Movie-Reviews-Pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/reviews.csv', index_col='filename')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>review</th>\n",
       "      <th>alpha_text</th>\n",
       "      <th>nostop_text</th>\n",
       "      <th>lemmatized_text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cv439_tok-13632.txt</th>\n",
       "      <td>the farrelly brothers' third film , after dumb...</td>\n",
       "      <td>1</td>\n",
       "      <td>the farrelly brothers  third film   after dumb...</td>\n",
       "      <td>farrelly brothers third film dumb dumber kingp...</td>\n",
       "      <td>farrelly brother third film dumb dumber kingpi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv180_tok-20034.txt</th>\n",
       "      <td>more movie views by jamey hughton at : http : ...</td>\n",
       "      <td>1</td>\n",
       "      <td>more movie views by jamey hughton at   http   ...</td>\n",
       "      <td>movie views jamey hughton http welcome movievi...</td>\n",
       "      <td>movie view jamey hughton http welcome movievie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv553_tok-13743.txt</th>\n",
       "      <td>if chris farley had strapped some fake mutton-...</td>\n",
       "      <td>1</td>\n",
       "      <td>if chris farley had strapped some fake mutton ...</td>\n",
       "      <td>chris farley strapped fake mutton chop sidebur...</td>\n",
       "      <td>chris farley strapped fake mutton chop sidebur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv026_tok-29622.txt</th>\n",
       "      <td>in zoolander , the world's most successful , i...</td>\n",
       "      <td>1</td>\n",
       "      <td>in zoolander   the world s most successful   i...</td>\n",
       "      <td>zoolander world successful influential intelle...</td>\n",
       "      <td>zoolander world successful influential intelle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv126_tok-13691.txt</th>\n",
       "      <td>if the current trends of hollywood filmmaking ...</td>\n",
       "      <td>1</td>\n",
       "      <td>if the current trends of hollywood filmmaking ...</td>\n",
       "      <td>current trends hollywood filmmaking continue p...</td>\n",
       "      <td>current trend hollywood filmmaking continue pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv344_tok-29737.txt</th>\n",
       "      <td>for more reviews and movie trailers , visit ht...</td>\n",
       "      <td>1</td>\n",
       "      <td>for more reviews and movie trailers   visit ht...</td>\n",
       "      <td>reviews movie trailers visit http www joblo co...</td>\n",
       "      <td>review movie trailer visit http www joblo com ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv345_tok-9908.txt</th>\n",
       "      <td>director : george armitage cast : john cusack ...</td>\n",
       "      <td>1</td>\n",
       "      <td>director   george armitage cast   john cusack ...</td>\n",
       "      <td>director george armitage cast john cusack minn...</td>\n",
       "      <td>director george armitage cast john cusack minn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv165_tok-11425.txt</th>\n",
       "      <td>written by : peter wang and shirley sunstarrin...</td>\n",
       "      <td>1</td>\n",
       "      <td>written by   peter wang and shirley sunstarrin...</td>\n",
       "      <td>written peter wang shirley sunstarring peter w...</td>\n",
       "      <td>written peter wang shirley sunstarring peter w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv245_tok-19462.txt</th>\n",
       "      <td>if you¹ve been paying attention to the media f...</td>\n",
       "      <td>1</td>\n",
       "      <td>if you ve been paying attention to the media f...</td>\n",
       "      <td>paying attention media frenzy blair witch proj...</td>\n",
       "      <td>paying attention medium frenzy blair witch pro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv156_tok-12349.txt</th>\n",
       "      <td>director : john sayles || screenplay : john sa...</td>\n",
       "      <td>1</td>\n",
       "      <td>director   john sayles    screenplay   john sa...</td>\n",
       "      <td>director john sayles screenplay john sayles st...</td>\n",
       "      <td>director john sayles screenplay john sayles st...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  text  \\\n",
       "filename                                                                 \n",
       "cv439_tok-13632.txt  the farrelly brothers' third film , after dumb...   \n",
       "cv180_tok-20034.txt  more movie views by jamey hughton at : http : ...   \n",
       "cv553_tok-13743.txt  if chris farley had strapped some fake mutton-...   \n",
       "cv026_tok-29622.txt  in zoolander , the world's most successful , i...   \n",
       "cv126_tok-13691.txt  if the current trends of hollywood filmmaking ...   \n",
       "cv344_tok-29737.txt  for more reviews and movie trailers , visit ht...   \n",
       "cv345_tok-9908.txt   director : george armitage cast : john cusack ...   \n",
       "cv165_tok-11425.txt  written by : peter wang and shirley sunstarrin...   \n",
       "cv245_tok-19462.txt  if you¹ve been paying attention to the media f...   \n",
       "cv156_tok-12349.txt  director : john sayles || screenplay : john sa...   \n",
       "\n",
       "                     review  \\\n",
       "filename                      \n",
       "cv439_tok-13632.txt       1   \n",
       "cv180_tok-20034.txt       1   \n",
       "cv553_tok-13743.txt       1   \n",
       "cv026_tok-29622.txt       1   \n",
       "cv126_tok-13691.txt       1   \n",
       "cv344_tok-29737.txt       1   \n",
       "cv345_tok-9908.txt        1   \n",
       "cv165_tok-11425.txt       1   \n",
       "cv245_tok-19462.txt       1   \n",
       "cv156_tok-12349.txt       1   \n",
       "\n",
       "                                                            alpha_text  \\\n",
       "filename                                                                 \n",
       "cv439_tok-13632.txt  the farrelly brothers  third film   after dumb...   \n",
       "cv180_tok-20034.txt  more movie views by jamey hughton at   http   ...   \n",
       "cv553_tok-13743.txt  if chris farley had strapped some fake mutton ...   \n",
       "cv026_tok-29622.txt  in zoolander   the world s most successful   i...   \n",
       "cv126_tok-13691.txt  if the current trends of hollywood filmmaking ...   \n",
       "cv344_tok-29737.txt  for more reviews and movie trailers   visit ht...   \n",
       "cv345_tok-9908.txt   director   george armitage cast   john cusack ...   \n",
       "cv165_tok-11425.txt  written by   peter wang and shirley sunstarrin...   \n",
       "cv245_tok-19462.txt  if you ve been paying attention to the media f...   \n",
       "cv156_tok-12349.txt  director   john sayles    screenplay   john sa...   \n",
       "\n",
       "                                                           nostop_text  \\\n",
       "filename                                                                 \n",
       "cv439_tok-13632.txt  farrelly brothers third film dumb dumber kingp...   \n",
       "cv180_tok-20034.txt  movie views jamey hughton http welcome movievi...   \n",
       "cv553_tok-13743.txt  chris farley strapped fake mutton chop sidebur...   \n",
       "cv026_tok-29622.txt  zoolander world successful influential intelle...   \n",
       "cv126_tok-13691.txt  current trends hollywood filmmaking continue p...   \n",
       "cv344_tok-29737.txt  reviews movie trailers visit http www joblo co...   \n",
       "cv345_tok-9908.txt   director george armitage cast john cusack minn...   \n",
       "cv165_tok-11425.txt  written peter wang shirley sunstarring peter w...   \n",
       "cv245_tok-19462.txt  paying attention media frenzy blair witch proj...   \n",
       "cv156_tok-12349.txt  director john sayles screenplay john sayles st...   \n",
       "\n",
       "                                                       lemmatized_text  \n",
       "filename                                                                \n",
       "cv439_tok-13632.txt  farrelly brother third film dumb dumber kingpi...  \n",
       "cv180_tok-20034.txt  movie view jamey hughton http welcome movievie...  \n",
       "cv553_tok-13743.txt  chris farley strapped fake mutton chop sidebur...  \n",
       "cv026_tok-29622.txt  zoolander world successful influential intelle...  \n",
       "cv126_tok-13691.txt  current trend hollywood filmmaking continue pr...  \n",
       "cv344_tok-29737.txt  review movie trailer visit http www joblo com ...  \n",
       "cv345_tok-9908.txt   director george armitage cast john cusack minn...  \n",
       "cv165_tok-11425.txt  written peter wang shirley sunstarring peter w...  \n",
       "cv245_tok-19462.txt  paying attention medium frenzy blair witch pro...  \n",
       "cv156_tok-12349.txt  director john sayles screenplay john sayles st...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_clf = Pipeline([\n",
    "    ('vect', CountVectorizer(ngram_range=(1,1))),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('clf', RandomForestClassifier())\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_type = 'lemmatized_text'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),\n",
       "                (&#x27;clf&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, CountVectorizer()), (&#x27;tfidf&#x27;, TfidfTransformer()),\n",
       "                (&#x27;clf&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfTransformer</label><div class=\"sk-toggleable__content\"><pre>TfidfTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),\n",
       "                ('clf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf.fit(df_train[text_type], df_train['review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['accept', 'acceptable', 'acceptance', 'accepted', 'accepting',\n",
       "       'acception', 'accepts', 'access', 'accessible', 'accessory'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf['vect'].get_feature_names_out()[100:110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22513"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text_clf['vect'].get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.14457669, 6.84932478, 6.84932478, ..., 6.84932478, 6.84932478,\n",
       "       5.75071249])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf['tfidf'].idf_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf['clf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.17379867e-04, 9.13693872e-05, 0.00000000e+00, ...,\n",
       "       4.95411712e-05, 0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf['clf'].feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_df = pd.DataFrame({\"feature\": text_clf['vect'].get_feature_names_out(), \n",
    "                           'importance': text_clf['clf'].feature_importances_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_sorted = feature_df.sort_values(by=['importance'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<p>22513 rows × 2 columns</p>\n",
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      "text/plain": [
       "           feature  importance\n",
       "1358           bad    0.007734\n",
       "19301       stupid    0.004430\n",
       "8555         great    0.003711\n",
       "21820       wasted    0.003370\n",
       "15856        quite    0.003172\n",
       "...            ...         ...\n",
       "8530      grateful    0.000000\n",
       "8529         grate    0.000000\n",
       "8528   grasshopper    0.000000\n",
       "8527       grassby    0.000000\n",
       "22512        zwick    0.000000\n",
       "\n",
       "[22513 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "features_sorted"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <th>13156</th>\n",
       "      <td>much</td>\n",
       "      <td>0.001351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11292</th>\n",
       "      <td>laugh</td>\n",
       "      <td>0.001350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>anything</td>\n",
       "      <td>0.001349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17623</th>\n",
       "      <td>see</td>\n",
       "      <td>0.001346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5406</th>\n",
       "      <td>director</td>\n",
       "      <td>0.001345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7425</th>\n",
       "      <td>first</td>\n",
       "      <td>0.001323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12165</th>\n",
       "      <td>many</td>\n",
       "      <td>0.001319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150</th>\n",
       "      <td>though</td>\n",
       "      <td>0.001318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22259</th>\n",
       "      <td>world</td>\n",
       "      <td>0.001315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6090</th>\n",
       "      <td>easily</td>\n",
       "      <td>0.001313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21045</th>\n",
       "      <td>unfunny</td>\n",
       "      <td>0.001292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>acting</td>\n",
       "      <td>0.001289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20127</th>\n",
       "      <td>thing</td>\n",
       "      <td>0.001287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19526</th>\n",
       "      <td>supposed</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12078</th>\n",
       "      <td>man</td>\n",
       "      <td>0.001271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17405</th>\n",
       "      <td>scene</td>\n",
       "      <td>0.001260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19778</th>\n",
       "      <td>talent</td>\n",
       "      <td>0.001250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7930</th>\n",
       "      <td>fun</td>\n",
       "      <td>0.001249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3373</th>\n",
       "      <td>cinematic</td>\n",
       "      <td>0.001232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5681</th>\n",
       "      <td>dog</td>\n",
       "      <td>0.001229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17543</th>\n",
       "      <td>script</td>\n",
       "      <td>0.001191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>best</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16829</th>\n",
       "      <td>ridiculous</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9485</th>\n",
       "      <td>hour</td>\n",
       "      <td>0.001184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20725</th>\n",
       "      <td>turn</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7453</th>\n",
       "      <td>five</td>\n",
       "      <td>0.001173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14278</th>\n",
       "      <td>pace</td>\n",
       "      <td>0.001166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6433</th>\n",
       "      <td>end</td>\n",
       "      <td>0.001165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19366</th>\n",
       "      <td>subtle</td>\n",
       "      <td>0.001160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12614</th>\n",
       "      <td>mess</td>\n",
       "      <td>0.001158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21848</th>\n",
       "      <td>way</td>\n",
       "      <td>0.001155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20151</th>\n",
       "      <td>thought</td>\n",
       "      <td>0.001154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16082</th>\n",
       "      <td>really</td>\n",
       "      <td>0.001145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13896</th>\n",
       "      <td>old</td>\n",
       "      <td>0.001143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19523</th>\n",
       "      <td>supporting</td>\n",
       "      <td>0.001141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16016</th>\n",
       "      <td>rating</td>\n",
       "      <td>0.001135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  importance\n",
       "1358           bad    0.007734\n",
       "19301       stupid    0.004430\n",
       "8555         great    0.003711\n",
       "21820       wasted    0.003370\n",
       "15856        quite    0.003172\n",
       "21927         well    0.003053\n",
       "553         always    0.003022\n",
       "17532   screenplay    0.002995\n",
       "12319     material    0.002792\n",
       "1176       attempt    0.002756\n",
       "11758         look    0.002723\n",
       "22321       writer    0.002565\n",
       "194          actor    0.002502\n",
       "13127        movie    0.002491\n",
       "7863        friend    0.002466\n",
       "1301         awful    0.002441\n",
       "13927          one    0.002437\n",
       "12037         make    0.002387\n",
       "14965         play    0.002329\n",
       "22284        would    0.002326\n",
       "4282         could    0.002213\n",
       "2182        boring    0.002163\n",
       "22212  wonderfully    0.002137\n",
       "16092       reason    0.002120\n",
       "15482     producer    0.002099\n",
       "1804        better    0.002052\n",
       "11373        least    0.002047\n",
       "15129       poorly    0.001975\n",
       "12693        might    0.001959\n",
       "3824    completely    0.001916\n",
       "8336            go    0.001901\n",
       "11064         know    0.001890\n",
       "13656      nothing    0.001806\n",
       "13143       moving    0.001784\n",
       "530           also    0.001783\n",
       "6750          even    0.001782\n",
       "3717          come    0.001760\n",
       "5978          dull    0.001735\n",
       "523          along    0.001709\n",
       "22278        worst    0.001699\n",
       "22211    wonderful    0.001697\n",
       "3704           com    0.001695\n",
       "10257  interesting    0.001641\n",
       "11187         lame    0.001626\n",
       "7015           eye    0.001587\n",
       "12380        maybe    0.001567\n",
       "1212      audience    0.001561\n",
       "10395     involves    0.001535\n",
       "542       although    0.001519\n",
       "9655          idea    0.001478\n",
       "11293    laughable    0.001474\n",
       "21819        waste    0.001456\n",
       "15007         plot    0.001434\n",
       "22427        young    0.001433\n",
       "11472          let    0.001432\n",
       "5192        detail    0.001430\n",
       "17791          sex    0.001428\n",
       "22336          www    0.001414\n",
       "16073    realistic    0.001412\n",
       "20370          top    0.001412\n",
       "8896          hard    0.001388\n",
       "21527         view    0.001384\n",
       "12778       minute    0.001381\n",
       "18161        since    0.001375\n",
       "13156         much    0.001351\n",
       "11292        laugh    0.001350\n",
       "790       anything    0.001349\n",
       "17623          see    0.001346\n",
       "5406      director    0.001345\n",
       "7425         first    0.001323\n",
       "12165         many    0.001319\n",
       "20150       though    0.001318\n",
       "22259        world    0.001315\n",
       "6090        easily    0.001313\n",
       "21045      unfunny    0.001292\n",
       "183         acting    0.001289\n",
       "20127        thing    0.001287\n",
       "19526     supposed    0.001286\n",
       "12078          man    0.001271\n",
       "17405        scene    0.001260\n",
       "19778       talent    0.001250\n",
       "7930           fun    0.001249\n",
       "3373     cinematic    0.001232\n",
       "5681           dog    0.001229\n",
       "17543       script    0.001191\n",
       "1789          best    0.001188\n",
       "16829   ridiculous    0.001188\n",
       "9485          hour    0.001184\n",
       "20725         turn    0.001175\n",
       "7453          five    0.001173\n",
       "14278         pace    0.001166\n",
       "6433           end    0.001165\n",
       "19366       subtle    0.001160\n",
       "12614         mess    0.001158\n",
       "21848          way    0.001155\n",
       "20151      thought    0.001154\n",
       "16082       really    0.001145\n",
       "13896          old    0.001143\n",
       "19523   supporting    0.001141\n",
       "16016       rating    0.001135"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\", None)\n",
    "features_sorted.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can do this to see the word distribution vector, but with this much data, you might not want to...\n",
    "#X_train = text_clf['vect'].fit_transform(df['alpha_text'])\n",
    "#print(X_train[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22513"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = text_clf.predict(df_test[text_type])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['pred'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_proba = text_clf.predict_proba(df_test[text_type])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.39, 0.61],\n",
       "       [0.56, 0.44],\n",
       "       [0.44, 0.56],\n",
       "       ...,\n",
       "       [0.43, 0.57],\n",
       "       [0.51, 0.49],\n",
       "       [0.59, 0.41]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_proba = [item[1] for item in y_proba]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pos_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['proba'] = pos_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>review</th>\n",
       "      <th>alpha_text</th>\n",
       "      <th>nostop_text</th>\n",
       "      <th>lemmatized_text</th>\n",
       "      <th>pred</th>\n",
       "      <th>proba</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cv333_tok-29224.txt</th>\n",
       "      <td>rat race ( 2001 ) . starring rowan atkinson , ...</td>\n",
       "      <td>1</td>\n",
       "      <td>rat race            starring rowan atkinson   ...</td>\n",
       "      <td>rat race starring rowan atkinson lanai chapman...</td>\n",
       "      <td>rat race starring rowan atkinson lanai chapman...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv157_tok-29786.txt</th>\n",
       "      <td>the uncompromising nudity bared throughout pet...</td>\n",
       "      <td>1</td>\n",
       "      <td>the uncompromising nudity bared throughout pet...</td>\n",
       "      <td>uncompromising nudity bared throughout petrice...</td>\n",
       "      <td>uncompromising nudity bared throughout petrice...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv682_tok-16618.txt</th>\n",
       "      <td>directed by neil jordan . cast : eomann owens ...</td>\n",
       "      <td>0</td>\n",
       "      <td>directed by neil jordan   cast   eomann owens ...</td>\n",
       "      <td>directed neil jordan cast eomann owens stephen...</td>\n",
       "      <td>directed neil jordan cast eomann owen stephen ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv041_tok-17672.txt</th>\n",
       "      <td>release date : april 9 , 1999 starring : katie...</td>\n",
       "      <td>0</td>\n",
       "      <td>release date   april          starring   katie...</td>\n",
       "      <td>release date april starring katie holmes sarah...</td>\n",
       "      <td>release date april starring katie holmes sarah...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv389_tok-8969.txt</th>\n",
       "      <td>film review ( c ) 1997 by kevin pattersonconta...</td>\n",
       "      <td>1</td>\n",
       "      <td>film review   c        by kevin pattersonconta...</td>\n",
       "      <td>film review c kevin pattersoncontact pg direct...</td>\n",
       "      <td>film review c kevin pattersoncontact pg direct...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv448_tok-10768.txt</th>\n",
       "      <td>seen on 31 december 1997 with tony for free at...</td>\n",
       "      <td>0</td>\n",
       "      <td>seen on    december      with tony for free at...</td>\n",
       "      <td>seen december tony free loews village viias mo...</td>\n",
       "      <td>seen december tony free loews village viias mo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv557_tok-4659.txt</th>\n",
       "      <td>carry on matron is the last great carry-on fil...</td>\n",
       "      <td>1</td>\n",
       "      <td>carry on matron is the last great carry on fil...</td>\n",
       "      <td>carry matron last great carry film opinion mad...</td>\n",
       "      <td>carry matron last great carry film opinion mad...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv460_tok-5367.txt</th>\n",
       "      <td>when i arrived in paris in june , 1992 , i was...</td>\n",
       "      <td>0</td>\n",
       "      <td>when i arrived in paris in june          i was...</td>\n",
       "      <td>arrived paris june surprised find france plast...</td>\n",
       "      <td>arrived paris june surprised find france plast...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv441_tok-9002.txt</th>\n",
       "      <td>new line / 1 : 30 / 1997 / r ( language , viol...</td>\n",
       "      <td>0</td>\n",
       "      <td>new line                   r   language   viol...</td>\n",
       "      <td>new line r language violence cast chris tucker...</td>\n",
       "      <td>new line r language violence cast chris tucker...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv697_tok-12765.txt</th>\n",
       "      <td>united states , 1998 u . s . release date : 5/...</td>\n",
       "      <td>0</td>\n",
       "      <td>united states        u   s   release date     ...</td>\n",
       "      <td>united states u release date wide running leng...</td>\n",
       "      <td>united state u release date wide running lengt...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  text  \\\n",
       "filename                                                                 \n",
       "cv333_tok-29224.txt  rat race ( 2001 ) . starring rowan atkinson , ...   \n",
       "cv157_tok-29786.txt  the uncompromising nudity bared throughout pet...   \n",
       "cv682_tok-16618.txt  directed by neil jordan . cast : eomann owens ...   \n",
       "cv041_tok-17672.txt  release date : april 9 , 1999 starring : katie...   \n",
       "cv389_tok-8969.txt   film review ( c ) 1997 by kevin pattersonconta...   \n",
       "cv448_tok-10768.txt  seen on 31 december 1997 with tony for free at...   \n",
       "cv557_tok-4659.txt   carry on matron is the last great carry-on fil...   \n",
       "cv460_tok-5367.txt   when i arrived in paris in june , 1992 , i was...   \n",
       "cv441_tok-9002.txt   new line / 1 : 30 / 1997 / r ( language , viol...   \n",
       "cv697_tok-12765.txt  united states , 1998 u . s . release date : 5/...   \n",
       "\n",
       "                     review  \\\n",
       "filename                      \n",
       "cv333_tok-29224.txt       1   \n",
       "cv157_tok-29786.txt       1   \n",
       "cv682_tok-16618.txt       0   \n",
       "cv041_tok-17672.txt       0   \n",
       "cv389_tok-8969.txt        1   \n",
       "cv448_tok-10768.txt       0   \n",
       "cv557_tok-4659.txt        1   \n",
       "cv460_tok-5367.txt        0   \n",
       "cv441_tok-9002.txt        0   \n",
       "cv697_tok-12765.txt       0   \n",
       "\n",
       "                                                            alpha_text  \\\n",
       "filename                                                                 \n",
       "cv333_tok-29224.txt  rat race            starring rowan atkinson   ...   \n",
       "cv157_tok-29786.txt  the uncompromising nudity bared throughout pet...   \n",
       "cv682_tok-16618.txt  directed by neil jordan   cast   eomann owens ...   \n",
       "cv041_tok-17672.txt  release date   april          starring   katie...   \n",
       "cv389_tok-8969.txt   film review   c        by kevin pattersonconta...   \n",
       "cv448_tok-10768.txt  seen on    december      with tony for free at...   \n",
       "cv557_tok-4659.txt   carry on matron is the last great carry on fil...   \n",
       "cv460_tok-5367.txt   when i arrived in paris in june          i was...   \n",
       "cv441_tok-9002.txt   new line                   r   language   viol...   \n",
       "cv697_tok-12765.txt  united states        u   s   release date     ...   \n",
       "\n",
       "                                                           nostop_text  \\\n",
       "filename                                                                 \n",
       "cv333_tok-29224.txt  rat race starring rowan atkinson lanai chapman...   \n",
       "cv157_tok-29786.txt  uncompromising nudity bared throughout petrice...   \n",
       "cv682_tok-16618.txt  directed neil jordan cast eomann owens stephen...   \n",
       "cv041_tok-17672.txt  release date april starring katie holmes sarah...   \n",
       "cv389_tok-8969.txt   film review c kevin pattersoncontact pg direct...   \n",
       "cv448_tok-10768.txt  seen december tony free loews village viias mo...   \n",
       "cv557_tok-4659.txt   carry matron last great carry film opinion mad...   \n",
       "cv460_tok-5367.txt   arrived paris june surprised find france plast...   \n",
       "cv441_tok-9002.txt   new line r language violence cast chris tucker...   \n",
       "cv697_tok-12765.txt  united states u release date wide running leng...   \n",
       "\n",
       "                                                       lemmatized_text  pred  \\\n",
       "filename                                                                       \n",
       "cv333_tok-29224.txt  rat race starring rowan atkinson lanai chapman...     1   \n",
       "cv157_tok-29786.txt  uncompromising nudity bared throughout petrice...     0   \n",
       "cv682_tok-16618.txt  directed neil jordan cast eomann owen stephen ...     1   \n",
       "cv041_tok-17672.txt  release date april starring katie holmes sarah...     0   \n",
       "cv389_tok-8969.txt   film review c kevin pattersoncontact pg direct...     1   \n",
       "cv448_tok-10768.txt  seen december tony free loews village viias mo...     0   \n",
       "cv557_tok-4659.txt   carry matron last great carry film opinion mad...     1   \n",
       "cv460_tok-5367.txt   arrived paris june surprised find france plast...     0   \n",
       "cv441_tok-9002.txt   new line r language violence cast chris tucker...     1   \n",
       "cv697_tok-12765.txt  united state u release date wide running lengt...     0   \n",
       "\n",
       "                     proba  \n",
       "filename                    \n",
       "cv333_tok-29224.txt   0.61  \n",
       "cv157_tok-29786.txt   0.44  \n",
       "cv682_tok-16618.txt   0.56  \n",
       "cv041_tok-17672.txt   0.50  \n",
       "cv389_tok-8969.txt    0.62  \n",
       "cv448_tok-10768.txt   0.39  \n",
       "cv557_tok-4659.txt    0.57  \n",
       "cv460_tok-5367.txt    0.50  \n",
       "cv441_tok-9002.txt    0.57  \n",
       "cv697_tok-12765.txt   0.36  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try a few lines of cleaned text - what would be retained from these sentences? \n",
    "document_text = \"\"\"its not especially good but it isnt especially bad either\"\"\"\n",
    "#document_text = \"\"\"movie was bad acting was poor script was terrible plot was trite\"\"\"\n",
    "#document_text = \"\"\"movie was great acting was excellent script was magnificent and wonderful plot was exciting\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's try: https://www.rogerebert.com/reviews/marmaduke-2010\n",
    "# what would be retained from this from our data cleaning pipeline? Does it make a big difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.43, 0.57]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_clf.predict_proba([document_text])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy:\n",
    "How often did the predicted value match the actual value\n",
    "\n",
    "Precision:\n",
    "When you predicted a positive result, how often was the actual value positive?\n",
    "\n",
    "Recall:\n",
    "When the actual value was positive, how often did you predict a positive value?\n",
    "\n",
    "Visual:\n",
    "https://en.wikipedia.org/wiki/Precision_and_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.757 / Recall: 0.708 / Accuracy: 0.743 / fscore: 0.732\n"
     ]
    }
   ],
   "source": [
    "precision, recall, fscore, train_support = precision_recall_fscore_support(df_test['review'], y_pred, pos_label=1, average='binary')\n",
    "print('Precision: {} / Recall: {} / Accuracy: {} / fscore: {}'.format(\n",
    "    round(precision, 3), round(recall, 3), (round((y_pred==df_test['review']).sum()/len(y_pred), 3)), round(fscore, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my results for multiple runs\n",
    "# body_text - Precision: 0.735 / Recall: 0.742 / Accuracy: 0.745\n",
    "# alpha_text - Precision: 0.788 / Recall: 0.65 / Accuracy: 0.74\n",
    "# nostop_text - Precision: 0.799 / Recall: 0.704 / Accuracy: 0.76\n",
    "# lemmatized_text - 0.789 / Recall: 0.65 / Accuracy: 0.734"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross validation\n",
    "#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html\n",
    "\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val = cross_val_score(text_clf, df_test['lemmatized_text'], y_pred, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.70503597, 0.62589928, 0.73381295, 0.76086957, 0.70289855])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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