Final_3_1_HP_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter training for part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "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": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cutoff\n",
    "cutoff = 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>verified</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>vote</th>\n",
       "      <th>image</th>\n",
       "      <th>style</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29189.000000</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29185</td>\n",
       "      <td>29189</td>\n",
       "      <td>29184</td>\n",
       "      <td>2.918900e+04</td>\n",
       "      <td>6047.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>15365</td>\n",
       "      <td>29189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4866</td>\n",
       "      <td>27588</td>\n",
       "      <td>24102</td>\n",
       "      <td>23305</td>\n",
       "      <td>28807</td>\n",
       "      <td>21768</td>\n",
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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",
       "      <td>25</td>\n",
       "      <td>1476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4296</td>\n",
       "      <td>4912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.971907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.409661e+09</td>\n",
       "      <td>7.295353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.410878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.115099e+08</td>\n",
       "      <td>18.749460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.947584e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.390781e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.439770e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.476317e+09</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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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": 308,
     "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": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\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 = ['ozzy', 'fyi', 'hydrated', '2x', 'exposure', 'extremely', 'execution', 'expert', '3x', '2wd', 'swivel', 'dwarf', 'sweat', 'owner', 'twins', 'rv', 'everybody', 'uv', 'evil', 'overseas', 'overlooked', 'overwhelmed', 'evenly', 'evo', 'quits', 'bux', 'rust', 'quit', 'burnout', 'sunshade', 'suction', 'ruined', 'bubbled', 'suck', 'purchasing', 'suspect', 'functionally', 'kurt', 'sucked', 'funky', 'suggestion', 'vu', 'rub', 'turner', 'bubblegum', 'puke', 'public', 'funniest', 'buble', 'julian', 'hunter', 'outshines', 'submerged', 'bullet', 'punish', 'puts', 'butterfly', 'guaranteed', 'rum', 'mushy', 'purposely', 'rubbed', 'nutrition', 'august', 'bucks', 'cumbersome', 'suffer', 'pumps', 'funko', 'cutie', 'pups', 'stopped', 'atv', 'strut', 'steer', 'wth', 'starters', 'stravinsky',\n",
    "                      'atleast', 'stinks', 'stage', 'stripped', 'styrofoam', 'starch', 'att', 'stylus', 'struggled', 'asian', 'isuzu', 'essence', 'es', 'asking', 'ashlee', '5star', 'os', '3s', 'broken', 'broke', 'trd', 'arctic', 'trucks', 'brightness', 'crappy', 'cracked', 'triple', 'transfer', 'prevents', 'fraction', 'trance', 'breath', 'written', 'orbital', 'proud', 'producing', 'freaking', 'dreadful', 'tripe', 'fred', 'grown', 'frustration', 'gross', 'fruits', 'grocery', 'artisan', 'fruitcake', 'preservatives', 'grainy', 'trump', 'crumbled', 'grains', 'pricing', 'mrs', 'draft', 'crash', 'privacy', 'arms', 'cracks', 'ar', 'erase', 'articulation', 'breyer', 'crayons', 'sponge', 'applications', 'appeared', 'tpms', 'spout', 'upon', 'specifications', 'spry', 'apricots', 'spots', 'spiced', '6p', 'springs', 'ipad3']\n",
    "STOP_WORDS_REVIEW_TEXT = ['ez', 'oza', 'azalea', 'ozawa', 'ozzmosis', 'typo', 'mysteriously', 'gy', 'tyler', 'oye', 'ryche', 'bytes', 'myung', 'wyclef', 'lydon', 'wynonna', 'nyquil', 'hypocrisy', 'dysfunctional', 'hydrated', 'xylichew', 'syrupi', 'symbols', '7yr', '9yr', '7yo', 'examine', 'ox', '3xl', 'axel', '5xl', 'expelled', 'execs', 'experiencewhat', 'experiece', 'excitment', 'exploited', 'experimentalism', 'existed', 'axemen', 'exacerbate', 'exiles', 'extremly', 'expendable', 'exhibit', 'exceptionthe', 'exits', 'exercised', 'extol', '7x', 'expenses', 'expectancy', 'experienceupdate', '9x9', 'expanders', 'lxl', 'excavator', 'awg', '5wt', 'pwc', 'tweaked', 'awareness', 'swifts', 'swearing', 'twinkie', 'swooping',\n",
    "                          'awash', 'swells', '3way', 'sweetie', 'sweaters', 'awakening', 'awfulthe', 'awfuli', 'twinings', 'swallows', 'swollen', 'twining', 'twopart', 'ownership', 'swivels', 'sweatwicking', 'awayit', 'overtightened', 'avenues', 'evacuation', 'evacuate', 'overrated', 'pvc', 'evidenced', 'dvds', 'oversexed', 'evangelical', 'evaporate', 'overuse', 'overprocessed', 'averages', 'evolutionary', 'overdo', 'everclear', 'dvdif', 'evens', 'overpowered', 'avocados', 'everhard', 'aviator', 'evey', 'sucks', 'sucked', 'buick', 'buyer', 'bux', 'junkyard', 'purolator', 'duralast', 'surge', 'autos', 'turbine', 'runaround', 'purchasesthe', 'buggy', 'fuseholder', 'ruben', 'outupdatethe', 'puddles', 'suck', 'sucksthe', 'bulks', 'purchasethis']\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 + STOP_WORDS_SUMMARY\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "\n",
    "# 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 + STOP_WORDS_REVIEW_TEXT\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "# Stop words search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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": 311,
   "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": 312,
   "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": 313,
   "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=0.651 total time=   2.2s\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=0.662 total time=   2.2s\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=0.638 total time=   2.0s\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=0.680 total time=   2.1s\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=0.670 total time=   2.2s\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=0.651 total time=   2.1s\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=0.653 total time=   2.2s\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=0.634 total time=   2.6s\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=0.676 total time=   2.6s\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=0.665 total time=   2.6s\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=0.646 total time=   2.2s\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=0.653 total time=   2.2s\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=0.633 total time=   2.0s\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=0.674 total time=   2.2s\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=0.666 total time=   1.9s\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=0.646 total time=   2.0s\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=0.646 total time=   2.0s\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=0.627 total time=   2.3s\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=0.668 total time=   2.4s\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=0.663 total time=   2.6s\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=0.651 total time=   2.0s\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=0.662 total time=   2.0s\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=0.638 total time=   2.1s\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=0.680 total time=   2.5s\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=0.670 total time=   2.2s\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=0.651 total time=   2.0s\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=0.653 total time=   2.0s\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=0.634 total time=   1.9s\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=0.676 total time=   1.9s\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=0.665 total time=   2.0s\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=0.646 total time=   1.9s\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=0.653 total time=   1.9s\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=0.633 total time=   2.0s\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=0.674 total time=   2.0s\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=0.666 total time=   1.9s\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=0.646 total time=   2.0s\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=0.646 total time=   2.1s\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=0.627 total time=   2.2s\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=0.668 total time=   2.0s\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=0.663 total time=   2.0s\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=0.651 total time=   1.9s\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=0.662 total time=   1.9s\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=0.638 total time=   2.2s\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=0.680 total time=   2.4s\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=0.670 total time=   2.3s\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=0.651 total time=   2.1s\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=0.653 total time=   1.9s\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=0.634 total time=   2.0s\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=0.676 total time=   1.9s\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=0.665 total time=   1.9s\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=0.646 total time=   1.9s\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=0.653 total time=   1.9s\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=0.633 total time=   1.9s\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=0.674 total time=   2.0s\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=0.666 total time=   1.9s\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=0.646 total time=   2.0s\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=0.646 total time=   1.9s\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=0.627 total time=   1.9s\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=0.668 total time=   2.0s\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=0.663 total time=   1.9s\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=0.768 total time=   1.9s\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=0.769 total time=   1.9s\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=0.745 total time=   1.9s\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=0.779 total time=   2.0s\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=0.768 total time=   2.0s\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=0.771 total time=   2.0s\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=0.765 total time=   2.0s\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=0.748 total time=   2.0s\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=0.779 total time=   2.0s\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=0.767 total time=   2.0s\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=0.764 total time=   1.9s\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=0.765 total time=   2.0s\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=0.747 total time=   2.0s\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=0.777 total time=   2.0s\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=0.764 total time=   2.0s\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=0.765 total time=   2.1s\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=0.762 total time=   2.8s\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=0.748 total time=   2.6s\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=0.779 total time=   3.3s\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=0.763 total time=   2.8s\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=0.768 total time=   2.1s\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=0.769 total time=   2.2s\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=0.745 total time=   1.9s\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=0.779 total time=   2.0s\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=0.768 total time=   1.9s\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=0.771 total time=   1.9s\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=0.765 total time=   2.0s\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=0.748 total time=   2.0s\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=0.779 total time=   2.0s\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=0.767 total time=   2.0s\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=0.764 total time=   2.0s\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=0.765 total time=   2.0s\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=0.747 total time=   2.3s\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=0.777 total time=   2.1s\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=0.764 total time=   2.0s\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=0.765 total time=   2.1s\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=0.762 total time=   2.4s\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=0.748 total time=   2.2s\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=0.779 total time=   2.9s\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=0.763 total time=   2.9s\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=0.768 total time=   2.8s\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=0.769 total time=   2.3s\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=0.745 total time=   2.0s\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=0.779 total time=   2.2s\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=0.768 total time=   2.2s\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=0.771 total time=   2.2s\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=0.765 total time=   2.9s\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=0.748 total time=   2.8s\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=0.779 total time=   2.1s\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=0.767 total time=   2.0s\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=0.764 total time=   2.0s\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=0.765 total time=   2.0s\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=0.747 total time=   2.0s\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=0.777 total time=   2.1s\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=0.764 total time=   2.0s\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=0.765 total time=   2.0s\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=0.762 total time=   2.0s\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=0.748 total time=   2.0s\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=0.779 total time=   2.1s\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=0.763 total time=   2.3s\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=0.773 total time=   2.1s\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=0.773 total time=   2.0s\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=0.758 total time=   2.0s\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=0.790 total time=   2.0s\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=0.773 total time=   2.0s\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=0.779 total time=   2.0s\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=0.774 total time=   2.0s\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=0.762 total time=   2.0s\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=0.791 total time=   2.2s\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=0.778 total time=   2.4s\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=0.771 total time=   2.9s\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=0.778 total time=   2.6s\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=0.758 total time=   2.4s\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=0.794 total time=   2.6s\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=0.770 total time=   2.6s\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=0.775 total time=   3.3s\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=0.775 total time=   3.3s\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=0.760 total time=   3.0s\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=0.792 total time=   4.1s\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=0.771 total time=   3.6s\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=0.773 total time=   4.2s\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=0.773 total time=   4.7s\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=0.758 total time=   5.6s\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=0.790 total time=   4.9s\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=0.773 total time=   4.2s\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=0.779 total time=   5.3s\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=0.774 total time=   4.9s\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=0.762 total time=   4.1s\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=0.791 total time=   5.2s\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=0.778 total time=   3.5s\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=0.771 total time=   5.4s\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=0.778 total time=   7.4s\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=0.758 total time=   7.4s\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=0.794 total time=   6.6s\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=0.770 total time=   5.0s\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=0.775 total time=   5.8s\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=0.775 total time=   6.7s\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=0.760 total time=   6.9s\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=0.792 total time=   5.7s\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=0.771 total time=   8.2s\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=0.773 total time=   8.8s\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=0.773 total time=   5.0s\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=0.758 total time=   7.9s\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=0.790 total time=   9.3s\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=0.773 total time=   8.8s\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=0.779 total time=  10.6s\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=0.774 total time=  10.1s\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=0.762 total time=  11.1s\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=0.791 total time=  10.1s\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=0.778 total time=  13.4s\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=0.771 total time=  12.4s\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=0.778 total time=  14.1s\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=0.758 total time=  12.2s\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=0.794 total time=   9.3s\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=0.770 total time=  14.2s\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=0.775 total time=   9.8s\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=0.775 total time=  10.3s\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=0.760 total time=  12.6s\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=0.792 total time=  11.0s\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=0.771 total time=  10.2s\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=0.772 total time=  15.3s\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=0.771 total time=  10.5s\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=0.759 total time=  14.3s\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=0.787 total time=   9.7s\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=0.774 total time=  12.4s\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=0.775 total time=  12.6s\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=0.775 total time=  16.7s\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=0.762 total time=  11.3s\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=0.788 total time=  13.3s\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=0.781 total time=  11.7s\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=0.770 total time=  12.9s\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=0.774 total time=  14.1s\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=0.763 total time=  15.8s\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=0.793 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=0.772 total time=  11.7s\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=0.774 total time=  14.9s\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=0.775 total time=  13.8s\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=0.767 total time=  16.1s\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=0.793 total time=  14.0s\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=0.775 total time=  14.1s\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=0.772 total time=  14.9s\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=0.771 total time=  13.6s\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=0.759 total time=  21.0s\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=0.787 total time=  19.0s\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=0.774 total time=  15.9s\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=0.775 total time=  16.3s\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=0.775 total time=  12.6s\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=0.762 total time=  14.6s\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=0.788 total time=  16.1s\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=0.781 total time=  12.1s\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=0.770 total time=  15.6s\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=0.774 total time=  19.1s\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=0.763 total time=  13.6s\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=0.793 total time=  17.4s\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=0.772 total time=  15.0s\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=0.774 total time=  14.2s\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=0.775 total time=  15.2s\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=0.767 total time=  13.4s\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=0.793 total time=  18.7s\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=0.775 total time=  17.3s\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=0.772 total time=  18.7s\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=0.771 total time=  15.8s\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=0.759 total time=  17.2s\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=0.787 total time=  21.4s\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=0.774 total time=  15.7s\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=0.775 total time=  21.8s\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=0.775 total time=  19.1s\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=0.762 total time=  16.0s\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=0.788 total time=  11.1s\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=0.781 total time=  12.7s\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=0.770 total time=  18.5s\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=0.774 total time=  16.6s\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=0.763 total time=  12.7s\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=0.793 total time=  11.9s\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=0.772 total time=  15.7s\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=0.774 total time=  17.7s\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=0.775 total time=  14.5s\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=0.767 total time=  16.8s\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=0.793 total time=  18.2s\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=0.775 total time=  17.5s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.776689 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:  0.7891564665233699\n",
      "accuracy:  0.8744433025008564\n",
      "auc score:  0.7693886791238338\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 = confusion_matrix(y_test, y_pred_log)\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_log = grid_log.best_estimator_.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.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Logistic Regression\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "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=0.754 total time=   6.2s\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=0.757 total time=   5.1s\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=0.761 total time=   7.3s\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=0.780 total time=   5.1s\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=0.774 total time=   5.6s\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=0.748 total time=   5.5s\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=0.746 total time=   6.4s\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=0.751 total time=   5.1s\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=0.769 total time=   7.7s\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=0.771 total time=   3.9s\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=0.749 total time=   8.7s\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=0.760 total time=   4.7s\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=0.756 total time=   7.2s\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=0.778 total time=   6.8s\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=0.775 total time=   4.4s\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=0.747 total time=   7.6s\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=0.749 total time=   3.9s\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=0.748 total time=   7.1s\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=0.774 total time=   4.8s\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=0.772 total time=   6.9s\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=0.756 total time=   5.2s\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=0.757 total time=   7.5s\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=0.755 total time=   4.5s\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=0.769 total time=   7.9s\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=0.765 total time=   5.6s\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=0.752 total time=   6.8s\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=0.742 total time=   6.6s\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=0.750 total time=   5.5s\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=0.767 total time=   7.4s\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=0.764 total time=   4.2s\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=0.760 total time=   8.8s\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=0.758 total time=   4.0s\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=0.758 total time=   7.8s\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=0.776 total time=   5.0s\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=0.768 total time=   5.8s\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=0.757 total time=   5.4s\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=0.739 total time=   5.6s\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=0.753 total time=   7.1s\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=0.776 total time=   4.0s\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=0.769 total time=   7.1s\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=0.758 total time=   4.9s\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=0.768 total time=   6.2s\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=0.767 total time=   5.9s\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=0.786 total time=   6.7s\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=   4.5s\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=0.749 total time=   7.2s\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=0.757 total time=   4.6s\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=0.756 total time=   5.7s\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=0.773 total time=   5.4s\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=0.771 total time=   6.6s\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=0.758 total time=   4.8s\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=0.772 total time=   9.1s\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=0.767 total time=   3.6s\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=0.786 total time=   8.5s\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=   4.5s\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=0.751 total time=   8.0s\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=0.760 total time=   4.6s\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=0.753 total time=   6.9s\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=0.777 total time=   4.0s\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=0.772 total time=   7.3s\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=0.771 total time=   3.9s\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=0.764 total time=   6.8s\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=0.765 total time=   4.0s\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=0.780 total time=   7.0s\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=0.769 total time=   4.3s\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=0.773 total time=   6.6s\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=0.768 total time=   5.9s\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=0.770 total time=   6.6s\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=0.788 total time=   5.9s\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=0.779 total time=   6.2s\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=0.772 total time=   5.8s\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=0.762 total time=   6.3s\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=0.768 total time=   5.2s\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=0.784 total time=   8.3s\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=0.765 total time=   4.1s\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=0.775 total time=   9.5s\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=0.767 total time=   4.5s\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=0.767 total time=   8.6s\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=0.795 total time=   5.7s\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=0.782 total time=   6.6s\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=0.748 total time=   5.7s\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=0.760 total time=   6.3s\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=0.743 total time=   5.4s\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=0.765 total time=   6.5s\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=0.764 total time=   4.9s\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=0.671 total time=   6.4s\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=0.667 total time=   4.1s\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=0.664 total time=   7.6s\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=0.691 total time=   4.4s\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=0.680 total time=   7.0s\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=0.746 total time=   5.5s\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=0.757 total time=   6.3s\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=0.742 total time=   4.6s\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=0.756 total time=   7.7s\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=0.756 total time=   4.1s\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=0.673 total time=   7.6s\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=0.679 total time=   4.3s\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=0.667 total time=   7.5s\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=0.693 total time=   4.0s\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=0.684 total time=   6.7s\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=0.772 total time=   4.4s\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=0.772 total time=   7.7s\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=0.779 total time=   3.8s\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=0.796 total time=   8.6s\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=0.782 total time=   3.6s\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=0.752 total time=   8.0s\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=0.765 total time=   5.5s\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=0.746 total time=   6.8s\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=0.767 total time=   7.4s\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=0.767 total time=   4.8s\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=0.774 total time=   8.2s\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=0.775 total time=   4.9s\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=0.779 total time=   6.2s\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=0.794 total time=   7.9s\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=0.785 total time=   5.5s\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=0.756 total time=   7.1s\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=0.767 total time=   4.9s\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=0.746 total time=   8.0s\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=0.766 total time=   4.7s\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=0.767 total time=   8.7s\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=0.703 total time=   5.5s\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=0.711 total time=   6.6s\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=0.700 total time=   6.4s\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=0.722 total time=   7.0s\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=0.715 total time=   7.5s\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=0.611 total time=   4.3s\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=0.597 total time=   8.2s\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=0.596 total time=   3.5s\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=0.629 total time=   7.7s\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=0.618 total time=   4.5s\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=0.684 total time=   6.4s\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=0.703 total time=   6.0s\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=0.685 total time=   5.3s\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=0.711 total time=   6.2s\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=0.701 total time=   7.4s\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=0.609 total time=   4.1s\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=0.596 total time=   7.0s\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=0.593 total time=   5.7s\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=0.628 total time=   6.5s\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=0.617 total time=   7.2s\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=0.774 total time=   5.2s\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=0.776 total time=   7.4s\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=0.779 total time=   5.8s\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=0.788 total time=   6.1s\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=0.784 total time=   7.3s\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=0.669 total time=   6.1s\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=0.656 total time=   5.5s\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=0.656 total time=   7.5s\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=0.684 total time=   4.7s\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=0.670 total time=   7.2s\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=0.767 total time=   5.2s\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=0.769 total time=   5.8s\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=0.766 total time=   5.9s\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=0.783 total time=   5.7s\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=0.778 total time=   6.6s\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=0.662 total time=   6.4s\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=0.656 total time=   6.5s\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=0.654 total time=   6.9s\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=0.685 total time=   5.6s\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=0.671 total time=   7.6s\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=0.488 total time=   5.0s\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=0.486 total time=   6.3s\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=0.492 total time=   7.1s\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=0.498 total time=   6.1s\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=0.494 total time=   7.4s\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=0.452 total time=   5.3s\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=0.449 total time=   6.1s\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=0.451 total time=   6.8s\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=0.448 total time=   6.6s\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=0.453 total time=   6.8s\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=0.482 total time=   7.0s\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=0.469 total time=   4.9s\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=0.476 total time=   6.0s\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=0.479 total time=   5.6s\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=0.480 total time=   5.3s\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=0.452 total time=   5.7s\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=0.448 total time=   7.0s\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=0.449 total time=   4.4s\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=0.449 total time=   7.3s\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=0.453 total time=   4.8s\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=0.530 total time=   7.1s\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=0.529 total time=   6.1s\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=0.526 total time=   5.0s\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=0.556 total time=   7.3s\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=0.546 total time=   5.2s\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=0.485 total time=   7.9s\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=0.472 total time=   6.4s\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=0.479 total time=   5.7s\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=0.486 total time=   7.0s\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=0.484 total time=   6.5s\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=0.515 total time=   6.9s\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=0.513 total time=   4.4s\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=0.516 total time=   7.4s\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=0.536 total time=   6.7s\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=0.529 total time=   7.0s\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=0.487 total time=   6.9s\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=0.469 total time=   6.1s\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=0.476 total time=   4.6s\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=0.485 total time=   7.4s\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=0.483 total time=   3.9s\n",
      "Best: 0.781420 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:  0.7854974048250059\n",
      "accuracy:  0.8475505310037684\n",
      "auc score:  0.8190131500989659\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.best_estimator_.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": 315,
   "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=   5.3s\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=   6.3s\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=   5.0s\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=   5.0s\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=   7.4s\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=   3.3s\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=   7.6s\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=   4.7s\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=   5.5s\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=   6.0s\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=   3.9s\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=   6.6s\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=   6.7s\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=   3.6s\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=   6.9s\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=   5.9s\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=   4.0s\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=   6.1s\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=   2.7s\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=   6.0s\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=0.731 total time=  13.7s\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=0.735 total time=  12.3s\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=0.716 total time=  13.0s\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=0.745 total time=  14.3s\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=0.732 total time=  11.9s\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=0.746 total time=  10.5s\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=0.747 total time=  10.6s\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=0.731 total time=  10.1s\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=0.757 total time=  10.8s\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=0.740 total time=  12.2s\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=0.733 total time=  11.5s\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=0.745 total time=   7.2s\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=0.729 total time=   7.8s\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=0.759 total time=   8.0s\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=0.740 total time=   7.5s\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=0.745 total time=   8.5s\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=0.756 total time=   8.5s\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=0.738 total time=   8.5s\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=0.769 total time=   8.5s\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=0.752 total time=   8.3s\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=   2.2s\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=   2.1s\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=   2.1s\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=   2.3s\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=   2.2s\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=   2.3s\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=   2.2s\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=   2.2s\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=   2.3s\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=   2.5s\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=   2.3s\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=   2.4s\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=   2.4s\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=   2.3s\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=0.758 total time=   6.3s\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=0.760 total time=   6.6s\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=0.739 total time=   8.1s\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=0.781 total time=   6.1s\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=0.761 total time=   7.1s\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=0.770 total time=   6.9s\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=0.769 total time=   6.5s\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=0.747 total time=   7.5s\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=0.778 total time=   8.2s\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=0.769 total time=   7.6s\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=0.760 total time=   7.7s\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=0.765 total time=   7.5s\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=0.747 total time=   6.5s\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=0.780 total time=   6.8s\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=0.761 total time=   6.2s\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=0.769 total time=   6.6s\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=0.769 total time=   6.3s\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=0.753 total time=   7.1s\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=0.786 total time=   6.9s\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=0.768 total time=   7.2s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.1s\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=   2.2s\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=   2.2s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.1s\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=   2.3s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.1s\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=   2.3s\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=   2.1s\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=0.780 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=0.777 total time=   3.1s\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=0.761 total time=   3.1s\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=0.793 total time=   3.2s\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=0.784 total time=   3.1s\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=0.783 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=0.779 total time=   3.3s\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=0.770 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=0.800 total time=   3.1s\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=0.784 total time=   3.2s\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=0.778 total time=   3.0s\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=0.778 total time=   3.1s\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=0.767 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=0.789 total time=   3.6s\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=0.786 total time=   3.3s\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=0.781 total time=   3.8s\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=0.780 total time=   3.5s\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=0.773 total time=   3.6s\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=0.795 total time=   3.5s\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=0.786 total time=   3.2s\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=   2.2s\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=   2.2s\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=   2.5s\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=   2.4s\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=   2.4s\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=   2.4s\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=   2.4s\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=   2.4s\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=   2.1s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.2s\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=   2.0s\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=   2.3s\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=   2.3s\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=   2.5s\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=0.763 total time=   3.2s\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=0.759 total time=   2.5s\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=0.752 total time=   2.6s\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=0.778 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=0.765 total time=   2.6s\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=0.762 total time=   2.7s\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=0.761 total time=   2.6s\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=0.760 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=0.778 total time=   2.7s\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=0.769 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=0.758 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=0.758 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=0.747 total time=   2.6s\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=0.779 total time=   2.5s\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=0.761 total time=   2.5s\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=0.760 total time=   2.6s\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=0.758 total time=   2.7s\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=0.753 total time=   2.7s\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=0.779 total time=   2.7s\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=0.765 total time=   2.6s\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 0.73191567 0.74417649\n",
      " 0.74118313 0.75181246        nan        nan        nan        nan\n",
      " 0.75987516 0.76673493 0.76273129 0.76910601        nan        nan\n",
      "        nan        nan 0.77907067 0.78307361 0.77970516 0.78323171\n",
      "        nan        nan        nan        nan 0.76322523 0.76623937\n",
      " 0.7605549  0.76277703]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.783232 using {'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",
      "f1_score:  0.7902613198458575\n",
      "accuracy:  0.8773552586502227\n",
      "auc score:  0.7660989600172037\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,\n",
    "                        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 = confusion_matrix(y_test, y_pred_svc)\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_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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "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=0.762 total time=   3.5s\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=0.754 total time=   2.9s\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=0.743 total time=   3.1s\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=0.774 total time=   3.1s\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=0.765 total time=   3.0s\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=0.766 total time=   2.9s\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=0.757 total time=   3.1s\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=0.748 total time=   3.2s\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=0.777 total time=   3.6s\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=0.768 total time=   3.7s\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=0.762 total time=   3.4s\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=0.754 total time=   3.3s\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=0.742 total time=   3.4s\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=0.771 total time=   2.9s\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=0.753 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=0.764 total time=   3.0s\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=0.757 total time=   3.5s\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=0.748 total time=   3.4s\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=0.778 total time=   3.1s\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=0.762 total time=   2.8s\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=0.757 total time=   2.8s\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=0.757 total time=   2.8s\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=0.734 total time=   2.8s\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=0.771 total time=   3.2s\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=0.768 total time=   2.9s\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=0.759 total time=   3.1s\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=0.764 total time=   3.0s\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=0.738 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=0.767 total time=   3.1s\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=0.770 total time=   2.9s\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=0.756 total time=   3.2s\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=0.765 total time=   3.2s\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=0.741 total time=   2.9s\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=0.769 total time=   3.0s\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=0.759 total time=   3.2s\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=0.747 total time=   3.1s\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=0.764 total time=   3.4s\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=0.750 total time=   3.3s\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=0.786 total time=   3.6s\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=0.763 total time=   3.2s\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=0.603 total time=   3.1s\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=0.590 total time=   3.3s\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=0.579 total time=   2.7s\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=0.609 total time=   3.1s\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=0.605 total time=   2.9s\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=0.604 total time=   2.8s\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=0.590 total time=   2.9s\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=0.579 total time=   2.6s\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=0.609 total time=   2.6s\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=0.605 total time=   2.8s\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=0.603 total time=   2.8s\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=0.591 total time=   2.7s\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=0.579 total time=   2.7s\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=0.609 total time=   2.7s\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=0.605 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=0.604 total time=   2.5s\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=0.591 total time=   2.6s\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=0.579 total time=   2.5s\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=0.609 total time=   2.5s\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=0.605 total time=   2.6s\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=0.738 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=0.756 total time=   2.5s\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=0.721 total time=   2.5s\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=0.773 total time=   2.5s\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=0.746 total time=   2.5s\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=0.762 total time=   2.5s\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=0.756 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=0.745 total time=   2.5s\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=0.764 total time=   2.6s\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=0.754 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=0.751 total time=   2.4s\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=0.754 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=0.741 total time=   2.5s\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=0.773 total time=   2.8s\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=0.748 total time=   3.1s\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=0.731 total time=   4.0s\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=0.758 total time=   5.6s\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=0.754 total time=   4.2s\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=0.779 total time=   3.5s\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=0.763 total time=   3.0s\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=0.443 total time=   2.7s\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=0.443 total time=   2.6s\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=0.443 total time=   2.9s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.443 total time=   2.8s\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=0.443 total time=   2.8s\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=0.443 total time=   2.7s\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=0.443 total time=   2.7s\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=0.443 total time=   2.7s\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=0.443 total time=   2.7s\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=0.443 total time=   2.6s\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=0.443 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=0.443 total time=   2.6s\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=0.443 total time=   2.6s\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=0.443 total time=   2.6s\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=0.443 total time=   2.6s\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=0.443 total time=   2.8s\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=0.443 total time=   3.2s\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=0.443 total time=   2.9s\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=0.718 total time=   3.5s\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=0.721 total time=   3.5s\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=0.697 total time=   3.3s\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=0.759 total time=   2.9s\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=0.725 total time=   2.7s\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=0.723 total time=   2.9s\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=0.752 total time=   3.0s\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=0.695 total time=   2.6s\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=0.745 total time=   2.6s\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=0.723 total time=   2.8s\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=0.725 total time=   2.9s\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=0.750 total time=   2.7s\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=0.712 total time=   2.8s\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=0.730 total time=   2.9s\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=0.712 total time=   3.8s\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=0.697 total time=   2.8s\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=0.717 total time=   2.7s\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=0.729 total time=   2.7s\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=0.747 total time=   2.6s\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=0.710 total time=   2.5s\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=0.443 total time=   2.5s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.443 total time=   2.5s\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=0.443 total time=   2.4s\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=0.443 total time=   2.4s\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=0.443 total time=   2.5s\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=0.443 total time=   2.5s\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=0.443 total time=   2.4s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.443 total time=   2.7s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.443 total time=   2.5s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.443 total time=   2.5s\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=0.443 total time=   2.5s\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=0.443 total time=   2.6s\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=0.538 total time=   2.9s\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=0.517 total time=   3.3s\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=0.471 total time=   3.4s\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=0.487 total time=   2.7s\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=0.491 total time=   3.4s\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=0.540 total time=   2.8s\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=0.500 total time=   2.6s\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=0.470 total time=   2.7s\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=0.579 total time=   2.7s\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=0.533 total time=   2.7s\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=0.472 total time=   2.7s\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=0.514 total time=   2.7s\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=0.507 total time=   2.8s\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=0.458 total time=   2.6s\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=0.515 total time=   2.5s\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=0.481 total time=   2.6s\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=0.472 total time=   2.5s\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=0.484 total time=   2.6s\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=0.473 total time=   2.6s\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=0.469 total time=   2.5s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.763209 using {'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",
      "f1_score:  0.7683551370683459\n",
      "accuracy:  0.8729016786570744\n",
      "auc score:  0.733602789184413\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": 318,
   "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>0.769389</td>\n",
       "      <td>0.789156</td>\n",
       "      <td>0.874443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.819013</td>\n",
       "      <td>0.785497</td>\n",
       "      <td>0.847551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.766099</td>\n",
       "      <td>0.790261</td>\n",
       "      <td>0.877355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient classifier</td>\n",
       "      <td>0.733603</td>\n",
       "      <td>0.768355</td>\n",
       "      <td>0.872902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       auc  f1 score  Accuracy\n",
       "0             Logistic Regression  0.769389  0.789156  0.874443\n",
       "1                   MultinomialNB  0.819013  0.785497  0.847551\n",
       "2                       LinearSVC  0.766099  0.790261  0.877355\n",
       "3  Stochastic Gradient classifier  0.733603  0.768355  0.872902"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\": model_arr, \"auc\": auc_arr,\n",
    "      \"f1 score\": f1_arr, \"Accuracy\": accuracy_arr, }).head()\n"
   ]
  }
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