23-text-mining-project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import gc\n",
    "import copy\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from nltk.stem import wordnet, WordNetLemmatizer, SnowballStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import pos_tag\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from textblob import TextBlob\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
    "from sklearn.tree import DecisionTreeClassifier as DTC\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from bayes_opt import BayesianOptimization\n",
    "from bayes_opt.observer import JSONLogger\n",
    "from bayes_opt.event import Events\n",
    "from sklearn.metrics import recall_score, precision_score, f1_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## READ DATASET AND CREATE TEXT AND TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>category</th>\n",
       "      <th>headline</th>\n",
       "      <th>short_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIME</td>\n",
       "      <td>There Were 2 Mass Shootings In Texas Last Week...</td>\n",
       "      <td>She left her husband. He killed their children...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Will Smith Joins Diplo And Nicky Jam For The 2...</td>\n",
       "      <td>Of course it has a song.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Hugh Grant Marries For The First Time At Age 57</td>\n",
       "      <td>The actor and his longtime girlfriend Anna Ebe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Jim Carrey Blasts 'Castrato' Adam Schiff And D...</td>\n",
       "      <td>The actor gives Dems an ass-kicking for not fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Julianna Margulies Uses Donald Trump Poop Bags...</td>\n",
       "      <td>The \"Dietland\" actress said using the bags is ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Morgan Freeman 'Devastated' That Sexual Harass...</td>\n",
       "      <td>\"It is not right to equate horrific incidents ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Donald Trump Is Lovin' New McDonald's Jingle I...</td>\n",
       "      <td>It's catchy, all right.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>What To Watch On Amazon Prime That’s New This ...</td>\n",
       "      <td>There's a great mini-series joining this week.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Mike Myers Reveals He'd 'Like To' Do A Fourth ...</td>\n",
       "      <td>Myer's kids may be pushing for a new \"Powers\" ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>What To Watch On Hulu That’s New This Week</td>\n",
       "      <td>You're getting a recent Academy Award-winning ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                                           headline  \\\n",
       "0          CRIME  There Were 2 Mass Shootings In Texas Last Week...   \n",
       "1  ENTERTAINMENT  Will Smith Joins Diplo And Nicky Jam For The 2...   \n",
       "2  ENTERTAINMENT    Hugh Grant Marries For The First Time At Age 57   \n",
       "3  ENTERTAINMENT  Jim Carrey Blasts 'Castrato' Adam Schiff And D...   \n",
       "4  ENTERTAINMENT  Julianna Margulies Uses Donald Trump Poop Bags...   \n",
       "5  ENTERTAINMENT  Morgan Freeman 'Devastated' That Sexual Harass...   \n",
       "6  ENTERTAINMENT  Donald Trump Is Lovin' New McDonald's Jingle I...   \n",
       "7  ENTERTAINMENT  What To Watch On Amazon Prime That’s New This ...   \n",
       "8  ENTERTAINMENT  Mike Myers Reveals He'd 'Like To' Do A Fourth ...   \n",
       "9  ENTERTAINMENT         What To Watch On Hulu That’s New This Week   \n",
       "\n",
       "                                   short_description  \n",
       "0  She left her husband. He killed their children...  \n",
       "1                           Of course it has a song.  \n",
       "2  The actor and his longtime girlfriend Anna Ebe...  \n",
       "3  The actor gives Dems an ass-kicking for not fi...  \n",
       "4  The \"Dietland\" actress said using the bags is ...  \n",
       "5  \"It is not right to equate horrific incidents ...  \n",
       "6                            It's catchy, all right.  \n",
       "7     There's a great mini-series joining this week.  \n",
       "8  Myer's kids may be pushing for a new \"Powers\" ...  \n",
       "9  You're getting a recent Academy Award-winning ...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_json('data/News_Category_Dataset_v2.json', lines=True)\n",
    "\n",
    "# keep only text and target category\n",
    "dataset = dataset.drop(['date', 'link', 'authors'], axis=1)\n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['category'] = dataset['category'].map(lambda x: \"WORLDPOST\" if x == \"THE WORLDPOST\" else x)\n",
    "dataset['category'] = dataset['category'].map(lambda x: \"ARTS & CULTURE\" if x in [\"ARTS\", \"CULTURE & ARTS\"] else x)\n",
    "dataset['category'] = dataset['category'].map(lambda x: \"STYLE & BEAUTY\" if x == \"STYLE\" else x)\n",
    "dataset['category'] = dataset['category'].map(lambda x: \"PARENTING\" if x == \"PARENTS\" else x)\n",
    "dataset['category'] = dataset['category'].map(lambda x: \"FOOD & DRINK\" if x == \"TASTE\" else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLITICS          32739\n",
       "WELLNESS          17827\n",
       "ENTERTAINMENT     16058\n",
       "PARENTING         12632\n",
       "STYLE & BEAUTY    11903\n",
       "TRAVEL             9887\n",
       "FOOD & DRINK       8322\n",
       "HEALTHY LIVING     6694\n",
       "QUEER VOICES       6314\n",
       "WORLDPOST          6243\n",
       "BUSINESS           5937\n",
       "COMEDY             5175\n",
       "SPORTS             4884\n",
       "BLACK VOICES       4528\n",
       "HOME & LIVING      4195\n",
       "ARTS & CULTURE     3878\n",
       "WEDDINGS           3651\n",
       "WOMEN              3490\n",
       "IMPACT             3459\n",
       "DIVORCE            3426\n",
       "CRIME              3405\n",
       "MEDIA              2815\n",
       "WEIRD NEWS         2670\n",
       "GREEN              2622\n",
       "RELIGION           2556\n",
       "SCIENCE            2178\n",
       "WORLD NEWS         2177\n",
       "TECH               2082\n",
       "MONEY              1707\n",
       "FIFTY              1401\n",
       "GOOD NEWS          1398\n",
       "ENVIRONMENT        1323\n",
       "COLLEGE            1144\n",
       "LATINO VOICES      1129\n",
       "EDUCATION          1004\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.sample(10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POLITICS          1612\n",
       "WELLNESS           887\n",
       "ENTERTAINMENT      790\n",
       "STYLE & BEAUTY     628\n",
       "PARENTING          601\n",
       "TRAVEL             487\n",
       "FOOD & DRINK       414\n",
       "HEALTHY LIVING     347\n",
       "QUEER VOICES       323\n",
       "BUSINESS           311\n",
       "WORLDPOST          287\n",
       "COMEDY             261\n",
       "SPORTS             260\n",
       "BLACK VOICES       238\n",
       "HOME & LIVING      210\n",
       "DIVORCE            186\n",
       "WEDDINGS           183\n",
       "CRIME              175\n",
       "IMPACT             175\n",
       "ARTS & CULTURE     174\n",
       "WOMEN              161\n",
       "MEDIA              136\n",
       "WEIRD NEWS         133\n",
       "GREEN              123\n",
       "TECH               117\n",
       "RELIGION           111\n",
       "WORLD NEWS         108\n",
       "SCIENCE            106\n",
       "MONEY               85\n",
       "GOOD NEWS           79\n",
       "FIFTY               68\n",
       "ENVIRONMENT         61\n",
       "COLLEGE             58\n",
       "EDUCATION           57\n",
       "LATINO VOICES       48\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "target = le.fit_transform(dataset['category'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WELLNESS', 'ENTERTAINMENT', 'BUSINESS', 'HEALTHY LIVING']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['category'].tolist()[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31,  8,  2, 14])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = (dataset['headline'] + ' ' + dataset['short_description']).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3 Easy Guided Meditations For Relaxation The stress and strain of constantly being connected can sometimes take your life -- and your well-being -- off course. GPS',\n",
       " 'Corrupt Politician From \\'The Wire\\' Says Donald Trump Is Just Like Him \"Sheeeeeeeee-it!\"',\n",
       " \"Wells Fargo's New CEO Already Sounding A Little Stressed He hasn't even had the job for a week yet.\",\n",
       " 'Is It Possible To Outrun Your Own Fart? Today in things you never knew you needed to know..']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## TEXT CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "patternAlphaNum = re.compile('[\\W_]+')\n",
    "stops = stopwords.words('english')\n",
    "wn = WordNetLemmatizer()\n",
    "stemmer = SnowballStemmer('english')\n",
    "\n",
    "# helper function to transform pos-tags\n",
    "# this is ridiculous btw\n",
    "def get_wordnet_pos(treebank_tag):\n",
    "\n",
    "    if treebank_tag.startswith('J'):\n",
    "        return 'a'\n",
    "    elif treebank_tag.startswith('V'):\n",
    "        return 'v'\n",
    "    elif treebank_tag.startswith('N'):\n",
    "        return 'n'\n",
    "    elif treebank_tag.startswith('R'):\n",
    "        return 'r'\n",
    "    else:\n",
    "        return 'n'\n",
    "\n",
    "def clean_text(text, lemma_or_stem='lemma', word_transformer=wn, pattern=patternAlphaNum, stops=stops):\n",
    "    # split to words and lowercase\n",
    "    words = [word.lower() for word in text.split()]\n",
    "    \n",
    "    # keep only alphanumeric characters\n",
    "    words_alphanum = [pattern.sub('', word) for word in words]\n",
    "    \n",
    "    # remove stopwords\n",
    "    words_notstop = [word for word in words_alphanum if not ((word in stops) or (word == ''))]\n",
    "    \n",
    "    # lemmatize using pos tags\n",
    "    if lemma_or_stem == \"lemma\":\n",
    "        pos_tags = [get_wordnet_pos(pos[1]) for pos in pos_tag(words_notstop)]\n",
    "        return ' '.join([word_transformer.lemmatize(word, pos=pos_tag) for word, pos_tag in zip(words_notstop, pos_tags)])\n",
    "    \n",
    "    # stemming\n",
    "    elif lemma_or_stem == \"stem\":\n",
    "        return ' '.join([word_transformer.stem(word) for word in words_notstop])\n",
    "    \n",
    "    # return raw words\n",
    "    else:\n",
    "        return ' '.join(words_notstop)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60f1a8a05be14aada6ec2a5faf60385f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ca045e646e24ae786eff30ffbe5518d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "texts_cleaned_lemma = [clean_text(text, 'lemma', wn) for text in tqdm(texts)]\n",
    "texts_cleaned_stem = [clean_text(text, 'stem', stemmer) for text in tqdm(texts)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine them to 1 text\n",
    "all_cleaned_text_lemma = '\\n'.join(texts_cleaned_lemma)\n",
    "all_cleaned_text_stem = '\\n'.join(texts_cleaned_stem)\n",
    "\n",
    "# write to file\n",
    "with open('cleaned_text/cleaned_text_lemma.txt', 'wb') as f:\n",
    "    f.write(all_cleaned_text_lemma.encode('utf-8'))\n",
    "with open('cleaned_text/cleaned_text_stem.txt', 'wb') as f:\n",
    "    f.write(all_cleaned_text_stem.encode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load text\n",
    "with open('cleaned_text/cleaned_text_stem.txt', 'rb') as f:\n",
    "    all_cleaned_text_stem = [line.decode('utf-8').strip('\\n') for line in f.readlines()]\n",
    "with open('cleaned_text/cleaned_text_lemma.txt', 'rb') as f:\n",
    "    all_cleaned_text_lemma = [line.decode('utf-8').strip('\\n') for line in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# metadata\n",
    "blobs = [TextBlob(text) for text in texts]\n",
    "polarities = [blob.polarity for blob in blobs]\n",
    "subjectivities = [blob.subjectivity for blob in blobs]\n",
    "lens = [len(text) for text in texts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_types = ['unigram_min3','unibigram_min3', 'unibitrigram_min3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = {}\n",
    "tfidf['unigram_min3'] = TfidfVectorizer(min_df=3, max_df=0.5)\n",
    "tfidf['unibigram_min3'] = TfidfVectorizer(ngram_range=(1,2), min_df=3, max_df=0.5)\n",
    "tfidf['unibitrigram_min3'] = TfidfVectorizer(ngram_range=(1,3), min_df=3, max_df=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_vectors = {\n",
    "    'lemma': {key: value.fit_transform(all_cleaned_text_lemma) for key, value in tfidf.items()},\n",
    "    'stem': {key: value.fit_transform(all_cleaned_text_stem) for key, value in tfidf.items()}\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lemma_unigram_min3: (10000, 7249)\n",
      "lemma_unibigram_min3: (10000, 11094)\n",
      "lemma_unibitrigram_min3: (10000, 11478)\n",
      "stem_unigram_min3: (10000, 6705)\n",
      "stem_unibigram_min3: (10000, 10665)\n",
      "stem_unibitrigram_min3: (10000, 11062)\n"
     ]
    }
   ],
   "source": [
    "for key1 in text_vectors.keys():\n",
    "    for key2 in text_vectors[key1].keys():\n",
    "        print(f'{key1}_{key2}: {text_vectors[key1][key2].shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FEATURE UNION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(key1, key2, target):\n",
    "    X = pd.DataFrame(text_vectors[key1][key2].todense())\n",
    "    X['polarity'] = polarities\n",
    "    X['subjectivity'] = subjectivities\n",
    "    X['lens'] = lens\n",
    "    \n",
    "    y = copy.copy(target)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.33, random_state=42)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test, le"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIMPLE MODEL TO FIND REPRESENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stem\n",
      "-------\n",
      "unigram_min3\n",
      "{'train': 0.41835820895522385, 'test': 0.3615151515151515}\n",
      "---\n",
      "unibigram_min3\n",
      "{'train': 0.43716417910447763, 'test': 0.36303030303030304}\n",
      "---\n",
      "unibitrigram_min3\n",
      "{'train': 0.4267164179104478, 'test': 0.3612121212121212}\n",
      "---\n",
      "lemma\n",
      "-------\n",
      "unigram_min3\n",
      "{'train': 0.41044776119402987, 'test': 0.353030303030303}\n",
      "---\n",
      "unibigram_min3\n",
      "{'train': 0.4, 'test': 0.3496969696969697}\n",
      "---\n",
      "unibitrigram_min3\n",
      "{'train': 0.43029850746268655, 'test': 0.36363636363636365}\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "scores = {}\n",
    "for i in ['stem', 'lemma']:\n",
    "    print(i)\n",
    "    print('-------')\n",
    "    scores[i] = {}\n",
    "    for j in tfidf_types:\n",
    "        print(j)\n",
    "        tree = DTC(min_samples_split=0.1)\n",
    "        scores[i][j] = {}\n",
    "        X_train, X_test, y_train, y_test, le = create_dataset(i, j, target)\n",
    "        tree.fit(X_train, y_train)\n",
    "        scores[i][j]['train'] = tree.score(X_train, y_train)\n",
    "        scores[i][j]['test'] = tree.score(X_test, y_test)\n",
    "        del X_train, X_test, y_train, y_test, tree\n",
    "        gc.collect()\n",
    "        print(scores[i][j])\n",
    "        print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's work with stemmed, uni and bigrams that appear on at least 3 texts and at most 50% of texts!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test, le = create_dataset('stem', 'unibigram_min3', target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save it to continue work later on\n",
    "X_train.to_csv('modeling_data/X_train.csv')\n",
    "X_test.to_csv('modeling_data/X_test.csv')\n",
    "pd.DataFrame(y_train).to_csv('modeling_data/y_train.csv')\n",
    "pd.DataFrame(y_test).to_csv('modeling_data/y_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OPTIMIZE MODEL HYPERPARAMETERS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LOAD MODEL DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.read_csv('modeling_data/X_train.csv')\n",
    "X_test = pd.read_csv('modeling_data/X_test.csv')\n",
    "y_train = pd.read_csv('modeling_data/y_train.csv')\n",
    "y_test = pd.read_csv('modeling_data/y_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SET UP OPTIMIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to optimize\n",
    "def rfccv(n_estimators, min_samples_split, max_features):\n",
    "    val = cross_val_score(\n",
    "        RFC(n_estimators=int(n_estimators),\n",
    "            min_samples_split=min_samples_split,\n",
    "            max_features=min(max_features, 0.999),\n",
    "            random_state=2\n",
    "        ),\n",
    "        X=X_train,\n",
    "        y=y_train,\n",
    "        scoring=None,\n",
    "        cv=3\n",
    "    ).mean()\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object which does optimization\n",
    "params_to_optimize = {'n_estimators': (10, 1000),\n",
    "                   'min_samples_split': (0.001, 0.2),\n",
    "                   'max_features': (0.1, 0.999)}\n",
    "\n",
    "rfcBO = BayesianOptimization(\n",
    "    rfccv,\n",
    "    params_to_optimize\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log to file rather than prints\n",
    "logger_rfc = JSONLogger(path=\"logs/rfc_logs.json\")\n",
    "rfcBO.subscribe(Events.OPTMIZATION_STEP, logger_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform optimization\n",
    "rfcBO.maximize(init_points=5, n_iter=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "Final Results\n",
      "RFC: 0.437894\n",
      "-----------------------------------------------------\n",
      "Best Parameters\n",
      "RFC: {'max_features': 0.10217901857765659, 'min_samples_split': 0.022427861157945508, 'n_estimators': 928.7684944755536}\n"
     ]
    }
   ],
   "source": [
    "print('-' * 53)\n",
    "print('Final Results')\n",
    "print('RFC: %f' % rfcBO.max['target'])\n",
    "\n",
    "print('-' * 53)\n",
    "print('Best Parameters')\n",
    "print(f'RFC: {rfcBO.max[\"params\"]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RFC(max_features=rfcBO.max['params']['max_features'], \n",
    "             min_samples_split=rfcBO.max['params']['min_samples_split'], \n",
    "             n_estimators=int(rfcBO.max['params']['n_estimators']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=0.10217901857765659,\n",
       "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                       min_impurity_split=None, min_samples_leaf=1,\n",
       "                       min_samples_split=0.022427861157945508,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=928,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15,  0,  1, ...,  1,  0,  1],\n",
       "       [ 0, 11,  1, ...,  0,  0,  0],\n",
       "       [ 0,  1, 23, ...,  5,  0,  0],\n",
       "       ...,\n",
       "       [ 0,  0,  0, ..., 13,  0,  0],\n",
       "       [ 0,  1,  0, ...,  1,  0,  6],\n",
       "       [ 0,  1,  1, ...,  0,  2, 13]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=https://miro.medium.com/max/621/1*uR09zTlPgIj5PvMYJZScVg.png>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62c0b51ac8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(conf_matrix, ax=ax, annot=True,\n",
    "            xticklabels=le.inverse_transform(list(range(35))),\n",
    "            yticklabels=le.inverse_transform(list(range(35))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kapronczaym/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/kapronczaym/.local/lib/python3.6/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# calculate metrics by label\n",
    "recalls = {}\n",
    "precisions = {}\n",
    "f1_scores = {}\n",
    "for label in range(35):\n",
    "    y_test_label = pd.np.where(y_test==label, 1, 0)\n",
    "    y_test_pred_label = pd.np.where(y_test_pred==label, 1, 0)\n",
    "    recalls[label] = recall_score(y_test_label, y_test_pred_label, pos_label=1)\n",
    "    precisions[label] = precision_score(y_test_label, y_test_pred_label, pos_label=1)\n",
    "    f1_scores[label] = f1_score(y_test_label, y_test_pred_label, pos_label=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a df of metrics by label\n",
    "metrics_df = pd.DataFrame.from_dict(recalls, orient=\"index\", columns=['recall/sens'])\n",
    "metrics_df['precision/spec'] = metrics_df.index.map(precisions)\n",
    "metrics_df['f1_score'] = metrics_df.index.map(f1_scores)\n",
    "metrics_df.index = le.inverse_transform(list(range(35)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>recall/sens</th>\n",
       "      <th>precision/spec</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ARTS &amp; CULTURE</th>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.344828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLACK VOICES</th>\n",
       "      <td>0.141026</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.209524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS</th>\n",
       "      <td>0.205357</td>\n",
       "      <td>0.450980</td>\n",
       "      <td>0.282209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLLEGE</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMEDY</th>\n",
       "      <td>0.091954</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.158416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIME</th>\n",
       "      <td>0.206349</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.268041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIVORCE</th>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.645161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.206897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTERTAINMENT</th>\n",
       "      <td>0.587302</td>\n",
       "      <td>0.273063</td>\n",
       "      <td>0.372796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENVIRONMENT</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>FIFTY</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOOD &amp; DRINK</th>\n",
       "      <td>0.436242</td>\n",
       "      <td>0.670103</td>\n",
       "      <td>0.528455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOD NEWS</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREEN</th>\n",
       "      <td>0.085714</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.127660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTHY LIVING</th>\n",
       "      <td>0.100917</td>\n",
       "      <td>0.183333</td>\n",
       "      <td>0.130178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOME &amp; LIVING</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>0.371681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMPACT</th>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LATINO VOICES</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDIA</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONEY</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARENTING</th>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.518828</td>\n",
       "      <td>0.564920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLITICS</th>\n",
       "      <td>0.768946</td>\n",
       "      <td>0.527919</td>\n",
       "      <td>0.626035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUEER VOICES</th>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.503145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELIGION</th>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.057143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCIENCE</th>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPORTS</th>\n",
       "      <td>0.234568</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.308943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STYLE &amp; BEAUTY</th>\n",
       "      <td>0.675532</td>\n",
       "      <td>0.444056</td>\n",
       "      <td>0.535865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TECH</th>\n",
       "      <td>0.093023</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAVEL</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.590164</td>\n",
       "      <td>0.507042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEDDINGS</th>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.637931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEIRD NEWS</th>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLNESS</th>\n",
       "      <td>0.724590</td>\n",
       "      <td>0.343701</td>\n",
       "      <td>0.466245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WOMEN</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.298851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLD NEWS</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WORLDPOST</th>\n",
       "      <td>0.154762</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                recall/sens  precision/spec  f1_score\n",
       "ARTS & CULTURE     0.254237        0.535714  0.344828\n",
       "BLACK VOICES       0.141026        0.407407  0.209524\n",
       "BUSINESS           0.205357        0.450980  0.282209\n",
       "COLLEGE            0.055556        0.250000  0.090909\n",
       "COMEDY             0.091954        0.571429  0.158416\n",
       "CRIME              0.206349        0.382353  0.268041\n",
       "DIVORCE            0.535714        0.810811  0.645161\n",
       "EDUCATION          0.176471        0.250000  0.206897\n",
       "ENTERTAINMENT      0.587302        0.273063  0.372796\n",
       "ENVIRONMENT        0.000000        0.000000  0.000000\n",
       "FIFTY              0.000000        0.000000  0.000000\n",
       "FOOD & DRINK       0.436242        0.670103  0.528455\n",
       "GOOD NEWS          0.111111        0.400000  0.173913\n",
       "GREEN              0.085714        0.250000  0.127660\n",
       "HEALTHY LIVING     0.100917        0.183333  0.130178\n",
       "HOME & LIVING      0.300000        0.488372  0.371681\n",
       "IMPACT             0.018182        0.200000  0.033333\n",
       "LATINO VOICES      0.111111        1.000000  0.200000\n",
       "MEDIA              0.043478        0.666667  0.081633\n",
       "MONEY              0.000000        0.000000  0.000000\n",
       "PARENTING          0.620000        0.518828  0.564920\n",
       "POLITICS           0.768946        0.527919  0.626035\n",
       "QUEER VOICES       0.370370        0.784314  0.503145\n",
       "RELIGION           0.030303        0.500000  0.057143\n",
       "SCIENCE            0.025641        0.333333  0.047619\n",
       "SPORTS             0.234568        0.452381  0.308943\n",
       "STYLE & BEAUTY     0.675532        0.444056  0.535865\n",
       "TECH               0.093023        0.800000  0.166667\n",
       "TRAVEL             0.444444        0.590164  0.507042\n",
       "WEDDINGS           0.660714        0.616667  0.637931\n",
       "WEIRD NEWS         0.057692        0.300000  0.096774\n",
       "WELLNESS           0.724590        0.343701  0.466245\n",
       "WOMEN              0.250000        0.371429  0.298851\n",
       "WORLD NEWS         0.000000        0.000000  0.000000\n",
       "WORLDPOST          0.154762        0.393939  0.222222"
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       "                recall/sens  precision/spec  f1_score\n",
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