Final_3_3_HP_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter training for part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import autograd.numpy as np\n",
    "from autograd import grad\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cutoff\n",
    "cutoff = 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>verified</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>vote</th>\n",
       "      <th>image</th>\n",
       "      <th>style</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29189.000000</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29185</td>\n",
       "      <td>29189</td>\n",
       "      <td>29184</td>\n",
       "      <td>2.918900e+04</td>\n",
       "      <td>6047.000000</td>\n",
       "      <td>600</td>\n",
       "      <td>15365</td>\n",
       "      <td>29189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4866</td>\n",
       "      <td>27588</td>\n",
       "      <td>24102</td>\n",
       "      <td>23305</td>\n",
       "      <td>28807</td>\n",
       "      <td>21768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600</td>\n",
       "      <td>6104</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>01 3, 2017</td>\n",
       "      <td>AA7918E9410D650A076221C7B2934A09</td>\n",
       "      <td>6E46168E5DFA83876F2E968A187B9F7A</td>\n",
       "      <td>760C63E8E5E8DC3FAA01878D37BA5678</td>\n",
       "      <td>works great</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['https://images-na.ssl-images-amazon.com/imag...</td>\n",
       "      <td>{'Format:': ' Audio CD'}</td>\n",
       "      <td>CDs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22996</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>1049</td>\n",
       "      <td>25</td>\n",
       "      <td>1476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4296</td>\n",
       "      <td>4912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.971907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.409661e+09</td>\n",
       "      <td>7.295353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.410878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.115099e+08</td>\n",
       "      <td>18.749460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.947584e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.390781e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.439770e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.476317e+09</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537488e+09</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             overall verified  reviewTime                        reviewerID  \\\n",
       "count   29189.000000    29189       29189                             29189   \n",
       "unique           NaN        2        4866                             27588   \n",
       "top              NaN     True  01 3, 2017  AA7918E9410D650A076221C7B2934A09   \n",
       "freq             NaN    22996          40                                32   \n",
       "mean        2.971907      NaN         NaN                               NaN   \n",
       "std         1.410878      NaN         NaN                               NaN   \n",
       "min         1.000000      NaN         NaN                               NaN   \n",
       "25%         2.000000      NaN         NaN                               NaN   \n",
       "50%         3.000000      NaN         NaN                               NaN   \n",
       "75%         4.000000      NaN         NaN                               NaN   \n",
       "max         5.000000      NaN         NaN                               NaN   \n",
       "\n",
       "                                    asin                      reviewerName  \\\n",
       "count                              29189                             29185   \n",
       "unique                             24102                             23305   \n",
       "top     6E46168E5DFA83876F2E968A187B9F7A  760C63E8E5E8DC3FAA01878D37BA5678   \n",
       "freq                                  36                              1049   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "         reviewText     summary  unixReviewTime         vote  \\\n",
       "count         29189       29184    2.918900e+04  6047.000000   \n",
       "unique        28807       21768             NaN          NaN   \n",
       "top     works great  Five Stars             NaN          NaN   \n",
       "freq             25        1476             NaN          NaN   \n",
       "mean            NaN         NaN    1.409661e+09     7.295353   \n",
       "std             NaN         NaN    1.115099e+08    18.749460   \n",
       "min             NaN         NaN    8.947584e+08     2.000000   \n",
       "25%             NaN         NaN    1.390781e+09     2.000000   \n",
       "50%             NaN         NaN    1.439770e+09     4.000000   \n",
       "75%             NaN         NaN    1.476317e+09     7.000000   \n",
       "max             NaN         NaN    1.537488e+09   682.000000   \n",
       "\n",
       "                                                    image  \\\n",
       "count                                                 600   \n",
       "unique                                                600   \n",
       "top     ['https://images-na.ssl-images-amazon.com/imag...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                           style category  \n",
       "count                      15365    29189  \n",
       "unique                      6104        6  \n",
       "top     {'Format:': ' Audio CD'}      CDs  \n",
       "freq                        4296     4912  \n",
       "mean                         NaN      NaN  \n",
       "std                          NaN      NaN  \n",
       "min                          NaN      NaN  \n",
       "25%                          NaN      NaN  \n",
       "50%                          NaN      NaN  \n",
       "75%                          NaN      NaN  \n",
       "max                          NaN      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data\n",
    "train_data = pd.read_csv('Training.csv')\n",
    "\n",
    "train_data.describe(include='all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\n",
    "# Stop words\n",
    "GENERIC_STOP_WORDS = ['ourselves', 'hers', 'between', 'yourself', 'but', 'again', 'there', 'about', 'once', 'during', 'out', 'very', 'having', 'with', 'they', 'own', 'an', 'be', 'some', 'for', 'do', 'its', 'yours', 'such', 'into', 'of', 'most', 'itself', 'other', 'off', 'is', 's', 'am', 'or', 'who', 'as', 'from', 'him', 'each', 'the', 'themselves', 'until', 'below', 'are', 'we', 'these', 'your', 'his', 'through', 'don', 'nor', 'me', 'were', 'her', 'more', 'himself', 'this', 'down', 'should', 'our', 'their', 'while',\n",
    "                      'above', 'both', 'up', 'to', 'ours', 'had', 'she', 'all', 'when', 'at', 'any', 'before', 'them', 'same', 'and', 'been', 'have', 'in', 'will', 'on', 'does', 'yourselves', 'then', 'that', 'because', 'what', 'over', 'why', 'so', 'can', 'did', 'now', 'under', 'he', 'you', 'herself', 'has', 'just', 'where', 'too', 'only', 'myself', 'which', 'those', 'i', 'after', 'few', 'whom', 't', 'being', 'if', 'theirs', 'my', 'against', 'a', 'by', 'doing', 'it', 'how', 'further', 'was', 'here', 'than', 'as']\n",
    "STOP_WORDS_SUMMARY = ['ozzy', 'oz', 'azoom', 'fyi', 'eyes', 'hybrid', 'lyrics', 'hydrated', '4yo', 'explorer', '2x', 'exposure', 'xxl', 'extreme', 'example', 'excellence', '2wd', 'owners', 'aware', 'awd', 'tweaking', 'twist', 'awaited', 'owe', 'twisted', 'sweater', 'tweezers', 'swimming', 'uv', 'rvs', 'everything', 'every', 'dvd', 'overlooked', 'avid', 'custom', 'cushion', 'super', 'durable', 'bux', 'multiple', 'duty', 'cute', 'pump', 'sunshade', 'purchases', 'suited', 'bunch', 'suv', 'fuses', 'fusion', 'buick', 'audi', 'funny', 'current', 'quads', 'quick', 'bumper', 'guide', 'gutter', 'public', 'business', 'audio', 'punk', 'guitars', 'outs', 'turkey', 'humor', 'buffett', 'bueno', 'sunday', 'buzz', 'judge', 'cube', 'buddy', 'submarine', 'guaranteed', 'jug', 'rum', 'gummies', 'rubbed', 'sugarfree', 'nutritious', 'quinoa', 'sufferers', 'gummy', 'justify', 'quest', 'cupcakes', 'quickie', 'punching', 'supposedly', 'ounces', 'turbo', 'substantially', 'suffer', 'supplies', 'building', 'numbers', 'cutie', 'puppets', 'hunt', 'outfits', 'still', 'strands', 'gt', 'stretched', 'xtreme', 'stores', 'utilitarian', 'stainless', 'starters', 'atleast', 'stage', 'standards', 'strength', 'stp', 'steve', 'styles', 'stages', 'stratovarius', 'steep', 'stevia', 'strawberries', 'strawberry', 'htc', 'bt', 'steal', 'study', 'stroller', 'stove', 'statue', 'isuzu', 'user', 'tsx', 'usa',\n",
    "                      'essence', 'ashlee', 'aspect', 'bs', '5star', 'usb', 'aside', 'product', 'around', 'crew', 'grand', 'trd', 'armor', 'arctic', 'driver', 'wrench', 'gray', 'pricey', 'protection', 'trim', 'fresh', 'drilling', 'true', 'ground', 'crossbar', 'wrx', 'practicality', 'transfer', 'traction', 'prevents', 'orignal', 'fraction', 'prius', 'groove', 'breath', 'breakfast', 'orbital', 'proud', 'freaking', 'art', 'travels', 'grace', 'brilliance', 'group', 'iron', 'drinking', 'brigade', 'organized', 'primus', 'groundbreaking', 'critical', 'orchestration', 'dressed', 'travelling', 'kryptonite', 'orleans', 'irons', 'dropping', 'programs', 'frustration', 'yr', 'crackers', 'artisan', 'fruitcake', 'crumbled', 'grains', 'grapefruit', 'creamy', 'pretzels', 'brownie', 'freshness', 'crispy', 'breakable', 'drinkers', 'crisp', 'produce', 'grill', 'pricy', 'draft', 'crash', 'branded', 'wrapped', 'droid', 'protectors', 'armorsuit', 'orientation', 'preference', 'frills', 'tray', 'grippy', 'prints', 'vr', 'practice', 'armour', 'drawstring', 'trek', 'trains', 'dragonfly', 'tracker', 'trike', 'dryer', 'breyer', 'princess', 'grandsons', 'dresses', 'crayon', 'equipped', 'sponge', 'applications', 'operate', 'iphone', 'spot', 'specifications', 'appropriate', 'sports', 'approach', 'appetite', 'spell', 'spins', 'spiced', 'spices', 'spanish', '6p', 'ipad3', 'operates', 'speeds', 'iphone4', '3pack']\n",
    "STOP_WORDS_REVIEW_TEXT = []\n",
    "\n",
    "\n",
    "# edit summary content\n",
    "def edit_summary(content):\n",
    "\n",
    "    # step 1 - convert the text to only lower case\n",
    "    content = content.lower()\n",
    "\n",
    "    # step 2 - remove unwanted and unicode characters\n",
    "    content = re.sub(\n",
    "        r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", content)\n",
    "\n",
    "    # step 3 - remove stop words\n",
    "    stopwords = GENERIC_STOP_WORDS\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "\n",
    "# edit review text content\n",
    "def edit_review_text(content):\n",
    "\n",
    "    # step 1 - convert the text to only lower case\n",
    "    content = content.lower()\n",
    "\n",
    "    # step 2 - remove unwanted and unicode characters\n",
    "    content = re.sub(\n",
    "        r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", content)\n",
    "\n",
    "    # step 3 - remove stop words\n",
    "    stopwords = GENERIC_STOP_WORDS\n",
    "\n",
    "    filtered_words = [\n",
    "        word for word in content.split() if word not in stopwords]\n",
    "    text = \" \".join(filtered_words)\n",
    "\n",
    "    return text\n",
    "\n",
    "\n",
    "# create categories for vote label\n",
    "def assign_vote_label(i):\n",
    "    if i <= 2.0:\n",
    "        return 'low'\n",
    "    if i > 2.0 and i <= 10.0:\n",
    "        return 'medium'\n",
    "    if i > 10.0 and i <= 50.0:\n",
    "        return 'good'\n",
    "    if i > 50.0:\n",
    "        return 'high'\n",
    "\n",
    "\n",
    "# preprocess\n",
    "def preprocess(data):\n",
    "\n",
    "    # filter out only important columns\n",
    "    X_cols = ['reviewText', 'summary', 'verified', 'vote', 'category']\n",
    "\n",
    "    # editing text of \"reviewText\" & \"summary\"\n",
    "    data['reviewText'] = data['reviewText'].astype(str).apply(\n",
    "        edit_review_text)\n",
    "    data['summary'] = data['summary'].astype(str).apply(edit_summary)\n",
    "\n",
    "    # editing vote - assigning NAN values to 0\n",
    "    data['vote'] = data['vote'].fillna(0)\n",
    "    data['vote'] = data['vote'].apply(lambda x: assign_vote_label(x))\n",
    "\n",
    "    X = train_data[X_cols]\n",
    "\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# creating X and y data\n",
    "\n",
    "# creating y labels less than cutoff to be 0 and 1 otherwise\n",
    "y = train_data['overall'].apply(lambda x: 0 if x <= cutoff else 1)\n",
    "\n",
    "# create features data\n",
    "X = preprocess(train_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# train test split function\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                    test_size=0.20, random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, f1_score, accuracy_score\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import warnings\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# define the column transformer to apply TfidfVectorizer & encoder to multiple columns\n",
    "ct = ColumnTransformer(\n",
    "    [(\"vect_summary\", TfidfVectorizer(), 'summary'),\n",
    "     (\"vect_reviewText\", TfidfVectorizer(), 'reviewText'),\n",
    "     (\"encd_verified\", OrdinalEncoder(dtype=int), ['verified']),\n",
    "     (\"encd_category\", OrdinalEncoder(dtype=int), ['category']),\n",
    "     (\"encd_votes\", OrdinalEncoder(dtype=int), ['vote'])\n",
    "     ])\n",
    "\n",
    "# For results\n",
    "model_arr, auc_arr, f1_arr, accuracy_arr = [], [], [], []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
      "[CV 1/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.802 total time=   5.9s\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=0.822 total time=   5.5s\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=0.829 total time=   4.0s\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=0.811 total time=   4.7s\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=0.817 total time=   4.1s\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=0.801 total time=   4.8s\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=0.823 total time=   3.2s\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=0.826 total time=   5.4s\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=0.812 total time=   5.5s\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=0.816 total time=   4.1s\n",
      "[CV 1/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   4.7s\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=0.816 total time=   7.1s\n",
      "[CV 3/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=   5.7s\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=0.808 total time=   3.5s\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=0.815 total time=   4.8s\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=0.796 total time=   7.1s\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=0.818 total time=   6.7s\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=0.821 total time=   5.3s\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=0.807 total time=   6.1s\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=0.813 total time=   7.6s\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=0.802 total time=   5.9s\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=0.822 total time=   6.5s\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=0.829 total time=   7.3s\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=0.811 total time=   5.3s\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=0.817 total time=   8.1s\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=0.801 total time=   8.8s\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=0.823 total time=   7.0s\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=0.826 total time=   8.9s\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=0.812 total time=   5.1s\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=0.816 total time=  10.1s\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=0.798 total time=   9.0s\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=0.816 total time=   8.4s\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=0.824 total time=   8.0s\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=0.808 total time=   9.4s\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=0.815 total time=   7.9s\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=0.796 total time=   8.1s\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=0.818 total time=   8.4s\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=0.821 total time=   9.0s\n",
      "[CV 4/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 8/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.807 total time=   8.9s\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=0.813 total time=   8.2s\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=0.802 total time=   7.9s\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=0.822 total time=  10.2s\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=0.829 total time=   7.3s\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=0.811 total time=   8.5s\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=0.817 total time=   9.3s\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=0.801 total time=   7.8s\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=0.823 total time=   8.0s\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=0.826 total time=   8.8s\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=0.812 total time=   8.5s\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=0.816 total time=   7.5s\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=0.798 total time=  10.7s\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=0.816 total time=   6.5s\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=0.824 total time=   8.1s\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=0.808 total time=   8.0s\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=0.815 total time=   6.2s\n",
      "[CV 1/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.796 total time=   8.8s\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=0.818 total time=   5.6s\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=0.821 total time=   8.3s\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=0.807 total time=   9.9s\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=0.813 total time=   7.2s\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=0.845 total time=  12.5s\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=0.859 total time=   9.9s\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=0.857 total time=   9.3s\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=0.853 total time=   9.1s\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=0.859 total time=  10.5s\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=0.847 total time=   6.7s\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=0.859 total time=   9.2s\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=0.861 total time=  12.3s\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=0.857 total time=  11.9s\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=0.860 total time=   8.7s\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=0.845 total time=  10.5s\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=0.859 total time=  12.1s\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=0.861 total time=   9.9s\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=0.858 total time=  10.3s\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=0.859 total time=  13.9s\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=0.848 total time=  11.9s\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=0.861 total time=  10.3s\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=0.866 total time=  10.7s\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=0.859 total time=   7.6s\n",
      "[CV 5/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.859 total time=  12.5s\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=0.845 total time=  10.2s\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=0.859 total time=   9.5s\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=0.857 total time=  11.8s\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=0.853 total time=  12.2s\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=0.859 total time=  10.1s\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=0.847 total time=   9.0s\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=0.859 total time=  11.9s\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=0.861 total time=  13.5s\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=0.857 total time=  12.1s\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=0.860 total time=  10.5s\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=0.845 total time=  12.0s\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=0.859 total time=  10.9s\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=0.861 total time=   6.1s\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=0.858 total time=  13.0s\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=0.859 total time=  12.2s\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=0.848 total time=  11.8s\n",
      "[CV 2/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.861 total time=  11.6s\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=0.866 total time=  12.7s\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=0.859 total time=  12.9s\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=0.859 total time=  11.7s\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=0.845 total time=  12.8s\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=0.859 total time=  11.8s\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=0.857 total time=  12.4s\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=0.853 total time=  10.7s\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=0.859 total time=  13.6s\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=0.847 total time=  10.9s\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=0.859 total time=  11.5s\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=0.861 total time=  15.1s\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=0.857 total time=   5.8s\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=0.860 total time=  13.6s\n",
      "[CV 1/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=  11.2s\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=0.859 total time=  11.5s\n",
      "[CV 3/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.861 total time=  11.4s\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=0.858 total time=  12.8s\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=0.859 total time=  13.1s\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=0.848 total time=  14.3s\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=0.861 total time=  11.2s\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=0.866 total time=  10.4s\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=0.859 total time=   8.9s\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=0.859 total time=  12.3s\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=0.845 total time=  15.3s\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=0.850 total time=  14.6s\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=0.855 total time=  13.7s\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=0.852 total time=  12.0s\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=0.850 total time=  14.3s\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=0.850 total time=  15.0s\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=0.856 total time=  15.4s\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=0.858 total time=  12.5s\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=0.855 total time=  11.4s\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=0.853 total time=  12.7s\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=0.845 total time=  10.8s\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=0.855 total time=  11.2s\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=0.858 total time=  11.0s\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=0.854 total time=  12.6s\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=0.851 total time=   9.0s\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=0.847 total time=  11.0s\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=0.862 total time=  11.8s\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=0.859 total time=  12.3s\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=0.857 total time=  12.5s\n",
      "[CV 5/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.856 total time=  12.1s\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=0.845 total time=  13.7s\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=0.850 total time=  11.9s\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=0.855 total time=   8.6s\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=0.852 total time=  10.0s\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=0.850 total time=  13.0s\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=0.850 total time=  12.5s\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=0.856 total time=   9.2s\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=0.858 total time=   8.9s\n",
      "[CV 4/5; 30/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.855 total time=  10.3s\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=0.853 total time=  11.7s\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=0.845 total time=  12.0s\n",
      "[CV 2/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.855 total time=  10.6s\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=0.858 total time=  10.2s\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=0.854 total time=   6.4s\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=0.851 total time=   9.5s\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=0.847 total time=  10.6s\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=0.862 total time=  11.9s\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=0.859 total time=  11.4s\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=0.857 total time=  12.0s\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=0.856 total time=  11.4s\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=0.845 total time=  10.1s\n",
      "[CV 2/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=   8.2s\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=0.855 total time=   7.2s\n",
      "[CV 4/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.852 total time=  10.2s\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=0.850 total time=  12.1s\n",
      "[CV 1/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=  14.0s\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=0.856 total time=  12.5s\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=0.858 total time=  11.0s\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=0.855 total time=  10.3s\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=0.853 total time=  12.2s\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=0.845 total time=  12.1s\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=0.855 total time=  12.8s\n",
      "[CV 3/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.858 total time=  12.0s\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=0.854 total time=  10.4s\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=0.851 total time=   7.4s\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=0.847 total time=   9.0s\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=0.862 total time=  10.4s\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=0.859 total time=  10.9s\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=0.857 total time=  11.7s\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=0.856 total time=  11.9s\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=0.846 total time=  10.5s\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=0.843 total time=  10.9s\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=0.849 total time=   8.2s\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=0.843 total time=   8.4s\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=0.846 total time=  11.2s\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=0.849 total time=  12.5s\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=0.850 total time=  13.4s\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=0.854 total time=  15.2s\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=0.852 total time=  15.5s\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=0.851 total time=  15.3s\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=0.843 total time=  11.9s\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=0.848 total time=  12.5s\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=0.852 total time=   8.1s\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=0.851 total time=  13.8s\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=0.844 total time=  13.1s\n",
      "[CV 1/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=  15.0s\n",
      "[CV 2/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.859 total time=  13.4s\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=0.854 total time=  12.7s\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=0.856 total time=  13.7s\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=0.851 total time=  10.7s\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=0.846 total time=  11.2s\n",
      "[CV 2/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.843 total time=  13.4s\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=0.849 total time=  15.0s\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=0.843 total time=  14.4s\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=0.846 total time=  11.6s\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=0.849 total time=  12.9s\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=0.850 total time=  15.0s\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=0.854 total time=  13.1s\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=0.852 total time=   8.2s\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=0.851 total time=  11.6s\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=0.843 total time=  13.5s\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=0.848 total time=  13.4s\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=0.852 total time=  15.4s\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=0.851 total time=  15.5s\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=0.844 total time=  16.0s\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=0.845 total time=  11.0s\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=0.859 total time=  15.4s\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=0.854 total time=  12.9s\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=0.856 total time=  11.0s\n",
      "[CV 5/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 44/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.851 total time=   9.3s\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=0.846 total time=  13.7s\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=0.843 total time=  12.8s\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=0.849 total time=  14.3s\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=0.843 total time=  12.7s\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=0.846 total time=  10.6s\n",
      "[CV 1/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.849 total time=  10.2s\n",
      "[CV 2/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=  12.2s\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=0.854 total time=  14.0s\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=0.852 total time=  14.9s\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=0.851 total time=  17.7s\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=0.843 total time=   8.1s\n",
      "[CV 2/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.848 total time=  11.7s\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=0.852 total time=  11.8s\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=0.851 total time=  13.1s\n",
      "[CV 5/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.844 total time=  15.0s\n",
      "[CV 1/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=  15.3s\n",
      "[CV 2/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 48/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.859 total time=  14.4s\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=0.854 total time=  12.4s\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=0.856 total time=  13.9s\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=0.851 total time=   9.8s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.858756 using {'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",
      "f1_score:  0.85826498139931\n",
      "accuracy:  0.8663926002055499\n",
      "auc score:  0.8541491136173713\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.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Logistic Regression\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "[CV 1/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.829 total time=   5.2s\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=0.829 total time=   4.9s\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=0.831 total time=   4.0s\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=0.833 total time=   6.0s\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=0.828 total time=   3.2s\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=0.821 total time=   6.1s\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=0.820 total time=   3.3s\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=0.819 total time=   6.0s\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=0.825 total time=   4.0s\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=0.820 total time=   5.0s\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=0.827 total time=   5.2s\n",
      "[CV 2/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.828 total time=   4.1s\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=0.833 total time=   4.6s\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=0.834 total time=   4.1s\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=0.829 total time=   5.2s\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=0.822 total time=   2.6s\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=0.821 total time=   3.7s\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=0.822 total time=   3.1s\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=0.823 total time=   3.6s\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=0.821 total time=   3.8s\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=0.832 total time=   3.3s\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=0.835 total time=   3.6s\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=0.830 total time=   2.4s\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=0.835 total time=   3.8s\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=0.827 total time=   2.9s\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=0.827 total time=   3.2s\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=0.829 total time=   3.2s\n",
      "[CV 3/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=   3.2s\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=0.824 total time=   3.9s\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=0.819 total time=   2.9s\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=0.837 total time=   4.4s\n",
      "[CV 2/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=   3.0s\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=0.832 total time=   3.6s\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=0.840 total time=   3.0s\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=0.829 total time=   3.5s\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=0.829 total time=   2.3s\n",
      "[CV 2/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.832 total time=   2.3s\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=0.819 total time=   2.2s\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=0.832 total time=   2.5s\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=0.823 total time=   2.6s\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=0.834 total time=   2.3s\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=0.834 total time=   2.4s\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=0.838 total time=   2.3s\n",
      "[CV 4/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   2.2s\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=0.832 total time=   2.2s\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=0.831 total time=   2.4s\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=0.832 total time=   2.3s\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=0.832 total time=   2.3s\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=0.835 total time=   2.2s\n",
      "[CV 5/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.3s\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=0.834 total time=   2.4s\n",
      "[CV 2/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.835 total time=   2.3s\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=0.840 total time=   2.4s\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=0.842 total time=   2.3s\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=0.837 total time=   2.4s\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=0.833 total time=   2.3s\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=0.833 total time=   2.4s\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=0.835 total time=   2.3s\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=0.839 total time=   2.2s\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=0.833 total time=   2.3s\n",
      "[CV 1/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   2.3s\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=0.842 total time=   2.3s\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=0.837 total time=   2.2s\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=0.843 total time=   2.3s\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=0.835 total time=   2.4s\n",
      "[CV 1/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   2.3s\n",
      "[CV 2/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   2.3s\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=0.835 total time=   2.3s\n",
      "[CV 4/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=   2.3s\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=0.833 total time=   2.2s\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=0.842 total time=   2.3s\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=0.845 total time=   2.2s\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=0.841 total time=   2.2s\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=0.847 total time=   2.4s\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=0.836 total time=   2.4s\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=0.842 total time=   2.3s\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=0.844 total time=   2.2s\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=0.836 total time=   2.4s\n",
      "[CV 4/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.846 total time=   2.4s\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=0.836 total time=   2.2s\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=0.838 total time=   2.4s\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=0.838 total time=   2.4s\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=0.840 total time=   2.4s\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=0.844 total time=   2.6s\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=0.840 total time=   2.2s\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=0.824 total time=   2.6s\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=0.820 total time=   2.4s\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=0.822 total time=   2.3s\n",
      "[CV 4/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.828 total time=   2.4s\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=0.826 total time=   2.6s\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=0.837 total time=   2.8s\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=0.841 total time=   2.9s\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=0.842 total time=   3.0s\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=0.847 total time=   2.8s\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=0.841 total time=   2.4s\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=0.825 total time=   2.4s\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=0.824 total time=   2.8s\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=0.831 total time=   2.4s\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=0.830 total time=   2.5s\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=0.832 total time=   2.6s\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=0.845 total time=   2.4s\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=0.852 total time=   2.3s\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=0.843 total time=   2.5s\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=0.853 total time=   2.3s\n",
      "[CV 5/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.843 total time=   2.9s\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=0.841 total time=   2.7s\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=0.841 total time=   2.4s\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=0.843 total time=   2.6s\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=0.844 total time=   2.6s\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=0.841 total time=   2.9s\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=0.848 total time=   2.8s\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=0.855 total time=   2.7s\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=0.845 total time=   2.3s\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=0.853 total time=   2.8s\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=0.845 total time=   2.8s\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=0.840 total time=   2.6s\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=0.845 total time=   2.8s\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=0.843 total time=   2.5s\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=0.849 total time=   2.5s\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=0.842 total time=   2.4s\n",
      "[CV 1/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.833 total time=   2.3s\n",
      "[CV 2/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 total time=   2.4s\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=0.836 total time=   2.5s\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=0.837 total time=   2.5s\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=0.840 total time=   2.2s\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=0.783 total time=   2.3s\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=0.776 total time=   2.2s\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=0.790 total time=   2.3s\n",
      "[CV 4/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.786 total time=   2.3s\n",
      "[CV 5/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.791 total time=   2.2s\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=0.830 total time=   2.3s\n",
      "[CV 2/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.834 total time=   2.3s\n",
      "[CV 3/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.836 total time=   2.3s\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=0.840 total time=   2.3s\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=0.842 total time=   2.3s\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=0.788 total time=   2.3s\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=0.782 total time=   2.3s\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=0.797 total time=   2.5s\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=0.792 total time=   2.2s\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=0.801 total time=   2.5s\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=0.845 total time=   2.3s\n",
      "[CV 2/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=   2.2s\n",
      "[CV 3/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=   2.3s\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=0.859 total time=   2.3s\n",
      "[CV 5/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=   2.2s\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=0.822 total time=   2.3s\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=0.819 total time=   2.1s\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=0.825 total time=   2.2s\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=0.829 total time=   2.3s\n",
      "[CV 5/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.826 total time=   2.4s\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=0.843 total time=   2.2s\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=0.852 total time=   2.3s\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=0.845 total time=   2.2s\n",
      "[CV 4/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.856 total time=   2.4s\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=0.847 total time=   2.4s\n",
      "[CV 1/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.823 total time=   2.3s\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=0.825 total time=   2.3s\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=0.832 total time=   2.3s\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=0.829 total time=   2.3s\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=0.828 total time=   2.4s\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=0.602 total time=   2.3s\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=0.596 total time=   2.5s\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=0.618 total time=   2.4s\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=0.612 total time=   2.4s\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=0.610 total time=   2.3s\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=0.580 total time=   2.3s\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=0.572 total time=   2.4s\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=0.600 total time=   2.4s\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=0.583 total time=   2.3s\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=0.585 total time=   2.5s\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=0.591 total time=   2.6s\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=0.584 total time=   2.2s\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=0.610 total time=   2.3s\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=0.599 total time=   2.4s\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=0.599 total time=   2.2s\n",
      "[CV 1/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.2s\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=0.572 total time=   2.3s\n",
      "[CV 3/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   2.7s\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=0.582 total time=   2.4s\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=0.585 total time=   2.5s\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=0.641 total time=   2.4s\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=0.642 total time=   2.5s\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=0.657 total time=   2.8s\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=0.645 total time=   2.5s\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=0.646 total time=   2.5s\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=0.587 total time=   2.4s\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=0.577 total time=   2.2s\n",
      "[CV 3/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   2.3s\n",
      "[CV 4/5; 38/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 38/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.4s\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=0.593 total time=   2.4s\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=0.620 total time=   2.7s\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=0.621 total time=   2.3s\n",
      "[CV 3/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.634 total time=   2.4s\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=0.628 total time=   2.6s\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=0.629 total time=   2.6s\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=0.587 total time=   2.4s\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=0.579 total time=   2.3s\n",
      "[CV 3/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   2.2s\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=0.593 total time=   2.3s\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=0.591 total time=   2.3s\n",
      "Best: 0.849247 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:  0.8614305827180329\n",
      "accuracy:  0.867591640972936\n",
      "auc score:  0.861790614503133\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,\n",
    "                        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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "[CV 1/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 2/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 3/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 5/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\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=   2.3s\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=   2.4s\n",
      "[CV 3/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 4/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 2/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 2/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 1/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\n",
      "[CV 2/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 3/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 4/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 3/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\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=   2.5s\n",
      "[CV 1/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 2/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.8s\n",
      "[CV 3/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\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=   2.6s\n",
      "[CV 5/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.9s\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=0.826 total time=  10.4s\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=0.832 total time=   9.4s\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=0.830 total time=   9.0s\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=0.827 total time=   8.5s\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=0.820 total time=   8.2s\n",
      "[CV 1/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.833 total time=   8.7s\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=0.838 total time=   8.9s\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=0.840 total time=   8.9s\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=0.835 total time=   8.8s\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=0.836 total time=   8.6s\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=0.829 total time=  11.4s\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=0.836 total time=  15.1s\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=0.836 total time=   9.1s\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=0.833 total time=   8.6s\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=0.834 total time=   9.0s\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=0.836 total time=  10.0s\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=0.848 total time=  10.0s\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=0.847 total time=  12.2s\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=0.842 total time=  12.6s\n",
      "[CV 5/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 8/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.841 total time=  10.6s\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=   2.3s\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=   2.3s\n",
      "[CV 4/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 9/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 9/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\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=   2.3s\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=   2.7s\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=   2.8s\n",
      "[CV 4/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 5/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.0s\n",
      "[CV 3/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 4/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 5/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 2/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 3/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.2s\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=   2.1s\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=0.844 total time=   6.5s\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=0.846 total time=   6.3s\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=0.847 total time=   6.8s\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=0.845 total time=   6.7s\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=0.845 total time=   7.0s\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=0.846 total time=   7.7s\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=0.852 total time=   8.7s\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=0.853 total time=   7.4s\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=0.851 total time=   7.0s\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=0.850 total time=   6.7s\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=0.840 total time=   7.0s\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=0.853 total time=   6.7s\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=0.850 total time=   6.7s\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=0.849 total time=   6.9s\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=0.847 total time=   5.9s\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=0.848 total time=   6.4s\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=0.859 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=0.856 total time=   6.8s\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=0.856 total time=   8.7s\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=0.854 total time=   6.2s\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=   1.9s\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=   1.9s\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=   1.8s\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=   2.1s\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=   2.0s\n",
      "[CV 2/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\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=   1.9s\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=   2.1s\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=   2.1s\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=   2.0s\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=   2.0s\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=   2.0s\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=   1.9s\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=   2.3s\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=   2.9s\n",
      "[CV 3/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 4/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.0s\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=0.847 total time=   3.0s\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=0.861 total time=   2.8s\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=0.857 total time=   2.8s\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=0.856 total time=   2.9s\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=0.860 total time=   2.9s\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=0.849 total time=   2.9s\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=0.862 total time=   2.9s\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=0.863 total time=   2.9s\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=0.856 total time=   2.8s\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=0.862 total time=   3.1s\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=0.848 total time=   2.8s\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=0.862 total time=   2.9s\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=0.860 total time=   2.8s\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=0.856 total time=   2.7s\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=0.862 total time=   2.7s\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=0.850 total time=   2.8s\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=0.864 total time=   2.8s\n",
      "[CV 3/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.864 total time=   2.8s\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=0.858 total time=   2.7s\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=0.862 total time=   2.8s\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=   2.0s\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=   1.9s\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=   1.9s\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=   1.9s\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=   2.1s\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=   2.0s\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=   1.9s\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=   1.9s\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=   2.0s\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=   1.9s\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=   2.0s\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=   1.8s\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=   1.9s\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=   1.9s\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=   1.9s\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=   1.9s\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=   1.9s\n",
      "[CV 4/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\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=0.830 total time=   2.4s\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=0.849 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=0.841 total time=   2.4s\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=0.836 total time=   2.3s\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=0.845 total time=   2.3s\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=0.831 total time=   2.4s\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=0.851 total time=   2.7s\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=0.842 total time=   2.7s\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=0.839 total time=   2.6s\n",
      "[CV 5/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.846 total time=   2.4s\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=0.828 total time=   2.1s\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=0.845 total time=   2.3s\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=0.840 total time=   2.4s\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=0.836 total time=   2.1s\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=0.843 total time=   2.0s\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=0.829 total time=   2.2s\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=0.849 total time=   2.0s\n",
      "[CV 3/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.841 total time=   2.1s\n",
      "[CV 4/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   2.1s\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=0.845 total time=   2.1s\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 0.82718169 0.8364825\n",
      " 0.83358294 0.84294465        nan        nan        nan        nan\n",
      " 0.84541305 0.85023047 0.84757414 0.85449535        nan        nan\n",
      "        nan        nan 0.85609571 0.85851218 0.85775948 0.85975293\n",
      "        nan        nan        nan        nan 0.84032453 0.84198736\n",
      " 0.83830861 0.84055274]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.859753 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:  0.8638068544020996\n",
      "accuracy:  0.8706748886605001\n",
      "auc score:  0.8619616318671124\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.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.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": 13,
   "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=0.832 total time=   2.2s\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=0.853 total time=   2.2s\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=0.845 total time=   2.1s\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=0.842 total time=   2.1s\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=0.846 total time=   2.1s\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=0.835 total time=   2.2s\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=0.854 total time=   2.1s\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=0.845 total time=   2.2s\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=0.844 total time=   2.2s\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=0.847 total time=   1.9s\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=0.826 total time=   2.0s\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=0.846 total time=   1.9s\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=0.844 total time=   1.9s\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=0.839 total time=   1.9s\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=0.845 total time=   1.9s\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=0.834 total time=   1.9s\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=0.851 total time=   1.8s\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=0.843 total time=   1.8s\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=0.845 total time=   2.0s\n",
      "[CV 5/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.843 total time=   1.9s\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=0.842 total time=   1.7s\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=0.851 total time=   1.8s\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=0.854 total time=   1.7s\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=0.853 total time=   1.8s\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=0.853 total time=   1.7s\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=0.837 total time=   1.7s\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=0.854 total time=   1.7s\n",
      "[CV 3/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.846 total time=   1.7s\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=0.856 total time=   1.7s\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=0.851 total time=   1.7s\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=0.840 total time=   1.8s\n",
      "[CV 2/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.854 total time=   1.7s\n",
      "[CV 3/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.854 total time=   1.8s\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=0.849 total time=   1.9s\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=0.850 total time=   1.8s\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=0.847 total time=   1.7s\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=0.859 total time=   1.8s\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=0.858 total time=   1.7s\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=0.854 total time=   1.7s\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=0.856 total time=   1.7s\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=0.759 total time=   1.7s\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=0.778 total time=   1.7s\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=0.784 total time=   1.7s\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=0.759 total time=   1.8s\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=0.774 total time=   1.7s\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=0.758 total time=   1.8s\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=0.777 total time=   1.7s\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=0.780 total time=   1.7s\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=0.766 total time=   1.8s\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=0.773 total time=   1.7s\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=0.750 total time=   1.7s\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=0.768 total time=   1.8s\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=0.771 total time=   1.7s\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=0.755 total time=   1.7s\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=0.767 total time=   1.8s\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=0.748 total time=   1.7s\n",
      "[CV 2/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.764 total time=   1.7s\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=0.774 total time=   1.7s\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=0.757 total time=   1.8s\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=0.767 total time=   1.7s\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=0.844 total time=   1.7s\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=0.855 total time=   1.7s\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=0.859 total time=   1.8s\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=0.847 total time=   1.7s\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=0.853 total time=   1.8s\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=0.844 total time=   1.8s\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=0.856 total time=   1.7s\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=0.860 total time=   1.7s\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=0.852 total time=   1.7s\n",
      "[CV 5/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=   1.7s\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=0.843 total time=   1.7s\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=0.852 total time=   1.7s\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=0.856 total time=   1.7s\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=0.857 total time=   1.7s\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=0.852 total time=   1.7s\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=0.842 total time=   1.8s\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=0.854 total time=   1.8s\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=0.861 total time=   1.7s\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=0.858 total time=   1.7s\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=0.857 total time=   1.7s\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=0.602 total time=   1.7s\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=0.606 total time=   1.8s\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=0.612 total time=   1.7s\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=0.596 total time=   1.7s\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=0.601 total time=   1.7s\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=0.604 total time=   1.7s\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=0.606 total time=   1.7s\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=0.610 total time=   1.8s\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=0.595 total time=   1.7s\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=0.600 total time=   1.7s\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=0.601 total time=   1.7s\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=0.607 total time=   1.8s\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=0.612 total time=   1.8s\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=0.594 total time=   1.7s\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=0.600 total time=   1.8s\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=0.599 total time=   1.8s\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=0.604 total time=   1.7s\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=0.610 total time=   1.7s\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=0.594 total time=   1.7s\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=0.599 total time=   1.8s\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=0.838 total time=   1.7s\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=0.844 total time=   1.7s\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=0.840 total time=   1.8s\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=0.848 total time=   1.8s\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=0.853 total time=   1.7s\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=0.845 total time=   1.8s\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=0.854 total time=   2.4s\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=0.853 total time=   1.8s\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=0.849 total time=   1.8s\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=0.850 total time=   1.8s\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=0.841 total time= 1.6min\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=0.845 total time=   1.8s\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=0.856 total time=   1.9s\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=0.843 total time=   1.7s\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=0.836 total time=   1.7s\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=0.835 total time=   2.0s\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=0.842 total time=   1.8s\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=0.846 total time=   1.8s\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=0.859 total time=   1.7s\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=0.854 total time=   1.8s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   2.0s\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=0.379 total time=   1.9s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.379 total time=   1.8s\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=0.379 total time=   1.7s\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=0.379 total time=   1.7s\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=0.379 total time=   1.8s\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=0.718 total time=   1.7s\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=0.719 total time=   1.7s\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=0.729 total time=   1.8s\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=0.732 total time=   1.7s\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=0.701 total time=   1.8s\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=0.639 total time=   1.7s\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=0.695 total time=   1.7s\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=0.709 total time=   1.7s\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=0.733 total time=   1.7s\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=0.695 total time=   1.7s\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=0.711 total time=   1.8s\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=0.705 total time=   1.8s\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=0.746 total time=   1.7s\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=0.746 total time=   1.7s\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=0.757 total time=   1.8s\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=0.684 total time=   1.7s\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=0.671 total time=   1.8s\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=0.710 total time=   1.8s\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=0.691 total time=   1.7s\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=0.751 total time=   1.8s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.854887 using {'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",
      "f1_score:  0.8553697510219249\n",
      "accuracy:  0.8629667694415896\n",
      "auc score:  0.8528544961891159\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABwHUlEQVR4nO3dd3xT1f8G8CdJm6R7UDoplL03UjYChbJFRYpsUBBFRetAFAXBH/h1MAQUkS2jLVMUBBFE2aO0DJllFShdlO6RJjm/P0oDsQVaSHqb9Hm/zAtycm/yyZU2T8499xyZEEKAiIiIyErIpS6AiIiIyJQYboiIiMiqMNwQERGRVWG4ISIiIqvCcENERERWheGGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNwQ0SOtWLECMpnMcLOxsYGfnx9GjRqFW7duFbuPEAI///wzOnXqBFdXV9jb26Nx48aYPn06srKyHvpamzdvRq9eveDh4QGlUglfX18MGjQIe/bsKVGtubm5mDNnDgIDA+Hi4gK1Wo06dergzTffxMWLF5/o/ROR5ZFxbSkiepQVK1Zg9OjRmD59OqpXr47c3FwcPnwYK1asQEBAAM6cOQO1Wm3YXqfTYciQIYiIiEDHjh3xwgsvwN7eHvv27cPatWvRoEED/Pnnn/Dy8jLsI4TAmDFjsGLFCjRv3hwDBw6Et7c3bt++jc2bNyMyMhIHDhxAu3btHlpncnIyevbsicjISPTt2xdBQUFwdHTEhQsXEBYWhvj4eGg0GrMeKyIqJwQR0SMsX75cABDHjh0zap80aZIAIMLDw43aZ86cKQCI999/v8hzbd26VcjlctGzZ0+j9q+//loAEO+8847Q6/VF9lu1apU4cuTII+vs06ePkMvlYsOGDUUey83NFe+9994j9y+p/Px8kZeXZ5LnIiLzYLghokd6WLj57bffBAAxc+ZMQ1t2drZwc3MTderUEfn5+cU+3+jRowUAcejQIcM+7u7uol69ekKr1T5RjYcPHxYAxNixY0u0fefOnUXnzp2LtI8cOVJUq1bNcP/q1asCgPj666/FnDlzRI0aNYRcLheHDx8WCoVCTJs2rchznD9/XgAQ8+fPN7TdvXtXTJw4UVSpUkUolUpRs2ZN8eWXXwqdTlfq90pEj8cxN0T0RK5duwYAcHNzM7Tt378fd+/exZAhQ2BjY1PsfiNGjAAA/Pbbb4Z9UlJSMGTIECgUiieqZevWrQCA4cOHP9H+j7N8+XLMnz8f48aNw7fffgsfHx907twZERERRbYNDw+HQqHASy+9BADIzs5G586dsXr1aowYMQLfffcd2rdvj8mTJyM0NNQs9RJVdMX/9iEi+o+0tDQkJycjNzcXR44cweeffw6VSoW+ffsatjl79iwAoGnTpg99nsLHzp07Z/Rn48aNn7g2UzzHo9y8eRMxMTGoXLmyoS0kJASvvfYazpw5g0aNGhnaw8PD0blzZ8OYotmzZ+Py5cuIiopC7dq1AQCvvfYafH198fXXX+O9996Dv7+/WeomqqjYc0NEJRIUFITKlSvD398fAwcOhIODA7Zu3YoqVaoYtsnIyAAAODk5PfR5Ch9LT083+vNR+zyOKZ7jUV588UWjYAMAL7zwAmxsbBAeHm5oO3PmDM6ePYuQkBBD2/r169GxY0e4ubkhOTnZcAsKCoJOp8M///xjlpqJKjL23BBRiSxcuBB16tRBWloali1bhn/++Qcqlcpom8JwURhyivPfAOTs7PzYfR7nwedwdXV94ud5mOrVqxdp8/DwQLdu3RAREYEZM2YAKOi1sbGxwQsvvGDY7tKlSzh16lSRcFQoMTHR5PUSVXQMN0RUIq1bt0arVq0AAAMGDECHDh0wZMgQXLhwAY6OjgCA+vXrAwBOnTqFAQMGFPs8p06dAgA0aNAAAFCvXj0AwOnTpx+6z+M8+BwdO3Z87PYymQyimFkwdDpdsdvb2dkV2z548GCMHj0a0dHRaNasGSIiItCtWzd4eHgYttHr9ejevTs+/PDDYp+jTp06j62XiEqHp6WIqNQUCgVmzZqFuLg4LFiwwNDeoUMHuLq6Yu3atQ8NCqtWrQIAw1idDh06wM3NDevWrXvoPo/Tr18/AMDq1atLtL2bmxtSU1OLtF+/fr1UrztgwAAolUqEh4cjOjoaFy9exODBg422qVmzJjIzMxEUFFTsrWrVqqV6TSJ6PIYbInoizz77LFq3bo25c+ciNzcXAGBvb4/3338fFy5cwCeffFJkn23btmHFihUIDg5GmzZtDPtMmjQJ586dw6RJk4rtUVm9ejWOHj360Fratm2Lnj17YsmSJdiyZUuRxzUaDd5//33D/Zo1a+L8+fNISkoytJ08eRIHDhwo8fsHAFdXVwQHByMiIgJhYWFQKpVFep8GDRqEQ4cOYefOnUX2T01NhVarLdVrEtHjcYZiInqkwhmKjx07ZjgtVWjDhg146aWX8MMPP2D8+PEACk7thISEYOPGjejUqRNefPFF2NnZYf/+/Vi9ejXq16+P3bt3G81QrNfrMWrUKPz8889o0aKFYYbi+Ph4bNmyBUePHsXBgwfRtm3bh9aZlJSEHj164OTJk+jXrx+6desGBwcHXLp0CWFhYbh9+zby8vIAFFxd1ahRIzRt2hSvvPIKEhMTsWjRInh5eSE9Pd1wmfu1a9dQvXp1fP3110bh6EFr1qzBsGHD4OTkhGeffdZwWXqh7OxsdOzYEadOncKoUaPQsmVLZGVl4fTp09iwYQOuXbtmdBqLiExA2ml2iKi8e9gkfkIIodPpRM2aNUXNmjWNJuDT6XRi+fLlon379sLZ2Vmo1WrRsGFD8fnnn4vMzMyHvtaGDRtEjx49hLu7u7CxsRE+Pj4iJCRE7N27t0S1Zmdni2+++UY888wzwtHRUSiVSlG7dm3x1ltviZiYGKNtV69eLWrUqCGUSqVo1qyZ2Llz5yMn8XuY9PR0YWdnJwCI1atXF7tNRkaGmDx5sqhVq5ZQKpXCw8NDtGvXTnzzzTdCo9GU6L0RUcmx54aIiIisCsfcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisioVbm0pvV6PuLg4ODk5QSaTSV0OERERlYAQAhkZGfD19YVc/ui+mQoXbuLi4uDv7y91GURERPQEbty4gSpVqjxymwoXbpycnAAUHBxnZ2eJqyEiIqKSSE9Ph7+/v+Fz/FEqXLgpPBXl7OzMcENERGRhSjKkhAOKiYiIyKow3BAREZFVYbghIiIiq8JwQ0RERFaF4YaIiIisCsMNERERWRWGGyIiIrIqDDdERERkVRhuiIiIyKow3BAREZFVkTTc/PPPP+jXrx98fX0hk8mwZcuWx+6zd+9etGjRAiqVCrVq1cKKFSvMXicRERFZDknDTVZWFpo2bYqFCxeWaPurV6+iT58+6NKlC6Kjo/HOO+/g1Vdfxc6dO81cKREREVkKSRfO7NWrF3r16lXi7RctWoTq1avj22+/BQDUr18f+/fvx5w5cxAcHGyuMomIiKyCEAJCAKLw7wD099oKHgcEBPTi/uNCFN1Pf2+7e/8ZniM7Ox8qtQIqGwU8ndWSvU+LWhX80KFDCAoKMmoLDg7GO++889B98vLykJeXZ7ifnp5urvKIiAiAXi+Qr9dDpxfQ6gV0uoI/tXo9tDpxv/3eTS/u/1nwd0CnFxBCQPfgY3pAJwRyNDrczdYgW6ODTAbDBzPw3w/he22434Z7H9j3/mr82ANtMGozfs7CZ7gfCB7+nPdru9cmgHy9Htl5Omi0euTpCv7UaPXQ6Ar+zNcJ5Ov0yNfpH3hf/wkWha8p7gWLB15bL3Cv/oLXfPBxc9IkZiPpl8twbumFzgPqYNMb7c37go9gUeEmPj4eXl5eRm1eXl5IT09HTk4O7Ozsiuwza9YsfP7552VVIhGRJIQQyNcJaHR65D/wQam59yGp0eqRo9EhI0+LzFwtUrI1uJqUhfTcfMOHqVZX8MGq1d/703C/8O/6eyHlXuDQPxBUxP37Zv4MpXJCJgNkAOQyGTRJ2YhfdQ5CJ3B3zw1ktfaTtDaLCjdPYvLkyQgNDTXcT09Ph7+/v4QVEVFFk5SRh5QsDTLztMjK0yIzLx8ZuQV/N/RsiPs9GboHw4O4HyCy8rQ4ezsdqdn50OoKeke0hcFEX/4jhY1cBkXhTSaDjUIGG4UcCllBm0wGw2OFf5fLCm4KuQxyuQyye8/lpLaBm70SKhv5vQ/ZgkdksoIbILv3Z8EHcOE2xm3397m3h+ED29Amu/fMj9rmgbbCxoe/JqAXBX+qbORQ2yqgtpFDaaOAna0cSlsFVDZyqGzkUCoU9973/eeRP/DeCoPFg69TcJMZXkv+QP0yyCD/z+MyyCCT3z8eDz4n/vMcRvsVbnCPEAIDErXYuvUCmjf1xsJRrUr/D8SELCrceHt7IyEhwagtISEBzs7OxfbaAIBKpYJKpSqL8ojIisWn5WLfpSTcTstFSpYGufk65Gn10Gh1yNXqkZuvw827OfdOK9wPHYW9KWVNLgNsFXLYKGSwlctho5BDqZDBXmUDe6UCDkobuNjbopq7PRxUNlDeCxpKGzls7+2jtLm/n+29x5Q2ctjIC57XRi6HQi4zhBZbxQP3FQV/2sjlsLkXTMh6yWQyLF/+HObMOYQpUzpBpZI2XlhUuGnbti22b99u1LZr1y60bdtWooqIyBLo9QUBIzdfh9x8PfK0BX8W3C8IKTn5OuRodIhNycaNlGykZGmQkq1BWnY+0nO1SM7Me/wLPYRcBjiobGBnq4C9UgF7pQ0cVAV/PtiTURgICnoqAIVMXvCnvKB3Q2krR2M/F1Rxs4Py3rd7W0MguRc+FHJDyCAyByEEFiw4ivr1KyMoqIah3d3dDjNmdJWwsvskDTeZmZmIiYkx3L969Sqio6Ph7u6OqlWrYvLkybh16xZWrVoFABg/fjwWLFiADz/8EGPGjMGePXsQERGBbdu2SfUWiMgMdHqB22k5iE/LRZZGh/ScfMQkZiIjNx+5+Trk3AsmefkFoSRXW/B3jU5v6E3J1wrDYM18nWlO2dTwcEBdbye42tvCzlYB9b1TCGpbBZQ2cgRUsoezna1x4FDI4eaghKPE32SJTOHu3Ry88spWbN58Hp6eDjh5cjy8vR2lLqsISX/ajh8/ji5duhjuF46NGTlyJFasWIHbt28jNjbW8Hj16tWxbds2vPvuu5g3bx6qVKmCJUuW8DJwIguQm69DWk4+0nLykZqdj5SsPMSl5eLW3Rz8eysNMUlZyNFokac17/gRuQxQ2SgM4UN171RL4TgHNwclans6orKTCpUclKjkoIKrgxKVHJSo4mZXZKwBUUVx9OgthIRswLVrqQCAxMQs/PbbRbz6agtpCyuGTAhzXxxWvqSnp8PFxQVpaWlwdnaWuhwiq3InMw9HrqbgYkIGUrI0uJutQWpWPmJTsnE9JbtUzyUDUNlJBUeVDeyUCvi52aGSgxJqGwXUSgXsbBWwUypgf+9PQ0+KrRwqGwXUtvcGaz7Qu6K+N4aEiEpOCIE5cw5j0qQ/odUWjB9zd7fDihXPoV+/umVWR2k+v9lPSkSPJIRAnlaPvHw9crU63EjJxoGYZFxJzkJGrtZwBVBGrhY3UrIfeRmwXAY4qmzgqLKBk50tKjuq4OOiRg0PB7SuUQlu9rZQ294PLmpbRZm9TyIqKiUlB6NGbcGvv140tLVr5491615E1aouElb2aAw3RBWQRqvHmVtpOHT5Dq7dyUJ8Wm7BvCj35kgpnPckJVuD1Oz8Uj13NXd7NPB1RiXHglM57g5K+LrYoWlVV3g4qHjVDJGFOHjwBgYP3oAbN+5PfjtpUnvMmNEFtuX8iwfDDZGVuxifgfBjN3DwSjKSMzXIzNUiJ1/3RM8llwHOdrao4+mEej5OqOJmB2e1LZzUNnC2s4Wfqx1qVC5/gwuJqHQyMvLQt+9a3L2bCwDw8LDHqlUD0KtXbYkrKxmGGyILl60pmHE2W1MwYPeX6DicvpWKxIw8JGfkIUtTfJCxkcvQKsAN9X2cUdXdHvZKBZQPzGWitCkYo1KtkgMc1TaGy46JyPo5OamwcGFvDBmyCR07VsW6dS/Cz89yxqky3BBZiNx8HTZE3sCVpGzczS4YrBuflovz8RmP3bexnwuGt62G2p6OcLNXws1eCSe1DU8REZGBEMLoasCXX24MOztb9O1bBzY2lvXFhuGGqJzKzdfhSlIWzt1Ox+lbaVh7NBYabfEz3RZe3qy2laNGZUf0aOCFet5O8HOzg5ezGk5q2zKunogshU6nx6xZ+xEXl4Hvv+9j9NiAAfUkqurpMNwQlRM6vcDV5ExExaZiz/lE7DqbUGS+F7WtHP2a+MLDUQk3ByU8HFVoWc0NVd3tOf8KEZVaQkImhg3bjD//vAIA6NixKl5+ubHEVT09hhuiMqDXC+Tk65Ct0eFqchYuJ2Vg/6U7OHc7HRl5WuRqdMjO10H3nzDjoFKguocD6no5oa63E4IbeKOah4NE74KIrMnu3VcwdOgmJCRkAQDkchlu3kx/zF6WgeGGyAzydXqsOXIdv0TH4WJ8xkMH9f6XykaOmpUd0cjPGSGt/NGimht7ZIjIpHQ6PaZP/xszZvyDwml8fXwcsXbti3j22QBJazMVhhsiExBCIC0nH+dup+PQlRSEH4tFQnrxCy16OCpR1d0e1T0cEdzQC57OajjeW0TRy1nNBQ+JyGzi4jIwdOgm7N17zdDWo0dN/Pzz8/D0tJ5eYYYbolKIS81BbEo2EjNyEXU9FVE3UpGUkYc7WXnIzTce7OtqZ4sxHaqjbc1KqOHhAHtlweXUvEKJiKSwc2cMhg/fjKSkgqVQFAoZZszogkmTOljd7yWGG6JH0OkF/r6QiF9OxiEq9i5iU3Ieub2nkwq1PB3Rp7EPBjT3gwNXgiaickAIgW++OWQINn5+TggLG4gOHapKXJl58Dcv0X8kpOVg3p4Y/HMhCTdTjcOMXAZ4O6vh7qCEj6sdnq1bGdXcHeDnZgdvZzXslOV7SnIiqphkMhl+/vl5NG26CK1a+WLlygHw8LCXuiyzYbghAhAdexebom7h37h0RMXexYMXLdnZytGlrif6N/NFYPVKcHNQSlcoEVEJZWTkwclJZbjv7e2Iw4dfQbVqrlZ3Guq/GG6oQruTmYd3wqOx71KyUXt1Dwd8GFwXDXyd4eNiB6WFzc5JRBVXfr4OH3+8G+vXn8WJE6/B3d3O8Fj16m4SVlZ2GG6owtDrBU7fSsOJ2Lu4lpyFuLRc7L2QiHxdQTdNjwZe6FSnMmp7OqJFNTeuo0REFuf69VQMHrwRhw/fBACMHv0LtmwJqXBTSjDckFW7kZKNiwkZOHzlDraficetu0UHBFdzt8eUvvXRvYG3BBUSEZnGli3nMXr0L0hNLVjJ29ZWjq5dA6QtSiIMN2R1cjQ6/HMpCYv/uYLI63eNHpMBeCbADbW9nFDJUYVW1dzQsbZHhftWQ0TWQ6PR4cMPd2HevCOGturVXREePhDPPOMnYWXSYbghq6DXCxy8fAerD1/H3guJyH1ggUlntQ0a+7mge0Mv9G7kA09ntYSVEhGZzpUrdxESsgHHj8cZ2gYObIAlS/rBxaXi/q5juCGLdjdLg22nb2P+nktGMwK72tuisa8L3gmqjZYB7hJWSERkHps2ncPo0b8g/d7vPqVSgTlzgvH6660qfG80ww1ZpAMxyQg7Govfz8QbVs5WyGXo39QXQwOroiXXZCIiK5eUlGUINrVquSMiYiCaN/eRuKrygeGGLMapG6mIiLyB6BupOHPr/sq1DioFght4Y0rfBnDnHDREVEGMG9cSf/11DXK5DD/+2NdoTpuKjuGGLMKvJ+PwXsRJaHT3x9J0rlMZr3WqgcAalbjYJBFZvejoeDRrdv+qTplMhlWrnoetrZw91f/BcEPlWmJ6LiZvOo3d5xMBAM39XdGniQ+a+buiFcfSEFEFkJOTj3fe2YHFi09g69bB6NevruExJZd8KRbDDZVbcanZ6PvdfqRk50MuA0a2C8CknvWgtuUPMxFVDOfPJ2PQoPU4fbrgC97IkVtw8eJbVr0ulCkw3FC5lJuvw+jlx5GSnY8qbnZYMKQ5mvlXjGnDiYgAYNWqk3j99W3Izs4HANjZ2WD27GAGmxJguKFyQwiBUzdTsT/mDsKP3UBsSjac1DZYNzYQ/u4OUpdHRFQmsrI0ePPN37FiRbShrWHDyoiIeAkNGlSWrjALwnBD5UJcag4mrD2BqNhUQ5uz2gY/DGvBYENEFca//yZi0KANOHs2ydA2ZkwzzJ/fG/b2thJWZlkYbkhS8Wm5mLzpFP6+mAS9AJQKOVoFuKF1gDtGtw+Aiz0v7SaiiuHXXy8gJGQDcnK0AAAHB1ssWtQXw4Y1kbgyy8NwQ5I4cysV3+2OwR9nEwxtDX2d8c3Apqjv6yxhZURE0mjc2AsqlQ1ycrRo0sQLEREDUbeuh9RlWSSGGypTer3Ad3suYf6eGOjuzSwcUMkeH/eujx4NuSo3EVVcAQGuWLHiOfz+ewzmzAmGnR1PQz0pmRBCSF1EWUpPT4eLiwvS0tLg7MwegrKUkZuPV1cex5GrKQCATnU88E63OmhRjVdBEVHFIoTAmjWn8dxzdTmzcAmV5vObPTdUJhIzcjFsyRFcTMiEjVyGyb3rYUz76pxVk4gqnPT0PIwb9yvCw//FkCGNsXr18/xdaGJyqQsg6xeTmIEes//BxYRMyGXAD8Na4JUONfjDTEQVzokTt9GixY8ID/8XALB27WkcOnRT4qqsD3tuyOy+3nkBqTn5cFApsHpMIJrzNBQRVTBCCCxceAzvvfcHNBodAMDFRYWlS/ujXTt/iauzPgw3ZBZCCETfSMXW6Dj8ea5g2vAfhrZgsCGiCic1NRevvLIVmzadM7Q984wvwsMHonp1/k40B4YbMikhBL7Ydg6rDl1Dvu7+WPUWVV3RsTZn1iSiiuXo0VsICdmAa9dSDW3vvtsGX34ZxEUvzYjhhkzm9M00vL4mEjfv5gAAbOQyNPR1xqsda6BPYx+OsSGiCiUyMg4dOixDfr4eAODmpsaKFQPQv3/dx+xJT4vhhkwiX6fH+NWRuJWaAxu5DK92rI53gupwBW8iqrCaN/dBjx41sW3bJbRtWwVhYQNRtaqL1GVVCAw3ZBI/7I3BrdQcOKtt8Od7neHppJa6JCIiScnlMqxcOQA//HAckya1hy2/7JUZXgpOT+1/v5/H7F2XAADvBNVmsCGiCkevF/j66wPYs+eqUXulSvaYMqUTg00ZY88NPZXE9Fws2X8FANC2RiWMaldd4oqIiMpWUlIWRo7cgt9/j4G3tyOio1+Dl5ej1GVVaOy5oSeWr9Nj+NKjyNcJuNjZYtHwlpDLOWiYiCqOffuuo1mzH/H77zEAgISETOzceVniqog9N/REhBCYuf0cLiRkwFFlgw3j28KFi7wRUQWh1wvMmrUPn322F/p7iwB7ejpg9ern0b17TYmrI4YbeiLf/nERyw9cAwBM7dcAtb2cpC2IiKiMJCRkYvjwzdi164qhrUuXAKxZ8wJ8fPi7sDxguKFSO34tBd/vLeiC/TC4Ll5qxanDiahi2LPnKoYO3YT4+EwAgEwGTJ3aGVOmdIJCwZEe5QXDDZVK5LUUjFx2FHoBdKrtgTe61JK6JCKiMpGamovnnw9HenoeAMDb2xFr176ALl14IUV5w5hJJZaWnY9XVx1HlkaHau72+O7l5lKXRERUZlxd1Vi4sDcAoHv3Gjh5cjyDTTnFnhsqkRPXUzBhbRTuZufD3V6JbRM7wlHFfz5EZN2EEEZLxwwb1gSurmr07l2bV4eWY/x0osf6+0IiRq84Br0APBxVWDSsBYMNEVk1rVaPadP24u7dHCxc2Mfosb5960hUFZUUP6HokXafS8ArK48DABr4OGPNq4Fwc1BKXBURkfncvJmOIUM2Yt++WABA584BGDSoocRVUWkw3FCxMnLz8d3uS/hpX8FU4na2Cqwc05rBhois2vbtlzBixGbcuZMDAFAoZEhIyJS4Kiothhsq4vi1FAxdcgR5Wj0AoGkVF8wd3ByVnVQSV0ZEZB75+Tp88skefP31QUNb1aouCAt7EW3bcroLS8NwQ0Yir9/F4MWHob034+ZXA5vgpZZVjAbUERFZk9jYNAwevAGHDt00tPXvXxfLlz8Hd3c7CSujJ8VwQwbZGi2mbDkNrV4goJI9VoxujQAPB6nLIiIym61bL2DUqC24ezcXAGBrK8dXX3XHxImB/FJnwRhuCABwIT4db6w5gctJWbCRy7BqTCCqVrKXuiwiIrMRQmDu3MOGYBMQ4IqIiIF45hk/iSujpyX5JH4LFy5EQEAA1Go1AgMDcfTo0UduP3fuXNStWxd2dnbw9/fHu+++i9zc3DKq1jodjEnGcwsO4HJSFtQ2ciwd2YrBhoisnkwmw+rVL6ByZXu88EJ9REW9xmBjJSTtuQkPD0doaCgWLVqEwMBAzJ07F8HBwbhw4QI8PT2LbL927Vp89NFHWLZsGdq1a4eLFy9i1KhRkMlkmD17tgTvwPLN3nUR3+2+BABo7OeC2YOachFMIrJaaWm5cHFRG+77+jrh+PFx8Pd35mkoKyJpz83s2bMxduxYjB49Gg0aNMCiRYtgb2+PZcuWFbv9wYMH0b59ewwZMgQBAQHo0aMHXn755cf29lDxLidlYsGegmDTrmYlhL/WhsGGiKxSbq4Wb721Hc2a/Yi7d3OMHqta1YXBxspIFm40Gg0iIyMRFBR0vxi5HEFBQTh06FCx+7Rr1w6RkZGGMHPlyhVs374dvXv3fujr5OXlIT093ehGBbafvg29KJicb+3YNrBXcggWEVmfmJgUtGu3FAsWHMO1a6kYM2YrhBBSl0VmJNmnWXJyMnQ6Hby8vIzavby8cP78+WL3GTJkCJKTk9GhQwcIIaDVajF+/Hh8/PHHD32dWbNm4fPPPzdp7dYgM0+LlQevAQAGtaoibTFERGYSHn4GY8f+iowMDQBArbZBr161JK6KzE3yAcWlsXfvXsycORPff/89Tpw4gU2bNmHbtm2YMWPGQ/eZPHky0tLSDLcbN26UYcXl17L9V5GcqYGDUoHnWzDcEJF1ycnJx/jxv2Hw4I2GYFO3biUcOfIqxo1rydNQVk6ynhsPDw8oFAokJCQYtSckJMDb27vYfT799FMMHz4cr776KgCgcePGyMrKwrhx4/DJJ59ALi+a1VQqFVQqzqz7oMV/X8ZP+64AAN7oUgsudrYSV0REZDoXLiRj0KANOHXq/ufL8OFN8P33feDoyCVkKgLJem6USiVatmyJ3bt3G9r0ej12796Ntm3bFrtPdnZ2kQCjUCgAgOdPS+hSQgZm/n4eGblauDso8XxzXvZIRNZj7drTaNlysSHY2NnZYNmy/li5cgCDTQUi6QjS0NBQjBw5Eq1atULr1q0xd+5cZGVlYfTo0QCAESNGwM/PD7NmzQIA9OvXD7Nnz0bz5s0RGBiImJgYfPrpp+jXr58h5NCjHbicDACo4+WI397qCKWNRZ2ZJCJ6pNTUXGRl5QMAGjSojIiIgWjYsOjUImTdJA03ISEhSEpKwmeffYb4+Hg0a9YMO3bsMAwyjo2NNeqpmTJlCmQyGaZMmYJbt26hcuXK6NevH/7v//5PqrdgUU5cv4vpv54FALSr6cFgQ0RW5/XXW+Gvv67ByUmJ+fN7wcGBvTUVkUxUsPM56enpcHFxQVpaGpydnaUup0yNWHoE/1wq6Ln5+4NnUa0S140iIsslhEBk5G20auVr1J6fr4OtLXvzrU1pPr/51b2C+PVkHP65lAwZgC1vtGOwISKLlpmpwYgRW/DMMz9h+/ZLRo8x2BDDTQUQdjQWE8OiAAC9G/ugWVU3iSsiInpyp04loFWrxVi9+hQAYMSIzUhN5RqDdB+npLVyR6/eweRNpyFQEGzmDm4mdUlERE9ECIGffjqBt9/+HXl5OgCAk5MSCxb0hqur+jF7U0XCcGPFbqRkYcTSoxAAGvo6Y/7LzaGQc+IqIrI86el5eO213xAWdsbQ1ry5N8LDB6J27UoSVkblEcONlUrLycfwpUeRq9Wjtqcj1o1rw2BDRBYpKuo2Bg3agJiYFEPbhAnP4JtvekCt5scYFcV/FVbq3fBoXLuTDbWtHLNeaAxnNWchJiLLs3HjWQwZsgkaTcFpKBcXFZYu7Y8XX2wgcWVUnjHcWKFfom9hz/lEyGXA6lcC0SrAXeqSiIieSIsWPrCzs4FGo8Mzz/giLGwgatTgRRH0aAw3VuZsXBo+2FBwBcFLrfwZbIjIolWv7oZly57Dvn3X8b//dYdSycu86fF4KbgVEULgo02nodHq0SrADTOeayR1SUREJSaEwNKlJ5CZqTFqf+GF+pgzpyeDDZUYw40VWXMkFqdupsFGLsM3A5tweQUishgpKTkYMCAcr776KyZM2C51OWTh+OlnJbQ6PX4+fB0A8PqzNRHg4ShxRUREJXPo0A00b/4jtm69AABYteokIiPjJK6KLBnDjRVISM9Fm1m7cSE+AwDQs5G3xBURET2eXi/w9dcH0KnTCsTGpgEAKlWyw7ZtQ9Cype9j9iZ6OA4otnAarR6DFx9G8r1z1G92rYWGvi4SV0VE9GjJydkYOXKL0bpQHTpUxbp1L6JKlYq1qDGZHsONhZvz50VcTc6C0kaONa8G4hleHUVE5dy+fdfx8ssbcetWQW+zTAZ8/HFHTJv2LGw4VpBMgOHGgu2/lIQf/74MAPisbwMGGyIq9w4fvokuXVZCpxMAgMqV7bFmzQvo3r2mxJWRNWFEtlArDlzFiGVHoRdAt3qeGBpYVeqSiIgeq3VrP0OQ6dIlACdPjmewIZNjz40FOnMrFdN+PQsA6FTbA/Nebg6ZjOtGEVH5J5fLsGrVACxfHo333msLhYLfscn0+K/KwqRma/DKyuMAgLpeTlg5pjUcVcyoRFT+6HR6TJ/+N/7++5pRe+XKDvjww/YMNmQ2/FS0MN/vvYyE9Dy42yuxdFQr9tgQUbl0+3YGhg3bjD17rsLX1wnR0a+hcmUHqcuiCoKx2cLsOZ8IAAjtUQdV3OwlroaIqKhduy6jWbMfsWfPVQBAfHwm/vrrmrRFUYXCcGNhEtJyAQAtq3FVXCIqX7RaPaZM2YPg4NVITMwCAPj6OuGvv0Zi0KCGEldHFQlPS1mQ1GwNMvK0AIBqldhrQ0Tlx82b6RgyZCP27Ys1tPXqVQsrVw7g6Sgqcww3FiQqNhUA4OWsgr2S/+uIqHz4/fdLGD58M+7cyQEAKBQyzJzZDe+/3w5yOccFUtnjJ6QF2RR1CwDQqhon6yOi8iE5ORsvvbQeWVn5AAB/f2eEhQ1Eu3b+EldGFRnH3FgIIQT+PJsAABj0TBWJqyEiKuDhYY8FC3oDAPr3r4vo6PEMNiQ59txYiFupOcjJ18FGLkO7mh5Sl0NEFZgQwmgailGjmsHLywE9e9bi9BRULrDnxkL88W9Br42fmx1sOfEVEUlAo9EhNHQnJk7cUeSxXr1qM9hQucGeGwtw/FoKvthWsNxCi6q8BJyIyt7Vq3cxePBGHD1aMPavc+dqePHFBhJXRVQ8hpty7lZqDsavPgG9AAIq2WNaf84VQURla9Omcxgz5hekpeUBAJRKBe7ezZW4KqKHY7gp5xbtvYzkzDz4uKgRNq4tXOxspS6JiCqIvDwt3n//DyxYcMzQVrOmG8LDB6JlS18JKyN6NIabci4mMRMA8FrnGvB2UUtcDRFVFDExKQgJ2YATJ24b2kJCGmLx4n5wdlZJWBnR4zHclGPxaTk4fj0FAFCzsqPE1RBRRREefgZjx/6KjAwNAEClUmDevJ4YN64lBw2TRWC4KcfeDotGvk7A00mFwOqVpC6HiCoAvV5g4cJjhmBTp04lREQMRNOm3hJXRlRyvKa4nDobl4ajVwt6bRYNawGlDf9XEZH5yeUyrF37IipVssOwYU0QGTmOwYYsDntuyqnvdscAALrW9UQLLrdARGZ0924O3NzsDPerVHFGdPR4+Pk58TQUWSR2B5RDWp0ef54rmLRvYlAtiashImuVnZ2PV1/dilatfkJamvGl3VWqODPYkMViuCmHEjLyoNUL2MhlaOznKnU5RGSFzp5NQuvWP2Hp0ihcuXIXr776K4QQUpdFZBI8LVUOXU0quPzbw1EFuZzfnIjItFasiMYbb2xDTo4WAGBvb4v+/euwp4asBsNNObT7XCIAoKm/i8SVEJE1yczUYMKE7Vi16qShrXFjT0REvIR69bggL1kPhpty6ETsXQBAp9qVJa6EiKzF6dMJGDRoA86fTza0jR3bAvPm9YQdZz4nK8NwU85sOnETJ2+mAQACa3BuGyJ6esuWRWHChO3IzS04DeXoqMTixX3x8suNJa6MyDwYbsoRrU6PGb8VrP49qm0AanlyVmIienqZmRpDsGnWzBsREQNRuza/PJH1YrgpR07dTMPd7Hw4qBT4pG99qcshIivx1lutsXfvNfj6OuGbb3pAreavfrJu/Bdejuy5UDCQuGU1N9gqeJU+EZWeEAJHj95CYGAVQ5tMJkNExEuw4UznVEHwX3o5cvjKHQAcSExETyYtLReDBm1AmzZLsXNnjNFjDDZUkfBfezmRr9PjzK2CgcTtavKSTCIqnePH49C8+Y/YsKFg3N7w4ZuRkZEncVVE0mC4KScuxKcjN18PB5UC9bydpC6HiCyEEALz5h1Gu3ZLcfVqKgDA1VWNxYv7wclJJW1xRBLhmJtyIiYxCwBQvZIDZyUmohJJScnBmDG/4JdfLhja2rSpgrCwF1Gtmqt0hRFJjOGmnIhJLFhyoaq7vcSVEJElOHz4JkJCNiA2Ns3Q9v77bTFzZjfY2iokrIxIegw35cTvZ24DAJpU4ZILRPRoa9acwqhRv0Cr1QMAKlWyw8qVA9CnTx2JKyMqHxhuyoHMPC0uJxWclnqplb/E1RBReRcYWAV2djbIyNCgfXt/hIUNRJUqzlKXRVRuMNyUAxfjMwAAbva2qOTIAYBE9Gi1arljyZL+iI6Ox/TpXXiZN9F/8CeiHAg7FgsAqO/Db15EZEyvF1i06DiysjRG7YMGNcTMmd0YbIiKwZ8Kien1Ar+eLBhvM75zDYmrIaLyJDExC716rcHrr2/DW2/9LnU5RBaD4UZicWk5yMnXwUYu4+R9RGSwd+81NGu2CH/8cRkAsGJFNE6dSpC4KiLLwHAjscJLwH1d7WDD9aSIKjydTo/p0/9Gt26rcPt2we8HLy8H7No1HE2aeElcHZFlkPzTdOHChQgICIBarUZgYCCOHj36yO1TU1MxYcIE+Pj4QKVSoU6dOti+fXsZVWt6heGmWiXOb0NU0cXHZ6JHj9WYOnUv9HoBAOjWrTqio8ejWzeetiYqKUmvlgoPD0doaCgWLVqEwMBAzJ07F8HBwbhw4QI8PT2LbK/RaNC9e3d4enpiw4YN8PPzw/Xr1+Hq6lr2xZvIoXuLZdb0cJS4EiKS0p9/XsGwYZuQkFAwLYRcLsPnnz+LyZM7QMFeXaJSkTTczJ49G2PHjsXo0aMBAIsWLcK2bduwbNkyfPTRR0W2X7ZsGVJSUnDw4EHY2toCAAICAsqyZJMSQuDIlRQAQM9G3hJXQ0RS+fvva+jR42eIgs4a+Po6Ye3aF9C5c4CkdRFZKsm+Dmg0GkRGRiIoKOh+MXI5goKCcOjQoWL32bp1K9q2bYsJEybAy8sLjRo1wsyZM6HT6R76Onl5eUhPTze6lRcJ6XnIzNNCLgOacx0YogqrY8dqCAoqOO3Us2ctREe/xmBD9BQkCzfJycnQ6XTw8jIeIOfl5YX4+Phi97ly5Qo2bNgAnU6H7du349NPP8W3336LL7744qGvM2vWLLi4uBhu/v7lZwbgc7cL1oTxc7OHyoZrwRBVVHK5DD///DzmzAnGtm1DULmyg9QlEVk0izqRq9fr4enpicWLF6Nly5YICQnBJ598gkWLFj10n8mTJyMtLc1wu3HjRhlW/GgnYlMBAHW8ON6GqKLIz9dh8uQ/sX9/rFG7l5cj3nmnDeRymUSVEVkPycbceHh4QKFQICHBeN6GhIQEeHsXP/7Ex8cHtra2UCju93LUr18f8fHx0Gg0UCqVRfZRqVRQqcrnkgbRN1IBAC2quklbCBGViRs30jB48EYcPHgDP/98CtHR4+HhwSsliUxNsp4bpVKJli1bYvfu3YY2vV6P3bt3o23btsXu0759e8TExECv1xvaLl68CB8fn2KDTXl34d6aUi2qukpbCBGZ3W+/XUSzZj/i4MGC3uOEhKwivTdEZBqSnpYKDQ3FTz/9hJUrV+LcuXN4/fXXkZWVZbh6asSIEZg8ebJh+9dffx0pKSmYOHEiLl68iG3btmHmzJmYMGGCVG/hid3N0iAxIw8A0MDXReJqiMhcNBod3ntvJ/r1W4eUlBwAQLVqLti/fzQGDKgncXVE1knSS8FDQkKQlJSEzz77DPHx8WjWrBl27NhhGGQcGxsLufx+/vL398fOnTvx7rvvokmTJvDz88PEiRMxadIkqd7CEzt8tWB+Gx8XNVzsbCWuhojM4dq1VISEbMDRo7cMbQMG1MOyZf3h5mYnYWVE1k0mROHMChVDeno6XFxckJaWBmdn6Vbhfu3n49j5bwKGtamKLwY0lqwOIjKPzZvPYcyYrUhNzQUAKJUKfPNNd7z5ZmvIZBw0TFRapfn8lrTnpiI7G1cw307XekVnYiYiy5aQkImhQzchJ0cLAKhRww0REQPRsqWvxJURVQwWdSm4tcjN1+FWasG594Ycb0Nkdby8HDF/fi8AwEsvNcCJE+MYbIjKEHtuJHAxIQN6ATirbeDpVD4vUyei0tHrhdEcNWPGNEfVqi4ICqrB01BEZYw9NxIoXE+qtpcTf+kRWbjcXC3eeGMbQkN3GrXLZDJ0716TP+NEEmDPjQT+vpgEAGhXs5LElRDR07h48Q4GDVqPkycLJiN99tkAXt5NVA4w3JSx9Nx8HLtW0HPTrb7XY7YmovJq7drTeO2135CZqQEA2NnZGP5ORNJiuCljp26kIU+rR2UnFZpW4WBiIkuTnZ2PiRN/x5IlUYa2+vU9EBHxEho14tWPROUBw00Zi0kqWHKhVmVHnosnsjDnziVh0KANOHMm0dA2alQzLFjQCw4OlrcEDJG1YrgpY3H3JvTycVVLXAkRlcbKldF4443tyM7OBwDY29vihx/6YMSIphJXRkT/xXBTxm7eLZjfxs+VU68TWQqdTo/Fi08Ygk2jRp6IiBiI+vUrS1wZERWHl4KXscjrBYOJG/lxvA2RpVAo5Fi37kW4u9vh1Veb48iRVxlsiMox9tyUIa1Oj8T0gpXAm1d1lbYYInooIQRSUnJQqZK9oa1qVRecOfM6fHycJKyMiEqCPTdlKD49FwKAXAZUcuDMxETlUUZGHoYO3YQ2bZYi/d6XkUIMNkSWgeGmDIUfuwEA8HGxg0LOK6WIypvo6Hi0bLkY69adQUxMCl577TepSyKiJ8BwU4Y2Rt4EAIzvXEPiSojoQUII/PDDMbRpswSXLhWMi3N2VuGFFzjbMJElMlm42bRpE5o0aWKqp7M6Wp0et9MKLgMPbuQtcTVEVCgtLRchIRvwxhvbkZenAwC0bOmDEyfG4aWXGkpcHRE9iVKFmx9//BEDBw7EkCFDcOTIEQDAnj170Lx5cwwfPhzt27c3S5HWICVLw/E2ROXM8eNxaNFiMdavP2toe/vt1jhwYAxq1nSXsDIieholDjdffvkl3nrrLVy7dg1bt25F165dMXPmTAwdOhQhISG4efMmfvjhB3PWatESMwoGJjqrbTnehqgc+P77Y2jXbimuXLkLAHB1VWPz5hDMm9cLKhUvJCWyZCX+CV6+fDl++uknjBw5Evv27UPnzp1x8OBBxMTEwMHBwZw1WoXYlGwAgKcze22IyoO8PC3y8/UAgMBAP4SFDURAgKu0RRGRSZQ43MTGxqJr164AgI4dO8LW1haff/45g00JXYwvWFOqipv9Y7YkorLwzjtt8Pff11GrljtmzuwGpVIhdUlEZCIlDjd5eXlQq++vh6RUKuHuznPSJXXgcjIAoHUAjxlRWdPrBQ4duoH27asa2mQyGTZuHASFgheNElmbUp1Y/vTTT2FvX9DzoNFo8MUXX8DFxXgZgdmzZ5uuOiuh1elx5lYaAKBTHU7ZTlSW7tzJxsiRW7B9+yX88cdwBAXdn4qBwYbIOpU43HTq1AkXLlww3G/Xrh2uXLlitI1MxoGyxbmdloucfD1sFTLU9eYMp0Rl5cCBWAwevBE3b6YDAIYP34zLl9+Gvb2txJURkTmVONzs3bvXjGVYt4T0gvlt3B2UvFKKqAzo9QJffXUAU6bsgU4nAAAeHvZYseI5BhuiCqBUp6XS09Nx5MgRaDQatG7dGpUr8xRLSRRO3sf5bYjMLzExCyNGbMbOnZcNbZ07V8PatS/C15c9p0QVQYnDTXR0NHr37o34+HgAgJOTEyIiIhAcHGy24qxFYc+Nh6NS4kqIrNvff1/Dyy9vxO3bmQAAmQz49NNO+PTTzrCx4fgaooqixD/tkyZNQvXq1XHgwAFERkaiW7duePPNN81Zm9WIS80BAHi7qB+zJRE9qaVLT6Br11WGYOPl5YBdu4bj88+7MNgQVTAl7rmJjIzEH3/8gRYtWgAAli1bBnd3d6Snp8PZ2dlsBVqDO5kaAEBlJ56WIjKXjh2rwd7eFpmZGnTrVh2rV78Ab29HqcsiIgmUONykpKSgSpUqhvuurq5wcHDAnTt3GG4e407WvXDjyHBDZC516lTC4sV9EROTgo8/7sjLvIkqsFINKD579qxhzA0ACCFw7tw5ZGRkGNq4MnhRKffCjacTT0sRmYJOp8fChccwdmwL2Nndv/rp5ZcbS1gVEZUXpQo33bp1gxDCqK1v376QyWQQQkAmk0Gn05m0QGtgCDdcV4roqcXFZWDIkI34++/rOHMmEYsX95O6JCIqZ0ocbq5evWrOOqxaag7H3BCZwo4dMRg+fDOSkwsWol22LAqhoW1Rr56HxJURUXlS4nCzcuVKvP/++4blF6hksvK0yL238rAHx9wQPRGtVo9PP92DL788YGirUsUZYWEvMtgQURElHnH3+eefIzMz05y1WKXkzDwAgNpWDgdVqc4CEhGAGzfS8OyzK4yCTd++dRAd/ZrRQphERIVK/Gn737E2VDJxqQUT+LnZcwI/otL67beLGDlyC1JSCuaKsrGR48svuyE0tC3XsiOihypVVwJ/mZTeiet3AQC1PDnfBlFp7Np1Gf36rTPcr1bNBWFhA9GmTZVH7EVEVMpwU6dOnccGnJSUlKcqyNpE3SgINy2ruUlcCZFl6dq1Orp2rY49e65iwIB6WLasP9zc7KQui4gsQKnCzeeffw4XFxdz1WKVbt4t6E6v580F+4hKQ6GQY82aF7B58zmMH9+KPcdEVGKlCjeDBw+Gp6enuWqxShm5WgCAO1cEJ3qovDwtJk36E4MGNUS7dv6Gdm9vR7z++jMSVkZElqjEV0vxW9OTycjNBwC42ts+Zkuiiuny5RS0b78M8+YdwcsvbzQMHiYielIlDje8Wqr09HqBzLyCnhtXO4Ybov9av/5ftGixGJGRtwEACQmZOHLkpsRVEZGlK/FpKb1eb846rFJGnhb6e5nQhT03RAa5uVqEhu7EDz8cN7TVru2OiIiX0KyZt4SVEZE14KxyZnQ36/4EfiobhcTVEJUPFy/ewaBB63HyZIKhbciQxli0qA+cuEQJEZkAw40Z3c0uGG/jpGavDREArF17Gq+99hsyMwvWW1OrbTB/fi+88kpzjusjIpNhuDGju9kFv8Cd1TzMRDdvpmPMmF+Ql6cDANSr54GIiIFo3NhL4sqIyNqUeEAxld6de99OXTiYmAhVqjhj3ryeAICRI5vi+PGxDDZEZBbsUjCjwp4bDiamikqvF5DL759uGjeuJerUqYQuXapLWBURWTv23JhR4ZgbXgZOFU1WlgYjR27BBx/8YdQuk8kYbIjI7NhzY0ap93puXLkiOFUgZ84k4qWX1uP8+WQAwLPPBqBfv7oSV0VEFQl7bswo+d6Ym0oODDdk/YQQWLLkBJ555idDsHFwsEXuvSVIiIjKCntuzOjm3WwAQEAlB4krITKvjIw8jB+/DWvXnja0NW3qhYiIl1CnTiUJKyOiiojhxowS0gsm8atayV7iSojMJzo6HiEhG3Dx4h1D2/jxLTFnTk+oOQ0CEUmAv3nMKEdTMJ8HJ/EjaySEwKJFx/HuuzsNc9c4OSmxZEl/DBrUUOLqiKgiY7gxEyEEcvMLfuE7qLj0AlkfrVaPlStPGoJNy5Y+CA8fiJo13SWujIgqOg4oNpPcfD0K11F3UDJDkvWxtVUgLGwgXF3VeOut1jhwYAyDDRGVC/zUNZMszf0rROxs2XNDlk8IgaSkbHh63h8gHxDgivPnJ8DLy1HCyoiIjJWLnpuFCxciICAAarUagYGBOHr0aIn2CwsLg0wmw4ABA8xb4BPIyisIN2pbudEMrUSW6O7dHLz4YgQ6dlyOjIw8o8cYbIiovJE83ISHhyM0NBRTp07FiRMn0LRpUwQHByMxMfGR+127dg3vv/8+OnbsWEaVlk5aTsHsxI4qdo6RZTty5CaaN/8Rmzefx8WLd/DGG9ulLomI6JEkDzezZ8/G2LFjMXr0aDRo0ACLFi2Cvb09li1b9tB9dDodhg4dis8//xw1atQow2pLLiWrcEVwXilFlkkIgW+/PYgOHZbj+vU0AICbmxqDBjWQuDIiokeTNNxoNBpERkYiKCjI0CaXyxEUFIRDhw49dL/p06fD09MTr7zySlmU+UQKVwR35aKZZIHu3MlG//5heP/9XdBq9QCAdu38ER09nkspEFG5J+k5k+TkZOh0Onh5eRm1e3l54fz588Xus3//fixduhTR0dEleo28vDzk5d0fI5Cenv7E9ZbGnayC1+S6UmRpDh68gcGDN+DGjfs/K5MmtceMGV1gy8HxRGQBJD8tVRoZGRkYPnw4fvrpJ3h4eJRon1mzZsHFxcVw8/f3N3OVBQp7btzYc0MW5NtvD6JTp+WGYOPhYY/ffx+KL78MYrAhIoshac+Nh4cHFAoFEhISjNoTEhLg7e1dZPvLly/j2rVr6Nevn6FNry/oMrexscGFCxdQs2ZNo30mT56M0NBQw/309PQyCTiFY27cuWgmWRC9XkCnK5ihqVOnali79gX4+TlLXBURUelIGm6USiVatmyJ3bt3Gy7n1uv12L17N958880i29erVw+nT582apsyZQoyMjIwb968YkOLSqWCSqUyS/2Pcj/clP1rEz2p995rh337YtG0qRemTn0WNjYW1blLRASgHEziFxoaipEjR6JVq1Zo3bo15s6di6ysLIwePRoAMGLECPj5+WHWrFlQq9Vo1KiR0f6urq4AUKRdasn3xtz4uKglroSoeDqdHgcO3ECnTtUMbXK5DFu2DObcTERk0SQPNyEhIUhKSsJnn32G+Ph4NGvWDDt27DAMMo6NjYVcbnnfHtOyC+a54WkpKo/i4zMxbNgm7NlzFX/+OQJdu1Y3PMZgQ0SWTiaEEI/fzHqkp6fDxcUFaWlpcHY231iCFjN2ISVLg9/e6oBGfi5mex2i0tq9+wqGDt2EhIQsAICfnxNiYt6GWi35dx0ioocqzee35XWJWAAhBNLvzVDMeW6ovNDp9Pjss7/QvfvPhmDj4+OI1atfYLAhIqvC32hmcPNuDrR6AbkMqMQBxVQOxMVlYMiQjfj77+uGth49auLnn583WgiTiMgaMNyYwambqQCAmp6OsFNybhCS1s6dMRg2bDOSk7MBAAqFDDNmdMGkSR04voaIrBLDjRkk3ls12duZV0qRtL7//hgmTLi/0KWfnxPCwgaiQ4eqElZFRGReDDdmkJCWCwDwcOQpKZJW167V4eBgi6ysfPTpUxsrVgyAh4e91GUREZkVw40ZJGUW9NxUZrghidWr54Eff+yL27czERralqehiKhCYLgxg+R760p5OjPcUNnJz9dh3rwjmDDhGdjZ3b9Kb+jQJhJWRURU9hhuzCA9lxP4Udm6di0VgwdvwJEjt3Dlyl18/30fqUsiIpIM57kxg6w8LQDAWc05bsj8tmw5j+bNf8SRI7cAAEuWnMDlyykSV0VEJB2GGzPIytMBAJw4MRqZUV6eFu+8swPPPx+O1NSCQezVq7viwIExqFnTXeLqiIikw09fM8jWFPTcODLckJlcvpyCkJANiIy8bWgbOLABlizpBxcu1kpEFRw/fU1MCIGM3IJw42rPMTdkeuvX/4tXX/0V6ekFV+UplQrMmROM119vBZmMV0MRETHcmNjttNwHll5guCHT+u23ixg0aIPhfq1a7oiIGIjmzX0krIqIqHzhmBsTu37n3oKELnZQ23LpBTKtXr1qoXPnagCAl19uhBMnxjHYEBH9B3tuTCzu3sBOznFD5qBQyLF27YvYsSMGo0c342koIqJisOfGxG6n5QDg7MT09LKz8zF+/G84evSWUbuvrxPGjGnOYENE9BAMNyYWn86eG3p6584lITBwCX78MRIhIRsMl3oTEdHjMdyYWGr2vdmJeaUUPaGVK6PRqtVPOHMmEQCQmJiFEyduP2YvIiIqxDE3JlY4gR/nuKHSysrSYMKE7Vi58qShrWHDyoiIeAkNGlSWsDIiIsvCT2ATy8kvCDcOSh5aKrkzZxIxaNB6nDuXbGgbM6YZ5s/vDXt7LuNBRFQa/AQ2sbx74cZexcvA6fGEEFi2LApvvvk7cu9N/ujgYItFi/pi2DCu5k1E9CQYbkyssOfGzpaHlh7v+vU0TJiwHXn3Tmc2aeKFiIiBqFvXQ+LKiIgsFwcUm1huYc+Nkj039HgBAa6YPTsYAPDaay1x+PArDDZERE+J3QsmlqfVA2C4oeIJIaDXCygU979XvP56KzRu7ImOHatJWBkRkfVgz42JFfbc2DHc0H+kpeVi8OCN+Pjj3UbtMpmMwYaIyITYc2NiufmFPTc8tHRfZGQcQkI24PLluwCAzp0D0Lt3bYmrIiKyTuy5MSEhBPK0hZeCs+eGCv5NzJ9/BO3aLTMEG1dXNXQ6vcSVERFZL3YvmFCeVg+9KPg7T0vR3bs5eOWVrdi8+byhrXVrP4SHD0RAgKt0hRERWTmGGxPK1ugMf+dpqYrt6NFbCAnZgGvXUg1toaFtMGtWEJQMvkREZsVPYBPKyiuYhE2pkEMh54rNFZEQAnPmHMakSX9Ce+/KOTc3NVauHIB+/epKXB0RUcXAcGNChRP4qW05lKmiys/XIyzsjCHYtGvnj3XrXkTVqi4SV0ZEVHHwU9iEClcEd1AxM1ZUSqUCYWED4eqqxqRJ7bF370gGGyKiMsZPYRO6eTcbAODppJK4Eiorer1AUlIWvLwcDW01arjh0qW34OFhL2FlREQVF3tuTCguNQcA4OWilrgSKgtJSVno02ctnn12JTIzNUaPMdgQEUmH4caEMu8NKHbkaSmr988/19Gs2Y/YsSMG588n4803t0tdEhER3cNwY0KFsxPb2fJSX2ul0+nxxRf/oEuXlYiLywAAeHo6YNiwJhJXRkREhdjFYEK5hqulGG6sUUJCJoYO3YTdu68a2rp2rY7Vq5+Hj4+ThJUREdGDGG5MqPBScPbcWJ/du69g6NBNSEjIAgDI5TJMndoZn3zS0WiFbyIikh7DjQlxRXDrNGPG35g6dS/EvaU1fHwcsXbti3j22QBJ6yIiouIx3JhQnmHMDb/JWxNbW4Uh2PToURM///w8PD0dpC2KiIgeiuHGhHK1HHNjjT78sD32749Fu3b++OijDpBzaQ0ionKN4caE8u5Nuc9wY7m0Wj327buOLl2qG9rkchm2bn2ZoYaIyELw/IkJFZ6WYrixTDdvpqNLl5UICvoZf/99zegxBhsiIsvBcGNChael7BluLM62bRfRrNki7N8fC71eYOTILdBodFKXRURET4DhxoQMPTe8Wspi5Ofr8MEHf6Bv33W4c6dg+YyqVV0QFjYQSv5/JCKySBxzY0IaHU9LWZLr11MxePBGHD5809D23HN1sWzZc3B3t5OwMiIiehoMNyakuTegWGXDDrHybsuW8xg9+hekpuYCAGxt5fj66+54++1AyGQcX0NEZMkYbkxIw6ulLMLs2Yfw3nt/GO5Xr+6K8PCBeOYZPwmrIiIiU2EXgwlp9QXhxlbBb/7lWc+etWBnV5DrX3yxPk6ceI3BhojIirDnxoQKZ7HlaY3yrUGDyli0qC8yMvLwxhvP8P8XEZGVYbgxA35Ulh+5uVrMnXsY777bBirV/X/uI0Y0lbAqIiIyJ4YbslqXLt1BSMgGREXF49atdMyf31vqkoiIqAxwzI0Z8CyH9NatO40WLRYjKioeALBkSRRiY9MkroqIiMoCw40JCakLIOTk5GPcuF8xZMgmZGZqAAB161bCkSOvompVF4mrIyKissDTUiYk7o0olnHUjSTOn0/GoEHrcfp0oqFt+PAm+P77PnB0VEpYGRERlSWGGzPgaamyt2rVSbz++jZkZ+cDAOzsbPD9930walQzaQsjIqIyVy5OSy1cuBABAQFQq9UIDAzE0aNHH7rtTz/9hI4dO8LNzQ1ubm4ICgp65PZk/TZuPIuRI7cYgk3DhpVx/Pg4BhsiogpK8nATHh6O0NBQTJ06FSdOnEDTpk0RHByMxMTEYrffu3cvXn75Zfz11184dOgQ/P390aNHD9y6dauMK384dtyUreeeq4cOHaoCAF55pTmOHh2LBg0qS1wVERFJRSYKB4pIJDAwEM888wwWLFgAANDr9fD398dbb72Fjz766LH763Q6uLm5YcGCBRgxYsRjt09PT4eLiwvS0tLg7Oz81PU/qPpH2yAAHP24Gzyd1SZ9bnq0mzfTsW/fdbz8cmOpSyEiIjMozee3pD03Go0GkZGRCAoKMrTJ5XIEBQXh0KFDJXqO7Oxs5Ofnw93d3Vxllh67bswmM1ODMWN+wfHjcUbtVao4M9gQEREAiQcUJycnQ6fTwcvLy6jdy8sL58+fL9FzTJo0Cb6+vkYB6UF5eXnIy8sz3E9PT3/ygh+jsAuMV0uZx8mT8Rg0aAMuXryDv/++jhMnxsHFhT1kRERkTPIxN0/jyy+/RFhYGDZv3gy1uvgPuVmzZsHFxcVw8/f3L+Mq6WkJIfDjj8cRGLgEFy/eAQAkJWXh1KkEiSsjIqLySNJw4+HhAYVCgYQE4w+phIQEeHt7P3Lfb775Bl9++SX++OMPNGnS5KHbTZ48GWlpaYbbjRs3TFL7o/BScNNJT8/Dyy9vxPjx25CXpwMAtGjhgxMnXkPHjtUkro6IiMojScONUqlEy5YtsXv3bkObXq/H7t270bZt24fu99VXX2HGjBnYsWMHWrVq9cjXUKlUcHZ2NrqZg8Tjsq3SiRO30aLFjwgP/9fQ9tZbrXHw4BjUqlWOxlgREVG5IvkkfqGhoRg5ciRatWqF1q1bY+7cucjKysLo0aMBACNGjICfnx9mzZoFAPjf//6Hzz77DGvXrkVAQADi4wvWDnJ0dISjo6Nk7+NB7Lh5OkIILFx4DO+99wc0moLeGhcXFZYtew4vvFBf4uqIiKi8kzzchISEICkpCZ999hni4+PRrFkz7NixwzDIODY2FnL5/Q6mH374ARqNBgMHDjR6nqlTp2LatGllWbqRBztuZDwv9VRiYlIQGroT+fl6AMAzz/giPHwgqld3k7gyIiKyBJLPc1PWzDXPjV4vUOPj7QCAE592h7sD1zJ6Gt99dwQTJ+7Au++2wZdfBkGpVEhdEhERSag0n9+S99xYiwcTIvttSkcIAb1eQKG430P31lut0bq1H9q0qSJhZUREZIks+lLw8qSCdYCZTEpKDgYMCMenn/5l1C6TyRhsiIjoibDnxgw45KZkDh68gcGDN+DGjXRs3XoBnTtXQ3BwLanLIiIiC8eeGxMxPi3FdPMoer3AV18dQKdOy3HjRsGM0ZUq2XEgNhERmQR7bqhMJSVlYeTILfj99xhDW8eOVbF27YuoUsU8cxAREVHFwnBjIoIjih9r377rGDx4I+LiMgAUnL77+OOOmDbtWdjYsBORiIhMg+HGRAQ4oPhh9HqBWbP24bPP9kKvLzhOnp4OWL36eXTvXlPi6oiIyNow3JgBh44Yy8/XYdOm84Zg06VLANaseQE+Pk4SV0ZERNaI5wJMxGiGYunKKJdUKhuEhw+Eq6sa06Z1xq5dwxlsiIjIbNhzQyan0+mRmJhlFGBq1XLH5ctvw93dTsLKiIioImDPjRlU5Euab9/OQPfuPyMo6GdkZWmMHmOwISKissBwQyaza9dlNGv2I/766xrOnk3CxIk7pC6JiIgqIIYbE6nIY260Wj2mTNmD4ODVSEzMAgD4+Tlh5MimEldGREQVEcfcmMiDl4JXpLNSN2+mY8iQjdi3L9bQ1qtXLaxa9Tw8POwlrIyIiCoqhht6Ytu3X8KIEZtx504OAEChkGHWrG547712kMsrUMIjIqJyheHGRIxPS1n/B/vHH+/GrFn7DferVnVBWNiLaNvWX8KqiIiIGG7oCTk42Br+3r9/XSxf/hyvhiIionKB4cZEjJaWsv6OG0ye3BGHDt1EUFANTJwYWKEvfyciovKF4cZEhLDetaU0Gh327buObt1qGNrkchl+/fVlhhoiIip3eCk4PdLVq3fRocMyBAevxv79sUaPMdgQEVF5xHBjItZ4WmrTpnNo3vxHHDsWB51OYNSoLdBq9VKXRURE9Eg8LUVF5OZq8cEHf2DBgmOGtlq13BERMRA2NszDRERUvjHcmIi1XAoeE5OCQYPWIyoq3tA2eHAj/PhjXzg7qySsjIiIqGQYbszAUk9LhYefwdixvyIjo2DBS5VKge++64WxY1twfA0REVkMhhtTsfCLpWbO3IdPPtljuF+3biVERLyEJk28JKyKiIio9DiAwkSM1paSsI4n1b9/XdjZFWTdYcOa4PjxcQw2RERkkdhzQwCARo088cMPfaDTCYwe3YynoYiIyGKx58ZEjAYUl/NgkJWlwRdf/AONRmfUPnJkM4wZ07zc109ERPQo7Lkxg/IcDf79NxGDBm3A2bNJuHMnG3Pm9JS6JCIiIpNiz42JlPfxxEIILF8ehWee+QlnzyYBAJYsiUJcXIbElREREZkWw40ZlLezOpmZGowYsQVjxmxFTo4WANC4sSeOHRsLX18niasjIiIyLZ6WMpHyunDmqVMJGDRoPS5cuGNoe+21lpgzJxh2drYSVkZERGQeDDcmYry2lPRdN0II/PTTCUycuAO5uQW9NU5OSixe3A+DBzeSuDoiIiLzYbixUmFhZ/Daa78Z7jdv7o3w8IGoXbuShFURERGZH8fcmEh5Oys1cGADtGvnDwCYMOEZHDz4CoMNERFVCOy5MTHpT0gVsLVVYN26F3H8eBxeeKG+1OUQERGVGfbcmIiQ8GLw1NRcDBmyEVFRt43aq1Z1YbAhIqIKhz03JlJ4WkouL9u+m2PHbiEkZAOuXk3FsWNxiIwcB2dnVZnWQEREVJ6w58ZE9PfSTVlFGyEE5s49jPbtl+Hq1VQAwJ072Th3LqmMKiAiIiqf2HNjIoU9N2VxFXhKSg5Gj/4FW7deMLS1aVMFYWEvolo1V/MXQEREVI4x3JiIoefGzOnm0KEbGDx4I2Jj0wxtH37YDl980RW2tgqzvjYREZElYLgxEUPPjZmeX68X+Pbbg/j44z3QavUAgEqV7LBq1fPo3bu2mV6ViIjI8jDcmIhhQLGZem4uXEjGJ5/cDzYdOlTFunUvokoVZ7O8HhERkaXigGITuX9ayjzPX79+Zfzvf0GQyYBPPumIv/4ayWBDRERUDPbcmIipr5bS6wWEEFAo7ufPd95pg44dq6FVK18TvQoREZH1YbgxEb3haqmnjzeJiVkYNmwT2rSpgunTuxjaZTIZgw1RMYQQ0Gq10Ol0UpdCRE/B1tYWCsXTXxzDcGMypjkt9ddfVzFkyCbEx2fizz+voFOnaggKqmGC+oisk0ajwe3bt5GdnS11KUT0lGQyGapUqQJHR8eneh6GGxPRP+XVUjqdHl988Q+mT/8H+ntP5uXlCFtbDosiehi9Xo+rV69CoVDA19cXSqXS7NMxEJF5CCGQlJSEmzdvonbt2k/Vg8NwYyKFY26e5Gqp27czMHToJvz11zVDW/fuNfDzz8/Dy+vp0iuRNdNoNNDr9fD394e9vb3U5RDRU6pcuTKuXbuG/Px8hpvyQF9whXapT0vt2nUZw4ZtRmJiFoCCtammT38Wkyd3LPN1qogslVzOHk4ia2CqnleGGxMpXBVcVsITU1qtHtOm7cXMmfsMc+T4+jph3boX0alTNXOVSUREZPUYbkyktGtLabV6/PbbRcN+vXrVwsqVA1C5soN5CiQiIqog2JdrIqVdW0qttkFExEtwc1Pjq6+C8NtvQxhsiCqgQ4cOQaFQoE+fPkbte/fuhUwmQ2pqapF9AgICMHfuXKO2v/76C71790alSpVgb2+PBg0a4L333sOtW7eeuLaFCxciICAAarUagYGBOHr06GP3mTt3LurWrQs7Ozv4+/vj3XffRW5uruHxadOmQSaTGd3q1atneDwlJQVvvfWW4TmqVq2Kt99+G2lpaUavc+zYMXTr1g2urq5wc3NDcHAwTp48aXj82rVrRV5HJpPh8OHDxdYdFhYGmUyGAQMGPPS9jR8/HjKZrMixP3HiBLp37w5XV1dUqlQJ48aNQ2ZmZpH9V6xYgSZNmkCtVsPT0xMTJkwwPJabm4tRo0ahcePGsLGxeWgda9asQdOmTWFvbw8fHx+MGTMGd+7cMTyen5+P6dOno2bNmlCr1WjatCl27Nhh9Bz//PMP+vXrB19fX8hkMmzZsqXI62zatAk9evRApUqVIJPJEB0dXWSbZ599tsjxHT9+vNE2u3fvRrt27eDk5ARvb29MmjQJWq222PdmSgw3JvK4npv8fB3i4jKM2urUqYQrVybigw/ac3wNUQW1dOlSvPXWW/jnn38QFxf3RM/x448/IigoCN7e3ti4cSPOnj2LRYsWIS0tDd9+++0TPWd4eDhCQ0MxdepUnDhxAk2bNkVwcDASExMfus/atWvx0UcfYerUqTh37hyWLl2K8PBwfPzxx0bbNWzYELdv3zbc9u/fb3gsLi4OcXFx+Oabb3DmzBmsWLECO3bswCuvvGLYJjMzEz179kTVqlVx5MgR7N+/H05OTggODkZ+fr7Ra/35559Gr9WyZcsidV+7dg3vv/8+Onbs+ND3tnnzZhw+fBi+vsZzjcXFxSEoKAi1atXCkSNHsGPHDvz7778YNWqU0XazZ8/GJ598go8++gj//vsv/vzzTwQHBxse1+l0sLOzw9tvv42goKBiazhw4ABGjBiBV155Bf/++y/Wr1+Po0ePYuzYsYZtpkyZgh9//BHz58/H2bNnMX78eDz//POIiooybJOVlYWmTZti4cKFD32/WVlZ6NChA/73v/89dBsAGDt2rNHx/eqrrwyPnTx5Er1790bPnj0RFRWF8PBwbN26FR999NEjn9MkRAWTlpYmAIi0tDSTPu+J6ymi2qTfxDNf7Cry2PXrqaJt2yWicePvRXa2xqSvS1SR5eTkiLNnz4qcnBypS3kiGRkZwtHRUZw/f16EhISI//u//zM89tdffwkA4u7du0X2q1atmpgzZ44QQogbN24IpVIp3nnnnWJfo7j9S6J169ZiwoQJhvs6nU74+vqKWbNmPXSfCRMmiK5duxq1hYaGivbt2xvuT506VTRt2rRUtURERAilUiny8/OFEEIcO3ZMABCxsbGGbU6dOiUAiEuXLgkhhLh69aoAIKKioh753FqtVrRr104sWbJEjBw5Ujz33HNFtrl586bw8/MTZ86cMTr2Qgjx448/Ck9PT6HT6R5aS0pKirCzsxN//vlnid7vw+r4+uuvRY0aNYzavvvuO+Hn52e47+PjIxYsWGC0zQsvvCCGDh1a7GsBEJs3b35oLY86jp07dxYTJ0586L6TJ08WrVq1MmrbunWrUKvVIj09vdh9HvUzXZrPb/bcmIj+IQtnbt16Ac2aLcKhQzdx+nQiQkN3SlAdUcUhhEC2RlvmN1HYfVsKERERqFevHurWrYthw4Zh2bJlpX6e9evXQ6PR4MMPPyz2cVdXVwBAbGwsHB0dH3mbOXMmgIJL7CMjI416EORyOYKCgnDo0KGH1tKuXTtERkYaTl9duXIF27dvR+/evY22u3TpEnx9fVGjRg0MHToUsbGxj3yPaWlpcHZ2ho1NwTDRunXrolKlSli6dCk0Gg1ycnKwdOlS1K9fHwEBAUb79u/fH56enujQoQO2bt1a5LmnT58OT09Po56hB+n1egwfPhwffPABGjZsWOTxvLw8KJVKoyv27OzsAMDQI7Vr1y7o9XrcunUL9evXR5UqVTBo0CDcuHHjke/7v9q2bYsbN25g+/btEEIgISEBGzZsMDq+eXl5UKvVRvvZ2dkZ9Y6Z0po1a+Dh4YFGjRph8uTJRpNpPqyW3NxcREZGmqWeQhxQbCKeTioMaOYHR3XBIdVodPjooz8xZ87987sBAa4YPbq5VCUSVQg5+To0+Kzsv0ScnR4Me2XpfqUuXboUw4YNAwD07NkTaWlp+Pvvv/Hss8+W+DkuXboEZ2dn+Pj4PHI7X1/fYsdNPMjd3R0AkJycDJ1OBy8vL6PHvby8cP78+YfuP2TIECQnJ6NDhw6GJTHGjx9vdFoqMDAQK1asQN26dXH79m18/vnn6NixI86cOQMnJ6ciz5mcnIwZM2Zg3LhxhjYnJyfs3bsXAwYMwIwZMwAAtWvXxs6dOw0ByNHREd9++y3at28PuVyOjRs3YsCAAdiyZQv69+8PoCB8LF269JHH5X//+x9sbGzw9ttvF/t4165dERoaiq+//hoTJ05EVlaW4bTL7du3ARSEPL1ej5kzZ2LevHlwcXHBlClT0L17d5w6dQpKpfKhr/+g9u3bY82aNQgJCUFubi60Wi369etndHopODgYs2fPRqdOnVCzZk3s3r0bmzZtMsvSJEOGDEG1atXg6+uLU6dOYdKkSbhw4QI2bdpkqGXu3LlYt24dBg0ahPj4eEyfPh3A/WNjLuWi56a0g9bWr1+PevXqQa1Wo3Hjxti+fXsZVfpw/u72eKVDdbzc2h9Xr95Fx47LjYLNCy/UR1TUa2jd2k/CKomovLhw4QKOHj2Kl19+GQBgY2ODkJAQLF26tFTPI4Qo0YUMNjY2qFWr1iNvheHmSe3duxczZ87E999/jxMnTmDTpk3Ytm2bIYAAQK9evfDSSy+hSZMmCA4Oxvbt25GamoqIiIgiz5eeno4+ffqgQYMGmDZtmqE9JycHr7zyCtq3b4/Dhw/jwIEDaNSoEfr06YOcnBwAgIeHB0JDQxEYGIhnnnkGX375JYYNG4avv/4aAJCRkYHhw4fjp59+goeHR7HvJzIyEvPmzcOKFSseeowbNmyIlStX4ttvv4W9vT28vb1RvXp1eHl5GXpz9Ho98vPz8d133yE4OBht2rTBunXrcOnSJfz1118lPr5nz57FxIkT8dlnnyEyMhI7duzAtWvXjAbxzps3D7Vr10a9evWgVCrx5ptvYvTo0WaZC2rcuHEIDg5G48aNMXToUKxatQqbN2/G5cuXAQA9evTA119/jfHjx0OlUqFOnTqGXiazz0312BNXZhYWFiaUSqVYtmyZ+Pfff8XYsWOFq6urSEhIKHb7AwcOCIVCIb766itx9uxZMWXKFGFraytOnz5dotcz15gbIYRIz9GI8IgzwsVllgCmCWCaUCpniPnzjwi9Xm/y1yOq6Io7P6/X60VWXn6Z30r7M/7BBx8IAEKhUBhucrlc2NnZidTUVBEZGSkAiGvXrhXZ18XFRSxbtkwIIcTs2bMFABEXF/fI17t+/bpwcHB45K1wzE9eXp5QKBRFxmKMGDFC9O/f/6Gv0aFDB/H+++8btf3888/Czs7OaEzKf7Vq1Up89NFHRm3p6emibdu2olu3bkXGXyxZsqTIOJe8vDxhb28v1q1b99DXWbBggfD29hZCCBEVFVXk+MtkMiGTyYRCoRAxMTFizpw5hvuFNwBCLpeLatWqFXn++Ph4kZGRITIzM4VcLhcRERFCCCGWLVsmAIgbN24Ybe/p6SkWL15c5HkeNuZm2LBhYuDAgUZt+/btK/b/f05Ojrh586bQ6/Xiww8/FA0aNCj2mOApxtz8V2ZmpgAgduzYYdSu1+vFrVu3RHZ2tjh79qwAII4ePVrsc5hqzI3kp6Vmz56NsWPHYvTo0QCARYsWYdu2bVi2bFmxI6rnzZuHnj174oMPPgAAzJgxA7t27cKCBQuwaNGiMq39QUIIfPrRbsybd8TQVrOmGyIiXkKLFo/uLiYi05HJZKU+PVTWtFotVq1ahW+//RY9evQwemzAgAFYt24dhg4dCrlcjsjISFSrdn9izytXriAtLQ116tQBAAwcOBAfffQRvvrqK8yZM6fIa6WmpsLV1bVUp6WUSiVatmyJ3bt3Gy5J1uv12L17N958882H7p+dnV3kG3nhFPriIWOJMjMzcfnyZQwfPtzQlp6ejuDgYKhUKmzdurXIuI3C13mwN6Xwvr5wuvhiREdHG07f1atXD6dPnzZ6fMqUKcjIyMC8efPg7++P4cOHF7lyKTg4GMOHDzd8Zj2o8DTesmXLoFar0b17dwAFp5OAgt66KlWqACi45D05Odno/+3jZGdnG067FXrY8VWr1fDz80N+fj42btyIQYMGlfh1nlThv6//niKVyWSGq8zWrVsHf39/tGjRwrzFPDb+mNGTfDvw9/c3GqkuhBCfffaZaNKkSbHb5+bmirS0NMPtxo0bZuu5mTbtL0OPTUjIepGWlmvy1yCi+yz1aqnNmzcLpVIpUlNTizz24YcfGq4wGTdunAgICBC//PKLuHLlivj7779FmzZtRJs2bYx6ihYuXChkMpkYM2aM2Lt3r7h27ZrYv3+/GDdunAgNDX2iGsPCwoRKpRIrVqwQZ8+eFePGjROurq4iPj7esM3w4cONelymTp0qnJycxLp168SVK1fEH3/8IWrWrCkGDRpk2Oa9994Te/fuFVevXhUHDhwQQUFBwsPDQyQmJgohCr6dBwYGisaNG4uYmBhx+/Ztw02r1QohhDh37pxQqVTi9ddfF2fPnhVnzpwRw4YNEy4uLoYejBUrVoi1a9eKc+fOiXPnzon/+7//E3K53NDjVZyH9Zg86L9XSwkhxPz580VkZKS4cOGCWLBggbCzsxPz5s0z2ua5554TDRs2FAcOHBCnT58Wffv2FQ0aNBAazf0raP/9918RFRUl+vXrJ5599lkRFRVl1GOyfPlyYWNjI77//ntx+fJlsX//ftGqVSvRunVrwzaHDx8WGzduFJcvXxb//POP6Nq1q6hevbrRVXMZGRmG5wYgZs+eLaKiosT169cN29y5c0dERUWJbdu2CQAiLCxMREVFidu3bwshhIiJiRHTp08Xx48fF1evXhW//PKLqFGjhujUqZPR+/7qq6/EqVOnxJkzZ8T06dOFra3tI3uKTNVzI2m4uXXrlgAgDh48aNT+wQcfGP3PepCtra1Yu3atUdvChQuFp6dnsdtPnTpVAChyM0e40Wp1onfvNWLRomM8DUVUBiw13PTt21f07t272MeOHDkiAIiTJ0+KnJwcMXXqVFGvXj1hZ2cnqlevLsaNGyeSkpKK7Ldr1y4RHBws3NzchFqtFvXq1RPvv//+Y09XPcr8+fNF1apVhVKpFK1btxaHDx82erxz585i5MiRhvv5+fli2rRpombNmkKtVgt/f3/xxhtvGH2whoSECB8fH6FUKoWfn58ICQkRMTExhscLL4Ev7nb16lXDdn/88Ydo3769cHFxEW5ubqJr167i0KFDhsdXrFgh6tevL+zt7YWzs7No3bq1WL9+/SPf75OGm+HDhwt3d3ehVCpFkyZNxKpVq4rsl5aWJsaMGSNcXV2Fu7u7eP75540uZS987uLe94O+++470aBBA2FnZyd8fHzE0KFDxc2bNw2P7927V9SvX1+oVCpRqVIlMXz4cHHr1i2j53jYMX7w/+Xy5cuL3Wbq1KlCCCFiY2NFp06dhLu7u1CpVKJWrVrigw8+KPLZ2qVLF+Hi4iLUarUIDAwU27dvf+TxNVW4kQnxBNcvmkhcXBz8/Pxw8OBBtG3b1tD+4Ycf4u+//8aRI0eK7KNUKrFy5UrDIDwA+P777/H5558jISGhyPZ5eXnIy8sz3E9PT4e/v7/h0kJTEyUc3EdETy83NxdXr15F9erVi5y6ICLL86if6fT0dLi4uJTo81vSE9MeHh5QKBRFQklCQgK8vb2L3cfb27tU26tUKqhUKtMUXAIMNkRERNKS9FLwBwetFSoctPZgT86D2rZta7Q9UDBB0sO2JyIioopF8ksKQkNDMXLkSLRq1QqtW7fG3LlzkZWVZRiJPmLECPj5+WHWrFkAgIkTJ6Jz58749ttv0adPH4SFheH48eNYvHixlG+DiIiIygnJw01ISAiSkpLw2WefIT4+Hs2aNcOOHTsMl9TFxsYaXVrYrl07rF27FlOmTMHHH3+M2rVrY8uWLWjUqJFUb4GIiIjKEUkHFEuhNAOSiKh844BiIutiqgHF5WL5BSKip1HBvqMRWS1T/Swz3BCRxbK1tQUAo5WIichyaTQaAPdnXn5Sko+5ISJ6UgqFAq6urkhMTAQA2NvbczoGIgul1+uRlJQEe3v7IstMlBbDDRFZtMI5rgoDDhFZLrlcjqpVqz71lxSGGyKyaDKZDD4+PvD09ER+fr7U5RDRU1AqlUUWX30SDDdEZBUUCsVTn6cnIuvAAcVERERkVRhuiIiIyKow3BAREZFVqXBjbgonCEpPT5e4EiIiIiqpws/tkkz0V+HCTUZGBgDA399f4kqIiIiotDIyMuDi4vLIbSrc2lJ6vR5xcXFwcnIy+WRf6enp8Pf3x40bN7hulRnxOJcNHueyweNcdnisy4a5jrMQAhkZGfD19X3s5eIVrudGLpejSpUqZn0NZ2dn/uCUAR7nssHjXDZ4nMsOj3XZMMdxflyPTSEOKCYiIiKrwnBDREREVoXhxoRUKhWmTp0KlUoldSlWjce5bPA4lw0e57LDY102ysNxrnADiomIiMi6seeGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYbkpp4cKFCAgIgFqtRmBgII4ePfrI7devX4969epBrVajcePG2L59exlVatlKc5x/+ukndOzYEW5ubnBzc0NQUNBj/79QgdL+ey4UFhYGmUyGAQMGmLdAK1Ha45yamooJEybAx8cHKpUKderU4e+OEijtcZ47dy7q1q0LOzs7+Pv7491330Vubm4ZVWuZ/vnnH/Tr1w++vr6QyWTYsmXLY/fZu3cvWrRoAZVKhVq1amHFihVmrxOCSiwsLEwolUqxbNky8e+//4qxY8cKV1dXkZCQUOz2Bw4cEAqFQnz11Vfi7NmzYsqUKcLW1lacPn26jCu3LKU9zkOGDBELFy4UUVFR4ty5c2LUqFHCxcVF3Lx5s4wrtyylPc6Frl69Kvz8/ETHjh3Fc889VzbFWrDSHue8vDzRqlUr0bt3b7F//35x9epVsXfvXhEdHV3GlVuW0h7nNWvWCJVKJdasWSOuXr0qdu7cKXx8fMS7775bxpVblu3bt4tPPvlEbNq0SQAQmzdvfuT2V65cEfb29iI0NFScPXtWzJ8/XygUCrFjxw6z1slwUwqtW7cWEyZMMNzX6XTC19dXzJo1q9jtBw0aJPr06WPUFhgYKF577TWz1mnpSnuc/0ur1QonJyexcuVKc5VoFZ7kOGu1WtGuXTuxZMkSMXLkSIabEijtcf7hhx9EjRo1hEajKasSrUJpj/OECRNE165djdpCQ0NF+/btzVqnNSlJuPnwww9Fw4YNjdpCQkJEcHCwGSsTgqelSkij0SAyMhJBQUGGNrlcjqCgIBw6dKjYfQ4dOmS0PQAEBwc/dHt6suP8X9nZ2cjPz4e7u7u5yrR4T3qcp0+fDk9PT7zyyitlUabFe5LjvHXrVrRt2xYTJkyAl5cXGjVqhJkzZ0Kn05VV2RbnSY5zu3btEBkZaTh1deXKFWzfvh29e/cuk5orCqk+ByvcwplPKjk5GTqdDl5eXkbtXl5eOH/+fLH7xMfHF7t9fHy82eq0dE9ynP9r0qRJ8PX1LfIDRfc9yXHev38/li5diujo6DKo0Do8yXG+cuUK9uzZg6FDh2L79u2IiYnBG2+8gfz8fEydOrUsyrY4T3KchwwZguTkZHTo0AFCCGi1WowfPx4ff/xxWZRcYTzsczA9PR05OTmws7Mzy+uy54asypdffomwsDBs3rwZarVa6nKsRkZGBoYPH46ffvoJHh4eUpdj1fR6PTw9PbF48WK0bNkSISEh+OSTT7Bo0SKpS7Mqe/fuxcyZM/H999/jxIkT2LRpE7Zt24YZM2ZIXRqZAHtuSsjDwwMKhQIJCQlG7QkJCfD29i52H29v71JtT092nAt98803+PLLL/Hnn3+iSZMm5izT4pX2OF++fBnXrl1Dv379DG16vR4AYGNjgwsXLqBmzZrmLdoCPcm/Zx8fH9ja2kKhUBja6tevj/j4eGg0GiiVSrPWbIme5Dh/+umnGD58OF599VUAQOPGjZGVlYVx48bhk08+gVzO7/6m8LDPQWdnZ7P12gDsuSkxpVKJli1bYvfu3YY2vV6P3bt3o23btsXu07ZtW6PtAWDXrl0P3Z6e7DgDwFdffYUZM2Zgx44daNWqVVmUatFKe5zr1auH06dPIzo62nDr378/unTpgujoaPj7+5dl+RbjSf49t2/fHjExMYbwCAAXL16Ej48Pg81DPMlxzs7OLhJgCgOl4JKLJiPZ56BZhytbmbCwMKFSqcSKFSvE2bNnxbhx44Srq6uIj48XQggxfPhw8dFHHxm2P3DggLCxsRHffPONOHfunJg6dSovBS+B0h7nL7/8UiiVSrFhwwZx+/Ztwy0jI0Oqt2ARSnuc/4tXS5VMaY9zbGyscHJyEm+++aa4cOGC+O2334Snp6f44osvpHoLFqG0x3nq1KnCyclJrFu3Tly5ckX88ccfombNmmLQoEFSvQWLkJGRIaKiokRUVJQAIGbPni2ioqLE9evXhRBCfPTRR2L48OGG7QsvBf/ggw/EuXPnxMKFC3kpeHk0f/58UbVqVaFUKkXr1q3F4cOHDY917txZjBw50mj7iIgIUadOHaFUKkXDhg3Ftm3byrhiy1Sa41ytWjUBoMht6tSpZV+4hSntv+cHMdyUXGmP88GDB0VgYKBQqVSiRo0a4v/+7/+EVqst46otT2mOc35+vpg2bZqoWbOmUKvVwt/fX7zxxhvi7t27ZV+4Bfnrr7+K/X1beGxHjhwpOnfuXGSfZs2aCaVSKWrUqCGWL19u9jplQrD/jYiIiKwHx9wQERGRVWG4ISIiIqvCcENERERWheGGiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNwQUbk3atQoyGSyIreYmBijx5RKJWrVqoXp06dDq9UCKFj9+cF9KleujN69e+P06dMSvysiMheGGyKyCD179sTt27eNbtWrVzd67NKlS3jvvfcwbdo0fP3110b7X7hwAbdv38bOnTuRl5eHPn36QKPRSPFWiMjMGG6IyCKoVCp4e3sb3QpXcS58rFq1anj99dcRFBSErVu3Gu3v6ekJb29vtGjRAu+88w5u3LiB8+fPS/FWiMjMGG6IyOrY2dk9tFcmLS0NYWFhAAClUlmWZRFRGbGRugAiopL47bff4OjoaLjfq1cvrF+/3mgbIQR2796NnTt34q233jJ6rEqVKgCArKwsAED//v1Rr149M1dNRFJguCEii9ClSxf88MMPhvsODg6GvxcGn/z8fOj1egwZMgTTpk0z2n/fvn2wt7fH4cOHMXPmTCxatKisSieiMsZwQ0QWwcHBAbVq1Sr2scLgo1Qq4evrCxubor/aqlevDldXV9StWxeJiYkICQnBP//8Y+6yiUgCHHNDRBavMPhUrVq12GDzXxMmTMCZM2ewefPmMqiOiMoaww0RVTj29vYYO3Yspk6dCiGE1OUQkYkx3BBRhfTmm2/i3LlzRQYlE5Hlkwl+bSEiIiIrwp4bIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVX5f6N1YKVeBHzVAAAAAElFTkSuQmCC",
      "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": 14,
   "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>0.854149</td>\n",
       "      <td>0.858265</td>\n",
       "      <td>0.866393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.861791</td>\n",
       "      <td>0.861431</td>\n",
       "      <td>0.867592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.861962</td>\n",
       "      <td>0.863807</td>\n",
       "      <td>0.870675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient classifier</td>\n",
       "      <td>0.852854</td>\n",
       "      <td>0.855370</td>\n",
       "      <td>0.862967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       auc  f1 score  Accuracy\n",
       "0             Logistic Regression  0.854149  0.858265  0.866393\n",
       "1                   MultinomialNB  0.861791  0.861431  0.867592\n",
       "2                       LinearSVC  0.861962  0.863807  0.870675\n",
       "3  Stochastic Gradient classifier  0.852854  0.855370  0.862967"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\": model_arr, \"auc\": auc_arr,\n",
    "              \"f1 score\": f1_arr, \"Accuracy\": accuracy_arr, }).head()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "7b70de28ce7f16dabb42d6daca88e435fe373ac8e95c52d0bdf357ad057a0126"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}