은 영화가 있지만, genre는 모든 영화에 부여되어 있다\n", + "# tag와 genre를 결합한 것을 영화 콘텐츠 정보로 하여 비슷한 영화를 찾아서 추천한다\n", + "# tag가 없는 영화는 NaN으로 되어 있으므로, 빈 리스트로 변환한 뒤 처리한다\n", + "movie_content[\"tag_genre\"] = movie_content[\"tag\"].fillna(\"\").apply(list) + movie_content[\"genre\"].apply(list)\n", + "movie_content[\"tag_genre\"] = movie_content[\"tag_genre\"].apply(lambda x: list(map(str, x)))\n", + "\n", + "# 태그와 장르 데이터를 사용해 lda를 학습한다\n", + "tag_genre_data = movie_content.tag_genre.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d5804af1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 7975, + "status": "ok", + "timestamp": 1672118468216, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "d5804af1", + "outputId": "04a68cd3-8b0e-45ee-a1ee-d1a952a63437" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting gensim==4.0.1\n", + " Downloading gensim-4.0.1-cp38-cp38-manylinux1_x86_64.whl (23.9 MB)\n", + "\u001b[K |████████████████████████████████| 23.9 MB 1.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.7.3)\n", + "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (1.21.6)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.8/dist-packages (from gensim==4.0.1) (6.3.0)\n", + "Installing collected packages: gensim\n", + " Attempting uninstall: gensim\n", + " Found existing installation: gensim 3.6.0\n", + " Uninstalling gensim-3.6.0:\n", + " Successfully uninstalled gensim-3.6.0\n", + "Successfully installed gensim-4.0.1\n" + ] + } + ], + "source": [ + "!pip install gensim==4.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c448ef53", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1442, + "status": "ok", + "timestamp": 1672118469654, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "c448ef53", + "outputId": "040b678f-bb33-4b0e-eea1-9c147aa589c6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "from gensim.corpora.dictionary import Dictionary\n", + "\n", + "# LDA의 입력으로 사용할 데이터를 작성한다\n", + "common_dictionary = Dictionary(tag_genre_data)\n", + "common_corpus = [common_dictionary.doc2bow(text) for text in tag_genre_data]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a4449eb1", + "metadata": { + "executionInfo": { + "elapsed": 94718, + "status": "ok", + "timestamp": 1672118564370, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "a4449eb1" + }, + "outputs": [], + "source": [ + "import gensim\n", + "\n", + "# LDA 학습\n", + "lda_model = gensim.models.LdaModel(\n", + " common_corpus, id2word=common_dictionary, num_topics=factors, passes=n_epochs\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a5911f4e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1672118564371, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "a5911f4e", + "outputId": "851e44c7-0605-4c9b-cd83-701340697d26" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "word=coen brothers, score=0.06292896717786789\n", + "word=bechdel test:fail, score=0.04510311409831047\n", + "word=based on book, score=0.04402820020914078\n", + "word=philip k. dick, score=0.04182061925530434\n", + "word=kidnapping, score=0.03534778952598572\n", + "word=quirky, score=0.02876952663064003\n", + "word=david lynch, score=0.022975284606218338\n", + "word=fascism, score=0.01680714637041092\n", + "word=sure thing, score=0.015899477526545525\n", + "word=robert altman, score=0.015737345442175865\n" + ] + } + ], + "source": [ + "# topic0인 단어 목록\n", + "for token_id, score in lda_model.get_topic_terms(0, topn=10):\n", + " word = common_dictionary.id2token[token_id]\n", + " print(f'word={word}, score={score}')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "858bf7ea", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1672118564371, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "858bf7ea", + "outputId": "df48bb09-d2ad-4570-fa8f-e5b38679a98c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0, 0.01000004),\n", + " (1, 0.01000004),\n", + " (2, 0.01000004),\n", + " (3, 0.01000004),\n", + " (4, 0.01000004),\n", + " (5, 0.01000004),\n", + " (6, 0.01000004),\n", + " (7, 0.01000004),\n", + " (8, 0.01000004),\n", + " (9, 0.01000004),\n", + " (10, 0.01000004),\n", + " (11, 0.01000004),\n", + " (12, 0.01000004),\n", + " (13, 0.01000004),\n", + " (14, 0.01000004),\n", + " (15, 0.01000004),\n", + " (16, 0.01000004),\n", + " (17, 0.01000004),\n", + " (18, 0.01000004),\n", + " (19, 0.01000004),\n", + " (20, 0.01000004),\n", + " (21, 0.01000004),\n", + " (22, 0.01000004),\n", + " (23, 0.01000004),\n", + " (24, 0.01000004),\n", + " (25, 0.01000004),\n", + " (26, 0.01000004),\n", + " (27, 0.01000004),\n", + " (28, 0.01000004),\n", + " (29, 0.01000004),\n", + " (30, 0.01000004),\n", + " (31, 0.01000004),\n", + " (32, 0.509998),\n", + " (33, 0.01000004),\n", + " (34, 0.01000004),\n", + " (35, 0.01000004),\n", + " (36, 0.01000004),\n", + " (37, 0.01000004),\n", + " (38, 0.01000004),\n", + " (39, 0.01000004),\n", + " (40, 0.01000004),\n", + " (41, 0.01000004),\n", + " (42, 0.01000004),\n", + " (43, 0.01000004),\n", + " (44, 0.01000004),\n", + " (45, 0.01000004),\n", + " (46, 0.01000004),\n", + " (47, 0.01000004),\n", + " (48, 0.01000004),\n", + " (49, 0.01000004)]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# samurai라는 단어의 토픽(각 토픽에 소속될 확률)\n", + "lda_model[common_dictionary.doc2bow(['samurai'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e03ab49d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 554 + }, + "executionInfo": { + "elapsed": 7036, + "status": "ok", + "timestamp": 1672118571397, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "e03ab49d", + "outputId": "04f31642-f84a-4a5b-a7a7-17f5e79ad431" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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movie_idtitlegenretagtag_genretopictopic_score
01Toy Story (1995)[Adventure, Animation, Children, Comedy, Fantasy][pixar, pixar, pixar, animation, pixar, animat...[pixar, pixar, pixar, animation, pixar, animat...340.777594
12Jumanji (1995)[Adventure, Children, Fantasy][for children, game, animals, joe johnston, ro...[for children, game, animals, joe johnston, ro...400.275520
23Grumpier Old Men (1995)[Comedy, Romance][funniest movies, comedinha de velhinhos engra...[funniest movies, comedinha de velhinhos engra...70.501525
34Waiting to Exhale (1995)[Comedy, Drama, Romance][girl movie][girl movie, Comedy, Drama, Romance]490.755000
45Father of the Bride Part II (1995)[Comedy][steve martin, pregnancy, remake, steve martin...[steve martin, pregnancy, remake, steve martin...340.692084
........................
1067665088Bedtime Stories (2008)[Adventure, Children, Comedy]NaN[Adventure, Children, Comedy]340.755000
1067765091Manhattan Melodrama (1934)[Crime, Drama, Romance]NaN[Crime, Drama, Romance]490.456076
1067865126Choke (2008)[Comedy, Drama][chuck palahniuk, based on book][chuck palahniuk, based on book, Comedy, Drama]490.505002
1067965130Revolutionary Road (2008)[Drama, Romance][toplist08][toplist08, Drama, Romance]490.673333
1068065133Blackadder Back & Forth (1999)[Comedy]NaN[Comedy]170.510000
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ro... \n", + "2 [funniest movies, comedinha de velhinhos engra... \n", + "3 [girl movie] \n", + "4 [steve martin, pregnancy, remake, steve martin... \n", + "... ... \n", + "10676 NaN \n", + "10677 NaN \n", + "10678 [chuck palahniuk, based on book] \n", + "10679 [toplist08] \n", + "10680 NaN \n", + "\n", + " tag_genre topic topic_score \n", + "0 [pixar, pixar, pixar, animation, pixar, animat... 34 0.777594 \n", + "1 [for children, game, animals, joe johnston, ro... 40 0.275520 \n", + "2 [funniest movies, comedinha de velhinhos engra... 7 0.501525 \n", + "3 [girl movie, Comedy, Drama, Romance] 49 0.755000 \n", + "4 [steve martin, pregnancy, remake, steve martin... 34 0.692084 \n", + "... ... ... ... \n", + "10676 [Adventure, Children, Comedy] 34 0.755000 \n", + "10677 [Crime, Drama, Romance] 49 0.456076 \n", + "10678 [chuck palahniuk, based on book, Comedy, Drama] 49 0.505002 \n", + "10679 [toplist08, Drama, Romance] 49 0.673333 \n", + "10680 [Comedy] 17 0.510000 \n", + "\n", + "[10681 rows x 7 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 각 영화의 토픽을 저장한다\n", + "lda_topics = lda_model[common_corpus]\n", + "\n", + "# 각 영화에 가장 확률이 높은 토픽을 1개 추출해서 저장한다\n", + "movie_topics = []\n", + "movie_topic_scores = []\n", + "for movie_index, lda_topic in enumerate(lda_topics):\n", + " sorted_topic = sorted(lda_topics[movie_index], key=lambda x: -x[1])\n", + " # 가장 확률이 높은 토픽\n", + " movie_topic, topic_score = sorted_topic[0]\n", + " movie_topics.append(movie_topic)\n", + " movie_topic_scores.append(topic_score)\n", + "movie_content[\"topic\"] = movie_topics\n", + "movie_content[\"topic_score\"] = movie_topic_scores\n", + "movie_content" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "961bb095", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1592, + "status": "ok", + "timestamp": 1672118572985, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "961bb095", + "outputId": "e05ef7b3-811d-4c1e-a76b-32c2721be3ad" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {1: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n", + " 2: [1196, 5378, 33493, 2628, 1200, 1580, 377, 1584, 1438, 788],\n", + " 3: [48161, 993, 930, 2178, 524, 2375, 904, 2186, 290, 43710],\n", + " 4: [1210, 1196, 260, 5378, 33493, 780, 2628, 1200, 1580, 1584],\n", + " 5: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n", + " 6: [6152, 7236, 3702, 2528, 4460, 6678, 4166, 2117, 1527, 1682],\n", + " 7: [25766, 910, 116, 5231, 2136, 5682, 2937, 464, 7080, 2494],\n", + " 8: [3683, 4881, 1394, 5756, 33823, 2583, 76, 663, 1464, 45028],\n", + " 9: [1513, 45221, 4452, 5802, 1285, 6807, 1602, 5439, 6816, 4102],\n", + " 10: [8749, 6921, 3134, 7176, 6649, 8126, 4046, 7238, 26151, 6985],\n", + " 11: [48161, 993, 930, 2178, 524, 2375, 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1225],\n", + " 1053: [5378, 780, 2628, 377, 1438, 788, 736, 1690, 4812, 3699]})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "from collections import Counter\n", + "\n", + "# 각 사용자에 대한 추천 리스트를 작성한다\n", + "# 사용자가 높게 평가한 영화가, 어떤 토픽에 많이 소속되어 있는지 카운트한다\n", + "# 가장 많은 토픽을 사용자가 좋아하는 토픽으로 간주하고, 해당 토픽의 영화를 추천한다\n", + "\n", + "movielens_train_high_rating = movielens_train[movielens_train.rating >= 4]\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "\n", + "movie_id2index = dict(zip(movie_content.movie_id.tolist(), range(len(movie_content))))\n", + "pred_user2items = defaultdict(list)\n", + "for user_id, data in movielens_train_high_rating.groupby(\"user_id\"):\n", + " # 사용자가 높이 평가한 영화\n", + " evaluated_movie_ids = user_evaluated_movies[user_id]\n", + " # 최근 열람한 영화를 얻는다\n", + " movie_ids = data.sort_values(\"timestamp\")[\"movie_id\"].tolist()[-10:]\n", + "\n", + " movie_indexes = [movie_id2index[id] for id in movie_ids]\n", + " \n", + " # 최근 열람한 영화의 토픽을 얻고, 풀현 횟수를 카운트한다\n", + " topic_counter = Counter([movie_topics[i] for i in movie_indexes])\n", + " # 가장 출현 횟수가 많았던 토픽을 얻는다\n", + " frequent_topic = topic_counter.most_common(1)[0][0]\n", + " # 해당 토픽의 영화 중에서도 점수가 높은 것을 추천한다\n", + " topic_movies = (\n", + " movie_content[movie_content.topic == frequent_topic]\n", + " .sort_values(\"topic_score\", ascending=False)\n", + " .movie_id.tolist()\n", + " )\n", + "\n", + " for movie_id in topic_movies:\n", + " if movie_id not in evaluated_movie_ids:\n", + " pred_user2items[user_id].append(movie_id)\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8d238322", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 175 + }, + "executionInfo": { + 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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
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movie_idtitlegenretag
7576Screamers (1995)[Action, Sci-Fi, Thriller][philip k. dick, artificial intelligence, post...
19222006Mask of Zorro, The (1998)[Action, Adventure, Romance][california, mexico, funny, banderas, anthony ...
20312115Indiana Jones and the Temple of Doom (1984)[Action, Adventure][lucas, want it, dvd collection, harrison ford...
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\n", + " " + ], + "text/plain": [ + " movie_id title \\\n", + "75 76 Screamers (1995) \n", + "1922 2006 Mask of Zorro, The (1998) \n", + "2031 2115 Indiana Jones and the Temple of Doom (1984) \n", + "\n", + " genre \\\n", + "75 [Action, Sci-Fi, Thriller] \n", + "1922 [Action, Adventure, Romance] \n", + "2031 [Action, Adventure] \n", + "\n", + " tag \n", + "75 [philip k. dick, artificial intelligence, post... \n", + "1922 [california, mexico, funny, banderas, anthony ... \n", + "2031 [lucas, want it, dvd collection, harrison ford... " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(2115, 76, 2006)\n", + "movies[movies.movie_id.isin([2115, 76, 2006])]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "129f2877", + "metadata": { + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1672118572986, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "129f2877" + }, + "outputs": [], + "source": [ + "# 여기에서는 각 사용자의 평가 이력으로부터 1개의 토픽을 픽업했지만, 토픽의 확률값을 사용해서 가중치를 주면서 아이템을 추출할 수도 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6a30f15c", + "metadata": { + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1672118572987, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "6a30f15c" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/MF.ipynb b/chapter5/colab/MF.ipynb index 9c5ef59..2b63efc 100644 --- a/chapter5/colab/MF.ipynb +++ b/chapter5/colab/MF.ipynb @@ -1 +1,1980 @@ -{"cells":[{"cell_type":"markdown","id":"88a3be74","metadata":{"id":"88a3be74"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/MF.ipynb)"]},{"cell_type":"markdown","id":"47e2fec5","metadata":{"id":"47e2fec5"},"source":["# Matrix Factorization"]},{"cell_type":"code","execution_count":1,"id":"99e09acf","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"99e09acf","executionInfo":{"status":"ok","timestamp":1672119437385,"user_tz":-540,"elapsed":3804,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"294107d4-f398-41b9-a605-09d7c15bb44f"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:35:29-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 63.1MB/s in 1.0s \n","\n","2022-12-27 05:35:30 (63.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"179ce864","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"179ce864","executionInfo":{"status":"ok","timestamp":1672119502987,"user_tz":-540,"elapsed":65608,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"4e5a1760-788a-4246-e64d-8cda917e1a11"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"8c10d538","metadata":{"id":"8c10d538","executionInfo":{"status":"ok","timestamp":1672119502988,"user_tz":-540,"elapsed":28,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 인자 수\n","factors = 5\n","# 평가수 임곗값\n","minimum_num_rating = 100\n","# 바이어스 항 사용\n","use_biase = False\n","# 학습률\n","lr_all = 0.005\n","# 에폭 수\n","n_epochs = 50"]},{"cell_type":"code","execution_count":4,"id":"af591e1a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"af591e1a","executionInfo":{"status":"ok","timestamp":1672119547233,"user_tz":-540,"elapsed":44271,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f5f3025d-2ead-479f-c315-94433d318640"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Collecting scikit-surprise\n"," Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n","\u001b[K |████████████████████████████████| 771 kB 4.8 MB/s \n","\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626477 sha256=a922027e3c7f6d8735b7192824bc8d1a46af450d003dde385ce514ec0ae33d2d\n"," Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"]}],"source":["!pip install surprise"]},{"cell_type":"code","execution_count":5,"id":"735ff486","metadata":{"id":"735ff486","executionInfo":{"status":"ok","timestamp":1672119548405,"user_tz":-540,"elapsed":1188,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from surprise import SVD, Reader\n","from surprise import Dataset as SurpriseDataset\n","\n","# 평가 수가 minimum_num_rating 건 이상 있는 영화로 줄인다\n","filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n"," lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n",")\n","\n","# Surprise용으로 데이터를 가공한다\n","reader = Reader(rating_scale=(0.5, 5))\n","data_train = SurpriseDataset.load_from_df(\n"," filtered_movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n",").build_full_trainset()"]},{"cell_type":"code","execution_count":6,"id":"cbc512a3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cbc512a3","executionInfo":{"status":"ok","timestamp":1672119549729,"user_tz":-540,"elapsed":1327,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b776c506-902c-4525-a5de-88ecc825e60b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6}],"source":["# Surprise로 행렬 분해를 학습한다\n","# 이름은 SVD지만, 특이값 분석이 아니라 Matrix Factorization가 실행된다\n","matrix_factorization = SVD(n_factors=factors, n_epochs=n_epochs, lr_all=lr_all, biased=use_biase)\n","matrix_factorization.fit(data_train)"]},{"cell_type":"code","execution_count":7,"id":"48876224","metadata":{"id":"48876224","executionInfo":{"status":"ok","timestamp":1672119549730,"user_tz":-540,"elapsed":13,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from collections import defaultdict\n","\n","def get_top_n(predictions, n=10):\n"," # 각 사용자별로 예측된 아이템을 저장한다\n"," top_n = defaultdict(list)\n"," for uid, iid, true_r, est, _ in predictions:\n"," top_n[uid].append((iid, est))\n","\n"," # 사용자별로 아이템을 예측 평갓값 순으로 나열해 상위 n개를 저장한다\n"," for uid, user_ratings in top_n.items():\n"," user_ratings.sort(key=lambda x: x[1], reverse=True)\n"," top_n[uid] = [d[0] for d in user_ratings[:n]]\n","\n"," return top_n"]},{"cell_type":"code","execution_count":8,"id":"a2b23304","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a2b23304","executionInfo":{"status":"ok","timestamp":1672119551833,"user_tz":-540,"elapsed":2114,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"2318faf1-5003-42d9-f6aa-bc551e8ab571"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {1: [110, 260, 590, 733, 802, 858, 1073, 1210, 1148, 1246],\n"," 22: [318, 260, 608, 1196, 912, 1234, 1213, 858, 904, 2959],\n"," 26: [260, 1210, 318, 527, 1196, 50, 593, 1234, 3578, 1704],\n"," 30: [1148, 912, 457, 3578, 1234, 2762, 1704, 3147, 1233, 2324],\n"," 34: [2324, 1148, 1234, 1207, 4306, 1233, 904, 497, 1247, 17],\n"," 38: [1380, 50, 920, 1036, 1234, 1210, 2324, 2081, 165, 589],\n"," 50: [260, 1207, 904, 2858, 1148, 4993, 1704, 5952, 912, 1233],\n"," 53: [318, 1233, 858, 912, 1193, 904, 2019, 4973, 4226, 1234],\n"," 54: [1210, 1380, 3578, 260, 1196, 1704, 527, 2324, 1234, 1198],\n"," 67: [2959, 1234, 1089, 858, 1213, 1036, 912, 4226, 1221, 1201],\n"," 71: [318, 260, 50, 593, 912, 1234, 527, 1196, 858, 904],\n"," 76: [1721, 597, 587, 3578, 356, 539, 780, 500, 527, 786],\n"," 88: [318, 1036, 50, 589, 1234, 3147, 2000, 457, 593, 733],\n"," 89: [593, 1234, 2959, 858, 912, 1213, 47, 4226, 1036, 1221],\n"," 94: [260, 1704, 1148, 4993, 1207, 2028, 17, 904, 2324, 2762],\n"," 95: [50, 260, 1196, 1210, 47, 1234, 1213, 2959, 4226, 1198],\n"," 100: [1199, 1206, 2019, 541, 1221, 750, 924, 1208, 923, 1219],\n"," 101: [3147, 3578, 1234, 1036, 919, 1242, 2000, 733, 1035, 2324],\n"," 102: [50, 260, 1196, 593, 1210, 1213, 2959, 1234, 4226, 1221],\n"," 103: [318, 1148, 1193, 858, 2858, 904, 912, 2571, 4226, 7153],\n"," 111: [50, 260, 1210, 1036, 47, 3578, 1196, 1234, 2571, 1198],\n"," 116: [1721, 1380, 920, 3578, 919, 527, 1704, 457, 2324, 832],\n"," 118: [260, 1196, 1210, 1234, 904, 5952, 4993, 912, 1704, 7153],\n"," 139: [50, 2542, 2692, 4011, 1200, 4226, 6874, 858, 1193, 1221],\n"," 143: [912, 1233, 1207, 2019, 908, 750, 1247, 1213, 1250, 1234],\n"," 144: [111, 1193, 908, 904, 2502, 1234, 1288, 923, 1225, 1247],\n"," 162: [318, 50, 593, 1234, 608, 912, 1213, 904, 4226, 2324],\n"," 172: [318, 260, 2571, 50, 1148, 527, 3578, 1234, 1198, 4993],\n"," 173: [3578, 1704, 2324, 1148, 2762, 2571, 4306, 2028, 3147, 4993],\n"," 187: [780, 3147, 587, 2396, 62, 11, 736, 1242, 733, 1641],\n"," 193: [3578, 1035, 919, 1234, 2324, 1704, 1036, 3147, 1242, 912],\n"," 208: [733, 780, 597, 919, 457, 3578, 1234, 1036, 2000, 3147],\n"," 210: [912, 50, 1234, 527, 858, 2324, 904, 919, 1193, 1207],\n"," 214: [318, 1207, 1233, 904, 2858, 1247, 2028, 912, 1288, 5952],\n"," 228: [3147, 733, 736, 2000, 802, 1610, 11, 786, 2706, 1242],\n"," 232: [2571, 7153, 5952, 4993, 2858, 1288, 1148, 1136, 4226, 1221],\n"," 236: [2959, 858, 1193, 1234, 1233, 1036, 4226, 4011, 1213, 912],\n"," 259: [318, 1036, 1240, 589, 1234, 380, 3578, 4011, 110, 1213],\n"," 260: [50, 318, 2959, 1234, 2571, 4226, 3578, 1213, 4011, 110],\n"," 267: [1148, 1213, 904, 36, 908, 4011, 912, 1197, 1304, 1250],\n"," 268: [1721, 1035, 919, 920, 1380, 1641, 3147, 2671, 1242, 3578],\n"," 271: [527, 3578, 1704, 356, 150, 318, 17, 2324, 590, 2762],\n"," 274: [527, 593, 608, 912, 50, 318, 904, 296, 1213, 4226],\n"," 281: [1234, 858, 912, 1233, 1193, 2959, 919, 904, 4973, 4226],\n"," 287: [318, 260, 2571, 50, 1148, 3578, 527, 1198, 4993, 1234],\n"," 292: [1219, 2019, 904, 908, 1199, 923, 1201, 1233, 1208, 778],\n"," 296: [260, 1234, 1196, 1148, 1198, 912, 1136, 1210, 608, 908],\n"," 303: [111, 1199, 2019, 923, 1208, 1148, 904, 1193, 1206, 908],\n"," 307: [4993, 1288, 7153, 1148, 5952, 2858, 318, 1136, 293, 4226],\n"," 310: [3578, 1210, 904, 150, 4306, 1148, 1233, 6539, 1242, 4226],\n"," 315: [1721, 494, 653, 1917, 2628, 1584, 3, 786, 832, 802],\n"," 320: [50, 1221, 1213, 318, 1201, 858, 608, 1234, 1198, 2019],\n"," 326: [260, 858, 1148, 527, 912, 1193, 904, 2858, 1233, 318],\n"," 332: [527, 912, 1207, 904, 919, 1233, 4973, 1394, 1035, 1247],\n"," 339: [260, 527, 50, 1234, 3578, 2571, 2324, 1210, 1196, 1704],\n"," 343: [318, 3578, 3147, 733, 1242, 1234, 2000, 1036, 527, 597],\n"," 355: [260, 1196, 1234, 4226, 1213, 527, 912, 2571, 858, 904],\n"," 364: [858, 912, 1234, 1233, 2959, 1193, 1213, 904, 4226, 4973],\n"," 365: [858, 912, 904, 1233, 318, 4973, 1394, 1207, 608, 919],\n"," 368: [1221, 1213, 1193, 318, 1233, 2858, 111, 1208, 904, 1222],\n"," 381: [260, 1148, 912, 1207, 1247, 1193, 2028, 2396, 3578, 904],\n"," 382: [527, 318, 1207, 1233, 1148, 904, 912, 1247, 1704, 2028],\n"," 383: [1219, 923, 1199, 1288, 904, 1094, 1394, 1136, 2019, 1080],\n"," 391: [1234, 50, 3578, 527, 1233, 1148, 3147, 2324, 1036, 912],\n"," 396: [858, 593, 608, 912, 1221, 1193, 50, 904, 296, 1213],\n"," 398: [1704, 318, 1210, 17, 2324, 3578, 1207, 6539, 919, 4993],\n"," 406: [318, 527, 1207, 912, 1233, 904, 1234, 919, 2324, 1148],\n"," 409: [780, 1917, 3578, 380, 2628, 733, 786, 2001, 2000, 3147],\n"," 410: [3147, 1148, 3578, 2762, 1233, 2000, 2396, 2028, 1230, 457],\n"," 416: [318, 912, 527, 919, 1207, 904, 1035, 1233, 1234, 4973],\n"," 418: [260, 1148, 1288, 5952, 7153, 527, 541, 923, 1199, 1196],\n"," 419: [2959, 1206, 6874, 2502, 7153, 223, 2019, 293, 1080, 1196],\n"," 421: [527, 1035, 1704, 1028, 17, 919, 364, 1721, 1207, 2324],\n"," 424: [1207, 1219, 1233, 4973, 1394, 111, 5952, 2019, 858, 1288],\n"," 426: [2502, 1374, 1242, 17, 1278, 2791, 1356, 112, 1376, 1073],\n"," 432: [912, 608, 904, 2858, 4973, 923, 1233, 858, 260, 1219],\n"," 434: [1288, 1148, 318, 17, 904, 34, 1207, 923, 509, 1358],\n"," 436: [260, 2571, 1234, 4226, 1213, 858, 1198, 1148, 1196, 912],\n"," 438: [4993, 7153, 50, 5952, 1288, 4226, 47, 1213, 6874, 527],\n"," 441: [260, 318, 527, 3578, 1210, 1704, 2571, 356, 1148, 4993],\n"," 446: [50, 260, 858, 4226, 2571, 1213, 1193, 1233, 1148, 1221],\n"," 452: [1036, 2959, 2000, 380, 1234, 589, 733, 919, 593, 1035],\n"," 456: [541, 1221, 2959, 2571, 1136, 4226, 858, 750, 2019, 1213],\n"," 459: [50, 2502, 1201, 1221, 223, 1206, 1213, 2692, 6, 1198],\n"," 460: [527, 50, 1234, 318, 3578, 260, 2324, 356, 912, 1036],\n"," 463: [1234, 1213, 4011, 912, 1148, 1304, 380, 919, 1247, 7153],\n"," 468: [260, 1196, 4993, 5952, 7153, 1210, 2571, 1288, 2858, 318],\n"," 469: [733, 858, 1252, 1288, 1207, 1247, 1193, 50, 904, 1617],\n"," 472: [1721, 919, 527, 1207, 2324, 3578, 500, 2396, 1246, 920],\n"," 476: [1193, 858, 904, 912, 1288, 1247, 1213, 111, 908, 1207],\n"," 480: [111, 1089, 1213, 923, 908, 778, 1208, 1233, 1201, 1193],\n"," 482: [1233, 1222, 912, 1230, 2019, 4973, 1252, 750, 2692, 904],\n"," 493: [912, 904, 1234, 593, 1196, 1207, 1233, 919, 1148, 1247],\n"," 494: [2959, 2502, 2542, 1206, 858, 1200, 1221, 2997, 2692, 1201],\n"," 496: [260, 1196, 318, 50, 527, 1234, 1198, 1704, 110, 2324],\n"," 501: [527, 1207, 318, 904, 1233, 2324, 1247, 4973, 2858, 1035],\n"," 504: [1233, 1148, 904, 1193, 2858, 912, 1247, 1207, 858, 1288],\n"," 505: [527, 1704, 3578, 2324, 1207, 1234, 919, 1148, 904, 912],\n"," 511: [527, 318, 1704, 356, 260, 3578, 2324, 919, 1035, 17],\n"," 516: [1233, 1193, 912, 904, 1148, 1247, 1252, 1207, 50, 923],\n"," 525: [260, 1210, 1196, 527, 50, 356, 1704, 3578, 2324, 1234],\n"," 530: [912, 919, 1035, 1207, 593, 1234, 904, 4973, 527, 1394],\n"," 531: [527, 1207, 919, 1035, 2324, 912, 1704, 2396, 1233, 904],\n"," 533: [1213, 111, 2329, 4011, 58, 778, 1090, 2194, 4995, 150],\n"," 536: [527, 318, 1704, 2324, 1207, 3578, 919, 356, 1035, 590],\n"," 543: [2028, 1234, 1233, 1288, 36, 1242, 497, 4011, 1250, 2329],\n"," 547: [260, 1210, 1196, 1704, 318, 4993, 5952, 17, 2571, 7153],\n"," 549: [912, 919, 1035, 1207, 527, 1233, 904, 1234, 4973, 364],\n"," 553: [858, 541, 778, 1206, 1221, 1193, 1732, 4226, 1089, 1200],\n"," 556: [260, 1196, 1210, 5952, 4993, 7153, 2571, 608, 1198, 1704],\n"," 558: [1233, 4973, 904, 919, 1193, 1250, 2019, 908, 1225, 750],\n"," 562: [1035, 1721, 919, 1380, 920, 2000, 1101, 3147, 2706, 786],\n"," 567: [780, 786, 733, 2000, 1917, 3147, 1721, 2001, 802, 3578],\n"," 571: [296, 608, 2019, 912, 1213, 750, 1233, 1193, 2959, 111],\n"," 572: [318, 1148, 36, 3578, 1036, 1233, 1704, 2019, 1225, 1304],\n"," 577: [356, 1036, 2959, 919, 47, 3578, 2324, 1213, 1380, 110],\n"," 581: [912, 1233, 904, 1207, 4973, 858, 2858, 1247, 923, 1193],\n"," 585: [1221, 4973, 1233, 1208, 4226, 2502, 3897, 2542, 1250, 7153],\n"," 593: [1210, 260, 1196, 1036, 2571, 1240, 1198, 3578, 1234, 2959],\n"," 601: [1035, 2324, 1704, 3578, 1234, 590, 1207, 6539, 50, 4306],\n"," 604: [2959, 2502, 1221, 1089, 2542, 1200, 1201, 1206, 1213, 541],\n"," 605: [593, 1213, 912, 1221, 858, 1234, 4226, 904, 2019, 2959],\n"," 609: [1234, 912, 260, 2324, 904, 919, 1233, 1207, 858, 3578],\n"," 628: [1234, 527, 50, 3578, 593, 2324, 260, 912, 1148, 1233],\n"," 629: [260, 4993, 318, 2571, 7153, 1148, 5952, 1288, 527, 2858],\n"," 634: [527, 3578, 50, 1234, 296, 318, 2324, 1704, 1213, 912],\n"," 645: [3147, 1193, 1233, 858, 2959, 1036, 2692, 733, 4011, 1242],\n"," 650: [111, 912, 923, 1221, 1136, 2959, 296, 1213, 1233, 750],\n"," 656: [527, 3578, 260, 1704, 2324, 1148, 1234, 2762, 2571, 2028],\n"," 657: [1394, 1199, 2019, 1233, 750, 1136, 1080, 1288, 1222, 1148],\n"," 669: [527, 3578, 919, 1035, 2324, 1234, 1704, 1207, 912, 260],\n"," 678: [3578, 2571, 47, 2324, 1704, 1148, 4011, 2000, 4226, 36],\n"," 683: [3147, 318, 50, 1240, 457, 4011, 2959, 1234, 2706, 1610],\n"," 688: [1288, 1221, 1208, 223, 293, 1148, 3996, 1079, 318, 1197],\n"," 689: [527, 912, 1207, 904, 919, 1234, 1233, 4973, 1035, 260],\n"," 693: [1035, 919, 912, 1207, 1233, 1234, 904, 4973, 2324, 1242],\n"," 701: [858, 912, 2959, 1233, 4973, 318, 593, 1193, 1234, 919],\n"," 716: [5952, 111, 1288, 7153, 4993, 923, 1136, 1208, 1219, 1080],\n"," 719: [3147, 1233, 1641, 2396, 919, 2706, 1242, 1035, 1230, 912],\n"," 720: [1233, 3147, 858, 1193, 912, 1242, 1234, 1230, 2959, 1222],\n"," 734: [527, 318, 3578, 2324, 1704, 150, 912, 904, 1148, 2762],\n"," 735: [318, 260, 1196, 1234, 527, 1210, 912, 1198, 1213, 2324],\n"," 739: [296, 2502, 858, 1036, 1234, 318, 1213, 1201, 1220, 4011],\n"," 745: [1234, 912, 919, 2324, 356, 4973, 904, 1201, 1304, 2019],\n"," 755: [858, 1233, 912, 2019, 1193, 750, 4973, 904, 1221, 2858],\n"," 758: [527, 1704, 17, 356, 260, 3578, 318, 1210, 2324, 1028],\n"," 767: [1193, 50, 1233, 1234, 1148, 4226, 2959, 912, 4011, 1213],\n"," 769: [260, 1196, 4993, 1210, 5952, 7153, 2571, 1288, 1148, 2858],\n"," 775: [260, 1210, 1148, 1288, 5952, 6539, 7153, 527, 1207, 1196],\n"," 785: [858, 2959, 1234, 912, 1213, 1193, 4226, 1233, 904, 1221],\n"," 786: [589, 293, 2692, 1148, 380, 4993, 7153, 1288, 296, 36],\n"," 789: [260, 1210, 1704, 3578, 17, 1196, 2324, 4993, 6539, 2571],\n"," 792: [527, 260, 1704, 4993, 1148, 3578, 17, 2571, 2028, 1210],\n"," 795: [260, 2571, 1196, 1221, 4226, 1213, 2959, 7153, 858, 1136],\n"," 798: [36, 4011, 1233, 2329, 223, 163, 1358, 1997, 4027, 16],\n"," 799: [919, 1035, 1234, 2324, 912, 3578, 260, 1207, 1704, 50],\n"," 802: [2571, 3578, 1036, 2000, 1148, 1240, 3147, 1917, 2001, 1291],\n"," 806: [1207, 912, 1233, 904, 2396, 1247, 923, 919, 4973, 1394],\n"," 807: [260, 858, 527, 593, 912, 50, 1234, 1213, 4226, 2324],\n"," 816: [527, 1721, 2396, 62, 1207, 1584, 539, 919, 597, 500],\n"," 827: [260, 1148, 527, 904, 4993, 318, 2858, 2571, 7153, 1196],\n"," 828: [356, 539, 920, 1035, 1380, 1721, 786, 919, 1101, 3578],\n"," 846: [2959, 858, 1193, 2542, 2502, 1233, 1036, 1234, 4011, 3147],\n"," 859: [50, 1221, 47, 1213, 608, 318, 1201, 750, 1234, 2502],\n"," 862: [1035, 1721, 919, 1028, 1207, 62, 590, 2324, 2396, 920],\n"," 870: [50, 260, 1234, 2571, 3578, 1148, 1198, 2324, 4226, 1036],\n"," 876: [1035, 919, 912, 1207, 1234, 364, 904, 2324, 1233, 4973],\n"," 881: [1035, 1380, 919, 920, 2324, 1234, 1210, 3578, 1704, 260],\n"," 885: [912, 4973, 1234, 1233, 2858, 4226, 1394, 923, 750, 2324],\n"," 886: [7153, 1148, 4993, 5952, 4226, 4011, 111, 6874, 1291, 1213],\n"," 889: [1148, 318, 1233, 527, 4993, 1288, 2762, 2028, 1247, 2858],\n"," 890: [380, 165, 318, 3147, 1148, 2001, 36, 1610, 1270, 1198],\n"," 891: [1288, 111, 1136, 1221, 1148, 750, 2019, 1213, 1199, 1208],\n"," 896: [912, 1219, 1394, 4973, 904, 858, 2019, 111, 2858, 923],\n"," 897: [1233, 912, 318, 858, 1193, 904, 4973, 1207, 1247, 1234],\n"," 898: [527, 318, 1148, 1704, 3578, 4993, 2762, 2028, 2324, 1207],\n"," 908: [1148, 593, 912, 1207, 1225, 1247, 1193, 3578, 50, 904],\n"," 922: [1242, 733, 2000, 919, 457, 1234, 1233, 1035, 1036, 2324],\n"," 930: [919, 1207, 1233, 1035, 912, 3147, 2396, 1242, 3578, 2324],\n"," 938: [3578, 50, 1234, 1036, 356, 527, 2324, 3147, 919, 1198],\n"," 956: [1196, 1210, 2959, 260, 6874, 1240, 1089, 223, 1201, 2571],\n"," 958: [1394, 912, 1035, 919, 4973, 608, 364, 1207, 1073, 904],\n"," 968: [527, 912, 1207, 904, 919, 1233, 1234, 2324, 1247, 1035],\n"," 977: [5952, 7153, 4993, 2997, 2019, 1219, 1233, 4034, 3996, 3897],\n"," 987: [1721, 1035, 919, 597, 539, 2396, 1641, 3147, 500, 1242],\n"," 990: [608, 912, 1394, 1035, 858, 2959, 4973, 919, 1089, 1234],\n"," 996: [1233, 912, 1207, 904, 1193, 1247, 2396, 858, 4973, 919],\n"," 1004: [527, 3578, 1234, 2324, 1148, 912, 1036, 1704, 919, 1233],\n"," 1005: [1148,\n"," 4993,\n"," 1704,\n"," 2028,\n"," 3578,\n"," 2762,\n"," 2571,\n"," 1288,\n"," 1207,\n"," 7153],\n"," 1008: [1234, 1213, 1233, 904, 2019, 1089, 750, 4011, 908, 2324],\n"," 1029: [2959, 1036, 589, 1240, 2502, 2542, 1234, 4011, 1213, 1089],\n"," 1037: [858, 1233, 912, 1193, 318, 904, 4973, 2019, 750, 4226],\n"," 1050: [260, 2571, 7153, 5952, 4993, 1288, 541, 2858, 1136, 1196],\n"," 4: [356, 260, 1210, 527, 1196, 3578, 1704, 17, 318, 2324],\n"," 8: [296, 2502, 2692, 1221, 318, 1201, 1213, 750, 1193, 1288],\n"," 18: [50, 4226, 1221, 1200, 293, 2692, 1213, 6874, 858, 1148],\n"," 36: [318, 1234, 912, 47, 1193, 2542, 4973, 1233, 1035, 1201],\n"," 47: [380, 1234, 356, 47, 2000, 457, 3147, 2324, 6, 1304],\n"," 59: [527, 260, 1704, 1207, 1148, 2324, 904, 912, 1234, 3578],\n"," 62: [356, 1035, 3578, 919, 1234, 2324, 457, 50, 1036, 527],\n"," 77: [318, 3578, 50, 1234, 260, 2324, 1210, 1704, 1036, 919],\n"," 79: [858, 912, 2959, 1221, 1213, 2019, 4973, 750, 4226, 1089],\n"," 80: [1233, 858, 1193, 912, 1234, 904, 1148, 4226, 4973, 1247],\n"," 119: [912, 50, 1234, 919, 904, 4973, 1233, 527, 608, 1207],\n"," 122: [541, 912, 923, 1199, 1219, 908, 1233, 750, 924, 2019],\n"," 125: [1201, 1288, 778, 1617, 908, 1304, 2329, 1090, 1234, 1252],\n"," 126: [4973, 908, 1035, 2019, 1233, 17, 750, 58, 551, 1199],\n"," 132: [912, 1234, 919, 1233, 858, 50, 1035, 1193, 1207, 904],\n"," 141: [3578, 318, 1704, 150, 2324, 919, 1035, 1234, 260, 1721],\n"," 154: [318, 4226, 858, 1234, 1221, 4011, 1233, 1136, 7153, 2019],\n"," 155: [1233, 912, 2019, 904, 4973, 1234, 4226, 750, 1221, 1222],\n"," 163: [2571, 2959, 1200, 541, 1206, 6874, 1240, 1221, 293, 7153],\n"," 164: [1233, 858, 912, 1193, 4973, 904, 1234, 1222, 1207, 919],\n"," 170: [1221, 6874, 1200, 1201, 1213, 2502, 541, 1089, 260, 589],\n"," 171: [912, 919, 1035, 364, 1207, 1234, 1233, 1242, 2324, 593],\n"," 176: [318, 527, 1207, 260, 904, 912, 1233, 1148, 2324, 1704],\n"," 182: [260, 1148, 904, 1233, 1584, 1035, 1288, 1198, 1252, 1210],\n"," 190: [527, 1288, 17, 1704, 2858, 1148, 34, 509, 318, 1358],\n"," 197: [2959, 296, 912, 2502, 608, 4973, 2019, 1222, 593, 1213],\n"," 198: [1233, 2019, 4973, 904, 908, 4011, 1201, 2542, 1198, 1247],\n"," 199: [527, 1035, 919, 318, 1207, 1704, 2324, 364, 356, 1028],\n"," 212: [2858, 2502, 1222, 1233, 2542, 3897, 1230, 1079, 1247, 4011],\n"," 221: [2959, 1240, 1234, 3578, 4011, 2542, 3147, 6, 2502, 4226],\n"," 223: [356, 364, 110, 260, 858, 1210, 1148, 1246, 1252, 1288],\n"," 226: [912, 1193, 2959, 1233, 2019, 4973, 750, 1252, 1230, 1247],\n"," 241: [2959, 858, 296, 50, 1193, 912, 2502, 1233, 318, 2542],\n"," 247: [1035, 919, 318, 3578, 1234, 2324, 1721, 1380, 527, 3147],\n"," 249: [858, 1234, 1233, 912, 1193, 904, 260, 1148, 2019, 1221],\n"," 254: [7153, 4226, 2019, 1213, 4993, 1148, 608, 1198, 1201, 904],\n"," 264: [318, 912, 260, 904, 858, 608, 527, 1234, 1233, 4973],\n"," 273: [1234, 1233, 912, 1193, 858, 904, 1148, 1207, 1242, 919],\n"," 275: [1219, 1394, 912, 4973, 858, 923, 2019, 1221, 908, 1213],\n"," 276: [1221, 2019, 1148, 1233, 908, 904, 1201, 1230, 912, 1266],\n"," 293: [527, 356, 3578, 2324, 1234, 919, 1035, 1704, 260, 50],\n"," 294: [2959, 593, 1213, 858, 1234, 1036, 1089, 1221, 4226, 1240],\n"," 295: [858, 912, 904, 1233, 318, 4973, 1193, 1250, 593, 923],\n"," 318: [1288, 1094, 1233, 1207, 924, 1199, 1358, 1219, 908, 1252],\n"," 321: [318, 527, 919, 912, 1207, 1035, 1234, 2324, 904, 1233],\n"," 345: [318, 50, 858, 1233, 4226, 1213, 1221, 912, 2019, 1148],\n"," 346: [3147, 733, 2000, 1242, 1233, 318, 1036, 1193, 858, 589],\n"," 348: [1234, 260, 1233, 912, 904, 1136, 1247, 908, 1225, 2324],\n"," 349: [260, 1704, 1210, 1196, 4993, 1148, 2324, 5952, 3578, 7153],\n"," 354: [527, 1207, 919, 2324, 260, 1234, 904, 1233, 1035, 1148],\n"," 359: [608, 111, 1394, 1199, 2019, 912, 1221, 858, 904, 1208],\n"," 366: [1233, 318, 1193, 1148, 912, 858, 904, 1247, 1230, 1207],\n"," 369: [2571, 50, 1240, 260, 47, 1210, 1036, 589, 6874, 1196],\n"," 384: [3578, 527, 1704, 260, 380, 2324, 1210, 1148, 2762, 2571],\n"," 390: [1221, 912, 2019, 750, 1233, 1089, 908, 541, 111, 1222],\n"," 392: [858, 912, 1233, 2019, 2858, 904, 608, 1193, 4973, 750],\n"," 403: [2959, 858, 912, 2019, 608, 4973, 1221, 1213, 1193, 1089],\n"," 407: [318, 36, 293, 1288, 4011, 4226, 1193, 3147, 527, 2858],\n"," 414: [858, 1193, 2502, 1222, 2019, 1233, 750, 1221, 2542, 912],\n"," 431: [858, 912, 1234, 1233, 904, 1193, 593, 527, 1207, 4226],\n"," 454: [260, 527, 1210, 318, 1196, 1704, 3578, 4993, 2324, 356],\n"," 465: [912, 2019, 750, 1394, 1233, 904, 541, 1199, 1201, 1136],\n"," 481: [1193, 1233, 1234, 1148, 750, 1222, 2692, 1247, 4973, 3147],\n"," 485: [2019, 750, 912, 4973, 2502, 1233, 1206, 2542, 1394, 1201],\n"," 503: [260, 50, 1196, 608, 1210, 1234, 912, 47, 904, 1089],\n"," 513: [7153, 5952, 4993, 4226, 4973, 3897, 1358, 1250, 235, 1213],\n"," 517: [260, 1210, 1196, 593, 527, 1234, 2571, 1198, 1213, 5952],\n"," 545: [1199, 1230, 912, 908, 1234, 1148, 1997, 1250, 36, 3147],\n"," 552: [260, 47, 2571, 1221, 593, 1196, 1240, 1198, 1036, 858],\n"," 554: [260, 1196, 1288, 1199, 1210, 1136, 541, 923, 1080, 1221],\n"," 555: [1036, 1221, 36, 1304, 2692, 541, 2324, 1200, 904, 6],\n"," 561: [50, 293, 318, 1198, 1148, 1288, 223, 36, 1136, 1221],\n"," 565: [318, 527, 1234, 912, 1233, 50, 2324, 904, 593, 260],\n"," 591: [1233, 2396, 1230, 912, 1193, 858, 1252, 1073, 4973, 3897],\n"," 617: [1704, 4993, 3578, 318, 2571, 2028, 2628, 2762, 5952, 1288],\n"," 621: [1233, 2542, 1080, 1278, 4011, 2329, 2692, 3897, 1387, 7153],\n"," 622: [858, 1148, 1193, 3578, 50, 1234, 1233, 4226, 1036, 912],\n"," 623: [1233, 912, 858, 1193, 50, 904, 1234, 1148, 4226, 2858],\n"," 633: [1199, 4973, 924, 2997, 1080, 778, 1247, 1079, 2329, 1148],\n"," 636: [2858, 1234, 1247, 4226, 1288, 1207, 1230, 36, 1252, 1242],\n"," 638: [1148, 4993, 1704, 2571, 7153, 1288, 2028, 5952, 2858, 2762],\n"," 641: [356, 3578, 50, 1210, 260, 527, 1234, 2324, 110, 1196],\n"," 646: [318, 50, 260, 1234, 527, 912, 858, 4226, 904, 1213],\n"," 654: [919, 527, 1035, 912, 1234, 2324, 1207, 904, 50, 3578],\n"," 655: [527, 593, 912, 3578, 50, 1234, 2324, 4226, 904, 1233],\n"," 658: [50, 858, 318, 1221, 1213, 1193, 2019, 750, 541, 2502],\n"," 660: [1233, 1207, 923, 912, 1247, 1148, 2396, 1252, 1230, 1250],\n"," 661: [50, 2571, 1148, 1036, 1193, 1234, 4226, 858, 1233, 4011],\n"," 677: [1207, 912, 904, 1233, 2324, 260, 1148, 1234, 919, 1247],\n"," 714: [912, 1234, 1233, 593, 1207, 1193, 1247, 1250, 1213, 1242],\n"," 715: [2959, 858, 1221, 50, 541, 2019, 750, 1193, 1213, 4226],\n"," 723: [912, 858, 4973, 1394, 904, 593, 1207, 2019, 919, 1234],\n"," 731: [3578, 50, 1148, 1234, 1198, 3147, 457, 4993, 4306, 36],\n"," 742: [1233, 1193, 1288, 1230, 36, 858, 904, 2692, 1252, 527],\n"," 743: [527, 318, 3578, 260, 1210, 1704, 2324, 1035, 110, 593],\n"," 752: [912, 318, 858, 4973, 904, 1233, 608, 2019, 1193, 593],\n"," 772: [1394, 912, 1219, 904, 858, 923, 318, 2019, 1207, 908],\n"," 780: [3578, 912, 919, 904, 1207, 1704, 1035, 1148, 4226, 1247],\n"," 788: [1234, 912, 919, 1233, 1193, 1213, 296, 904, 1035, 4973],\n"," 814: [858, 1193, 912, 1222, 4973, 1230, 1252, 2019, 904, 750],\n"," 823: [593, 50, 318, 1234, 356, 2324, 919, 527, 47, 3578],\n"," 826: [1148, 1199, 2692, 2502, 293, 36, 1247, 1198, 1225, 1266],\n"," 833: [1233, 1193, 904, 2019, 912, 4226, 7153, 50, 1247, 4973],\n"," 838: [2019, 1394, 750, 1199, 1221, 924, 1222, 1080, 1250, 1252],\n"," 842: [858, 1148, 527, 912, 919, 1207, 1247, 1193, 904, 1234],\n"," 852: [50, 3578, 1234, 1036, 260, 110, 527, 1210, 2324, 1704],\n"," 878: [2858, 7153, 1148, 5952, 4993, 1233, 2571, 4226, 1617, 2997],\n"," 887: [260, 858, 1148, 1252, 1288, 5952, 7153, 111, 527, 541],\n"," 895: [527, 1704, 3578, 1721, 318, 17, 2324, 62, 587, 1584],\n"," 899: [318, 50, 858, 260, 1233, 1234, 912, 1193, 4226, 1148],\n"," 902: [912, 1234, 1233, 4973, 904, 1221, 2019, 919, 908, 2324],\n"," 907: [1233, 1193, 1148, 858, 904, 912, 2858, 1247, 1230, 4226],\n"," 928: [912, 1233, 904, 858, 2019, 1207, 111, 1394, 1247, 1219],\n"," 936: [318, 1234, 1213, 2324, 904, 1304, 1148, 750, 908, 1250],\n"," 964: [1233, 912, 904, 4973, 1234, 1207, 1247, 1148, 1250, 1252],\n"," 970: [858, 912, 1073, 1233, 919, 1035, 2959, 104, 4973, 2791],\n"," 972: [260, 1234, 1148, 912, 904, 1233, 50, 1198, 919, 1250],\n"," 988: [7153, 1221, 908, 36, 6874, 4034, 1732, 1222, 4027, 2329],\n"," 1001: [318, 527, 912, 919, 1234, 260, 1207, 2324, 1035, 904],\n"," 1013: [1233, 1193, 1230, 1148, 912, 1252, 1247, 904, 3147, 1207],\n"," 1018: [50, 1221, 1213, 858, 2019, 750, 1193, 1201, 912, 1234],\n"," 1020: [912, 1230, 2959, 3147, 1233, 318, 1090, 1242, 2692, 2542],\n"," 1028: [260, 1210, 527, 1196, 3578, 1704, 318, 2324, 17, 110],\n"," 1031: [50, 1234, 318, 912, 858, 1233, 1193, 2959, 4226, 1213],\n"," 1035: [2502, 2542, 50, 1625, 6, 1220, 1234, 1201, 4011, 1278],\n"," 1038: [1148, 150, 527, 912, 1207, 1247, 2396, 904, 1234, 2762],\n"," 1045: [1035, 1234, 4973, 608, 1394, 904, 1207, 1233, 908, 1250],\n"," 1046: [318, 3578, 1234, 1036, 50, 527, 2324, 3147, 919, 1242],\n"," 35: [2997, 858, 231, 2019, 924, 1221, 1199, 541, 1193, 2502],\n"," 72: [318, 50, 2571, 1036, 1234, 1148, 1193, 858, 1233, 4226],\n"," 91: [1233, 111, 1199, 2019, 858, 4973, 1252, 7153, 5952, 908],\n"," 106: [1721, 597, 539, 587, 3147, 500, 62, 2396, 150, 3578],\n"," 128: [260, 858, 1148, 1288, 5952, 7153, 608, 912, 923, 1207],\n"," 158: [260, 318, 2571, 1196, 4226, 7153, 1213, 1221, 1198, 1148],\n"," 160: [47, 111, 541, 593, 608, 912, 1219, 1221, 1193, 50],\n"," 175: [1207, 908, 1247, 1199, 1148, 3897, 1230, 1073, 2324, 1242],\n"," 196: [3578, 1148, 1704, 3147, 733, 2762, 2324, 50, 4993, 36],\n"," 203: [2959, 858, 3578, 4011, 4226, 1193, 912, 3147, 2571, 1233],\n"," 206: [858, 923, 1199, 1207, 1247, 1193, 2858, 1233, 1394, 2019],\n"," 207: [2858, 1288, 7153, 5952, 4993, 111, 260, 1148, 1136, 2571],\n"," 215: [527, 2329, 3147, 1246, 58, 1090, 337, 1035, 1201, 509],\n"," 255: [50, 1234, 3578, 1036, 2324, 527, 260, 1198, 47, 919],\n"," 265: [356, 597, 527, 150, 1704, 587, 318, 2000, 457, 3147],\n"," 340: [1380, 3578, 2959, 2324, 920, 858, 1968, 2081, 4011, 1304],\n"," 404: [3578, 1036, 1234, 50, 3147, 593, 1242, 733, 2000, 2324],\n"," 433: [1148, 1233, 318, 2858, 1288, 1193, 2571, 7153, 4993, 904],\n"," 440: [1234, 912, 1233, 919, 904, 4973, 4011, 2324, 2019, 1250],\n"," 444: [318, 3578, 527, 1234, 2324, 1704, 1148, 3147, 150, 260],\n"," 450: [912, 1234, 2324, 1035, 2019, 923, 1704, 1247, 364, 1250],\n"," 479: [260, 1196, 5952, 1210, 7153, 4993, 1219, 1288, 1221, 904],\n"," 491: [2997, 541, 1193, 750, 1233, 2502, 296, 1208, 924, 1732],\n"," 506: [2858, 7153, 1221, 2019, 5952, 2571, 4226, 4993, 1233, 904],\n"," 523: [2858, 1233, 904, 912, 1148, 111, 2019, 923, 7153, 1288],\n"," 539: [260, 1207, 904, 912, 1219, 923, 2858, 17, 1196, 1704],\n"," 541: [7153, 318, 4993, 5952, 1148, 4226, 111, 904, 1233, 2019],\n"," 616: [1234, 2324, 919, 912, 1704, 1035, 904, 1207, 1196, 1233],\n"," 647: [3147, 2000, 597, 2706, 587, 539, 1242, 3578, 104, 1036],\n"," 659: [1148, 1288, 1233, 904, 1193, 2028, 2019, 750, 50, 36],\n"," 695: [1233, 2858, 904, 1193, 4993, 1247, 1288, 2762, 1207, 7153],\n"," 700: [260, 50, 296, 1196, 2571, 1210, 4226, 1213, 1221, 7153],\n"," 707: [260, 318, 1196, 1210, 4993, 5952, 1148, 7153, 3578, 904],\n"," 748: [260, 593, 1234, 1196, 1210, 3578, 1198, 2324, 2571, 296],\n"," 759: [296, 2959, 50, 47, 593, 1089, 1221, 1213, 318, 608],\n"," 784: [912, 1233, 1193, 904, 4973, 111, 908, 1199, 1222, 1247],\n"," 790: [1233, 1148, 1207, 912, 2762, 1193, 2028, 2324, 3578, 2858],\n"," 835: [858, 111, 608, 912, 923, 1199, 1221, 1193, 904, 908],\n"," 844: [318, 527, 1704, 50, 2324, 1234, 150, 1148, 733, 4306],\n"," 871: [2019, 2858, 2959, 2997, 4226, 5952, 4973, 7153, 1394, 1233],\n"," 875: [1148, 4993, 260, 1288, 2028, 2762, 2858, 1704, 7153, 2571],\n"," 892: [4226, 2959, 2019, 750, 912, 1193, 1136, 541, 1234, 1233],\n"," 919: [3578, 3147, 318, 597, 1036, 589, 1148, 356, 587, 150],\n"," 935: [1148, 2692, 318, 1193, 296, 2959, 541, 293, 7153, 1288],\n"," 942: [356, 3578, 1210, 260, 110, 50, 1036, 527, 1704, 1234],\n"," 960: [260, 318, 2858, 50, 1221, 2019, 858, 1213, 111, 1196],\n"," 1006: [912, 2858, 904, 1233, 923, 858, 4973, 1207, 5952, 2019],\n"," 1026: [912, 923, 1207, 904, 2858, 1233, 1247, 318, 527, 858],\n"," 1047: [260, 912, 904, 1233, 5952, 1196, 1221, 1193, 908, 1148],\n"," 65: [1233, 1207, 527, 904, 1193, 2762, 3147, 2028, 1250, 1252],\n"," 135: [318, 50, 2571, 1148, 4993, 7153, 527, 4226, 5952, 2858],\n"," 152: [296, 527, 1234, 4226, 1213, 7153, 4993, 5952, 1148, 2324],\n"," 166: [1035, 919, 318, 1234, 356, 527, 2324, 912, 364, 50],\n"," 179: [912, 2959, 4973, 318, 904, 4226, 2997, 2502, 1136, 1208],\n"," 188: [318, 260, 3578, 2324, 1234, 1704, 50, 919, 912, 1207],\n"," 217: [318, 527, 1207, 912, 1233, 919, 904, 1035, 1234, 1247],\n"," 253: [1148, 1233, 318, 1288, 904, 4993, 1193, 527, 7153, 1247],\n"," 262: [589, 2000, 1234, 1148, 36, 2324, 2001, 1270, 1304, 1917],\n"," 277: [318, 260, 50, 2571, 1148, 4226, 1196, 1198, 1234, 2858],\n"," 289: [1233, 1199, 541, 6377, 1198, 1073, 364, 778, 1035, 2194],\n"," 300: [1394, 4973, 1207, 1219, 1250, 1234, 593, 2858, 1035, 4226],\n"," 302: [4993, 7153, 1198, 5952, 36, 1036, 293, 4226, 1234, 2324],\n"," 308: [318, 912, 919, 1233, 1207, 1035, 1234, 904, 527, 858],\n"," 311: [318, 527, 919, 1035, 2324, 1234, 912, 1207, 260, 593],\n"," 328: [1288, 5952, 7153, 111, 1221, 1136, 4993, 296, 2019, 4226],\n"," 329: [318, 912, 1234, 50, 858, 919, 1233, 904, 1193, 1035],\n"," 344: [1233, 1193, 1230, 858, 1252, 923, 904, 1247, 4973, 1207],\n"," 347: [2858, 111, 5952, 608, 7153, 923, 2019, 904, 318, 1136],\n"," 358: [50, 2959, 1036, 47, 4011, 1234, 858, 4226, 2542, 3578],\n"," 474: [318, 1148, 4993, 7153, 2858, 1288, 1233, 50, 5952, 527],\n"," 483: [3578, 919, 1035, 1721, 2324, 1704, 1207, 1234, 3147, 1242],\n"," 509: [260, 1148, 912, 1207, 1221, 1230, 1247, 1198, 3578, 904],\n"," 578: [2959, 2502, 6874, 2542, 47, 50, 2692, 1206, 293, 1221],\n"," 589: [318, 5952, 4993, 7153, 2858, 2571, 1288, 4226, 912, 1213],\n"," 643: [4973, 1394, 2324, 2019, 4226, 1242, 2329, 1246, 1198, 2959],\n"," 672: [318, 1234, 4226, 5952, 7153, 4993, 1240, 1036, 1148, 1136],\n"," 679: [919, 1207, 1035, 912, 1233, 2396, 318, 527, 904, 364],\n"," 680: [318, 527, 3578, 2324, 1704, 919, 1234, 150, 457, 593],\n"," 691: [1148, 1233, 2762, 904, 1247, 1207, 1193, 1234, 3147, 912],\n"," 702: [2571, 4993, 7153, 5952, 1148, 2858, 1288, 50, 4226, 1198],\n"," 708: [318, 1148, 1234, 527, 1233, 1193, 4226, 1198, 3578, 904],\n"," 730: [858, 912, 1193, 50, 1234, 3147, 1233, 1148, 1242, 4011],\n"," 751: [318, 527, 1234, 50, 919, 1035, 260, 2324, 356, 912],\n"," 773: [527, 4993, 1704, 2028, 17, 150, 509, 2762, 1288, 497],\n"," 803: [62, 736, 597, 3147, 802, 653, 1148, 586, 527, 509],\n"," 809: [1210, 318, 2571, 7153, 1704, 1198, 1288, 593, 1234, 3578],\n"," 820: [1288, 318, 1233, 111, 904, 1221, 1213, 1247, 1198, 1617],\n"," 824: [4973, 364, 318, 527, 923, 1183, 588, 1213, 50, 595],\n"," 863: [858, 1193, 912, 2019, 4226, 1221, 2959, 750, 904, 1136],\n"," 865: [527, 1234, 1148, 912, 904, 1233, 1198, 858, 2324, 1193],\n"," 867: [912, 593, 357, 1193, 17, 1682, 58, 1148, 2291, 2762],\n"," 911: [608, 318, 527, 912, 904, 5952, 593, 50, 1207, 4973],\n"," 915: [527, 3578, 1704, 318, 110, 2324, 150, 1196, 1198, 17],\n"," 939: [527, 919, 1035, 2324, 912, 1207, 1234, 1704, 904, 3578],\n"," 946: [912, 1221, 1193, 908, 1234, 4226, 2959, 296, 1213, 1233],\n"," 954: [527, 318, 593, 1704, 50, 356, 2324, 1234, 110, 1198],\n"," 957: [1148, 5952, 7153, 4993, 1199, 923, 750, 2019, 904, 1094],\n"," 971: [1252, 541, 923, 1247, 1193, 904, 1208, 1233, 1207, 1148],\n"," 986: [260, 1210, 1196, 3578, 1234, 1704, 2324, 1198, 1036, 2571],\n"," 992: [2858, 527, 1148, 4993, 904, 5952, 1288, 7153, 1233, 923],\n"," 92: [593, 919, 50, 1035, 1380, 1234, 920, 2324, 296, 2959],\n"," 107: [1193, 318, 2019, 1148, 750, 904, 2858, 4226, 1252, 1230],\n"," 131: [2959, 1200, 1206, 6874, 2571, 541, 1240, 2502, 1221, 293],\n"," 138: [223, 288, 1221, 1288, 111, 1527, 1213, 2502, 2997, 2692],\n"," 145: [2959, 1221, 858, 1213, 608, 2019, 318, 4226, 750, 541],\n"," 183: [356, 150, 3578, 2324, 17, 4306, 50, 1148, 6539, 2762],\n"," 209: [1233, 912, 1193, 923, 858, 904, 2858, 4973, 2019, 1247],\n"," 230: [318, 858, 1193, 904, 2019, 4226, 111, 7153, 4973, 908],\n"," 263: [904, 1234, 1233, 1148, 2324, 1247, 4011, 1250, 919, 1225],\n"," 305: [318, 919, 1234, 912, 1207, 3578, 1233, 2324, 1035, 904],\n"," 314: [50, 1213, 2019, 4226, 608, 7153, 2571, 1233, 2959, 1148],\n"," 319: [858, 608, 912, 1394, 2019, 4973, 2959, 1221, 1233, 1193],\n"," 325: [2571, 2692, 733, 4011, 780, 3147, 3578, 293, 2959, 36],\n"," 341: [5952, 7153, 111, 541, 608, 912, 1221, 1193, 50, 904],\n"," 471: [318, 1233, 912, 904, 1193, 858, 1207, 527, 1148, 1234],\n"," 488: [318, 1036, 3147, 2000, 3578, 733, 50, 1234, 1242, 2959],\n"," 495: [527, 3578, 1704, 318, 780, 17, 1148, 2028, 11, 356],\n"," 532: [231, 2959, 1732, 2502, 924, 1222, 750, 2019, 2542, 1193],\n"," 564: [1148, 527, 912, 919, 1207, 1247, 2028, 3578, 1234, 2762],\n"," 574: [356, 3578, 2000, 318, 733, 3147, 50, 1234, 1101, 110],\n"," 590: [912, 858, 904, 1234, 4973, 527, 1233, 1207, 4226, 919],\n"," 603: [1035, 919, 364, 62, 2396, 500, 1028, 1641, 920, 1207],\n"," 674: [2959, 296, 2502, 2692, 2542, 858, 1221, 750, 50, 4226],\n"," 753: [858, 1148, 541, 912, 1221, 1193, 50, 904, 4226, 1136],\n"," 810: [1035, 919, 912, 593, 318, 1234, 858, 50, 4973, 2324],\n"," 830: [318, 3578, 17, 4993, 1148, 2762, 6539, 4306, 1234, 2571],\n"," 841: [50, 318, 1148, 7153, 2858, 858, 1233, 296, 1198, 1136],\n"," 856: [318, 50, 912, 593, 858, 1234, 904, 1233, 1213, 4973],\n"," 921: [50, 318, 593, 260, 1234, 296, 1036, 3578, 2571, 1198],\n"," 933: [1233, 912, 904, 4973, 1207, 858, 1193, 2858, 1394, 924],\n"," 976: [912, 904, 608, 4973, 1233, 858, 260, 2858, 1207, 923],\n"," 10: [1148, 2858, 1288, 1247, 1207, 1230, 904, 923, 2396, 318],\n"," 19: [1704, 4993, 3578, 2571, 2324, 5952, 7153, 2028, 2762, 4306],\n"," 41: [318, 527, 2324, 593, 1234, 3578, 1704, 590, 150, 1207],\n"," 43: [780, 1148, 1252, 150, 527, 1207, 1230, 1247, 1584, 2028],\n"," 44: [858, 1148, 1252, 7153, 111, 541, 912, 923, 1199, 1207],\n"," 45: [2959, 318, 1234, 2542, 4011, 296, 1193, 2502, 2000, 733],\n"," 51: [47, 1221, 50, 1089, 2959, 1213, 318, 1201, 1394, 2019],\n"," 56: [608, 1199, 1394, 1183, 1233, 1073, 318, 778, 2997, 2959],\n"," 61: [919, 1035, 2396, 1233, 904, 150, 2324, 1242, 1246, 364],\n"," 68: [318, 527, 3578, 1035, 1234, 912, 150, 1233, 1704, 3147],\n"," 69: [1207, 904, 1233, 923, 1148, 2028, 919, 4973, 1704, 1250],\n"," 78: [2019, 1221, 1213, 3897, 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750, 924, 1136, 608, 1080],\n"," 279: [858, 608, 912, 1221, 1233, 750, 2019, 4973, 1193, 1394],\n"," 280: [318, 3147, 1233, 3578, 1242, 1207, 1148, 1234, 919, 150],\n"," 285: [858, 541, 923, 1199, 1193, 1208, 2858, 1233, 750, 924],\n"," 298: [912, 1394, 608, 923, 4973, 904, 1233, 858, 1219, 2019],\n"," 301: [111, 7153, 5952, 1233, 4226, 1193, 904, 1148, 4993, 924],\n"," 304: [527, 318, 1704, 1207, 3578, 1584, 62, 2028, 1148, 17],\n"," 333: [150, 2396, 1207, 500, 3578, 919, 11, 3147, 1035, 1961],\n"," 334: [318, 527, 3578, 2324, 1704, 593, 1035, 50, 150, 1207],\n"," 338: [50, 2959, 608, 1213, 1221, 858, 260, 912, 1089, 4226],\n"," 350: [2858, 527, 923, 904, 1233, 1207, 1288, 5952, 1148, 4993],\n"," 351: [1219, 5952, 7153, 2019, 4993, 1199, 4226, 1233, 541, 1080],\n"," 353: [1136, 1221, 2858, 6874, 260, 1080, 750, 223, 2997, 1208],\n"," 378: [318, 1233, 1193, 2858, 904, 1148, 912, 2019, 1247, 4226],\n"," 386: [1148, 1234, 1233, 50, 2571, 1207, 904, 1193, 4306, 919],\n"," 397: [260, 1207, 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550: [3578, 3147, 2324, 1242, 2028, 1704, 4306, 1246, 6377, 4973],\n"," 560: [858, 2502, 1221, 1222, 2019, 1213, 912, 1201, 750, 4973],\n"," 573: [260, 1196, 1210, 912, 1207, 904, 1219, 919, 1035, 5952],\n"," 579: [1240, 6874, 110, 4226, 223, 1221, 7153, 1200, 1213, 4993],\n"," 582: [1233, 923, 924, 4973, 750, 2858, 1148, 3897, 2997, 1199],\n"," 583: [1233, 2858, 1193, 858, 2019, 923, 111, 924, 904, 1288],\n"," 584: [318, 919, 3578, 1234, 1035, 2324, 593, 3147, 912, 1242],\n"," 587: [111, 923, 2858, 2019, 1199, 912, 858, 1233, 4973, 904],\n"," 588: [260, 1148, 4993, 2571, 7153, 1288, 2858, 5952, 1233, 904],\n"," 596: [1094, 3114, 5952, 4993, 1148, 1394, 7153, 1250, 1358, 1246],\n"," 602: [2858, 318, 1148, 7153, 1233, 1288, 1193, 2571, 1136, 5952],\n"," 618: [1233, 912, 904, 1193, 1207, 1247, 858, 1148, 4973, 923],\n"," 624: [589, 260, 858, 1210, 1148, 1252, 1288, 5952, 7153, 32],\n"," 627: [260, 7153, 1148, 5952, 1288, 1136, 1196, 527, 904, 1233],\n"," 635: [858, 1148, 912, 1207, 1247, 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1234, 1210],\n"," 1033: [912, 858, 1234, 1233, 1193, 904, 4226, 1213, 4973, 1148],\n"," 1039: [111, 2858, 1136, 2019, 7153, 750, 1221, 1288, 1199, 5952],\n"," 1040: [1721, 539, 500, 587, 1035, 62, 919, 2396, 3147, 527],\n"," 1053: [50, 318, 296, 593, 2959, 47, 1213, 4226, 858, 912],\n"," 42: [1233, 1148, 2858, 1193, 1288, 1230, 318, 1252, 1247, 904],\n"," 73: [912, 527, 1234, 4973, 4226, 2324, 1193, 2019, 1247, 1210],\n"," 82: [1035, 919, 912, 318, 1207, 1234, 527, 1394, 593, 904],\n"," 159: [527, 318, 260, 1207, 1148, 1704, 904, 2028, 4993, 1233],\n"," 161: [1221, 111, 1213, 2019, 750, 858, 1288, 1089, 1080, 1199],\n"," 192: [1233, 912, 1199, 1208, 318, 904, 908, 924, 1201, 1080],\n"," 216: [527, 1704, 3578, 150, 356, 1207, 6539, 1148, 2028, 590],\n"," 219: [17, 1210, 318, 1196, 590, 6539, 1207, 4993, 2324, 2028],\n"," 290: [318, 912, 1035, 1234, 593, 527, 1207, 1233, 904, 2324],\n"," 379: [318, 912, 1234, 1233, 527, 593, 50, 904, 858, 919],\n"," 389: [260, 1196, 50, 1288, 1148, 904, 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2858, 2028, 318, 1584, 3114, 2762, 6377],\n"," 352: [1207, 527, 62, 1233, 919, 318, 539, 1584, 150, 3147],\n"," 429: [2858, 111, 923, 912, 2019, 858, 1233, 904, 4973, 1199],\n"," 625: [356, 318, 3578, 527, 1035, 919, 2324, 1234, 1704, 1380],\n"," 808: [318, 1233, 1234, 50, 1193, 1148, 3147, 858, 3578, 912],\n"," 843: [923, 2858, 1233, 1288, 1148, 7153, 3897, 5952, 4993, 912],\n"," 847: [318, 1233, 1234, 912, 527, 3147, 919, 3578, 1242, 1207],\n"," 963: [318, 593, 50, 1234, 356, 919, 1035, 3578, 1036, 2324],\n"," 975: [2959, 1221, 47, 1213, 858, 318, 1089, 593, 4226, 1201],\n"," 998: [2858, 1233, 4973, 1207, 1219, 318, 858, 260, 527, 2019],\n"," 75: [50, 260, 1196, 1210, 318, 47, 593, 2571, 2959, 1213],\n"," 427: [1233, 912, 1193, 904, 1148, 4226, 1247, 1207, 2858, 4973],\n"," 466: [1148, 1288, 2858, 4993, 7153, 318, 2571, 260, 1233, 1193],\n"," 801: [608, 318, 2858, 50, 1219, 111, 904, 912, 1213, 1221],\n"," 848: [260, 527, 318, 50, 1234, 593, 2324, 912, 904, 1704],\n"," 888: [260, 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[1240, 293, 1148, 1288, 1136, 1221, 5952, 6874, 1196, 36],\n"," 251: [1219, 111, 1089, 1394, 2019, 2959, 912, 4973, 50, 750],\n"," 258: [318, 1234, 50, 3578, 1233, 1148, 3147, 1036, 527, 593],\n"," 278: [318, 1234, 1704, 2324, 3578, 904, 1148, 1207, 912, 50],\n"," 388: [318, 260, 296, 593, 1196, 1234, 1213, 47, 1210, 858],\n"," 551: [318, 3147, 3578, 780, 1148, 150, 597, 11, 2762, 2396],\n"," 606: [1233, 231, 1193, 1230, 924, 1252, 2997, 1222, 2019, 923],\n"," 614: [318, 912, 1233, 904, 1234, 1193, 527, 1207, 50, 1148],\n"," 681: [1233, 923, 912, 904, 1207, 4973, 1247, 1193, 858, 924],\n"," 686: [2959, 912, 4973, 1193, 231, 1213, 296, 2502, 318, 919],\n"," 704: [1148, 1036, 3147, 318, 1233, 1234, 733, 1198, 527, 1193],\n"," 711: [2959, 50, 1221, 858, 318, 1213, 4226, 593, 2019, 608],\n"," 718: [260, 318, 50, 1196, 527, 1210, 1234, 2571, 1198, 904],\n"," 873: [318, 1148, 527, 1207, 904, 1247, 1193, 912, 2762, 3147],\n"," 962: [1233, 318, 858, 1193, 912, 904, 1234, 1148, 50, 1247],\n"," 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229: [318, 50, 593, 912, 1196, 296, 858, 1213, 904, 1234],\n"," 237: [527, 17, 1721, 150, 1028, 590, 1035, 62, 919, 508],\n"," 242: [858, 1148, 1252, 1288, 527, 912, 923, 1207, 1230, 1247],\n"," 252: [318, 4973, 923, 1247, 1234, 858, 1148, 1219, 1250, 1394],\n"," 266: [858, 111, 541, 1221, 50, 4226, 1136, 296, 1213, 750],\n"," 270: [2858, 318, 527, 904, 5952, 1148, 1233, 4993, 7153, 1288],\n"," 282: [1233, 318, 912, 1193, 904, 858, 1207, 1247, 1148, 4973],\n"," 297: [318, 50, 296, 1196, 1213, 593, 4226, 1221, 2858, 858],\n"," 309: [1148, 1233, 318, 527, 1247, 1193, 2858, 1288, 904, 2762],\n"," 316: [1148, 1233, 318, 1230, 1193, 1288, 1247, 2762, 3147, 2858],\n"," 327: [593, 1221, 50, 2959, 296, 1213, 318, 912, 4226, 2019],\n"," 356: [318, 1233, 912, 904, 1148, 1193, 858, 527, 1247, 1234],\n"," 393: [1233, 318, 1193, 912, 1148, 3147, 1207, 904, 1247, 858],\n"," 394: [1148, 1288, 7153, 527, 1247, 1193, 2571, 904, 1234, 4226],\n"," 395: [780, 733, 1148, 2000, 1233, 1193, 1036, 589, 2571, 2692],\n"," 399: [1233, 1193, 858, 318, 912, 1230, 1148, 1252, 904, 2019],\n"," 401: [2959, 1233, 1193, 912, 2019, 50, 750, 4226, 296, 4973],\n"," 402: [318, 2959, 50, 1234, 858, 593, 1036, 1193, 912, 3147],\n"," 405: [527, 1207, 2396, 1148, 1233, 318, 2028, 1247, 904, 1584],\n"," 417: [318, 912, 50, 904, 858, 1233, 2858, 1213, 4226, 608],\n"," 422: [318, 1148, 1233, 527, 904, 1247, 1207, 2858, 1193, 912],\n"," 437: [5952, 7153, 4993, 923, 924, 4226, 1148, 858, 1206, 1233],\n"," 455: [318, 527, 1148, 1233, 904, 4993, 2858, 1207, 912, 1247],\n"," 461: [318, 50, 858, 2959, 1193, 1233, 1234, 4226, 912, 1213],\n"," 473: [318, 527, 1196, 5952, 2858, 904, 4993, 912, 1210, 7153],\n"," 484: [1148, 1233, 318, 2858, 1288, 1193, 4993, 2571, 7153, 904],\n"," 499: [318, 1196, 593, 50, 904, 1219, 1213, 4973, 527, 1234],\n"," 526: [318, 50, 858, 1233, 1193, 2858, 4226, 1148, 2019, 2571],\n"," 537: [318, 527, 50, 1196, 904, 912, 593, 2858, 1234, 5952],\n"," 542: [318, 2858, 2019, 4973, 4226, 1213, 608, 750, 1234, 1247],\n"," 557: [527, 1148, 1207, 2858, 1233, 2028, 4993, 1288, 2396, 904],\n"," 563: [296, 1221, 2959, 50, 111, 541, 1213, 2019, 858, 750],\n"," 570: [858, 1148, 5952, 7153, 593, 912, 1196, 2571, 50, 904],\n"," 575: [1148, 1288, 5952, 7153, 527, 912, 919, 923, 1207, 1219],\n"," 594: [11, 1148, 3147, 2762, 2396, 527, 597, 2028, 150, 1584],\n"," 607: [2571, 1148, 1234, 296, 527, 7153, 1213, 1193, 3578, 1036],\n"," 631: [912, 858, 318, 1233, 4973, 904, 50, 1234, 2019, 1213],\n"," 651: [4973, 904, 318, 1222, 4226, 2959, 50, 1208, 111, 924],\n"," 664: [318, 1233, 1148, 912, 904, 858, 2858, 1247, 1207, 527],\n"," 685: [1219, 912, 1394, 904, 4973, 923, 2858, 318, 1207, 1196],\n"," 690: [318, 1148, 527, 1233, 2858, 904, 4993, 1288, 7153, 1247],\n"," 696: [318, 527, 1234, 912, 904, 50, 1148, 1207, 2324, 1233],\n"," 724: [318, 912, 904, 1233, 527, 858, 50, 2858, 4973, 1234],\n"," 738: [527, 318, 1148, 4993, 1704, 2571, 3578, 2028, 2762, 1288],\n"," 762: [1196, 318, 2858, 50, 5952, 904, 7153, 912, 527, 1213],\n"," 763: [318, 3578, 1234, 1704, 2324, 2571, 50, 2762, 1233, 1198],\n"," 770: [1233, 2396, 923, 1230, 1207, 1252, 1247, 3897, 904, 1193],\n"," 796: [318, 912, 904, 2858, 1233, 858, 527, 4973, 923, 1207],\n"," 800: [318, 50, 1148, 2571, 1233, 1193, 858, 4226, 1234, 904],\n"," 829: [1148, 1252, 1288, 5952, 7153, 111, 527, 608, 912, 923],\n"," 836: [527, 318, 904, 1207, 912, 1233, 1148, 2858, 1247, 1704],\n"," 882: [858, 912, 1207, 1193, 904, 1233, 318, 4973, 1247, 919],\n"," 900: [296, 50, 318, 1221, 1213, 912, 2019, 1196, 593, 4226],\n"," 903: [318, 1148, 527, 1233, 904, 3578, 2762, 1207, 1247, 1234],\n"," 914: [318, 1233, 912, 1234, 527, 1193, 904, 1148, 858, 1207],\n"," 918: [1148, 2571, 1288, 318, 1233, 4993, 1230, 3147, 1193, 2762],\n"," 920: [2959, 1193, 2019, 296, 1233, 750, 318, 50, 1213, 4226],\n"," 934: [318, 1148, 1233, 2858, 527, 904, 1193, 7153, 1288, 4993],\n"," 945: [527, 1207, 2396, 1233, 904, 923, 1247, 912, 1148, 2858],\n"," 950: [912, 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2324, 912],\n"," 134: [858, 1148, 1252, 1288, 541, 912, 923, 1207, 1221, 1225],\n"," 142: [260, 318, 50, 1196, 296, 1210, 1213, 1234, 527, 912],\n"," 156: [2396, 11, 1148, 1233, 527, 1584, 2028, 2762, 1207, 1230],\n"," 167: [593, 1196, 50, 296, 1089, 2959, 1213, 260, 912, 318],\n"," 177: [1233, 858, 1193, 912, 318, 904, 2019, 2858, 4973, 750],\n"," 224: [1148, 3147, 1233, 318, 733, 1230, 1193, 1242, 2000, 3578],\n"," 269: [318, 527, 260, 1148, 1234, 3578, 904, 2324, 1233, 1704],\n"," 312: [318, 1233, 1148, 1193, 858, 2858, 2571, 4226, 50, 1288],\n"," 360: [527, 318, 260, 912, 1207, 904, 919, 1196, 2324, 4973],\n"," 385: [527, 318, 2858, 260, 904, 1148, 1233, 1207, 4993, 1288],\n"," 411: [318, 527, 1233, 260, 1234, 912, 1148, 904, 50, 1207],\n"," 464: [1148, 527, 912, 904, 2858, 1233, 318, 260, 1247, 1207],\n"," 568: [11, 3147, 1230, 1148, 1233, 2396, 802, 2762, 1252, 1193],\n"," 612: [1233, 1148, 318, 904, 1193, 1247, 2858, 1207, 912, 1230],\n"," 630: [2396, 11, 1233, 1230, 62, 1584, 1148, 1207, 3147, 1247],\n"," 706: [296, 50, 318, 2571, 2959, 260, 4226, 1221, 1213, 47],\n"," 737: [296, 2959, 50, 1221, 858, 1213, 318, 1089, 593, 4226],\n"," 749: [318, 1148, 50, 1233, 1234, 527, 260, 2571, 1193, 912],\n"," 756: [260, 527, 318, 912, 904, 1196, 1207, 2858, 1234, 4973],\n"," 811: [1233, 1193, 1230, 3147, 858, 1252, 318, 912, 1242, 1148],\n"," 853: [318, 1233, 1148, 527, 1193, 904, 912, 1207, 1234, 1247],\n"," 884: [318, 1148, 527, 2571, 3578, 260, 1233, 2762, 4993, 1234],\n"," 917: [318, 527, 260, 904, 1148, 1233, 912, 1207, 2858, 1247],\n"," 951: [3147, 1233, 1193, 318, 1230, 1242, 858, 1148, 2000, 2692],\n"," 955: [260, 858, 1148, 47, 593, 912, 1193, 1198, 2571, 3578],\n"," 997: [858, 1148, 1252, 1288, 527, 912, 923, 1207, 1225, 1230],\n"," 1032: [260, 733, 858, 1148, 1246, 1252, 1288, 4995, 5952, 6377],\n"," 1043: [1148, 3147, 11, 733, 597, 2396, 2762, 802, 1230, 1233],\n"," 670: [318, 50, 593, 296, 1234, 2959, 858, 912, 1213, 260],\n"," 923: [260, 1148, 150, 527, 912, 1207, 1247, 2028, 3578, 904],\n"," 931: [11, 597, 3147, 802, 1148, 2000, 587, 2396, 539, 500],\n"," 969: [527, 318, 1704, 260, 1207, 1148, 150, 2028, 2324, 904],\n"," 12: [1148, 318, 1233, 1193, 1230, 1247, 2571, 527, 1288, 904],\n"," 370: [1148, 318, 1233, 527, 260, 2571, 1193, 904, 1234, 4993],\n"," 597: [318, 1233, 904, 858, 1193, 50, 260, 1234, 1148, 527],\n"," 195: [1233, 912, 904, 318, 2396, 1222, 923, 1242, 3147, 1148],\n"," 337: [858, 912, 50, 296, 608, 2019, 2959, 4973, 904, 1193],\n"," 910: [608, 1221, 1219, 111, 2019, 912, 750, 1394, 4973, 541],\n"," 63: [318, 260, 50, 1148, 1196, 2571, 4993, 1234, 7153, 5952],\n"," 70: [1233, 1247, 923, 1250, 919, 2858, 1394, 2396, 1230, 1225],\n"," 99: [2997, 2019, 2959, 1221, 1208, 1233, 1222, 2858, 1732, 2502],\n"," 121: [260, 4993, 5952, 7153, 1704, 1148, 1234, 110, 3578, 4226],\n"," 130: [2959, 2019, 1221, 912, 750, 1222, 2502, 1213, 4226, 318],\n"," 244: [318, 1233, 260, 1207, 4973, 923, 2019, 4226, 908, 1250],\n"," 291: [912, 904, 4973, 608, 1233, 923, 1394, 318, 2858, 1219],\n"," 335: [318, 608, 1233, 50, 2959, 1213, 296, 1221, 1193, 593],\n"," 361: [1233, 912, 904, 318, 923, 1193, 1207, 1247, 4973, 1148],\n"," 470: [2019, 260, 912, 1196, 904, 1193, 908, 1233, 1201, 7153],\n"," 497: [296, 2959, 1221, 2019, 608, 541, 1206, 750, 1213, 50],\n"," 620: [260, 318, 2571, 7153, 4993, 5952, 2858, 50, 1148, 1196],\n"," 648: [3147, 780, 1230, 733, 1242, 2762, 3578, 2692, 2571, 11],\n"," 666: [260, 527, 318, 1288, 904, 923, 111, 1148, 912, 1207],\n"," 710: [260, 912, 904, 1233, 1148, 1247, 1234, 4973, 2324, 923],\n"," 794: [2571, 4993, 1288, 260, 5952, 2858, 318, 1136, 293, 541],\n"," 854: [260, 318, 2571, 1148, 4993, 7153, 1288, 5952, 3578, 1704],\n"," 861: [318, 912, 1233, 904, 1193, 4973, 1234, 50, 2019, 1207],\n"," 819: [1233, 912, 1207, 904, 2396, 4973, 1193, 923, 1247, 858],\n"," 87: [589, 260, 858, 1148, 1252, 1288, 4995, 5952, 6377, 7153],\n"," 317: [1233, 318, 858, 912, 904, 2858, 1148, 2019, 4973, 1247],\n"," 324: [296, 608, 1221, 50, 1196, 593, 260, 1201, 2019, 858],\n"," 387: [318, 912, 50, 593, 1234, 858, 260, 904, 527, 1233],\n"," 451: [296, 260, 50, 1221, 2858, 318, 541, 1136, 111, 1213],\n"," 502: [527, 150, 1207, 62, 1584, 1704, 1721, 17, 2028, 318],\n"," 559: [260, 1148, 527, 904, 2858, 4993, 318, 1233, 2571, 7153],\n"," 973: [858, 1233, 1193, 2019, 912, 750, 318, 4973, 904, 1221],\n"," 1015: [318, 912, 1233, 858, 1207, 1193, 527, 1234, 1247, 4973],\n"," 1017: [318, 2571, 1148, 50, 260, 4226, 1233, 1193, 2858, 1288],\n"," 85: [260, 1196, 1210, 318, 110, 1234, 47, 1198, 1704, 2324],\n"," 306: [318, 1233, 912, 858, 1193, 904, 1234, 1207, 4973, 1247],\n"," 653: [318, 1233, 912, 904, 858, 1193, 50, 1148, 1234, 2858],\n"," 371: [318, 50, 260, 2571, 4226, 1234, 593, 1213, 1198, 858],\n"," 566: [780, 3147, 736, 104, 733, 802, 1233, 2000, 11, 597],\n"," 331: [296, 858, 1221, 2019, 1089, 912, 1222, 750, 2997, 608],\n"," 665: [50, 318, 858, 2959, 1221, 1213, 1233, 1136, 2571, 260],\n"," 872: [318, 912, 858, 1233, 904, 1234, 4973, 4226, 593, 1213],\n"," 879: [858, 527, 912, 923, 1207, 2858, 1233, 318, 4973, 1247],\n"," 486: [318, 1233, 1148, 1193, 858, 50, 2571, 912, 4226, 904],\n"," 817: [2396, 1233, 1584, 1148, 2028, 3147, 2762, 1230, 904, 1242],\n"," 864: [260, 318, 1704, 1207, 904, 1148, 2324, 17, 4993, 912],\n"," 52: [1233, 912, 858, 4973, 1207, 1247, 1230, 318, 923, 2019],\n"," 9: [2571, 1148, 7153, 318, 541, 1136, 1193, 1233, 4226, 858],\n"," 117: [527, 260, 318, 1207, 904, 1704, 1148, 912, 2858, 1233],\n"," 220: [318, 50, 858, 1233, 1193, 4226, 912, 1234, 2959, 1213],\n"," 544: [1206, 1221, 541, 2997, 608, 111, 1201, 2019, 750, 1200],\n"," 546: [2858, 1148, 1288, 1233, 2571, 260, 1193, 1136, 904, 541],\n"," 363: [318, 1233, 1148, 260, 904, 2858, 1193, 912, 527, 858],\n"," 445: [318, 2571, 1148, 260, 50, 2858, 1233, 1193, 4226, 1288],\n"," 492: [260, 4993, 1288, 7153, 1148, 2858, 2571, 527, 318, 2028],\n"," 534: [2858, 541, 111, 1136, 750, 2019, 7153, 1288, 1221, 858],\n"," 234: [296, 589, 1240, 1036, 47, 2502, 380, 2542, 1200, 6874],\n"," 430: [296, 50, 318, 2959, 593, 858, 1213, 4226, 260, 1234],\n"," 1027: [318, 912, 1233, 1234, 858, 1193, 50, 904, 593, 1207],\n"," 140: [1233, 904, 858, 4973, 923, 1207, 2019, 1247, 608, 908],\n"," 373: [919, 356, 1234, 593, 3578, 2324, 50, 527, 1380, 457],\n"," 926: [2959, 296, 1206, 2997, 858, 1221, 2502, 2019, 541, 1222],\n"," 781: [260, 858, 1148, 541, 608, 912, 923, 1221, 1193, 50],\n"," 825: [318, 912, 1207, 904, 1148, 1247, 527, 3147, 1242, 2396],\n"," 913: [1035, 912, 318, 1234, 858, 1233, 364, 4973, 3147, 1207],\n"," 453: [318, 3578, 356, 527, 2324, 1704, 1234, 260, 50, 1036],\n"," 540: [260, 47, 593, 1221, 1196, 50, 1234, 4226, 2959, 296],\n"," 66: [50, 1221, 858, 2019, 318, 4226, 593, 750, 608, 541],\n"," 185: [318, 527, 1207, 1233, 3578, 1148, 3147, 150, 2324, 2762],\n"," 222: [296, 2959, 50, 858, 318, 1221, 4226, 1193, 1213, 2019],\n"," 498: [1233, 2858, 858, 1193, 2019, 111, 750, 923, 904, 1136],\n"," 994: [912, 608, 904, 1394, 527, 1207, 4973, 1219, 260, 923],\n"," 165: [318, 2571, 260, 50, 7153, 1148, 2858, 4226, 1288, 1136],\n"," 202: [260, 858, 1148, 150, 912, 919, 1207, 1247, 1193, 3578],\n"," 180: [260, 1148, 1288, 5952, 7153, 527, 912, 923, 1207, 1225],\n"," 261: [318, 912, 527, 904, 1207, 1233, 4973, 919, 1234, 260],\n"," 435: [318, 1233, 527, 912, 904, 1207, 1148, 1247, 2858, 1193],\n"," 322: [318, 50, 858, 912, 1233, 1193, 4226, 904, 1213, 260],\n"," 238: [260, 7153, 4993, 318, 5952, 1288, 1148, 2571, 527, 904],\n"," 425: [2858, 923, 1233, 1199, 924, 904, 1288, 2019, 912, 1208],\n"," 512: [858, 1233, 912, 2019, 1193, 4973, 904, 750, 923, 1221],\n"," 725: [296, 50, 858, 318, 1221, 1213, 4226, 2019, 1193, 750],\n"," 732: [1148, 527, 4993, 1288, 2028, 1233, 318, 2762, 1247, 7153],\n"," 916: [296, 541, 1221, 2571, 7153, 111, 1136, 1206, 260, 2858],\n"," 736: [2571, 50, 296, 318, 2959, 4226, 1221, 541, 260, 7153],\n"," 961: [318, 50, 1233, 912, 1234, 858, 904, 1148, 593, 260],\n"," 974: [318, 50, 260, 912, 1234, 593, 904, 858, 4226, 1148],\n"," 443: [50, 296, 2571, 260, 1234, 4226, 1036, 1198, 858, 2959],\n"," 1012: [318, 912, 1234, 1233, 858, 919, 904, 527, 50, 1207],\n"," 515: [318, 593, 50, 1234, 858, 904, 1233, 4973, 527, 919],\n"," 288: [260, 527, 912, 1207, 1234, 318, 2324, 1704, 1233, 3578],\n"," 978: [1207, 1035, 527, 912, 318, 364, 2396, 1233, 904, 1247],\n"," 28: [318, 260, 50, 527, 2571, 1148, 1234, 4226, 593, 1198],\n"," 458: [527, 1207, 904, 318, 912, 1233, 1247, 923, 260, 1148],\n"," 475: [858, 1233, 1193, 318, 912, 2019, 50, 750, 4226, 1221],\n"," 592: [318, 2571, 50, 1148, 260, 4226, 1193, 1233, 7153, 858],\n"," 598: [912, 318, 858, 1233, 904, 4973, 1193, 2019, 608, 2858],\n"," 943: [2858, 318, 260, 1148, 1233, 1288, 7153, 904, 4993, 5952],\n"," 55: [858, 2959, 912, 2019, 1233, 4973, 608, 750, 296, 1213],\n"," 520: [1233, 858, 1193, 912, 904, 2019, 4973, 1148, 1247, 50],\n"," 697: [1233, 2396, 923, 1230, 1207, 1252, 1193, 1247, 912, 904],\n"," 849: [858, 1148, 593, 912, 1193, 2571, 50, 1234, 4226, 1233],\n"," 1003: [260, 1148, 1288, 7153, 50, 2858, 4993, 318, 5952, 527],\n"," 705: [912, 608, 858, 318, 4973, 593, 904, 50, 1213, 2019],\n"," 231: [296, 318, 260, 593, 2959, 1213, 1196, 4226, 1221, 47],\n"," 699: [858, 912, 1234, 1233, 593, 1193, 904, 4226, 1213, 260],\n"," 448: [318, 1233, 904, 912, 1148, 1193, 260, 858, 1247, 527],\n"," 750: [318, 527, 260, 1148, 904, 912, 1233, 1234, 1207, 50],\n"," 782: [318, 50, 858, 912, 1233, 1234, 1193, 4226, 904, 593],\n"," 108: [296, 858, 2959, 1221, 2019, 50, 1213, 318, 750, 912],\n"," 29: [318, 1036, 50, 589, 3578, 380, 2571, 1234, 1240, 3147]})"]},"metadata":{},"execution_count":8}],"source":["# 학습 데이터에 나오지 않는 사용자와 아이템의 조합을 준비한다\n","data_test = data_train.build_anti_testset(None)\n","predictions = matrix_factorization.test(data_test)\n","pred_user2items = get_top_n(predictions, n=10)\n","pred_user2items"]},{"cell_type":"code","execution_count":9,"id":"afa174f5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"afa174f5","executionInfo":{"status":"ok","timestamp":1672119551834,"user_tz":-540,"elapsed":26,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"cc01c6d1-c6f5-4616-a63f-2add1448726a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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\n"," "]},"metadata":{},"execution_count":9}],"source":["# user_id=2인 사용자가 학습 데이터로 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":10,"id":"06bb1a90","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"06bb1a90","executionInfo":{"status":"ok","timestamp":1672119551835,"user_tz":-540,"elapsed":23,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"d1d3d0cf-cae7-41ff-bd6c-76121fe4689e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title genre \\\n","2487 2571 Matrix, The (1999) [Action, Sci-Fi, Thriller] \n","2874 2959 Fight Club (1999) [Action, Crime, Drama, Thriller] \n","3489 3578 Gladiator (2000) [Action, Adventure, Drama] \n","\n"," tag \n","2487 [artificial intelligence, hackers, heroine in ... \n","2874 [based on a book, chuck palahniuk, edward nort... \n","3489 [revenge, crowe's best, gfei own it, dvd, glad... "],"text/html":["\n","
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
34893578Gladiator (2000)[Action, Adventure, Drama][revenge, crowe's best, gfei own it, dvd, glad...
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\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2에 대한 추천(3578, 2571, 2959)\n","movies[movies.movie_id.isin([3578, 2571, 2959])]"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "88a3be74", + "metadata": { + "id": "88a3be74" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/MF.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "47e2fec5", + "metadata": { + "id": "47e2fec5" + }, + "source": [ + "# 행렬 분해(Matrix Factorization, MF)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "99e09acf", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3804, + "status": "ok", + "timestamp": 1672119437385, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "99e09acf", + "outputId": "294107d4-f398-41b9-a605-09d7c15bb44f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:35:29-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 63.1MB/s in 1.0s \n", + "\n", + "2022-12-27 05:35:30 (63.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "179ce864", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 65608, + "status": "ok", + "timestamp": 1672119502987, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "179ce864", + "outputId": "4e5a1760-788a-4246-e64d-8cda917e1a11" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8c10d538", + "metadata": { + "executionInfo": { + "elapsed": 28, + "status": "ok", + "timestamp": 1672119502988, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "8c10d538" + }, + "outputs": [], + "source": [ + "# 인자 수\n", + "factors = 5\n", + "# 평가수 임곗값\n", + "minimum_num_rating = 100\n", + "# 바이어스 항 사용\n", + "use_biase = False\n", + "# 학습률\n", + "lr_all = 0.005\n", + "# 에폭 수\n", + "n_epochs = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "af591e1a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 44271, + "status": "ok", + "timestamp": 1672119547233, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "af591e1a", + "outputId": "f5f3025d-2ead-479f-c315-94433d318640" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting surprise\n", + " Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n", + "Collecting scikit-surprise\n", + " Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n", + "\u001b[K |████████████████████████████████| 771 kB 4.8 MB/s \n", + "\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n", + "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n", + "Building wheels for collected packages: scikit-surprise\n", + " Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626477 sha256=a922027e3c7f6d8735b7192824bc8d1a46af450d003dde385ce514ec0ae33d2d\n", + " Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n", + "Successfully built scikit-surprise\n", + "Installing collected packages: scikit-surprise, surprise\n", + "Successfully installed scikit-surprise-1.1.3 surprise-0.1\n" + ] + } + ], + "source": [ + "!pip install surprise" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "735ff486", + "metadata": { + "executionInfo": { + "elapsed": 1188, + "status": "ok", + "timestamp": 1672119548405, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "735ff486" + }, + "outputs": [], + "source": [ + "from surprise import SVD, Reader\n", + "from surprise import Dataset as SurpriseDataset\n", + "\n", + "# 평가 수가 minimum_num_rating 건 이상 있는 영화로 줄인다\n", + "filtered_movielens_train = movielens_train.groupby(\"movie_id\").filter(\n", + " lambda x: len(x[\"movie_id\"]) >= minimum_num_rating\n", + ")\n", + "\n", + "# Surprise용으로 데이터를 가공한다\n", + "reader = Reader(rating_scale=(0.5, 5))\n", + "data_train = SurpriseDataset.load_from_df(\n", + " filtered_movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n", + ").build_full_trainset()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cbc512a3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1327, + "status": "ok", + "timestamp": 1672119549729, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "cbc512a3", + "outputId": "b776c506-902c-4525-a5de-88ecc825e60b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Surprise로 행렬 분해를 학습한다\n", + "# 이름은 SVD지만, 특이값 분석이 아니라 Matrix Factorization가 실행된다\n", + "matrix_factorization = SVD(n_factors=factors, n_epochs=n_epochs, lr_all=lr_all, biased=use_biase)\n", + "matrix_factorization.fit(data_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48876224", + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1672119549730, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "48876224" + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "def get_top_n(predictions, n=10):\n", + " # 각 사용자별로 예측된 아이템을 저장한다\n", + " top_n = defaultdict(list)\n", + " for uid, iid, true_r, est, _ in predictions:\n", + " top_n[uid].append((iid, est))\n", + "\n", + " # 사용자별로 아이템을 예측 평갓값 순으로 나열해 상위 n개를 저장한다\n", + " for uid, user_ratings in top_n.items():\n", + " user_ratings.sort(key=lambda x: x[1], reverse=True)\n", + " top_n[uid] = [d[0] for d in user_ratings[:n]]\n", + "\n", + " return top_n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a2b23304", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 2114, + "status": "ok", + "timestamp": 1672119551833, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "a2b23304", + "outputId": "2318faf1-5003-42d9-f6aa-bc551e8ab571" + }, + "outputs": [ + { + "data": { + 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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
34893578Gladiator (2000)[Action, Adventure, Drama][revenge, crowe's best, gfei own it, dvd, glad...
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" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(3578, 2571, 2959)\n", + "movies[movies.movie_id.isin([3578, 2571, 2959])]" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/NMF.ipynb b/chapter5/colab/NMF.ipynb index 0f8192f..3c34734 100644 --- a/chapter5/colab/NMF.ipynb +++ b/chapter5/colab/NMF.ipynb @@ -1 +1,2286 @@ -{"cells":[{"cell_type":"markdown","id":"b9224173","metadata":{"id":"b9224173"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/NMF.ipynb)"]},{"cell_type":"markdown","id":"f8caaceb","metadata":{"id":"f8caaceb"},"source":["# 비부값 행렬 분석"]},{"cell_type":"code","execution_count":1,"id":"9cb2f773","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cb2f773","executionInfo":{"status":"ok","timestamp":1672119380127,"user_tz":-540,"elapsed":4459,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3b3441b6-3940-4b5a-ef0c-99ca4e312e13"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:36:13-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 64.1MB/s in 1.0s \n","\n","2022-12-27 05:36:14 (64.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"ed3c6a08","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ed3c6a08","executionInfo":{"status":"ok","timestamp":1672119449934,"user_tz":-540,"elapsed":69811,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"740a21e4-a8ae-42cc-f142-e51190fa0f81"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"8e6879e3","metadata":{"id":"8e6879e3","executionInfo":{"status":"ok","timestamp":1672119449934,"user_tz":-540,"elapsed":21,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 결손값을 채우는 방법\n","fillna_with_zero = True\n","# 인자 수\n","factors = 5"]},{"cell_type":"code","execution_count":4,"id":"52281452","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"52281452","executionInfo":{"status":"ok","timestamp":1672119449935,"user_tz":-540,"elapsed":20,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1bb4e599-cc7a-4fc5-cb95-4ef09185a7a2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," ...,\n"," [5., 0., 3., ..., 0., 0., 0.],\n"," [0., 0., 0., ..., 0., 0., 0.],\n"," [5., 0., 0., ..., 0., 0., 0.]])"]},"metadata":{},"execution_count":4}],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","if fillna_with_zero:\n"," matrix = user_movie_matrix.fillna(0).to_numpy()\n","else:\n"," matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n","matrix"]},{"cell_type":"code","execution_count":5,"id":"708dda3c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"708dda3c","executionInfo":{"status":"ok","timestamp":1672119463518,"user_tz":-540,"elapsed":13601,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"e404fb9c-9bd5-4466-95a6-79c39e5ea205"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n"," warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n"," warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["NMF(n_components=5)"]},"metadata":{},"execution_count":5}],"source":["from sklearn.decomposition import NMF\n","\n","# NMF 실행\n","nmf = NMF(n_components=factors)\n","nmf.fit(matrix)\n"]},{"cell_type":"code","execution_count":6,"id":"687a16b1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"687a16b1","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":12450,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"64397446-be94-4869-bc6c-61d351679634"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n"," warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n"," warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[0.02183306, 0.53589472, 0. , 0. , 0. ],\n"," [0.21316649, 0.10881363, 0. , 0. , 0. ],\n"," [0. , 0.02976153, 0.05290897, 0.15364729, 0.02014201],\n"," ...,\n"," [0.13635649, 0.14281654, 0. , 0. , 0.00656846],\n"," [0.09753435, 0.01077205, 0. , 0.13237952, 0.13516597],\n"," [0.99758645, 0. , 0. , 0.64704016, 0.0597298 ]])"]},"metadata":{},"execution_count":6}],"source":["P = nmf.fit_transform(matrix)\n","P"]},{"cell_type":"code","execution_count":7,"id":"a731dbb1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a731dbb1","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"f6e9c43b-428e-4a65-e742-25a037fbe084"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1.96418272, 0.50117827, 0.39871676, ..., 0. , 0. ,\n"," 0. ],\n"," [1.15067071, 0.96077987, 0.40196914, ..., 0. , 0.00289232,\n"," 0.00216924],\n"," [0.47176639, 0. , 0. , ..., 0. , 0. ,\n"," 0. ],\n"," [0.79016059, 0.30417491, 0. , ..., 0.02803924, 0.01446954,\n"," 0.01085215],\n"," [0.29616062, 0.30060155, 0.17846866, ..., 0. , 0. ,\n"," 0. ]])"]},"metadata":{},"execution_count":7}],"source":["Q = nmf.components_\n","Q"]},{"cell_type":"code","execution_count":8,"id":"36669c15","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"36669c15","executionInfo":{"status":"ok","timestamp":1672119475949,"user_tz":-540,"elapsed":3,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6c3b7cb4-feb2-460f-8f90-42484580128c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[6.59522475e-01, 5.25819116e-01, 2.24118350e-01, ...,\n"," 0.00000000e+00, 1.54997784e-03, 1.16248338e-03],\n"," [5.43906588e-01, 2.11380357e-01, 1.28732773e-01, ...,\n"," 0.00000000e+00, 3.14723598e-04, 2.36042698e-04],\n"," [1.86577692e-01, 8.13846441e-02, 1.55579328e-02, ...,\n"," 4.30815294e-03, 2.30928521e-03, 1.73196391e-03],\n"," ...,\n"," [4.34109190e-01, 2.07528655e-01, 1.12947724e-01, ...,\n"," 0.00000000e+00, 4.13070817e-04, 3.09803112e-04],\n"," [3.48602292e-01, 1.40129296e-01, 6.73415036e-02, ...,\n"," 3.71182091e-03, 1.94662680e-03, 1.45997010e-03],\n"," [2.48839731e+00, 7.14736905e-01, 4.08414332e-01, ...,\n"," 1.81425136e-02, 9.36237290e-03, 7.02177967e-03]])"]},"metadata":{},"execution_count":8}],"source":["import numpy as np\n","# 예측 평갓값 행렬\n","pred_matrix = np.dot(P, Q)\n","pred_matrix"]},{"cell_type":"code","execution_count":9,"id":"08eb2849","metadata":{"id":"08eb2849","executionInfo":{"status":"ok","timestamp":1672119476234,"user_tz":-540,"elapsed":287,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용에 나타나지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다\n","average_score = movielens_train.rating.mean()\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results"]},{"cell_type":"code","execution_count":10,"id":"7aa822a0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7aa822a0","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":1330,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"12551108-c9b0-445a-fe32-eb39af8205c8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 149: [4226, 2571, 2762, 47, 3996, 318, 6539, 5445, 4963, 1704],\n"," 182: [47, 6874, 2329, 3793, 4011, 7361, 4878, 7438, 6333, 2502],\n"," 215: [2329, 527, 6711, 3147, 2683, 8360, 5669, 6502, 3948, 1246],\n"," 281: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 326: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 351: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n"," 357: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 426: [2502,\n"," 33794,\n"," 3949,\n"," 7147,\n"," 33493,\n"," 5902,\n"," 44191,\n"," 33166,\n"," 4848,\n"," 6016],\n"," 456: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 459: [318, 4886, 2997, 50, 4995, 4973, 7361, 4878, 3897, 2502],\n"," 494: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 517: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 524: [7153, 3996, 6539, 5349, 4886, 3793, 7361, 593, 7438, 527],\n"," 556: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 588: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 589: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 590: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 4306, 3996, 6539],\n"," 601: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 621: [5952, 7153, 4306, 2329, 4995, 4011, 4878, 1682, 3897, 7438],\n"," 634: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 672: [4993, 5952, 4226, 7153, 4306, 3996, 318, 6539, 5445, 6874],\n"," 701: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 719: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 745: [5952, 7153, 4306, 6539, 5445, 6874, 2329, 6377, 356, 4973],\n"," 757: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 775: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 780: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 4306, 3996],\n"," 785: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 788: [296, 4973, 4878, 3897, 1089, 2502, 1136, 1961, 2542, 4034],\n"," 870: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 987: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 988: [7153, 6539, 5445, 6874, 4963, 2329, 6377, 4995, 7361, 4027],\n"," 1020: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 1: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 22: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 26: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 30: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 34: [4993, 5952, 4226, 2858, 7153, 4306, 3996, 6539, 5445, 6874],\n"," 38: [4226, 2858, 4306, 6539, 5445, 4886, 2329, 3793, 2997, 50],\n"," 50: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 53: [2959, 4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996],\n"," 54: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 67: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 71: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 76: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 88: [4226, 296, 47, 4306, 3996, 318, 1704, 2997, 50, 4995],\n"," 89: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 94: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 95: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 100: [2959, 4993, 4226, 7153, 3578, 2762, 4306, 5445, 6874, 4963],\n"," 101: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 102: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 103: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 111: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 116: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 118: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 143: [7153, 6539, 6874, 6377, 50, 7361, 4027, 7438, 8961, 6711],\n"," 144: [1704, 4886, 3897, 5989, 2502, 2706, 3147, 1784, 3114, 4979],\n"," 162: [4226, 318, 4963, 1704, 2329, 2997, 50, 4011, 7361, 4878],\n"," 172: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 173: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 187: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 193: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 208: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 210: [2959, 5952, 47, 6539, 5445, 6874, 4963, 5349, 4886, 2329],\n"," 214: [2959, 5952, 2858, 7153, 4306, 318, 6539, 6874, 2329, 6377],\n"," 228: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 232: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 236: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 259: [3578, 318, 6377, 4011, 4027, 1089, 8961, 1961, 2542, 4034],\n"," 260: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 267: [4306, 1704, 4886, 4011, 4878, 1682, 5989, 2502, 6711, 4034],\n"," 268: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 271: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 274: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306],\n"," 287: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 292: [6874, 4963, 527, 6333, 5989, 2542, 3147, 5418, 1517, 3481],\n"," 296: [3996, 1704, 6377, 1136, 2683, 1270, 1784, 1258, 364, 1517],\n"," 303: [6539, 4963, 1704, 5349, 2329, 6377, 2997, 50, 4995, 4973],\n"," 307: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 310: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539],\n"," 315: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 320: [318, 6874, 1704, 5349, 2329, 3793, 2997, 50, 7361, 4878],\n"," 332: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 339: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 343: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 355: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 364: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 365: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 368: [2858, 2762, 318, 1704, 2329, 4995, 2028, 4878, 593, 7438],\n"," 381: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 382: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 383: [2959, 47, 4306, 6539, 6874, 4963, 5349, 2329, 356, 4011],\n"," 391: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 396: [4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 3996, 318],\n"," 398: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 406: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 409: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 410: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 416: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 418: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 419: [2959, 7153, 3578, 6539, 6874, 1704, 4886, 2329, 6377, 356],\n"," 421: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 424: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 432: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 434: [4226, 296, 47, 318, 2329, 2997, 50, 4973, 7361, 4878],\n"," 436: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 438: [2959, 4993, 5952, 4226, 7153, 47, 6539, 5445, 6874, 4963],\n"," 441: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 446: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 452: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 296, 47, 3996],\n"," 460: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 463: [7153, 6874, 4963, 6377, 4973, 4011, 7361, 1682, 3897, 7438],\n"," 468: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 469: [2959, 47, 2329, 50, 4995, 4011, 7361, 4878, 1682, 3897],\n"," 472: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 476: [1704, 4886, 4995, 7438, 2502, 2291, 1923, 1721, 4034, 1270],\n"," 480: [7153, 318, 6539, 6874, 1704, 2329, 6377, 7361, 4878, 1682],\n"," 482: [2858, 2762, 3996, 5445, 6874, 2997, 50, 4973, 1682, 3897],\n"," 493: [4993, 5952, 7153, 593, 7438, 6333, 2502, 1136, 6365, 1258],\n"," 496: [2959, 4993, 5952, 2858, 7153, 3578, 4306, 3996, 318, 6539],\n"," 501: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 504: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 505: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 511: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 516: [6377, 50, 7361, 1682, 4027, 2542, 33794, 364, 3481, 2918],\n"," 525: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 530: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 531: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 533: [4886, 2329, 4995, 4011, 4878, 2706, 3147, 6365, 2683, 778],\n"," 536: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 543: [2329, 2028, 4011, 3897, 5989, 1923, 2542, 4034, 3481, 3052],\n"," 547: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 549: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 553: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 558: [7153, 2762, 6874, 4886, 2329, 2997, 4973, 4011, 7361, 4878],\n"," 562: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 567: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 571: [2959, 4993, 5952, 7153, 2762, 296, 3996, 6874, 356, 4878],\n"," 572: [3578, 3996, 318, 1704, 4973, 1961, 4034, 33794, 5418, 1784],\n"," 577: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 581: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 585: [4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996, 6539],\n"," 593: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 604: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 605: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 609: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 628: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 629: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 645: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 650: [2959, 5952, 7153, 296, 4306, 6539, 5445, 6874, 4963, 4886],\n"," 656: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 657: [4306, 4963, 4886, 3793, 4011, 7438, 6333, 2502, 1136, 8961],\n"," 669: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 678: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 683: [2959, 5952, 4226, 2858, 2762, 296, 3996, 318, 6874, 4963],\n"," 688: [3996, 318, 5445, 4973, 2028, 7361, 4878, 1682, 3897, 2502],\n"," 689: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 693: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 716: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 720: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 734: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 735: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 739: [4993, 5952, 4226, 2858, 7153, 3578, 296, 4306, 3996, 318],\n"," 755: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 758: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 767: [2959, 4226, 2858, 296, 47, 4306, 5445, 4963, 1704, 4886],\n"," 769: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 786: [2959, 4993, 5952, 2858, 7153, 296, 47, 6539, 5445, 6874],\n"," 789: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 792: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 795: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 798: [2329, 6377, 4011, 7361, 4027, 2502, 2706, 4034, 1784, 3481],\n"," 799: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 802: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 806: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 807: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 816: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 827: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 828: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 846: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 859: [2858, 47, 4306, 318, 6874, 4963, 2329, 6377, 2997, 50],\n"," 862: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 876: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 881: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 885: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 4306, 3996],\n"," 886: [4993, 5952, 4226, 7153, 4306, 6539, 5445, 6874, 4963, 5349],\n"," 889: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 890: [4993, 5952, 4226, 7153, 47, 4306, 318, 6539, 5445, 6874],\n"," 891: [6539, 5445, 6874, 4963, 2329, 6377, 4995, 4011, 7361, 593],\n"," 896: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 897: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 898: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 908: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 922: [4226, 2858, 296, 3996, 5445, 6874, 1704, 2329, 3793, 2997],\n"," 930: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 938: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 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125: [2329,\n"," 7361,\n"," 4878,\n"," 1682,\n"," 7438,\n"," 8961,\n"," 33794,\n"," 5418,\n"," 32587,\n"," 8636],\n"," 126: [4973, 7361, 4878, 6711, 2542, 6365, 3052, 778, 1206, 5669],\n"," 132: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 141: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 154: [4993, 5952, 4226, 2858, 7153, 3578, 4306, 3996, 318, 6874],\n"," 155: [4226, 7153, 6539, 5445, 6874, 4963, 5349, 4886, 6377, 3793],\n"," 163: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 164: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 170: [4993, 5952, 7153, 3578, 3996, 6539, 5445, 6874, 1704, 5349],\n"," 171: [4993, 5952, 4226, 2571, 7153, 3578, 4306, 3996, 6539, 5445],\n"," 176: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 190: [4226, 2858, 47, 318, 4963, 1704, 2329, 6377, 2997, 50],\n"," 197: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 198: [2997,\n"," 4973,\n"," 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3052, 110],\n"," 293: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 294: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 295: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 318: [4226, 2329, 4878, 1923, 3147, 32, 2692, 293, 5669, 1213],\n"," 321: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 345: [4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 318, 6539],\n"," 346: [2959, 4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996],\n"," 348: [1136, 6365, 5418, 3481, 2918, 3052, 1580, 4022, 1732, 1265],\n"," 349: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 354: [4226, 296, 47, 6874, 4886, 2329, 3793, 2997, 50, 2028],\n"," 359: [47, 318, 6874, 1704, 5349, 4886, 2329, 356, 50, 2028],\n"," 366: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 369: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 384: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 390: [47, 6539, 6377, 7361, 3897, 1089, 7438, 527, 2291, 1961],\n"," 392: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 403: [2959, 4993, 5952, 2858, 3578, 4306, 3996, 318, 6539, 6874],\n"," 407: [4226, 2858, 318, 1704, 5349, 4995, 4973, 4011, 7361, 4878],\n"," 414: [4226, 7153, 3578, 4306, 318, 6539, 5445, 5349, 4886, 2329],\n"," 431: [2959, 4226, 2571, 2858, 3578, 2762, 296, 47, 3996, 5445],\n"," 454: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 465: [5952, 2571, 7153, 5445, 4963, 4886, 3793, 2028, 3897, 6333],\n"," 481: [2762, 6377, 2997, 4973, 4027, 527, 5989, 8961, 1961, 3147],\n"," 485: [4993, 5952, 4226, 7153, 47, 4306, 3996, 6539, 5445, 6874],\n"," 503: [47, 3996, 4963, 1704, 5349, 50, 4011, 3897, 4027, 1089],\n"," 513: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539],\n"," 545: [3578, 4306, 6539, 5445, 4963, 5349, 3793, 1682, 6333, 2706],\n"," 552: [4993, 5952, 2571, 7153, 3578, 47, 3996, 5445, 6874, 4963],\n"," 554: [4226, 2571, 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6874, 1704],\n"," 970: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 972: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 1001: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1013: [2959, 4226, 2858, 3578, 2762, 47, 6539, 5445, 6874, 1704],\n"," 1018: [3578, 4963, 1704, 6377, 50, 4995, 4027, 527, 2502, 8961],\n"," 1028: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 1031: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1035: [3996, 6539, 4963, 1704, 2329, 2997, 50, 4995, 4973, 4011],\n"," 1038: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1045: [5952,\n"," 3996,\n"," 4973,\n"," 6711,\n"," 2542,\n"," 33794,\n"," 5418,\n"," 2692,\n"," 1580,\n"," 480],\n"," 1046: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 35: [5952, 7153, 3578, 296, 318, 6539, 1704, 5349, 4886, 6377],\n"," 72: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 91: [2959, 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5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 440: [3578, 4306, 3996, 5445, 6874, 4963, 1704, 5349, 4886, 6377],\n"," 444: [2959, 4993, 5952, 4226, 7153, 3578, 3996, 318, 6539, 5445],\n"," 450: [2959, 7153, 6539, 6874, 1704, 6377, 4011, 7361, 4878, 1682],\n"," 479: [4993, 5952, 7153, 3578, 47, 4306, 3996, 6539, 5445, 6874],\n"," 491: [4993, 7153, 296, 4306, 6539, 5445, 4886, 2329, 6377, 3793],\n"," 506: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 523: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 539: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 541: [4993, 5952, 4226, 7153, 3578, 2762, 296, 4306, 3996, 318],\n"," 616: [4306, 3996, 6874, 4963, 1704, 5349, 2329, 6377, 3793, 4995],\n"," 647: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 659: [2959, 3578, 6539, 4963, 5349, 4886, 2329, 3793, 50, 2028],\n"," 695: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 4306, 3996],\n"," 700: [2959, 4993, 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679: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 680: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 318],\n"," 691: [2959, 4993, 5952, 4226, 2858, 7153, 2762, 47, 4306, 3996],\n"," 702: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 708: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 730: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 751: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 773: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n"," 803: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 47, 4306, 3996],\n"," 809: [2959, 4226, 2571, 7153, 3578, 296, 4306, 3996, 318, 6539],\n"," 820: [2762, 318, 1704, 2329, 4995, 4011, 3897, 527, 5989, 2542],\n"," 824: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 4306],\n"," 863: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 4306, 3996, 6539],\n"," 865: [4993, 5952, 2762, 3996, 6539, 5445, 6874, 5349, 4886, 2329],\n"," 867: [2762, 47, 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387: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 428: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 451: [2959, 2858, 3578, 296, 47, 4306, 3996, 318, 6539, 5445],\n"," 584: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 874: [4226, 318, 50, 4011, 4878, 1089, 527, 2291, 1961, 6711],\n"," 995: [2959, 4993, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 10: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 19: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 41: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 43: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 44: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 45: [2959, 4993, 5952, 4226, 7153, 3578, 296, 47, 4306, 3996],\n"," 51: [2959, 4993, 5952, 2571, 7153, 47, 4306, 318, 6539, 5445],\n"," 56: [2959, 2762, 47, 3996, 318, 6539, 5445, 4963, 1704, 5349],\n"," 61: [2959, 4993, 5952, 4226, 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386: [4993, 5952, 2571, 7153, 47, 4306, 6539, 5445, 6874, 4963],\n"," 397: [4993, 5952, 7153, 47, 4306, 3996, 6539, 5445, 6874, 4963],\n"," 420: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 439: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 449: [4993, 5952, 4226, 7153, 296, 4306, 3996, 318, 6539, 5445],\n"," 478: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 487: [4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539, 5445],\n"," 489: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 500: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 510: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 521: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 522: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 527: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 529: [2959, 4993, 5952, 2571, 2858, 7153, 3578, 2762, 47, 3996],\n"," 535: [4226, 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4306],\n"," 213: [2959, 4993, 5952, 4226, 7153, 3578, 4306, 3996, 318, 6539],\n"," 243: [4993, 5952, 4226, 7153, 3578, 296, 47, 4306, 3996, 318],\n"," 323: [2959, 4226, 2571, 2858, 3578, 2762, 47, 4306, 3996, 318],\n"," 352: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 429: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 625: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 808: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 843: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 847: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 963: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 975: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 998: [4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47, 318],\n"," 75: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 47, 4306],\n"," 427: [2959, 4993, 5952, 4226, 2858, 7153, 296, 4306, 3996, 5445],\n"," 466: 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4306, 3996],\n"," 718: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 873: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 296, 47, 4306],\n"," 962: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 985: [2959, 4226, 2571, 2858, 2762, 296, 47, 318, 6539, 6874],\n"," 993: [2959, 2571, 2858, 7153, 2762, 296, 47, 318, 6539, 6874],\n"," 184: [4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47, 4306],\n"," 246: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 373: [2959, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47, 4306],\n"," 430: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 534: [2959, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47, 4306],\n"," 805: [2959, 4226, 2858, 2762, 296, 47, 3996, 318, 5445, 6874],\n"," 58: [2959, 4993, 5952, 4226, 7153, 3578, 47, 4306, 3996, 6539],\n"," 112: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n"," 367: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 548: 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47, 4306, 3996, 318],\n"," 1016: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 1019: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1044: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1051: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 917: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 951: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 997: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 174: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 676: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n"," 764: [4993, 5952, 4226, 2571, 7153, 3578, 47, 4306, 3996, 6539],\n"," 1052: [4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47, 4306],\n"," 5: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 27: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 33: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 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296],\n"," 630: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 706: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 737: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 749: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 756: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 811: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 853: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 884: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 955: [2959, 4993, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47],\n"," 1032: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1043: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 370: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 670: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 923: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 931: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 969: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 12: [4993, 5952, 4226, 2571, 7153, 296, 47, 4306, 3996, 318],\n"," 597: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 195: [2959, 5952, 2571, 7153, 47, 318, 6539, 5445, 6874, 1704],\n"," 337: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 910: [2571, 3578, 2762, 6539, 5445, 4963, 5349, 356, 2997, 4973],\n"," 63: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 70: [2959, 5952, 4226, 2571, 2858, 3578, 2762, 296, 47, 3996],\n"," 99: [2959, 5952, 2571, 2858, 7153, 3578, 47, 4306, 3996, 6539],\n"," 121: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 47, 4306],\n"," 130: [2959, 4226, 2571, 3578, 2762, 47, 4306, 318, 6539, 5445],\n"," 244: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 291: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 335: [2959, 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47],\n"," 864: [4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306, 3996],\n"," 52: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 288: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 9: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 117: [2959, 4993, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 220: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 544: [4226, 2858, 3578, 2762, 4306, 3996, 318, 6539, 5445, 4963],\n"," 999: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 458: [2959, 4993, 5952, 2571, 7153, 296, 47, 4306, 3996, 318],\n"," 974: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 546: [2959, 4226, 2571, 2858, 2762, 296, 47, 6874, 4963, 1704],\n"," 55: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 363: [4226, 2571, 2858, 3578, 2762, 47, 4306, 3996, 318, 6539],\n"," 445: [2959, 4993, 4226, 2571, 2858, 3578, 2762, 296, 47, 4306],\n"," 492: [2959, 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2858, 7153, 3578, 2762, 296, 47],\n"," 165: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 202: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 180: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 296, 47],\n"," 93: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 261: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n"," 435: [4993, 5952, 2858, 7153, 3578, 2762, 296, 4306, 3996, 318],\n"," 322: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 238: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 425: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 512: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 47, 4306],\n"," 725: [4993, 5952, 4226, 7153, 3578, 296, 47, 4306, 3996, 318],\n"," 732: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 108: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 592: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 443: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 916: [5952, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47, 4306],\n"," 736: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 2762, 296, 47],\n"," 961: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 296, 47, 4306],\n"," 1012: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 515: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 978: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 28: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 475: [4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296, 47],\n"," 598: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 943: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 520: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 697: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 849: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 1003: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 296, 47, 4306],\n"," 705: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 48: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 29: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 231: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 699: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n"," 448: [2959, 4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47],\n"," 750: [4993, 5952, 4226, 2571, 7153, 3578, 2762, 296, 47, 4306],\n"," 782: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 860: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 768: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 127: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296],\n"," 894: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]})"]},"metadata":{},"execution_count":10}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," if user_id not in user_id2index:\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"]},{"cell_type":"code","execution_count":11,"id":"27b87c98","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"27b87c98","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":25,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"3b53ae05-8f2b-4bcd-e3e5-72cac813ffcf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2의 사용자가 학습 데이터에 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":12,"id":"1940df09","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1940df09","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"c9d4698b-99ae-48dd-8628-92b6791aaa0e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 296]"]},"metadata":{},"execution_count":12}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":13,"id":"d1786a85","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"d1786a85","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":5,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9bf45053-0677-4bd4-b3e2-e27b4f1c6384"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","2874 2959 Fight Club (1999) \n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","5852 5952 Lord of the Rings: The Two Towers, The (2002) \n","\n"," genre \\\n","2874 [Action, Crime, Drama, Thriller] \n","4899 [Action, Adventure, Fantasy] \n","5852 [Action, Adventure, Fantasy] \n","\n"," tag \n","2874 [based on a book, chuck palahniuk, edward nort... \n","4899 [based on a book, big budget, new zealand, sce... \n","5852 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
58525952Lord of the Rings: The Two Towers, The (2002)[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":13}],"source":["# user_id=2에 대한 추천(2959, 4993, 5952)\n","movies[movies.movie_id.isin([2959, 4993, 5952])]"]},{"cell_type":"code","execution_count":13,"id":"441ff6da","metadata":{"id":"441ff6da","executionInfo":{"status":"ok","timestamp":1672119477563,"user_tz":-540,"elapsed":4,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b9224173", + "metadata": { + "id": "b9224173" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/NMF.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "f8caaceb", + "metadata": { + "id": "f8caaceb" + }, + "source": [ + "# 비음수 행렬 분해(Non-negative Matrix Factorization, NMF)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9cb2f773", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4459, + "status": "ok", + "timestamp": 1672119380127, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "9cb2f773", + "outputId": "3b3441b6-3940-4b5a-ef0c-99ca4e312e13" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:36:13-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 64.1MB/s in 1.0s \n", + "\n", + "2022-12-27 05:36:14 (64.1 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ed3c6a08", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 69811, + "status": "ok", + "timestamp": 1672119449934, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "ed3c6a08", + "outputId": "740a21e4-a8ae-42cc-f142-e51190fa0f81" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8e6879e3", + "metadata": { + "executionInfo": { + "elapsed": 21, + "status": "ok", + "timestamp": 1672119449934, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "8e6879e3" + }, + "outputs": [], + "source": [ + "# 결손값을 채우는 방법\n", + "fillna_with_zero = True\n", + "# 인자 수\n", + "factors = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "52281452", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 20, + "status": "ok", + "timestamp": 1672119449935, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "52281452", + "outputId": "1bb4e599-cc7a-4fc5-cb95-4ef09185a7a2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [5., 0., 3., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [5., 0., 0., ..., 0., 0., 0.]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", + "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", + "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", + "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", + "if fillna_with_zero:\n", + " matrix = user_movie_matrix.fillna(0).to_numpy()\n", + "else:\n", + " matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n", + "matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "708dda3c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 13601, + "status": "ok", + "timestamp": 1672119463518, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "708dda3c", + "outputId": "e404fb9c-9bd5-4466-95a6-79c39e5ea205" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", + " warnings.warn(\n", + "/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "NMF(n_components=5)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.decomposition import NMF\n", + "\n", + "# NMF 실행\n", + "nmf = NMF(n_components=factors)\n", + "nmf.fit(matrix)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "687a16b1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 12450, + "status": "ok", + "timestamp": 1672119475949, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "687a16b1", + "outputId": "64397446-be94-4869-bc6c-61d351679634" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:289: FutureWarning: The 'init' value, when 'init=None' and n_components is less than n_samples and n_features, will be changed from 'nndsvd' to 'nndsvda' in 1.1 (renaming of 0.26).\n", + " warnings.warn(\n", + "/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_nmf.py:1637: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.02183306, 0.53589472, 0. , 0. , 0. ],\n", + " [0.21316649, 0.10881363, 0. , 0. , 0. ],\n", + " [0. , 0.02976153, 0.05290897, 0.15364729, 0.02014201],\n", + " ...,\n", + " [0.13635649, 0.14281654, 0. , 0. , 0.00656846],\n", + " [0.09753435, 0.01077205, 0. , 0.13237952, 0.13516597],\n", + " [0.99758645, 0. , 0. , 0.64704016, 0.0597298 ]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "P = nmf.fit_transform(matrix)\n", + "P" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a731dbb1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 5, + "status": "ok", + "timestamp": 1672119475949, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "a731dbb1", + "outputId": "f6e9c43b-428e-4a65-e742-25a037fbe084" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.96418272, 0.50117827, 0.39871676, ..., 0. , 0. ,\n", + " 0. ],\n", + " [1.15067071, 0.96077987, 0.40196914, ..., 0. , 0.00289232,\n", + " 0.00216924],\n", + " [0.47176639, 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [0.79016059, 0.30417491, 0. , ..., 0.02803924, 0.01446954,\n", + " 0.01085215],\n", + " [0.29616062, 0.30060155, 0.17846866, ..., 0. , 0. ,\n", + " 0. ]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Q = nmf.components_\n", + "Q" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "36669c15", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1672119475949, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "36669c15", + "outputId": "6c3b7cb4-feb2-460f-8f90-42484580128c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[6.59522475e-01, 5.25819116e-01, 2.24118350e-01, ...,\n", + " 0.00000000e+00, 1.54997784e-03, 1.16248338e-03],\n", + " [5.43906588e-01, 2.11380357e-01, 1.28732773e-01, ...,\n", + " 0.00000000e+00, 3.14723598e-04, 2.36042698e-04],\n", + " [1.86577692e-01, 8.13846441e-02, 1.55579328e-02, ...,\n", + " 4.30815294e-03, 2.30928521e-03, 1.73196391e-03],\n", + " ...,\n", + " [4.34109190e-01, 2.07528655e-01, 1.12947724e-01, ...,\n", + " 0.00000000e+00, 4.13070817e-04, 3.09803112e-04],\n", + " [3.48602292e-01, 1.40129296e-01, 6.73415036e-02, ...,\n", + " 3.71182091e-03, 1.94662680e-03, 1.45997010e-03],\n", + " [2.48839731e+00, 7.14736905e-01, 4.08414332e-01, ...,\n", + " 1.81425136e-02, 9.36237290e-03, 7.02177967e-03]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "# 예측 평갓값 행렬\n", + "pred_matrix = np.dot(P, Q)\n", + "pred_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "08eb2849", + "metadata": { + "executionInfo": { + "elapsed": 287, + "status": "ok", + "timestamp": 1672119476234, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "08eb2849" + }, + "outputs": [], + "source": [ + "# 학습용에 나타나지 않는 사용자나 영화의 예측 평갓값은 평균 평갓값으로 한다\n", + "average_score = movielens_train.rating.mean()\n", + "movie_rating_predict = movielens_test.copy()\n", + "pred_results = []\n", + "for i, row in movielens_test.iterrows():\n", + " user_id = row[\"user_id\"]\n", + " if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n", + " pred_results.append(average_score)\n", + " continue\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_index = movie_id2index[row[\"movie_id\"]]\n", + " pred_score = pred_matrix[user_index, movie_index]\n", + " pred_results.append(pred_score)\n", + "movie_rating_predict[\"rating_pred\"] = pred_results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7aa822a0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1330, + "status": "ok", + "timestamp": 1672119477563, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7aa822a0", + "outputId": "12551108-c9b0-445a-fe32-eb39af8205c8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {139: [4993, 5952, 4226, 7153, 4306, 3996, 6539, 5445, 6874, 4963],\n", + " 149: [4226, 2571, 2762, 47, 3996, 318, 6539, 5445, 4963, 1704],\n", + " 182: [47, 6874, 2329, 3793, 4011, 7361, 4878, 7438, 6333, 2502],\n", + " 215: [2329, 527, 6711, 3147, 2683, 8360, 5669, 6502, 3948, 1246],\n", + " 281: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 326: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n", + " 351: [2959, 4993, 5952, 4226, 7153, 3578, 2762, 47, 4306, 3996],\n", + " 357: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 2762, 296, 47],\n", + " 426: [2502,\n", + " 33794,\n", + " 3949,\n", + " 7147,\n", + " 33493,\n", + " 5902,\n", + " 44191,\n", + " 33166,\n", + " 4848,\n", + " 6016],\n", + " 456: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 459: [318, 4886, 2997, 50, 4995, 4973, 7361, 4878, 3897, 2502],\n", + " 494: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 517: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 524: [7153, 3996, 6539, 5349, 4886, 3793, 7361, 593, 7438, 527],\n", + " 556: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 588: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 47],\n", + " 589: [2959, 4993, 5952, 4226, 2571, 2858, 7153, 3578, 2762, 4306],\n", + " 590: [2959, 4993, 5952, 4226, 2858, 7153, 3578, 4306, 3996, 6539],\n", + " 601: [2959, 4993, 5952, 4226, 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"from collections import defaultdict\n", + "\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자가 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", + "pred_user2items = defaultdict(list)\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "for user_id in movielens_train.user_id.unique():\n", + " if user_id not in user_id2index:\n", + " continue\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", + " for movie_index in movie_indexes:\n", + " movie_id = user_movie_matrix.columns[movie_index]\n", + " if movie_id not in user_evaluated_movies[user_id]:\n", + " pred_user2items[user_id].append(movie_id)\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "27b87c98", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "executionInfo": { + "elapsed": 25, + "status": "ok", + "timestamp": 1672119477563, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "27b87c98", + "outputId": "3b53ae05-8f2b-4bcd-e3e5-72cac813ffcf" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"],"id":"52eedd0b"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":73811,"status":"ok","timestamp":1672119850668,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"d3c02fa7","outputId":"2703b391-837b-4ced-8876-73fe4b6b7a9e"},"outputs":[{"name":"stdout","output_type":"stream","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"],"id":"d3c02fa7"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1672119850668,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"683d7c01-91e8-4835-b9cc-332b16c13966","outputId":"3df466b9-dbeb-47f1-ecd2-105e2288c100"},"outputs":[{"data":{"text/html":["\n","
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2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51.0NaNNaNNaNNaNNaN3.0NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1000 rows × 6673 columns

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\n"," "],"text/plain":["movie_id 1 2 3 4 5 6 7 8 9 \\\n","user_id \n","1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","4 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","5 1.0 NaN NaN NaN NaN NaN 3.0 NaN NaN \n","... ... ... ... ... ... ... ... ... ... \n","1048 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","1050 NaN 3.0 NaN NaN NaN 3.0 NaN NaN NaN \n","1051 5.0 NaN 3.0 NaN 3.0 NaN 4.0 NaN NaN \n","1052 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n","1053 5.0 NaN NaN NaN NaN NaN NaN NaN NaN \n","\n","movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n","user_id ... \n","1 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","2 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","3 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","5 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","... ... ... ... ... ... ... ... ... ... ... \n","1048 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1050 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1051 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1052 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","1053 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n","\n","movie_id 63992 64716 \n","user_id \n","1 NaN NaN \n","2 NaN NaN \n","3 NaN NaN \n","4 NaN NaN \n","5 NaN NaN \n","... ... ... \n","1048 NaN NaN \n","1050 NaN NaN \n","1051 NaN NaN \n","1052 NaN NaN \n","1053 NaN NaN \n","\n","[1000 rows x 6673 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","user_movie_matrix"],"id":"683d7c01-91e8-4835-b9cc-332b16c13966"},{"cell_type":"code","execution_count":null,"metadata":{"id":"1f34610c"},"outputs":[],"source":["# 학습에 사용하는 학습용 데이터 안의 사용자와 영화의 조합을 얻는다\n","train_keys = movielens_train[[\"user_id\", \"movie_id\"]]\n","# 학습용 데이터 안의 평갓값을 학습의 정답 데이터로 얻는다\n","train_y = movielens_train.rating.values\n","\n","# 평갓값을 예측할 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다\n","test_keys = movielens_test[[\"user_id\", \"movie_id\"]]\n","# 순위 형식의 추천 리스트 작성을 위해 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 얻는다\n","train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[[\"user_id\", \"movie_id\"]]\n"],"id":"1f34610c"},{"cell_type":"code","execution_count":null,"metadata":{"id":"9f5053fe"},"outputs":[],"source":["# 특징량을 작성한다\n","train_x = train_keys.copy()\n","test_x = test_keys.copy()\n","train_all_x = train_all_keys.copy()"],"id":"9f5053fe"},{"cell_type":"code","execution_count":null,"metadata":{"id":"3b756eca"},"outputs":[],"source":["# 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값\n","# 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량으로 추가한다\n","aggregators = [\"min\", \"max\", \"mean\"]\n","user_features = movielens_train.groupby(\"user_id\").rating.agg(aggregators).to_dict()\n","movie_features = movielens_train.groupby(\"movie_id\").rating.agg(aggregators).to_dict()\n","for agg in aggregators:\n"," train_x[f\"u_{agg}\"] = train_x[\"user_id\"].map(user_features[agg])\n"," test_x[f\"u_{agg}\"] = test_x[\"user_id\"].map(user_features[agg])\n"," train_all_x[f\"u_{agg}\"] = train_all_x[\"user_id\"].map(user_features[agg])\n"," train_x[f\"m_{agg}\"] = train_x[\"movie_id\"].map(movie_features[agg])\n"," test_x[f\"m_{agg}\"] = test_x[\"movie_id\"].map(movie_features[agg])\n"," train_all_x[f\"m_{agg}\"] = train_all_x[\"movie_id\"].map(movie_features[agg])\n","# 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다\n","average_rating = train_y.mean()\n","test_x.fillna(average_rating, inplace=True)"],"id":"3b756eca"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1492,"status":"ok","timestamp":1672119853162,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"3499ae5a","outputId":"ae542425-5ea1-46bd-fe3f-b68df0abae71"},"outputs":[{"name":"stderr","output_type":"stream","text":[":7: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"," movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n"]}],"source":["import itertools\n","\n","# 영화가 특정한 genre인지 나타내는 특징량을 추가한다\n","movie_genres = movies[[\"movie_id\", \"genre\"]]\n","genres = set(list(itertools.chain(*movie_genres.genre)))\n","for genre in genres:\n"," movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n","movie_genres.drop(\"genre\", axis=1, inplace=True)\n","train_x = train_x.merge(movie_genres, on=\"movie_id\")\n","test_x = test_x.merge(movie_genres, on=\"movie_id\")\n","train_all_x = train_all_x.merge(movie_genres, on=\"movie_id\")"],"id":"3499ae5a"},{"cell_type":"code","execution_count":null,"metadata":{"id":"eca70ebb"},"outputs":[],"source":["# 특징량으로 사용하지 않는 정보는 삭제한다\n","train_x = train_x.drop(columns=[\"user_id\", \"movie_id\"])\n","test_x = test_x.drop(columns=[\"user_id\", \"movie_id\"])\n","train_all_x = train_all_x.drop(columns=[\"user_id\", \"movie_id\"])"],"id":"eca70ebb"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47235,"status":"ok","timestamp":1672119900826,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"},"user_tz":-540},"id":"78bde7fd","outputId":"6aa184b5-c138-4343-8e8c-918a5383c6a4"},"outputs":[{"data":{"text/plain":["RandomForestRegressor(n_jobs=-1, random_state=0)"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.ensemble import RandomForestRegressor as RFR\n","\n","# Random Forest를 사용한 학습\n","reg = RFR(n_jobs=-1, random_state=0)\n","reg.fit(train_x.values, train_y)\n"],"id":"78bde7fd"},{"cell_type":"code","execution_count":null,"metadata":{"id":"ca01da90"},"outputs":[],"source":["# 테스트용 데이터 안의 사용와 영화의 조합에 대해 평갓값을 예측한다\n","test_pred = reg.predict(test_x.values)\n","\n","movie_rating_predict = test_keys.copy()\n","movie_rating_predict[\"rating_pred\"] = test_pred"],"id":"ca01da90"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"3ec280f6","outputId":"2757cf67-bb2c-4111-8871-e88cbe01853b"},"outputs":[{"data":{"text/plain":["defaultdict(list,\n"," {139: [7986, 6356, 6565, 4270, 6538, 5817, 4828, 3588, 631, 26974],\n"," 149: [5914, 1672, 1570, 555, 5928, 616, 640, 5996, 1326, 1671],\n"," 182: [4956,\n"," 5971,\n"," 6729,\n"," 8379,\n"," 6713,\n"," 5662,\n"," 30894,\n"," 6890,\n"," 7647,\n"," 5090],\n"," 215: [5598, 4626, 4679, 6286, 4458, 4802, 4887, 848, 3274, 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31594,\n"," 32179,\n"," 25805],\n"," 943: [1251, 1365, 1373, 1152, 1797, 1144, 1785, 1125, 1421, 1428],\n"," 520: [4039, 4104, 4795, 3600, 3738, 3518, 3527, 4677, 4251, 2461],\n"," 697: [938, 2166, 1693, 1669, 1046, 1284, 1468, 1968, 981, 964],\n"," 849: [5024, 4970, 4968, 5602, 4961, 4960, 4958, 4957, 4951, 5606],\n"," 1003: [7194,\n"," 7828,\n"," 8484,\n"," 31367,\n"," 7013,\n"," 8827,\n"," 7237,\n"," 8811,\n"," 6855,\n"," 7047],\n"," 705: [2421, 2306, 1754, 2445, 2365, 2600, 1857, 2095, 2677, 2864],\n"," 48: [3073, 2613, 7134, 5172, 8275, 2754, 2542, 3743, 3204, 6244],\n"," 29: [7943,\n"," 1103,\n"," 7698,\n"," 1593,\n"," 1040,\n"," 1045,\n"," 26745,\n"," 25753,\n"," 8604,\n"," 1733],\n"," 231: [627, 58, 1032, 56949, 33836, 1113, 5499, 164, 719, 157],\n"," 699: [485, 468, 301, 37741, 436, 8537, 50794, 73, 378, 69],\n"," 448: [4722, 4168, 5111, 4477, 4886, 6686, 5523, 4085, 1841, 5099],\n"," 750: [3968, 4453, 5295, 4509, 5202, 3849, 4121, 5214, 4599, 4683],\n"," 782: [2473, 2885, 2930, 2408, 3061, 3519, 2610, 2975, 2443, 3135],\n"," 860: [4366, 4521, 5823, 4355, 5236, 4517, 5187, 4531, 5464, 5560],\n"," 768: [960, 2017, 1126, 1997, 961, 1371, 1575, 1465, 1145, 1780],\n"," 127: [47099,\n"," 3189,\n"," 2824,\n"," 7373,\n"," 3528,\n"," 3605,\n"," 58047,\n"," 3252,\n"," 3592,\n"," 8636],\n"," 894: [118, 177, 900, 830, 1014, 486, 868, 298, 640, 969]})"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["from collections import defaultdict\n","import numpy as np\n","\n","# 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합에 대해 평갓값을 예측한다\n","train_all_pred = reg.predict(train_all_x.values)\n","\n","pred_train_all = train_all_keys.copy()\n","pred_train_all[\"rating_pred\"] = train_all_pred\n","pred_matrix = pred_train_all.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating_pred\")\n","\n","# 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서\n","# 예측 평갓값이 높은 순으로 10편의 영화를 순위 형식으로 추천 리스트로 만든다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," movie_indexes = np.argsort(-pred_matrix.loc[user_id, :]).values\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in (user_evaluated_movies[user_id]):\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","pred_user2items"],"id":"3ec280f6"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"c3a25c25","outputId":"aa1f9306-1bdb-43c6-f692-1731569f185f"},"outputs":[{"data":{"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
40134105Evil Dead, The (1981)[Fantasy, Horror][directorial debut, bruce campbell, cult class...
41184210Manhunter (1986)[Action, Crime, Drama, Horror, Thriller][hannibal lecter, serial killer, ei muista, er...
48674961Pornstar: The Legend of Ron Jeremy (2001)[Documentary][pornography]
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\n"," "],"text/plain":[" movie_id title \\\n","4013 4105 Evil Dead, The (1981) \n","4118 4210 Manhunter (1986) \n","4867 4961 Pornstar: The Legend of Ron Jeremy (2001) \n","\n"," genre \\\n","4013 [Fantasy, Horror] \n","4118 [Action, Crime, Drama, Horror, Thriller] \n","4867 [Documentary] \n","\n"," tag \n","4013 [directorial debut, bruce campbell, cult class... \n","4118 [hannibal lecter, serial killer, ei muista, er... \n","4867 [pornography] "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# user_id=2에 대한 추천(4210, 4961, 4105)\n","movies[movies.movie_id.isin([4210, 4961, 4105])]"],"id":"09555a1f"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7a42fa17"},"outputs":[],"source":[],"id":"7a42fa17"}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d5889b3", + "metadata": { + "id": "0d5889b3" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/RF.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "4935d845", + "metadata": { + "id": "4935d845" + }, + "source": [ + "# 회귀 모델, 랜덤 포레스트(Random Forest)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52eedd0b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4283, + "status": "ok", + "timestamp": 1672119776862, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "52eedd0b", + "outputId": "d53d23e4-53b5-440a-b5d5-5432ccedb2d6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:42:50-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 64.4MB/s in 1.0s \n", + "\n", + "2022-12-27 05:42:51 (64.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3c02fa7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 73811, + "status": "ok", + "timestamp": 1672119850668, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "d3c02fa7", + "outputId": "2703b391-837b-4ced-8876-73fe4b6b7a9e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "683d7c01-91e8-4835-b9cc-332b16c13966", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 455 + }, + "executionInfo": { + "elapsed": 24, + "status": "ok", + "timestamp": 1672119850668, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "683d7c01-91e8-4835-b9cc-332b16c13966", + "outputId": "3df466b9-dbeb-47f1-ecd2-105e2288c100" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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user_id
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2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51.0NaNNaNNaNNaNNaN3.0NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
1048NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1050NaN3.0NaNNaNNaN3.0NaNNaNNaN3.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10515.0NaN3.0NaN3.0NaN4.0NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1052NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10535.0NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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1000 rows × 6673 columns

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\n", + " " + ], + "text/plain": [ + "movie_id 1 2 3 4 5 6 7 8 9 \\\n", + "user_id \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "5 1.0 NaN NaN NaN NaN NaN 3.0 NaN NaN \n", + "... ... ... ... ... ... ... ... ... ... \n", + "1048 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1050 NaN 3.0 NaN NaN NaN 3.0 NaN NaN NaN \n", + "1051 5.0 NaN 3.0 NaN 3.0 NaN 4.0 NaN NaN \n", + "1052 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1053 5.0 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "movie_id 10 ... 62000 62113 62293 62344 62394 62801 62803 63113 \\\n", + "user_id ... \n", + "1 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "5 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... ... ... ... ... \n", + "1048 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1050 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1051 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1052 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1053 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + "movie_id 63992 64716 \n", + "user_id \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "... ... ... \n", + "1048 NaN NaN \n", + "1050 NaN NaN \n", + "1051 NaN NaN \n", + "1052 NaN NaN \n", + "1053 NaN NaN \n", + "\n", + "[1000 rows x 6673 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다\n", + "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", + "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", + "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", + "user_movie_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f34610c", + "metadata": { + "id": "1f34610c" + }, + "outputs": [], + "source": [ + "# 학습에 사용하는 학습용 데이터 안의 사용자와 영화의 조합을 얻는다\n", + "train_keys = movielens_train[[\"user_id\", \"movie_id\"]]\n", + "# 학습용 데이터 안의 평갓값을 학습의 정답 데이터로 얻는다\n", + "train_y = movielens_train.rating.values\n", + "\n", + "# 평갓값을 예측할 테스트용 데이터 안의 사용자와 영화의 조합을 얻는다\n", + "test_keys = movielens_test[[\"user_id\", \"movie_id\"]]\n", + "# 순위 형식의 추천 리스트 작성을 위해 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합을 얻는다\n", + "train_all_keys = user_movie_matrix.stack(dropna=False).reset_index()[[\"user_id\", \"movie_id\"]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f5053fe", + "metadata": { + "id": "9f5053fe" + }, + "outputs": [], + "source": [ + "# 특징량을 작성한다\n", + "train_x = train_keys.copy()\n", + "test_x = test_keys.copy()\n", + "train_all_x = train_all_keys.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b756eca", + "metadata": { + "id": "3b756eca" + }, + "outputs": [], + "source": [ + "# 학습용 데이터에 존재하는 사용자별 평갓값의 최솟값, 최댓값, 평균값\n", + "# 및, 영화별 평갓값의 최솟값, 최댓값, 평균값을 특징량으로 추가한다\n", + "aggregators = [\"min\", \"max\", \"mean\"]\n", + "user_features = movielens_train.groupby(\"user_id\").rating.agg(aggregators).to_dict()\n", + "movie_features = movielens_train.groupby(\"movie_id\").rating.agg(aggregators).to_dict()\n", + "for agg in aggregators:\n", + " train_x[f\"u_{agg}\"] = train_x[\"user_id\"].map(user_features[agg])\n", + " test_x[f\"u_{agg}\"] = test_x[\"user_id\"].map(user_features[agg])\n", + " train_all_x[f\"u_{agg}\"] = train_all_x[\"user_id\"].map(user_features[agg])\n", + " train_x[f\"m_{agg}\"] = train_x[\"movie_id\"].map(movie_features[agg])\n", + " test_x[f\"m_{agg}\"] = test_x[\"movie_id\"].map(movie_features[agg])\n", + " train_all_x[f\"m_{agg}\"] = train_all_x[\"movie_id\"].map(movie_features[agg])\n", + "# 테스트용 데이터에만 존재하는 사용자나 영화의 특징량을, 학습용 데이터 전체의 평균 평갓값으로 채운다\n", + "average_rating = train_y.mean()\n", + "test_x.fillna(average_rating, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3499ae5a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1492, + "status": "ok", + "timestamp": 1672119853162, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "3499ae5a", + "outputId": "ae542425-5ea1-46bd-fe3f-b68df0abae71" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n" + ] + } + ], + "source": [ + "import itertools\n", + "\n", + "# 영화가 특정한 genre인지 나타내는 특징량을 추가한다\n", + "movie_genres = movies[[\"movie_id\", \"genre\"]]\n", + "genres = set(list(itertools.chain(*movie_genres.genre)))\n", + "for genre in genres:\n", + " movie_genres[f\"is_{genre}\"] = movie_genres.genre.apply(lambda x: genre in x)\n", + "movie_genres.drop(\"genre\", axis=1, inplace=True)\n", + "train_x = train_x.merge(movie_genres, on=\"movie_id\")\n", + "test_x = test_x.merge(movie_genres, on=\"movie_id\")\n", + "train_all_x = train_all_x.merge(movie_genres, on=\"movie_id\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eca70ebb", + "metadata": { + "id": "eca70ebb" + }, + "outputs": [], + "source": [ + "# 특징량으로 사용하지 않는 정보는 삭제한다\n", + "train_x = train_x.drop(columns=[\"user_id\", \"movie_id\"])\n", + "test_x = test_x.drop(columns=[\"user_id\", \"movie_id\"])\n", + "train_all_x = train_all_x.drop(columns=[\"user_id\", \"movie_id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78bde7fd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 47235, + "status": "ok", + "timestamp": 1672119900826, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "78bde7fd", + "outputId": "6aa184b5-c138-4343-8e8c-918a5383c6a4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor(n_jobs=-1, random_state=0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor as RFR\n", + "\n", + "# Random Forest를 사용한 학습\n", + "reg = RFR(n_jobs=-1, random_state=0)\n", + "reg.fit(train_x.values, train_y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca01da90", + "metadata": { + "id": "ca01da90" + }, + "outputs": [], + "source": [ + "# 테스트용 데이터 안의 사용와 영화의 조합에 대해 평갓값을 예측한다\n", + "test_pred = reg.predict(test_x.values)\n", + "\n", + "movie_rating_predict = test_keys.copy()\n", + "movie_rating_predict[\"rating_pred\"] = test_pred" + ] + }, + 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+ " 3252,\n", + " 3592,\n", + " 8636],\n", + " 894: [118, 177, 900, 830, 1014, 486, 868, 298, 640, 969]})" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "import numpy as np\n", + "\n", + "# 학습용 데이터에 존재하는 모든 사용자와 모든 영화의 조합에 대해 평갓값을 예측한다\n", + "train_all_pred = reg.predict(train_all_x.values)\n", + "\n", + "pred_train_all = train_all_keys.copy()\n", + "pred_train_all[\"rating_pred\"] = train_all_pred\n", + "pred_matrix = pred_train_all.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating_pred\")\n", + "\n", + "# 사용자가 학습용 데이터 안에서 평가하지 않은 영화 중에서\n", + "# 예측 평갓값이 높은 순으로 10편의 영화를 순위 형식으로 추천 리스트로 만든다\n", + "pred_user2items = defaultdict(list)\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "for user_id in movielens_train.user_id.unique():\n", + " movie_indexes = np.argsort(-pred_matrix.loc[user_id, :]).values\n", + " for movie_index in movie_indexes:\n", + " movie_id = user_movie_matrix.columns[movie_index]\n", + " if movie_id not in (user_evaluated_movies[user_id]):\n", + " pred_user2items[user_id].append(movie_id)\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3a25c25", + "metadata": { + "colab": { + "background_save": true + }, + "id": "c3a25c25", + "outputId": "aa1f9306-1bdb-43c6-f692-1731569f185f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
40134105Evil Dead, The (1981)[Fantasy, Horror][directorial debut, bruce campbell, cult class...
41184210Manhunter (1986)[Action, Crime, Drama, Horror, Thriller][hannibal lecter, serial killer, ei muista, er...
48674961Pornstar: The Legend of Ron Jeremy (2001)[Documentary][pornography]
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"display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/SVD.ipynb b/chapter5/colab/SVD.ipynb index b2c9df3..ce786a4 100644 --- a/chapter5/colab/SVD.ipynb +++ b/chapter5/colab/SVD.ipynb @@ -1 +1,2381 @@ -{"cells":[{"cell_type":"markdown","id":"4fb1700b","metadata":{"id":"4fb1700b"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/SVD.ipynb)"]},{"cell_type":"markdown","id":"05fd5c37","metadata":{"id":"05fd5c37"},"source":["# 특이값 분석(SVD)"]},{"cell_type":"code","execution_count":1,"id":"ae97b569","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ae97b569","executionInfo":{"status":"ok","timestamp":1672119890093,"user_tz":-540,"elapsed":4522,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"52b03925-db6b-47e9-e26c-764d0eba7a5b"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:43:01-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 65.4MB/s in 1.0s \n","\n","2022-12-27 05:43:02 (65.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"395a5042","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"395a5042","executionInfo":{"status":"ok","timestamp":1672119956404,"user_tz":-540,"elapsed":66319,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"d2cab3b4-2205-46e7-c70a-e37c5d6b611c"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"1bcee149","metadata":{"id":"1bcee149","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":30,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 결손값을 채우는 방법\n","fillna_with_zero = True\n","# 인자 수\n","factors = 5"]},{"cell_type":"code","execution_count":4,"id":"2cf8dfe4","metadata":{"id":"2cf8dfe4","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":25,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","if fillna_with_zero:\n"," matrix = user_movie_matrix.fillna(0).to_numpy()\n","else:\n"," matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n"]},{"cell_type":"code","execution_count":5,"id":"22c0909b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"22c0909b","executionInfo":{"status":"ok","timestamp":1672119956405,"user_tz":-540,"elapsed":22,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"73d96ca8-6fd5-42b8-c085-568f67d0d2e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["ユーザー数=1000, アイテム数=6673, 密度=0.02\n"]}],"source":["# 희소 정보\n","user_num = len(user_movie_matrix.index)\n","item_num = len(user_movie_matrix.columns)\n","non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n","non_null_ratio = non_null_num / (user_num*item_num)\n","\n","print(f'ユーザー数={user_num}, アイテム数={item_num}, 密度={non_null_ratio:.2f}')"]},{"cell_type":"code","execution_count":6,"id":"7f1f97fa","metadata":{"scrolled":true,"id":"7f1f97fa","executionInfo":{"status":"ok","timestamp":1672119957450,"user_tz":-540,"elapsed":607,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from scipy.sparse.linalg import svds\n","import numpy as np\n","\n","# 인자 수 k로 특이점 분석을 수행한다\n","P, S, Qt = svds(matrix, k=factors)"]},{"cell_type":"code","execution_count":7,"id":"65a9563b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"65a9563b","executionInfo":{"status":"ok","timestamp":1672119957451,"user_tz":-540,"elapsed":4,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b73e0f1a-457a-4e3f-866f-78010e4c4bd3"},"outputs":[{"output_type":"stream","name":"stdout","text":["P: (1000, 5), S: (5,), Qt: (5, 6673), pred_matrix: (1000, 6673)\n"]}],"source":["# 예측 평갓값 행렬\n","pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n","\n","print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")"]},{"cell_type":"code","execution_count":8,"id":"75ee52d8","metadata":{"id":"75ee52d8","executionInfo":{"status":"ok","timestamp":1672119957898,"user_tz":-540,"elapsed":449,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 학습용에 나타나지 않은 사용자와 영화 예측 평갓값은 평균 평갓값으로 한다\n","average_score = movielens_train.rating.mean()\n","movie_rating_predict = movielens_test.copy()\n","pred_results = []\n","for i, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_index = movie_id2index[row[\"movie_id\"]]\n"," pred_score = pred_matrix[user_index, movie_index]\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results"]},{"cell_type":"code","execution_count":9,"id":"2a761397","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2a761397","executionInfo":{"status":"ok","timestamp":1672119959233,"user_tz":-540,"elapsed":1343,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"275c7093-7ffa-4628-d041-649854a18e3a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [4993, 4226, 5952, 7153, 4306, 3996, 5349, 4963, 6539, 4886],\n"," 149: [2571, 4226, 2762, 3996, 5349, 47, 2028, 4963, 6539, 4886],\n"," 182: [3793, 47, 6874, 2329, 4011, 7361, 2706, 4878, 2502, 6333],\n"," 215: [2329, 3147, 6711, 527, 2683, 3948, 8360, 5218, 6502, 1246],\n"," 281: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 326: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 351: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 357: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 426: [2502,\n"," 33794,\n"," 7147,\n"," 3949,\n"," 5902,\n"," 4848,\n"," 33166,\n"," 33493,\n"," 2791,\n"," 5995],\n"," 456: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 459: [4886, 318, 2997, 4995, 4973, 50, 3897, 7361, 1923, 4878],\n"," 494: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 517: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 524: [7153, 3996, 3793, 5349, 6539, 4886, 7361, 593, 1923, 5989],\n"," 556: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 588: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 589: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 590: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 601: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 621: [5952, 7153, 4306, 4995, 2329, 3897, 1682, 4011, 4878, 3147],\n"," 634: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 672: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 4963, 6539],\n"," 701: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 719: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 745: [5952, 7153, 4306, 6539, 5445, 356, 6377, 6874, 4973, 2329],\n"," 757: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 775: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 780: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996],\n"," 785: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 788: [296, 4973, 3897, 1089, 4878, 2502, 1961, 1196, 1136, 4034],\n"," 870: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 987: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 988: [7153, 4963, 6539, 5445, 6377, 6874, 4995, 2329, 4027, 7361],\n"," 1020: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306],\n"," 1: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 22: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 26: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 30: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 34: [4993, 4226, 5952, 2858, 7153, 4306, 3996, 5349, 4963, 6539],\n"," 38: [4226, 2858, 4306, 3793, 2028, 6539, 4886, 5445, 2997, 2329],\n"," 50: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 53: [2959, 2571, 4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 54: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 67: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 71: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 76: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 88: [4226, 4306, 3996, 47, 2028, 296, 1704, 318, 2997, 4995],\n"," 89: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 94: [2959, 2571, 4993, 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2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 292: [4963, 6874, 5989, 6333, 3147, 527, 1517, 2542, 1617, 3481],\n"," 296: [3996, 1704, 6377, 1270, 1196, 1136, 2716, 2683, 1517, 2918],\n"," 303: [5349, 4963, 6539, 1704, 2997, 6377, 4995, 4973, 2329, 50],\n"," 307: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 3996, 47],\n"," 310: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 315: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 320: [3793, 5349, 1704, 318, 2997, 6874, 2329, 50, 3897, 4027],\n"," 332: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 339: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 343: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 355: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 364: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 365: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 368: [2858, 2762, 2028, 1704, 318, 4995, 2329, 593, 4878, 5989],\n"," 381: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 382: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 383: [2959, 4306, 5349, 47, 4963, 6539, 356, 6874, 2329, 3897],\n"," 391: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 396: [4993, 3578, 4226, 5952, 2762, 7153, 3996, 3793, 5349, 47],\n"," 398: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 406: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 409: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 410: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 416: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 418: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 419: [2959, 3578, 7153, 2028, 6539, 4886, 1704, 356, 6377, 6874],\n"," 421: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 424: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 432: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 434: [4226, 47, 296, 318, 2997, 4973, 2329, 50, 3897, 1682],\n"," 436: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 438: [2959, 4993, 4226, 5952, 7153, 5349, 47, 2028, 4963, 6539],\n"," 441: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 446: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 452: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 3996, 47, 2028],\n"," 460: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 463: [7153, 4963, 6377, 6874, 4973, 3897, 1682, 4011, 7361, 2291],\n"," 468: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 469: [2959, 47, 4995, 2329, 50, 3897, 1682, 4011, 7361, 1089],\n"," 472: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 476: [4886, 1704, 4995, 1270, 1923, 2291, 2502, 2716, 4034, 1517],\n"," 480: [7153, 6539, 1704, 318, 6377, 6874, 2329, 1682, 7361, 1089],\n"," 482: [2858, 2762, 3996, 5445, 2997, 6874, 4973, 50, 3897, 1682],\n"," 493: [4993, 5952, 7153, 593, 2502, 6333, 1196, 1136, 7438, 589],\n"," 496: [2959, 4993, 3578, 5952, 2858, 7153, 4306, 3996, 3793, 5349],\n"," 501: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 504: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 505: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 511: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 516: [6377, 50, 1682, 4027, 7361, 2918, 2542, 3052, 3481, 364],\n"," 525: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 530: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 531: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 533: [4886, 4995, 2329, 4011, 2706, 4878, 3147, 2683, 6365, 1732],\n"," 536: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 543: [2028, 2329, 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2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 609: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 628: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 629: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 645: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 650: [2959, 5952, 7153, 4306, 2028, 296, 4963, 6539, 4886, 5445],\n"," 656: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 657: [4306, 3793, 4963, 4886, 4011, 2502, 6333, 1136, 1517, 7438],\n"," 669: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 678: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 683: [2959, 4226, 5952, 2858, 2762, 3996, 296, 4963, 1704, 318],\n"," 688: [3996, 2028, 5445, 318, 4973, 3897, 1682, 7361, 4878, 2291],\n"," 689: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 693: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 716: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 4963, 6539],\n"," 720: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 734: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 735: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 739: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793, 5349],\n"," 755: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 758: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 767: [2959, 4226, 2858, 4306, 3793, 47, 2028, 296, 4963, 4886],\n"," 769: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 786: [2959, 4993, 5952, 2858, 7153, 5349, 47, 2028, 296, 4963],\n"," 789: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 792: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 795: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 798: [6377, 2329, 4011, 4027, 7361, 2706, 2502, 4034, 3052, 3481],\n"," 799: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 802: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 806: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 807: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 816: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 827: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 828: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 846: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 859: [2858, 4306, 47, 4963, 318, 2997, 6377, 6874, 4995, 2329],\n"," 862: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 876: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 881: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 885: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 886: [4993, 4226, 5952, 7153, 4306, 3793, 5349, 4963, 6539, 4886],\n"," 889: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 890: [4993, 4226, 5952, 7153, 4306, 5349, 47, 4963, 6539, 4886],\n"," 891: [4963, 6539, 5445, 6377, 6874, 4995, 2329, 4011, 4027, 7361],\n"," 896: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 897: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 898: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 908: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 922: [4226, 2858, 3996, 3793, 2028, 296, 5445, 1704, 2997, 6874],\n"," 930: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 938: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 956: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 958: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793, 47],\n"," 968: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 977: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 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2502],\n"," 18: [4993, 4226, 5952, 7153, 3793, 5349, 4886, 5445, 2997, 6874],\n"," 36: [7153, 3996, 47, 4886, 5445, 1704, 318, 2997, 6874, 4995],\n"," 47: [5349, 47, 4963, 4886, 1704, 356, 6377, 3897, 1682, 4027],\n"," 59: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 62: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 77: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 79: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 80: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 119: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 122: [4993, 4226, 5952, 7153, 4306, 3793, 5349, 47, 2028, 4963],\n"," 125: [2329, 1682, 7361, 4878, 7438, 1617, 5418, 8961, 1527, 8636],\n"," 126: [4973, 7361, 4878, 6711, 2542, 3052, 6365, 3948, 3751, 5378],\n"," 132: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 141: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 154: [4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 2028, 4963],\n"," 155: [4226, 7153, 3793, 5349, 4963, 6539, 4886, 5445, 6377, 6874],\n"," 163: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 164: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 170: [4993, 3578, 5952, 7153, 3996, 5349, 2028, 6539, 5445, 1704],\n"," 171: [2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 6539],\n"," 176: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 190: [4226, 2858, 47, 4963, 1704, 318, 2997, 6377, 4973, 2329],\n"," 197: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 198: [2997, 4973, 4011, 5989, 2291, 6333, 3147, 4034, 2542, 1291],\n"," 199: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 212: [3578, 2858, 4886, 1704, 6377, 2329, 3897, 1682, 4011, 2706],\n"," 221: [2959, 4993, 3578, 4226, 2762, 7153, 4306, 3996, 5349, 4963],\n"," 223: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 226: [2959, 2858, 7153, 4963, 6539, 2997, 6377, 6874, 4973, 1682],\n"," 241: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 247: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 249: [2858, 3996, 2997, 6874, 4973, 3897, 6333, 527, 7438, 1617],\n"," 254: [4993, 4226, 7153, 3793, 4963, 6539, 4886, 1704, 6377, 6874],\n"," 264: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 273: [2959, 4226, 2762, 4306, 3996, 5349, 47, 296, 4963, 4886],\n"," 275: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3793, 5349, 47],\n"," 276: [4963, 6539, 4995, 4027, 2716, 1517, 2918, 3052, 110, 5418],\n"," 293: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 294: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 295: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 3996],\n"," 318: [4226, 2329, 1923, 4878, 3147, 32, 2692, 293, 223, 3949],\n"," 321: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 345: [4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47],\n"," 346: [2959, 2571, 4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 348: [1580, 1196, 1136, 2918, 3052, 3481, 5418, 1265, 1210, 6365],\n"," 349: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 354: [4226, 3793, 47, 2028, 296, 4886, 2997, 6874, 2329, 50],\n"," 359: [5349, 47, 2028, 4886, 1704, 356, 318, 6874, 2329, 50],\n"," 366: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 369: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 384: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 390: [47, 6539, 6377, 3897, 7361, 1580, 1089, 1270, 2291, 1961],\n"," 392: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 403: [2959, 4993, 3578, 5952, 2858, 4306, 3996, 5349, 2028, 6539],\n"," 407: [4226, 2858, 5349, 1704, 318, 4995, 4973, 3897, 4011, 4027],\n"," 414: [3578, 4226, 7153, 4306, 3793, 5349, 2028, 6539, 4886, 5445],\n"," 431: [2959, 2571, 3578, 4226, 2858, 2762, 3996, 3793, 5349, 47],\n"," 454: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 465: [2571, 5952, 7153, 3793, 2028, 4963, 4886, 5445, 3897, 2291],\n"," 481: [2762, 2997, 6377, 4973, 4027, 5989, 1961, 3147, 527, 2918],\n"," 485: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47, 2028],\n"," 503: [3996, 5349, 47, 4963, 1704, 50, 3897, 4011, 4027, 1089],\n"," 513: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 545: [3578, 4306, 3793, 5349, 4963, 6539, 5445, 1682, 2706, 1923],\n"," 552: [2571, 4993, 3578, 5952, 7153, 3996, 3793, 5349, 47, 2028],\n"," 554: [2571, 3578, 4226, 5349, 47, 6539, 4886, 5445, 4995, 2329],\n"," 555: [5445, 1704, 4973, 1682, 7361, 1923, 5989, 4034, 3481, 480],\n"," 561: [1704, 318, 2997, 4995, 50, 3897, 1682, 4027, 7361, 1089],\n"," 565: [2959, 2571, 4993, 4226, 5952, 7153, 4306, 5349, 47, 2028],\n"," 591: [2959, 2571, 4993, 4226, 5952, 2762, 7153, 4306, 3793, 5349],\n"," 617: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 622: [3578, 4226, 3996, 3793, 6539, 4886, 2997, 6377, 6874, 4995],\n"," 623: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 633: [2762, 4963, 5445, 2997, 4995, 4973, 2329, 1682, 4011, 1270],\n"," 636: [2959, 4226, 2858, 4306, 3996, 5349, 47, 4886, 1704, 356],\n"," 638: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 641: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 646: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 654: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 655: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 658: [4993, 5952, 2762, 7153, 4306, 3996, 3793, 5349, 4886, 5445],\n"," 660: [4993, 4226, 5952, 7153, 3793, 5349, 6539, 4886, 5445, 1704],\n"," 661: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 677: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 714: [3793, 5349, 47, 4011, 7361, 1089, 593, 4878, 5989, 1961],\n"," 715: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 723: [2858, 3996, 3793, 2028, 296, 4963, 4886, 5445, 1704, 2997],\n"," 731: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 742: [1089, 593, 2502, 527, 7438, 1258, 858, 2692, 1732, 2396],\n"," 743: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 752: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 772: [2959, 2571, 4226, 5952, 2858, 2762, 7153, 5349, 47, 2028],\n"," 814: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 823: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 826: [2762, 4306, 5349, 4886, 2329, 5989, 2291, 2502, 2716, 527],\n"," 833: [2959, 3578, 4226, 2762, 7153, 47, 6539, 4886, 6377, 6874],\n"," 838: [2959, 7153, 4306, 3996, 3793, 5349, 4963, 4886, 6874, 4995],\n"," 842: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 852: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 878: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 887: [4993, 4226, 5952, 2762, 7153, 4306, 3996, 3793, 5349, 4963],\n"," 895: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 899: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 902: [2997, 6377, 4973, 1682, 4027, 1923, 4878, 2291, 2502, 1961],\n"," 907: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 928: [7153, 3793, 5349, 2028, 6539, 1704, 6874, 4995, 1682, 4027],\n"," 936: [3793, 5349, 318, 1580, 1270, 2706, 5989, 2291, 2716, 4034],\n"," 964: [4993, 5952, 2858, 2762, 7153, 4306, 3996, 3793, 5349, 5445],\n"," 970: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 972: [4993, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 4963, 6539],\n"," 1001: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306],\n"," 1013: 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2858, 7153, 4306, 3996, 3793],\n"," 128: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 158: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 160: [2959, 4993, 4226, 5952, 2762, 7153, 4306, 5349, 47, 296],\n"," 175: [4306, 3996, 3793, 5349, 4963, 6539, 5445, 1704, 4995, 3897],\n"," 196: [4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 3793],\n"," 203: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 206: [2959, 4993, 3578, 4226, 5952, 2858, 7153, 4306, 3996, 3793],\n"," 207: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 255: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 265: [2959, 4993, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 3793],\n"," 340: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 404: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 433: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 440: [3578, 4306, 3996, 3793, 5349, 4963, 4886, 5445, 1704, 2997],\n"," 444: [2959, 4993, 3578, 4226, 5952, 7153, 3996, 3793, 5349, 2028],\n"," 450: [2959, 7153, 6539, 1704, 6377, 6874, 1682, 4011, 7361, 1923],\n"," 479: [4993, 3578, 5952, 7153, 4306, 3996, 3793, 5349, 47, 4963],\n"," 491: [4993, 7153, 4306, 3793, 2028, 296, 6539, 4886, 5445, 2997],\n"," 506: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 523: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 539: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 541: [4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793, 5349],\n"," 616: [4306, 3996, 3793, 5349, 4963, 1704, 6377, 6874, 4995, 4973],\n"," 647: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 659: [2959, 3578, 3793, 5349, 2028, 4963, 6539, 4886, 2329, 50],\n"," 695: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 700: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 707: [2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 748: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 759: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 784: [4973, 1682, 4011, 4027, 7361, 1089, 5989, 2502, 3147, 6711],\n"," 790: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 835: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 844: [2959, 4993, 4226, 5952, 7153, 4306, 3996, 5349, 47, 2028],\n"," 871: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 875: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 892: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 919: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 935: [2959, 4993, 5952, 2858, 2762, 7153, 4306, 5349, 47, 296],\n"," 942: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 960: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 1006: [2959,\n"," 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7153, 4306, 3996],\n"," 679: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 680: [2959, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349],\n"," 691: [2959, 4993, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 3793],\n"," 702: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 708: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 730: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 751: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 773: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 803: [2959, 2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793],\n"," 809: [2959, 2571, 3578, 4226, 7153, 4306, 3996, 3793, 5349, 2028],\n"," 820: [2762, 1704, 318, 4995, 2329, 3897, 4011, 5989, 3147, 4034],\n"," 824: [2959, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 863: [2959, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 865: [4993, 5952, 2762, 3996, 3793, 5349, 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4306, 3996],\n"," 435: [4993, 3578, 5952, 2858, 2762, 7153, 4306, 3996, 3793, 5349],\n"," 322: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 238: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 425: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 512: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 725: [4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793, 5349, 47],\n"," 732: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 108: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 592: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 443: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 916: [2571, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996, 5349],\n"," 736: [2959, 2571, 4993, 4226, 5952, 2858, 2762, 7153, 4306, 5349],\n"," 961: [2959, 2571, 4993, 3578, 4226, 5952, 7153, 4306, 3996, 3793],\n"," 1012: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2762,\n"," 7153,\n"," 4306,\n"," 3996],\n"," 515: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 978: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 28: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 475: [2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306, 3996],\n"," 598: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 943: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 520: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 697: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 849: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 1003: [2959,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 7153,\n"," 4306,\n"," 3996,\n"," 3793],\n"," 705: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 48: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 29: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 231: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 699: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 448: [2959, 2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996],\n"," 750: [2571, 4993, 3578, 4226, 5952, 2762, 7153, 4306, 3996, 3793],\n"," 782: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 860: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 768: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 127: [2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306],\n"," 894: [2959,\n"," 2571,\n"," 4993,\n"," 3578,\n"," 4226,\n"," 5952,\n"," 2858,\n"," 2762,\n"," 7153,\n"," 4306]})"]},"metadata":{},"execution_count":9}],"source":["from collections import defaultdict\n","\n","# 각 사용자에 대한 추천 영화는 해당 사용자 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n","pred_user2items = defaultdict(list)\n","user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n","for user_id in movielens_train.user_id.unique():\n"," if user_id not in user_id2index:\n"," continue\n"," user_index = user_id2index[row[\"user_id\"]]\n"," movie_indexes = np.argsort(-pred_matrix[user_index, :])\n"," for movie_index in movie_indexes:\n"," movie_id = user_movie_matrix.columns[movie_index]\n"," if movie_id not in user_evaluated_movies[user_id]:\n"," pred_user2items[user_id].append(movie_id)\n"," if len(pred_user2items[user_id]) == 10:\n"," break\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":10,"id":"14a45925","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"14a45925","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":33,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"27d652fa-b864-4756-d5fc-74bd77a05a82"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
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\n","
\n"," "]},"metadata":{},"execution_count":10}],"source":["# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":11,"id":"47543b66","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"47543b66","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":31,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"779b6217-68dc-416f-eadc-345a38857d60"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[2959, 2571, 4993, 3578, 4226, 5952, 2858, 2762, 7153, 4306]"]},"metadata":{},"execution_count":11}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":12,"id":"ac73e03a","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":187},"id":"ac73e03a","executionInfo":{"status":"ok","timestamp":1672119959234,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1fb5be98-7592-4696-d496-571b130f9b1d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","2487 2571 Matrix, The (1999) \n","2874 2959 Fight Club (1999) \n","4899 4993 Lord of the Rings: The Fellowship of the Ring,... \n","\n"," genre \\\n","2487 [Action, Sci-Fi, Thriller] \n","2874 [Action, Crime, Drama, Thriller] \n","4899 [Action, Adventure, Fantasy] \n","\n"," tag \n","2487 [artificial intelligence, hackers, heroine in ... \n","2874 [based on a book, chuck palahniuk, edward nort... \n","4899 [based on a book, big budget, new zealand, sce... "],"text/html":["\n","
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":12}],"source":["# user_id=2에 대한 추천(2959, 4993, 5952)\n","movies[movies.movie_id.isin([2959, 2571, 4993])]"]},{"cell_type":"code","execution_count":12,"id":"93ce1b7a","metadata":{"id":"93ce1b7a","executionInfo":{"status":"ok","timestamp":1672119959235,"user_tz":-540,"elapsed":9,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4fb1700b", + "metadata": { + "id": "4fb1700b" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/SVD.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "05fd5c37", + "metadata": { + "id": "05fd5c37" + }, + "source": [ + "# 특잇값 분해(Singular Value Decomposition, SVD)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ae97b569", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4522, + "status": "ok", + "timestamp": 1672119890093, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "ae97b569", + "outputId": "52b03925-db6b-47e9-e26c-764d0eba7a5b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:43:01-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 65.4MB/s in 1.0s \n", + "\n", + "2022-12-27 05:43:02 (65.4 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "395a5042", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 66319, + "status": "ok", + "timestamp": 1672119956404, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "395a5042", + "outputId": "d2cab3b4-2205-46e7-c70a-e37c5d6b611c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1bcee149", + "metadata": { + "executionInfo": { + "elapsed": 30, + "status": "ok", + "timestamp": 1672119956405, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "1bcee149" + }, + "outputs": [], + "source": [ + "# 결손값을 채우는 방법\n", + "fillna_with_zero = True\n", + "# 인자 수\n", + "factors = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2cf8dfe4", + "metadata": { + "executionInfo": { + "elapsed": 25, + "status": "ok", + "timestamp": 1672119956405, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "2cf8dfe4" + }, + "outputs": [], + "source": [ + "# 평갓값을 사용자 x 영화 행렬로 변환한다. 결손값은 평균값 또는 0으로 채운다.\n", + "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", + "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", + "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", + "if fillna_with_zero:\n", + " matrix = user_movie_matrix.fillna(0).to_numpy()\n", + "else:\n", + " matrix = user_movie_matrix.fillna(movielens_train.rating.mean()).to_numpy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "22c0909b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 22, + "status": "ok", + "timestamp": 1672119956405, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "22c0909b", + "outputId": "73d96ca8-6fd5-42b8-c085-568f67d0d2e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ユーザー数=1000, アイテム数=6673, 密度=0.02\n" + ] + } + ], + "source": [ + "# 희소 정보\n", + "user_num = len(user_movie_matrix.index)\n", + "item_num = len(user_movie_matrix.columns)\n", + "non_null_num = user_num*item_num - user_movie_matrix.isnull().sum().sum()\n", + "non_null_ratio = non_null_num / (user_num*item_num)\n", + "\n", + "print(f'ユーザー数={user_num}, アイテム数={item_num}, 密度={non_null_ratio:.2f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7f1f97fa", + "metadata": { + "executionInfo": { + "elapsed": 607, + "status": "ok", + "timestamp": 1672119957450, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "7f1f97fa", + "scrolled": true + }, + "outputs": [], + "source": [ + "from scipy.sparse.linalg import svds\n", + "import numpy as np\n", + "\n", + "# 인자 수 k로 특이점 분석을 수행한다\n", + "P, S, Qt = svds(matrix, k=factors)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "65a9563b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4, + "status": "ok", + "timestamp": 1672119957451, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "65a9563b", + "outputId": "b73e0f1a-457a-4e3f-866f-78010e4c4bd3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "P: (1000, 5), S: (5,), Qt: (5, 6673), pred_matrix: (1000, 6673)\n" + ] + } + ], + "source": [ + "# 예측 평갓값 행렬\n", + "pred_matrix = np.dot(np.dot(P, np.diag(S)), Qt)\n", + "\n", + "print(f\"P: {P.shape}, S: {S.shape}, Qt: {Qt.shape}, pred_matrix: {pred_matrix.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "75ee52d8", + "metadata": { + "executionInfo": { + "elapsed": 449, + "status": "ok", + "timestamp": 1672119957898, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "75ee52d8" + }, + "outputs": [], + "source": [ + "# 학습용에 나타나지 않은 사용자와 영화 예측 평갓값은 평균 평갓값으로 한다\n", + "average_score = movielens_train.rating.mean()\n", + "movie_rating_predict = movielens_test.copy()\n", + "pred_results = []\n", + "for i, row in movielens_test.iterrows():\n", + " user_id = row[\"user_id\"]\n", + " if user_id not in user_id2index or row[\"movie_id\"] not in movie_id2index:\n", + " pred_results.append(average_score)\n", + " continue\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_index = movie_id2index[row[\"movie_id\"]]\n", + " pred_score = pred_matrix[user_index, movie_index]\n", + " pred_results.append(pred_score)\n", + "movie_rating_predict[\"rating_pred\"] = pred_results" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2a761397", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 1343, + "status": "ok", + "timestamp": 1672119959233, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "2a761397", + "outputId": "275c7093-7ffa-4628-d041-649854a18e3a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " {139: [4993, 4226, 5952, 7153, 4306, 3996, 5349, 4963, 6539, 4886],\n", + " 149: [2571, 4226, 2762, 3996, 5349, 47, 2028, 4963, 6539, 4886],\n", + " 182: 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4226,\n", + " 5952,\n", + " 2858,\n", + " 2762,\n", + " 7153,\n", + " 4306]})" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "# 각 사용자에 대한 추천 영화는 해당 사용자 아직 평가하지 않은 영화 중에서 예측값이 높은 순으로 한다\n", + "pred_user2items = defaultdict(list)\n", + "user_evaluated_movies = movielens_train.groupby(\"user_id\").agg({\"movie_id\": list})[\"movie_id\"].to_dict()\n", + "for user_id in movielens_train.user_id.unique():\n", + " if user_id not in user_id2index:\n", + " continue\n", + " user_index = user_id2index[row[\"user_id\"]]\n", + " movie_indexes = np.argsort(-pred_matrix[user_index, :])\n", + " for movie_index in movie_indexes:\n", + " movie_id = user_movie_matrix.columns[movie_index]\n", + " if movie_id not in user_evaluated_movies[user_id]:\n", + " pred_user2items[user_id].append(movie_id)\n", + " if len(pred_user2items[user_id]) == 10:\n", + " break\n", + "\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "14a45925", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 841 + }, + "executionInfo": { + "elapsed": 33, + "status": "ok", + "timestamp": 1672119959234, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "14a45925", + "outputId": "27d652fa-b864-4756-d5fc-74bd77a05a82" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
24872571Matrix, The (1999)[Action, Sci-Fi, Thriller][artificial intelligence, hackers, heroine in ...
28742959Fight Club (1999)[Action, Crime, Drama, Thriller][based on a book, chuck palahniuk, edward nort...
48994993Lord of the Rings: The Fellowship of the Ring,...[Action, Adventure, Fantasy][based on a book, big budget, new zealand, sce...
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" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# user_id=2에 대한 추천(2959, 4993, 5952)\n", + "movies[movies.movie_id.isin([2959, 2571, 4993])]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "93ce1b7a", + "metadata": { + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1672119959235, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "93ce1b7a" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chapter5/colab/UMCF.ipynb b/chapter5/colab/UMCF.ipynb index 7f20a42..7a0821f 100644 --- a/chapter5/colab/UMCF.ipynb +++ b/chapter5/colab/UMCF.ipynb @@ -1 +1,4927 @@ -{"cells":[{"cell_type":"markdown","id":"66d2e720","metadata":{"id":"66d2e720"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/UMCF.ipynb)"]},{"cell_type":"markdown","id":"f8caaceb","metadata":{"id":"f8caaceb"},"source":["# 사용자-사용자 메모리 기반 협조 필터링"]},{"cell_type":"code","execution_count":1,"id":"9cb2f773","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9cb2f773","executionInfo":{"status":"ok","timestamp":1672120577940,"user_tz":-540,"elapsed":3786,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"1da0f943-c87e-4e98-9eca-45f624108983"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-12-27 05:54:30-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n","Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n","Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 65566137 (63M) [application/zip]\n","Saving to: ‘../data/ml-10m.zip’\n","\n","ml-10m.zip 100%[===================>] 62.53M 91.0MB/s in 0.7s \n","\n","2022-12-27 05:54:31 (91.0 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n","\n","Archive: ../data/ml-10m.zip\n"," creating: ../data/ml-10M100K/\n"," inflating: ../data/ml-10M100K/allbut.pl \n"," inflating: ../data/ml-10M100K/movies.dat \n"," inflating: ../data/ml-10M100K/ratings.dat \n"," inflating: ../data/ml-10M100K/README.html \n"," inflating: ../data/ml-10M100K/split_ratings.sh \n"," inflating: ../data/ml-10M100K/tags.dat \n"]}],"source":["# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n","# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n","# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n","\n","# 데이터 다운로드와 압축 풀기\n","!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n","!unzip -n ../data/ml-10m.zip -d ../data/"]},{"cell_type":"code","execution_count":2,"id":"ed3c6a08","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ed3c6a08","executionInfo":{"status":"ok","timestamp":1672120647715,"user_tz":-540,"elapsed":69778,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"0c864b95-f3a3-4b20-ab41-d26580399203"},"outputs":[{"output_type":"stream","name":"stdout","text":["unique_users=1000, unique_movies=6736\n"]}],"source":["# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n","import pandas as pd\n","\n","# movieID와 제목만 사용\n","m_cols = ['movie_id', 'title', 'genre']\n","movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n","\n","# genre를 list 형식으로 저장한다\n","movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n","\n","\n","# 사용자가 부여한 영화의 태그 정보를 로딩한다\n","t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n","user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n","\n","# tag를 소문자로 바꾼다\n","user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n","\n","\n","# tag를 영화별로 list 형식으로 저장한다\n","movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n","\n","# 태그 정보를 결합한다\n","movies = movies.merge(movie_tags, on='movie_id', how='left')\n","\n","# 평갓값 데이터만 로딩한다\n","r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n","ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n","\n","\n","# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n","valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n","ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n","\n","\n","# 영화 데이터와 평가 데이터를 결합한다\n","movielens = ratings.merge(movies, on='movie_id')\n","\n","print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n","\n","# 학습용과 데이터용으로 데이터를 나눈다\n","# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n","# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n","# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n","\n","movielens['timestamp_rank'] = movielens.groupby(\n"," 'user_id')['timestamp'].rank(ascending=False, method='first')\n","movielens_train = movielens[movielens['timestamp_rank'] > 5]\n","movielens_test = movielens[movielens['timestamp_rank']<= 5]"]},{"cell_type":"code","execution_count":3,"id":"06409f5a","metadata":{"id":"06409f5a","executionInfo":{"status":"ok","timestamp":1672120647715,"user_tz":-540,"elapsed":24,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["import numpy as np\n","# 피어슨 상관 계수\n","def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float:\n"," u_diff = u - np.mean(u)\n"," v_diff = v - np.mean(v)\n"," numerator = np.dot(u_diff, v_diff)\n"," denominator = np.sqrt(sum(u_diff ** 2)) * np.sqrt(sum(v_diff ** 2))\n"," if denominator == 0:\n"," return 0.0\n"," return numerator / denominator\n"]},{"cell_type":"code","execution_count":4,"id":"ff148555-35f5-4362-87fb-75f0a8d5d33f","metadata":{"id":"ff148555-35f5-4362-87fb-75f0a8d5d33f","executionInfo":{"status":"ok","timestamp":1672120647716,"user_tz":-540,"elapsed":23,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["from collections import defaultdict\n","\n","# 평갓값을 사용자 x 화면의 행렬로 변환한다\n","user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n","user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n","movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n","\n","# 예측 대상 사용자와 영화의 조합\n","movie_rating_predict = movielens_test.copy()\n","pred_user2items = defaultdict(list)"]},{"cell_type":"code","execution_count":5,"id":"fc0361fa","metadata":{"id":"fc0361fa","executionInfo":{"status":"ok","timestamp":1672120648148,"user_tz":-540,"elapsed":453,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":["# 우직하게 유사도를 계산한다(이 계산은 매우 무겁습니다. 그렇기 떄문에 도중에 break를 넣었습니다. 속도 향상을 위해 라이브러리르 사용한 구현도 다음 셀에서 소개합니다)\n","\n","# 예측 대상 사용자 ID\n","test_users = movie_rating_predict.user_id.unique()\n","\n","# 예측 대상 사용자(사용자 1)에 주목한다\n","for user1_id in test_users:\n"," similar_users = []\n"," similarities = []\n"," avgs = []\n","\n"," # 사용자 1과 평갓값 행렬 안의 기타 사용자(사용자 2)와의 유사도를 산출한다\n"," for user2_id in user_movie_matrix.index:\n"," if user1_id == user2_id:\n"," continue\n","\n"," # 사용자 1과 사용자 2의 평갓값 벡터\n"," u_1 = user_movie_matrix.loc[user1_id, :].to_numpy()\n"," u_2 = user_movie_matrix.loc[user2_id, :].to_numpy()\n","\n"," # `u_1`과 `u_2`로부터, 동시에 결손값이 없는 요소만 추출한 벡터를 얻는다\n"," common_items = ~np.isnan(u_1) & ~np.isnan(u_2)\n","\n"," # 공통으로 평가한 아이템이 없으면 스킵\n"," if not common_items.any():\n"," continue\n","\n"," u_1, u_2 = u_1[common_items], u_2[common_items]\n","\n"," # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출\n"," rho_12 = peason_coefficient(u_1, u_2)\n","\n"," # 사용자 1과의 유사도가 0 보다 큰 경우, 사용자 2를 유사 사용자로 간주한다\n"," if rho_12 > 0:\n"," similar_users.append(user2_id)\n"," similarities.append(rho_12)\n"," avgs.append(np.mean(u_2))\n","\n"," # 사용자 1의 평균 평갓값\n"," avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy())\n","\n"," # 예측 대상 영화의 ID\n"," test_movies = movie_rating_predict[movie_rating_predict[\"user_id\"] == user1_id].movie_id.values\n"," # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다\n"," movie_rating_predict.loc[(movie_rating_predict[\"user_id\"] == user1_id), \"rating_pred\"] = avg_1\n","\n"," if similar_users:\n"," for movie_id in test_movies:\n"," if movie_id in movie_id2index:\n"," r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy()\n"," rating_exists = ~np.isnan(r_xy)\n","\n"," # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않은 경우는 스킵한다\n"," if not rating_exists.any():\n"," continue\n","\n"," r_xy = r_xy[rating_exists]\n"," rho_1x = np.array(similarities)[rating_exists]\n"," avg_x = np.array(avgs)[rating_exists]\n"," r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum()\n","\n"," # 예측 평갓값을 저장\n"," movie_rating_predict.loc[\n"," (movie_rating_predict[\"user_id\"] == user1_id)\n"," & (movie_rating_predict[\"movie_id\"] == movie_id),\n"," \"rating_pred\",\n"," ] = r_hat_1y\n"," break # 계산이 무거우므로, for 루프는 1번만 수행한 뒤 종료합니다. 각 변수에 어떤 값이 들어있는지 확인하면 알고리즘을 더 깊이 이해할 수 있습니다."]},{"cell_type":"code","execution_count":6,"id":"665ebafd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"665ebafd","executionInfo":{"status":"ok","timestamp":1672120691833,"user_tz":-540,"elapsed":43701,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"114ccdfc-023b-4a0b-d695-4b2c631818a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting surprise\n"," Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n","Collecting scikit-surprise\n"," Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n","\u001b[K |████████████████████████████████| 771 kB 33.9 MB/s \n","\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n","Building wheels for collected packages: scikit-surprise\n"," Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626503 sha256=0e6db95d2a4c33d1ddb4f8a1bdfe526bfa9ce95d16c0f4728ccec069f280cbd1\n"," Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n","Successfully built scikit-surprise\n","Installing collected packages: scikit-surprise, surprise\n","Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"]}],"source":["!pip install surprise"]},{"cell_type":"code","execution_count":8,"id":"b42f291a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b42f291a","executionInfo":{"status":"ok","timestamp":1672121035097,"user_tz":-540,"elapsed":203038,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"d9542b8c-d6ab-44a2-ca47-dbb7e74fc6e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Computing the pearson similarity matrix...\n","Done computing similarity matrix.\n"]},{"output_type":"execute_result","data":{"text/plain":["defaultdict(list,\n"," {139: [6319, 2721, 2131, 3415, 1121, 2509, 4221, 4418, 4509, 5101],\n"," 149: [2494,\n"," 26425,\n"," 137,\n"," 2930,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 2425,\n"," 31116],\n"," 182: [6319, 3415, 3473, 146, 1121, 2509, 4221, 4418, 4509, 6122],\n"," 215: [1564, 3365, 1204, 1131, 2931, 2938, 1192, 1489, 2679, 1147],\n"," 281: [137,\n"," 1477,\n"," 26425,\n"," 6319,\n"," 27255,\n"," 2911,\n"," 4850,\n"," 5069,\n"," 2494,\n"," 31116],\n"," 326: [1192, 2679, 3341, 7064, 32316, 3415, 3473, 4208, 4453, 146],\n"," 351: [3473,\n"," 6122,\n"," 2911,\n"," 137,\n"," 27255,\n"," 3777,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116],\n"," 357: [7064,\n"," 3473,\n"," 4850,\n"," 2911,\n"," 27255,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116,\n"," 1444],\n"," 426: [26425,\n"," 137,\n"," 2930,\n"," 1477,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 31116,\n"," 1444,\n"," 3415],\n"," 456: 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1572, 4405, 6271, 6918, 7067],\n"," 66: [1046, 2075, 2852, 2931, 2938, 3516, 1192, 1489, 2550, 3929],\n"," 185: [4387, 6595, 6219, 3473, 3090, 869, 3857, 4846, 220, 3777],\n"," 222: [4135, 2704, 137, 26425, 4850, 1184, 2425, 2721, 6772, 2935],\n"," 498: [7064, 8199, 3415, 3473, 3645, 4928, 6241, 6271, 7067, 7749],\n"," 994: [2704, 3694, 3695, 7064, 6122, 3823, 1444, 3415, 6023, 8195],\n"," 165: [3473,\n"," 3700,\n"," 2704,\n"," 1444,\n"," 27255,\n"," 7297,\n"," 1477,\n"," 26425,\n"," 31116,\n"," 27648],\n"," 202: [1148, 28, 299, 321, 904, 1348, 2648, 2804, 1611, 2618],\n"," 180: [260, 3684, 28, 47, 308, 326, 527, 912, 926, 1046],\n"," 93: [242, 1172, 908, 1248, 1260, 2206, 3365, 4327, 5017, 5292],\n"," 261: [5017, 1192, 2679, 7064, 7933, 8199, 927, 2043, 3415, 3473],\n"," 435: [324, 3477, 5255, 2075, 2931, 1468, 2550, 3918, 7064, 32316],\n"," 322: [26131,\n"," 40826,\n"," 7064,\n"," 2721,\n"," 3415,\n"," 3473,\n"," 7063,\n"," 6023,\n"," 3511,\n"," 1121],\n"," 238: [1646, 3341, 3415, 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31116,\n"," 1444],\n"," 515: [2721, 715, 3415, 3645, 4928, 6271, 7067, 7749, 7759, 8256],\n"," 978: [1148, 8533, 321, 1172, 1260, 3365, 4432, 324, 2060, 2618],\n"," 28: [5438, 2075, 3516, 188, 1034, 615, 3097, 2721, 1361, 146],\n"," 475: [3473,\n"," 2704,\n"," 1477,\n"," 2930,\n"," 26425,\n"," 31116,\n"," 1444,\n"," 3415,\n"," 3645,\n"," 668],\n"," 598: [1046, 506, 1056, 2931, 1192, 1489, 250, 2696, 1929, 199],\n"," 943: [4135, 6122, 4850, 2704, 1477, 26425, 1046, 850, 7064, 2721],\n"," 520: [8132,\n"," 26425,\n"," 27255,\n"," 2970,\n"," 31116,\n"," 5017,\n"," 5304,\n"," 7064,\n"," 8199,\n"," 3415],\n"," 697: [2347, 28, 1719, 2721, 328, 4103, 30803, 4307, 55280, 57243],\n"," 849: [260, 1210, 1148, 28, 527, 541, 1199, 1207, 1193, 1197],\n"," 1003: [3516, 250, 7064, 7933, 32316, 2721, 715, 5034, 212, 1121],\n"," 705: [2721, 3415, 3473, 121, 212, 6023, 1121, 2509, 4221, 4418],\n"," 48: [242, 562, 1103, 1199, 1206, 1207, 1244, 904, 913, 1086],\n"," 29: [4135,\n"," 4850,\n"," 26425,\n"," 1184,\n"," 1827,\n"," 1870,\n"," 3360,\n"," 6772,\n"," 8712,\n"," 31116],\n"," 231: [250, 2714, 1646, 3415, 1904, 6791, 3076, 212, 1121, 2271],\n"," 699: [1489, 2679, 1147, 615, 3341, 2731, 5304, 6783, 7064, 8125],\n"," 448: [7064,\n"," 2704,\n"," 26425,\n"," 2494,\n"," 3473,\n"," 27255,\n"," 2911,\n"," 3694,\n"," 3695,\n"," 850],\n"," 750: [213, 242, 926, 26131, 613, 2075, 2931, 2938, 4633, 1192],\n"," 782: [5017,\n"," 26131,\n"," 4356,\n"," 7064,\n"," 8199,\n"," 2721,\n"," 3224,\n"," 3415,\n"," 6023,\n"," 8195],\n"," 860: [5840,\n"," 26425,\n"," 3473,\n"," 59315,\n"," 54995,\n"," 3823,\n"," 4327,\n"," 8712,\n"," 31116,\n"," 1444],\n"," 768: [213, 2704, 26425, 1034, 1361, 2419, 2852, 1477, 2714, 2935],\n"," 127: [1833, 3503, 2721, 2732, 82, 1237, 6881, 2704, 3694, 3695],\n"," 894: [299,\n"," 4103,\n"," 30803,\n"," 4307,\n"," 55280,\n"," 57243,\n"," 1244,\n"," 49932,\n"," 7327,\n"," 4437]})"]},"metadata":{},"execution_count":8}],"source":["# surprise를 사용한 구현\n","\n","from surprise import KNNWithMeans, Reader\n","from surprise import Dataset as SurpriseDataset\n","# surprise용으로 데이터를 가공한다\n","reader = Reader(rating_scale=(0.5, 5))\n","data_train = SurpriseDataset.load_from_df(\n"," movielens_train[[\"user_id\", \"movie_id\", \"rating\"]], reader\n",").build_full_trainset()\n","\n","sim_options = {\"name\": \"pearson\", \"user_based\": True} # 유사도를 계산하는 방법을 정의한다 # False로 하면 아이템 기반이 된다\n","knn = KNNWithMeans(k=30, min_k=1, sim_options=sim_options)\n","knn.fit(data_train)\n","\n","# 학습 데이터셋에서 평갓값이 없는 사용자와 아이템의 조합을 준비한다\n","data_test = data_train.build_anti_testset(None)\n","predictions = knn.test(data_test)\n","\n","def get_top_n(predictions, n=10):\n"," # 각 사용자별로 예측된 아이템을 저장한다\n"," top_n = defaultdict(list)\n"," for uid, iid, true_r, est, _ in predictions:\n"," top_n[uid].append((iid, est))\n","\n"," # 사용자별로 아이템을 예측 평갓값 순으로 나열하고, 상위 n개를 저장한다\n"," for uid, user_ratings in top_n.items():\n"," user_ratings.sort(key=lambda x: x[1], reverse=True)\n"," top_n[uid] = [d[0] for d in user_ratings[:n]]\n","\n"," return top_n\n","\n","pred_user2items = get_top_n(predictions, n=10)\n","\n","average_score = movielens_train.rating.mean()\n","pred_results = []\n","for _, row in movielens_test.iterrows():\n"," user_id = row[\"user_id\"]\n"," movie_id = row[\"movie_id\"]\n"," # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자와 영화에 대한 예측 평갓값은 전체의 평균 평갓값으로 한다\n"," if user_id not in user_id2index or movie_id not in movie_id2index:\n"," pred_results.append(average_score)\n"," continue\n"," pred_score = knn.predict(uid=user_id, iid=movie_id).est\n"," pred_results.append(pred_score)\n","movie_rating_predict[\"rating_pred\"] = pred_results\n","\n","pred_user2items"]},{"cell_type":"code","execution_count":9,"id":"1fccc723","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":841},"id":"1fccc723","executionInfo":{"status":"ok","timestamp":1672121035097,"user_tz":-540,"elapsed":30,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"6420b02f-837f-4f24-adc1-0c986fe8d916"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" user_id movie_id rating timestamp \\\n","4732 2 110 5.0 868245777 \n","5246 2 260 5.0 868244562 \n","5798 2 590 5.0 868245608 \n","6150 2 648 2.0 868244699 \n","6531 2 733 3.0 868244562 \n","6813 2 736 3.0 868244698 \n","7113 2 780 3.0 868244698 \n","7506 2 786 3.0 868244562 \n","7661 2 802 2.0 868244603 \n","7779 2 858 2.0 868245645 \n","8077 2 1049 3.0 868245920 \n","8127 2 1073 3.0 868244562 \n","8381 2 1210 4.0 868245644 \n","8771 2 1356 3.0 868244603 \n","9097 2 1544 3.0 868245920 \n","\n"," title \\\n","4732 Braveheart (1995) \n","5246 Star Wars: Episode IV - A New Hope (a.k.a. Sta... \n","5798 Dances with Wolves (1990) \n","6150 Mission: Impossible (1996) \n","6531 Rock, The (1996) \n","6813 Twister (1996) \n","7113 Independence Day (a.k.a. ID4) (1996) \n","7506 Eraser (1996) \n","7661 Phenomenon (1996) \n","7779 Godfather, The (1972) \n","8077 Ghost and the Darkness, The (1996) \n","8127 Willy Wonka & the Chocolate Factory (1971) \n","8381 Star Wars: Episode VI - Return of the Jedi (1983) \n","8771 Star Trek: First Contact (1996) \n","9097 Lost World: Jurassic Park, The (Jurassic Park ... \n","\n"," genre \\\n","4732 [Action, Drama, War] \n","5246 [Action, Adventure, Sci-Fi] \n","5798 [Adventure, Drama, Western] \n","6150 [Action, Adventure, Mystery, Thriller] \n","6531 [Action, Adventure, Thriller] \n","6813 [Action, Adventure, Romance, Thriller] \n","7113 [Action, Adventure, Sci-Fi, War] \n","7506 [Action, Drama, Thriller] \n","7661 [Drama, Romance] \n","7779 [Crime, Drama] \n","8077 [Action, Adventure] \n","8127 [Children, Comedy, Fantasy, Musical] \n","8381 [Action, Adventure, Sci-Fi] \n","8771 [Action, Adventure, Sci-Fi, Thriller] \n","9097 [Action, Adventure, Horror, Sci-Fi, Thriller] \n","\n"," tag timestamp_rank \n","4732 [bullshit history, medieval, bloodshed, hero, ... 8.0 \n","5246 [desert, quotable, lucas, gfei own it, seen mo... 17.0 \n","5798 [afi 100, lame, native, biopic, american india... 11.0 \n","6150 [confusing, confusing plot, memorable sequence... 12.0 \n","6531 [gfei own it, alcatraz, nicolas cage, sean con... 18.0 \n","6813 [disaster, disaster, storm, bill paxton, helen... 13.0 \n","7113 [action, alien invasion, aliens, will smith, a... 14.0 \n","7506 [arnold schwarzenegger, action, arnold, arnold... 19.0 \n","7661 [interesting concept, own, john travolta, john... 15.0 \n","7779 [oscar (best picture), marlon brando, classic,... 9.0 \n","8077 [lions, own, seen at the cinema, adventure, af... 6.0 \n","8127 [based on a book, roald dahl, based on a book,... 20.0 \n","8381 [desert, fantasy, sci-fi, space, lucas, gfei o... 10.0 \n","8771 [space, borg, futuristmovies.com, patrick stew... 16.0 \n","9097 [dinosaurs, dinosaurs, steven spielberg, borin... 7.0 "],"text/html":["\n","
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":9}],"source":["# user_id=2인 사용자가 학습 데이터에서 평가를 부여한 영화 목록\n","movielens_train[movielens_train.user_id==2]"]},{"cell_type":"code","execution_count":10,"id":"874b6e3e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"874b6e3e","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":19,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"b39c148b-c27c-4f83-a2b5-ddf05e1f8545"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[3473, 4135, 4850, 2425, 2704, 137, 27255, 26425, 8712, 31116]"]},"metadata":{},"execution_count":10}],"source":["pred_user2items[2]"]},{"cell_type":"code","execution_count":11,"id":"c9b77fbd","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"c9b77fbd","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}},"outputId":"9a9700f9-87ac-459c-b415-1df2a8dceb5c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" movie_id title \\\n","3386 3473 Jonah Who Will Be 25 in the Year 2000 (Jonas q... \n","4043 4135 Monster Squad, The (1987) \n","4756 4850 Spriggan (Supurigan) (1998) \n","\n"," genre \\\n","3386 [Comedy] \n","4043 [Adventure, Comedy, Horror] \n","4756 [Action, Animation, Sci-Fi] \n","\n"," tag \n","3386 [cerebral, humorous, intersecting lives, intim... \n","4043 [can't remember] \n","4756 [library, anime, spectacle, over expositive] "],"text/html":["\n","
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movie_idtitlegenretag
33863473Jonah Who Will Be 25 in the Year 2000 (Jonas q...[Comedy][cerebral, humorous, intersecting lives, intim...
40434135Monster Squad, The (1987)[Adventure, Comedy, Horror][can't remember]
47564850Spriggan (Supurigan) (1998)[Action, Animation, Sci-Fi][library, anime, spectacle, over expositive]
\n","
\n"," \n"," \n"," \n","\n"," \n","
\n","
\n"," "]},"metadata":{},"execution_count":11}],"source":["# user_id=2에 대한 추천(1198, 1196, 1097)\n","movies[movies.movie_id.isin([3473, 4135, 4850])]"]},{"cell_type":"code","execution_count":11,"id":"99c25fe6","metadata":{"id":"99c25fe6","executionInfo":{"status":"ok","timestamp":1672121035098,"user_tz":-540,"elapsed":17,"user":{"displayName":"Moses Kim","userId":"10365665687963761023"}}},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.8"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "66d2e720", + "metadata": { + "id": "66d2e720" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/oreilly-japan/RecommenderSystems/blob/main/chapter5/colab/UMCF.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "f8caaceb", + "metadata": { + "id": "f8caaceb" + }, + "source": [ + "# 사용자-사용자 메모리 기반 방법 협조 필터링(User-User Memory Based Collaborative Filtering)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9cb2f773", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 3786, + "status": "ok", + "timestamp": 1672120577940, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "9cb2f773", + "outputId": "1da0f943-c87e-4e98-9eca-45f624108983" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-12-27 05:54:30-- https://files.grouplens.org/datasets/movielens/ml-10m.zip\n", + "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n", + "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 65566137 (63M) [application/zip]\n", + "Saving to: ‘../data/ml-10m.zip’\n", + "\n", + "ml-10m.zip 100%[===================>] 62.53M 91.0MB/s in 0.7s \n", + "\n", + "2022-12-27 05:54:31 (91.0 MB/s) - ‘../data/ml-10m.zip’ saved [65566137/65566137]\n", + "\n", + "Archive: ../data/ml-10m.zip\n", + " creating: ../data/ml-10M100K/\n", + " inflating: ../data/ml-10M100K/allbut.pl \n", + " inflating: ../data/ml-10M100K/movies.dat \n", + " inflating: ../data/ml-10M100K/ratings.dat \n", + " inflating: ../data/ml-10M100K/README.html \n", + " inflating: ../data/ml-10M100K/split_ratings.sh \n", + " inflating: ../data/ml-10M100K/tags.dat \n" + ] + } + ], + "source": [ + "# Colab용 notebook입니다. 이 notebook 한 장에서 여러 데이터의 다운로드부터, 추천까지 완결하도록 되어 있습니다(예측 평가는 미포함)\n", + "# MovieLens 데이터를 아직 다운로드 하지 않았다면, 이 셀을 실행해서 다운로드합니다.\n", + "# MovieLens 데이터 분석은 data_download.ipynb를 참조합니다.\n", + "\n", + "# 데이터 다운로드와 압축 풀기\n", + "!wget -nc --no-check-certificate https://files.grouplens.org/datasets/movielens/ml-10m.zip -P ../data\n", + "!unzip -n ../data/ml-10m.zip -d ../data/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ed3c6a08", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 69778, + "status": "ok", + "timestamp": 1672120647715, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "ed3c6a08", + "outputId": "0c864b95-f3a3-4b20-ab41-d26580399203" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_users=1000, unique_movies=6736\n" + ] + } + ], + "source": [ + "# Movielens 데이터 로딩(데이터량이 많으므로, 로딩에 시간이 걸릴 수 있습니다)\n", + "import pandas as pd\n", + "\n", + "# movieID와 제목만 사용\n", + "m_cols = ['movie_id', 'title', 'genre']\n", + "movies = pd.read_csv('../data/ml-10M100K/movies.dat', names=m_cols, sep='::' , encoding='latin-1', engine='python')\n", + "\n", + "# genre를 list 형식으로 저장한다\n", + "movies['genre'] = movies.genre.apply(lambda x:x.split('|'))\n", + "\n", + "\n", + "# 사용자가 부여한 영화의 태그 정보를 로딩한다\n", + "t_cols = ['user_id', 'movie_id', 'tag', 'timestamp']\n", + "user_tagged_movies = pd.read_csv('../data/ml-10M100K/tags.dat', names=t_cols, sep='::', engine='python')\n", + "\n", + "# tag를 소문자로 바꾼다\n", + "user_tagged_movies['tag'] = user_tagged_movies['tag'].str.lower()\n", + "\n", + "\n", + "# tag를 영화별로 list 형식으로 저장한다\n", + "movie_tags = user_tagged_movies.groupby('movie_id').agg({'tag':list})\n", + "\n", + "# 태그 정보를 결합한다\n", + "movies = movies.merge(movie_tags, on='movie_id', how='left')\n", + "\n", + "# 평갓값 데이터만 로딩한다\n", + "r_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n", + "ratings = pd.read_csv('../data/ml-10M100K/ratings.dat', names=r_cols, sep='::', engine='python')\n", + "\n", + "\n", + "# 데이터량이 많으므로 사용자수를 1000으로 줄여서 시험해본다\n", + "valid_user_ids = sorted(ratings.user_id.unique())[:1000]\n", + "ratings = ratings[ratings[\"user_id\"].isin(valid_user_ids)]\n", + "\n", + "\n", + "# 영화 데이터와 평가 데이터를 결합한다\n", + "movielens = ratings.merge(movies, on='movie_id')\n", + "\n", + "print(f'unique_users={len(movielens.user_id.unique())}, unique_movies={len(movielens.movie_id.unique())}')\n", + "\n", + "# 학습용과 데이터용으로 데이터를 나눈다\n", + "# 각 사용자의 최근 5건의 영화를 평가용으로 사용하고, 나머지는 학습용으로 사용한다\n", + "# 우선, 각 사용자가 평가한 영화의 순서를 계산한다\n", + "# 최근 부여한 영화부터 순서를 부여한다(1에서 시작)\n", + "\n", + "movielens['timestamp_rank'] = movielens.groupby(\n", + " 'user_id')['timestamp'].rank(ascending=False, method='first')\n", + "movielens_train = movielens[movielens['timestamp_rank'] > 5]\n", + "movielens_test = movielens[movielens['timestamp_rank']<= 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "06409f5a", + "metadata": { + "executionInfo": { + "elapsed": 24, + "status": "ok", + "timestamp": 1672120647715, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "06409f5a" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "# 피어슨 상관 계수\n", + "def peason_coefficient(u: np.ndarray, v: np.ndarray) -> float:\n", + " u_diff = u - np.mean(u)\n", + " v_diff = v - np.mean(v)\n", + " numerator = np.dot(u_diff, v_diff)\n", + " denominator = np.sqrt(sum(u_diff ** 2)) * np.sqrt(sum(v_diff ** 2))\n", + " if denominator == 0:\n", + " return 0.0\n", + " return numerator / denominator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ff148555-35f5-4362-87fb-75f0a8d5d33f", + "metadata": { + "executionInfo": { + "elapsed": 23, + "status": "ok", + "timestamp": 1672120647716, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "ff148555-35f5-4362-87fb-75f0a8d5d33f" + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "\n", + "# 평갓값을 사용자 x 화면의 행렬로 변환한다\n", + "user_movie_matrix = movielens_train.pivot(index=\"user_id\", columns=\"movie_id\", values=\"rating\")\n", + "user_id2index = dict(zip(user_movie_matrix.index, range(len(user_movie_matrix.index))))\n", + "movie_id2index = dict(zip(user_movie_matrix.columns, range(len(user_movie_matrix.columns))))\n", + "\n", + "# 예측 대상 사용자와 영화의 조합\n", + "movie_rating_predict = movielens_test.copy()\n", + "pred_user2items = defaultdict(list)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fc0361fa", + "metadata": { + "executionInfo": { + "elapsed": 453, + "status": "ok", + "timestamp": 1672120648148, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "fc0361fa" + }, + "outputs": [], + "source": [ + "# 우직하게 유사도를 계산한다(이 계산은 매우 무겁습니다. 그렇기 떄문에 도중에 break를 넣었습니다. 속도 향상을 위해 라이브러리르 사용한 구현도 다음 셀에서 소개합니다)\n", + "\n", + "# 예측 대상 사용자 ID\n", + "test_users = movie_rating_predict.user_id.unique()\n", + "\n", + "# 예측 대상 사용자(사용자 1)에 주목한다\n", + "for user1_id in test_users:\n", + " similar_users = []\n", + " similarities = []\n", + " avgs = []\n", + "\n", + " # 사용자 1과 평갓값 행렬 안의 기타 사용자(사용자 2)와의 유사도를 산출한다\n", + " for user2_id in user_movie_matrix.index:\n", + " if user1_id == user2_id:\n", + " continue\n", + "\n", + " # 사용자 1과 사용자 2의 평갓값 벡터\n", + " u_1 = user_movie_matrix.loc[user1_id, :].to_numpy()\n", + " u_2 = user_movie_matrix.loc[user2_id, :].to_numpy()\n", + "\n", + " # `u_1`과 `u_2`로부터, 동시에 결손값이 없는 요소만 추출한 벡터를 얻는다\n", + " common_items = ~np.isnan(u_1) & ~np.isnan(u_2)\n", + "\n", + " # 공통으로 평가한 아이템이 없으면 스킵\n", + " if not common_items.any():\n", + " continue\n", + "\n", + " u_1, u_2 = u_1[common_items], u_2[common_items]\n", + "\n", + " # 피어슨 상관 계수를 사용해 사용자 1과 사용자 2의 유사도를 산출\n", + " rho_12 = peason_coefficient(u_1, u_2)\n", + "\n", + " # 사용자 1과의 유사도가 0 보다 큰 경우, 사용자 2를 유사 사용자로 간주한다\n", + " if rho_12 > 0:\n", + " similar_users.append(user2_id)\n", + " similarities.append(rho_12)\n", + " avgs.append(np.mean(u_2))\n", + "\n", + " # 사용자 1의 평균 평갓값\n", + " avg_1 = np.mean(user_movie_matrix.loc[user1_id, :].dropna().to_numpy())\n", + "\n", + " # 예측 대상 영화의 ID\n", + " test_movies = movie_rating_predict[movie_rating_predict[\"user_id\"] == user1_id].movie_id.values\n", + " # 예측할 수 없는 영화에 대한 평갓값은 사용자 1의 평균 평갓값으로 한다\n", + " movie_rating_predict.loc[(movie_rating_predict[\"user_id\"] == user1_id), \"rating_pred\"] = avg_1\n", + "\n", + " if similar_users:\n", + " for movie_id in test_movies:\n", + " if movie_id in movie_id2index:\n", + " r_xy = user_movie_matrix.loc[similar_users, movie_id].to_numpy()\n", + " rating_exists = ~np.isnan(r_xy)\n", + "\n", + " # 유사 사용자가 대상이 되는 영화에 대한 평갓값을 갖지 않은 경우는 스킵한다\n", + " if not rating_exists.any():\n", + " continue\n", + "\n", + " r_xy = r_xy[rating_exists]\n", + " rho_1x = np.array(similarities)[rating_exists]\n", + " avg_x = np.array(avgs)[rating_exists]\n", + " r_hat_1y = avg_1 + np.dot(rho_1x, (r_xy - avg_x)) / rho_1x.sum()\n", + "\n", + " # 예측 평갓값을 저장\n", + " movie_rating_predict.loc[\n", + " (movie_rating_predict[\"user_id\"] == user1_id)\n", + " & (movie_rating_predict[\"movie_id\"] == movie_id),\n", + " \"rating_pred\",\n", + " ] = r_hat_1y\n", + " break # 계산이 무거우므로, for 루프는 1번만 수행한 뒤 종료합니다. 각 변수에 어떤 값이 들어있는지 확인하면 알고리즘을 더 깊이 이해할 수 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "665ebafd", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 43701, + "status": "ok", + "timestamp": 1672120691833, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "665ebafd", + "outputId": "114ccdfc-023b-4a0b-d695-4b2c631818a5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting surprise\n", + " Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n", + "Collecting scikit-surprise\n", + " Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n", + "\u001b[K |████████████████████████████████| 771 kB 33.9 MB/s \n", + "\u001b[?25hRequirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.2.0)\n", + "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.21.6)\n", + "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-surprise->surprise) (1.7.3)\n", + "Building wheels for collected packages: scikit-surprise\n", + " Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp38-cp38-linux_x86_64.whl size=2626503 sha256=0e6db95d2a4c33d1ddb4f8a1bdfe526bfa9ce95d16c0f4728ccec069f280cbd1\n", + " Stored in directory: /root/.cache/pip/wheels/af/db/86/2c18183a80ba05da35bf0fb7417aac5cddbd93bcb1b92fd3ea\n", + "Successfully built scikit-surprise\n", + "Installing collected packages: scikit-surprise, surprise\n", + "Successfully installed scikit-surprise-1.1.3 surprise-0.1\n" + ] + } + ], + "source": [ + "!pip install surprise" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b42f291a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 203038, + "status": "ok", + "timestamp": 1672121035097, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "b42f291a", + "outputId": "d9542b8c-d6ab-44a2-ca47-dbb7e74fc6e0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing the pearson similarity matrix...\n", + "Done computing similarity matrix.\n" + ] + }, + { + "data": { + "text/plain": [ + "defaultdict(list,\n", + " 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data_train.build_anti_testset(None)\n", + "predictions = knn.test(data_test)\n", + "\n", + "def get_top_n(predictions, n=10):\n", + " # 각 사용자별로 예측된 아이템을 저장한다\n", + " top_n = defaultdict(list)\n", + " for uid, iid, true_r, est, _ in predictions:\n", + " top_n[uid].append((iid, est))\n", + "\n", + " # 사용자별로 아이템을 예측 평갓값 순으로 나열하고, 상위 n개를 저장한다\n", + " for uid, user_ratings in top_n.items():\n", + " user_ratings.sort(key=lambda x: x[1], reverse=True)\n", + " top_n[uid] = [d[0] for d in user_ratings[:n]]\n", + "\n", + " return top_n\n", + "\n", + "pred_user2items = get_top_n(predictions, n=10)\n", + "\n", + "average_score = movielens_train.rating.mean()\n", + "pred_results = []\n", + "for _, row in movielens_test.iterrows():\n", + " user_id = row[\"user_id\"]\n", + " movie_id = row[\"movie_id\"]\n", + " # 학습 데이터에 존재하지 않고 테스트 데이터에만 존재하는 사용자와 영화에 대한 예측 평갓값은 전체의 평균 평갓값으로 한다\n", + " if user_id not in user_id2index or movie_id not in movie_id2index:\n", + " pred_results.append(average_score)\n", + " continue\n", + " pred_score = knn.predict(uid=user_id, iid=movie_id).est\n", + " pred_results.append(pred_score)\n", + "movie_rating_predict[\"rating_pred\"] = pred_results\n", + "\n", + "pred_user2items" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1fccc723", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 841 + }, + "executionInfo": { + "elapsed": 30, + "status": "ok", + "timestamp": 1672121035097, + "user": { + "displayName": "Moses Kim", + "userId": "10365665687963761023" + }, + "user_tz": -540 + }, + "id": "1fccc723", + "outputId": "6420b02f-837f-4f24-adc1-0c986fe8d916" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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user_idmovie_idratingtimestamptitlegenretagtimestamp_rank
473221105.0868245777Braveheart (1995)[Action, Drama, War][bullshit history, medieval, bloodshed, hero, ...8.0
524622605.0868244562Star Wars: Episode IV - A New Hope (a.k.a. Sta...[Action, Adventure, Sci-Fi][desert, quotable, lucas, gfei own it, seen mo...17.0
579825905.0868245608Dances with Wolves (1990)[Adventure, Drama, Western][afi 100, lame, native, biopic, american india...11.0
615026482.0868244699Mission: Impossible (1996)[Action, Adventure, Mystery, Thriller][confusing, confusing plot, memorable sequence...12.0
653127333.0868244562Rock, The (1996)[Action, Adventure, Thriller][gfei own it, alcatraz, nicolas cage, sean con...18.0
681327363.0868244698Twister (1996)[Action, Adventure, Romance, Thriller][disaster, disaster, storm, bill paxton, helen...13.0
711327803.0868244698Independence Day (a.k.a. ID4) (1996)[Action, Adventure, Sci-Fi, War][action, alien invasion, aliens, will smith, a...14.0
750627863.0868244562Eraser (1996)[Action, Drama, Thriller][arnold schwarzenegger, action, arnold, arnold...19.0
766128022.0868244603Phenomenon (1996)[Drama, Romance][interesting concept, own, john travolta, john...15.0
777928582.0868245645Godfather, The (1972)[Crime, Drama][oscar (best picture), marlon brando, classic,...9.0
8077210493.0868245920Ghost and the Darkness, The (1996)[Action, Adventure][lions, own, seen at the cinema, adventure, af...6.0
8127210733.0868244562Willy Wonka & the Chocolate Factory (1971)[Children, Comedy, Fantasy, Musical][based on a book, roald dahl, based on a book,...20.0
8381212104.0868245644Star Wars: Episode VI - Return of the Jedi (1983)[Action, Adventure, Sci-Fi][desert, fantasy, sci-fi, space, lucas, gfei o...10.0
8771213563.0868244603Star Trek: First Contact (1996)[Action, Adventure, Sci-Fi, Thriller][space, borg, futuristmovies.com, patrick stew...16.0
9097215443.0868245920Lost World: Jurassic Park, The (Jurassic Park ...[Action, Adventure, Horror, Sci-Fi, Thriller][dinosaurs, dinosaurs, steven spielberg, borin...7.0
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movie_idtitlegenretag
33863473Jonah Who Will Be 25 in the Year 2000 (Jonas q...[Comedy][cerebral, humorous, intersecting lives, intim...
40434135Monster Squad, The (1987)[Adventure, Comedy, Horror][can't remember]
47564850Spriggan (Supurigan) (1998)[Action, Animation, Sci-Fi][library, anime, spectacle, over expositive]
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"# 어소시에이션 분석" + "# 어소시에이션 분석(Apriori 알고리즘)" ] }, { diff --git a/chapter5/notebook/BPR.ipynb b/chapter5/notebook/BPR.ipynb index d421bcf..4002ead 100644 --- a/chapter5/notebook/BPR.ipynb +++ b/chapter5/notebook/BPR.ipynb @@ -5,7 +5,7 @@ "id": "1dd49045", "metadata": {}, "source": [ - "# Bayesian Personalized Ranking(BPR)" + "# 개인화 된 랭킹 문제(Bayesian Personalized Ranking, BPR)" ] }, { diff --git a/chapter5/notebook/IMF.ipynb b/chapter5/notebook/IMF.ipynb index 8a5439a..3d983d3 100644 --- a/chapter5/notebook/IMF.ipynb +++ b/chapter5/notebook/IMF.ipynb @@ -5,7 +5,7 @@ "id": "c0dccd9c", "metadata": {}, "source": [ - "# Implicit Matrix Factorization" + "# 암묵적 행렬 분해(Implicit Matrix Factorization, IMF)" ] }, { diff --git a/chapter5/notebook/LDA_collaboration.ipynb b/chapter5/notebook/LDA_collaboration.ipynb index a49e8ac..9807fc3 100644 --- a/chapter5/notebook/LDA_collaboration.ipynb +++ b/chapter5/notebook/LDA_collaboration.ipynb @@ -5,7 +5,7 @@ "id": "942a9897", "metadata": {}, "source": [ - "# Latent Dirichlet Allocation (LDA)의 행동 데이터로의 적용" + "# LDA를 행동 데이터로의 적용" ] }, { diff --git a/chapter5/notebook/LDA_content.ipynb b/chapter5/notebook/LDA_content.ipynb index 8f2b2f8..fb7aa5e 100644 --- a/chapter5/notebook/LDA_content.ipynb +++ b/chapter5/notebook/LDA_content.ipynb @@ -5,7 +5,7 @@ "id": "98853cdc", "metadata": {}, "source": [ - "# Latent Dirichlet Allocation (LDA)" + "# 잠재 디리클레 할당(Latent Dirichlet Allocation, LDA)" ] }, { diff --git a/chapter5/notebook/MF.ipynb b/chapter5/notebook/MF.ipynb index b55d1eb..7a4d612 100644 --- a/chapter5/notebook/MF.ipynb +++ b/chapter5/notebook/MF.ipynb @@ -5,7 +5,7 @@ "id": "47e2fec5", "metadata": {}, "source": [ - "# Matrix Factorization" + "# 행렬 분해(Matrix Factorization, MF)" ] }, { diff --git a/chapter5/notebook/NMF.ipynb b/chapter5/notebook/NMF.ipynb index 6949cb4..7bd1614 100644 --- a/chapter5/notebook/NMF.ipynb +++ b/chapter5/notebook/NMF.ipynb @@ -5,7 +5,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# 비부값 행렬 분석" + "# 비음수 행렬 분해(Non-negative Matrix Factorization, NMF)" ] }, { diff --git a/chapter5/notebook/SVD.ipynb b/chapter5/notebook/SVD.ipynb index d55536e..008a07a 100644 --- a/chapter5/notebook/SVD.ipynb +++ b/chapter5/notebook/SVD.ipynb @@ -5,7 +5,7 @@ "id": "05fd5c37", "metadata": {}, "source": [ - "# 특이값 분해SVD)" + "# 특잇값 분해(Singular Value Decomposition, SVD)" ] }, { diff --git a/chapter5/notebook/UMCF.ipynb b/chapter5/notebook/UMCF.ipynb index fff77cc..55d6848 100644 --- a/chapter5/notebook/UMCF.ipynb +++ b/chapter5/notebook/UMCF.ipynb @@ -5,7 +5,7 @@ "id": "f8caaceb", "metadata": {}, "source": [ - "# 사용자-사용자 메모리 기반 방법 협조 필터링" + "# 사용자-사용자 메모리 기반 방법 협조 필터링(User-User Memory Based Collaborative Filtering)" ] }, { From 7780219a33c1c8107d21403699680a8cc3598d03 Mon Sep 17 00:00:00 2001 From: moseskim Date: Thu, 7 Nov 2024 10:47:29 +0900 Subject: [PATCH 17/17] Correct typo in text --- chapter5/README.md | 2 +- chapter5/colab/BPR.ipynb | 2 +- chapter5/notebook/BPR.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/chapter5/README.md b/chapter5/README.md index 1dfcd20..0dea092 100644 --- a/chapter5/README.md +++ b/chapter5/README.md @@ -52,7 +52,7 @@ chapter5 |`NMF.ipynb`|비음수 행렬 분해Non-negative Matrix Factorization, NMF|5.8.3|p.148| |`MF.ipynb`|행렬 분해Matrix Factorization, MF|5.8.4|p.150| |`IMF.ipynb`|암묵적 행렬 분해Implicit Matrix Factorization, IMF|5.8.5|p.154| -|`BPR.ipynb`|개인화 된 랭킹 문제Bayesian Personalized Ranking, BPR|5.8.6|p.158| +|`BPR.ipynb`|개인화된 랭킹 문제Bayesian Personalized Ranking, BPR|5.8.6|p.158| |`FM.ipynb`|Factorization Machines, FM|5.8.7|p.160| |`LDA_content.ipynb`|잠재 디리클레 할당Latent Dirichlet Allocation, LDA|5.9.1|p.165| |`LDA_collaboration.ipynb`|LDA를 행동 데이터에 적용|5.9.3|p.169| diff --git a/chapter5/colab/BPR.ipynb b/chapter5/colab/BPR.ipynb index 468e916..dfc9448 100644 --- a/chapter5/colab/BPR.ipynb +++ b/chapter5/colab/BPR.ipynb @@ -17,7 +17,7 @@ "id": "1dd49045" }, "source": [ - "# 개인화 된 랭킹 문제(Bayesian Personalized Ranking, BPR)" + "# 개인화된 랭킹 문제(Bayesian Personalized Ranking, BPR)" ] }, { diff --git a/chapter5/notebook/BPR.ipynb b/chapter5/notebook/BPR.ipynb index 4002ead..e9df5ba 100644 --- a/chapter5/notebook/BPR.ipynb +++ b/chapter5/notebook/BPR.ipynb @@ -5,7 +5,7 @@ "id": "1dd49045", "metadata": {}, "source": [ - "# 개인화 된 랭킹 문제(Bayesian Personalized Ranking, BPR)" + "# 개인화된 랭킹 문제(Bayesian Personalized Ranking, BPR)" ] }, {