Final_3_2_HP_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter training for part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cutoff\n",
    "cutoff = 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>verified</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>vote</th>\n",
       "      <th>image</th>\n",
       "      <th>style</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29189.000000</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <td>29189</td>\n",
       "      <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": 51,
     "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\n",
    "\n",
    "# Stop words\n",
    "GENERIC_STOP_WORDS = ['ourselves', 'hers', 'between', 'yourself', 'but', 'again', 'there', 'about', 'once', 'during', 'out', 'very', 'having', 'with', 'they', 'own', 'an', 'be', 'some', 'for', 'do', 'its', 'yours', 'such', 'into', 'of', 'most', 'itself', 'other', 'off', 'is', 's', 'am', 'or', 'who', 'as', 'from', 'him', 'each', 'the', 'themselves', 'until', 'below', 'are', 'we', 'these', 'your', 'his', 'through', 'don', 'nor', 'me', 'were', 'her', 'more', 'himself', 'this', 'down', 'should', 'our', 'their', 'while',\n",
    "                      'above', 'both', 'up', 'to', 'ours', 'had', 'she', 'all', 'when', 'at', 'any', 'before', 'them', 'same', 'and', 'been', 'have', 'in', 'will', 'on', 'does', 'yourselves', 'then', 'that', 'because', 'what', 'over', 'why', 'so', 'can', 'did', 'now', 'under', 'he', 'you', 'herself', 'has', 'just', 'where', 'too', 'only', 'myself', 'which', 'those', 'i', 'after', 'few', 'whom', 't', 'being', 'if', 'theirs', 'my', 'against', 'a', 'by', 'doing', 'it', 'how', 'further', 'was', 'here', 'than', 'as']\n",
    "\n",
    "# edit summary content\n",
    "\n",
    "\n",
    "def edit_summary(content):\n",
    "\n",
    "    # step 1 - convert the text to only lower case\n",
    "    content = content.lower()\n",
    "\n",
    "    # step 2 - remove unwanted and unicode characters\n",
    "    content = re.sub(\n",
    "        r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", \"\", content)\n",
    "\n",
    "    # step 3 - remove stop words\n",
    "    stopwords = GENERIC_STOP_WORDS\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": 53,
   "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": 54,
   "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": 55,
   "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": 56,
   "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=0.819 total time=   4.7s\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=0.817 total time=   7.1s\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=0.819 total time=   5.9s\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=0.829 total time=   3.7s\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=0.823 total time=   5.0s\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=0.816 total time=   6.8s\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=0.816 total time=   7.0s\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=0.814 total time=   6.0s\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=0.830 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=0.820 total time=   8.0s\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=0.812 total time=   6.8s\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=0.815 total time=   5.9s\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=0.817 total time=   7.8s\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=0.828 total time=   4.4s\n",
      "[CV 5/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 3/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   7.7s\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=0.811 total time=   9.2s\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=0.813 total time=   7.0s\n",
      "[CV 3/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/48] END classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   9.0s\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=0.827 total time=   7.1s\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=0.816 total time=   8.3s\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=0.819 total time=   9.0s\n",
      "[CV 2/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   7.6s\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=0.819 total time=   8.1s\n",
      "[CV 4/5; 5/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.829 total time=   9.6s\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=0.823 total time=   7.7s\n",
      "[CV 1/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=   7.8s\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=0.816 total time=   8.5s\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=0.814 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=0.830 total time=   8.9s\n",
      "[CV 5/5; 6/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   8.2s\n",
      "[CV 1/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/48] END classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   7.8s\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=0.815 total time=  10.1s\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=0.817 total time=   7.5s\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=0.828 total time=   8.6s\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=0.820 total time=   9.1s\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=0.811 total time=   7.9s\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=0.813 total time=   8.0s\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=0.811 total time=   7.9s\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=0.827 total time=   8.3s\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=0.816 total time=   6.6s\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=0.819 total time=   8.4s\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=0.817 total time=   7.8s\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=0.819 total time=   7.4s\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=0.829 total time=   5.9s\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=0.823 total time=   9.7s\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=0.816 total time=   6.3s\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=0.816 total time=   6.8s\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=0.814 total time=   8.5s\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=0.830 total time=   6.1s\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=0.820 total time=  10.1s\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=0.812 total time=   7.9s\n",
      "[CV 2/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=  10.2s\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=0.817 total time=   8.7s\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=0.828 total time=   7.6s\n",
      "[CV 5/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   8.9s\n",
      "[CV 1/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 12/48] END classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   7.5s\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=0.813 total time=   7.1s\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=0.811 total time=   8.0s\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=0.827 total time=   6.7s\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=0.816 total time=  10.7s\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=0.839 total time=  11.3s\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=0.840 total time=   8.2s\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=0.840 total time=  10.9s\n",
      "[CV 4/5; 13/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.846 total time=  12.7s\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=0.844 total time=   9.7s\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=0.838 total time=  10.5s\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=0.841 total time=  12.9s\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=0.842 total time=   8.6s\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=0.847 total time=  13.3s\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=0.845 total time=   8.9s\n",
      "[CV 1/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=   9.8s\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=0.839 total time=  11.9s\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=0.842 total time=  11.1s\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=0.849 total time=  10.8s\n",
      "[CV 5/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 15/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=  11.5s\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=0.840 total time=  12.7s\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=0.838 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=0.846 total time=   9.6s\n",
      "[CV 4/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/48] END classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.851 total time=  11.1s\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=0.849 total time=  15.3s\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=0.839 total time=  11.4s\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=0.840 total time=  10.7s\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=0.840 total time=  11.0s\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=0.846 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=0.844 total time=   5.7s\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=0.838 total time=  11.8s\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=0.841 total time=  12.1s\n",
      "[CV 3/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.842 total time=  11.9s\n",
      "[CV 4/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.847 total time=  11.0s\n",
      "[CV 5/5; 18/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=  13.2s\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=0.838 total time=  11.9s\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=0.839 total time=  10.8s\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=0.842 total time=  13.8s\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=0.849 total time=  12.2s\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=0.850 total time=  12.3s\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=0.840 total time=  12.1s\n",
      "[CV 2/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=  12.8s\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=0.846 total time=  12.0s\n",
      "[CV 4/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 20/48] END classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.851 total time=  10.1s\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=0.849 total time=  16.4s\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=0.839 total time=   9.9s\n",
      "[CV 2/5; 21/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   9.9s\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=0.840 total time=  12.5s\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=0.846 total time=  10.1s\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=0.844 total time=  10.4s\n",
      "[CV 1/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=  12.2s\n",
      "[CV 2/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.841 total time=  13.0s\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=0.842 total time=  14.4s\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=0.847 total time=  11.1s\n",
      "[CV 5/5; 22/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.845 total time=  11.7s\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=0.838 total time=   9.6s\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=0.839 total time=  11.2s\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=0.842 total time=  13.0s\n",
      "[CV 4/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.849 total time=  11.4s\n",
      "[CV 5/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/48] END classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=   7.5s\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=0.840 total time=  14.1s\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=0.838 total time=  11.1s\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=0.846 total time=  12.8s\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=0.851 total time=  13.0s\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=0.849 total time=  10.6s\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=0.826 total time=   9.5s\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=0.830 total time=  10.4s\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=0.829 total time=  10.6s\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=0.837 total time=  11.9s\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=0.838 total time=  11.7s\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=0.829 total time=  11.5s\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=0.832 total time=  11.1s\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=0.836 total time=  13.8s\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=0.841 total time=  13.7s\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=0.845 total time=  12.4s\n",
      "[CV 1/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.829 total time=   7.9s\n",
      "[CV 2/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/48] END classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 total time=   9.2s\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=0.835 total time=  11.8s\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=0.842 total time=  11.9s\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=0.843 total time=  13.8s\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=0.829 total time=  13.9s\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=0.833 total time=  13.1s\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=0.841 total time=  13.4s\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=0.846 total time=  10.6s\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=0.848 total time=   7.5s\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=0.826 total time=   9.7s\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=0.830 total time=  12.6s\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=0.829 total time=   9.1s\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=0.837 total time=   7.6s\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=0.838 total time=  11.4s\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=0.829 total time=  11.1s\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=0.832 total time=  11.6s\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=0.836 total time=  10.4s\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=0.841 total time=  10.9s\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=0.845 total time=  12.8s\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=0.829 total time=  11.3s\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=0.831 total time=  12.4s\n",
      "[CV 3/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/48] END classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.835 total time=  12.2s\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=0.842 total time=  11.1s\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=0.843 total time=  10.6s\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=0.829 total time=   9.5s\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=0.833 total time=   7.5s\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=0.841 total time=  11.1s\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=0.846 total time=  12.1s\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=0.848 total time=  12.0s\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=0.826 total time=  11.0s\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=0.830 total time=  10.6s\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=0.829 total time=  13.1s\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=0.837 total time=  11.9s\n",
      "[CV 5/5; 33/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 33/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 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=0.829 total time=  12.6s\n",
      "[CV 2/5; 34/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 34/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.832 total time=  12.2s\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=0.836 total time=  10.9s\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=0.841 total time=  10.6s\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=0.845 total time=  10.5s\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=0.829 total time=   8.8s\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=0.831 total time=   8.2s\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=0.835 total time=  12.2s\n",
      "[CV 4/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 35/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.842 total time=  10.9s\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=0.843 total time=  11.4s\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=0.829 total time=  10.5s\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=0.833 total time=  11.8s\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=0.841 total time=  12.6s\n",
      "[CV 4/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 36/48] END classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.846 total time=  10.5s\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=0.848 total time=   9.4s\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=0.817 total time=  12.2s\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=0.820 total time=  14.8s\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=0.821 total time=  15.0s\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=0.828 total time=  13.4s\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=0.830 total time=  15.5s\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=0.823 total time=  14.3s\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=0.822 total time=  13.9s\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=0.831 total time=  16.0s\n",
      "[CV 4/5; 38/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 38/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.836 total time=  13.5s\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=0.838 total time=  12.5s\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=0.821 total time=  11.0s\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=0.824 total time=   9.5s\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=0.827 total time=  14.2s\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=0.831 total time=  13.4s\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=0.835 total time=  13.2s\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=0.826 total time=  11.4s\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=0.829 total time=  12.0s\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=0.835 total time=  17.0s\n",
      "[CV 4/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 40/48] END classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.843 total time=  14.2s\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=0.844 total time=  10.8s\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=0.817 total time=   7.9s\n",
      "[CV 2/5; 41/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 41/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=  11.3s\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=0.821 total time=  14.1s\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=0.828 total time=  15.8s\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=0.830 total time=  16.8s\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=0.823 total time=  13.2s\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=0.822 total time=  13.1s\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=0.831 total time=   9.9s\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=0.836 total time=  15.6s\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=0.838 total time=  17.0s\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=0.821 total time=  10.6s\n",
      "[CV 2/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=  10.1s\n",
      "[CV 3/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.827 total time=  14.2s\n",
      "[CV 4/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 43/48] END classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 total time=  12.3s\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=0.835 total time=  13.2s\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=0.826 total time=   7.8s\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=0.829 total time=  12.5s\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=0.835 total time=  14.7s\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=0.843 total time=  12.6s\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=0.844 total time=  13.3s\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=0.817 total time=  10.8s\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=0.820 total time=  16.6s\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=0.821 total time=  12.9s\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=0.828 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=0.830 total time=  12.7s\n",
      "[CV 1/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.823 total time=  13.2s\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=0.822 total time=  14.8s\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=0.831 total time=  14.3s\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=0.836 total time=  13.1s\n",
      "[CV 5/5; 46/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 46/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.838 total time=   9.9s\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=0.821 total time=   7.9s\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=0.824 total time=  14.5s\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=0.827 total time=  14.3s\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=0.831 total time=  12.9s\n",
      "[CV 5/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 47/48] END classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.835 total time=  10.8s\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=0.826 total time=  11.2s\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=0.829 total time=   9.7s\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=0.835 total time=  10.9s\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=0.843 total time=  10.6s\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=0.844 total time=   8.4s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.844711 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:  0.8497676814130793\n",
      "accuracy:  0.8561151079136691\n",
      "auc score:  0.8469277983886101\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": 57,
   "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=0.803 total time=   3.3s\n",
      "[CV 2/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   3.4s\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=0.809 total time=   3.9s\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=0.821 total time=   3.3s\n",
      "[CV 5/5; 1/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 1/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.819 total time=   3.7s\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=0.794 total time=   2.7s\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=0.798 total time=   3.6s\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=0.798 total time=   3.1s\n",
      "[CV 4/5; 2/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 2/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   3.1s\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=0.808 total time=   3.8s\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=0.808 total time=   2.7s\n",
      "[CV 2/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 3/40] END classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 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=0.813 total time=   3.8s\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=0.824 total time=   3.7s\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=0.817 total time=   4.1s\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=0.799 total time=   2.8s\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=0.801 total time=   3.8s\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=0.800 total time=   2.7s\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=0.815 total time=   2.4s\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=0.807 total time=   2.5s\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=0.802 total time=   2.3s\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=0.809 total time=   2.7s\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=0.807 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=0.820 total time=   2.5s\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=0.815 total time=   2.4s\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=0.792 total time=   2.3s\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=0.800 total time=   2.3s\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=0.799 total time=   2.3s\n",
      "[CV 4/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.811 total time=   2.4s\n",
      "[CV 5/5; 6/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   2.3s\n",
      "[CV 1/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   2.3s\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=0.816 total time=   2.3s\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=0.809 total time=   2.3s\n",
      "[CV 4/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   2.4s\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=0.817 total time=   2.3s\n",
      "[CV 1/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/40] END classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.799 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=0.803 total time=   2.4s\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=0.799 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=0.811 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=0.810 total time=   2.3s\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=0.810 total time=   2.2s\n",
      "[CV 2/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.3s\n",
      "[CV 3/5; 9/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 9/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 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=0.827 total time=   2.4s\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=0.827 total time=   2.3s\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=0.806 total time=   2.3s\n",
      "[CV 2/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.810 total time=   2.4s\n",
      "[CV 3/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=   2.4s\n",
      "[CV 4/5; 10/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/40] END classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.825 total time=   2.5s\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=0.821 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=0.814 total time=   2.3s\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=0.820 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=0.823 total time=   2.3s\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=0.830 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=0.823 total time=   2.3s\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=0.808 total time=   2.4s\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=0.815 total time=   2.3s\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=0.816 total time=   2.4s\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=0.829 total time=   2.3s\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=0.825 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=0.809 total time=   2.4s\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=0.819 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=0.816 total time=   2.6s\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=0.828 total time=   2.4s\n",
      "[CV 5/5; 13/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.823 total time=   2.5s\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=0.806 total time=   2.6s\n",
      "[CV 2/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.816 total time=   2.6s\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=0.816 total time=   2.7s\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=0.829 total time=   2.5s\n",
      "[CV 5/5; 14/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.826 total time=   2.4s\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=0.818 total time=   2.5s\n",
      "[CV 2/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 15/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.825 total time=   3.0s\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=0.820 total time=   2.7s\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=0.824 total time=   3.2s\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=0.825 total time=   2.8s\n",
      "[CV 1/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   2.5s\n",
      "[CV 2/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/40] END classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.815 total time=   2.4s\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=0.819 total time=   2.8s\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=0.827 total time=   2.5s\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=0.824 total time=   2.5s\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=0.815 total time=   2.5s\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=0.820 total time=   2.5s\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=0.828 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=0.833 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=0.827 total time=   2.4s\n",
      "[CV 1/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.812 total time=   2.7s\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=0.814 total time=   2.7s\n",
      "[CV 3/5; 18/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/40] END classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   2.5s\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=0.830 total time=   2.8s\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=0.827 total time=   2.7s\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=0.820 total time=   3.0s\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=0.823 total time=   2.7s\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=0.828 total time=   2.7s\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=0.837 total time=   2.4s\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=0.832 total time=   2.9s\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=0.813 total time=   2.7s\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=0.816 total time=   2.7s\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=0.821 total time=   2.9s\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=0.834 total time=   2.7s\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=0.825 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=0.819 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=0.827 total time=   2.4s\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=0.826 total time=   2.8s\n",
      "[CV 4/5; 21/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.835 total time=   2.4s\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=0.833 total time=   2.4s\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=0.820 total time=   2.3s\n",
      "[CV 2/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=   2.3s\n",
      "[CV 3/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.829 total time=   2.2s\n",
      "[CV 4/5; 22/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 22/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.837 total time=   2.3s\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=0.834 total time=   2.3s\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=0.824 total time=   2.2s\n",
      "[CV 2/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 total time=   2.3s\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=0.830 total time=   2.4s\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=0.834 total time=   2.4s\n",
      "[CV 5/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 23/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.836 total time=   2.3s\n",
      "[CV 1/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=   2.3s\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=0.825 total time=   2.3s\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=0.829 total time=   2.3s\n",
      "[CV 4/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/40] END classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.839 total time=   2.4s\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=0.837 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=0.816 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=0.819 total time=   2.3s\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=0.826 total time=   2.2s\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=0.835 total time=   2.3s\n",
      "[CV 5/5; 25/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.832 total time=   2.3s\n",
      "[CV 1/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.787 total time=   2.2s\n",
      "[CV 2/5; 26/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.792 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=0.796 total time=   2.2s\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=0.803 total time=   2.4s\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=0.806 total time=   2.3s\n",
      "[CV 1/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.2s\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=0.818 total time=   2.2s\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=0.826 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=0.834 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=0.830 total time=   2.5s\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=0.793 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=0.795 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=0.793 total time=   2.3s\n",
      "[CV 4/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.808 total time=   2.3s\n",
      "[CV 5/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/40] END classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.806 total time=   2.4s\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=0.825 total time=   2.2s\n",
      "[CV 2/5; 29/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.828 total time=   2.5s\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=0.835 total time=   2.4s\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=0.838 total time=   2.5s\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=0.837 total time=   2.4s\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=0.813 total time=   2.4s\n",
      "[CV 2/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.817 total time=   2.4s\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=0.819 total time=   2.5s\n",
      "[CV 4/5; 30/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 30/40] END classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 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=0.828 total time=   2.6s\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=0.826 total time=   2.7s\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=0.829 total time=   2.2s\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=0.835 total time=   2.3s\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=0.841 total time=   2.5s\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=0.841 total time=   2.3s\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=0.814 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=0.817 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=0.822 total time=   2.6s\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=0.834 total time=   2.7s\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=   2.5s\n",
      "[CV 1/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.611 total time=   2.5s\n",
      "[CV 2/5; 33/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 33/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.614 total time=   2.8s\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=0.600 total time=   2.5s\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=0.621 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=0.608 total time=   2.6s\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=0.551 total time=   2.6s\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=0.546 total time=   2.3s\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=0.537 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=0.565 total time=   2.5s\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=0.555 total time=   2.7s\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=0.589 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=0.586 total time=   2.5s\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=0.578 total time=   2.7s\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=0.601 total time=   2.7s\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=0.595 total time=   2.6s\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=0.547 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=0.544 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=0.530 total time=   2.3s\n",
      "[CV 4/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 36/40] END classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 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=0.553 total time=   2.4s\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=0.672 total time=   2.2s\n",
      "[CV 2/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.660 total time=   2.1s\n",
      "[CV 3/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.652 total time=   2.2s\n",
      "[CV 4/5; 37/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 37/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.678 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=0.664 total time=   2.3s\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=0.579 total time=   2.6s\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=0.570 total time=   2.9s\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=0.563 total time=   2.4s\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=0.586 total time=   2.3s\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=0.578 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=0.639 total time=   2.4s\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=0.637 total time=   2.4s\n",
      "[CV 3/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 39/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.628 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=0.651 total time=   2.4s\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=0.632 total time=   2.3s\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=0.575 total time=   2.3s\n",
      "[CV 2/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.565 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=0.561 total time=   2.3s\n",
      "[CV 4/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.4s\n",
      "[CV 5/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 40/40] END classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   2.3s\n",
      "Best: 0.834432 using {'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",
      "f1_score:  0.8412778514087741\n",
      "accuracy:  0.8461801986981843\n",
      "auc score:  0.8423100677556231\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", MultinomialNB())\n",
    "])\n",
    "\n",
    "mnbModel_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__alpha\": [0.01, 0.1, 0.5, 1, 10],\n",
    "    'classifier__fit_prior': [True, False],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_mnb = GridSearchCV(pipe, mnbModel_params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_mnb.fit(X_train, y_train)\n",
    "print(\"Best: %f using %s\" % (grid_mnb.best_score_, grid_mnb.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_mnb = grid_mnb.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_mnb, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_mnb)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_mnb)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_mnb)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_mnb = grid_mnb.best_estimator_.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_mnb[:,1])\n",
    "sns.lineplot(x=fpr, y=tpr,label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"MultinomialNB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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=   2.2s\n",
      "[CV 2/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 1/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 1/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.1s\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=   2.1s\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=   2.1s\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=   2.2s\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=   2.2s\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=   2.1s\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=   2.1s\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=   2.3s\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=   2.4s\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=   2.7s\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=   2.3s\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=   2.5s\n",
      "[CV 3/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 4/32] END classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\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=   2.5s\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=   2.5s\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=0.799 total time=   8.7s\n",
      "[CV 2/5; 5/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 5/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.800 total time=   8.0s\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=0.802 total time=   8.7s\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=0.807 total time=   8.6s\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=0.807 total time=   9.1s\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=0.805 total time=  10.0s\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=0.806 total time=  10.0s\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=0.812 total time=  10.7s\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=0.820 total time=  10.9s\n",
      "[CV 5/5; 6/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.819 total time=  11.4s\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=0.808 total time=  10.9s\n",
      "[CV 2/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/32] END classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.803 total time=  10.6s\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=0.802 total time=  11.7s\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=0.815 total time=  10.4s\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=0.820 total time=  10.4s\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=0.813 total time=  12.0s\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=0.810 total time=  12.8s\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=0.818 total time=  11.8s\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=0.826 total time=  12.1s\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=0.831 total time= 1.4min\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= 1.6min\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=   2.6s\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=   2.3s\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=   2.1s\n",
      "[CV 2/5; 10/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\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=   2.2s\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=   2.5s\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=   2.1s\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=   2.1s\n",
      "[CV 3/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 4/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.1s\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=   2.3s\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=   2.1s\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=   2.3s\n",
      "[CV 5/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 12/32] END classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 1/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.818 total time=  10.3s\n",
      "[CV 2/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.820 total time=   8.2s\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=0.822 total time=   8.1s\n",
      "[CV 4/5; 13/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.826 total time=   7.0s\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=0.830 total time=   7.8s\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=0.822 total time=   8.1s\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=0.825 total time=   8.3s\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=0.829 total time=   8.0s\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=0.834 total time=   7.6s\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=0.839 total time=   9.3s\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=0.823 total time=  11.0s\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=0.822 total time=  11.0s\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=0.827 total time=   9.9s\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=0.832 total time=  10.5s\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=0.832 total time=   9.0s\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=0.824 total time=  11.8s\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=0.828 total time=   9.9s\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=0.836 total time=  11.5s\n",
      "[CV 4/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 16/32] END classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.840 total time=   9.5s\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=0.843 total time=   8.9s\n",
      "[CV 1/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 2/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 3/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 17/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\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=   2.4s\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=   2.5s\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=   2.6s\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=   2.5s\n",
      "[CV 3/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 18/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.7s\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=   2.7s\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=   2.5s\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=   2.7s\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=   2.7s\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=   2.6s\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=   2.7s\n",
      "[CV 5/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.8s\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=   2.5s\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=   2.6s\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=   3.0s\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=   2.8s\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=   2.8s\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=0.839 total time=   4.0s\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=0.842 total time=   3.4s\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=0.840 total time=   4.1s\n",
      "[CV 4/5; 21/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 21/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.848 total time=   3.6s\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=0.848 total time=   3.5s\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=0.841 total time=   3.5s\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=0.843 total time=   3.4s\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=0.844 total time=   3.6s\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=0.852 total time=   3.4s\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=0.848 total time=   3.4s\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=0.839 total time=   3.3s\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=0.844 total time=   3.3s\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=0.843 total time=   3.3s\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=0.850 total time=   3.3s\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=0.853 total time=   3.3s\n",
      "[CV 1/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.841 total time=   3.5s\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=0.844 total time=   3.2s\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=0.845 total time=   3.4s\n",
      "[CV 4/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.852 total time=   3.4s\n",
      "[CV 5/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/32] END classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.850 total time=   3.3s\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=   2.1s\n",
      "[CV 2/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 4/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 5/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 25/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\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=   2.2s\n",
      "[CV 2/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.1s\n",
      "[CV 3/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 4/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.4s\n",
      "[CV 5/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 26/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.9s\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=   3.5s\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=   5.0s\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=   3.4s\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=   3.2s\n",
      "[CV 5/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.6s\n",
      "[CV 1/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.5s\n",
      "[CV 2/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.3s\n",
      "[CV 3/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\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=   2.6s\n",
      "[CV 5/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 28/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.2s\n",
      "[CV 1/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.827 total time=   2.6s\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=0.828 total time=   2.9s\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=0.828 total time=   2.9s\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=0.837 total time=   2.8s\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=0.830 total time=   2.8s\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=0.825 total time=   2.7s\n",
      "[CV 2/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.831 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=0.830 total time=   2.8s\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=   2.7s\n",
      "[CV 5/5; 30/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 30/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.7s\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=0.823 total time=   2.7s\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=0.826 total time=   2.7s\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=0.829 total time=   2.6s\n",
      "[CV 4/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 31/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.836 total time=   3.1s\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=0.831 total time=   3.3s\n",
      "[CV 1/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.824 total time=   3.0s\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=0.828 total time=   3.9s\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=0.828 total time=   3.9s\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=0.838 total time=   3.0s\n",
      "[CV 5/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/32] END classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.8s\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 0.80297517 0.81252761\n",
      " 0.80954813 0.81947201        nan        nan        nan        nan\n",
      " 0.82292302 0.82959257 0.82721854 0.83412232        nan        nan\n",
      "        nan        nan 0.84346728 0.84561374 0.84561316 0.84630691\n",
      "        nan        nan        nan        nan 0.8300333  0.83095722\n",
      " 0.82896385 0.82978248]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.846307 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:  0.8508080372339801\n",
      "accuracy:  0.8568002740664611\n",
      "auc score:  0.8486133994229507\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, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_svc.fit(X_train, y_train)\n",
    "print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Best: %f using %s\" % (grid_svc.best_score_, grid_svc.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_svc = grid_svc.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_svc, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_svc)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_svc)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_svc)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_svc = grid_svc.best_estimator_.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_svc[:, 1])\n",
    "sns.lineplot(x=fpr, y=tpr, label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"LinearSVC\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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=0.830 total time=   2.9s\n",
      "[CV 2/5; 1/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 1/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.833 total time=   3.0s\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=0.831 total time=   2.8s\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=0.843 total time=   2.9s\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=0.837 total time=   3.0s\n",
      "[CV 1/5; 2/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 2/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.827 total time=   3.8s\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=0.836 total time=   2.9s\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=0.830 total time=   2.9s\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=   2.7s\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=0.837 total time=   2.7s\n",
      "[CV 1/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.828 total time=   2.8s\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=0.833 total time=   2.7s\n",
      "[CV 3/5; 3/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 3/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.7s\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=0.843 total time=   2.7s\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=0.836 total time=   2.6s\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=0.828 total time=   2.6s\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=0.835 total time=   2.8s\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=0.823 total time=   2.7s\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=0.841 total time=   2.6s\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=0.835 total time=   2.7s\n",
      "[CV 1/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.825 total time=   2.7s\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=0.829 total time=   2.6s\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=0.831 total time=   2.5s\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=0.840 total time=   2.5s\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=0.836 total time=   2.5s\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=0.826 total time=   2.6s\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=0.833 total time=   2.4s\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=0.834 total time=   2.4s\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=0.844 total time=   2.5s\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=0.838 total time=   2.4s\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=0.824 total time=   2.6s\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=0.830 total time=   3.0s\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=0.833 total time=   3.4s\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=0.844 total time=   3.2s\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=0.838 total time=   2.7s\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=0.824 total time=   2.7s\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=0.830 total time=   2.7s\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=0.839 total time=   2.5s\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=0.849 total time=   2.7s\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=0.843 total time=   2.7s\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=0.754 total time=   2.6s\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=0.756 total time=   2.6s\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=0.764 total time=   2.7s\n",
      "[CV 4/5; 9/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 9/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.771 total time=   2.8s\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=0.770 total time=   2.5s\n",
      "[CV 1/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.750 total time=   2.5s\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=0.754 total time=   2.5s\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=0.758 total time=   2.4s\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=0.765 total time=   2.5s\n",
      "[CV 5/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.768 total time=   2.5s\n",
      "[CV 1/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.744 total time=   2.4s\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=0.740 total time=   2.6s\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=0.760 total time=   2.5s\n",
      "[CV 4/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.755 total time=   2.4s\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=0.765 total time=   2.5s\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=0.739 total time=   2.3s\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=0.747 total time=   2.3s\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=0.744 total time=   2.5s\n",
      "[CV 4/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.753 total time=   2.3s\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=0.759 total time=   2.4s\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=0.827 total time=   2.3s\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=0.826 total time=   2.2s\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=0.824 total time=   2.3s\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=0.841 total time=   2.4s\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=0.835 total time=   2.6s\n",
      "[CV 1/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   3.0s\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=0.838 total time=   2.7s\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=0.837 total time=   2.5s\n",
      "[CV 4/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.847 total time=   2.3s\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=   2.3s\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=0.829 total time=   2.2s\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=0.839 total time=   2.3s\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=0.833 total time=   2.3s\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=0.839 total time=   2.6s\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=0.840 total time=   2.4s\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=0.831 total time=   2.4s\n",
      "[CV 2/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.4s\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=0.831 total time=   2.6s\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=0.841 total time=   2.6s\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=0.847 total time=   2.5s\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=0.571 total time=   2.4s\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=0.578 total time=   2.2s\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=0.569 total time=   2.6s\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=0.589 total time=   3.4s\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=0.588 total time=   2.7s\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=0.572 total time=   2.6s\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=0.578 total time=   2.3s\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=0.569 total time=   2.4s\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=0.591 total time=   2.3s\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=0.590 total time=   2.4s\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=0.571 total time=   2.6s\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=0.578 total time=   2.4s\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=0.570 total time=   2.5s\n",
      "[CV 4/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   2.4s\n",
      "[CV 5/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.5s\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=0.573 total time=   2.4s\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=0.578 total time=   2.4s\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=0.569 total time=   2.6s\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=0.591 total time=   2.3s\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=0.588 total time=   2.4s\n",
      "[CV 1/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.798 total time=   2.3s\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=0.815 total time=   2.2s\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=0.835 total time=   2.2s\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=0.843 total time=   2.2s\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=0.830 total time=   2.2s\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=0.826 total time=   2.3s\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=0.828 total time=   2.2s\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=0.832 total time=   2.2s\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=0.833 total time=   2.2s\n",
      "[CV 5/5; 22/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 22/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.830 total time=   2.2s\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=0.830 total time=   2.2s\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=0.835 total time=   2.2s\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=0.813 total time=   2.2s\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=0.841 total time=   2.2s\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=0.843 total time=   2.4s\n",
      "[CV 1/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.835 total time=   2.3s\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=0.825 total time=   2.2s\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=0.823 total time=   2.2s\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=0.848 total time=   2.3s\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=0.813 total time=   2.3s\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=0.372 total time=   2.3s\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=0.372 total time=   2.3s\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=0.372 total time=   2.3s\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=0.372 total time=   2.2s\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=0.372 total time=   2.3s\n",
      "[CV 1/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.2s\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=0.372 total time=   2.5s\n",
      "[CV 3/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.3s\n",
      "[CV 4/5; 26/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 26/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.2s\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=0.372 total time=   2.3s\n",
      "[CV 1/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.3s\n",
      "[CV 2/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.3s\n",
      "[CV 3/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.4s\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=0.372 total time=   2.3s\n",
      "[CV 5/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.8s\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=0.372 total time=   2.5s\n",
      "[CV 2/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.6s\n",
      "[CV 3/5; 28/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 28/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.372 total time=   2.2s\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=0.372 total time=   2.2s\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=0.372 total time=   2.1s\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=0.692 total time=   2.3s\n",
      "[CV 2/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.707 total time=   2.0s\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=0.747 total time=   2.0s\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=0.745 total time=   2.1s\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=0.683 total time=   2.0s\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=0.739 total time=   2.1s\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=0.713 total time=   2.0s\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=0.738 total time=   2.0s\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=0.704 total time=   2.1s\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=0.775 total time=   2.1s\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=0.738 total time=   1.9s\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=0.747 total time=   1.9s\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=0.695 total time=   2.1s\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=0.794 total time=   2.2s\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=0.748 total time=   2.1s\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=0.729 total time=   2.0s\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=0.670 total time=   2.0s\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=0.599 total time=   2.1s\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=0.703 total time=   2.1s\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=0.657 total time=   2.2s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.839433 using {'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",
      "f1_score:  0.8450031982199846\n",
      "accuracy:  0.8516615279205207\n",
      "auc score:  0.8419933039930089\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "pipe = Pipeline([\n",
    "    (\"transformer\", ct),\n",
    "    (\"classifier\", CalibratedClassifierCV(estimator=SGDClassifier()))\n",
    "])\n",
    "\n",
    "sgd_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [10000, 32000],\n",
    "    \"transformer__vect_reviewText__max_features\": [5000, 10000],\n",
    "    \"classifier__estimator__alpha\": [0.001, 0.01, 0.1, 1],\n",
    "    \"classifier__estimator__loss\": ['hinge', 'perceptron'],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_sgd = GridSearchCV(pipe, sgd_params, cv=5, scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_sgd.fit(X_train, y_train)\n",
    "print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Best: %f using %s\" % (grid_sgd.best_score_, grid_sgd.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_sgd = grid_sgd.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_sgd, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accur = accuracy_score(y_test, y_pred_sgd)\n",
    "accuracy_arr.append(accur)\n",
    "print(\"accuracy: \", accur)\n",
    "\n",
    "# auc score\n",
    "aucscore = roc_auc_score(y_test, y_pred_sgd)\n",
    "print(\"auc score: \", aucscore)\n",
    "auc_arr.append(aucscore)\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_sgd)\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "fpr = fp / (fp + tn)\n",
    "tpr = tp / (tp + fn)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
    "            yticklabels=['Actual Negative\\nFPR={:.2f}'.format(fpr), 'Actual Positive\\nTPR={:.2f}'.format(tpr)])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# ROC curve\n",
    "y_pred_prob_sgd = grid_sgd.best_estimator_.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pred_prob_sgd[:, 1])\n",
    "sns.lineplot(x=fpr, y=tpr, label=\"AUC=\"+str(aucscore))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Stochastic Gradient classifier\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>0.846928</td>\n",
       "      <td>0.849768</td>\n",
       "      <td>0.856115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.842310</td>\n",
       "      <td>0.841278</td>\n",
       "      <td>0.846180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.848613</td>\n",
       "      <td>0.850808</td>\n",
       "      <td>0.856800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient classifier</td>\n",
       "      <td>0.841993</td>\n",
       "      <td>0.845003</td>\n",
       "      <td>0.851662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       auc  f1 score  Accuracy\n",
       "0             Logistic Regression  0.846928  0.849768  0.856115\n",
       "1                   MultinomialNB  0.842310  0.841278  0.846180\n",
       "2                       LinearSVC  0.848613  0.850808  0.856800\n",
       "3  Stochastic Gradient classifier  0.841993  0.845003  0.851662"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\": model_arr, \"auc\": auc_arr,\n",
    "              \"f1 score\": f1_arr, \"Accuracy\": accuracy_arr, }).head()\n"
   ]
  }
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