Final_3_4_HP_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter training for part 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import autograd.numpy as np\n",
    "from autograd import grad\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cutoff\n",
    "cutoff = 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</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",
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       "      <td>2.918900e+04</td>\n",
       "      <td>6047.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>15365</td>\n",
       "      <td>29189</td>\n",
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       "    <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",
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       "      <td>28807</td>\n",
       "      <td>21768</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "train_data = pd.read_csv('Training.csv')\n",
    "\n",
    "train_data.describe(include='all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\n",
    "# Stop words\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",
    "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",
    "STOP_WORDS_REVIEW_TEXT = []\n",
    "\n",
    "\n",
    "# edit summary content\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\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",
    "# edit review text content\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\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",
    "# create categories for vote label\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",
    "# preprocess\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",
    "    X = train_data[X_cols]\n",
    "\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# creating X and y data\n",
    "\n",
    "# creating y labels less than cutoff to be 0 and 1 otherwise\n",
    "y = train_data['overall'].apply(lambda x: 0 if x <= cutoff else 1)\n",
    "\n",
    "# create features data\n",
    "X = preprocess(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# train test split function\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.20, random_state = 101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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, RandomizedSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, f1_score, accuracy_score\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import warnings\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# define the column transformer to apply TfidfVectorizer & 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), ['category']),\n",
    "     (\"encd_votes\", OrdinalEncoder(dtype=int), ['vote'])\n",
    "     ])\n",
    "\n",
    "# For results\n",
    "model_arr, auc_arr, f1_arr, accuracy_arr = [], [], [], []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
      "[CV 1/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.715 total time=   5.3s\n",
      "[CV 2/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.708 total time=   6.3s\n",
      "[CV 3/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.716 total time=   6.6s\n",
      "[CV 4/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.730 total time=   4.8s\n",
      "[CV 5/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.710 total time=   7.9s\n",
      "[CV 1/5; 2/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 2/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.712 total time=   8.3s\n",
      "[CV 2/5; 2/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 2/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.703 total time=   4.8s\n",
      "[CV 3/5; 2/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 2/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.718 total time=   7.8s\n",
      "[CV 4/5; 2/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.728 total time=   6.3s\n",
      "[CV 5/5; 2/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 2/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   7.1s\n",
      "[CV 1/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=  10.1s\n",
      "[CV 2/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=   8.1s\n",
      "[CV 3/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=   6.2s\n",
      "[CV 4/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.725 total time=  11.4s\n",
      "[CV 5/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.706 total time=   9.0s\n",
      "[CV 1/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.705 total time=   8.5s\n",
      "[CV 2/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.698 total time=   7.9s\n",
      "[CV 3/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=   9.7s\n",
      "[CV 4/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.722 total time=   7.7s\n",
      "[CV 5/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=   7.8s\n",
      "[CV 1/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.715 total time=   9.0s\n",
      "[CV 2/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.708 total time=   9.0s\n",
      "[CV 3/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.716 total time=   8.6s\n",
      "[CV 4/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.730 total time=   7.7s\n",
      "[CV 5/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.710 total time=   7.7s\n",
      "[CV 1/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.712 total time=  10.4s\n",
      "[CV 2/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.703 total time=   7.3s\n",
      "[CV 3/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.718 total time=   8.7s\n",
      "[CV 4/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.728 total time=   9.3s\n",
      "[CV 5/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   8.2s\n",
      "[CV 1/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   8.1s\n",
      "[CV 2/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=   8.9s\n",
      "[CV 3/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=   9.4s\n",
      "[CV 4/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.725 total time=   7.2s\n",
      "[CV 5/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.706 total time=  10.3s\n",
      "[CV 1/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.705 total time=   6.4s\n",
      "[CV 2/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.698 total time=   8.3s\n",
      "[CV 3/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=  10.0s\n",
      "[CV 4/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.722 total time=   5.0s\n",
      "[CV 5/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=   8.3s\n",
      "[CV 1/5; 9/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 9/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.715 total time=   8.7s\n",
      "[CV 2/5; 9/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 9/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.708 total time=   6.0s\n",
      "[CV 3/5; 9/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 9/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.716 total time=  10.0s\n",
      "[CV 4/5; 9/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.730 total time=   9.3s\n",
      "[CV 5/5; 9/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 9/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.710 total time=   9.8s\n",
      "[CV 1/5; 10/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 10/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.712 total time=   9.7s\n",
      "[CV 2/5; 10/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.703 total time=   8.3s\n",
      "[CV 3/5; 10/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.718 total time=   8.7s\n",
      "[CV 4/5; 10/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.728 total time=   8.7s\n",
      "[CV 5/5; 10/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   5.3s\n",
      "[CV 1/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=  10.5s\n",
      "[CV 2/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=   6.9s\n",
      "[CV 3/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=  11.2s\n",
      "[CV 4/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.725 total time=  10.6s\n",
      "[CV 5/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.706 total time=   7.2s\n",
      "[CV 1/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.705 total time=  10.0s\n",
      "[CV 2/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.698 total time=  10.7s\n",
      "[CV 3/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.714 total time=   7.2s\n",
      "[CV 4/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.722 total time=   9.6s\n",
      "[CV 5/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.704 total time=  10.9s\n",
      "[CV 1/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  10.8s\n",
      "[CV 2/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=  12.8s\n",
      "[CV 3/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   8.0s\n",
      "[CV 4/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.821 total time=  13.6s\n",
      "[CV 5/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  11.9s\n",
      "[CV 1/5; 14/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   9.9s\n",
      "[CV 2/5; 14/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   9.7s\n",
      "[CV 3/5; 14/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 14/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  12.7s\n",
      "[CV 4/5; 14/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=  13.5s\n",
      "[CV 5/5; 14/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   8.9s\n",
      "[CV 1/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   7.4s\n",
      "[CV 2/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  12.8s\n",
      "[CV 3/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=  15.0s\n",
      "[CV 4/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=  10.8s\n",
      "[CV 5/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  10.0s\n",
      "[CV 1/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  13.0s\n",
      "[CV 2/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   9.7s\n",
      "[CV 3/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   8.0s\n",
      "[CV 4/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.813 total time=  12.6s\n",
      "[CV 5/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=  12.9s\n",
      "[CV 1/5; 17/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 17/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  10.7s\n",
      "[CV 2/5; 17/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 17/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   8.1s\n",
      "[CV 3/5; 17/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 17/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  14.3s\n",
      "[CV 4/5; 17/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 17/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.821 total time=  11.0s\n",
      "[CV 5/5; 17/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 17/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   9.0s\n",
      "[CV 1/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  14.5s\n",
      "[CV 2/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   9.1s\n",
      "[CV 3/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  10.9s\n",
      "[CV 4/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=  14.4s\n",
      "[CV 5/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=  13.4s\n",
      "[CV 1/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 19/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  11.1s\n",
      "[CV 2/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 19/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  14.0s\n",
      "[CV 3/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 19/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=  16.0s\n",
      "[CV 4/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 19/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   7.7s\n",
      "[CV 5/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  14.0s\n",
      "[CV 1/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  10.8s\n",
      "[CV 2/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  13.3s\n",
      "[CV 3/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  12.0s\n",
      "[CV 4/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.813 total time=  12.6s\n",
      "[CV 5/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=  13.3s\n",
      "[CV 1/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  14.9s\n",
      "[CV 2/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=  11.4s\n",
      "[CV 3/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  11.2s\n",
      "[CV 4/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.821 total time=  10.6s\n",
      "[CV 5/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  10.4s\n",
      "[CV 1/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  15.0s\n",
      "[CV 2/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=  12.6s\n",
      "[CV 3/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   8.6s\n",
      "[CV 4/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=  12.4s\n",
      "[CV 5/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=  13.6s\n",
      "[CV 1/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   9.8s\n",
      "[CV 2/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=  12.5s\n",
      "[CV 3/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=  15.1s\n",
      "[CV 4/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=  11.4s\n",
      "[CV 5/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   9.8s\n",
      "[CV 1/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   9.2s\n",
      "[CV 2/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 24/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   6.8s\n",
      "[CV 3/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 24/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  10.7s\n",
      "[CV 4/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.813 total time=  10.8s\n",
      "[CV 5/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=  10.4s\n",
      "[CV 1/5; 25/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 25/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  14.8s\n",
      "[CV 2/5; 25/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  15.5s\n",
      "[CV 3/5; 25/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 25/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  13.1s\n",
      "[CV 4/5; 25/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=  12.1s\n",
      "[CV 5/5; 25/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  11.9s\n",
      "[CV 1/5; 26/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=  13.0s\n",
      "[CV 2/5; 26/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  14.8s\n",
      "[CV 3/5; 26/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  14.2s\n",
      "[CV 4/5; 26/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=  13.9s\n",
      "[CV 5/5; 26/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  14.2s\n",
      "[CV 1/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=  13.6s\n",
      "[CV 2/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  11.8s\n",
      "[CV 3/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=  14.9s\n",
      "[CV 4/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=  12.7s\n",
      "[CV 5/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=  13.1s\n",
      "[CV 1/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=  11.3s\n",
      "[CV 2/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  12.0s\n",
      "[CV 3/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  10.2s\n",
      "[CV 4/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.819 total time=  10.5s\n",
      "[CV 5/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  11.4s\n",
      "[CV 1/5; 29/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  10.9s\n",
      "[CV 2/5; 29/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  12.2s\n",
      "[CV 3/5; 29/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 29/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   7.2s\n",
      "[CV 4/5; 29/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 29/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=   9.5s\n",
      "[CV 5/5; 29/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 29/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  10.2s\n",
      "[CV 1/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=  10.6s\n",
      "[CV 2/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  11.7s\n",
      "[CV 3/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  11.9s\n",
      "[CV 4/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=  14.4s\n",
      "[CV 5/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  10.9s\n",
      "[CV 1/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=  11.0s\n",
      "[CV 2/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  10.7s\n",
      "[CV 3/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=  15.1s\n",
      "[CV 4/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=  13.0s\n",
      "[CV 5/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=  12.0s\n",
      "[CV 1/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=  12.0s\n",
      "[CV 2/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 32/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  12.3s\n",
      "[CV 3/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  11.6s\n",
      "[CV 4/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.819 total time=  11.5s\n",
      "[CV 5/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  11.2s\n",
      "[CV 1/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  12.1s\n",
      "[CV 2/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  13.9s\n",
      "[CV 3/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  11.2s\n",
      "[CV 4/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=  11.9s\n",
      "[CV 5/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  10.1s\n",
      "[CV 1/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=  12.5s\n",
      "[CV 2/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  13.4s\n",
      "[CV 3/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  13.6s\n",
      "[CV 4/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=  14.7s\n",
      "[CV 5/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  12.0s\n",
      "[CV 1/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   8.2s\n",
      "[CV 2/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  13.8s\n",
      "[CV 3/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=  13.3s\n",
      "[CV 4/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=  14.5s\n",
      "[CV 5/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=  15.2s\n",
      "[CV 1/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=  12.8s\n",
      "[CV 2/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=  15.7s\n",
      "[CV 3/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  12.4s\n",
      "[CV 4/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.819 total time=   9.6s\n",
      "[CV 5/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   7.5s\n",
      "[CV 1/5; 37/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 37/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=  13.9s\n",
      "[CV 2/5; 37/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 37/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=  15.4s\n",
      "[CV 3/5; 37/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 37/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   9.4s\n",
      "[CV 4/5; 37/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 37/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  10.6s\n",
      "[CV 5/5; 37/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 37/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  13.8s\n",
      "[CV 1/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  16.1s\n",
      "[CV 2/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  15.3s\n",
      "[CV 3/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  10.0s\n",
      "[CV 4/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=  12.2s\n",
      "[CV 5/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  12.2s\n",
      "[CV 1/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 39/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=  11.4s\n",
      "[CV 2/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 39/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=  12.9s\n",
      "[CV 3/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 39/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=  17.4s\n",
      "[CV 4/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 39/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  12.8s\n",
      "[CV 5/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 39/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=  10.1s\n",
      "[CV 1/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  15.0s\n",
      "[CV 2/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  15.2s\n",
      "[CV 3/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  14.0s\n",
      "[CV 4/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.814 total time=  18.1s\n",
      "[CV 5/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=   9.2s\n",
      "[CV 1/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=  11.2s\n",
      "[CV 2/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=  12.6s\n",
      "[CV 3/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   8.6s\n",
      "[CV 4/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  12.0s\n",
      "[CV 5/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  13.9s\n",
      "[CV 1/5; 42/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 42/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  14.8s\n",
      "[CV 2/5; 42/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 42/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  14.3s\n",
      "[CV 3/5; 42/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 42/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  10.3s\n",
      "[CV 4/5; 42/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 42/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=  10.8s\n",
      "[CV 5/5; 42/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 42/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=  10.8s\n",
      "[CV 1/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=  17.9s\n",
      "[CV 2/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=  17.0s\n",
      "[CV 3/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=  12.2s\n",
      "[CV 4/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  11.2s\n",
      "[CV 5/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=  13.3s\n",
      "[CV 1/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  10.6s\n",
      "[CV 2/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  10.6s\n",
      "[CV 3/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=  13.5s\n",
      "[CV 4/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.814 total time=  14.0s\n",
      "[CV 5/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=  12.2s\n",
      "[CV 1/5; 45/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 45/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=  16.9s\n",
      "[CV 2/5; 45/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 45/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=  16.7s\n",
      "[CV 3/5; 45/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 45/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=  11.5s\n",
      "[CV 4/5; 45/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 45/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  11.0s\n",
      "[CV 5/5; 45/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 45/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=  11.6s\n",
      "[CV 1/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=  10.2s\n",
      "[CV 2/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=  10.5s\n",
      "[CV 3/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=  10.9s\n",
      "[CV 4/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   8.7s\n",
      "[CV 5/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   9.4s\n",
      "[CV 1/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   6.1s\n",
      "[CV 2/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   5.9s\n",
      "[CV 3/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=   7.2s\n",
      "[CV 4/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   5.1s\n",
      "[CV 5/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   6.0s\n",
      "[CV 1/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   5.3s\n",
      "[CV 2/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   5.9s\n",
      "[CV 3/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   5.3s\n",
      "[CV 4/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.814 total time=   7.1s\n",
      "[CV 5/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=   6.1s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.807604 using {'classifier__C': 5.0, 'classifier__max_iter': 100, 'classifier__solver': 'liblinear', 'transformer__vect_reviewText__max_features': 5000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 32000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.8143643540061167\n",
      "accuracy:  0.8898595409386776\n",
      "auc score:  0.7959306256175568\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__solver\": ['liblinear'],\n",
    "    \"classifier__C\": [0.1, 1.0, 5.0, 10.0],\n",
    "    \"classifier__max_iter\": [100, 500, 1000]\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_log = GridSearchCV(pipe, params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_log.fit(X_train, y_train)\n",
    "print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Best: %f using %s\" % (grid_log.best_score_, grid_log.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_log = grid_log.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_log, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_log)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_log)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm_log = confusion_matrix(y_test, y_pred_log)\n",
    "tn, fp, fn, tp = cm_log.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm_log, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_log = grid_log.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_log[:,1])\n",
    "sns.lineplot(x=fpr, y=tpr,label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Logistic Regression\")\n",
    "\n",
    "# Best: 0.808420 using {'classifier__C': 5.0, 'classifier__max_iter': 100, 'classifier__solver': 'liblinear', 'transformer__vect_reviewText__max_features': 5000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 40000, 'transformer__vect_summary__ngram_range': (1, 2)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "[CV 1/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.792 total time=   2.3s\n",
      "[CV 2/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   2.3s\n",
      "[CV 3/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.6s\n",
      "[CV 4/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   2.3s\n",
      "[CV 5/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.778 total time=   2.4s\n",
      "[CV 1/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.4s\n",
      "[CV 2/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.2s\n",
      "[CV 3/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.2s\n",
      "[CV 4/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.2s\n",
      "[CV 5/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.3s\n",
      "[CV 1/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   2.2s\n",
      "[CV 2/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.782 total time=   2.2s\n",
      "[CV 3/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.775 total time=   2.2s\n",
      "[CV 4/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=   2.3s\n",
      "[CV 5/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.774 total time=   2.3s\n",
      "[CV 1/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.2s\n",
      "[CV 2/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 4/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.3s\n",
      "[CV 3/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.3s\n",
      "[CV 4/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 4/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.3s\n",
      "[CV 5/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.774 total time=   2.3s\n",
      "[CV 1/5; 5/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 5/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   2.4s\n",
      "[CV 2/5; 5/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.3s\n",
      "[CV 3/5; 5/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 5/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.1s\n",
      "[CV 4/5; 5/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.3s\n",
      "[CV 5/5; 5/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 5/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.783 total time=   2.3s\n",
      "[CV 1/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   2.4s\n",
      "[CV 2/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.2s\n",
      "[CV 3/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   2.3s\n",
      "[CV 4/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.4s\n",
      "[CV 5/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.777 total time=   2.3s\n",
      "[CV 1/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   2.4s\n",
      "[CV 2/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.2s\n",
      "[CV 3/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   2.3s\n",
      "[CV 4/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   2.2s\n",
      "[CV 5/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.3s\n",
      "[CV 1/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   2.3s\n",
      "[CV 2/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.2s\n",
      "[CV 3/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.4s\n",
      "[CV 4/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.3s\n",
      "[CV 5/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.2s\n",
      "[CV 1/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.2s\n",
      "[CV 2/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   2.3s\n",
      "[CV 3/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.775 total time=   2.3s\n",
      "[CV 4/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.803 total time=   2.3s\n",
      "[CV 5/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.4s\n",
      "[CV 1/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.777 total time=   2.3s\n",
      "[CV 2/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.778 total time=   2.4s\n",
      "[CV 3/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.4s\n",
      "[CV 4/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.2s\n",
      "[CV 5/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.772 total time=   2.6s\n",
      "[CV 1/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   2.4s\n",
      "[CV 2/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.4s\n",
      "[CV 3/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.5s\n",
      "[CV 4/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   2.6s\n",
      "[CV 5/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.8s\n",
      "[CV 1/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.778 total time=   2.7s\n",
      "[CV 2/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.780 total time=   3.1s\n",
      "[CV 3/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.778 total time=   2.6s\n",
      "[CV 4/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.4s\n",
      "[CV 5/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 12/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.775 total time=   2.3s\n",
      "[CV 1/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   2.7s\n",
      "[CV 2/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   2.4s\n",
      "[CV 3/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   2.4s\n",
      "[CV 4/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   2.4s\n",
      "[CV 5/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.4s\n",
      "[CV 1/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=   2.3s\n",
      "[CV 2/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   2.5s\n",
      "[CV 3/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   2.3s\n",
      "[CV 4/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   2.5s\n",
      "[CV 5/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   2.6s\n",
      "[CV 1/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=   2.5s\n",
      "[CV 2/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   2.6s\n",
      "[CV 3/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   2.6s\n",
      "[CV 4/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   2.5s\n",
      "[CV 5/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.8s\n",
      "[CV 1/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=   2.5s\n",
      "[CV 2/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=   2.6s\n",
      "[CV 3/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   2.4s\n",
      "[CV 4/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   2.8s\n",
      "[CV 5/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   2.6s\n",
      "[CV 1/5; 17/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 17/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.765 total time=   2.8s\n",
      "[CV 2/5; 17/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 17/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.762 total time=   2.6s\n",
      "[CV 3/5; 17/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 17/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.761 total time=   2.5s\n",
      "[CV 4/5; 17/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 17/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   2.5s\n",
      "[CV 5/5; 17/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 17/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.756 total time=   2.4s\n",
      "[CV 1/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.689 total time=   2.3s\n",
      "[CV 2/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.681 total time=   2.7s\n",
      "[CV 3/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.694 total time=   2.3s\n",
      "[CV 4/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   2.3s\n",
      "[CV 5/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.684 total time=   2.3s\n",
      "[CV 1/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 19/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.764 total time=   2.3s\n",
      "[CV 2/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 19/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.760 total time=   2.2s\n",
      "[CV 3/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 19/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.754 total time=   2.3s\n",
      "[CV 4/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 19/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.766 total time=   2.3s\n",
      "[CV 5/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.755 total time=   2.2s\n",
      "[CV 1/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 20/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.690 total time=   2.3s\n",
      "[CV 2/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.685 total time=   2.3s\n",
      "[CV 3/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.696 total time=   2.3s\n",
      "[CV 4/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.710 total time=   2.3s\n",
      "[CV 5/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 20/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.679 total time=   2.3s\n",
      "[CV 1/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=   2.2s\n",
      "[CV 2/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.803 total time=   2.2s\n",
      "[CV 3/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   2.3s\n",
      "[CV 4/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.821 total time=   2.3s\n",
      "[CV 5/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.803 total time=   2.3s\n",
      "[CV 1/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.765 total time=   2.3s\n",
      "[CV 2/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   2.3s\n",
      "[CV 3/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.760 total time=   2.2s\n",
      "[CV 4/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.770 total time=   2.2s\n",
      "[CV 5/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.757 total time=   2.2s\n",
      "[CV 1/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.2s\n",
      "[CV 2/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   2.2s\n",
      "[CV 3/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   2.2s\n",
      "[CV 4/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=   2.4s\n",
      "[CV 5/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   2.3s\n",
      "[CV 1/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.768 total time=   2.2s\n",
      "[CV 2/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.766 total time=   2.2s\n",
      "[CV 3/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.760 total time=   2.3s\n",
      "[CV 4/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.765 total time=   2.3s\n",
      "[CV 5/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.757 total time=   2.4s\n",
      "[CV 1/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.713 total time=   2.3s\n",
      "[CV 2/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.701 total time=   2.2s\n",
      "[CV 3/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.723 total time=   2.3s\n",
      "[CV 4/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.719 total time=   2.3s\n",
      "[CV 5/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.708 total time=   2.3s\n",
      "[CV 1/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.665 total time=   2.2s\n",
      "[CV 2/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.661 total time=   2.5s\n",
      "[CV 3/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.671 total time=   2.3s\n",
      "[CV 4/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.671 total time=   2.3s\n",
      "[CV 5/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.656 total time=   2.3s\n",
      "[CV 1/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.701 total time=   2.3s\n",
      "[CV 2/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.691 total time=   2.3s\n",
      "[CV 3/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.709 total time=   2.4s\n",
      "[CV 4/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.716 total time=   2.2s\n",
      "[CV 5/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.695 total time=   2.4s\n",
      "[CV 1/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.665 total time=   2.6s\n",
      "[CV 2/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.663 total time=   2.2s\n",
      "[CV 3/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.670 total time=   2.3s\n",
      "[CV 4/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.672 total time=   2.3s\n",
      "[CV 5/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.656 total time=   2.2s\n",
      "[CV 1/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.3s\n",
      "[CV 2/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=   2.2s\n",
      "[CV 3/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   2.5s\n",
      "[CV 4/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.803 total time=   2.3s\n",
      "[CV 5/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.5s\n",
      "[CV 1/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.685 total time=   2.4s\n",
      "[CV 2/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.681 total time=   2.3s\n",
      "[CV 3/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.692 total time=   2.9s\n",
      "[CV 4/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.701 total time=   2.3s\n",
      "[CV 5/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.676 total time=   2.3s\n",
      "[CV 1/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.5s\n",
      "[CV 2/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.5s\n",
      "[CV 3/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.2s\n",
      "[CV 4/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   2.3s\n",
      "[CV 5/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.775 total time=   2.4s\n",
      "[CV 1/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.689 total time=   2.6s\n",
      "[CV 2/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.679 total time=   2.5s\n",
      "[CV 3/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.692 total time=   2.4s\n",
      "[CV 4/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.700 total time=   2.4s\n",
      "[CV 5/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.673 total time=   2.5s\n",
      "[CV 1/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.515 total time=   2.5s\n",
      "[CV 2/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.501 total time=   2.2s\n",
      "[CV 3/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.500 total time=   2.2s\n",
      "[CV 4/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.504 total time=   2.2s\n",
      "[CV 5/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.484 total time=   2.2s\n",
      "[CV 1/5; 34/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 34/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.453 total time=   2.2s\n",
      "[CV 2/5; 34/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 34/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.448 total time=   2.2s\n",
      "[CV 3/5; 34/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 34/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.454 total time=   2.1s\n",
      "[CV 4/5; 34/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 34/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.453 total time=   2.1s\n",
      "[CV 5/5; 34/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 34/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.451 total time=   2.2s\n",
      "[CV 1/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 35/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.488 total time=   2.3s\n",
      "[CV 2/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 35/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.482 total time=   2.3s\n",
      "[CV 3/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 35/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.489 total time=   2.5s\n",
      "[CV 4/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 35/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.485 total time=   2.9s\n",
      "[CV 5/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 35/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.471 total time=   2.2s\n",
      "[CV 1/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.450 total time=   2.3s\n",
      "[CV 2/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 3/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.453 total time=   2.3s\n",
      "[CV 4/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.450 total time=   2.3s\n",
      "[CV 5/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.450 total time=   2.3s\n",
      "[CV 1/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.2s\n",
      "[CV 2/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.2s\n",
      "[CV 3/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   2.2s\n",
      "[CV 4/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.564 total time=   2.2s\n",
      "[CV 5/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.554 total time=   2.2s\n",
      "[CV 1/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.491 total time=   2.2s\n",
      "[CV 2/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.487 total time=   2.2s\n",
      "[CV 3/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.493 total time=   2.3s\n",
      "[CV 4/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.491 total time=   2.2s\n",
      "[CV 5/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.476 total time=   2.2s\n",
      "[CV 1/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.558 total time=   2.1s\n",
      "[CV 2/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.545 total time=   2.3s\n",
      "[CV 3/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.545 total time=   2.2s\n",
      "[CV 4/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.545 total time=   2.3s\n",
      "[CV 5/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.536 total time=   2.2s\n",
      "[CV 1/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.494 total time=   2.3s\n",
      "[CV 2/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.485 total time=   2.3s\n",
      "[CV 3/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.488 total time=   2.3s\n",
      "[CV 4/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.487 total time=   2.2s\n",
      "[CV 5/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.474 total time=   2.1s\n",
      "Best: 0.809570 using {'classifier__alpha': 0.5, 'classifier__fit_prior': False, 'transformer__vect_reviewText__max_features': 10000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 10000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.8210957623021629\n",
      "accuracy:  0.880781089414183\n",
      "auc score:  0.8392909147641584\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", MultinomialNB())\n",
    "])\n",
    "\n",
    "mnbModel_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__alpha\": [0.01, 0.1, 0.5, 1, 10],\n",
    "    'classifier__fit_prior': [True, False],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_mnb = GridSearchCV(pipe, mnbModel_params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_mnb.fit(X_train, y_train)\n",
    "print(\"Best: %f using %s\" % (grid_mnb.best_score_, grid_mnb.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_mnb = grid_mnb.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_mnb, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_mnb)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_mnb)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_mnb)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_mnb = grid_mnb.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_mnb[:,1])\n",
    "sns.lineplot(x=fpr, y=tpr,label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"MultinomialNB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "[CV 1/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 2/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 5/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 1/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 2/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 3/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 5/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 1/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 2/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 4/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 2/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 3/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 4/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.780 total time=   6.8s\n",
      "[CV 2/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.758 total time=   7.5s\n",
      "[CV 3/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.771 total time=   7.2s\n",
      "[CV 4/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   6.4s\n",
      "[CV 5/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   7.2s\n",
      "[CV 1/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.792 total time=   8.4s\n",
      "[CV 2/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.774 total time=   7.8s\n",
      "[CV 3/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.781 total time=   8.0s\n",
      "[CV 4/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   8.9s\n",
      "[CV 5/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.778 total time=   8.8s\n",
      "[CV 1/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.781 total time=   8.9s\n",
      "[CV 2/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.765 total time=   7.4s\n",
      "[CV 3/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.777 total time=   9.3s\n",
      "[CV 4/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   8.5s\n",
      "[CV 5/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.773 total time=   8.7s\n",
      "[CV 1/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.788 total time=   8.8s\n",
      "[CV 2/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.775 total time=  10.1s\n",
      "[CV 3/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   9.5s\n",
      "[CV 4/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   9.6s\n",
      "[CV 5/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.781 total time=   9.8s\n",
      "[CV 1/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 2/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time= 1.3min\n",
      "[CV 4/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 2/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time= 1.6min\n",
      "[CV 3/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 4/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 5/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 2/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 3/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 4/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 5/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 2/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 3/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 4/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 5/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   6.1s\n",
      "[CV 2/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.785 total time=   5.6s\n",
      "[CV 3/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.792 total time=   7.3s\n",
      "[CV 4/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   7.6s\n",
      "[CV 5/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   6.1s\n",
      "[CV 1/5; 14/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   6.5s\n",
      "[CV 2/5; 14/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   5.8s\n",
      "[CV 3/5; 14/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 14/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=   5.6s\n",
      "[CV 4/5; 14/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   6.4s\n",
      "[CV 5/5; 14/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   6.5s\n",
      "[CV 1/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 15/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.794 total time=   6.9s\n",
      "[CV 2/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 15/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   6.5s\n",
      "[CV 3/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 15/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=   6.0s\n",
      "[CV 4/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 15/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   5.6s\n",
      "[CV 5/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 15/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   7.3s\n",
      "[CV 1/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   9.0s\n",
      "[CV 2/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   8.8s\n",
      "[CV 3/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   7.5s\n",
      "[CV 4/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   8.3s\n",
      "[CV 5/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   8.1s\n",
      "[CV 1/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 2/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 3/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 4/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 5/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 1/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   3.0s\n",
      "[CV 2/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.9s\n",
      "[CV 3/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.8s\n",
      "[CV 4/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 5/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 1/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 2/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 3/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 1/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 2/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 3/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   3.2s\n",
      "[CV 2/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   3.3s\n",
      "[CV 3/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.814 total time=   3.7s\n",
      "[CV 4/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.829 total time=   3.4s\n",
      "[CV 5/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   3.7s\n",
      "[CV 1/5; 22/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 22/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   3.3s\n",
      "[CV 2/5; 22/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   3.6s\n",
      "[CV 3/5; 22/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 22/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=   3.5s\n",
      "[CV 4/5; 22/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 22/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.832 total time=   3.6s\n",
      "[CV 5/5; 22/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=   3.7s\n",
      "[CV 1/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 23/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=   3.5s\n",
      "[CV 2/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 23/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   3.8s\n",
      "[CV 3/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 23/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   3.5s\n",
      "[CV 4/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 23/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.827 total time=   4.2s\n",
      "[CV 5/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   3.8s\n",
      "[CV 1/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   4.1s\n",
      "[CV 2/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   3.8s\n",
      "[CV 3/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=   4.4s\n",
      "[CV 4/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   3.4s\n",
      "[CV 5/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   3.7s\n",
      "[CV 1/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 2/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 3/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 4/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 1/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 2/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 3/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 1/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 2/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 3/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 4/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 5/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 2/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 4/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.5s\n",
      "[CV 2/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.781 total time=   2.6s\n",
      "[CV 3/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.7s\n",
      "[CV 4/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   2.7s\n",
      "[CV 5/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   2.5s\n",
      "[CV 1/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.6s\n",
      "[CV 2/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.8s\n",
      "[CV 3/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.5s\n",
      "[CV 4/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   2.6s\n",
      "[CV 5/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.782 total time=   2.5s\n",
      "[CV 1/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.6s\n",
      "[CV 2/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.6s\n",
      "[CV 3/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   3.0s\n",
      "[CV 4/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   3.6s\n",
      "[CV 5/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.780 total time=   5.3s\n",
      "[CV 1/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   4.7s\n",
      "[CV 2/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.783 total time=   4.0s\n",
      "[CV 3/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   3.1s\n",
      "[CV 4/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   3.0s\n",
      "[CV 5/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.780 total time=   2.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
      "80 fits failed out of a total of 160.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "80 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/pipeline.py\", line 405, in fit\n",
      "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/calibration.py\", line 395, in fit\n",
      "    self.calibrated_classifiers_ = parallel(\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 63, in __call__\n",
      "    return super().__call__(iterable_with_config)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/parallel.py\", line 1085, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/parallel.py\", line 901, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/parallel.py\", line 819, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 597, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/joblib/parallel.py\", line 288, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/utils/parallel.py\", line 123, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/calibration.py\", line 577, in _fit_classifier_calibrator_pair\n",
      "    estimator.fit(X_train, y_train, **fit_params_train)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/svm/_classes.py\", line 274, in fit\n",
      "    self.coef_, self.intercept_, n_iter_ = _fit_liblinear(\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/svm/_base.py\", line 1223, in _fit_liblinear\n",
      "    solver_type = _get_liblinear_solver_type(multi_class, penalty, loss, dual)\n",
      "  File \"/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/svm/_base.py\", line 1062, in _get_liblinear_solver_type\n",
      "    raise ValueError(\n",
      "ValueError: Unsupported set of arguments: The combination of penalty='l1' and loss='squared_hinge' are not supported when dual=True, Parameters: penalty='l1', loss='squared_hinge', dual=True\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/Users/shyamreddy/.pyenv/versions/3.9.2/envs/mlenv/lib/python3.9/site-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan 0.7729519  0.78321129\n",
      " 0.77729853 0.78489979        nan        nan        nan        nan\n",
      " 0.79443138 0.79874286 0.79523805 0.80037105        nan        nan\n",
      "        nan        nan 0.81020155 0.81316082 0.80777574 0.81203205\n",
      "        nan        nan        nan        nan 0.78982468 0.79122516\n",
      " 0.78729283 0.78878806]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.813161 using {'classifier__estimator__C': 0.1, 'classifier__estimator__penalty': 'l2', 'transformer__vect_reviewText__max_features': 5000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 32000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.821174767865851\n",
      "accuracy:  0.894998287084618\n",
      "auc score:  0.7994578501222113\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", CalibratedClassifierCV(estimator = LinearSVC()))\n",
    "])\n",
    "\n",
    "svcModel_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__estimator__C\": [10, 1, 0.1, 0.01],\n",
    "    \"classifier__estimator__penalty\": ['l1', 'l2'],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_svc = GridSearchCV(pipe, svcModel_params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_svc.fit(X_train, y_train)\n",
    "print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Best: %f using %s\" % (grid_svc.best_score_, grid_svc.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_svc = grid_svc.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_svc, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_svc)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_svc)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm_svc = confusion_matrix(y_test, y_pred_svc)\n",
    "tn, fp, fn, tp = cm_svc.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm_svc, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_svc = grid_svc.best_estimator_.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_svc[:, 1])\n",
    "sns.lineplot(x=fpr, y=tpr, label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"LinearSVC\")\n",
    "\n",
    "# Best: 0.805811 using {'classifier__C': 1, 'classifier__penalty': 'l2', 'transformer__vect_reviewText__max_features': 10000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 40000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "[CV 1/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.9s\n",
      "[CV 2/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.6s\n",
      "[CV 3/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.7s\n",
      "[CV 4/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   2.7s\n",
      "[CV 5/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.6s\n",
      "[CV 1/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.7s\n",
      "[CV 2/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.780 total time=   2.6s\n",
      "[CV 3/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.7s\n",
      "[CV 4/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.805 total time=   2.6s\n",
      "[CV 5/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.779 total time=   2.6s\n",
      "[CV 1/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.783 total time=   2.6s\n",
      "[CV 2/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.772 total time=   2.7s\n",
      "[CV 3/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.6s\n",
      "[CV 4/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   3.3s\n",
      "[CV 5/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   3.0s\n",
      "[CV 1/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.785 total time=   3.5s\n",
      "[CV 2/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.777 total time=   3.4s\n",
      "[CV 3/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   3.5s\n",
      "[CV 4/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   3.0s\n",
      "[CV 5/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.777 total time=   2.7s\n",
      "[CV 1/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.801 total time=   2.9s\n",
      "[CV 2/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.783 total time=   2.9s\n",
      "[CV 3/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   2.6s\n",
      "[CV 4/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   2.6s\n",
      "[CV 5/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.782 total time=   2.8s\n",
      "[CV 1/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.804 total time=   2.9s\n",
      "[CV 2/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.7s\n",
      "[CV 3/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.782 total time=   2.9s\n",
      "[CV 4/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.9s\n",
      "[CV 5/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   3.6s\n",
      "[CV 1/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.797 total time=   2.8s\n",
      "[CV 2/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.795 total time=   2.7s\n",
      "[CV 3/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   2.6s\n",
      "[CV 4/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.809 total time=   2.6s\n",
      "[CV 5/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   2.5s\n",
      "[CV 1/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   2.5s\n",
      "[CV 2/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.789 total time=   2.5s\n",
      "[CV 3/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.6s\n",
      "[CV 4/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.5s\n",
      "[CV 5/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.5s\n",
      "[CV 1/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.654 total time=   2.4s\n",
      "[CV 2/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.640 total time=   2.5s\n",
      "[CV 3/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.660 total time=   2.5s\n",
      "[CV 4/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.656 total time=   2.5s\n",
      "[CV 5/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.647 total time=   2.5s\n",
      "[CV 1/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.655 total time=   2.6s\n",
      "[CV 2/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.640 total time=   2.6s\n",
      "[CV 3/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.662 total time=   2.5s\n",
      "[CV 4/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.655 total time=   2.5s\n",
      "[CV 5/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.648 total time=   2.5s\n",
      "[CV 1/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.654 total time=   2.5s\n",
      "[CV 2/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.641 total time=   2.5s\n",
      "[CV 3/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.662 total time=   2.5s\n",
      "[CV 4/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.657 total time=   2.5s\n",
      "[CV 5/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.649 total time=   2.6s\n",
      "[CV 1/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.657 total time=   2.7s\n",
      "[CV 2/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.640 total time=   3.2s\n",
      "[CV 3/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.664 total time=   3.4s\n",
      "[CV 4/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.657 total time=   3.0s\n",
      "[CV 5/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.650 total time=   2.8s\n",
      "[CV 1/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=   2.6s\n",
      "[CV 2/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.776 total time=   2.6s\n",
      "[CV 3/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.6s\n",
      "[CV 4/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   2.6s\n",
      "[CV 5/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.772 total time=   2.6s\n",
      "[CV 1/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.6s\n",
      "[CV 2/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.782 total time=   2.5s\n",
      "[CV 3/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.792 total time=   2.6s\n",
      "[CV 4/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   2.7s\n",
      "[CV 5/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   2.5s\n",
      "[CV 1/5; 15/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 15/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   2.5s\n",
      "[CV 2/5; 15/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 15/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   2.5s\n",
      "[CV 3/5; 15/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 15/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 total time=   2.4s\n",
      "[CV 4/5; 15/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 15/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   2.5s\n",
      "[CV 5/5; 15/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 15/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.793 total time=   2.5s\n",
      "[CV 1/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.784 total time=   2.4s\n",
      "[CV 2/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.770 total time=   2.5s\n",
      "[CV 3/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.788 total time=   2.5s\n",
      "[CV 4/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.3s\n",
      "[CV 5/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.4s\n",
      "[CV 1/5; 17/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 17/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.454 total time=   2.2s\n",
      "[CV 2/5; 17/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 17/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.3s\n",
      "[CV 3/5; 17/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 17/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.455 total time=   2.3s\n",
      "[CV 4/5; 17/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 17/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.2s\n",
      "[CV 5/5; 17/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 17/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.459 total time=   2.4s\n",
      "[CV 1/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.454 total time=   2.3s\n",
      "[CV 2/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.2s\n",
      "[CV 3/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.455 total time=   2.3s\n",
      "[CV 4/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.5s\n",
      "[CV 5/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.459 total time=   2.6s\n",
      "[CV 1/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.454 total time=   3.0s\n",
      "[CV 2/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.9s\n",
      "[CV 3/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.455 total time=   2.5s\n",
      "[CV 4/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.4s\n",
      "[CV 5/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.459 total time=   2.4s\n",
      "[CV 1/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.454 total time=   2.2s\n",
      "[CV 2/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.3s\n",
      "[CV 3/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.455 total time=   2.5s\n",
      "[CV 4/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.449 total time=   2.5s\n",
      "[CV 5/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.459 total time=   2.4s\n",
      "[CV 1/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.735 total time=   2.4s\n",
      "[CV 2/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.759 total time=   2.3s\n",
      "[CV 3/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.766 total time=   2.6s\n",
      "[CV 4/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   2.5s\n",
      "[CV 5/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.753 total time=   2.4s\n",
      "[CV 1/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.769 total time=   2.4s\n",
      "[CV 2/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.761 total time=   2.3s\n",
      "[CV 3/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.749 total time=   2.7s\n",
      "[CV 4/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.790 total time=   3.2s\n",
      "[CV 5/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.764 total time=   2.6s\n",
      "[CV 1/5; 23/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 23/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.740 total time=   2.6s\n",
      "[CV 2/5; 23/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 23/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.763 total time=   2.3s\n",
      "[CV 3/5; 23/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 23/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.758 total time=   2.4s\n",
      "[CV 4/5; 23/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 23/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.773 total time=   2.3s\n",
      "[CV 5/5; 23/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.730 total time=   2.4s\n",
      "[CV 1/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.756 total time=   2.5s\n",
      "[CV 2/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.753 total time=   2.4s\n",
      "[CV 3/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.749 total time=   2.4s\n",
      "[CV 4/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   2.4s\n",
      "[CV 5/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.767 total time=   2.4s\n",
      "[CV 1/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.4s\n",
      "[CV 2/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.4s\n",
      "[CV 3/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.5s\n",
      "[CV 4/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 5/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 1/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 2/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 3/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 4/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 5/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 1/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 2/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 3/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 4/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 5/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.2s\n",
      "[CV 1/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 2/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 3/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 4/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.4s\n",
      "[CV 5/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.447 total time=   2.3s\n",
      "[CV 1/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.481 total time=   2.3s\n",
      "[CV 2/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.2s\n",
      "[CV 3/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.2s\n",
      "[CV 4/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.621 total time=   2.3s\n",
      "[CV 5/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.610 total time=   2.2s\n",
      "[CV 1/5; 30/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 30/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.547 total time=   2.2s\n",
      "[CV 2/5; 30/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.537 total time=   2.3s\n",
      "[CV 3/5; 30/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 30/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.615 total time=   2.3s\n",
      "[CV 4/5; 30/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.629 total time=   2.3s\n",
      "[CV 5/5; 30/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.608 total time=   2.3s\n",
      "[CV 1/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.551 total time=   2.3s\n",
      "[CV 2/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.479 total time=   2.5s\n",
      "[CV 3/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.637 total time=   2.3s\n",
      "[CV 4/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.448 total time=   2.3s\n",
      "[CV 5/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   2.3s\n",
      "[CV 1/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   2.3s\n",
      "[CV 2/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.669 total time=   2.4s\n",
      "[CV 3/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.507 total time=   2.3s\n",
      "[CV 4/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.560 total time=   2.4s\n",
      "[CV 5/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.667 total time=   2.7s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.798111 using {'classifier__estimator__alpha': 0.01, 'classifier__estimator__loss': 'perceptron', 'transformer__vect_reviewText__max_features': 10000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 10000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.8013125715299603\n",
      "accuracy:  0.8876327509421035\n",
      "auc score:  0.7718485100629258\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", CalibratedClassifierCV(estimator = SGDClassifier()))\n",
    "])\n",
    "\n",
    "sgd_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__estimator__alpha\": [0.001, 0.01, 0.1, 1],\n",
    "    \"classifier__estimator__loss\": ['hinge', 'perceptron'],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_sgd = GridSearchCV(pipe, sgd_params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_sgd.fit(X_train, y_train)\n",
    "print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Best: %f using %s\" % (grid_sgd.best_score_, grid_sgd.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_sgd = grid_sgd.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_sgd, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_sgd)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_sgd)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_sgd)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_sgd = grid_sgd.best_estimator_.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_sgd[:,1])\n",
    "sns.lineplot(x=fpr, y=tpr,label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Stochastic Gradient classifier\")\n"
   ]
  },
  {
   "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>Model</th>\n",
       "      <th>auc</th>\n",
       "      <th>f1 score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.795931</td>\n",
       "      <td>0.814364</td>\n",
       "      <td>0.889860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.839291</td>\n",
       "      <td>0.821096</td>\n",
       "      <td>0.880781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.799458</td>\n",
       "      <td>0.821175</td>\n",
       "      <td>0.894998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient classifier</td>\n",
       "      <td>0.771849</td>\n",
       "      <td>0.801313</td>\n",
       "      <td>0.887633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       auc  f1 score  Accuracy\n",
       "0             Logistic Regression  0.795931  0.814364  0.889860\n",
       "1                   MultinomialNB  0.839291  0.821096  0.880781\n",
       "2                       LinearSVC  0.799458  0.821175  0.894998\n",
       "3  Stochastic Gradient classifier  0.771849  0.801313  0.887633"
      ]
     },
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    "pd.DataFrame({\"Model\": model_arr, \"auc\": auc_arr,\n",
    "      \"f1 score\": f1_arr, \"Accuracy\": accuracy_arr, }).head()\n"
   ]
  }
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