code/classify_lyrics/nb_whitelist_model.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext watermark"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%watermark -d -v -a 'Sebastian Raschka' -p scikit-learn,nltk,numpy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sebastian Raschka 13/12/2014 \n",
        "\n",
        "CPython 3.4.2\n",
        "IPython 2.3.0\n",
        "\n",
        "scikit-learn 0.15.2\n",
        "nltk 3.0.0\n",
        "numpy 1.9.1\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lyrics Mood Classification - Training"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Sections"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- [Preprocessing](#Preprocessing)\n",
      "    - [Reading the dataset](#Reading-the-dataset)\n",
      "    - [Label Encoder](#Label-Encoder)\n",
      "    - [Feature extraction: Word counts and Vectorizers](#Feature-extraction:-Word-counts-and-Vectorizers)\n",
      "        - [Porter Stemmer](#Porter-Stemmer)\n",
      "        - [Stop word list](#Stop-word-list)\n",
      "        - [Initial vocabularies](Initial-vocabularies)\n",
      "        - [Whitelists of positive and negative words](#Whitelists-of-positive-and-negative-words)\n",
      "        - [Summary vocabulary sizes](#Summary-vocabulary-sizes)\n",
      "- [Model Selection](#Model-Selection)\n",
      "    - [Setup](#Setup)\n",
      "    - [Grid Search](#Grid-Search)\n",
      "    - [ROC Curves](#ROC-Curves)\n",
      "- [Validation](#Validation)\n",
      "    - [Confusion matrix](#Confusion-matrix)\n",
      "    - [ROC AUC, Accuracy, Precision, Recall, and F1-score](#ROC-AUC,-Accuracy,-Precision,-Recall,-and-F1-score)\n",
      "- [Save classifier](#Save-classifier)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Preprocessing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Reading the dataset"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "df_train = pd.read_csv('../../dataset/training/train_lyrics_1000.csv')\n",
      "df_test = pd.read_csv('../../dataset/validation/valid_lyrics_200.csv')\n",
      "\n",
      "X_train = df_train['lyrics'].values \n",
      "y_train = df_train['mood'].values\n",
      "\n",
      "X_test = df_test['lyrics'].values \n",
      "y_test = df_test['mood'].values\n",
      "\n",
      "df_train.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>file</th>\n",
        "      <th>artist</th>\n",
        "      <th>title</th>\n",
        "      <th>lyrics</th>\n",
        "      <th>mood</th>\n",
        "      <th>year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> TRAAAAW128F429D538.h5</td>\n",
        "      <td>        Casual</td>\n",
        "      <td>        I Didn't Mean To</td>\n",
        "      <td> Verse One:\\n\\nAlright I might\\nHave had a litt...</td>\n",
        "      <td>   sad</td>\n",
        "      <td> 1994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> TRAAAEF128F4273421.h5</td>\n",
        "      <td>      Adam Ant</td>\n",
        "      <td>         Something Girls</td>\n",
        "      <td> Adam Ant/Marco Pirroni\\nEvery girl is a someth...</td>\n",
        "      <td> happy</td>\n",
        "      <td> 1982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> TRAAAFD128F92F423A.h5</td>\n",
        "      <td>           Gob</td>\n",
        "      <td>          Face the Ashes</td>\n",
        "      <td> I've just erased it's been a while, I've got a...</td>\n",
        "      <td>   sad</td>\n",
        "      <td> 2007</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> TRAABJV128F1460C49.h5</td>\n",
        "      <td> Lionel Richie</td>\n",
        "      <td> Tonight Will Be Alright</td>\n",
        "      <td> Little darling \\nWhere you've been so long \\nI...</td>\n",
        "      <td> happy</td>\n",
        "      <td> 1986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> TRAABLR128F423B7E3.h5</td>\n",
        "      <td>    Blue Rodeo</td>\n",
        "      <td>                Floating</td>\n",
        "      <td> Lead Vocal by Greg\\n\\nWell, these late night c...</td>\n",
        "      <td>   sad</td>\n",
        "      <td> 1987</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                    file         artist                    title  \\\n",
        "0  TRAAAAW128F429D538.h5         Casual         I Didn't Mean To   \n",
        "1  TRAAAEF128F4273421.h5       Adam Ant          Something Girls   \n",
        "2  TRAAAFD128F92F423A.h5            Gob           Face the Ashes   \n",
        "3  TRAABJV128F1460C49.h5  Lionel Richie  Tonight Will Be Alright   \n",
        "4  TRAABLR128F423B7E3.h5     Blue Rodeo                 Floating   \n",
        "\n",
        "                                              lyrics   mood  year  \n",
        "0  Verse One:\\n\\nAlright I might\\nHave had a litt...    sad  1994  \n",
        "1  Adam Ant/Marco Pirroni\\nEvery girl is a someth...  happy  1982  \n",
        "2  I've just erased it's been a while, I've got a...    sad  2007  \n",
        "3  Little darling \\nWhere you've been so long \\nI...  happy  1986  \n",
        "4  Lead Vocal by Greg\\n\\nWell, these late night c...    sad  1987  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Label Encoder"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "import numpy as np\n",
      "\n",
      "pickle_in = open('./label_encoder.p', 'rb')\n",
      "le = pickle.load(pickle_in)\n",
      "pickle_in.close()\n",
      "\n",
      "print('before: %s ...' %y_train[:5])\n",
      "\n",
      "y_train = le.transform(y_train)\n",
      "y_test = le.transform(y_test)\n",
      "\n",
      "print('after: %s ...' %y_train[:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "before: ['sad' 'happy' 'sad' 'happy' 'sad'] ...\n",
        "after: [1 0 1 0 1] ...\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Stop word list"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('./stopwords_eng.txt') as sw:\n",
      "    stopwords = sw.read().split()\n",
      "stopwords[-10:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "['too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now']"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Feature extraction: Word counts and Vectorizers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Initial vocabularies"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# With Porter Stemming\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from nltk.stem import PorterStemmer\n",
      "porter = PorterStemmer()\n",
      "\n",
      "import re\n",
      "\n",
      "vec_porter = CountVectorizer(analyzer='word',\n",
      "                      decode_error='replace',\n",
      "                      tokenizer=lambda text: text.split(),\n",
      "                      preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()), \n",
      "                      stop_words=stopwords,) \n",
      "\n",
      "\n",
      "vec_porter.fit(X_train)\n",
      "\n",
      "print('Vocabular size: %s words' % len(vec_porter.vocabulary_))\n",
      "\n",
      "vocab_1 = vec_porter.get_feature_names()\n",
      "print(vocab_1[-10:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Vocabular size: 11378 words\n",
        "['zipped', 'zippin', 'zipping', 'zippo', 'zodiac', 'zoli', 'zombie', 'zone', 'zonin', 'zoo']\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# With Porter Stemming\n",
      "\n",
      "porter = PorterStemmer()\n",
      "\n",
      "vec_porter = CountVectorizer(analyzer='word',\n",
      "                      decode_error='replace',\n",
      "                      tokenizer=lambda text: [porter.stem(word) for word in text.split()],\n",
      "                      preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()), \n",
      "                      stop_words=stopwords,) \n",
      "\n",
      "\n",
      "vec_porter.fit(X_train)\n",
      "\n",
      "print('Vocabular size: %s words' % len(vec_porter.vocabulary_))\n",
      "\n",
      "vocab_2 = vec_porter.get_feature_names()\n",
      "print(vocab_2[-10:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Vocabular size: 8551 words\n",
        "['zip', 'ziplock', 'zippin', 'zippo', 'zodiac', 'zoli', 'zombi', 'zone', 'zonin', 'zoo']\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Whitelists of positive and negative words"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('./whitelist_dicts/negative-words.txt', 'r') as w:\n",
      "    negative_words = w.read().split()\n",
      "print('number of negative words: %s' % len(negative_words))\n",
      "negative_words[-5:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "number of negative words: 4783\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "['zaps', 'zealot', 'zealous', 'zealously', 'zombie']"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('./whitelist_dicts/positive-words.txt', 'r') as w:\n",
      "    positive_words = w.read().split()\n",
      "print('number of positive words: %s' % len(positive_words))\n",
      "positive_words[-5:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "number of positive words: 2006\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "['youthful', 'zeal', 'zenith', 'zest', 'zippy']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "negative_words = set(negative_words)\n",
      "positive_words = set(positive_words)\n",
      "semantic_words = negative_words.union(positive_words)\n",
      "\n",
      "#pickle.dump(semantic_words, open('./whitelist_dicts/semantic_words.p', 'wb'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# With whitelist \n",
      "\n",
      "# With Porter Stemming\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from nltk.stem import PorterStemmer\n",
      "import re\n",
      "\n",
      "porter = PorterStemmer()\n",
      "\n",
      "vec_porter = CountVectorizer(analyzer='word',\n",
      "                      decode_error='replace',\n",
      "                      tokenizer=lambda text: [word for word in text.split() if word in semantic_words],\n",
      "                      preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()), \n",
      "                      stop_words=stopwords,) \n",
      "\n",
      "\n",
      "vec_porter.fit(X_train)\n",
      "\n",
      "print('Vocabular size: %s words' % len(vec_porter.vocabulary_))\n",
      "\n",
      "vocab_3 = vec_porter.get_feature_names()\n",
      "print(vocab_3[-10:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Vocabular size: 1666 words\n",
        "['wrath', 'wreck', 'wrest', 'wrestle', 'wretched', 'wrong', 'yawn', 'yay', 'zest', 'zombie']\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# With whitelist and Porter Stemming\n",
      "\n",
      "# With Porter Stemming\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from nltk.stem import PorterStemmer\n",
      "import re\n",
      "\n",
      "porter = PorterStemmer()\n",
      "\n",
      "vec_porter = CountVectorizer(analyzer='word',\n",
      "                      decode_error='replace',\n",
      "                      tokenizer=lambda text: [porter.stem(word) for word in text.split() if word in semantic_words],\n",
      "                      preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()), \n",
      "                      stop_words=stopwords,) \n",
      "\n",
      "\n",
      "vec_porter.fit(X_train)\n",
      "\n",
      "print('Vocabular size: %s words' % len(vec_porter.vocabulary_))\n",
      "\n",
      "vocab_4 = vec_porter.get_feature_names()\n",
      "print(vocab_4[-10:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Vocabular size: 1349 words\n",
        "['wrath', 'wreck', 'wrest', 'wrestl', 'wretch', 'wrong', 'yawn', 'yay', 'zest', 'zombi']\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Summary vocabulary sizes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print('All words: %s ' % len(vocab_1))\n",
      "print('Porter: %s ' % len(vocab_2))\n",
      "print('Whitelist: %s ' % len(vocab_3))\n",
      "print('Porter + Whitelist: %s ' % len(vocab_4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "All words: 11378 \n",
        "Porter: 8551 \n",
        "Whitelist: 1666 \n",
        "Porter + Whitelist: 1349 \n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Model Selection"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setup"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Requirements\n",
      "\n",
      "from sklearn.naive_bayes import MultinomialNB\n",
      "from sklearn.naive_bayes import BernoulliNB\n",
      "from sklearn.pipeline import Pipeline\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "from nltk.stem import PorterStemmer\n",
      "import re\n",
      "import pickle\n",
      "from sklearn import metrics\n",
      "\n",
      "porter = PorterStemmer()\n",
      "\n",
      "semantic_words = pickle.load(open('./whitelist_dicts/semantic_words.p', 'rb'))\n",
      "\n",
      "with open('./stopwords_eng.txt') as sw:\n",
      "    stop_words = sw.read().split()\n",
      "    \n",
      "f1_scorer = metrics.make_scorer(metrics.f1_score, greater_is_better=True, pos_label=0)    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Grid Search"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nltk.stem.porter import PorterStemmer\n",
      "from nltk.stem.snowball import EnglishStemmer\n",
      "\n",
      "porter = PorterStemmer()\n",
      "snowball = EnglishStemmer()\n",
      "\n",
      "# raw words\n",
      "tokenizer = lambda text: text.split()\n",
      "\n",
      "# words after Porter stemming \n",
      "tokenizer_porter = lambda text: [porter.stem(word) for word in text.split()]\n",
      "\n",
      "# Words after Snowball stemming\n",
      "tokenizer_snowball = lambda text: [snowball.stem(word) for word in text.split()]\n",
      "\n",
      "# Only words that are in a list of 'positive' or 'negative' words ('whitelist')\n",
      "# http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon\n",
      "tokenizer_whitelist = lambda text: [word for word in text.split() if word in semantic_words]\n",
      "\n",
      "# Porter-stemmed words in whitelist\n",
      "tokenizer_porter_wl = lambda text: [porter.stem(word) for word in text.split() if word in semantic_words]\n",
      "\n",
      "# Snowball-stemmed words in whitelist\n",
      "tokenizer_snowball_wl = lambda text: [snowball.stem(word) for word in text.split() if word in semantic_words]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pipeline_1 = Pipeline([\n",
      "    ('vect', CountVectorizer(binary=False,\n",
      "                             stop_words=stop_words,\n",
      "                             ngram_range=(1,1),\n",
      "                             preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                             )),\n",
      "    ('clf', MultinomialNB(fit_prior=False, \n",
      "                          alpha=1.0))\n",
      "])\n",
      "\n",
      "parameters_1 = dict(\n",
      "    vect__tokenizer=[tokenizer, tokenizer_porter, tokenizer_whitelist, tokenizer_porter_wl],\n",
      ")\n",
      "\n",
      "grid_search_1 = GridSearchCV(pipeline_1, \n",
      "                           parameters_1, \n",
      "                           n_jobs=1, \n",
      "                           verbose=1,\n",
      "                           scoring=f1_scorer,\n",
      "                           cv=10\n",
      "                )\n",
      "\n",
      "\n",
      "print(\"Performing grid search...\")\n",
      "print(\"pipeline:\", [name for name, _ in pipeline_1.steps])\n",
      "grid_search_1.fit(X_train, y_train)\n",
      "print(\"Best score: %0.3f\" % grid_search_1.best_score_)\n",
      "print(\"Best parameters set:\")\n",
      "best_parameters_1 = grid_search_1.best_estimator_.get_params()\n",
      "for param_name in sorted(parameters_1.keys()):\n",
      "    print(\"\\t%s: %r\" % (param_name, best_parameters_1[param_name]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    1.2s\n",
        "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:  1.2min finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Performing grid search...\n",
        "pipeline: ['vect', 'clf']\n",
        "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
        "Best score: 0.632"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Best parameters set:\n",
        "\tvect__tokenizer: <function <lambda> at 0x10a4d2f28>\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pipeline_2 = Pipeline([\n",
      "    ('vect', TfidfVectorizer(binary=False,\n",
      "                             stop_words=stop_words,\n",
      "                             ngram_range=(1,1),\n",
      "                             preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                             )),\n",
      "    ('clf', MultinomialNB(fit_prior=False, \n",
      "                          alpha=1.0))\n",
      "])\n",
      "\n",
      "parameters_2 = dict(\n",
      "    vect__tokenizer=[tokenizer, tokenizer_porter, tokenizer_whitelist, tokenizer_porter_wl],\n",
      ")\n",
      "\n",
      "grid_search_2 = GridSearchCV(pipeline_2, \n",
      "                           parameters_2, \n",
      "                           n_jobs=1, \n",
      "                           verbose=1,\n",
      "                           scoring=f1_scorer,\n",
      "                           cv=10\n",
      "                )\n",
      "\n",
      "\n",
      "print(\"Performing grid search...\")\n",
      "print(\"pipeline:\", [name for name, _ in pipeline_2.steps])\n",
      "grid_search_2.fit(X_train, y_train)\n",
      "print(\"Best score: %0.3f\" % grid_search_2.best_score_)\n",
      "print(\"Best parameters set:\")\n",
      "best_parameters_2 = grid_search_2.best_estimator_.get_params()\n",
      "for param_name in sorted(parameters_2.keys()):\n",
      "    print(\"\\t%s: %r\" % (param_name, best_parameters_1[param_name]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "[Parallel(n_jobs=1)]: Done   1 jobs       | elapsed:    1.0s\n",
        "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:  1.0min finished\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Performing grid search...\n",
        "pipeline: ['vect', 'clf']\n",
        "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n",
        "Best score: 0.606"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Best parameters set:\n",
        "\tvect__tokenizer: <function <lambda> at 0x10a4d2f28>\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "ROC Curves"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import roc_curve, auc\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "from sklearn.cross_validation import StratifiedKFold\n",
      "from scipy import interp\n",
      "\n",
      "sns.set()\n",
      "sns.set_style(\"whitegrid\")\n",
      "\n",
      "vect_1 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer)\n",
      "\n",
      "vect_2 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_porter)\n",
      "    \n",
      "vect_3 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_snowball)  \n",
      "\n",
      "vect_4 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_whitelist)  \n",
      "\n",
      "vect_5 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_porter_wl)\n",
      "\n",
      "vect_6 = CountVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_snowball_wl)\n",
      "\n",
      "vect_7 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer)\n",
      "\n",
      "vect_8 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_porter)\n",
      "    \n",
      "vect_9 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_snowball)\n",
      "\n",
      "vect_10 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_whitelist)    \n",
      "\n",
      "vect_11 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_porter_wl)\n",
      "\n",
      "vect_12 = TfidfVectorizer(binary=False,\n",
      "                         stop_words=stop_words,\n",
      "                         ngram_range=(1,1),\n",
      "                         preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),\n",
      "                         tokenizer=tokenizer_snowball_wl)\n",
      "\n",
      "pipelines = []\n",
      "vectorizers = [vect_1, vect_2, vect_3, vect_4, vect_5, vect_6, vect_7, vect_8, vect_9, vect_10, vect_11, vect_12]\n",
      "for v in vectorizers:\n",
      "    pipelines.append(Pipeline([('vect', v),\n",
      "                               ('clf', MultinomialNB(fit_prior=False, alpha=1.0))]))\n",
      "    \n",
      "for v in vectorizers[:6]:\n",
      "    pipelines.append(Pipeline([('vect', v),\n",
      "                               ('clf', BernoulliNB(fit_prior=False, alpha=1.0))]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['blue', 'red', 'black', 'green', 'cyan', 'magenta']\n",
      "linestyles = ['-', '--', '-.', ':', '-', '--']\n",
      "classifiers = [pipelines[i] for i in range(6)]\n",
      "labels = ['1: all words', \n",
      "          '2: porter stemmer',\n",
      "          '3: snowball stemmer',\n",
      "          '4: whitelist',\n",
      "          '5: porter stemmer + whitelist',\n",
      "          '6: snowball stemmer + whitelist',\n",
      "          ]\n",
      "\n",
      "for clf,col,ls,lab in zip(classifiers, colors, linestyles, labels):\n",
      "    \n",
      "    mean_tpr = 0.0\n",
      "    mean_fpr = np.linspace(0, 1, 100)\n",
      "    all_tpr = []\n",
      "    cv = StratifiedKFold(y_train, n_folds=10, random_state=123)\n",
      "\n",
      "    for i, (train, test) in enumerate(cv):\n",
      "        probas_ = clf.fit(X_train[train], y_train[train]).predict_proba(X_train[test])\n",
      "        # Compute ROC curve and area the curve\n",
      "        fpr, tpr, thresholds = roc_curve(y_train[test], probas_[:, 1])\n",
      "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
      "        mean_tpr[0] = 0.0\n",
      "        roc_auc = auc(fpr, tpr)\n",
      "\n",
      "    mean_tpr /= len(cv)\n",
      "    mean_tpr[-1] = 1.0\n",
      "    mean_auc = auc(mean_fpr, mean_tpr)\n",
      "    plt.plot(mean_fpr, \n",
      "             mean_tpr, \n",
      "             color=col, \n",
      "             linestyle=ls,\n",
      "             label='%s (ROC AUC = %0.2f)' % (lab, mean_auc), \n",
      "             lw=2\n",
      "    )\n",
      "\n",
      "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random Guessing')    \n",
      "plt.xlim([-0.05, 1.05])\n",
      "plt.ylim([-0.05, 1.05])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.title('CounterVectorizer')\n",
      "plt.tight_layout()\n",
      "plt.savefig('./nb_whitelist_model_images/roc_gridsearch_1.eps', dpi=300)\n",
      "plt.show()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MbGwsbm5utGvXTt5gXIok5AghREHffqsPbypq5sz8kKNpGkePHqVOnTrwyCPQ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qAAAg\nAElEQVS1tdk5OTklgpubtDp06FAmTpzIrl27SEhIYPr06VhYWPD6668zceJEpkyZQqVKlRg1alSm\n+R6HDx9m8+bNWFlZ8cUXX2BpacnixYvp168fjo6ONGjQgDt37mQ6btbjF9fk8jFjxnDgwIFHSi6f\nNWvWM59cnrFPsvZjsU8uN5fa+Sx+PWsp5Ls1U8Nefch2w4drGmha9+6aFhZ2S3N2dtYAbcSIEY/b\n1CLneU4Gfp5Jvxce6fu8y8/k8uLW789ScnlhpZDL5arHpM2HvW1g4Nact7lzB1au0ACNKVPAyqoE\nL730Eu7u7nqF/rzRNC3HyXBCCJHfRo8erV8yEo9GksvlctVjM2yNpM3+ciS9H40ppiu7b78Fz8TV\njH9hFS/FzYEXXmX79u1ER0frtzk+TyIjIwkKCiI6OhpPT8/n8nsQQjxfJLn88UlyuRQ5jyU1FZqc\nNM3gr+Nu/kT/4AEsXZREMFOode8ynDsHr76KpaUl5cuXf4qtfXJGo5Hff/+d48ePk5qaSs2aNTEa\njYXdLCGEECJXUuQ8hr9VcIk0EOYMtWubzxlftgy6Ra2gFpehXj3w8nq6jcwnd+7c4cCBA/pt4e7u\n7tnWrRBCCCGeRVLkPIYbv6biggWXW4CTmZujkpJg8fx49jGdLsDwd96h03Oa05ScnMz9+/d58cUX\nadq0KSVLlizsJgkhhBB5IkXOY7i2/yZQlZsN7wHZ1x9Yvx7eurmEaoTxYZ06TDt8mLZJSc/lHBZn\nZ2d69uyZbfVOIYQQ4lknd1c9htn/dab+7+DQN3vRkpoKc+ZAHKVIKO3A6wsWsHfv3ueywEknBY4Q\npsXlWrduzd9///3Y+0iPRsgoMDBQX5Ru7dq1ub7/jTfeICkpiaVLl3Lq1Kkct8tpP8eOHWP16tXc\nuHGDRo0a4e3tjbe3Nz179uT999/XF96LjY1lxowZ9O3bF29vb4YOHcrly5f1/Vy4cIEhQ4bQr18/\nunfvnuPqu7dv36ZBgwbs3LlTfy44ODjbui3z5s0jMDAQgLCwMEaPHo23tzeenp5MnTqV5OTkXPvF\nnCtXrtC7d2/69OnDlClTsi2QGBgYqH//np6e1K9fX19tGOCLL77QAzY1TcPHx4fExMRHbocoXFLk\nPKJI4EwJA2p9aFmjTLbXt26FP/+E7dWGYnHlMvzrX0+9jY9K0zT+/PPPTKuBCvGsMkw15OvjvEhO\nTmby5MnY2to+8nszHdvMgnddu3bljTfeAMDX1zdP+xk8eDD169fP8XVz+9E0jUWLFuHl5YWmadSt\nW1dfeXfjxo288sor/N///R8AkyZNolatWqxdu5Y1a9YwZswYPvjgA2JiYoiOjmbcuHF8+umnrF69\nmoCAAM6fP282cXvz5s3069cvU5CkuT5IX8TOaDQyfPhwBgwYwJo1awgICKBEiRJ6XtijmDVrFuPG\njWPdunVomsYvv/yS6fWuXbvq3//LL7/MpEmTsLOz02Mn9u3bl2lhwy5durBs2bJHbocoXFLkPKK9\nSbEANAKyzk5JTYWZMzUglXHjwPqFMs98dENkZCRbt27l4MGDnDlzRv5SEcKMOXPm0Lt370xBjFu3\nbiUgICDTdiNGjODMmTOAaaXk3bt3AzBgwABu375NUlIS48ePZ+rUqQwfPpyUlBQWLlyIv78/vr6+\nREZGMm3aNFJSUpg4cSJ9+/bFy8uLo0ePZjqOj48PBw8e5O+//6ZXr154e3vTp08fbt26xZIlS/T9\nZPTbb79Rp06dTFED6TRNIywsDHt7eyIiIrhw4UKmYEo3NzfatGnDzz//zN69e2nRogXVq1cHTHEM\nX375Jd26dcu2zy1btvD++++TnJzMhQsX9OfN0TSN0NBQnJycMhVwH330ER988EGmbXft2qWPwqR/\nnT59OtM2f/zxh57z1apVKw4dOmT2uKdPn+bChQt6GnhcXByjRo3i7bffztTWFi1aZBqREs8HmZPz\niP4TdhxqvEaZO2eg4suZXvPzg6NH12BltYL27VcDNQqnkXmQflv4iRMnMBqN1KxZE3d3d5lYLJ55\n2udavj5+mM2bN+Pg4ICHhwd+fn76ic9cCnnbtm0JCgqiXLlylCxZkkOHDtGiRQsSExOpVKkScXFx\njB8/nrCwML755hv++OMPfRRj6NChrF27lsmTJ7N+/XocHBz44osvuH//Pt7e3mzd+r+VR9NHGA4d\nOkTDhg358MMPOXbsGA8ePGDYsGGsW7eOyZMnZ2pbetZVuosXL+Lt7U1UVBSJiYm8+eabvPPOO5w9\ne1bPqcqoWrVq3Lx5E2tr62yxAunhoxkdPnwYV1dXHBwc6NatG+vWrWPKlCk59rPBYCA8PDzbsc1d\n6u/QoQMdOnTIcV+QuZgqVapUjouY+vn5MXLkSP1x1apVqVq1qp4hlc7S0hIHBwdUVS22id7PIxnJ\neUSWNV4DoLd95gLm6lX48MMbwEiSk4MICdn/9Bv3CEJCQjh27BglS5akXbt2tG/fPs/ZNEIUJ5s3\nb+bQoUN4e3tz7tw5fHx8uHv3rtlt33jjDQ4dOsTBgwcZNGgQp06dIigoSL8cZW9vT5UqVQBTGnlC\nQoLZ/Zw/f54DBw7g7e3NqFGjMBqN3L9/P9M2BoOBHj16YGdnx8CBA1m3bh2WudzFef/+/UxBjXXq\n1GHNmjVs2rSJKlWq8MILL2BpaUnFihW5efNmtvdfvnyZKlWqUKVKFcLCwjK9du3aNY4dO5bpuYCA\nAK5fv87AgQPZunUrO3bsICYmBhsbGz1EM11cXBw2NjZm933//n327duX6bmdO3dmG8nJOkcpY+Bn\nTone0dHRXL58maZNm5rrsmwcHR2JjIzM07bi2SAjOY8g1Qg3rwPVoX3J/83H0TQYOkRDiZvLCWJ4\n66236NevX+E1NA/q169PamoqjRs3ltEbIXKRcRKvt7c306ZNo0KFCma3LVu2LDY2Nmzfvp1Fixax\na9cuVq9erYdM5hTImz7qkP5fFxcXnJycGDJkCDExMaxcuZJy5cple8+ePXto0qQJI0aMYOvWrSxb\ntoxZs2aZvSTk4OBgNtG7ZMmSzJs3j7fffpt//OMfuLm5Ub16ddatW6dfsjp79iz79u3TLxv5+fnh\n5eVFtWrVSE5OZvbs2Xh4eOip2REREZw6dYpffvlF/54nTZpEYGAgXbt25Y8//iA8PBxHR0cSExMJ\nCQmhf//+VK1alevXr3Pq1Cnq16+vzyOytbWlTZs2eps7duxIx44dzfZluhdffJGjR4/StGlTgoKC\naNGiRbZtQkJCaN68ea77ySg6OjrHn714NslIziP4/fgDztSE0BbgnOH5devAbucmjrOAQ6825z+L\nFj3z6eKlSpWiZcuWUuAI8ZjMzckB+Oc//0liYiL29vZ4eHiQkJBg9vJPRumfFy4uLkyYMIGePXty\n6dIlfZTCyckpW8K0wWDglVde4dtvv+W9995j48aN+h9X6fvJqFmzZplGOzLu64UXXuDjjz/m888/\nB+DLL7/k4sWLeHp60qtXLxYuXMjixYuxs7PDzs6O2bNn89lnn+l3Zr344ov07t1b39+WLVvo0KFD\npmP06NGDDRs2YGdnxyeffMKQIUPw8vLS7+CqVq0aBoOBBQsWsGjRIry9venevTsGg4ExY8Y89OeR\nlY+PDwsXLqRXr14YjUa9KPr444+5desWYBqdSp9bZE7G9qempnL79u1MKeXiOWAutfNZ/HoWUsjX\nf3lV09C0Le/c05+7fVvTapa7r92ksilufOnSgmzmI4uNjdWioqIKuxm5Km7JwM8K6ffCUxh9n5qa\nqvXt21dLSkp66sd+VjxJv+/fv19bsmRJPrameJEU8udA9bOmv8ZsW//vUtXIkfBR5EScuIXm7g4D\nBhRW8zLRNI1z584REBDAvn37cryjQQhRPBgMBkaMGCGJ3o9B0zS2bt1K//79C7sp4hHJnJxH4HzY\n9F8nDysAfvwRzgf8yFqWoJUogcHXFywKv26MjIzk4MGDhIWFYWVlRZ06dQq7SUKIZ0CzZs1o1qxZ\nYTfjuWMwGPR5VeL5IkVOHl27EUPNC3bE2WrUbWAgORnGjwdnJtASCBwyhKovv/zQ/RS0U6dOERIS\ngtFopEaNGri7u2NnZ1fYzRJCCCGeOily8iAR+L/r8bT+hx0PSsfS2sqOFSvgr7+gRJ2zTHr9Aypk\nWXirsFhYWGBtba2nhT/rE6CFEEKIgiJFTg4igK2axrgrB0io0YrYZo5wHHxSStMiCaZPN203eWoJ\nvLz8CrWtGdWrV4+6devKXVNCCCGKPSlyzLicCq9YQIzBADVfB6AB8A4wpoSBlb5w5QrUqwc9exZe\nOzVNyzZSY2FhIQWOEEIIgayTY9Yt31QuOcLcL2BaQhTnUhI5CUwBbBJg+nTTnUpTpkAuC4wWmLi4\nOPbs2aNnwQghClbXrl31NWsmTpxYIMc4f/58tlWDn8TGjRtJSUnJt/0VtvSMr6zSIxlUVc21/zZv\n3oy/vz93795l6tSpOW6X089B0zQ++eQT4uLi8PHx4a233tJ/J9588002b96sb3vkyBHee+89vL29\n6dWrF6tWrcq0H19fX/r06YO3tzf9+vXj/PnzZtsyZcoUunbtmuk5b29vLl26pD9OTEzUV9QG0889\nfe2h3r17Z8s9y6tFixbRo0cPevXqZTbxPuNq0+7u7nz11VeAaY2lXr160b17dzZt2gRAUFAQ+/fv\nf6x2PCkZyTHjcsh1mt+tTsW4MPrZOGV6bamfRvjNUZQvH0ezZlOBquZ3UgA0TUNVVYKDg0lMTCQl\nJQVXV9endnwhCltnYHs+77MTsC2X19NDa9esWZPPR85s165dODo66qsGPyk/P79sJ8jnWU7zCxcu\nXAjAzz//nGv/pb+/QoUK+qKH5uT0c9ixYwcvv/wypUqVwmAwMGHCBDw8PACIioqic+fOvPvuu5w/\nf545c+awdOlSKlSogNFoZMqUKaxYsYIBAwawbNkyoqKi9GT206dPM3z4cHbt2pUpliM+Pp7jx4/j\n6uqqr9z8sL7Ytm0bhw4d4r///S+WlpZcv36dvn378sMPP2RbMTs3Z8+eJSQkhE2bNhEWFsbIkSP1\nhPp06f8/XLt2jbFjxzJs2DCOHDnC9evX8ff3JykpiS5dutCxY0datWrFokWLiImJeeo3wkiRY4Zy\n07QeTsWG9pmej4uDA5O/w8AiIu8biIoaw9MqcqKioggKCtJvC2/ZsiX16tV7KscWojg7d+4c8fHx\nDBgwgJSUFMaNG0eDBg3YunUrcXFxeHp66tsGBwfz3XffkZSUxN27d+nduze9e/fmjz/+YMaMGVha\nWmJtbU3Pnj25fv06w4YNo1y5cjRr1ozAwECsrKyoV68eCQkJfPPNN1haWlKtWjWmTZvGli1b+P77\n79E0jZEjR+oxBREREYwZMwZN00hKSmLq1KmcPn2au3fvMm7cOBYtWsT8+fMJDQ0lNTWV/v3707Fj\nR7y9vXFzc+PChQuUKlWKJk2a8OuvvxIdHc3KlSvZs2cP+/btIzExkfDwcPr168cvv/zChQsXmDBh\nAv/85z/ZsWMH//3vf7GwsKBx48aMHz+ehQsXcuLECeLi4pg5c2a2FYIXLlyIo6MjvXr1ytbP33zz\nDb6+vmzbtg0/Pz+2bNlCaGgoP/74IxUrVuSXX35h586dREZGMnr0aNq0aYO7uzubN28mMDAQa2vr\nHPsv3Y0bNxg3bhwbN27k66+/Jjg4GKPRSPv27Xnrrbf0/bz00ku88sor+vvWrl3Lf/7zH/1xxrXH\nwsPDsbGxAcDf35+hQ4fq8Q+WlpZ8/PHHvPvuuwwYMICAgAACAwP1977yyit8//332XLHduzYQcuW\nLXnttddYu3ZtnvK1/P39+fTTT/V9Va1alR9//BF7+8znsqFDhxIbG6s/rlu3bqZA19DQUL2Ac3Jy\n0rPTypcvn+2YM2fO5MMPP8TW1pZGjRplOi8ZjUY99b5hw4YEBgbi7e390O8jP0mRY0alc6YquXqD\nzMm6fguTqBvtQxLQs2HDTP8DFCRN09i3bx937tyhevXqeHh4yG3holjKbcSloNja2jJgwAB69OjB\n5cuXGTRoELt27TKbQg6mdarWrVtHUlISb731Fh06dOCzzz7jiy++wM3NjV9++YVVq1Yxe/Zs7t69\nS2BgoH4icHR0pH79+nTo0IENGzbg4ODAggUL9G3s7e1ZvHhxpuOdPn2a8uXLM2fOHC5evEhcXBw9\nevRgyZIlfPXVVxw4cIAbN26wfv16EhMT6dmzJ+7u7gA0aNCATz/9lIEDB2Jra8vKlSvx8fHh6NGj\nGAwG4uLiWLFiBdu3b2fVqlUEBAQQHBzM6tWradKkCYsWLWLz5s2ULFmSCRMmcOjQIQwGA3Xq1Ml2\nWW/79u1s2LCBGzduYGVlxbZt2xg2bBgtW7YEwM3NjRs3bpCUlERQUBCWlpbcu3ePvXv30r59e06e\nPEnlypWZPn06R48eZfny5bRp0waDwUClSpV49913H9p/Wf3000+sXbuWChUqEBgYmGk/GT/fExIS\nCAsL00/ymqYxd+5cfH19uXnzJi4uLixYsACA69evZ4uKsLOzIz4+Hk3TSEhIoEyZMplez1qEAGza\ntInp06dTu3ZtpkyZwp07d6hYsaLZ37n0kZ3w8PBsCfHm9u3r62t2P+liY2MzjfyULl2amJiYbEXO\nuXPniI2N1fO/rK2tsba2Jjk5GR8fH3r27ImtrS0A1atXJzg4WIqcwpYQbaTKVUsSraFWrf89HxMD\nd6cvYTbhtK5Qlbrr1ua8k3xmMBhwd3fnwYMHclu4EE9ZzZo1qVGjhv7vcuXKER4eTqVKlbJtazAY\nePXVV7G0tMTW1pa6dety7do1wsPDcXNzA6BJkybMnDkTMP2lnfXkGxERQXh4OKNHjwZMl8tatmxJ\njRo1qJXxQylNq1atuHz5MsOHD6dEiRIMGzYs0+vnz5/n7Nmz+snFaDRy48YNAP2v7rJly+qLhpYt\nW1ZPCX/xxRcB00k6fUSmbNmyJCYmcuXKFSIiIhg4cCBgOjFevXpV76esOnXqRKdOnVi0aBGOjo70\nNHPXhoeHB0eOHOHWrVu8+eab/Pbbb4SGhjJ27FhOnjzJSy+9BJiytuLj4zO9N31kJbf+y2revHnM\nmzeP8PBwWrVqlW1f6aKiojKd4DNerjpw4ADz5s3T88kqVarE9evX9Z83QExMDFZWVhgMBsqWLZvt\nss3u3btp0aKF/txff/3FxYsXmT17NmC6oWTDhg2MHj0aGxsb/RJqer+njyI5OzsTFhZG3bp19dcP\nHjyIm5sbjo6O+nNDhgwhLi5Of1ynTp1Ml/Ds7OwyjfTExsZmK8zAlFGW9ecYFRXF6NGjadasGYMH\nD9afL1euXKEkuMvE4ywOn7pOcgm46JKMbYbPntXfRDA+1jRZreOKxdR5ypeKHB0dqV27thQ4Qjxl\nmzdv1k82t2/fJiYmJtMJIyNN0zhz5gxgmlPx119/UaNGDSpWrIiqqoAp+drJyTTXzyLDCukGgwGj\n0Ui5cuWoXLkyS5YsYc2aNQwePFgf7bAws6J6cHAwjo6OrFixgqFDh+oTQC0sLEhNTcXFxYVmzZqx\nZs0avvvuOzp06KCfkB/2eZLb61WrVsXJyYlVq1axZs0avLy8aNiwYY7tzIt27dqxbNky3NzccHd3\nZ+3atdSoUcPsKExWFhYWD+2/jJKSkti5cydfffUVq1evJjAwkJs3b2IwGEhNTc20bfny5TOd9OF/\nhVDr1q1p27YtkyZNAqB3794sXryYu3fvApCcnMzMmTPx8vIC4J133sl02ev48ePMnj1bL1TANIoz\nduxYli9fzvLly1m1ahXff/89ycnJ1KtXj59//lnfNigoiPr16wPQrVs3Fi9ejNFoBODvv/9m0qRJ\n2S6F+fn5sWbNGv0r6xylRo0a8euvv6JpGjdv3iQ1NdXsnJ4jR47w2muv6Y8TEhLo378/3bt3z1Zs\nx8bG4uDgkG0fBU1GcrK471GDUnHQ9aaRAEzxDZoGfP01DtwnvP4/cXzT/DB1frh27RoVK1aU28CF\neEZ0796dTz75hD59+gAwa9YsLCwszM7JAdNf7e+//z5RUVGMGDGCcuXKMWPGDKZPn46maZQoUQJv\nb+9sS0C8/PLLzJkzBxcXFz799FMGDx5MamoqZcqU4csvv+TGjRtmiw43NzfGjRvHhg0bMBqNjBgx\nAjCNGA0ePJjVq1dz9OhR+vTpQ1xcHO3ataN06dJ5+t7Tj5f1uAaDAQcHB/r370+fPn1ITU2latWq\n+iW83Iqj9PaZ07BhQ/7++28GDRqEoiiEhYVlGg3ImsKeUV76L+N7ra2tsbe3x9PTk5IlS+Lh4UGV\nKlX0/dSpU0efB2NtbU2FChWIiIjQT9QZjz98+HC6du3KgQMHaN26NePGjWPs2LEYjUZSUlJo3749\nA9JyDQcOHMiCBQvo2bMnJUqUwMrKCl9fX72QS0pKYtu2bfz000/6/p2cnHBzc+Pnn39m0KBBfP75\n57z77ruULFlS//0C02jZnTt38PLywsrKCqPRyLx58x65uHjppZdo0qQJPXv2JDU1VS+Cjhw5Qmho\nKB988AEA9+7dy3Q5zN/fn+vXr7Nx40Y2btwIwOzZs3F2dubixYtmi80CZy6181n8elop5DPSDjg+\nw2v792vaG+zR9pTspCXvCyqQ48fGxmp79uzR/Pz8tKCggjnGs0rSsAuH9Hv+O3LkiDZt2rSHbid9\nXziepN+3bt2qfffdd/nXmGKmR48eWkxMTIHtX1LI8+hkiula54sZnlu4MIK9rGdD36mUeP018298\nTFrabeGbNm3ir7/+omLFivp1ZyHE88VgMMgl5SKqc+fO/PHHH5nmsoi8OXDgAE2bNs3zCGJ+kstV\nWeyKugIvuGJz/28oX4t792DLFitAITbWD8ifNSzANAFw586d+t0G6beFP+71bCFE4WratGmebvUV\nz6c5c+YUdhOeS61bty60O4KlyMkgFTC+YFpcr729aWLe6tWQnFyGf/1rAhs25O/xLC0tKVOmjNwW\nLoQQQhQAKXIyuB4HpWOgjCM4WpRA02DpUtNrGea+5St3d3csLCxkiFsIIYTIZ3JdJINr2+O5UwkC\nupseH9oTx51z93Bygs6dn2zfWW9JTGdpaSkFjhBCCFEApMjJ4GqoaV2D6+XCAAj9eDEXqcJ3bl9i\nZfUE+716lY0bN3Lnzp38aKYQQggh8kCKnAxqhJnm4Tg2qUjEPY2gE7OoQxK3aj9ecRIfH69nrcTE\nxOiLQwkhni9+fn706tWLbt26Zcodetp8fHz0xQYfJj21++jRo4wbNy5P75Hk8sw2b97M/PnzJbk8\nj/I7uTxrKOjjkDk5GZT70/Tf8i9Z8sPEHWwngnig2aj3Hmk/mqZx/vx5jhw5QmJiIo6OjrRq1YoX\nXngh/xstRHGT09VdLYfnH3X7LIKDgzlx4gT+/v7ExcWxfPnyvL2xADzKpe3HuQwuyeXm3y/J5Q9X\nEMnlgwYNomPHjk90U44UOWm0VI2qf2qAgSoKBK1bQCXAuWIt3NKWzM6rxMREjhw5gtFolNvChXjO\n/fbbbyiKwvDhw4mJiWHChAkAZlc8NpcIbmdnx/jx43FycuLq1avUr1+fN998k+joaD766CNiY2NJ\nSUlhzJgxxMbGcujQISZNmsTSpUs5ceIES5YsYcuWLYSFmS6jL126lKioKDRNY8aMGVSvXp358+dz\n9uxZIiMjURSFWbNm5fo9Fdfk8s6dO9O4cWNJLs/gWU4ub9269RMnl0uRk+Z2jBHbagYcIlK5dvQu\nH8TuYyRwZ8umR96XjY0Nbdu2xd7eXm4LFyK/5XEE5rG3zyIiIoKwsDD8/Py4du0aw4YNY+fOnWZT\nyM0lgtvZ2XH58mW+++47/bOhVatWLFmyBA8PD7y9vbl9+zZeXl5s375dT7MOCQkhIiICo9HIvn37\nGDlyJMuWLcPDwwNPT08OHDjA3LlzmTVrFvb29qxcuZLU1FS6dOnC7du3c/2eimty+fz58xk0aJAk\nl/N8JJcrisLq1aulyMkPN6uUo8tZaGY00MIzgXP04h9u8dRv1vix9ufs7JzPLRRCFIby5cvj4uJC\niRIlqFWrFiVLlsyUYZRRTongNWrUoFSpUoApbDcpKYlLly7x9ttvA6bkajs7O2JiYqhZsyanT5/G\nysqKhg0bcvToUcLCwqhduzYAr776KmDKeZo7dy42Njbcu3eP8ePHU6pUKeLi4h46r6a4JpentzFd\n+siKJJc/m8nlFSpUeOLkcrmGkuZS2g+8dooB3+3V6c9/sdu6Mdf3JCcnc/r06RxvDxdCPP8aN27M\nwYMHAVMKeXx8vNkheDCfCJ5T1EPt2rUJCQnR9xsdHU358uVp164dc+bMoXnz5ri7u/P1119nCjb8\n/fffAdNIj6IoBAUFcevWLebPn8/YsWNJSEjIdsLNSztBksslufzZSi6Pjo5+4uRyGclJ87ulqcJ/\ncCyVhAQL2raF2i45/8967do1fv31Vx48eICVlVWmClwIUXS8/vrrhISE0L17dz2R2WAwmJ2Tk1Mi\nuLkU76FDhzJx4kR27dpFQkIC06dPx8LCgtdff52JEycyZcoUKlWqxKhRozLNDzl8+DCbN2/GysqK\nL774AktLSxYvXky/fv1wdHSkQYMG+nIVOaWIF9fk8qweJfldkstz97Dk8vTLU4+SXP77778/cXK5\n4WEV/7MiNDRUa9z48S4d5UWVm2cIq/IytuO+JP7ru/j5jWPwYKds28XHx3P48GEuXryIwWCgQYMG\nNGrUKE9/NQjzQkNDKcifrTBP+r3wSN8Xjqfd79u2bSM8PJz+/fs/tWM+qx6n79OLu7wU2Gn7z1bR\nyuWqNKkVTJlV8T/1xtZWo1mz7NcBIyMjCQgI4OLFizg6OvLuu+/StGlTKXCEEFo99/kAABAWSURB\nVEJkI8nlj+/AgQN06NDhiZPL5ewMPIgHt2BrDHU0bv1dnZHj59GgQfbt7O3tqVChAtWrV+ell16S\n28KFEELkSpLLH0/r1q3zZT9S5ABnQqLY38aeP5VELhm7ofTyB7LPCjcYDHTq1EmypoQQQojngAxF\nADdPxwNw0fkOrnZh1Glol+1Ww3RS4AghhBDPhwIbyVEUxQJYDNQHEoGBqqr+leH1N4FJQAqwUlXV\nQlsr3eFaZQBKp17iXt9h3AsO5vz583Tv3l1f20IIIYQQz5eCHMl5B7BWVbUl4APMT39BURQr4Cug\nHdAaGKwoivllGZ8Ci2OmkZxdL5zhknspTp06hbW1tUwWE0IIIZ5jBTknxx3YCaCqarCiKBmTy14E\nLqqqGgWgKMqvQCvgySNHH8MLYcnsff8wLs2tiY2Po0GDBjRu3FjumhJCEBwczJgxY6hTpw4Gg4GY\nmBiqVavGvHnzsLKyeuz9Tps2jY4dO+YpXygvDh06xNKlS0lKSsLS0pKqVavy6aefFli0TGBgIPb2\n9pnSrIV41hTkWbwsEJ3hsVFRFAtVVVPTXovK8NoDIHtYxlOQmqpxomU88Y3/okRqCd7q/pYekiaE\nePasX7/e7PPpq8M+6fZZGQwGWrZsyfz5+mA048ePZ+/evXTo0CFP+8hpv/nl3LlzzJs3D19fXz2r\naNWqVSxfvpwxY8bk23EyKkpp5aLoKsgiJ5rMtyilFzhgKnAyvlYGuP+wHYaGhuZf6zL49q261HjQ\nEh/XeK5cucKVK1cK5DgiZwX1sxW5ex77PaebAnL6Xh51+6xUVeXevXv69ikpKVy6dInbt28TEhLC\n8uXLiYiIIDIykkaNGuHp6Ymvry9WVlaEh4cTGRnJ0KFDqVmzJnv27GHv3r16llOtWrXQNA0/Pz/C\nw8NJTU2lU6dONG/enOnTp1OjRg2uXbuGjY0Nbm5unDp1itjYWD755JNM64esWLGC9u3bc+3aNa5d\nuwagh0WGhoYyatQovvrqK0qUKMGGDRtwdnamVatW+Pv7o6qqftxmzZqxe/duDh48iMFgoHbt2rz3\n3nscPXqUrVu3YmlpSfny5Rk5ciTff/895cuXp0qVKmzZsoUSJUpw584dWrRowTvvvMOtW7f0VXkr\nVKjA3bt3+eyzz/LU5wXpefydLyoKpe81TSuQL1dX13ddXV2/S/t3c1dX120ZXrNydXU97+rqWt7V\n1dXa1dX1mKurq1Nu+zt27JhWkAp6/yJn0veFQ/o9b44cOaK1aNFC69u3r9apUyetS5cu2po1azRN\n07Tr169rAQEBmqZpWkJCgtasWTNN0zTNx8dH8/Pz0zRN0wICArTJkydr9+7d0zp06KAlJSVpR48e\n1by8vLTg4GBtzZo12qxZszRN07SYmBitffv2WkREhNa3b1/tp59+0jRN0wYMGKCtX79e0zRN+/jj\nj7Xdu3dnauOAAQM0VVU1TdO0q1evan379tX69u2r9e7dW9M0TWvTpo2WmJioaZqmzZs3T9u8ebO2\nf/9+bezYsXrb3377bS06Olrr1q2bdvr0aU3TNG39+vVaSkqKNmrUKG3Xrl2apmlaYGCgFh0drS1c\nuFDbsGGDFhwcrHXq1EkzGo1aXFyc1rhxY03TNG348OHagQMH9D7o27dv/v1QHpP8zheep3QOz1Y7\nFORITiDQTlGU39Iev68oSm/ATlXVZYqijAN2YZr8vEJV1bACbIsQQjy25s2b89VXXxEZGcm///1v\nnJ2dAdMCoadPnyY4OBg7u8xLT6SncVeuXJnjx49z5coVXFxcsLKywsLCgkaNGgFw6dIlPZ+ndOnS\nuLi46KMxD0v8Tufk5MS1a9dwdXWlWrVqrFmzhqSkJDp27Jjte9HSonwuXLhgNml81qxZrFy5kuvX\nr9OwYUM0TeOTTz7RAxpr165N27ZtM+3T1dUVCwsLbG1t9dDIS5cu6d9jo0aN2LJly2P1vRBPosCK\nHFVVNWBYlqfPZ3h9K7C1oI4vhBD5rVy5csydO5d+/frxww8/sGPHDsqWLcu0adO4cuUKAQEB2d6T\nXlTUqFGDixcv6inhp06d4rXXXsPFxYVjx47Rtm1bYmJiOH/+PFWrVgXyPm+nV69efP7559SvXx9H\nR0fAFOSZvip7yZIluXPnDs7Ozvz555+4uLhQu3ZtmjVrxrRp00hJScHX15dq1arxzTffMHXqVKyt\nrRkwYAAnTpzg0KFDjBw5EgcHByZPnsyePXsyHd9cO+vWrcvx48dp1aqVnpwuxNMmtw8JIUQuDAZD\nppO4i4sL3t7ezJgxg5EjRzJ+/HjOnj2rp1Hfvn1bf1/G/zo4ODBs2DC8vLwwGAyUK1cOg8GAp6cn\nkyZNwsvLi4SEBEaMGPFYCdATJkzAx8eH5ORk4uPjqVy5Mt9++y1gCjocPHgwzs7O+nHfeOMNs0nj\nrq6ueHl5Ubp0aSpXrkyDBg2IiYlhyJAhlC5dmtKlS/P666+zdu3aHBPHAT766CMmTpzIypUrKVOm\nzBPdiSbE45IU8v/tX1KBC4n0feGQfi88xaHvf/rpJxo0aED16tXZtGkTJ0+eZObMmYXapuLQ78+q\ngu77nFLIZSRHCCFEvnNycmLs2LHY2tpiaWlZ6AWOKJ6kyBFCCJHvmjRpwvfff1/YzRDFnAR0CiGE\nEKJIkiJHCCGEEEWSFDlCCCGEKJKkyBFCCCFEkSRFjhBCCCGKJClyhBBCCFEkSZEjhBBCiCLpuVrx\nuLDbIIQQQohnk7kVj5+bIkcIIYQQ4lHI5SohhBBCFElS5AghhBCiSJIiRwghhBBFkhQ5QgghhCiS\npMgRQgghRJEkRY4QQgghiqQShd2Ap0lRFAtgMVAfSAQGqqr6V4bX3wQmASnASlVVlxdKQ4ugPPR9\nb2A0pr4/DQxXVVXWN8gHD+v7DNstBe6pqvrJU25ikZSH3/lXgfmAAbgB9FNVNakw2lrU5KHvuwIT\nAQ3TZ71voTS0iFIUpRkwW1XVNlmef+rn2OI2kvMOYK2qakvAB9MHDACKolgBXwHtgNbAYEVRKhZK\nK4um3PreFpgOvK6qqgdgD3QplFYWTTn2fTpFUYYAL2P60Bf5I7ffeQOwFOivquprwC9ArUJpZdH0\nsN/59M96d2C8oij2T7l9RZaiKBOAZUDJLM8Xyjm2uBU57sBOAFVVg4EmGV57EbioqmqUqqrJwK9A\nq6ffxCIrt75PAFqoqpqQ9rgEEP90m1ek5db3KIrSEmgK+GEaVRD5I7d+dwXuAeMURdkPlFNVVX3q\nLSy6cv2dB5KBcoAtpt95Ke7zz0XgXbJ/lhTKOba4FTllgegMj41pw5rpr0VleO0BphEFkT9y7HtV\nVTVVVcMBFEUZCZRWVXVPIbSxqMqx7xVFcQImAyOQAie/5fZ5UwFoCSwE2gL/VBSlDSK/5Nb3YBrZ\nCQXOAD+pqppxW/EEVFXdjOlyVFaFco4tbkVONFAmw2MLVVVT0/4dleW1MsD9p9WwYiC3vkdRFAtF\nUeYB/wS6Pe3GFXG59X13TCfc7cDHgJeiKP2ecvuKqtz6/R6mv2pVVVVTMI06ZB1tEI8vx75XFKU6\npqK+BlATqKQoSven3sLip1DOscWtyPkN6ASgKEpz4FSG184BdRVFKa8oijWmYbTDT7+JRVZufQ+m\nSyUlga4ZLluJ/JFj36uqulBV1SZpEwRnA+tVVV1dOM0scnL7nb8E2CmK4pL2+DVMowoif+TW9zaA\nEUhMK3zuYLp0JQpWoZxji1VAZ9pkv/QZ9wDvA40BO1VVlymK0gXT0L0FsEJV1SWF09KiJ7e+B46l\nfQVleMsCVVV/eKqNLKIe9nufYbv3AEVV1YlPv5VFTx4+b9ILSwPwm6qqYwunpUVPHvp+LOCFaT7g\nRWBQ2oiayAeKotTE9AdTy7Q7ZwvtHFusihwhhBBCFB/F7XKVEEIIIYoJKXKEEEIIUSRJkSOEEEKI\nIkmKHCGEEEIUSVLkCCGEEKJIkiJHCCGEEEVSsUohF0L8T9paFueBs1le6qKq6o0c3jMF0FRVnfoE\nx+2PKajvStpTtsABTMnzxkfc11QgRFXVrYqi7EtPPVYU5YSqqv943Dam7WM/4AzEpD1VFtMifn1U\nVb2Ty/sGA9Gqqvo/yfGFEE9Oihwhircbj1gM5MfCWhrwg6qq/wZTpAewH/gA+PZRdqSq6ucZHrbO\n8PwTFThpNGCAqqpBoC8w93/AOEzJ1jlpCezLh+MLIZ6QFDlCiGwURXkZU8FhB1QE5ququjDD6yWA\n74CX0p5arKrqckVRKgG+QDUgFfhEVdVfzBxCDwNVVTVVUZTDQN20fb+PqZDQMIUojgCSgJVmjrcK\nU0HRKO29h1VVbaEoSipgBVwDGqqqekdRFAfgNFAdaAdMTdvmb0wr3kbk1s60vqgAHEk7Vo+0dtqm\nfQ0ErIE3gTaKotzEFCfgB1R9SH8IIQqAzMkRoniroijKiQxf49OeHwBMV1W1KfAGMDPL+1oC5VVV\nbYQpRbtl2vMLgJWqqjYB3gb8FEWxy60BiqK8AHQEflMU5RVgItBKVdX6QCzwOdAih+NpmC6fjQZQ\nVbVF+n7TLn0FAD3SnuoGBALlgVlA+7T9/Qx8aaZpBmC5oign0wqWw2nbfp02+jQE6KyqasO093+U\nVsBsASapqro7rT9WPEp/CCHyj4zkCFG83czh0s544F+KovgADYDSac8bMBUWZwBFUZSdmBLM0y/f\ntE17flra4xJAbTIHJBqAtxRFOZH2bwvge1VV/RVFGQFsUVU1PZ14KaYRo9k5HO9h1gDfAP8BemMq\noJpjGs3ZrygKgCWmVPCs9MtViqK0AL4HdqRnHCmK0jXt+1AwXSozl32Ul/4QQhQQKXKEEOZswnTi\n/wnwB3pmfFFV1QhFUV7CdNmnE3A87bEF0EZV1UgARVGcgbAs+9aAH9Pn5GRhIPMlIgugRC7Hy5Wq\nqqGKojgoivIq4Kyq6hFFUd4GflVV9e20NtoAZXLYhSFtP4cVRfkWWK0oSn2gFKZQ2f9imk/0O6bL\nalnlpT+EEAVELlcJIcxpC3yuqupPwOugTxAm7d+dgbWqqm4DRmO6A6kasBfTBGLSipDfMc1XyShr\nIZPRfkyjI+XTHg8C9uZyvIyMiqJYmtnnOkzzYjakPQ4GWiiKUjft8WfAnBzak3Gi9VeYRrSGYZo/\nZMR02Ws/psIr/dgpmOb6QN76QwhRQKTIEaJ4y+luqSnAr4qi/Aa4AX8CtdK214BdQJyiKGcxFQ3f\nq6p6BhgJNFcU5XdMRUUfVVVjzRzT7HFVVT2NqXA4oCjKn5hu2/4sl+Nl9CNwUlGUkln2vw6oD6xN\nO8Yt4N9AgKIop4B/YJpAnCtVVZOAT4HJwF/AybR+OYDp8lP1tE33ABMVRXk3j/0hhCggBk3LjztC\nhRBCCCGeLTKSI4QQQogiSYocIYQQQhRJUuQIIYQQokiSIkcIIYQQRZIUOUIIIYQokqTIEUIIIUSR\nJEWOEEIIIYqk/weitGLxXcIFFAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b0bd390>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['blue', 'red', 'black', 'green', 'cyan', 'magenta']\n",
      "linestyles = ['-', '--', '-.', ':', '-', '--']\n",
      "classifiers = [pipelines[i] for i in range(6,12)]\n",
      "labels = ['1: all words', \n",
      "          '2: porter stemmer',\n",
      "          '3: snowball stemmer',\n",
      "          '4: whitelist',\n",
      "          '5: porter stemmer + whitelist',\n",
      "          '6: snowball stemmer + whitelist',\n",
      "          ]\n",
      "\n",
      "for clf,col,ls,lab in zip(classifiers, colors, linestyles, labels):\n",
      "    \n",
      "    mean_tpr = 0.0\n",
      "    mean_fpr = np.linspace(0, 1, 100)\n",
      "    all_tpr = []\n",
      "    cv = StratifiedKFold(y_train, n_folds=10, random_state=123)\n",
      "\n",
      "    for i, (train, test) in enumerate(cv):\n",
      "        probas_ = clf.fit(X_train[train], y_train[train]).predict_proba(X_train[test])\n",
      "        # Compute ROC curve and area the curve\n",
      "        fpr, tpr, thresholds = roc_curve(y_train[test], probas_[:, 1])\n",
      "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
      "        mean_tpr[0] = 0.0\n",
      "        roc_auc = auc(fpr, tpr)\n",
      "\n",
      "    mean_tpr /= len(cv)\n",
      "    mean_tpr[-1] = 1.0\n",
      "    mean_auc = auc(mean_fpr, mean_tpr)\n",
      "    plt.plot(mean_fpr, \n",
      "             mean_tpr, \n",
      "             color=col, \n",
      "             linestyle=ls,\n",
      "             label='%s (ROC AUC = %0.2f)' % (lab, mean_auc), \n",
      "             lw=2\n",
      "    )\n",
      "\n",
      "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random Guessing')    \n",
      "plt.xlim([-0.05, 1.05])\n",
      "plt.ylim([-0.05, 1.05])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.title('TfidfVectorizer')\n",
      "plt.tight_layout()\n",
      "plt.savefig('./nb_whitelist_model_images/roc_gridsearch_2.eps', dpi=300)\n",
      "plt.show()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9/uHNhucaVzOm9Zhc6SXAKdjp06f5448/ZFp4MZIgRwghgJSUFDp16EBcfDwh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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b9ad0f0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['blue', 'red', 'black', 'green', 'cyan', 'magenta']\n",
      "linestyles = ['-', '--', '-.', ':', '-', '--']\n",
      "classifiers = [pipelines[i] for i in range(12,18)]\n",
      "labels = ['1: all words', \n",
      "          '2: porter stemmer',\n",
      "          '3: snowball stemmer',\n",
      "          '4: whitelist',\n",
      "          '5: porter stemmer + whitelist',\n",
      "          '6: snowball stemmer + whitelist',\n",
      "          ]\n",
      "\n",
      "plt.figure(figsize=(8,6))\n",
      "for clf,col,ls,lab in zip(classifiers, colors, linestyles, labels):\n",
      "    \n",
      "    mean_tpr = 0.0\n",
      "    mean_fpr = np.linspace(0, 1, 100)\n",
      "    all_tpr = []\n",
      "    cv = StratifiedKFold(y_train, n_folds=10, random_state=123)\n",
      "\n",
      "    for i, (train, test) in enumerate(cv):\n",
      "        probas_ = clf.fit(X_train[train], y_train[train]).predict_proba(X_train[test])\n",
      "        # Compute ROC curve and area the curve\n",
      "        fpr, tpr, thresholds = roc_curve(y_train[test], probas_[:, 1])\n",
      "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
      "        mean_tpr[0] = 0.0\n",
      "        roc_auc = auc(fpr, tpr)\n",
      "\n",
      "    mean_tpr /= len(cv)\n",
      "    mean_tpr[-1] = 1.0\n",
      "    mean_auc = auc(mean_fpr, mean_tpr)\n",
      "    plt.plot(mean_fpr, \n",
      "             mean_tpr, \n",
      "             color=col, \n",
      "             linestyle=ls,\n",
      "             label='%s (ROC AUC = %0.2f)' % (lab, mean_auc), \n",
      "             lw=2\n",
      "    )\n",
      "\n",
      "\n",
      "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random Guessing')    \n",
      "plt.xlim([-0.05, 1.05])\n",
      "plt.ylim([-0.05, 1.05])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.title('Bernoulli')\n",
      "plt.tight_layout()\n",
      "plt.savefig('./nb_whitelist_model_images/roc_gridsearch_3.eps', dpi=300)\n",
      "plt.show()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Lbffh8pKL1luzNQd2nYNVhRGMF/Ai8B/ApRw2272QfYGu87uS9koaBjvtV9DQ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OcI+i1wycnJNGjQgHbt2olgTUHIhZSUFL777jtsbGxe6DymNrz7+OOPadGiBUCu9zPp378/NWrU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\nIwiCIAhCkSSKHEEQBEEQiqT/B8jA77+ZjEO8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b698710>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics import roc_curve, auc\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "from sklearn.cross_validation import StratifiedKFold\n",
      "from scipy import interp\n",
      "\n",
      "sns.set()\n",
      "sns.set_style(\"whitegrid\")\n",
      "\n",
      "classifier = pipelines[7] # Tfidf + porter\n",
      "\n",
      "\n",
      "cv = StratifiedKFold(y_train, n_folds=10, random_state=123)\n",
      "\n",
      "mean_tpr = 0.0\n",
      "mean_fpr = np.linspace(0, 1, 100)\n",
      "all_tpr = []\n",
      "\n",
      "for i, (train, test) in enumerate(cv):\n",
      "    probas_ = classifier.fit(X_train[train], y_train[train]).predict_proba(X_train[test])\n",
      "    # Compute ROC curve and area the curve\n",
      "    fpr, tpr, thresholds = roc_curve(y_train[test], probas_[:, 1])\n",
      "    mean_tpr += interp(mean_fpr, fpr, tpr)\n",
      "    mean_tpr[0] = 0.0\n",
      "    roc_auc = auc(fpr, tpr)\n",
      "    plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i+1, roc_auc))\n",
      "\n",
      "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random Guessing')\n",
      "\n",
      "mean_tpr /= len(cv)\n",
      "mean_tpr[-1] = 1.0\n",
      "mean_auc = auc(mean_fpr, mean_tpr)\n",
      "plt.plot(mean_fpr, mean_tpr, 'k--',\n",
      "         label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)\n",
      "\n",
      "plt.xlim([-0.05, 1.05])\n",
      "plt.ylim([-0.05, 1.05])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.title('Receiver Operator Characteristic of the Lyrics Classifier')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.savefig('./nb_whitelist_model_images/roc_final.eps', dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h1Wtz600jCrU/d/435n7+fKHpEfVb8PvBuoWmn/3zN+Lj4/Hz9UfvO1luv2VPJe/DQEul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\nAImJibm3KVzNmeT9IkbC3g8MB9YCzj3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yMkePHiUiIoIqVarQtGlTeSBNCFGkIi3Cxhg+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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b3554a8>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['blue', 'red', 'black', 'green', 'cyan', 'magenta']\n",
      "linestyles = ['-', '--', '-.', ':', '-', '--']\n",
      "classifiers = [pipelines[12], pipelines[0], pipelines[6]]\n",
      "labels = ['1: BNB', \n",
      "          '2: MNB Tf',\n",
      "          '3: MNB Tfidf',\n",
      "          ]\n",
      "\n",
      "plt.figure(figsize=(8,6))\n",
      "\n",
      "for clf,col,ls,lab in zip(classifiers, colors, linestyles, labels):\n",
      "    \n",
      "    mean_tpr = 0.0\n",
      "    mean_fpr = np.linspace(0, 1, 100)\n",
      "    all_tpr = []\n",
      "    cv = StratifiedKFold(y_train, n_folds=10, random_state=123)\n",
      "\n",
      "    for i, (train, test) in enumerate(cv):\n",
      "        probas_ = clf.fit(X_train[train], y_train[train]).predict_proba(X_train[test])\n",
      "        # Compute ROC curve and area the curve\n",
      "        fpr, tpr, thresholds = roc_curve(y_train[test], probas_[:, 1])\n",
      "        mean_tpr += interp(mean_fpr, fpr, tpr)\n",
      "        mean_tpr[0] = 0.0\n",
      "        roc_auc = auc(fpr, tpr)\n",
      "\n",
      "    mean_tpr /= len(cv)\n",
      "    mean_tpr[-1] = 1.0\n",
      "    mean_auc = auc(mean_fpr, mean_tpr)\n",
      "    plt.plot(mean_fpr, \n",
      "             mean_tpr, \n",
      "             color=col, \n",
      "             linestyle=ls,\n",
      "             label='%s (ROC AUC = %0.2f)' % (lab, mean_auc), \n",
      "             lw=2\n",
      "    )\n",
      "\n",
      "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random Guessing')    \n",
      "plt.xlim([-0.05, 1.05])\n",
      "plt.ylim([-0.05, 1.05])\n",
      "plt.xlabel('False Positive Rate')\n",
      "plt.ylabel('True Positive Rate')\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.title('Different Naive Bayes Models')\n",
      "plt.tight_layout()\n",
      "plt.savefig('./nb_whitelist_model_images/roc_different_nb.eps', dpi=300)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VbNu2DbvdTvv27alcubK3wyrVJMkRQpQ6DoeDu+66i9mzZ5Oens6+ffvcBg7b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\nK0fjHwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b634320>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Validation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Confusion matrix"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib as mpl\n",
      "import numpy as np\n",
      "\n",
      "def plot_cm(X, y, clf, title, dest):\n",
      "\n",
      "    cm = metrics.confusion_matrix(y, clf.predict(X))\n",
      "\n",
      "    np.set_printoptions(suppress=True)\n",
      "    mpl.rc(\"figure\", figsize=(4, 2))\n",
      "\n",
      "    hm = sns.heatmap(cm, \n",
      "            cbar=False,\n",
      "            annot=True, \n",
      "            square=True,\n",
      "            fmt='d',\n",
      "            yticklabels=['happy','sad'],\n",
      "            xticklabels=['happy','sad'],\n",
      "            cmap='Blues'\n",
      "            )\n",
      "    plt.title(title)\n",
      "    plt.ylabel('actual class')\n",
      "    plt.xlabel('predicted class')\n",
      "    plt.tight_layout()\n",
      "    plt.savefig(dest, dpi=300)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = pipelines[7] # Tfidf + porter\n",
      "\n",
      "clf.fit(X_train, y_train)\n",
      "\n",
      "plot_cm(X_train, y_train, clf, 'Training - TfidfVectorizer Porter', './nb_whitelist_model_images/confmat_tfidf_porter_train.eps')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10c00b240>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_cm(X_test, y_test, clf, 'Test - TfidfVectorizer Porter', './nb_whitelist_model_images/confmat_tfidf_porter_test.eps')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zNmH+4s9o2CCbd564nuZNG3Ha4HvZcbtcOrfflblvf8pxXfZmi5z6sdpYlUQR3wlAWwBJ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\nxMb/A3w7fqcH6nTmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bf520b8>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "ROC AUC, Accuracy, Precision, Recall, and F1-score"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[[back to top](#Sections)]"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.naive_bayes import BernoulliNB"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Custom scorer methods to account for positive-negative class labels\n",
      "\n",
      "from sklearn import metrics\n",
      "\n",
      "# `pos_label` for positive class, since we have sad=1, happy=0\n",
      "\n",
      "acc_scorer = metrics.make_scorer(metrics.accuracy_score, greater_is_better=True)\n",
      "pre_scorer = metrics.make_scorer(metrics.precision_score, greater_is_better=True, pos_label=0)\n",
      "rec_scorer = metrics.make_scorer(metrics.recall_score, greater_is_better=True, pos_label=0)\n",
      "f1_scorer = metrics.make_scorer(metrics.f1_score, greater_is_better=True, pos_label=0)\n",
      "auc_scorer = metrics.make_scorer(metrics.roc_auc_score, greater_is_better=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "labels = ['BNB Training', 'BNB Training porter', 'BNB Training wl', 'BNB Training porter+wl',\n",
      "          'MNB Training-CountVec', 'MNB Training-CountVec porter', 'MNB Training-CountVec wl', 'MNB Training-CountVec porter+wl',\n",
      "          'MNB Training-TfidfVec', 'MNB Training-TfidfVec porter', 'MNB Training-TfidfVec wl', 'MNB Training-TfidfVec porter+wl',\n",
      "          'BNB Test', 'BNB Test porter', 'BNB Test wl', 'BNB Test porter+wl',\n",
      "          'MNB Test-CountVec', 'MNB Test-CountVec porter', 'MNB Test-CountVec wl', 'MNB Test-CountVec porter+wl',\n",
      "          'MNB Test-TfidfVec', 'MNB Test-TfidfVec porter', 'MNB Test-TfidfVec wl', 'MNB Test-TfidfVec porter+wl'\n",
      "          ]\n",
      "\n",
      "d = {'Data':labels,\n",
      "     'ACC (%)':[],\n",
      "     'PRE (%)':[],\n",
      "     'REC (%)':[],\n",
      "     'F1 (%)':[],\n",
      "     'ROC AUC (%)':[],\n",
      "}\n",
      "\n",
      "\n",
      "for clf in [clf_9, clf_10, clf_11, clf_12, clf_1, clf_2, clf_3, clf_4, clf_5, clf_6, clf_7, clf_8]:\n",
      "    clf.fit(X_train, y_train)\n",
      "\n",
      "for clf in [clf_9, clf_10, clf_11, clf_12, clf_1, clf_2, clf_3, clf_4, clf_5, clf_6, clf_7, clf_8]:\n",
      "\n",
      "    d['ACC (%)'].append(acc_scorer(estimator=clf, X=X_train, y_true=y_train))\n",
      "    d['PRE (%)'].append(pre_scorer(estimator=clf, X=X_train, y_true=y_train))\n",
      "    d['REC (%)'].append(rec_scorer(estimator=clf, X=X_train, y_true=y_train))\n",
      "    d['F1 (%)'].append(f1_scorer(estimator=clf, X=X_train, y_true=y_train))\n",
      "    d['ROC AUC (%)'].append(auc_scorer(estimator=clf, X=X_train, y_true=y_train))\n",
      "\n",
      "for clf in [clf_9, clf_10, clf_11, clf_12, clf_1, clf_2, clf_3, clf_4, clf_5, clf_6, clf_7, clf_8]:  \n",
      "\n",
      "    d['ACC (%)'].append(acc_scorer(estimator=clf, X=X_test, y_true=y_test))\n",
      "    d['PRE (%)'].append(pre_scorer(estimator=clf, X=X_test, y_true=y_test))\n",
      "    d['REC (%)'].append(rec_scorer(estimator=clf, X=X_test, y_true=y_test))\n",
      "    d['F1 (%)'].append(f1_scorer(estimator=clf, X=X_test, y_true=y_test))\n",
      "    d['ROC AUC (%)'].append(auc_scorer(estimator=clf, X=X_test, y_true=y_test))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.set_option('precision', 2)\n",
      "\n",
      "df_perform = pd.DataFrame(d)\n",
      "df_perform = df_perform[['ACC (%)', 'PRE (%)', 'REC (%)', 'F1 (%)', 'ROC AUC (%)']]\n",
      "df_perform.index=(labels)\n",
      "df_perform = df_perform*100\n",
      "df_perform = np.round(df_perform, decimals=2)\n",
      "df_perform"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ACC (%)</th>\n",
        "      <th>PRE (%)</th>\n",
        "      <th>REC (%)</th>\n",
        "      <th>F1 (%)</th>\n",
        "      <th>ROC AUC (%)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BNB Training</th>\n",
        "      <td> 96.5</td>\n",
        "      <td> 93.8</td>\n",
        "      <td> 98.7</td>\n",
        "      <td> 96.2</td>\n",
        "      <td> 96.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Training porter</th>\n",
        "      <td> 93.4</td>\n",
        "      <td> 89.3</td>\n",
        "      <td> 96.9</td>\n",
        "      <td> 92.9</td>\n",
        "      <td> 93.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Training wl</th>\n",
        "      <td> 81.1</td>\n",
        "      <td> 72.1</td>\n",
        "      <td> 94.0</td>\n",
        "      <td> 81.6</td>\n",
        "      <td> 82.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Training porter+wl</th>\n",
        "      <td> 78.8</td>\n",
        "      <td> 70.0</td>\n",
        "      <td> 91.9</td>\n",
        "      <td> 79.5</td>\n",
        "      <td> 80.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-CountVec</th>\n",
        "      <td> 95.7</td>\n",
        "      <td> 94.1</td>\n",
        "      <td> 96.4</td>\n",
        "      <td> 95.2</td>\n",
        "      <td> 95.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-CountVec porter</th>\n",
        "      <td> 93.6</td>\n",
        "      <td> 92.8</td>\n",
        "      <td> 92.8</td>\n",
        "      <td> 92.8</td>\n",
        "      <td> 93.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-CountVec wl</th>\n",
        "      <td> 81.2</td>\n",
        "      <td> 75.8</td>\n",
        "      <td> 85.0</td>\n",
        "      <td> 80.1</td>\n",
        "      <td> 81.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-CountVec porter+wl</th>\n",
        "      <td> 80.5</td>\n",
        "      <td> 75.9</td>\n",
        "      <td> 82.5</td>\n",
        "      <td> 79.0</td>\n",
        "      <td> 80.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-TfidfVec</th>\n",
        "      <td> 90.5</td>\n",
        "      <td> 99.7</td>\n",
        "      <td> 78.9</td>\n",
        "      <td> 88.1</td>\n",
        "      <td> 89.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-TfidfVec porter</th>\n",
        "      <td> 86.2</td>\n",
        "      <td> 99.0</td>\n",
        "      <td> 69.7</td>\n",
        "      <td> 81.8</td>\n",
        "      <td> 84.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-TfidfVec wl</th>\n",
        "      <td> 84.8</td>\n",
        "      <td> 82.0</td>\n",
        "      <td> 84.5</td>\n",
        "      <td> 83.2</td>\n",
        "      <td> 84.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Training-TfidfVec porter+wl</th>\n",
        "      <td> 83.2</td>\n",
        "      <td> 80.5</td>\n",
        "      <td> 82.3</td>\n",
        "      <td> 81.4</td>\n",
        "      <td> 83.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Test</th>\n",
        "      <td> 70.5</td>\n",
        "      <td> 75.0</td>\n",
        "      <td> 65.7</td>\n",
        "      <td> 70.0</td>\n",
        "      <td> 70.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Test porter</th>\n",
        "      <td> 70.0</td>\n",
        "      <td> 74.2</td>\n",
        "      <td> 65.7</td>\n",
        "      <td> 69.7</td>\n",
        "      <td> 70.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Test wl</th>\n",
        "      <td> 65.5</td>\n",
        "      <td> 65.2</td>\n",
        "      <td> 73.3</td>\n",
        "      <td> 69.1</td>\n",
        "      <td> 65.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BNB Test porter+wl</th>\n",
        "      <td> 67.0</td>\n",
        "      <td> 67.0</td>\n",
        "      <td> 73.3</td>\n",
        "      <td> 70.0</td>\n",
        "      <td> 66.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-CountVec</th>\n",
        "      <td> 72.0</td>\n",
        "      <td> 78.8</td>\n",
        "      <td> 63.8</td>\n",
        "      <td> 70.5</td>\n",
        "      <td> 72.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-CountVec porter</th>\n",
        "      <td> 68.0</td>\n",
        "      <td> 76.0</td>\n",
        "      <td> 57.1</td>\n",
        "      <td> 65.2</td>\n",
        "      <td> 68.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-CountVec wl</th>\n",
        "      <td> 64.5</td>\n",
        "      <td> 66.7</td>\n",
        "      <td> 64.8</td>\n",
        "      <td> 65.7</td>\n",
        "      <td> 64.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-CountVec porter+wl</th>\n",
        "      <td> 63.5</td>\n",
        "      <td> 66.0</td>\n",
        "      <td> 62.9</td>\n",
        "      <td> 64.4</td>\n",
        "      <td> 63.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-TfidfVec</th>\n",
        "      <td> 59.5</td>\n",
        "      <td> 80.0</td>\n",
        "      <td> 30.5</td>\n",
        "      <td> 44.1</td>\n",
        "      <td> 61.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-TfidfVec porter</th>\n",
        "      <td> 60.5</td>\n",
        "      <td> 84.2</td>\n",
        "      <td> 30.5</td>\n",
        "      <td> 44.8</td>\n",
        "      <td> 62.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-TfidfVec wl</th>\n",
        "      <td> 65.5</td>\n",
        "      <td> 70.5</td>\n",
        "      <td> 59.0</td>\n",
        "      <td> 64.2</td>\n",
        "      <td> 65.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MNB Test-TfidfVec porter+wl</th>\n",
        "      <td> 63.5</td>\n",
        "      <td> 68.6</td>\n",
        "      <td> 56.2</td>\n",
        "      <td> 61.8</td>\n",
        "      <td> 63.9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "                                 ACC (%)  PRE (%)  REC (%)  F1 (%)  \\\n",
        "BNB Training                        96.5     93.8     98.7    96.2   \n",
        "BNB Training porter                 93.4     89.3     96.9    92.9   \n",
        "BNB Training wl                     81.1     72.1     94.0    81.6   \n",
        "BNB Training porter+wl              78.8     70.0     91.9    79.5   \n",
        "MNB Training-CountVec               95.7     94.1     96.4    95.2   \n",
        "MNB Training-CountVec porter        93.6     92.8     92.8    92.8   \n",
        "MNB Training-CountVec wl            81.2     75.8     85.0    80.1   \n",
        "MNB Training-CountVec porter+wl     80.5     75.9     82.5    79.0   \n",
        "MNB Training-TfidfVec               90.5     99.7     78.9    88.1   \n",
        "MNB Training-TfidfVec porter        86.2     99.0     69.7    81.8   \n",
        "MNB Training-TfidfVec wl            84.8     82.0     84.5    83.2   \n",
        "MNB Training-TfidfVec porter+wl     83.2     80.5     82.3    81.4   \n",
        "BNB Test                            70.5     75.0     65.7    70.0   \n",
        "BNB Test porter                     70.0     74.2     65.7    69.7   \n",
        "BNB Test wl                         65.5     65.2     73.3    69.1   \n",
        "BNB Test porter+wl                  67.0     67.0     73.3    70.0   \n",
        "MNB Test-CountVec                   72.0     78.8     63.8    70.5   \n",
        "MNB Test-CountVec porter            68.0     76.0     57.1    65.2   \n",
        "MNB Test-CountVec wl                64.5     66.7     64.8    65.7   \n",
        "MNB Test-CountVec porter+wl         63.5     66.0     62.9    64.4   \n",
        "MNB Test-TfidfVec                   59.5     80.0     30.5    44.1   \n",
        "MNB Test-TfidfVec porter            60.5     84.2     30.5    44.8   \n",
        "MNB Test-TfidfVec wl                65.5     70.5     59.0    64.2   \n",
        "MNB Test-TfidfVec porter+wl         63.5     68.6     56.2    61.8   \n",
        "\n",
        "                                 ROC AUC (%)  \n",
        "BNB Training                            96.7  \n",
        "BNB Training porter                     93.7  \n",
        "BNB Training wl                         82.3  \n",
        "BNB Training porter+wl                  80.1  \n",
        "MNB Training-CountVec                   95.8  \n",
        "MNB Training-CountVec porter            93.5  \n",
        "MNB Training-CountVec wl                81.6  \n",
        "MNB Training-CountVec porter+wl         80.7  \n",
        "MNB Training-TfidfVec                   89.4  \n",
        "MNB Training-TfidfVec porter            84.6  \n",
        "MNB Training-TfidfVec wl                84.8  \n",
        "MNB Training-TfidfVec porter+wl         83.1  \n",
        "BNB Test                                70.8  \n",
        "BNB Test porter                         70.2  \n",
        "BNB Test wl                             65.1  \n",
        "BNB Test porter+wl                      66.7  \n",
        "MNB Test-CountVec                       72.4  \n",
        "MNB Test-CountVec porter                68.6  \n",
        "MNB Test-CountVec wl                    64.5  \n",
        "MNB Test-CountVec porter+wl             63.5  \n",
        "MNB Test-TfidfVec                       61.0  \n",
        "MNB Test-TfidfVec porter                62.1  \n",
        "MNB Test-TfidfVec wl                    65.8  \n",
        "MNB Test-TfidfVec porter+wl             63.9  "
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_perform.to_csv('./nb_whitelist_model_data/clf_performance.csv', index_label=False, float_format='%2.2f')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Cross Validation Scores"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "from sklearn import cross_validation\n",
      "\n",
      "# Since cross-validation, use whole dataset\n",
      "X_train = np.concatenate((X_train, X_test))\n",
      "y_train = np.concatenate((y_train, y_test))\n",
      "\n",
      "labels = ['CountVec', 'CountVec porter', 'CountVec wl', 'CountVec porter+wl',\n",
      "             'TfidfVec', 'TfidfVec porter', 'TfidfVec wl', 'TfidfVec porter+wl']\n",
      "\n",
      "d = {'Data':labels,\n",
      "     'ACC (%)':[],\n",
      "     'PRE (%)':[],\n",
      "     'REC (%)':[],\n",
      "     'F1 (%)':[],\n",
      "     'ROC AUC (%)':[],\n",
      "}\n",
      "\n",
      "for clf in [clf_1, clf_2, clf_3, clf_4, clf_5, clf_6, clf_7, clf_8]:\n",
      "    clf.fit(X_train, y_train)\n",
      "    \n",
      "   \n",
      "    scores = cross_validation.cross_val_score(estimator=clf, X=X_train, y=y_train, scoring=acc_scorer, cv=10)\n",
      "    d['ACC (%)'].append('%0.2f (+/- %0.2f)' % (scores.mean()*100, scores.std()*100))\n",
      "    \n",
      "    scores = cross_validation.cross_val_score(estimator=clf, X=X_train, y=y_train, scoring=pre_scorer, cv=10)\n",
      "    d['PRE (%)'].append('%0.2f (+/- %0.2f)' % (scores.mean()*100, scores.std()*100))\n",
      "    \n",
      "    scores = cross_validation.cross_val_score(estimator=clf, X=X_train, y=y_train, scoring=rec_scorer, cv=10)\n",
      "    d['REC (%)'].append('%0.2f (+/- %0.2f)' % (scores.mean()*100, scores.std()*100))\n",
      "    \n",
      "    scores = cross_validation.cross_val_score(estimator=clf, X=X_train, y=y_train, scoring=f1_scorer, cv=10)\n",
      "    d['F1 (%)'].append('%0.2f (+/- %0.2f)' % (scores.mean()*100, scores.std()*100))\n",
      "    \n",
      "    scores = cross_validation.cross_val_score(estimator=clf, X=X_train, y=y_train, scoring=auc_scorer, cv=10)\n",
      "    d['ROC AUC (%)'].append('%0.2f (+/- %0.2f)' % (scores.mean()*100, scores.std()*100))\n",
      "\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_perform = pd.DataFrame(d)\n",
      "df_perform = df_perform[['ACC (%)', 'PRE (%)', 'REC (%)', 'F1 (%)', 'ROC AUC (%)']]\n",
      "df_perform.index=(labels)\n",
      "df_perform"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ACC (%)</th>\n",
        "      <th>PRE (%)</th>\n",
        "      <th>REC (%)</th>\n",
        "      <th>F1 (%)</th>\n",
        "      <th>ROC AUC (%)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CountVec</th>\n",
        "      <td> 67.00 (+/- 4.07)</td>\n",
        "      <td> 65.30 (+/- 4.80)</td>\n",
        "      <td> 60.06 (+/- 6.20)</td>\n",
        "      <td> 62.48 (+/- 5.17)</td>\n",
        "      <td> 66.48 (+/- 4.16)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CountVec porter</th>\n",
        "      <td> 67.66 (+/- 2.76)</td>\n",
        "      <td> 66.78 (+/- 3.68)</td>\n",
        "      <td> 59.16 (+/- 6.31)</td>\n",
        "      <td> 62.54 (+/- 4.27)</td>\n",
        "      <td> 67.02 (+/- 2.89)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CountVec wl</th>\n",
        "      <td> 64.83 (+/- 5.30)</td>\n",
        "      <td> 60.72 (+/- 5.07)</td>\n",
        "      <td> 67.32 (+/- 8.33)</td>\n",
        "      <td> 63.63 (+/- 5.60)</td>\n",
        "      <td> 65.01 (+/- 5.26)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CountVec porter+wl</th>\n",
        "      <td> 66.49 (+/- 4.26)</td>\n",
        "      <td> 62.52 (+/- 4.70)</td>\n",
        "      <td> 68.41 (+/- 7.88)</td>\n",
        "      <td> 65.09 (+/- 4.93)</td>\n",
        "      <td> 66.64 (+/- 4.26)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TfidfVec</th>\n",
        "      <td> 67.75 (+/- 4.18)</td>\n",
        "      <td> 76.67 (+/- 6.99)</td>\n",
        "      <td> 42.63 (+/- 7.36)</td>\n",
        "      <td> 54.56 (+/- 7.41)</td>\n",
        "      <td> 65.85 (+/- 4.39)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TfidfVec porter</th>\n",
        "      <td> 67.09 (+/- 4.30)</td>\n",
        "      <td> 78.91 (+/- 6.08)</td>\n",
        "      <td> 38.47 (+/- 9.51)</td>\n",
        "      <td> 51.13 (+/- 9.57)</td>\n",
        "      <td> 64.92 (+/- 4.69)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TfidfVec wl</th>\n",
        "      <td> 64.41 (+/- 3.93)</td>\n",
        "      <td> 61.30 (+/- 4.08)</td>\n",
        "      <td> 61.70 (+/- 6.78)</td>\n",
        "      <td> 61.32 (+/- 4.52)</td>\n",
        "      <td> 64.21 (+/- 3.94)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TfidfVec porter+wl</th>\n",
        "      <td> 66.75 (+/- 3.73)</td>\n",
        "      <td> 63.76 (+/- 4.15)</td>\n",
        "      <td> 64.61 (+/- 7.09)</td>\n",
        "      <td> 63.96 (+/- 4.47)</td>\n",
        "      <td> 66.59 (+/- 3.76)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "                             ACC (%)           PRE (%)           REC (%)  \\\n",
        "CountVec            67.00 (+/- 4.07)  65.30 (+/- 4.80)  60.06 (+/- 6.20)   \n",
        "CountVec porter     67.66 (+/- 2.76)  66.78 (+/- 3.68)  59.16 (+/- 6.31)   \n",
        "CountVec wl         64.83 (+/- 5.30)  60.72 (+/- 5.07)  67.32 (+/- 8.33)   \n",
        "CountVec porter+wl  66.49 (+/- 4.26)  62.52 (+/- 4.70)  68.41 (+/- 7.88)   \n",
        "TfidfVec            67.75 (+/- 4.18)  76.67 (+/- 6.99)  42.63 (+/- 7.36)   \n",
        "TfidfVec porter     67.09 (+/- 4.30)  78.91 (+/- 6.08)  38.47 (+/- 9.51)   \n",
        "TfidfVec wl         64.41 (+/- 3.93)  61.30 (+/- 4.08)  61.70 (+/- 6.78)   \n",
        "TfidfVec porter+wl  66.75 (+/- 3.73)  63.76 (+/- 4.15)  64.61 (+/- 7.09)   \n",
        "\n",
        "                              F1 (%)       ROC AUC (%)  \n",
        "CountVec            62.48 (+/- 5.17)  66.48 (+/- 4.16)  \n",
        "CountVec porter     62.54 (+/- 4.27)  67.02 (+/- 2.89)  \n",
        "CountVec wl         63.63 (+/- 5.60)  65.01 (+/- 5.26)  \n",
        "CountVec porter+wl  65.09 (+/- 4.93)  66.64 (+/- 4.26)  \n",
        "TfidfVec            54.56 (+/- 7.41)  65.85 (+/- 4.39)  \n",
        "TfidfVec porter     51.13 (+/- 9.57)  64.92 (+/- 4.69)  \n",
        "TfidfVec wl         61.32 (+/- 4.52)  64.21 (+/- 3.94)  \n",
        "TfidfVec porter+wl  63.96 (+/- 4.47)  66.59 (+/- 3.76)  "
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}