Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CATALOGIQUE\n",
    "### PUBLICATION TOPIC CLASSIFACTION\n",
    "#### We organize your stuff so you don't have to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### problem statement:\n",
    "Our client at Elsevier is looking for a better way to clasify their scientific articles. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. (soruce: https://www.elsevier.com/about). With the large volume of scientific research articles flowing into the pipeline, we expertly classify and store your data for quick and efficient searching."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA and Modeling on combined dataframes\n",
    "#### In this notebook, we will import our cleaned data and combine dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from nltk.tokenize import word_tokenize\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.feature_extraction import text\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import clean datasets created in Cleaning notebook\n",
    "And inpsect head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multiplex vaccine</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>be there a biology equivalent of the american ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>be in   grade ap biology and the online progr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>virus dna</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>help for exam</td>\n",
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      "text/plain": [
       "                                               title  class_label\n",
       "0                                  multiplex vaccine            0\n",
       "1  be there a biology equivalent of the american ...            0\n",
       "2   be in   grade ap biology and the online progr...            0\n",
       "3                                          virus dna            0\n",
       "4                                      help for exam            0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bio_df = pd.read_csv('datasets/bio_clean.csv')\n",
    "bio_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>re all genius</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>what s the space fill with</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>restructure of physics undergrad a dialogue</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>help please</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>think of get a phd in quantum mechanic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  class_label\n",
       "0                                re all genius            1\n",
       "1                   what s the space fill with            1\n",
       "2  restructure of physics undergrad a dialogue            1\n",
       "3                                  help please            1\n",
       "4       think of get a phd in quantum mechanic            1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phys_df = pd.read_csv('datasets/phys_clean.csv')\n",
    "phys_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenate biology and physics subreddit data frames\n",
    "Inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>title</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multiplex vaccine</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>be there a biology equivalent of the american ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>be in   grade ap biology and the online progr...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>virus dna</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>help for exam</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3003</th>\n",
       "      <td>how important be program for physics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>what be a follow up to stephen hawking a brief...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>parallel universe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>what do apply physicist actually do</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>would a photon clock wind down</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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       "<p>6055 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                                                  title  class_label\n",
       "0                                     multiplex vaccine            0\n",
       "1     be there a biology equivalent of the american ...            0\n",
       "2      be in   grade ap biology and the online progr...            0\n",
       "3                                             virus dna            0\n",
       "4                                         help for exam            0\n",
       "...                                                 ...          ...\n",
       "3003               how important be program for physics            1\n",
       "3004  what be a follow up to stephen hawking a brief...            1\n",
       "3005                                  parallel universe            1\n",
       "3006                what do apply physicist actually do            1\n",
       "3007                     would a photon clock wind down            1\n",
       "\n",
       "[6055 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_df = pd.concat([bio_df, phys_df])\n",
    "sci_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for balanced classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.50322\n",
       "1    0.49678\n",
       "Name: class_label, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci_df['class_label'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set X and y values. Train Test Split\n",
    "\n",
    "Set the target as the `'class_label'` column which labels which subreddit the content is from  \n",
    "Added `.values.astype('str')` because they were not the right format for CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sci_df['title'].values.astype('str')\n",
    "y = sci_df['class_label'].values.astype('str')\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add custom stop words to English set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_stop_words = text.ENGLISH_STOP_WORDS.union(['biology', 'physics', 'question','help', 'need', \n",
    "                                               'like', 'look', 'use', 'career', 'work', 'vs', 'know', \n",
    "                                               'book', 'science', 'happen', 'job', 'advice', 'school', \n",
    "                                               'good', 'just', 'thing', 'way', 'idea', 'long', 'high', \n",
    "                                               'student', 'start', 'different', 'problem', 'study', \n",
    "                                               'learn', 'textbook', 'understand', 'research', \n",
    "                                               'possible', 'want', 'make', 'biologist', 'physicist',\n",
    "                                              'really', 'master', 'degree'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is about 50% since we are using well balanced binary classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatiate Count Vectorizer and Tfidf Vectorizers for NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvec = CountVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))\n",
    "tvec = TfidfVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics(y_test, y_pred): \n",
    "    \n",
    "    precision = precision_score(y_test, y_pred, pos_label=None,\n",
    "                                    average='weighted')             \n",
    "    recall = recall_score(y_test, y_pred, pos_label=None,\n",
    "                              average='weighted')\n",
    "    f1 = metrics.f1_score(y_test, y_pred, average='macro')  \n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    return accuracy, precision, recall, f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fit and Transform training and testing data for both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count Vectorizer\n",
    "X_train_cv = cvec.fit_transform(X_train)\n",
    "X_test_cv = cvec.transform(X_test)\n",
    "\n",
    "#Tfidf Vectorizer\n",
    "X_train_tv = tvec.fit_transform(X_train)\n",
    "X_test_tv = tvec.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.792, precision = 0.794, recall = 0.792, f1 = 0.791\n"
     ]
    }
   ],
   "source": [
    "clf = MultinomialNB(alpha=.01)\n",
    "clf.fit(X_train_cv, y_train)\n",
    "y_test_cv = cvec.transform(X_test)\n",
    "\n",
    "y_pred_cv = clf.predict(y_test_cv)\n",
    "\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred_cv)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tfidf Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.788, precision = 0.791, recall = 0.788, f1 = 0.787\n"
     ]
    }
   ],
   "source": [
    "clf = MultinomialNB(alpha=.01)\n",
    "clf.fit(X_train_tv, y_train)\n",
    "y_test_tv = cvec.transform(X_test)\n",
    "y_pred_tv = clf.predict(y_test_tv)\n",
    "\n",
    "accuracy, precision, recall, f1 = get_metrics(y_test, y_pred_tv)\n",
    "print(\"accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Store the vectorized data in a dataframe\n",
    "Inspect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ability  abiotic  able  able live  abroad  absolute  absolute zero  absorb  \\\n",
       "0        0        0     0          0       0         0              0       0   \n",
       "1        0        0     0          0       0         0              0       0   \n",
       "2        0        0     0          0       0         0              0       0   \n",
       "3        0        0     0          0       0         0              0       0   \n",
       "4        0        0     0          0       0         0              0       0   \n",
       "\n",
       "   ac  academia  ...  youtube  youtube channel  youtube video  zero  zinc  \\\n",
       "0   0         0  ...        0                0              0     0     0   \n",
       "1   0         0  ...        0                0              0     0     0   \n",
       "2   0         0  ...        0                0              0     0     0   \n",
       "3   0         0  ...        0                0              0     0     0   \n",
       "4   0         0  ...        0                0              0     0     0   \n",
       "\n",
       "   zone  zoologist  zoology  zoology ecology  zoom  \n",
       "0     0          0        0                0     0  \n",
       "1     0          0        0                0     0  \n",
       "2     0          0        0                0     0  \n",
       "3     0          0        0                0     0  \n",
       "4     0          0        0                0     0  \n",
       "\n",
       "[5 rows x 5000 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df = pd.DataFrame(X_train_cv.toarray(), columns=cvec.get_feature_names())\n",
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatiate and fit Logistic Regression Model\n",
    "Using Count Vectorized Data\n",
    "Evaluate using cross_val_score\n",
    "C regularization set to 0.4\n",
    "Best cv value = ~7. Tested 5, 7, 10, 15 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.7927764326069411\n",
      "Test Score: 0.7391193036354327\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(C=.4)\n",
    "lr.fit(X_train_cv, y_train)\n",
    "y_pred = lr.predict(y_test_cv)\n",
    "\n",
    "print(f'Training Score: {cross_val_score(lr, X_train_cv, y_train, cv=7).mean()}')\n",
    "print(f'Test Score: {cross_val_score(lr, X_test_cv, y_test, cv=7).mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1510618740936884"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lir = LinearRegression().fit(X_train_cv, y_train)\n",
    "lir.score(X_test_cv, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a list of words being analyzed in Phys data set with their corresponding coefficients from logistic regression\n",
    "Create a table with top 20 coefs  \n",
    "Plot coefs \n",
    "Plots are optimized for presentation which makes them hard to read here. Can be converted back to black text etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 567,
       "width": 712
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_coefs = pd.DataFrame(list(zip(X_train_df, np.exp(lr.coef_[0]))))\n",
    "\n",
    "table = log_coefs.sort_values(1, ascending=False).head(20)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "# Grab physics data\n",
    "plt.barh(table[0], table[1], color='white')\n",
    "plt.xlabel('Weight', fontsize=20, color='aquamarine')\n",
    "plt.xticks(fontsize=18, color='aquamarine' )\n",
    "plt.ylim(plt.ylim()[::-1])\n",
    "plt.yticks(fontsize=16, color='aquamarine')\n",
    "plt.title(r\"$\\bf{Physics:\\ Top\\ Predictive\\ Words}$\", fontsize=24, color='white', pad=20)\n",
    "plt.tick_params(color='aquamarine');\n",
    "plt.savefig('Visuals/physics_word_importance.png', dpi=360, transparent=True)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a list of words being analyzed in Bio data set with their corresponding coefficients from logistic regression\n",
    "Create a table with top 20 coefs  \n",
    "Plot coefs   \n",
    "\n",
    "Plots are optimized for presentation which makes them hard to read here. Can be converted back to black text etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 712
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# table = log_coefs.sort_values(1).head(20)\n",
    "\n",
    "# plt.figure(figsize=(6,8))\n",
    "# # Grab biology data\n",
    "# plt.barh(table[0], (1 - table[1]), color='mediumturquoise')\n",
    "# plt.ylim(plt.ylim()[::-1])\n",
    "# plt.yticks(fontsize=16)\n",
    "# plt.xlabel('Weight', fontsize=20)\n",
    "# plt.xticks(fontsize=18)\n",
    "# plt.title('Biology: Top Predictive Words', fontsize=26);\n",
    "# log_coefs = pd.DataFrame(list(zip(X_train_df, np.exp(lr.coef_[0]))))\n",
    "\n",
    "table = log_coefs.sort_values(1).head(20)\n",
    "plt.figure(figsize=(10,8))\n",
    "# Grab physics data\n",
    "plt.barh(table[0], (1 - table[1]), color='white')\n",
    "plt.xlabel('Weight', fontsize=20, color='aquamarine')\n",
    "plt.xticks(fontsize=14, color='aquamarine' )\n",
    "plt.ylim(plt.ylim()[::-1])\n",
    "plt.yticks(fontsize=16, color='aquamarine')\n",
    "plt.title(r\"$\\bf{Biology:\\ Top\\ Predictive\\ Words}$\", fontsize=24, color='White', pad=20)\n",
    "plt.tick_params(color='aquamarine');\n",
    "plt.savefig('Visuals/biology_word_importance.png', dpi=360, transparent=True)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Searching\n",
    "## CountVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatniate a pipeline for model testing\n",
    "* count vectorizer with the optimal parameters from above\n",
    "* random forest classifier\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('cvec', CountVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup parameters for Grid Searching for CountVectorizer RandomForestClassifier\n",
    "n_jobs are set for my machine but will be commented out in favor of settings better for the average machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_params = {\n",
    "    'rf__min_samples_split': [14, 15, 16],\n",
    "    'rf__n_jobs': [30, 34, 40] # commented out as this may not apply to other machine specs running this\n",
    "#     'rf__n_jobs': [10, 12, 16]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate Grid Search\n",
    "Fit data and print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7 µs, sys: 5 µs, total: 12 µs\n",
      "Wall time: 23.4 µs\n",
      "0.7643590193025221\n",
      "{'rf__min_samples_split': 15, 'rf__n_jobs': 30}\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=7)\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(gs.best_score_)\n",
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatiate pipeline for TfidfVectorizer  and RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('tvec', TfidfVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))),\n",
    "    ('rf', RandomForestClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup parameters for Grid Searching TfidfVectorizer and RandomForestClassifier\n",
    "n_jobs are set for my machine but will be commented out in favor of settings better for the average machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up a parameter grid to search over\n",
    "pipe_params = {\n",
    "    # different parameter combinations to search over\n",
    "    'rf__min_samples_split': [11, 12, 13, 14, 15],\n",
    "    'rf__n_jobs': [30, 34, 40] # commented out as this only applies for my machine specs\n",
    "#     'rf__n_jobs': [10, 12, 16]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate Grid Search\n",
    "Fit data and print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 µs, sys: 2 µs, total: 7 µs\n",
      "Wall time: 12.6 µs\n",
      "0.7683248954435395\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'rf__min_samples_split': 12, 'rf__n_jobs': 30}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=7)\n",
    "gs.fit(X_train, y_train)\n",
    "\n",
    "print(gs.best_score_)\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate Random Forect Classifier\n",
    "Fit model\n",
    "Use optimal parameters from grid search\n",
    "\n",
    "Print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.7672310182560105\n",
      "Test Score: 0.7186217297226412\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(min_samples_split=13, n_jobs=-1)\n",
    "rf.fit(X_train_cv, y_train)\n",
    "\n",
    "print(f'Training Score: {cross_val_score(rf, X_train_cv, y_train, cv=5).mean()}')\n",
    "print(f'Test Score: {cross_val_score(rf, X_test_cv, y_test, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatiate pipeline for CountVectorizer  and KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('cvec', CountVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))),\n",
    "    ('knn', KNeighborsClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup parameters for Grid Searching CountVectorizer and KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_params = {\n",
    "    'knn__n_neighbors': [1, 3, 5, 7, 9, 11, 13]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate Grid Search\n",
    "Fit data and print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 µs, sys: 1 µs, total: 6 µs\n",
      "Wall time: 11 µs\n",
      "0.6529319968585506\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'knn__n_neighbors': 1}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=7)\n",
    "gs.fit(X_train, y_train)\n",
    "print(gs.best_score_)\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instatiate pipeline for TfidfVectorizer  and KNeighborsClassifier\n",
    "Set Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('tvec', TfidfVectorizer(max_features=5_000, stop_words=my_stop_words, ngram_range=(1,2))),\n",
    "    ('knn', KNeighborsClassifier())\n",
    "])\n",
    "\n",
    "pipe_params = {\n",
    "    'knn__n_neighbors': [2, 3, 5, 7, 9, 11, 13]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate Grid Search\n",
    "Fit data and print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6511747825307148\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'knn__n_neighbors': 2}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = GridSearchCV(pipe, param_grid=pipe_params, cv=7)\n",
    "gs.fit(X_train, y_train)\n",
    "print(gs.best_score_)\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run KNN Model with optemized params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.6606405730699516\n",
      "Test Score: 0.6361245214931851\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train_cv, y_train)\n",
    "\n",
    "print(f'Training Score: {cross_val_score(knn, X_train_cv, y_train, cv=7).mean()}')\n",
    "print(f'Test Score: {cross_val_score(knn, X_test_cv, y_test, cv=7).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import cm\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.BuGn):\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=30, color='white', pad=20)\n",
    "    color_bar = plt.colorbar(fraction=0.046, pad=0.04)\n",
    "    plt.setp(plt.getp(color_bar.ax.axes, 'yticklabels'), color='aquamarine')\n",
    "    color_bar.ax.yaxis.set_tick_params(color='aquamarine')\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, fontsize=20, color='aquamarine')\n",
    "    plt.yticks(tick_marks, classes, fontsize=20, color='aquamarine')\n",
    "    \n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", \n",
    "                 color=\"teal\" if cm[i, j] < thresh else \"white\", fontsize=40)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label', fontsize=26, color='white')\n",
    "    plt.xlabel('Predicted label', fontsize=26, color='white')\n",
    "\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots are optimized for presentation which makes them hard to read here. Can be converted back to black text etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 626,
       "width": 712
      },
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plot = plot_confusion_matrix(cm, classes=['Biology','Physics'], normalize=False, title='Confusion matrix')\n",
    "plt.savefig('Visuals/confusion_matrix.png', dpi=360, transparent=True)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[629, 133],\n",
       "       [214, 538]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}