Final_3_4_PR_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions for part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import autograd.numpy as np\n",
    "from autograd import grad\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>overall</th>\n",
       "      <th>verified</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>vote</th>\n",
       "      <th>image</th>\n",
       "      <th>style</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29189.000000</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29185</td>\n",
       "      <td>29189</td>\n",
       "      <td>29184</td>\n",
       "      <td>2.918900e+04</td>\n",
       "      <td>6047.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>15365</td>\n",
       "      <td>29189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4866</td>\n",
       "      <td>27588</td>\n",
       "      <td>24102</td>\n",
       "      <td>23305</td>\n",
       "      <td>28807</td>\n",
       "      <td>21768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>6104</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>01 3, 2017</td>\n",
       "      <td>AA7918E9410D650A076221C7B2934A09</td>\n",
       "      <td>6E46168E5DFA83876F2E968A187B9F7A</td>\n",
       "      <td>760C63E8E5E8DC3FAA01878D37BA5678</td>\n",
       "      <td>works great</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['https://images-na.ssl-images-amazon.com/imag...</td>\n",
       "      <td>{'Format:': ' Audio CD'}</td>\n",
       "      <td>CDs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22996</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>1049</td>\n",
       "      <td>25</td>\n",
       "      <td>1476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4296</td>\n",
       "      <td>4912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.971907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.409661e+09</td>\n",
       "      <td>7.295353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.410878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.115099e+08</td>\n",
       "      <td>18.749460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.947584e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.390781e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.439770e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.476317e+09</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537488e+09</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             overall verified  reviewTime                        reviewerID  \\\n",
       "count   29189.000000    29189       29189                             29189   \n",
       "unique           NaN        2        4866                             27588   \n",
       "top              NaN     True  01 3, 2017  AA7918E9410D650A076221C7B2934A09   \n",
       "freq             NaN    22996          40                                32   \n",
       "mean        2.971907      NaN         NaN                               NaN   \n",
       "std         1.410878      NaN         NaN                               NaN   \n",
       "min         1.000000      NaN         NaN                               NaN   \n",
       "25%         2.000000      NaN         NaN                               NaN   \n",
       "50%         3.000000      NaN         NaN                               NaN   \n",
       "75%         4.000000      NaN         NaN                               NaN   \n",
       "max         5.000000      NaN         NaN                               NaN   \n",
       "\n",
       "                                    asin                      reviewerName  \\\n",
       "count                              29189                             29185   \n",
       "unique                             24102                             23305   \n",
       "top     6E46168E5DFA83876F2E968A187B9F7A  760C63E8E5E8DC3FAA01878D37BA5678   \n",
       "freq                                  36                              1049   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "         reviewText     summary  unixReviewTime         vote  \\\n",
       "count         29189       29184    2.918900e+04  6047.000000   \n",
       "unique        28807       21768             NaN          NaN   \n",
       "top     works great  Five Stars             NaN          NaN   \n",
       "freq             25        1476             NaN          NaN   \n",
       "mean            NaN         NaN    1.409661e+09     7.295353   \n",
       "std             NaN         NaN    1.115099e+08    18.749460   \n",
       "min             NaN         NaN    8.947584e+08     2.000000   \n",
       "25%             NaN         NaN    1.390781e+09     2.000000   \n",
       "50%             NaN         NaN    1.439770e+09     4.000000   \n",
       "75%             NaN         NaN    1.476317e+09     7.000000   \n",
       "max             NaN         NaN    1.537488e+09   682.000000   \n",
       "\n",
       "                                                    image  \\\n",
       "count                                                 600   \n",
       "unique                                                600   \n",
       "top     ['https://images-na.ssl-images-amazon.com/imag...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                           style category  \n",
       "count                      15365    29189  \n",
       "unique                      6104        6  \n",
       "top     {'Format:': ' Audio CD'}      CDs  \n",
       "freq                        4296     4912  \n",
       "mean                         NaN      NaN  \n",
       "std                          NaN      NaN  \n",
       "min                          NaN      NaN  \n",
       "25%                          NaN      NaN  \n",
       "50%                          NaN      NaN  \n",
       "75%                          NaN      NaN  \n",
       "max                          NaN      NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "test_data = pd.read_csv('Test.csv')\n",
    "train_data = pd.read_csv('Training.csv')\n",
    "\n",
    "train_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\n",
    "GENERIC_STOP_WORDS = ['ourselves', 'hers', 'between', 'yourself', 'but', 'again', 'there', 'about', 'once', 'during', 'out', 'very', 'having', 'with', 'they', 'own', 'an', 'be', 'some', 'for', 'do', 'its', 'yours', 'such', 'into', 'of', 'most', 'itself', 'other', 'off', 'is', 's', 'am', 'or', 'who', 'as', 'from', 'him', 'each', 'the', 'themselves', 'until', 'below', 'are', 'we', 'these', 'your', 'his', 'through', 'don', 'nor', 'me', 'were', 'her', 'more', 'himself', 'this', 'down', 'should', 'our', 'their', 'while',\n",
    "                      'above', 'both', 'up', 'to', 'ours', 'had', 'she', 'all', 'when', 'at', 'any', 'before', 'them', 'same', 'and', 'been', 'have', 'in', 'will', 'on', 'does', 'yourselves', 'then', 'that', 'because', 'what', 'over', 'why', 'so', 'can', 'did', 'now', 'under', 'he', 'you', 'herself', 'has', 'just', 'where', 'too', 'only', 'myself', 'which', 'those', 'i', 'after', 'few', 'whom', 't', 'being', 'if', 'theirs', 'my', 'against', 'a', 'by', 'doing', 'it', 'how', 'further', 'was', 'here', 'than', 'as']\n",
    "\n",
    "STOP_WORDS_SUMMARY = ['hyundai', 'eyed', '4yo', 'cycling', '2x', 'excellence', 'excelent', 'exquisite', '2wd', 'awsome', 'awhile', 'awaited', 'owe', 'twisted', 'swimming', 'rvs', 'overlooked', 'sunshade', 'purchases', 'subaru', 'buick', 'audi', 'quads', 'tuner', 'tune', 'gutter', 'junior', 'musical', 'guitars', 'funk', 'humor', 'bueno', 'sunday', 'buzz', 'dude', 'hurrah', 'guessing', 'junkie', 'jug', 'rum', 'rubbed', 'nutritious', 'muffins', 'turmeric', 'hunters', 'supposedly', 'ounces', 'turbo', 'suffer', 'supplies', 'cutie', 'puppets', 'cuddly', 'gt', 'utilitarian', 'attaches', 'stroke', 'starters', 'standards', '6th', 'stretching', 'stream', 'bt', 'stout', 'strategy',\n",
    "                      'isuzu', 'tsx', 'usefull', 'usa', 'ass', 'bs', 'crew', 'trd', 'drilling', 'transmission', 'draw', 'brother', 'traction', 'prevents', 'orignal', 'fraction', 'prius', 'groove', 'proud', 'grace', 'brilliance', 'brigade', 'orchestration', 'treasure', 'previously', 'irons', 'franklin', 'programs', 'frustration', 'protein', 'artisan', 'crumbled', 'grains', 'grapefruit', 'drinkers', 'crisp', 'breads', 'brewing', 'creme', 'branded', 'wrapped', 'armorsuit', 'orientation', 'preference', 'tray', 'prints', 'vr', 'armour', 'dragonfly', 'dryer', 'breyer', 'dresses', 'promo', 'transformers', 'spread', 'appetite', 'ipod', 'spices', 'spanish', 'operates', 'speeds', 'iphone4']\n",
    "\n",
    "STOP_WORDS_REVIEW_TEXT = []\n",
    "\n",
    "\n",
    "def edit_summary(content):\n",
    "\n",
    "    # step 1 - convert the text to only lower case\n",
    "    content = content.lower()\n",
    "\n",
    "    # step 2 - remove unwanted and unicode characters\n",
    "    content = re.sub(\n",
    "        r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", content)\n",
    "\n",
    "    # step 3 - remove stop words\n",
    "    stopwords = GENERIC_STOP_WORDS + STOP_WORDS_SUMMARY\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "\n",
    "def edit_review_text(content):\n",
    "\n",
    "    # step 1 - convert the text to only lower case\n",
    "    content = content.lower()\n",
    "\n",
    "    # step 2 - remove unwanted and unicode characters\n",
    "    content = re.sub(\n",
    "        r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", content)\n",
    "\n",
    "    # step 3 - remove stop words\n",
    "    stopwords = GENERIC_STOP_WORDS + STOP_WORDS_REVIEW_TEXT\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "# create categories for vote label\n",
    "\n",
    "\n",
    "def assign_vote_label(i):\n",
    "    if i <= 2.0:\n",
    "        return 'low'\n",
    "    if i > 2.0 and i <= 10.0:\n",
    "        return 'medium'\n",
    "    if i > 10.0 and i <= 50.0:\n",
    "        return 'good'\n",
    "    if i > 50.0:\n",
    "        return 'high'\n",
    "\n",
    "\n",
    "def preprocess(data):\n",
    "\n",
    "    # filter out only important columns\n",
    "    X_cols = ['reviewText', 'summary', 'verified', 'vote', 'category']\n",
    "\n",
    "    # editing text of \"reviewText\" & \"summary\"\n",
    "    data['reviewText'] = data['reviewText'].astype(str).apply(\n",
    "        edit_review_text)\n",
    "    data['summary'] = data['summary'].astype(str).apply(edit_summary)\n",
    "\n",
    "    # editing vote - assigning NAN values to 0\n",
    "    data['vote'] = data['vote'].fillna(0)\n",
    "    data['vote'] = data['vote'].apply(lambda x: assign_vote_label(x))\n",
    "\n",
    "    data['category'] = data['category'].fillna('Unknown')\n",
    "\n",
    "    X = data[X_cols]\n",
    "\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y = train_data['overall'].apply(lambda x: 0 if x <= cutoff else 1)\n",
    "X_main_train = preprocess(train_data)\n",
    "\n",
    "# Test Data\n",
    "X_main_test = preprocess(test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "# define the column transformer to apply TfidfVectorizer and encoder to multiple columns\n",
    "ct = ColumnTransformer(\n",
    "    [(\"vect_summary\", TfidfVectorizer(), 'summary'),\n",
    "     (\"vect_reviewText\", TfidfVectorizer(), 'reviewText'),\n",
    "     (\"encd_verified\", OrdinalEncoder(dtype=int), ['verified']),\n",
    "     (\"encd_category\", OrdinalEncoder(dtype=int,\n",
    "      handle_unknown='use_encoded_value', unknown_value=99), ['category']),\n",
    "     (\"encd_votes\", OrdinalEncoder(dtype=int), ['vote'])\n",
    "     ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;vect_summary&#x27;,\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         &#x27;summary&#x27;),\n",
       "                                                                        (&#x27;vect_reviewText&#x27;,\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         &#x27;reviewText&#x27;),\n",
       "                                                                        (&#x27;encd_verified&#x27;,\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                                         [&#x27;verified&#x27;]),\n",
       "                                                                        (&#x27;encd_category&#x27;,\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                                        handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                        unknown_val...\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                                         [&#x27;vote&#x27;])])),\n",
       "                                       (&#x27;classifier&#x27;, MultinomialNB())]),\n",
       "             param_grid={&#x27;classifier__alpha&#x27;: [0.5],\n",
       "                         &#x27;classifier__fit_prior&#x27;: [False],\n",
       "                         &#x27;transformer__vect_reviewText__max_features&#x27;: [10000],\n",
       "                         &#x27;transformer__vect_reviewText__ngram_range&#x27;: [(1, 2)],\n",
       "                         &#x27;transformer__vect_summary__max_features&#x27;: [10000],\n",
       "                         &#x27;transformer__vect_summary__ngram_range&#x27;: [(1, 2)]})</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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                        ColumnTransformer(transformers=[(&#x27;vect_summary&#x27;,\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         &#x27;summary&#x27;),\n",
       "                                                                        (&#x27;vect_reviewText&#x27;,\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         &#x27;reviewText&#x27;),\n",
       "                                                                        (&#x27;encd_verified&#x27;,\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                                         [&#x27;verified&#x27;]),\n",
       "                                                                        (&#x27;encd_category&#x27;,\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                                        handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                        unknown_val...\n",
       "                                                                         OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                                         [&#x27;vote&#x27;])])),\n",
       "                                       (&#x27;classifier&#x27;, MultinomialNB())]),\n",
       "             param_grid={&#x27;classifier__alpha&#x27;: [0.5],\n",
       "                         &#x27;classifier__fit_prior&#x27;: [False],\n",
       "                         &#x27;transformer__vect_reviewText__max_features&#x27;: [10000],\n",
       "                         &#x27;transformer__vect_reviewText__ngram_range&#x27;: [(1, 2)],\n",
       "                         &#x27;transformer__vect_summary__max_features&#x27;: [10000],\n",
       "                         &#x27;transformer__vect_summary__ngram_range&#x27;: [(1, 2)]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;vect_summary&#x27;,\n",
       "                                                  TfidfVectorizer(),\n",
       "                                                  &#x27;summary&#x27;),\n",
       "                                                 (&#x27;vect_reviewText&#x27;,\n",
       "                                                  TfidfVectorizer(),\n",
       "                                                  &#x27;reviewText&#x27;),\n",
       "                                                 (&#x27;encd_verified&#x27;,\n",
       "                                                  OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                  [&#x27;verified&#x27;]),\n",
       "                                                 (&#x27;encd_category&#x27;,\n",
       "                                                  OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=99),\n",
       "                                                  [&#x27;category&#x27;]),\n",
       "                                                 (&#x27;encd_votes&#x27;,\n",
       "                                                  OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                                  [&#x27;vote&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">transformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;vect_summary&#x27;, TfidfVectorizer(), &#x27;summary&#x27;),\n",
       "                                (&#x27;vect_reviewText&#x27;, TfidfVectorizer(),\n",
       "                                 &#x27;reviewText&#x27;),\n",
       "                                (&#x27;encd_verified&#x27;,\n",
       "                                 OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                 [&#x27;verified&#x27;]),\n",
       "                                (&#x27;encd_category&#x27;,\n",
       "                                 OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=99),\n",
       "                                 [&#x27;category&#x27;]),\n",
       "                                (&#x27;encd_votes&#x27;,\n",
       "                                 OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;),\n",
       "                                 [&#x27;vote&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">vect_summary</label><div class=\"sk-toggleable__content\"><pre>summary</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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">vect_reviewText</label><div class=\"sk-toggleable__content\"><pre>reviewText</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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">encd_verified</label><div class=\"sk-toggleable__content\"><pre>[&#x27;verified&#x27;]</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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">encd_category</label><div class=\"sk-toggleable__content\"><pre>[&#x27;category&#x27;]</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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;, handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "               unknown_value=99)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">encd_votes</label><div class=\"sk-toggleable__content\"><pre>[&#x27;vote&#x27;]</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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(dtype=&lt;class &#x27;int&#x27;&gt;)</pre></div></div></div></div></div></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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('transformer',\n",
       "                                        ColumnTransformer(transformers=[('vect_summary',\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         'summary'),\n",
       "                                                                        ('vect_reviewText',\n",
       "                                                                         TfidfVectorizer(),\n",
       "                                                                         'reviewText'),\n",
       "                                                                        ('encd_verified',\n",
       "                                                                         OrdinalEncoder(dtype=<class 'int'>),\n",
       "                                                                         ['verified']),\n",
       "                                                                        ('encd_category',\n",
       "                                                                         OrdinalEncoder(dtype=<class 'int'>,\n",
       "                                                                                        handle_unknown='use_encoded_value',\n",
       "                                                                                        unknown_val...\n",
       "                                                                         OrdinalEncoder(dtype=<class 'int'>),\n",
       "                                                                         ['vote'])])),\n",
       "                                       ('classifier', MultinomialNB())]),\n",
       "             param_grid={'classifier__alpha': [0.5],\n",
       "                         'classifier__fit_prior': [False],\n",
       "                         'transformer__vect_reviewText__max_features': [10000],\n",
       "                         'transformer__vect_reviewText__ngram_range': [(1, 2)],\n",
       "                         'transformer__vect_summary__max_features': [10000],\n",
       "                         'transformer__vect_summary__ngram_range': [(1, 2)]})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", MultinomialNB())\n",
    "])\n",
    "\n",
    "params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000],\n",
    "    \"transformer__vect_reviewText__max_features\": [10000],\n",
    "    \"classifier__alpha\": [0.5],\n",
    "    'classifier__fit_prior': [False],\n",
    "}\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_search = GridSearchCV(pipe, params, cv=5)\n",
    "\n",
    "grid_search.fit(X_main_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict test data using Logistic regression\n",
    "y_pred_test_log = grid_search.best_estimator_.predict(X_main_test)\n",
    "\n",
    "test_ids = list(test_data.index)\n",
    "test_submission = pd.DataFrame({'id':test_ids, 'predicted':y_pred_test_log})\n",
    "test_submission.head()\n",
    "\n",
    "test_submission.to_csv('test_submission_part_4.csv', index = False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# f1score: 0.81691"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# pipe = Pipeline([\n",
    "#     (\"transformer\", ct),\n",
    "#     (\"classifier\", LogisticRegression())\n",
    "# ])\n",
    "\n",
    "# params = {\n",
    "#     \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "#     \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "#     \"transformer__vect_summary__max_features\": [32000],\n",
    "#     \"transformer__vect_reviewText__max_features\": [5000],\n",
    "#     \"classifier__solver\": ['liblinear'],\n",
    "#     \"classifier__C\": [5],\n",
    "#     \"classifier__max_iter\": [100]\n",
    "# }\n",
    "\n",
    "# # perform grid search cross-validation to find the best hyperparameters\n",
    "# grid_search = GridSearchCV(pipe, params, cv=5)\n",
    "\n",
    "# grid_search.fit(X_main_train, y)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# f1 score: 0.82121"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.svm import LinearSVC\n",
    "\n",
    "# pipe = Pipeline([\n",
    "#     (\"transformer\", ct),\n",
    "#     (\"classifier\", LinearSVC())\n",
    "# ])\n",
    "\n",
    "# params = {\n",
    "#     \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "#     \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "#     \"transformer__vect_summary__max_features\": [32000],\n",
    "#     \"transformer__vect_reviewText__max_features\": [5000],\n",
    "#     \"classifier__C\": [0.1],\n",
    "#     \"classifier__penalty\": ['l2']\n",
    "# }\n",
    "\n",
    "# # perform grid search cross-validation to find the best hyperparameters\n",
    "# grid_search = GridSearchCV(pipe, params, cv=5)\n",
    "\n",
    "# grid_search.fit(X_main_train, y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# f1score: 0.81558"
   ]
  }
 ],
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