Final_3_5_HP_COSC_74_274_Winter23_ShyamReddy.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter training for part 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <td>2.918900e+04</td>\n",
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       "      <td>600</td>\n",
       "      <td>15365</td>\n",
       "      <td>29189</td>\n",
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       "      <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",
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       "      <td>6104</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>01 3, 2017</td>\n",
       "      <td>AA7918E9410D650A076221C7B2934A09</td>\n",
       "      <td>6E46168E5DFA83876F2E968A187B9F7A</td>\n",
       "      <td>760C63E8E5E8DC3FAA01878D37BA5678</td>\n",
       "      <td>works great</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['https://images-na.ssl-images-amazon.com/imag...</td>\n",
       "      <td>{'Format:': ' Audio CD'}</td>\n",
       "      <td>CDs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22996</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
       "      <td>1049</td>\n",
       "      <td>25</td>\n",
       "      <td>1476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4296</td>\n",
       "      <td>4912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.971907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.409661e+09</td>\n",
       "      <td>7.295353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.410878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.115099e+08</td>\n",
       "      <td>18.749460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.947584e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.390781e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.439770e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.476317e+09</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537488e+09</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "             overall verified  reviewTime                        reviewerID  \\\n",
       "count   29189.000000    29189       29189                             29189   \n",
       "unique           NaN        2        4866                             27588   \n",
       "top              NaN     True  01 3, 2017  AA7918E9410D650A076221C7B2934A09   \n",
       "freq             NaN    22996          40                                32   \n",
       "mean        2.971907      NaN         NaN                               NaN   \n",
       "std         1.410878      NaN         NaN                               NaN   \n",
       "min         1.000000      NaN         NaN                               NaN   \n",
       "25%         2.000000      NaN         NaN                               NaN   \n",
       "50%         3.000000      NaN         NaN                               NaN   \n",
       "75%         4.000000      NaN         NaN                               NaN   \n",
       "max         5.000000      NaN         NaN                               NaN   \n",
       "\n",
       "                                    asin                      reviewerName  \\\n",
       "count                              29189                             29185   \n",
       "unique                             24102                             23305   \n",
       "top     6E46168E5DFA83876F2E968A187B9F7A  760C63E8E5E8DC3FAA01878D37BA5678   \n",
       "freq                                  36                              1049   \n",
       "mean                                 NaN                               NaN   \n",
       "std                                  NaN                               NaN   \n",
       "min                                  NaN                               NaN   \n",
       "25%                                  NaN                               NaN   \n",
       "50%                                  NaN                               NaN   \n",
       "75%                                  NaN                               NaN   \n",
       "max                                  NaN                               NaN   \n",
       "\n",
       "         reviewText     summary  unixReviewTime         vote  \\\n",
       "count         29189       29184    2.918900e+04  6047.000000   \n",
       "unique        28807       21768             NaN          NaN   \n",
       "top     works great  Five Stars             NaN          NaN   \n",
       "freq             25        1476             NaN          NaN   \n",
       "mean            NaN         NaN    1.409661e+09     7.295353   \n",
       "std             NaN         NaN    1.115099e+08    18.749460   \n",
       "min             NaN         NaN    8.947584e+08     2.000000   \n",
       "25%             NaN         NaN    1.390781e+09     2.000000   \n",
       "50%             NaN         NaN    1.439770e+09     4.000000   \n",
       "75%             NaN         NaN    1.476317e+09     7.000000   \n",
       "max             NaN         NaN    1.537488e+09   682.000000   \n",
       "\n",
       "                                                    image  \\\n",
       "count                                                 600   \n",
       "unique                                                600   \n",
       "top     ['https://images-na.ssl-images-amazon.com/imag...   \n",
       "freq                                                    1   \n",
       "mean                                                  NaN   \n",
       "std                                                   NaN   \n",
       "min                                                   NaN   \n",
       "25%                                                   NaN   \n",
       "50%                                                   NaN   \n",
       "75%                                                   NaN   \n",
       "max                                                   NaN   \n",
       "\n",
       "                           style category  \n",
       "count                      15365    29189  \n",
       "unique                      6104        6  \n",
       "top     {'Format:': ' Audio CD'}      CDs  \n",
       "freq                        4296     4912  \n",
       "mean                         NaN      NaN  \n",
       "std                          NaN      NaN  \n",
       "min                          NaN      NaN  \n",
       "25%                          NaN      NaN  \n",
       "50%                          NaN      NaN  \n",
       "75%                          NaN      NaN  \n",
       "max                          NaN      NaN  "
      ]
     },
     "execution_count": 170,
     "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": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Preprocessing data\n",
    "\n",
    "# Stop words\n",
    "GENERIC_STOP_WORDS = []  # removed stop words to gather more data for multiclassification\n",
    "\n",
    "\n",
    "# edit text\n",
    "def edit_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(edit_text)\n",
    "    data['summary'] = data['summary'].astype(str).apply(edit_text)\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": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# creating X and y data\n",
    "\n",
    "# creating y labels\n",
    "y = train_data['overall'].astype(int)\n",
    "\n",
    "# create features data\n",
    "X = preprocess(train_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\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, LabelBinarizer\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, f1_score, accuracy_score, auc\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "import warnings\n",
    "\n",
    "\n",
    "# define the column transformer to apply TfidfVectorizer & encoder to multiple columns\n",
    "ct = ColumnTransformer(\n",
    "    [\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",
    "# ignore warnings\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# For results\n",
    "model_arr, auc_arr, f1_arr, accuracy_arr = [], [], [], []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.5s\n",
      "[CV 2/5; 1/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 3/5; 1/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.554 total time=   2.6s\n",
      "[CV 4/5; 1/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.568 total time=   2.7s\n",
      "[CV 5/5; 1/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.4s\n",
      "[CV 1/5; 2/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.555 total time=   2.3s\n",
      "[CV 2/5; 2/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.558 total time=   2.3s\n",
      "[CV 3/5; 2/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.552 total time=   2.3s\n",
      "[CV 4/5; 2/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.565 total time=   2.3s\n",
      "[CV 5/5; 2/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.562 total time=   2.3s\n",
      "[CV 1/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.564 total time=   2.3s\n",
      "[CV 2/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.557 total time=   2.3s\n",
      "[CV 3/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.560 total time=   2.3s\n",
      "[CV 4/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.567 total time=   2.3s\n",
      "[CV 5/5; 3/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.570 total time=   2.3s\n",
      "[CV 1/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=   2.3s\n",
      "[CV 2/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 3/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 4/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 5/5; 4/40] START classifier__alpha=0.01, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=   2.3s\n",
      "[CV 1/5; 5/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=   2.3s\n",
      "[CV 2/5; 5/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 3/5; 5/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.553 total time=   2.3s\n",
      "[CV 4/5; 5/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.568 total time=   2.4s\n",
      "[CV 5/5; 5/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.3s\n",
      "[CV 1/5; 6/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.555 total time=   2.3s\n",
      "[CV 2/5; 6/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.557 total time=   2.3s\n",
      "[CV 3/5; 6/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.554 total time=   2.3s\n",
      "[CV 4/5; 6/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.565 total time=   2.3s\n",
      "[CV 5/5; 6/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=   2.3s\n",
      "[CV 1/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.565 total time=   2.3s\n",
      "[CV 2/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 3/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=   2.3s\n",
      "[CV 4/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.567 total time=   2.3s\n",
      "[CV 5/5; 7/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=   2.3s\n",
      "[CV 1/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=   2.3s\n",
      "[CV 2/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 3/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=   2.3s\n",
      "[CV 4/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 5/5; 8/40] START classifier__alpha=0.01, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.564 total time=   2.3s\n",
      "[CV 1/5; 9/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 2/5; 9/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 3/5; 9/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 4/5; 9/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 5/5; 9/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 1/5; 10/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 2/5; 10/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 3/5; 10/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 4/5; 10/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.3s\n",
      "[CV 5/5; 10/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   2.3s\n",
      "[CV 1/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.3s\n",
      "[CV 2/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 3/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.3s\n",
      "[CV 4/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 5/5; 11/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   2.3s\n",
      "[CV 1/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 2/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 3/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.3s\n",
      "[CV 4/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.3s\n",
      "[CV 5/5; 12/40] START classifier__alpha=0.1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 1/5; 13/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 2/5; 13/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 3/5; 13/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 4/5; 13/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 5/5; 13/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.3s\n",
      "[CV 1/5; 14/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 2/5; 14/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 3/5; 14/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.3s\n",
      "[CV 4/5; 14/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 5/5; 14/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 1/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.3s\n",
      "[CV 2/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 3/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   2.3s\n",
      "[CV 4/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.3s\n",
      "[CV 5/5; 15/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   2.3s\n",
      "[CV 1/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 2/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 3/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.3s\n",
      "[CV 4/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.3s\n",
      "[CV 5/5; 16/40] START classifier__alpha=0.1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 1/5; 17/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 2/5; 17/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.3s\n",
      "[CV 3/5; 17/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 4/5; 17/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   2.3s\n",
      "[CV 5/5; 17/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   2.3s\n",
      "[CV 1/5; 18/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.3s\n",
      "[CV 2/5; 18/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.3s\n",
      "[CV 3/5; 18/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 4/5; 18/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 5/5; 18/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 1/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.3s\n",
      "[CV 2/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   2.3s\n",
      "[CV 3/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 4/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 5/5; 19/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 1/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.3s\n",
      "[CV 2/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 3/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 4/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 5/5; 20/40] START classifier__alpha=0.5, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 1/5; 21/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 2/5; 21/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.3s\n",
      "[CV 3/5; 21/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.3s\n",
      "[CV 4/5; 21/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   2.3s\n",
      "[CV 5/5; 21/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 1/5; 22/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 2/5; 22/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.3s\n",
      "[CV 3/5; 22/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 4/5; 22/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 5/5; 22/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   2.3s\n",
      "[CV 1/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 2/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 3/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 4/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 5/5; 23/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 1/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.3s\n",
      "[CV 2/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 3/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.3s\n",
      "[CV 4/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 5/5; 24/40] START classifier__alpha=0.5, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 1/5; 25/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.3s\n",
      "[CV 2/5; 25/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 3/5; 25/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   2.3s\n",
      "[CV 4/5; 25/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   2.3s\n",
      "[CV 5/5; 25/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   2.3s\n",
      "[CV 1/5; 26/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.3s\n",
      "[CV 2/5; 26/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 3/5; 26/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.3s\n",
      "[CV 4/5; 26/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   2.3s\n",
      "[CV 5/5; 26/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   2.3s\n",
      "[CV 1/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.3s\n",
      "[CV 2/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 3/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.594 total time=   2.3s\n",
      "[CV 4/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   2.3s\n",
      "[CV 5/5; 27/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 1/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   2.4s\n",
      "[CV 2/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.4s\n",
      "[CV 3/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.4s\n",
      "[CV 4/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   2.4s\n",
      "[CV 5/5; 28/40] START classifier__alpha=1, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.4s\n",
      "[CV 1/5; 29/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.3s\n",
      "[CV 2/5; 29/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   2.3s\n",
      "[CV 3/5; 29/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   2.4s\n",
      "[CV 4/5; 29/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   2.4s\n",
      "[CV 5/5; 29/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   2.3s\n",
      "[CV 1/5; 30/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.3s\n",
      "[CV 2/5; 30/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 3/5; 30/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   2.3s\n",
      "[CV 4/5; 30/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   2.3s\n",
      "[CV 5/5; 30/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   2.3s\n",
      "[CV 1/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.3s\n",
      "[CV 2/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.3s\n",
      "[CV 3/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.3s\n",
      "[CV 4/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   2.3s\n",
      "[CV 5/5; 31/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 1/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   2.3s\n",
      "[CV 2/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   2.3s\n",
      "[CV 3/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   2.3s\n",
      "[CV 4/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   2.3s\n",
      "[CV 5/5; 32/40] START classifier__alpha=1, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   2.3s\n",
      "[CV 1/5; 33/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.560 total time=   2.3s\n",
      "[CV 2/5; 33/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=   2.3s\n",
      "[CV 3/5; 33/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 4/5; 33/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.3s\n",
      "[CV 5/5; 33/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 1/5; 34/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.547 total time=   2.3s\n",
      "[CV 2/5; 34/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=   2.3s\n",
      "[CV 3/5; 34/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=   2.3s\n",
      "[CV 4/5; 34/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 5/5; 34/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 1/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.556 total time=   2.3s\n",
      "[CV 2/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.572 total time=   2.3s\n",
      "[CV 3/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=   2.3s\n",
      "[CV 4/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.3s\n",
      "[CV 5/5; 35/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   2.3s\n",
      "[CV 1/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.550 total time=   2.3s\n",
      "[CV 2/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.562 total time=   2.3s\n",
      "[CV 3/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=   2.3s\n",
      "[CV 4/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.564 total time=   2.3s\n",
      "[CV 5/5; 36/40] START classifier__alpha=10, classifier__fit_prior=True, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.567 total time=   2.3s\n",
      "[CV 1/5; 37/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.3s\n",
      "[CV 2/5; 37/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.3s\n",
      "[CV 3/5; 37/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   2.3s\n",
      "[CV 4/5; 37/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   2.3s\n",
      "[CV 5/5; 37/40] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   2.3s\n",
      "[CV 1/5; 38/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.553 total time=   2.3s\n",
      "[CV 2/5; 38/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.3s\n",
      "[CV 3/5; 38/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 4/5; 38/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.572 total time=   2.3s\n",
      "[CV 5/5; 38/40] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   2.3s\n",
      "[CV 1/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=   2.3s\n",
      "[CV 2/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   2.3s\n",
      "[CV 3/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.3s\n",
      "[CV 4/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   2.3s\n",
      "[CV 5/5; 39/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.3s\n",
      "[CV 1/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.555 total time=   2.3s\n",
      "[CV 2/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.569 total time=   2.3s\n",
      "[CV 3/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=   2.3s\n",
      "[CV 4/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=   2.3s\n",
      "[CV 5/5; 40/40] START classifier__alpha=10, classifier__fit_prior=False, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=   2.3s\n",
      "Best: 0.595551 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': 15000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.6020228531383159\n",
      "accuracy:  0.6010620075368277\n",
      "auc score for Y = 1:  0.923848214298776\n",
      "auc score for Y = 2:  0.8290147746942559\n",
      "auc score for Y = 3:  0.845672984662622\n",
      "auc score for Y = 4:  0.8703878176447999\n",
      "auc score for Y = 5:  0.9384453726885731\n",
      "auc score average:  0.8871093405704155\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\": [15000, 35000],\n",
    "    \"transformer__vect_reviewText__max_features\": [10000, 15000],\n",
    "    \"classifier__alpha\": [0.01, 0.1, 0.5, 1, 10],\n",
    "    'classifier__fit_prior': [True, False],\n",
    "}\n",
    "\n",
    "\n",
    "# perform grid search cross-validation to find the best hyperparameters\n",
    "grid_mnb = GridSearchCV(pipe, mnbModel_params, cv=5,\n",
    "                        scoring='f1_macro', verbose=10)\n",
    "\n",
    "grid_mnb.fit(X_train, y_train)\n",
    "print(\"Best: %f using %s\" % (grid_mnb.best_score_, grid_mnb.best_params_))\n",
    "\n",
    "# predict\n",
    "y_pred_mnb = grid_mnb.best_estimator_.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "fscore = f1_score(y_test, y_pred_mnb, average='macro')\n",
    "print(\"f1_score: \", fscore)\n",
    "f1_arr.append(fscore)\n",
    "\n",
    "# accuracy\n",
    "accu = accuracy_score(y_test, y_pred_mnb)\n",
    "accuracy_arr.append(accu)\n",
    "print(\"accuracy: \", accu)\n",
    "\n",
    "# ROC curve & AUC Score\n",
    "y_pred_prob_mnb = grid_mnb.predict_proba(X_test)\n",
    "label_binarizer = LabelBinarizer().fit(y_train)\n",
    "y_onehot_test = label_binarizer.transform(y_test)\n",
    "\n",
    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
    "for i in range(5):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_onehot_test[:, i], y_pred_prob_mnb[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    print(f\"auc score for Y = {i+1}: \", roc_auc[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(\n",
    "    y_onehot_test.ravel(), y_pred_prob_mnb.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "print(\"auc score average: \", roc_auc[\"micro\"])\n",
    "auc_arr.append(roc_auc[\"micro\"])\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_mnb)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted 1', 'Predicted 2',\n",
    "                         'Predicted 3', 'Predicted 4', 'Predicted 5'],\n",
    "            yticklabels=['Actual 1', 'Actual 2', 'Actual 3', 'Actual 4', 'Actual 5'])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the ROC curve for each class\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr[0], tpr[0], color='orange', lw=lw,\n",
    "         label='Y Label 1 (area = %0.2f)' % roc_auc[0])\n",
    "plt.plot(fpr[1], tpr[1], color='green', lw=lw,\n",
    "         label='Y Label 2 (area = %0.2f)' % roc_auc[1])\n",
    "plt.plot(fpr[2], tpr[2], color='blue', lw=lw,\n",
    "         label='Y Label 3 (area = %0.2f)' % roc_auc[2])\n",
    "plt.plot(fpr[3], tpr[3], color='cyan', lw=lw,\n",
    "         label='Y Label 4 (area = %0.2f)' % roc_auc[3])\n",
    "plt.plot(fpr[4], tpr[4], color='purple', lw=lw,\n",
    "         label='Y Label 5 (area = %0.2f)' % roc_auc[4])\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], color='black', lw=lw, label='Average')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"MultinomialNB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   3.9s\n",
      "[CV 2/5; 1/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   3.9s\n",
      "[CV 3/5; 1/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.0s\n",
      "[CV 4/5; 1/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.1s\n",
      "[CV 5/5; 1/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   4.0s\n",
      "[CV 1/5; 2/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.1s\n",
      "[CV 2/5; 2/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   4.0s\n",
      "[CV 3/5; 2/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.2s\n",
      "[CV 4/5; 2/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   4.2s\n",
      "[CV 5/5; 2/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   4.1s\n",
      "[CV 1/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   4.1s\n",
      "[CV 2/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.0s\n",
      "[CV 3/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   4.1s\n",
      "[CV 4/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.0s\n",
      "[CV 5/5; 3/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.2s\n",
      "[CV 1/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   4.2s\n",
      "[CV 2/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   4.1s\n",
      "[CV 3/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.2s\n",
      "[CV 4/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.3s\n",
      "[CV 5/5; 4/48] START classifier__C=0.1, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.2s\n",
      "[CV 1/5; 5/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   4.4s\n",
      "[CV 2/5; 5/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   4.2s\n",
      "[CV 3/5; 5/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.2s\n",
      "[CV 4/5; 5/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.0s\n",
      "[CV 5/5; 5/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   4.3s\n",
      "[CV 1/5; 6/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.2s\n",
      "[CV 2/5; 6/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   4.0s\n",
      "[CV 3/5; 6/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.2s\n",
      "[CV 4/5; 6/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   4.1s\n",
      "[CV 5/5; 6/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   4.2s\n",
      "[CV 1/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   4.1s\n",
      "[CV 2/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.1s\n",
      "[CV 3/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   4.3s\n",
      "[CV 4/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.1s\n",
      "[CV 5/5; 7/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.2s\n",
      "[CV 1/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   6.1s\n",
      "[CV 2/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   7.0s\n",
      "[CV 3/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.8s\n",
      "[CV 4/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.8s\n",
      "[CV 5/5; 8/48] START classifier__C=0.1, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   6.0s\n",
      "[CV 1/5; 9/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   4.9s\n",
      "[CV 2/5; 9/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   4.0s\n",
      "[CV 3/5; 9/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.1s\n",
      "[CV 4/5; 9/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.3s\n",
      "[CV 5/5; 9/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   4.1s\n",
      "[CV 1/5; 10/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.3s\n",
      "[CV 2/5; 10/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   4.4s\n",
      "[CV 3/5; 10/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   4.1s\n",
      "[CV 4/5; 10/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   4.0s\n",
      "[CV 5/5; 10/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   4.0s\n",
      "[CV 1/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   4.1s\n",
      "[CV 2/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   4.1s\n",
      "[CV 3/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   4.1s\n",
      "[CV 4/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.0s\n",
      "[CV 5/5; 11/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.3s\n",
      "[CV 1/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   4.3s\n",
      "[CV 2/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   4.1s\n",
      "[CV 3/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.2s\n",
      "[CV 4/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   4.4s\n",
      "[CV 5/5; 12/48] START classifier__C=0.1, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.2s\n",
      "[CV 1/5; 13/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   5.0s\n",
      "[CV 2/5; 13/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   4.9s\n",
      "[CV 3/5; 13/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   5.0s\n",
      "[CV 4/5; 13/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   4.6s\n",
      "[CV 5/5; 13/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   4.9s\n",
      "[CV 1/5; 14/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   4.9s\n",
      "[CV 2/5; 14/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   4.9s\n",
      "[CV 3/5; 14/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   4.8s\n",
      "[CV 4/5; 14/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.607 total time=   4.8s\n",
      "[CV 5/5; 14/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.604 total time=   5.2s\n",
      "[CV 1/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   4.9s\n",
      "[CV 2/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   4.9s\n",
      "[CV 3/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   4.9s\n",
      "[CV 4/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.0s\n",
      "[CV 5/5; 15/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.1s\n",
      "[CV 1/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   5.1s\n",
      "[CV 2/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   4.9s\n",
      "[CV 3/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   5.2s\n",
      "[CV 4/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.608 total time=   4.9s\n",
      "[CV 5/5; 16/48] START classifier__C=1.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   6.7s\n",
      "[CV 1/5; 17/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   5.7s\n",
      "[CV 2/5; 17/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   5.9s\n",
      "[CV 3/5; 17/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   5.6s\n",
      "[CV 4/5; 17/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   5.5s\n",
      "[CV 5/5; 17/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   5.8s\n",
      "[CV 2/5; 18/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   6.0s\n",
      "[CV 3/5; 18/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   5.6s\n",
      "[CV 4/5; 18/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.607 total time=   5.2s\n",
      "[CV 5/5; 18/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.604 total time=   5.2s\n",
      "[CV 1/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   5.1s\n",
      "[CV 2/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   5.1s\n",
      "[CV 3/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   5.2s\n",
      "[CV 4/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.3s\n",
      "[CV 5/5; 19/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.1s\n",
      "[CV 1/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   5.9s\n",
      "[CV 2/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   5.5s\n",
      "[CV 3/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   5.4s\n",
      "[CV 4/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.608 total time=   5.1s\n",
      "[CV 5/5; 20/48] START classifier__C=1.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.2s\n",
      "[CV 1/5; 21/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   4.8s\n",
      "[CV 2/5; 21/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   4.8s\n",
      "[CV 3/5; 21/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   4.9s\n",
      "[CV 4/5; 21/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   4.6s\n",
      "[CV 5/5; 21/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   4.7s\n",
      "[CV 1/5; 22/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   4.7s\n",
      "[CV 2/5; 22/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.598 total time=   4.8s\n",
      "[CV 3/5; 22/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   4.9s\n",
      "[CV 4/5; 22/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.607 total time=   4.7s\n",
      "[CV 5/5; 22/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.604 total time=   4.8s\n",
      "[CV 1/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   4.7s\n",
      "[CV 2/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   4.9s\n",
      "[CV 3/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   4.8s\n",
      "[CV 4/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   4.6s\n",
      "[CV 5/5; 23/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   4.8s\n",
      "[CV 1/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   5.0s\n",
      "[CV 2/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   4.8s\n",
      "[CV 3/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   5.1s\n",
      "[CV 4/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.608 total time=   4.9s\n",
      "[CV 5/5; 24/48] START classifier__C=1.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   5.1s\n",
      "[CV 1/5; 25/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.6s\n",
      "[CV 2/5; 25/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.6s\n",
      "[CV 3/5; 25/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   5.9s\n",
      "[CV 4/5; 25/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   5.7s\n",
      "[CV 5/5; 25/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.7s\n",
      "[CV 1/5; 26/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   6.1s\n",
      "[CV 2/5; 26/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   5.8s\n",
      "[CV 3/5; 26/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   6.0s\n",
      "[CV 4/5; 26/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   5.8s\n",
      "[CV 5/5; 26/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.8s\n",
      "[CV 1/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   5.9s\n",
      "[CV 2/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.9s\n",
      "[CV 3/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.1s\n",
      "[CV 4/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   5.8s\n",
      "[CV 5/5; 27/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   6.3s\n",
      "[CV 1/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.3s\n",
      "[CV 2/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.4s\n",
      "[CV 3/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.3s\n",
      "[CV 4/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   6.1s\n",
      "[CV 5/5; 28/48] START classifier__C=5.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   6.1s\n",
      "[CV 1/5; 29/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.7s\n",
      "[CV 2/5; 29/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.6s\n",
      "[CV 3/5; 29/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   5.9s\n",
      "[CV 4/5; 29/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   5.7s\n",
      "[CV 5/5; 29/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.7s\n",
      "[CV 1/5; 30/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   5.9s\n",
      "[CV 2/5; 30/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   6.0s\n",
      "[CV 3/5; 30/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   5.9s\n",
      "[CV 4/5; 30/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   5.8s\n",
      "[CV 5/5; 30/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.8s\n",
      "[CV 1/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   5.9s\n",
      "[CV 2/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.8s\n",
      "[CV 3/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.1s\n",
      "[CV 4/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   5.9s\n",
      "[CV 5/5; 31/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   5.9s\n",
      "[CV 1/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.2s\n",
      "[CV 2/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.3s\n",
      "[CV 3/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.1s\n",
      "[CV 4/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   6.2s\n",
      "[CV 5/5; 32/48] START classifier__C=5.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   6.2s\n",
      "[CV 1/5; 33/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.5s\n",
      "[CV 2/5; 33/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.5s\n",
      "[CV 3/5; 33/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   5.7s\n",
      "[CV 4/5; 33/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   5.7s\n",
      "[CV 5/5; 33/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.5s\n",
      "[CV 1/5; 34/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   6.4s\n",
      "[CV 2/5; 34/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   5.6s\n",
      "[CV 3/5; 34/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   5.8s\n",
      "[CV 4/5; 34/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   5.8s\n",
      "[CV 5/5; 34/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   5.9s\n",
      "[CV 1/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   5.7s\n",
      "[CV 2/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   5.6s\n",
      "[CV 3/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.1s\n",
      "[CV 4/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   5.8s\n",
      "[CV 5/5; 35/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   6.0s\n",
      "[CV 1/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.1s\n",
      "[CV 2/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.5s\n",
      "[CV 3/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   6.1s\n",
      "[CV 4/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.596 total time=   6.1s\n",
      "[CV 5/5; 36/48] START classifier__C=5.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   6.0s\n",
      "[CV 1/5; 37/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   6.1s\n",
      "[CV 2/5; 37/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   6.1s\n",
      "[CV 3/5; 37/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.570 total time=   6.6s\n",
      "[CV 4/5; 37/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   6.2s\n",
      "[CV 5/5; 37/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.5s\n",
      "[CV 1/5; 38/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.4s\n",
      "[CV 2/5; 38/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.9s\n",
      "[CV 3/5; 38/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.3s\n",
      "[CV 4/5; 38/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.2s\n",
      "[CV 5/5; 38/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   6.3s\n",
      "[CV 1/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   6.4s\n",
      "[CV 2/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.2s\n",
      "[CV 3/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   6.5s\n",
      "[CV 4/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   6.4s\n",
      "[CV 5/5; 39/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.6s\n",
      "[CV 1/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.4s\n",
      "[CV 2/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.7s\n",
      "[CV 3/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   6.6s\n",
      "[CV 4/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   6.9s\n",
      "[CV 5/5; 40/48] START classifier__C=10.0, classifier__max_iter=100, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   7.0s\n",
      "[CV 1/5; 41/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   5.9s\n",
      "[CV 2/5; 41/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   5.8s\n",
      "[CV 3/5; 41/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.570 total time=   6.3s\n",
      "[CV 4/5; 41/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   6.1s\n",
      "[CV 5/5; 41/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.2s\n",
      "[CV 1/5; 42/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.2s\n",
      "[CV 2/5; 42/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.9s\n",
      "[CV 3/5; 42/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.4s\n",
      "[CV 4/5; 42/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.2s\n",
      "[CV 5/5; 42/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   6.3s\n",
      "[CV 1/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   6.6s\n",
      "[CV 2/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.2s\n",
      "[CV 3/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   6.5s\n",
      "[CV 4/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   6.3s\n",
      "[CV 5/5; 43/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.5s\n",
      "[CV 1/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.4s\n",
      "[CV 2/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.6s\n",
      "[CV 3/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   6.7s\n",
      "[CV 4/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   6.9s\n",
      "[CV 5/5; 44/48] START classifier__C=10.0, classifier__max_iter=500, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   7.0s\n",
      "[CV 1/5; 45/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   6.1s\n",
      "[CV 2/5; 45/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   6.0s\n",
      "[CV 3/5; 45/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.570 total time=   6.5s\n",
      "[CV 4/5; 45/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   6.0s\n",
      "[CV 5/5; 45/48] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.3s\n",
      "[CV 1/5; 46/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.3s\n",
      "[CV 2/5; 46/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   5.9s\n",
      "[CV 3/5; 46/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.2s\n",
      "[CV 4/5; 46/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   6.4s\n",
      "[CV 5/5; 46/48] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   6.3s\n",
      "[CV 1/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   6.5s\n",
      "[CV 2/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=   6.2s\n",
      "[CV 3/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=   6.5s\n",
      "[CV 4/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=   6.3s\n",
      "[CV 5/5; 47/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.8s\n",
      "[CV 1/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=   6.3s\n",
      "[CV 2/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   6.7s\n",
      "[CV 3/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   6.7s\n",
      "[CV 4/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   6.9s\n",
      "[CV 5/5; 48/48] START classifier__C=10.0, classifier__max_iter=1000, classifier__solver=liblinear, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   6.9s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.601284 using {'classifier__C': 1.0, 'classifier__max_iter': 100, 'classifier__solver': 'liblinear', 'transformer__vect_reviewText__max_features': 15000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 35000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.6002048937885699\n",
      "accuracy:  0.6026036313806098\n",
      "auc score for Y = 1:  0.9237604396922622\n",
      "auc score for Y = 2:  0.8280107229725643\n",
      "auc score for Y = 3:  0.8497265400115142\n",
      "auc score for Y = 4:  0.8711170765940917\n",
      "auc score for Y = 5:  0.9418583723988321\n",
      "auc score average:  0.8887286294881288\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",
    "log_params = {\n",
    "    \"transformer__vect_summary__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_reviewText__ngram_range\": [(1, 2)],\n",
    "    \"transformer__vect_summary__max_features\": [15000, 35000],\n",
    "    \"transformer__vect_reviewText__max_features\": [10000, 15000],\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, log_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",
    "accu = accuracy_score(y_test, y_pred_log)\n",
    "accuracy_arr.append(accu)\n",
    "print(\"accuracy: \", accu)\n",
    "\n",
    "# ROC curve & AUC Score\n",
    "y_pred_prob_log = grid_log.predict_proba(X_test)\n",
    "label_binarizer = LabelBinarizer().fit(y_train)\n",
    "y_onehot_test = label_binarizer.transform(y_test)\n",
    "\n",
    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
    "for i in range(5):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_onehot_test[:, i], y_pred_prob_log[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    print(f\"auc score for Y = {i+1}: \", roc_auc[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(\n",
    "    y_onehot_test.ravel(), y_pred_prob_log.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "print(\"auc score average: \", roc_auc[\"micro\"])\n",
    "auc_arr.append(roc_auc[\"micro\"])\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_log)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted 1', 'Predicted 2',\n",
    "                         'Predicted 3', 'Predicted 4', 'Predicted 5'],\n",
    "            yticklabels=['Actual 1', 'Actual 2', 'Actual 3', 'Actual 4', 'Actual 5'])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the ROC curve for each class\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr[0], tpr[0], color='orange', lw=lw,\n",
    "         label='Y Label 1 (area = %0.2f)' % roc_auc[0])\n",
    "plt.plot(fpr[1], tpr[1], color='green', lw=lw,\n",
    "         label='Y Label 2 (area = %0.2f)' % roc_auc[1])\n",
    "plt.plot(fpr[2], tpr[2], color='blue', lw=lw,\n",
    "         label='Y Label 3 (area = %0.2f)' % roc_auc[2])\n",
    "plt.plot(fpr[3], tpr[3], color='cyan', lw=lw,\n",
    "         label='Y Label 4 (area = %0.2f)' % roc_auc[3])\n",
    "plt.plot(fpr[4], tpr[4], color='purple', lw=lw,\n",
    "         label='Y Label 5 (area = %0.2f)' % roc_auc[4])\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], color='black', lw=lw, label='Average')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Logistic Regression\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 1/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 1/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 1/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 1/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 1/5; 2/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 2/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 2/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 2/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 2/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 3/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 2/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 4/32] START classifier__estimator__C=10, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 5/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=  33.8s\n",
      "[CV 2/5; 5/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=  33.1s\n",
      "[CV 3/5; 5/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.553 total time=  33.1s\n",
      "[CV 4/5; 5/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=  33.4s\n",
      "[CV 5/5; 5/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.560 total time=  33.4s\n",
      "[CV 1/5; 6/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=  36.4s\n",
      "[CV 2/5; 6/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.561 total time=  36.4s\n",
      "[CV 3/5; 6/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.559 total time=  36.4s\n",
      "[CV 4/5; 6/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.568 total time=  36.9s\n",
      "[CV 5/5; 6/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.567 total time=  36.6s\n",
      "[CV 1/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.560 total time=  36.2s\n",
      "[CV 2/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.555 total time=  36.3s\n",
      "[CV 3/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.556 total time=  36.1s\n",
      "[CV 4/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=  37.4s\n",
      "[CV 5/5; 7/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.566 total time=  36.6s\n",
      "[CV 1/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.567 total time=  39.9s\n",
      "[CV 2/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.563 total time=  39.8s\n",
      "[CV 3/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.562 total time=  39.9s\n",
      "[CV 4/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=  39.8s\n",
      "[CV 5/5; 8/32] START classifier__estimator__C=10, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.565 total time=  40.5s\n",
      "[CV 1/5; 9/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 9/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 9/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 9/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 9/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 10/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 10/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 10/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 10/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 5/5; 10/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 5/5; 11/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 5/5; 12/32] START classifier__estimator__C=1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 1/5; 13/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=  23.6s\n",
      "[CV 2/5; 13/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.572 total time=  22.7s\n",
      "[CV 3/5; 13/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=  22.3s\n",
      "[CV 4/5; 13/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=  22.6s\n",
      "[CV 5/5; 13/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=  23.3s\n",
      "[CV 1/5; 14/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=  22.6s\n",
      "[CV 2/5; 14/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=  22.4s\n",
      "[CV 3/5; 14/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.573 total time=  23.5s\n",
      "[CV 4/5; 14/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=  23.0s\n",
      "[CV 5/5; 14/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.581 total time=  23.5s\n",
      "[CV 1/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.572 total time=  23.6s\n",
      "[CV 2/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=  24.2s\n",
      "[CV 3/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.571 total time=  24.1s\n",
      "[CV 4/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=  22.4s\n",
      "[CV 5/5; 15/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=  24.3s\n",
      "[CV 1/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.577 total time=  22.8s\n",
      "[CV 2/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=  24.0s\n",
      "[CV 3/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.574 total time=  23.9s\n",
      "[CV 4/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=  23.2s\n",
      "[CV 5/5; 16/32] START classifier__estimator__C=1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=  24.0s\n",
      "[CV 1/5; 17/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 17/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 17/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 17/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 18/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 18/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 18/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 18/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 5/5; 18/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 19/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 20/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 21/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   5.9s\n",
      "[CV 2/5; 21/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   5.9s\n",
      "[CV 3/5; 21/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   6.0s\n",
      "[CV 4/5; 21/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.601 total time=   6.0s\n",
      "[CV 5/5; 21/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.607 total time=   6.0s\n",
      "[CV 1/5; 22/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   6.1s\n",
      "[CV 2/5; 22/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   6.1s\n",
      "[CV 3/5; 22/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   6.2s\n",
      "[CV 4/5; 22/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   6.3s\n",
      "[CV 5/5; 22/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   6.2s\n",
      "[CV 1/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   6.2s\n",
      "[CV 2/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   6.2s\n",
      "[CV 3/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   6.2s\n",
      "[CV 4/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.602 total time=   6.4s\n",
      "[CV 5/5; 23/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.606 total time=   6.3s\n",
      "[CV 1/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   6.5s\n",
      "[CV 2/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.599 total time=   6.4s\n",
      "[CV 3/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.603 total time=   6.5s\n",
      "[CV 4/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.604 total time=   6.5s\n",
      "[CV 5/5; 24/32] START classifier__estimator__C=0.1, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.605 total time=   6.5s\n",
      "[CV 1/5; 25/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 25/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 25/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 25/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 26/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 26/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 26/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 5/5; 26/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 27/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 2/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 3/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 4/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   2.0s\n",
      "[CV 5/5; 28/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l1, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=nan total time=   1.9s\n",
      "[CV 1/5; 29/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   3.2s\n",
      "[CV 2/5; 29/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   3.2s\n",
      "[CV 3/5; 29/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   3.2s\n",
      "[CV 4/5; 29/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   3.2s\n",
      "[CV 5/5; 29/32] 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=15000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   3.2s\n",
      "[CV 1/5; 30/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 total time=   3.3s\n",
      "[CV 2/5; 30/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   3.3s\n",
      "[CV 3/5; 30/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   3.3s\n",
      "[CV 4/5; 30/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   3.3s\n",
      "[CV 5/5; 30/32] 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=35000, 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=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   3.3s\n",
      "[CV 1/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   3.3s\n",
      "[CV 2/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   3.3s\n",
      "[CV 3/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   3.3s\n",
      "[CV 4/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 total time=   3.4s\n",
      "[CV 5/5; 31/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=15000, transformer__vect_summary__ngram_range=(1, 2);, score=0.591 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   3.4s\n",
      "[CV 2/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   3.4s\n",
      "[CV 3/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   3.4s\n",
      "[CV 4/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   3.4s\n",
      "[CV 5/5; 32/32] START classifier__estimator__C=0.01, classifier__estimator__penalty=l2, transformer__vect_reviewText__max_features=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, 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=15000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=35000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   3.4s\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 0.5586962  0.56344617\n",
      " 0.56040939 0.56520698        nan        nan        nan        nan\n",
      " 0.57485203 0.57836479 0.57533364 0.58028615        nan        nan\n",
      "        nan        nan 0.59948337 0.59999152 0.59917229 0.60028265\n",
      "        nan        nan        nan        nan 0.58599674 0.58688795\n",
      " 0.58515266 0.58550556]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.600283 using {'classifier__estimator__C': 0.1, 'classifier__estimator__penalty': 'l2', 'transformer__vect_reviewText__max_features': 15000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 35000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.6029277215858294\n",
      "accuracy:  0.606200753682768\n",
      "auc score for Y = 1:  0.9238213333255312\n",
      "auc score for Y = 2:  0.8261164810646304\n",
      "auc score for Y = 3:  0.8496069704618927\n",
      "auc score for Y = 4:  0.8677330477919823\n",
      "auc score for Y = 5:  0.9407421268470985\n",
      "auc score average:  0.887788543207425\n"
     ]
    },
    {
     "data": {
      "image/png": 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q07nnANyLe6rr7P9lC0cO/sqta1dIepLIsh8PYGVto9HOtnXfcvr4YW7f+JtChUxYvqXgPdVz2eIFLF+ySKOshKcXP2z7mfB793gvoHG2500NmsW7TZrlRoh52g8b1rN543rCw+8BULJ0aXr37U+duvUASElJYdY3X/Prrp9JTU3Dr3ZtvhgznsJFihgy7DxhxbIl7N+7h5s3b2Bmbk7VqtUYMmwEnl4l1XVSUlKYGTSd3bt+ITU1lXdq1+HLsQXk/knPgW5MTEwwMso8zdnZmbCwMADs7Oy4c+eOfqPLIcM/9qfXe7UYOvMnqnaaxZhFuxjWxZ9PO7yjUe/X0Ct4BnylfgWO25Bte1P7tyAiJi43Qs8TLp39k8atOjBpzreMnraA9PSnTP9iIMnJSeo6qcnJVKnhR5tO3V7YztOnadSs14hGAe1zIeq8q2Sp0vyy75D6tXTldwC4uLpqlP+y7xC9+g3A0tISvzr//ZTUgsLF1YWBQ4fz/Q8/8v3Gzbz9di2GDuzP9WtXAZjx9TRCfjtI0Ky5LF+1hujoKIYPGWjgqPOGkyeO80HnLqxd/wNLlq3k6dOn9O3VkydPnqjrfPP1VA79dpBvZs3h29VriY6OYtjgAQaM+s2Xnp7O2LFj8fLywsLCglKlSjF58mQURVHXURSFcePG4ebmhoWFBY0aNeLq1asa7cTGxtKlSxdsbW2xt7enZ8+eJCQkaB2Hzj0H1apV48SJE3h7e+Pv78+4ceOIiYlh7dq1VKxYUdfmDKJWpRLs/P0iu49cASAs8iEdG1elhk9xjXqpqU+5H/vfN7NJrTK8W9ObzqO/o9k75XIs5rzk86nzNd73HT6evh804ebVS5SvVB2A5u0+BODiX6de2M77XfsAcGjPjhyKNH8wNjamcBEnrcoPHdjHu02aYWlplVvh5Wn+9RtqvB8weCibNm7g7F9/4eziyrYtPzI16BverlkLgImTp9GudQvO/nWGylWqGiDivGPx0hUa7ydNmU6Dun5cungB3xpvER8fz9Yff2R60Axq1vLLrPPVVNq2KiD3z0DDAV9//TWLFy9m9erVVKhQgZMnT9K9e3fs7OzUc/qCgoKYN28eq1evxsvLi7Fjx9K0aVMuXryIubk5AF26dCEiIoK9e/eSlpZG9+7d6d27N+vWrdMqDp17DqZOnYqbmxsAU6ZMwcHBgX79+hEdHc3SpUt1bc4gjp67TYMapSldPLNrrFJpN/yqlGBP6BWNenWrl+T2z2P4a8Nw5o5si6OtpcZxZwdrFo1uT8+JG3mSnJZr8ec1TxIzEyhrG1sDR5I/3QkLI6CxP+8FNGHc6JFERoRnW+/SxQv8feUyrdsW7J6WF0lPT2f3Lz+TlPSEylWrcuniBZ4+TaNWrX96BL1KlsTVzZ2zf50xXKB5VEJ8PAC2dnYAXLxwPrN3z+/f968Ubm7u/HXmjCFCzF0qI/29dHDkyBHatGlDQEAAnp6evP/++zRp0oTjx48Dmb0Gc+bMYcyYMbRp04bKlSuzZs0awsPD2bZtGwCXLl1i9+7dLF++nJo1a1KnTh3mz5/Phg0bCA/P/ufL83TuOahRo4b6a2dnZ3bv3q1rEwY3Y80hbC3N+WvDMNIzFIyNVIxfsocNe86o6+w9eoWffjvPrYhYShYtzMS+Tflpdnf8ey0iIyOze2fp2A4s23qMPy/fw8PVwUBXY1gZGRmsDZ5FmQpVKO5Z2tDh5DsVKlVm3KQpeHh68SAmmuXBi+jT42PWbd6OlZVm78COrT/iWbIklatWM1C0edPVv68Q2KUzqakpWFhaMnPuAkqVKs3fly9hYmKCja1m0lq4cGEexMQYKNq8KSMjg6Cvp1K1WnW8vcsA8CAmBhMTE2yfu3+OhQsTExNtiDDzrZSUFFJSUjTKzMzMMDMzy1L3nXfeYenSpfz999+UKVOGv/76i8OHDzNr1iwAbt68SWRkJI0aNVKfY2dnR82aNQkNDaVTp06EhoZib2+v8fu6UaNGGBkZcezYMd57772XxmyQTZCyu1FKxlNURrkTzvvvVqJT06p0G7+BizfvU9nbnW+GtCQiJo7vf/kTgE37zqrrX7h+n3PXIrn042fUq16S305e59MO72BjacY3awreJLp/W7kgiDu3rzN+5jJDh5IvvVOnnvpr7zJlqVCxMm1aNGL/nt20fu+fHoLk5GR+3fUzPXr3NUSYeZqnlxcbftxKQnw8+/b8yrgvP2f5qrWGDitfmfrVRK5fvcqqtdp1ORcIehxWmDZtGhMnTtQoGz9+PBMmTMhS9/PPPycuLo5y5cphbGxMeno6U6ZMoUuXLgBERkYC4OLionGei4uL+lhkZCTOzs4axwsVKoSjo6O6zsvo/NvYy8sL1X/ctBs3bry0jexulHHR2pgUr6NrOK9k6oAWzFj7mzoBuHD9Ph6uDozsWl+dHDzvVngs0Q8TKFWsML+dvE5931LUrOjB40NfadT749sBbNhzhl6TN+X0ZRjcygVBnD72O+NmLqWwk8vLTxAvZWNri4eHJ3fu3NYoP7BvD8nJSbRo2cZAkeVdJiameHiUAMCnQkUuXDjP+u/W0KRZC9LS0oiPi9PoPXjw4EHBmG2vpalfTSLk0G98u/o7XFxd1eWFixQhLS2NuLg4jd6D2AcPKJLNHJk3jh5XK4wePZphw4ZplGXXawDwww8/8P3337Nu3ToqVKjAmTNnGDJkCO7u7gQGBuotppfROTkYMmSIxvu0tDROnz7N7t27GTlypFZtZHejnBtP0jWUV2ZhbqIeGngmPSMDo/9Ieoo62VLYzpLImMxxueGztzNh6R71cbcituyc25OPx67nxIWwnAk8j1AUhVULv+Hkkd8Y800wzq5FDR3SG+PJk0Tu3Q2jeZFWGuU7tv5I3foNcXB0NFBk+YeSkUFqairlfSpQqJAJx46F0qhxUwBu3bxBZET4mz+ZTguKojBtymQO7N/LilVrKVZMc0K2T4WKFCpkwvGjoTRq8s/9i4gIp0rVqgaIOP960RBCdkaOHMnnn39Op06dAKhUqRK3b99m2rRpBAYG4vr/BO7+/fvq+X/P3lf9/38XV1dXoqKiNNp9+vQpsbGx6vNfRufkYPDgwdmWL1y4kJMnT2rVRnY3KreGFAB+OXyZUd0acuf+Iy7eiKJqWXcGdarDmp2Z8VtZmPJlz3fZdvA8kQ8SKFnMkSn9m3P97gP2HvsbgDv3HwOP1W0mPMncRvrGvQfci36zlzWuXPA1Rw7+yvAJM7CwsORRbOb4raWVNaZmmTNlH8XG8OjhA+6HZy5vvXPzGuaWlhRxcsXaNnPCU0xUJAnxj3kQFUlGRga3rmdOCHV1L465hWU2n/zmmTsriLr1GuDq5k5MdBTLFi/AyNiYJs0C1HXuhN3m9J8nmb0g2ICR5k3zZs+kdt16uLm5kZiYyK6fd3LyxHEWLVmOjY0Nbdu1Z2bQ19jZ2WFlZc3XU7+icpWqkhwAUydPZNcvO5kzfxFWllbERGfOI7C2scHc3BwbGxvea9+eGUHTsbWzw9ramulTv6JK1WoF4/4ZaJ+DJ0+eqLcLeMbY2Fj9AEQvLy9cXV3Zv3+/OhmIi4vj2LFj9OvXDwA/Pz8ePXrEqVOn8PXN3K/nwIEDZGRkULNmTa3i0Ntv5ObNmzN69GhWrsy6SVBeM2zWT4zv3YS5I9ri5GhNRHQcK7YdZ+q3+4HMXoSKpdzo0twXextzImLi2XfsbyYt3UtqmjySet/OHwGYPFJz/LvP8HH4N8n8i3ffz1vY8t0/8xAmjeidpc7mNcGE7P1ZXeeLTz8CYExQMD5VNDegelNF3b/P2NEjePzoEfYOjlSpVp0Va9Zr9BDs2LYFZxcXavrVNmCkeVNsbCxjvxhFTHQ01jY2eJcpy6Ily6n1Tua9GjFqNEZGRowYMpjUtFTeeacOo8eOM3DUecMPG9cD0LPbxxrlk76aRpv3MjezGznqC4xURgwfMijz/tWuw5djxud6rAZhoKWMrVq1YsqUKXh4eFChQgVOnz7NrFmz6NGjx//DUjFkyBC++uorvL291UsZ3d3dadu2LQDly5enWbNm9OrVi+DgYNLS0hgwYACdOnXC3d1dqzhUyr93VngNQUFBLFq0iFu3br3S+RZ+n+sjjALp8PovDB1CvlXKRfYLeB2mhQr2LnKv47+GMcXLmedwZ7NF68V6aytpez+t68bHxzN27Fi2bt1KVFQU7u7udO7cmXHjxmFqagpkDgmNHz+epUuX8ujRI+rUqcOiRYsoU6aMup3Y2FgGDBjAjh07MDIyon379sybNw9ra2ut4tA5OahWrZrGhERFUYiMjCQ6OppFixbRu3dvXZpTk+Tg1Uly8OokOXg9khy8OkkOXk+OJwdtluitraSf+uitrdyi8+1t06aNRnJgZGSEk5MT9evXp1y5grFDoBBCiDdcAU/edE4OsluXKYQQQrxR5MFLujE2Ns6yRAIy1w4bGxvrJSghhBBCGI7OPQcvmqKQkpKiniwhhBBC5GsyrKCdefPmAZnLKJYvX64x4zE9PZ2QkBCZcyCEEOKN8F87ARcEWicHs2fPBjJ7DoKDgzWGEExNTfH09CQ4WDZpEUIIIfI7rZODmzdvAtCgQQO2bNmCg0PBfAqhEEKIN5/0HOjo4MGC/RRCIYQQBUDBzg10X63Qvn17vv766yzlQUFBdOjQQS9BCSGEEMJwdE4OQkJCaNGiRZby5s2bExISopeghBBCCENSqVR6e+VHOg8rJCQkZLtk0cTEhLi4N/tphEIIIQqG/PpLXV907jmoVKkSGzduzFK+YcMGfHx89BKUEEIIIQxH556DsWPH0q5dO65fv07Dhg0B2L9/P+vXr2fTpk16D1AIIYTIbQW950Dn5KBVq1Zs27aNqVOnsnnzZiwsLKhcuTL79u3D398/J2IUQgghcpUkB68gICCAgICALOXnz5+nYsWKrx2UEEIIYVAFOzfQfc7B8+Lj41m6dClvv/02VapU0UdMQgghhDCgV04OQkJC6Nq1K25ubsyYMYOGDRty9OhRfcYmhBBCGIQsZdRBZGQkq1atYsWKFcTFxdGxY0dSUlLYtm2brFQQQgjxxsivv9T1Reueg1atWlG2bFnOnj3LnDlzCA8PZ/78+TkZmxBCCCEMQOueg127djFo0CD69euHt7d3TsYkhBBCGJT0HGjp8OHDxMfH4+vrS82aNVmwYAExMTE5GZsQQghhEAV9zoHWyUGtWrVYtmwZERER9OnThw0bNuDu7k5GRgZ79+4lPj4+J+MUQgghRC7RebWClZUVPXr04PDhw5w7d47hw4czffp0nJ2dad26dU7EKIQQQuQulR5f+dBr7XNQtmxZgoKCuHv3LuvXr9dXTEIIIYRBybCCHhgbG9O2bVu2b9+uj+aEEEIIYUCvtH2yEEII8SbLr3/x64skB0IIIcRzJDkQQgghhKaCnRvoZ86BEEIIId4c0nMghBBCPEeGFYQQQgihQZKDPGLJ/P6GDiHfGrn9gqFDyLe+aV3B0CHkayWKWBo6hHzL0tTY0CHkcwX7l3dOkzkHQgghxHMMtQmSp6dntm3075/5B3RycjL9+/encOHCWFtb0759e+7fv6/RRlhYGAEBAVhaWuLs7MzIkSN5+vSpTnHkmZ4DIYQQIq8w1LDCiRMnSE9PV78/f/48jRs3pkOHDgAMHTqUn3/+mU2bNmFnZ8eAAQNo164df/zxBwDp6ekEBATg6urKkSNHiIiIoGvXrpiYmDB16lSt45DkQAghhMhBKSkppKSkaJSZmZlhZmaWpa6Tk5PG++nTp1OqVCn8/f15/PgxK1asYN26dTRs2BCAlStXUr58eY4ePUqtWrXYs2cPFy9eZN++fbi4uFC1alUmT57MqFGjmDBhAqamplrFLMMKQgghxPP0+OCladOmYWdnp/GaNm3aS0NITU3lu+++o0ePHqhUKk6dOkVaWhqNGjVS1ylXrhweHh6EhoYCEBoaSqVKlXBxcVHXadq0KXFxcVy4oP38NOk5EEIIIZ6jz2GF0aNHM2zYMI2y7HoNnrdt2zYePXpEt27dAIiMjMTU1BR7e3uNei4uLkRGRqrr/DsxeHb82TFtSXIghBBC5KAXDSG8zIoVK2jevDnu7u45ENV/k2EFIYQQ4jmGfmTz7du32bdvH5988om6zNXVldTUVB49eqRR9/79+7i6uqrrPL964dn7Z3W0IcmBEEII8RxDJwcrV67E2dmZgIAAdZmvry8mJibs379fXXblyhXCwsLw8/MDwM/Pj3PnzhEVFaWus3fvXmxtbfHx8dH682VYQQghhHieAfdYysjIYOXKlQQGBlKo0D+/pu3s7OjZsyfDhg3D0dERW1tbBg4ciJ+fH7Vq1QKgSZMm+Pj48PHHHxMUFERkZCRjxoyhf//+Og1tSHIghBBC5CH79u0jLCyMHj16ZDk2e/ZsjIyMaN++PSkpKTRt2pRFixapjxsbG7Nz50769euHn58fVlZWBAYGMmnSJJ1iUCmKomhbOSkpiVOnTuHo6JileyI5OZkffviBrl276hTAM2tO3nml8wSsOnLX0CHkW7J98uuR7ZNfnWyf/HosTXP2T3uPgdv11lbY/NZ6ayu3aD3n4O+//6Z8+fLUq1ePSpUq4e/vT0REhPr448eP6d69e44EKYQQQuQmQ885MDStk4NRo0ZRsWJFoqKiuHLlCjY2NtSuXZuwsLCcjE8IIYQQuUzrOQdHjhxh3759FClShCJFirBjxw4+/fRT6taty8GDB7GyssrJOIUQQohck1//4tcXrXsOkpKSNGZNqlQqFi9eTKtWrfD39+fvv//OkQCFEEKI3FbQhxW07jkoV64cJ0+epHz58hrlCxYsAKB16/w34UIIIYQQWWndc/Dee++xfv36bI8tWLCAzp07o8PCByGEECLv0uODl/IjrZOD0aNH88svv7zw+KJFi8jIyNBLUEIIIYQhFfRhBdk+WQghhBAaZIdEIYQQ4jn59S9+fZHkQAghhHhOAc8NJDkQQgghnlfQew5kzoEQQgghNGjVc7B9u/YPoJD9DoQQQuR3BbzjQLvkoG3btlo1plKpSE9Pf514hBBCCIMr6MMKWiUHsn+BEEIIUXDIhEQhhBDiOQW84+DVkoPExEQOHTpEWFgYqampGscGDRqkl8CEEEIIQzEyKtjZgc7JwenTp2nRogVPnjwhMTERR0dHYmJisLS0xNnZWZIDIYQQIp/TeSnj0KFDadWqFQ8fPsTCwoKjR49y+/ZtfH19mTFjRk7EKIQQQuQqlUp/r/xI556DM2fOsGTJEoyMjDA2NiYlJYWSJUsSFBREYGAg7dq1y4k49erUvu38uW8Hj6LvA+BUrAR13vuY0lXfBuCXFbO5ef5PEh4+wNTcgqLePjTs3Isi7h7qNqZ0aZSl3bYDvqSCX4PcuQgDCqxZjMCaxTXKwmKT6PbdGfV7H1drevp5UM7VmgxF4Xr0Ez7bdonU9MzJrTZmhRjo74lfSQcUBUKuxbIg5CbJaW/25NefNqzkxB8HCb9zG1NTM7x9KtO55wDci3uq6+z/ZQtHDv7KrWtXSHqSyLIfD2BlbaM+Hh0ZztZ1K7hw5iSPHj7AoXAR6jRsTtvOPShkYmKAqzKc9i0bExkRnqW8XYdO9Oo3kOVLFnL86BHuR0bgYO9A3frv0qvfQKxtbLJprWD5YeN6Nm9cT3j4PQBKlipN7779qVO3Ho8fP2LxwvkcDf2DyIgIHBwcqd/wXT4dMBibAnLvZLWCjkxMTDAyyuxwcHZ2JiwsjPLly2NnZ8edO3f0HmBOsHF0okGnT3B0LYqiwNnf97Bp1jg+mRqMUzFPXL28qfjOu9gWcSYpIZ7ft6xh/fRR9J/zHUZGxup2WvYeSakqb6nfm1taG+JyDOLmgyeM2HpR/T4945/Hdfu4WjO9TXnWn7zH/EM3Sc9QKOlkhcI/db5oWprCVqaM3HqJQkYqPmtciuENSzHl16u5eh257dLZP2ncqgOlyviQnp7OxlWLmP7FQIKW/YC5uQUAqcnJVKnhR5Uafmz4dmGWNsLv3CIjI4Oeg0fj4l6Mu7eus2zOVFKSk+jSe0guX5FhLV+7kYx/LZ++cf0aQz79hAaNmhITHU1MdBQDhozA06sU9yPC+WbaJGJiopgSNMdwQecRLi4uDBwyHI8SJUBR2LF9G0MH9WfDpi0oikJ0dBRDh39GyVKliQgPZ8rk8URHRzFj1jxDhy5ygc7JQbVq1Thx4gTe3t74+/szbtw4YmJiWLt2LRUrVsyJGPWuTHU/jfcNOvbgz307uHftEk7FPKnesKX6mL2TK/4durN8dG8eR9/HwcVdfczcyhpre8dcizsvSc9QePgkLdtjn9bzZOtfkaw/9c9fdHceJau/9nCwoKanA303nOXvqEQA5h+6xbTW5Qg+fIsHidm3+yb4fOp8jfd9h4+n7wdNuHn1EuUrVQegebsPAbj416ls26jy1jtUeesd9XsXt2KE3w1j387NBS45cHDQ/Pe3dtVyihYrTjXft1CpVEz9Zq76WLHiHvT+dDCTxo7i6dOnFCpUsBdr+ddvqPF+wKChbNq4gbNn/+K9du8zc/Y/36vFi3swYOBQvhw9ssDcuwLecaB7cjB16lTi4+MBmDJlCl27dqVfv354e3vz7bff6j3AnJaRkc6lYyGkpSRTtLRPluOpyUmcPbQbeydXbAs7aRzbvWoePy+bib2zG9XfbUkV/2YFpiuqqL05P/TwJTU9g4sR8Sw/EkZUQir2FoXwcbVh/+UY5neoiJudGXceJrPiSBjnIzK/b3zcrIlPfqpODABOhT1CUaC8iw2Hb8Qa6rJy3ZPEBACsbWxfq52kxASsbez0EVK+lZaWyp5fdvLBR4Ev/HeYkBCPlZV1gfjlpov09HT27tlNUtITKlepmm2d+IR4rKwLzr0rKD/LX0Tn/8o1atRQf+3s7Mzu3bv1GlBuiQq7waoJg3ialoqpuQXvD52AU7ES6uMn9/7EgfXLSEtJprBbcT4cHYRxoX/Gc+u93w1Pn6qYmJlx49wpdq+aR1pyMm81e88Ql5OrLkUmELT3GnceJuNoZUJgzeLMfb8iPb4/g5udOQBdaxZjyeHbXItJpEk5J2a086Hnd39x73EyjpamPErS7B3IUCAu+SmOVgVnzDwjI4O1wbMoU6EKxT1Lv3I7kffu8OtPG+nSa7Aeo8t/Qg4eICEhnhat2mZ7/NHDh6xaHkzrdh1yN7A87OrfVwj8qDOpqSlYWFoyc84CSpXK+r348OFDli1ZTPv3OxogSsOQ5MAAUlJSSElJ0ShLS03BxNQs12Io7F6cT6YuISUpkcvHQtgRHMRHY2apE4SKtd+lZCVfEh7GcvSXTWyZN5nA8XMpZGoKQN33PlK35erpTVpKMqE//1AgkoPjtx+pv77xIDNZWN+9OvW9ixAW+wSAnefvs/tSNADXom9TrbgdzSs4s/xImCFCzpNWLgjizu3rjJ+57JXbiI2J4usvB1GzXiMatnjzv/f+y86ffqTWO3VwcnLOciwxIYGRg/vhVbIUPXt/aoDo8iZPLy82bN5KQnw8+/b+yrgxn7N85VqNBCEhIYFB/ftQsmQp+vQbYMBoRW7SeSmjl5cXJUuWfOFLG9OmTcPOzk7jtXNV1olXOcm4kAmOrkVx8ypDg06f4OxRkhO/blEfN7e0xtG1GB7lK9N+8DgeRNzhysnDL2zPvVQ54mOjeZqW+sI6b6rE1HTuPkqmqL05D/4/D+F2bJJGnbDYJJytMxOr2Cep2Fto9hAYqcDWvBCxb/B8g39buSCI08d+Z0zQYgo7ubxSGw8fRPPVZ/3w9qnMJ4O/0HOE+UtkRDgnjx+lVdv3sxxLTExk2MA+WFpZMXXGvAK3ouO/mJiY4uFRAp8KFRk0ZDhlypRj/Xdr1McTExPo3/cTLC2tmDV3ASYF6N7JUkYdDRkyRON9Wloap0+fZvfu3YwcOVKrNkaPHs2wYcM0yjadj9I1FL1SFIX0tOx/MSmKgqIoPH3BcYD7t69jbmVDIRPTnAoxzzI3McLdzpy9l6OJjEshJiGV4g4WGnWKOVhw/NZDAC5GJGBjXghvJyuuRmfOO6he3A6VCi7dj8/1+HOToiisWvgNJ4/8xphvgnF2LfpK7cTGRPHVZ/3w8i5H3+Hj1CuICqqft2/FwcERvzr1NMoTExIYOqA3pqamfD1rAWZmudc7mR8pSoZ619uEhAQ+7dMTU1NT5sxfVODunQwr6Gjw4OzHNRcuXMjJkye1asPMzCzLN5qJ6WNdQ3llBzcsp1SVt7Et4kxq0hMuHDnA7Ut/0XnUdB5GhXMx9DdKVq6BpY0d8bExHNmxARNTU/U+CH//GUri44cULV2eQiam3Dx/iiPb11OzRcEYy+xbpwRHbj7kflwKRaxMCKxVnAxF4cDfMQBs/PMegTWLcz0mkWvRT2ha3gkPBwsm/nIFgLCHSRy79ZAR75Zk9sGbGBupGOjvxcG/H7zRKxUAVi74miMHf2X4hBlYWFjyKDbznllaWWNqljlf41FsDI8ePuB+eObS4Ds3r2FuaUkRJ1esbe2IjYli8si+FHF2pUuvwcQ9fqhu396xSO5flIFlZGTw8/atNG/ZRmOyXGJCAkP69yIlOZlxk6eTmJhA4v8ngNo7OGJsbPyiJguEeXNmUrtOPdzc3EhMTGTXLzs5eeI4i4KXqxOD5KQkpkz/RuPeOci9KxBUiqIoL6/2cjdu3KBq1arExcW90vlrTubeHgk7l87g1oXTJDyKxczSCufiXvi16kTJSr7EP4zh52WziLz5N0mJCVjZOeBRrhJ13/uYwu6ZG/9c/+s4Bzeu4OH9cBRFwcGlKL6NWlGtQQtUBvgLbtWRu7n6eWOaeVPZ3RZbi0I8TkrjXHg834aGEf74n3kknX3daVPZFRvzQtyIecKSw7fVqxUgcxOkQfW98PNyIENR+P1aLPMNsAnSN60r5Ornfdj0rWzL+wwfh3+TVgBsXruULd9lnYfwrM6hPTtYMnNStu2s+/WE/oLVQokilrn6edk5FvoHwwb0Zv2Wn/Eo4aku//PkcQb26Z7tOZt37MHN/dV6bfTF0tSwv2AnjPuS48dCiYmOxtrGBm/vsnTv8Qm13qnNyRPH6NUjMNvzft69D/eixXI52qwsTXP2L/vqkw7ora0/xzV8eaU8Rm/JQVBQEIsWLeLWrVuvdH5uJgdvmtxODt4kuZ0cvGnyQnKQXxk6Ocjvcjo58J18UG9tnRqb/3bOfaVNkP49FqMoCpGRkURHR7No0SK9BieEEEKI3KdzH3ibNm00Xu3atWP8+PGcP3+e3r1750SMQgghRK4y5GqFe/fu8dFHH1G4cGEsLCyoVKmSxpw+RVEYN24cbm5uWFhY0KhRI65e1dx6PjY2li5dumBra4u9vT09e/YkISFB6xh07jmYMGGCrqcIIYQQ+YqhVis8fPiQ2rVr06BBA3bt2oWTkxNXr17FwcFBXScoKIh58+axevVqvLy8GDt2LE2bNuXixYuYm2dObO7SpQsRERHs3buXtLQ0unfvTu/evVm3bp1WceicHBgbGxMREYGzs+ZGIw8ePMDZ2Zn0fz0ERQghhBDa+/rrrylevDgrV65Ul3l5eam/VhSFOXPmMGbMGNq0aQPAmjVrcHFxYdu2bXTq1IlLly6xe/duTpw4od7VeP78+bRo0YIZM2bg7u7Oy+g8rPCi+YspKSmYmha8Nf5CCCHePPocVkhJSSEuLk7j9fwuwc9s376dGjVq0KFDB5ydnalWrRrLlv2zeunmzZtERkbSqFEjdZmdnR01a9YkNDQUgNDQUOzt7TUed9CoUSOMjIw4duyYVtevdc/BvHnz/n/DVCxfvhxr638eT5yenk5ISAjlypXTtjkhhBAiz9LnsMK0adOYOHGiRtn48eOzHaa/ceMGixcvZtiwYXzxxRecOHGCQYMGYWpqSmBgIJGRkUDmI7f/zcXFRX0sMjIyS+9+oUKFcHR0VNd5Ga2Tg9mzZwOZPQfBwcEam2CYmpri6elJcHCwts0JIYQQeZY+pxxktyvwi3aczMjIoEaNGkydOhXIXCF4/vx5goODCQzMfu+JnKB1cnDz5k0AGjRowJYtWzQmRwghhBAie9ntCvwibm5u+Pj4aJSVL1+eH3/8EQBXV1cA7t+/j5ubm7rO/fv3qVq1qrpOVJTmIwmePn1KbGys+vyX0XnOwcGDByUxEEII8UZTqVR6e+midu3aXLlyRaPs77//pkSJzCcGe3l54erqyv79+9XH4+LiOHbsGH5+fgD4+fnx6NEjTp06pa5z4MABMjIyqFmzplZx6JwctG/fnq+//jpLeVBQEB06FIxnCwghhHizGWqfg6FDh3L06FGmTp3KtWvXWLduHUuXLqV///7/j0vFkCFD+Oqrr9i+fTvnzp2ja9euuLu707ZtWyCzp6FZs2b06tWL48eP88cffzBgwAA6deqk1UoFeIXkICQkhBYtWmQpb968OSEhIbo2J4QQQoj/e+utt9i6dSvr16+nYsWKTJ48mTlz5tClSxd1nc8++4yBAwfSu3dv3nrrLRISEti9e7d6jwOA77//nnLlyvHuu+/SokUL6tSpw9KlS7WOQ+d9DhISErJdsmhiYvLKD10SQggh8hJDPrK5ZcuWtGzZ8oXHVSoVkyZNYtKk7B/ABuDo6Kj1hkfZ0bnnoFKlSmzcuDFL+YYNG7JMohBCCCHyI0Nun5wX6NxzMHbsWNq1a8f169dp2DDzMZT79+9n/fr1bNq0Se8BCiGEECJ36ZwctGrVim3btjF16lQ2b96MhYUFlStXZt++ffj7++dEjEIIIUSuMuSwQl6gc3IAEBAQQEBAQJby8+fPU7FixdcOSgghhDCkgp4c6Dzn4Hnx8fEsXbqUt99+mypVqugjJiGEEEIY0CsnByEhIXTt2hU3NzdmzJhBw4YNOXr0qD5jE0IIIQxCJiTqIDIyklWrVrFixQri4uLo2LEjKSkpbNu2TVYqCCGEeGPIsIKWWrVqRdmyZTl79ixz5swhPDyc+fPn52RsQgghhEFIz4GWdu3axaBBg+jXrx/e3t45GZMQQgghDEjrnoPDhw8THx+Pr68vNWvWZMGCBcTExORkbEIIIYRBGOrBS3mF1slBrVq1WLZsGREREfTp04cNGzbg7u5ORkYGe/fuJT4+PifjFEIIIXJNQR9W0Hm1gpWVFT169ODw4cOcO3eO4cOHM336dJydnWndunVOxCiEEEKIXPRa+xyULVuWoKAg7t69y/r16/UVkxBCCGFQRiqV3l750SvtkPg8Y2Nj2rZtq36WtBBCCJGf5dPf6Xrz2jskCiGEEOLNopeeAyGEEOJNkl9XGeiLJAdCCCHEc4wKdm4gyYEQQgjxvILecyBzDoQQQgihQXoOhBBCiOcU8I6DvJMcNC/rZugQ8q2KTnaGDiHf6rXmlKFDyNeWdfU1dAj5lpeTlaFDyNcsTY1ztH0VBTs7kGEFIYQQQmjIMz0HQgghRF4hqxWEEEIIoUFWKwghhBBC/Iv0HAghhBDPKeAdB5IcCCGEEM/Lr09T1BcZVhBCCCGEBuk5EEIIIZ5TwDsOJDkQQgghnlfQVytIciCEEEI8p4DnBrrNObh06RIrV67k8uXLAFy+fJl+/frRo0cPDhw4kCMBCiGEECJ3aZ0c7N69m6pVqzJixAiqVavG7t27qVevHteuXeP27ds0adJEEgQhhBBvBCOVSm8vXUyYMAGVSqXxKleunPp4cnIy/fv3p3DhwlhbW9O+fXvu37+v0UZYWBgBAQFYWlri7OzMyJEjefr0qW7Xr23FSZMmMXLkSB48eMDKlSv58MMP6dWrF3v37mX//v2MHDmS6dOn6/ThQgghRF6k0uNLVxUqVCAiIkL9Onz4sPrY0KFD2bFjB5s2beLQoUOEh4fTrl079fH09HQCAgJITU3lyJEjrF69mlWrVjFu3DidYtA6Obhw4QLdunUDoGPHjsTHx/P++++rj3fp0oWzZ8/q9OFCCCGE0FSoUCFcXV3VryJFigDw+PFjVqxYwaxZs2jYsCG+vr6sXLmSI0eOcPToUQD27NnDxYsX+e6776hatSrNmzdn8uTJLFy4kNTUVK1j0GnOwbPZm0ZGRpibm2Nn98+jgm1sbHj8+LEuzQkhhBB50vNd+6/zSklJIS4uTuOVkpLyws++evUq7u7ulCxZki5duhAWFgbAqVOnSEtLo1GjRuq65cqVw8PDg9DQUABCQ0OpVKkSLi4u6jpNmzYlLi6OCxcuaH39WicHnp6eXL16Vf0+NDQUDw8P9fuwsDDc3Ny0/mAhhBAirzJS6e81bdo07OzsNF7Tpk3L9nNr1qzJqlWr2L17N4sXL+bmzZvUrVuX+Ph4IiMjMTU1xd7eXuMcFxcXIiMjAYiMjNRIDJ4df3ZMW1ovZezXrx/p6enq9xUrVtQ4vmvXLho2bKj1BwshhBAFwejRoxk2bJhGmZmZWbZ1mzdvrv66cuXK1KxZkxIlSvDDDz9gYWGRo3H+m9bJQd++ff/z+NSpU187GCGEECIv0OcmSGZmZi9MBl7G3t6eMmXKcO3aNRo3bkxqaiqPHj3S6D24f/8+rq6uALi6unL8+HGNNp6tZnhWRxvybAUhhBDiOSqV/l6vIyEhgevXr+Pm5oavry8mJibs379fffzKlSuEhYXh5+cHgJ+fH+fOnSMqKkpdZ+/evdja2uLj46P158oOiUIIIUQeMWLECFq1akWJEiUIDw9n/PjxGBsb07lzZ+zs7OjZsyfDhg3D0dERW1tbBg4ciJ+fH7Vq1QKgSZMm+Pj48PHHHxMUFERkZCRjxoyhf//+OvVeSHIghBBCPMdQz1a4e/cunTt35sGDBzg5OVGnTh2OHj2Kk5MTALNnz8bIyIj27duTkpJC06ZNWbRokfp8Y2Njdu7cSb9+/fDz88PKyorAwEAmTZqkUxwqRVEUvV7ZK4qO1233JvGPO7FPDB1CvtVrzSlDh5CvLevqa+gQ8i0vJytDh5CvOVga52j73dbrb9+eVZ0r662t3CI9B0IIIcRz5KmMWti+fbvWDbZu3fqVgxFCCCGE4WmVHLRt21arxlQqlcZeCEIIIUR+VLD7DbRMDjIyMnI6DiGEECLP0PVpim8a2edACCGEEBpeaUJiYmIihw4dIiwsLMtTngYNGqSXwIQQQghDKeAdB7onB6dPn6ZFixY8efKExMREHB0diYmJwdLSEmdnZ0kOhBBC5HsFfbWCzsMKQ4cOpVWrVjx8+BALCwuOHj3K7du38fX1ZcaMGTkRoxBCCCFykc49B2fOnGHJkiUYGRlhbGxMSkoKJUuWJCgoiMDAQNq1a5cTcea491s1JjIiPEv5ex06MXzUWO7dDWPBnBmcO/MnqWmp1PSrw9CRX+BYuIgBojWsbetXcuKPg4TfuY2pqRllfCrT+ZMBuBf3VNdJTU3huyVzCP1tL2lpqVSpUYvuA0dh71A4S3vxcY/4vG8XYmOiWL7lAFbWNrl4NbnPycaUQe+W4p1ShTE3MeLuwyQmbL/MpYh4ABqUK8L71YtSzs0Ge0sTOi89wd/3EzTaeK+aG80qulDOzQZrs0L4B/1OQsqbvZGYfN/pX1TUfRbOnUnoH7+TkpxMseIejJkwhfIVMp+6qygKyxYv4Ketm0iIj6dSlWp89sU4PEp4GjbwXFDAOw507zkwMTHByCjzNGdnZ8LCwgCws7Pjzp07+o0uFy1bs5Gfdv+mfs1euByABu82JSnpCUP790alUjE3+FsWr/iOp2lpjBrav0Cu5Lh07k+atO7ApLnf8sX0BTxNf8q00QNJTkpS11kbPJs/j/7O4DHTGDdjCQ8fxDB74mfZtrd05ld4eJXOrfANysa8EN92q87TdIVB6/+iQ/BxZu+9RnxymrqOhYkxZ+48Zv7+6y9sx9zEmNDrsaw8fDs3ws4T5PtOv+LiHtO7WxcKFSrE7AVLWP/jDgYN+wwbW1t1nbWrVvDD+u8Y9cV4lq/ZgIWFBUP69yYlJcWAkecOI5VKb6/8SOeeg2rVqnHixAm8vb3x9/dn3LhxxMTEsHbtWipWrJgTMeYKBwdHjfffrV5O0WLFqeb7FieOHSEy4h4rv9+MlbU1AF9OnErzBn6cOnGMt2r6GSJkgxk9db7G+34jxtOnYxNuXr1E+crVeZKYwMHdPzHw86+oWO0tAPoMH8eITzpw9dI5vMtXUp+7d8dmEhPjadflE86cOJKr12EI3d7x4H5cChN3XFaXhT9K1qjzy7nMx6u62Zm/sJ31x+8C4FvCXv9B5lHyfadfa1euwMXVlbETp6rL3IsWU3+tKAob162he68+1GvwLgDjJ0+nRaO6hBzcT+NmLXI9ZpF7dO45mDp1Km5ubgBMmTIFBwcH+vXrR3R0NEuXLtV7gIaQlpbKnl92EtC6HSqVitTUVFQqFSampuo6pqZmGBkZcfbMnwaMNG94kpjZ5W1tk/kXx42/L5H+9CkVq7+trlPUw5Mizq5cvXhOXXb39g22fL+cTz+bqO6NetPVK1OEi+HxfN2+AnuH1eb7XjV4r5qbocPKl+T77vX8fugA5X0q8sXIITRvWIeundqxbcsm9fHwe3d5EBOj8cePtY0NFSpW5tzZMwaIOHfllUc2G4rOPQc1atRQf+3s7Mzu3bt1/tCUlJQs3VIpqcY6PU4yJ4X8doCEhHhatGoLQIVKVTA3t2Dx/Jn06T8ERVEInj+b9PR0HsREGzZYA8vIyGBN8CzKVqhC8f930T5++IBCJiZZxnDtHBx59PABAGmpqcyfNoYPPxlEEWdXoiLu5XrshlDUwZz3a7jz/dG7fPvHbXzcbBjR1Ju0dIWdZyMNHV6+Id93ry/83l22bNpA548CCezZm0sXzjM7aComhUwIaN2WBzExADg6as6rcixcmAcPYgwRcq6S1QoGMG3aNOzs7DRec2d+bYhQsvXzTz9S8506FHFyBjKHHCZ/PYs/Qg7RuO5bNKtfi4T4eMqU8ynQf3kArFwQxJ1b1xn4xRSdztvw7UKKFvekbqOC1TVppFJxOSKBhQdvcCUyga2nI9h2OoL2vu6GDi1fke+715eRkUHZcj70GziUsuV8aNu+I63fe5+tmzcaOrQ8wUiPr/xI554DLy+v/8yobty48dI2Ro8ezbBhwzTK4lJz9vGb2oqMCOfk8aNMCZqrUf52rdr88NNuHj16iLGxMTY2trRuWg/3os0NFKnhrVwQxJ9Hf2f8zKUUdnJRl9s5FOZpWhqJCfEaf8U9fhirnjV+4cwJwm5d51izWgAoZD45vPf7jWn7YXc6dO2Ti1eSe2LiU7kZk6hRdjMmkYblnAwUUf4j33f6UaSIE54lS2mUeXqV4rf9ewEoXCSzxyA2NoYiTv98f8Y+eIB32XK5F6gwCJ2TgyFDhmi8T0tL4/Tp0+zevZuRI0dq1YaZmVmWIYSU+LyxDOvn7VtxcHDEr069bI/b2zsAcOrEUR7GxlKnXoPcDC9PUBSFVQu/4cQfvzF2RjDObkU1jpcsUx7jQoU4f/oENes2BCD8zi1ioiLx9smcFDZ0XBCpKf9MxLv+90WWzJzM+FlLcXErxpvqr7uPKVHYUqPMw9GSiMfJLzhDPCPfd/pVuWp1wm7f1Ci7E3YLV7fMXiz3osUoXKQIJ44dpUzZ8gAkJiRw4fxZ2nXolOvx5raCPqygc3IwePDgbMsXLlzIyZMnXzsgQ8rIyOCXHVtp1rINhQpp3pqft2+lhFdJHBwcOH/2L+bOnEbHD7vi4elloGgN59v5X3Pk4K8MnzgDCwtLHsVmjj9aWlljamaOpZU1DZq14bsls7G2scXC0opVi77B26eSesa4i7vmD+L4uMcAFPXweqPXm39/9A4ru1ene+0S7L0YRcWiNrSr7s6Un6+o69iaF8LVzhwnm8wJsM+SiQcJqTxIzNyuvLCVKYWtTSnuYAFAaWcrnqSmE/k4mbjkvJFo65t83+lXp4+60qtbF1atWMK7jZtx8cI5tv24ic/HTgAyfzl+8GFXVi1fQnGPErgXLcbSRfMo4uSsXr3wJjMq2LnBqz1bITvNmzdn9OjRrFy5Ul9N5rqTx0O5HxlBQOusGzmF3b7JkoWziXv8GFf3onTt3psPugQaIErD27fzRwAmj+irUd53xDj8m7QC4OO+Q1GpVMyePIqnqalUrlGLHgNH5Xqsec3FiHhGbDrPgIYl6VWvBOGPkpm55yq7zt9X1/EvU4QJbcqr309vXwGAJYdusjTkFgDtfd3p4/9PYrqiW3UAJvx0iR1v6MRG+b7TL58Klfh65jwWz5/Nt0sX41a0GENGfk6zFq3UdT7u1pPkpCSmfzWehPh4KletzpyFS/PM5HGRc1SKoij6aCgoKIhFixZx69atVzo/Oo8MK+RHd2KfGDqEfKvXmlOGDiFfW9bV19Ah5FteTlaGDiFfc7DM2Xlqw7ZffnklLc1qnf/maLzSJkj/HotRFIXIyEiio6NZtGiRXoMTQgghDEHmHOioTZs2GjfNyMgIJycn6tevT7ly+S87EkIIIYQmnZODCRMm5EAYQgghRN5R0Cck6rw/g7GxMVFRUVnKHzx4gLFx3tirQAghhHgdBX37ZJ2TgxfNX0xJScH0X88eEEIIIUT+pPWwwrx584DMSRrLly/H+v9PJwRIT08nJCRE5hwIIYR4I+TXRy3ri9bJwezZs4HMnoPg4GCNIQRTU1M8PT0JDg7Wf4RCCCFELsuvz0TQF62Tg5s3M7fZbNCgAVu2bMHBwSHHghJCCCEMqYB3HOi+WuHgwYM5EYcQQggh8gide07at2/P119nfbxyUFAQHTp00EtQQgghhCEZqVR6e+VHOicHISEhtGiR9VnozZs3JyQkRC9BCSGEEIYkSxl1lJCQkO2SRRMTE+Li4vQSlBBCCCEMR+fkoFKlSmzcuDFL+YYNG/Dx8dFLUEIIIYQhGan098qPdE4Oxo4dy+TJkwkMDGT16tWsXr2arl27MmXKFMaOHZsTMQohhBC5Ki/MOZg+fToqlYohQ4aoy5KTk+nfvz+FCxfG2tqa9u3bc//+fY3zwsLCCAgIwNLSEmdnZ0aOHMnTp7o9+Vjn1QqtWrVi27ZtTJ06lc2bN2NhYUHlypXZt28f/v7+ujYnhBBCiOecOHGCJUuWULlyZY3yoUOH8vPPP7Np0ybs7OwYMGAA7dq1448//gAyNyUMCAjA1dWVI0eOEBERQdeuXTExMWHq1Klaf/4r7fMQEBDAH3/8QWJiIjExMRw4cAB/f3/Onz//Ks0JIYQQeYohJyQmJCTQpUsXli1bprGn0OPHj1mxYgWzZs2iYcOG+Pr6snLlSo4cOcLRo0cB2LNnDxcvXuS7776jatWqNG/enMmTJ7Nw4UJSU1O1juG1N4GKj49n6dKlvP3221SpUuV1mxNCCCEMTp9zDlJSUoiLi9N4paSkvPCz+/fvT0BAAI0aNdIoP3XqFGlpaRrl5cqVw8PDg9DQUABCQ0OpVKkSLi4u6jpNmzYlLi6OCxcuaH/9Wtd8TkhICF27dsXNzY0ZM2bQsGFDdeYihBBCiEzTpk3Dzs5O4zVt2rRs627YsIE///wz2+ORkZGYmppib2+vUe7i4kJkZKS6zr8Tg2fHnx3Tlk5zDiIjI1m1ahUrVqwgLi6Ojh07kpKSwrZt22SlghBCiDeGCv0tMxg9ejTDhg3TKDMzM8tS786dOwwePJi9e/dibm6ut89/FVr3HLRq1YqyZcty9uxZ5syZQ3h4OPPnz8/J2IQQQgiD0OewgpmZGba2thqv7JKDU6dOERUVRfXq1SlUqBCFChXi0KFDzJs3j0KFCuHi4kJqaiqPHj3SOO/+/fu4uroC4OrqmmX1wrP3z+podf3aVty1axc9e/Zk4sSJBAQEaDyVUQghhHiTGGKfg3fffZdz585x5swZ9atGjRp06dJF/bWJiQn79+9Xn3PlyhXCwsLw8/MDwM/Pj3PnzhEVFaWus3fvXmxtbXXq4dd6WOHw4cOsWLECX19fypcvz8cff0ynTp20/iAhhBBCvJiNjQ0VK1bUKLOysqJw4cLq8p49ezJs2DAcHR2xtbVl4MCB+Pn5UatWLQCaNGmCj48PH3/8MUFBQURGRjJmzBj69++fbW/Fi2jdc1CrVi2WLVtGREQEffr0YcOGDbi7u5ORkcHevXuJj4/X+kOFEEKIvEylUuntpU+zZ8+mZcuWtG/fnnr16uHq6sqWLVvUx42Njdm5cyfGxsb4+fnx0Ucf0bVrVyZNmqTb9SuKorxqkFeuXGHFihWsXbuWR48e0bhxY7Zv3/5KbUXH67Z7k/jHndgnhg4h3+q15pShQ8jXlnX1NXQI+ZaXk5WhQ8jXHCxzdmh75qEbemtruH9JvbWVW15rn4OyZcsSFBTE3bt3Wb9+vb5iEkIIIYQB6bx9cnaMjY1p27Ytbdu21UdzQgghhEHl10ct64tekgMhhBDiTfI6D0x6E7z29slCCCGEeLNIz4EQQgjxHF32J3gTSXIghBBCPKeAjyrIsIIQQgghNOWZngMrM9mO+VXJeulX913Ptw0dQr72QXCooUPIt+Z2qWboEPK1BmUL52j7Rnp88FJ+lGeSAyGEECKvKOjDCpIcCCGEEM8p6BMSZc6BEEIIITRIz4EQQgjxnIK+CZIkB0IIIcRzCnhuIMMKQgghhNAkPQdCCCHEc2RYQQghhBAaCnhuIMMKQgghhND02j0HiqKgKugplhBCiDdKQf/L+bWv38zMjEuXLukjFiGEECJPUKlUenvlR1r3HAwbNizb8vT0dKZPn07hwpn7XM+aNUs/kQkhhBDCILRODubMmUOVKlWwt7fXKFcUhUuXLmFlZZVvMyQhhBDi3wr6bzOtk4OpU6eydOlSZs6cScOGDdXlJiYmrFq1Ch8fnxwJUAghhMhtBX0po9ZzDj7//HM2btxIv379GDFiBGlpaTkZlxBCCGEwKj2+8iOdJiS+9dZbnDp1iujoaGrUqMH58+dlKEEIIYR4w+i8lNHa2prVq1ezYcMGGjVqRHp6ek7EJYQQQhhMQf+795X3OejUqRN16tTh1KlTlChRQp8xCSGEEAZV0HvFX2sTpGLFilGsWDF9xSKEEEKIPECerSCEEEI8p6DvkCjJgRBCCPGcgj6sUNCTIyGEEEI8R3oOhBBCiOcU7H4DLZOD7du3a91g69atXzkYIYQQIi8o6MMKWiUHbdu21aoxlUol+x4IIYQQr2jx4sUsXryYW7duAVChQgXGjRtH8+bNAUhOTmb48OFs2LCBlJQUmjZtyqJFi3BxcVG3ERYWRr9+/Th48CDW1tYEBgYybdo0ChXSfrBAqzkHGRkZWr0kMRBCCPEmMNLjSxfFihVj+vTpnDp1ipMnT9KwYUPatGnDhQsXABg6dCg7duxg06ZNHDp0iPDwcNq1a6c+Pz09nYCAAFJTUzly5AirV69m1apVjBs3Tqc4VIqiKDrGniOepOaJMPKllKcZhg4h34p8lGzoEPK1D4JDDR1CvjW3SzVDh5CvNShbOEfb33o2Um9tvVfZ9bXOd3R05JtvvuH999/HycmJdevW8f777wNw+fJlypcvT2hoKLVq1WLXrl20bNmS8PBwdW9CcHAwo0aNIjo6GlNTU60+85UmJCYmJnLo0CHCwsJITU3VODZo0KBXaVIIIYTIM/Q54yAlJYWUlBSNMjMzM8zMzP7zvPT0dDZt2kRiYiJ+fn6cOnWKtLQ0GjVqpK5Trlw5PDw81MlBaGgolSpV0hhmaNq0Kf369ePChQtUq6ZdUqpzcnD69GlatGjBkydPSExMxNHRkZiYGCwtLXF2dpbkQAghhPiXadOmMXHiRI2y8ePHM2HChGzrnzt3Dj8/P5KTk7G2tmbr1q34+Phw5swZTE1Nsbe316jv4uJCZGRmT0dkZKRGYvDs+LNj2tI5ORg6dCitWrUiODgYOzs7jh49iomJCR999BGDBw/WtTkhhBAiz9HnYoXRo0czbNgwjbL/6jUoW7YsZ86c4fHjx2zevJnAwEAOHTqkv4C0oHNycObMGZYsWYKRkRHGxsakpKRQsmRJgoKCCAwM1JgYIYQQQuRHRnocWNBmCOHfTE1NKV26NAC+vr6cOHGCuXPn8sEHH5CamsqjR480eg/u37+Pq2vmvAZXV1eOHz+u0d79+/fVx7Slc3JgYmKCkVHm/EtnZ2fCwsIoX748dnZ23LlzR9fm8owfNq5n88b1hIffA6BkqdL07tufOnXrAfDVxHEcOxpKdHQUFpaWVKlSjcFDR+BVsqQhw84zoqLus3DuTEL/+J2U5GSKFfdgzIQplK9QkadpaQQvmkfo4RDu3b2LtbU1b9X049NBw3BydjZ06Lnqwl+n2LpxDdf/vsTDBzF8Pnkmteo00Khz5/YN1iydx4W//iQ9/SnFS5Rk1MRvcHJx435kOH06t8y27ZHjv6Z2/ca5cRkG42xjxpAmpanjXRhzE2PuxCYxdusFLobHA9CvQUmaVXTB1c6ctPQMLobHMX//dc7djVO3YWtRiNEtyuJf1okMRWHfxSi+3vU3Salv9mqr3ZvWcDr0NyLvhWFqakrJcpV4L/BTXIv981TdmV/05+r50xrn1W3Wli6ffgbAkf0/s2bulGzbD1qzE1t7x5y7gAIsIyODlJQUfH19MTExYf/+/bRv3x6AK1euEBYWhp+fHwB+fn5MmTKFqKgonP//83Xv3r3Y2tri4+Oj9WfqnBxUq1aNEydO4O3tjb+/P+PGjSMmJoa1a9dSsWJFXZvLM1xcXBg4ZDgeJUqAorBj+zaGDurPhk1bKFXam/I+FWge0Ao3NzceP35M8OIFfNqnJzt378PY2NjQ4RtUXNxjenfrgu9bbzN7wRIcHBy5E3YbG1tbIHNd7pVLF+neqy/eZcoRHxfHrG+mMnJIf1at22Tg6HNXcnIyXqXK0Kh5G6aPG5HleMS9O3wxqCfvNm9D5259sbC04s6tG5iYZv7VUcTJhZU/7tE4Z8+OLWzduIbqNWvnyjUYio15IVZ/UoMTNx/y6dozPExMxaOwJXFJT9V1bsckMvXnK9x9mIR5ISM+fseD4K7VaTnnDx4+SQNg+vsVKWJtRp81f1LISMWk9yowvnV5Pt983lCXliv+Pn8a/4D2eHqXJyM9nW1rg5k3fgjjF67DzNxCXa9Ok9a06tJL/d7UzFz9dY06jahQvZZGu6vnfEVaWuoblxgYag+k0aNH07x5czw8PIiPj2fdunX89ttv/Prrr9jZ2dGzZ0+GDRuGo6Mjtra2DBw4ED8/P2rVyvzv0qRJE3x8fPj4448JCgoiMjKSMWPG0L9/f516L3RODqZOnUp8fGaWPmXKFLp27Uq/fv3w9vbm22+/1bW5PMO/fkON9wMGDWXTxg2cPfsXpUp7077DB+pj7kWL0X/AED54vw3h4fcoXtwjt8PNU9auXIGLqytjJ05Vl7kX/edR3tY2NswPXqFxzojPx9Djow+IjAjH1c0912I1NN+atfH9j1/i369YSPWatenWd4i6zK1ocfXXxsbGODgW0Tjn6OGD1K7fGAsLS73Hm5f0qOvJ/bhkxm27qC6799xS1F/O3dd4/83uv2nnW5QyrtYcu/EQryKW1PEuQqfgY+rehuk/X2HhR1WZ+evfRMdrrr56kwyaOFvjfeDgMYz8OICwa5fxrvjPDHZTM3PsHLJfJmhqZobpv37BxD9+yJVzp/h44OicCdqAVAbaQDkqKoquXbsSERGBnZ0dlStX5tdff6Vx48xewdmzZ2NkZET79u01NkF6xtjYmJ07d9KvXz/8/PywsrIiMDCQSZMm6RSHzslBjRo11F87Ozuze/duXZvI89LT09m7ZzdJSU+oXKVqluNJT56wfdsWihYtptMYzpvq90MHqPVOHb4YOYTTp07i5OxMu46daduuwwvPSYiPR6VSYWNjm4uR5m0ZGRmcPHqY9zoFMmHkp9y8dgVn16K079I9y9DDM9euXOTmtSv0Gfx5Lkeb++qXLcKRa7HM6FiJGp4O3I9P4Yfjd/jxVHi29QsZq3i/RlHiktK4EpkAQJXi9sQlpakTA4CjN2LJUBQqFbPjwKXoXLmWvCApMREAy+f+DR4/tIdjv/2KnYMjld6qQ0Cn7hq9B/929MAuTM3Mqf5Ow2yPC92tWLHiP4+bm5uzcOFCFi5c+MI6JUqU4JdffnmtOAzy4KXs1nymq0x16vLICVf/vkLgR51JTU3BwtKSmXMWUKpUafXxHzasY86sGSQlPcHT04vFy77FxES7DSXeZOH37rJl0wY6fxRIYM/eXLpwntlBUzEpZEJA67ZZ6qekpLBw3iwaN2uBlbV17gecRz1+FEty0hO2rF9Jlx6f0rXPYE4fP8LX40YwedZSKlb1zXLOvl9+olgJL8pVrGKAiHNXMQcLOr5VlLWhYSwPuUWForaMalGWtHSF7Wci1PXqlSlCUIeKmJsYE52QQp/Vp3n0/yGFIjamxCZq9g6kZyjEJT2liHXB+beckZHBpuVzKFW+MkVLlFKXv12vMY7Ortg7OnH31jW2rl7E/Xth9P1iWrbt/LFvJ2/Va6zRm/CmKOCPVtA9OfDy8vrPB1LcuHHjpW1kt+bzizHj+HLsBF3D0StPLy82bN5KQnw8+/b+yrgxn7N85Vp1gtA8oBU1/d4hJjqaNau/ZdTwIaxcu97gSY2hZWRkUN6nIv0GDgWgbDkfrl+7ytbNG7MkB0/T0vjys2EoisKoL8YbINq8S8nI3CX07Xfq07rDRwCULF2Wyxf+4tcdm7MkBykpyYTs30XHrr2ytPUmMlKpuBAex7x91wG4HBlPaRcrOrxVVCM5OHEzlg6Lj+FgaUI736LM+KASXZYeJzYxzVCh5zkbgmdyL+wGI6cHa5TXbdZW/XVRz1LYORRmzthBREfcxcmtmEbdG5fPEXnnFt2H6rYtb36hz9UK+ZHOycGQIUM03qelpXH69Gl2797NyJEjtWojuzWf6SrDZ+0mJqZ4eGTO3PWpUJEL58+z/rs1jBmfOVZjY2ODjY0NJUp4UrlKFerVrsmB/Xtp3iL72eMFRZEiTniWLKVR5ulVit/279Uoe5qWxpejhhEZEc7CpSul1+A5Nnb2GBsXorin5gqYYh5eXDp3Jkv9I4f2kZqSTIMmBeP7LzohhRvRiRplN6MTaeSjueIlKS2DO7FJ3IlN4uzdOHYMfof3qhdlxe+3iIlPxdFK82eNsZEKW4tCxCS8ufMN/m198EzOnfyD4VMX4VDkv1cLeZWtAEBUNsnB4T07KO7lTYnS5XIsVmE4OicHL9roaOHChZw8eVKrNrJb85kXn62gKBlZtof+51jm/6W94HhBUrlqdcJu39QouxN2S2Oi4bPE4E7YbRYuXYXdczt8icxlwqXL+XDvzi2N8vC7YTi5uGWpv++Xn3jrHX/s7B1yKULDOhP2GM8impMuSxS2IuIlz8cwUoFpoczl13/deYSthQnl3Wy4FJE57+BtLweMVCrO3X2cM4HnEYqisGHJLM4cPcSwqQsp4vryicB3blwFwM5BcxJsctITTv1xgLYf982RWPOCgj6soOsDo16oefPm/Pjjj/pqLtfNmzOTUydPEH7vLlf/vsK8OTM5eeI4LQJacffOHVYsX8LFC+eJiAjnzJk/GTl8MGZmZtSp62/o0A2u00ddOX/uLKtWLOFO2G1+3bWTbT9uov0HnYHMxGD0yCFcuniBiVOCyMhI50FMNA9ioklLK1jJVVLSE25cu8KNa1cAiIq4x41rV4i+n9kt/t4HXfnj4B727NxCxL0wft66gRNHQmjeVnNyZ8S9MC6e/ZPGAW1z+xIMZu2RMCoVs+OTep4Ud7SgRSUX3q9RlA3H7wJgYWLEoEalqFzMFjc7c8q72TCxrQ/ONmbsOZ+5iuFmzBMOX41hQpvyVCxqS1UPO0YHlGX3+ftv9EoFgPXBMzh+6Fd6jpiIuYUljx8+4PHDB6T+f/5XdMRdft6wktvXLhNzP4K/jv3OqjmT8K5QlWJepTXaOvX7fjLSn1KzflNDXEquUKn098qP9PZUxqCgIBYtWqR+BrWuDN1zMGHclxw/FkpMdDTWNjZ4e5ele49PqPVObaKi7jNp/FguXbxAXFwchQsXprpvDXr3/RRPL8NvgpQXnsp4OOQ3Fs+fzZ2w27gVLUbnjwLVqxXCw+/RLiD7zXkWLluFb423czNUDbn9VMZzZ04ydmjvLOUNmrZi8OeZ83D2/bKNH9et5EF0FO7FS9C5W19q1qmvUX/tsvkc2reLpet3qjclM4TcfipjvTJFGNy4NB6OFtx7lMzaI7fVqxVMCxkx/f2KVCpmi4OlKY+epHHhXhxLD93kQrjmJkhfBJTDv2wRMhTYdzGK6b9cyfVNkHL7qYx9W7+TbXnXwV/yzrsBxEbfZ+WsiYSH3SAlORmHIs5UreVPiw+6YWFppXFO0Ge9KeziTs/hE3Ih8uzl9FMZ916K0VtbjcsXeXmlPEbn5KBatWoaExIVRSEyMpLo6GgWLVpE795Zf/Bpw9DJQX6WF5KD/Eoe2fx65JHNr04e2fx6JDnIWTrPOWjTpo1GcmBkZISTkxP169enXDmZmCKEECL/M8qnwwH6onNy8KJHTAohhBBvCkPtkJhX6DxYaWxsTFRUVJbyBw8eFPhnDAghhBBvAp17Dl40RSElJQVTU8PvVSCEEEK8rvy6ykBftE4O5s2bB4BKpWL58uVY/2sDm/T0dEJCQmTOgRBCiDdCQR9W0Do5mD0784leiqIQHBysMYRgamqKp6cnwcHBLzpdCCGEEPmE1snBzZuZO+A1aNCALVu24OBQMHZlE0IIUfDIagUdHTx4MCfiEEIIIfKMgj6soPNqhfbt2/P1119nKQ8KCqJDhw7ZnCGEEEKI/ETn5CAkJIQWLVpkKW/evDkhISF6CUoIIYQwpIL+bAWdhxUSEhKyXbJoYmJCXFxcNmcIIYQQ+Us+/Z2uNzr3HFSqVImNGzdmKd+wYQM+Pj56CUoIIYQwJCOVSm+v/EjnnoOxY8fSrl07rl+/TsOGDQHYv38/69evZ9OmTXoPUAghhBC5S+fkoFWrVmzbto2pU6eyefNmLCwsqFy5Mvv27cPf3z8nYhRCCCFyVf78e19/dE4OAAICAggICMhSfv78eSpWrPjaQQkhhBAGVcCzA53nHDwvPj6epUuX8vbbb1OlShV9xCSEEEIIA3rl5CAkJISuXbvi5ubGjBkzaNiwIUePHtVnbEIIIYRBqPT4v/xIp2GFyMhIVq1axYoVK4iLi6Njx46kpKSwbds2WakghBDijZFPFxnojdY9B61ataJs2bKcPXuWOXPmEB4ezvz583MyNiGEEEIYgNY9B7t27WLQoEH069cPb2/vnIxJCCGEMKgC3nGgfc/B4cOHiY+Px9fXl5o1a7JgwQJiYmJyMjYhhBDCMFR6fOVDWicHtWrVYtmyZURERNCnTx82bNiAu7s7GRkZ7N27l/j4+JyMUwghhBC5ROfVClZWVvTo0YPDhw9z7tw5hg8fzvTp03F2dqZ169Y5EaMQQgiRqwr6aoXX2uegbNmyBAUFcffuXdavX6+vmIQQQgiDKuhPZXztTZAAjI2Nadu2Ldu3b9dHc0IIIYRBGWrKwbRp03jrrbewsbHB2dmZtm3bcuXKFY06ycnJ9O/fn8KFC2NtbU379u25f/++Rp2wsDACAgKwtLTE2dmZkSNH8vTpU63j0EtyIIQQQojXd+jQIfr378/Ro0fZu3cvaWlpNGnShMTERHWdoUOHsmPHDjZt2sShQ4cIDw+nXbt26uPp6ekEBASQmprKkSNHWL16NatWrWLcuHFax6FSFEXR65W9oiepeSKMfCnlaYahQ8i3Ih8lGzqEfO2D4FBDh5Bvze1SzdAh5GsNyhbO0fb/vB2nt7aql7B95XOjo6Nxdnbm0KFD1KtXj8ePH+Pk5MS6det4//33Abh8+TLly5cnNDSUWrVqsWvXLlq2bEl4eDguLi4ABAcHM2rUKKKjozE1NX3p50rPgRBCCPEcfU5ITElJIS4uTuOVkpKiVRyPHz8GwNHREYBTp06RlpZGo0aN1HXKlSuHh4cHoaGZyXpoaCiVKlVSJwYATZs2JS4ujgsXLmj1uZIcCCGEEDlo2rRp2NnZabymTZv20vMyMjIYMmQItWvXVj/xODIyElNTU+zt7TXquri4EBkZqa7z78Tg2fFnx7TxSo9sFkIIId5k+lxlMHr0aIYNG6ZRZmZm9tLz+vfvz/nz5zl8+LD+gtGSJAdCCCHEc/S5AtHMzEyrZODfBgwYwM6dOwkJCaFYsWLqcldXV1JTU3n06JFG78H9+/dxdXVV1zl+/LhGe89WMzyr8zJ5JjmQ6YivzsLU2NAh5FvFClsYOoR8bWM/P0OHkG9VbzHK0CHka0mnFxg6hByhKAoDBw5k69at/Pbbb3h5eWkc9/X1xcTEhP3799O+fXsArly5QlhYGH5+mf8e/fz8mDJlClFRUTg7OwOwd+9ebG1ttX6Ccp5JDoQQQog8w0CbF/Xv359169bx008/YWNjo54jYGdnh4WFBXZ2dvTs2ZNhw4bh6OiIra0tAwcOxM/Pj1q1agHQpEkTfHx8+PjjjwkKCiIyMpIxY8bQv39/rXswJDkQQgghnmOobY8XL14MQP369TXKV65cSbdu3QCYPXs2RkZGtG/fnpSUFJo2bcqiRYvUdY2Njdm5cyf9+vXDz88PKysrAgMDmTRpktZx5Jl9DhJln4NXZmyUT/fnzAPS0mWPiNdxNzbJ0CHkWzKs8Hpyeljh7J0EvbVVubi13trKLdJzIIQQQjwnvz4TQV8kORBCCCGeU8BzA0kOhBBCiCwKeHYgOyQKIYQQQoP0HAghhBDPMdRqhbxCkgMhhBDiOQV9QqIMKwghhBBCwyv3HCQmJvLDDz9w7do13Nzc6Ny5M4UL5+zztYUQQojcUMA7DrRPDnx8fDh8+DCOjo7cuXOHevXq8fDhQ8qUKcP169eZPHkyR48ezbIPtBBCCJHvFPDsQOthhcuXL/P06VMg8/GT7u7u3L59m+PHj3P79m0qV67Ml19+mWOBCiGEECJ3vNKwQmhoKMHBwdjZ2QFgbW3NxIkT6dSpk16DE0IIIQxBVivoQPX/6ZvJycm4ublpHCtatCjR0dH6i0wIIYQwkIK+WkGn5ODdd9+lUKFCxMXFceXKFSpWrKg+dvv2bZmQKIQQQrwBtE4Oxo8fr/He2lrzKVM7duygbt26+olKCCGEMKAC3nEgj2x+E8gjm1+dPLL59cgjm1+dPLL59eT0I5v/vv9Eb22VcbHUW1u5RXZIFEIIIZ5T0Cckyg6JQgghhNAgPQdCCCHEc2S1ghBCCCE0FPDcQIYVhBBCCKFJq56D7du3a91g69atXzkYIYQQIk8o4F0HWiUHbdu21aoxlUpFenr668QjhBBCGFxBX62gVXKQkSFrwYUQQoiCQiYkCiGEEM+R1QqvIDExkUOHDhEWFkZqaqrGsUGDBuklMCGEEMJQCnhuoHtycPr0aVq0aMGTJ09ITEzE0dGRmJgYLC0tcXZ2luRACCGEyOd0Xso4dOhQWrVqxcOHD7GwsODo0aPcvn0bX19fZsyYkRMxCiGEELlLpcdXPqRzcnDmzBmGDx+OkZERxsbGpKSkULx4cYKCgvjiiy9yIkYhhBAiV6n0+L/8SOdhBRMTE4yMMnMKZ2dnwsLCKF++PHZ2dty5c0fvAeaWTRvXs2njeiLC7wFQslRpevftT+269TTqKYrCwH69OfLH78ycs4AG7zYyRLh5yoplS9i/dw83b97AzNycqlWrMWTYCDy9SgLw+NEjFi2cT+iRw0RGRODg4EiDdxvRf+BgbGxsDBx93rJqxTIWzJ1F5y4fM3xUZrI9ZdJ4jh8NJSY6CgtLSypXqcagocPV97cgufDXKbZuWMP1vy/x8EEMn0+eSa26DTTq3Ll9gzVL5nHhrz9JT39K8RIlGTXpG5xc3AD4cnAvLvx1SuOcpq3a02/4l7l2HYZgZKRiTN8WdG7xFi6FbYmIfszaHceYvmy3uo6VhSlfDWpDqwaVcbSz4lb4AxatP8TyzYezbXPbgn40rV2BjkOXsuO3s7l1KblCJiTqqFq1apw4cQJvb2/8/f0ZN24cMTExrF27looVK+ZEjLnC2cWFQUOG41GiBIqisGP7NoYO6s/6TVsoVdpbXe/7tatRFfTvmuecPHGcDzp3oUKlSqQ/TWf+3Fn07dWTLdt/xtLSkqjoKKKjohg2YhSlSpUmPPweX02aQHRUFDPnzDN0+HnGhfPn2LJpI95lymqUl/epQPMWLXF1cyfu8SOWLF5I/z6fsH3XXoyNjQ0UrWEkJyfjVaoMjVq0YfrYEVmOR9y7wxcDe/JuizZ07t4XC0sr7ty6gYmpmUa9xi3f48Pu/dTvzczNczx2QxverTG93q9Lr3FruXg9At8KHiyZ8BFxCUksWn8IgK+Ht6f+W2Xo/uUaboc/oJFfeeaO7khE9GN+PnROo72BXRqgKIa4EpEbdE4Opk6dSnx8PABTpkyha9eu9OvXD29vb7799lu9B5hb/Os31Hg/YNBQNm/cwLmzf6mTgyuXL/Hd6pV8t3EzTRrUNUSYedLipSs03k+aMp0Gdf24dPECvjXewtu7DLPmzlcfL+7hwcDBQ/hi1EiePn1KoUKyovbJk0TGjh7JlxMmsWJpsMaxdu93VH/tXrQonw4cTOf32xIRfo9ixT1yO1SD8q1ZG9+atV94/PvlC6leszbd+g5Rl7kVLZ6lnpmZOQ6Fi+REiHlWrSol2XnoLLsPXwAgLCKWjs1qUKNCiX/V8eK7ncf4/dRVAL7d8gc929emRoUSGslB5TJFGfxxQ2p3CeLWvmm5eyG5pKD/CajznIMaNWrQoEFmN56zszO7d+8mLi6OU6dOUaVKFb0HaAjp6en8uutnkpKeULlKVQCSkpL4YtQIPv9yHEWKOBk2wDwu4f/Jo62d3X/UScDa2loSg//7espkatf1p2atd/6zXtKTJ2zftoWiRYvh4uqaS9HlDxkZGZw8ehj34iWYMPJTAtu+y8h+XTn6+8EsdUP27eLj1g0Z1K0Da5fOJyU5yQAR566jf92gwdtlKe3hDEClMkXxq1qSPX9c/Fedm7T0r4S7U+a/3Xo1vPEu4cy+o5fUdSzMTVg1rRtDpv/A/QfxuXsRuUil0t8rPzLIT+aUlBRSUlI0yp6qTDEzM3vBGbnj6t9X6PZRZ1JTU7CwtGTmnAWULFUagJlB06hStRr1G75r0BjzuoyMDIK+nkrVatXx9i6TbZ2HD2NZGryI9h0+yOXo8qZfd/3M5UsXWbN+0wvrbNqwjnmzZ5KU9IQSnl4sXLoCExPTXIwy73v8MJbkpCdsWbeSLj0/pWvvwZw+foSvx41g8uylVKzqC0C9Rs1wdnHDoYgTt69fZc2Sedy7c4vPJ8808BXkrBkr92Jrbc5fW8eQnq5gbKxi/MKdbNh1Ul1n2NebWDi2M9f3TCEtLZ0MJYNPJ6/njz+vq+sEDW/P0b9usvO3c9l9jHhNISEhfPPNN5w6dYqIiAi2bt2q8QgDRVEYP348y5Yt49GjR9SuXZvFixfj7f3P8HdsbCwDBw5kx44dGBkZ0b59e+bOnYu1tbXWceicHHh5ef3nmPuNGzde2sa0adOYOHGiRtnoMeP4cuwEXcPRK08vL9Zv3kpCfDz79/7KuDGfs3zlWu6EhXHi+DHWb9pi0Pjyg6lfTeT61ausWrsu2+MJCQkM6NeHkqVK0ffTAbkcXd4TGRnBzK+nsXDpiv9MjpsHtKKm3zvEREezdvVKPh8xlBVr1hk8oc5LlP8PgL9duz6tO3wEQEnvsly+8Be/bt+sTg6atmqvPsezpDcOhYswblhfIu7dyXYI4k3xfpPqdGr+Ft2+WM3F6xFULluUb0a8T0T0Y77fcQyATzv583YlT9oPDiYsIpY61Usz5/PMOQcHj10hwL8S9d8uQ61O0w18NbnBMH/yJyYmUqVKFXr06EG7du2yHA8KCmLevHmsXr0aLy8vxo4dS9OmTbl48SLm/58706VLFyIiIti7dy9paWl0796d3r17s25d9j+Xs6NzcjBkyBCN92lpaZw+fZrdu3czcuRIrdoYPXo0w4YN0yh7qjL8X0EmJqZ4eGSOv/lUqMiF8+dZ990azMzNuXsnDP933taoP3LYIKpV92XZyrWGCDfPmfrVJEIO/ca3q7/Ltss7MTGBT/t8gpWVFbPnLcTExMQAUeYtly9eIDb2AR998M8vrPT0dE6fOskPG9Zx5ORfGBsbY21jg7WNDR4lPKlUpQoNatfi4P59NGsRYMDo8xYbO3uMjQtRvITmKo5iJby4dO7MC88rU74SAJFveHIwdUhbZqzcy6ZfM1dqXLgWjoebIyO7N+b7HccwNzNh4sBWfDBsmXpewvmr4VQuW4whH7/LwWNXqP9WGUoWK0JkyDcaba+f8Ql/nL5O015zc/26coqhhgOaN29O8+bNsz2mKApz5sxhzJgxtGnTBoA1a9bg4uLCtm3b6NSpE5cuXWL37t2cOHGCGjVqADB//nxatGjBjBkzcHd31yoOnZODwYMHZ1u+cOFCTp48me2x55mZmWX5iycxNe9Ne81QMkhLTaVv/4G81+59jWMd27Vm+GefU8+/4QvOLjgURWHalMkc2L+XFavWUqxY1h+wCQkJ9OvdE1NTU+YuWCx/8f7fWzX92PDjTxplk8Z9SQkvLwK7f5LtagRFAQWFtLTULMcKMhMTE0qX8+HenVsa5eF3wtTLGLNz89oVgDd+gqKFuSkZiuZD9NIzFPXSdJNCxpiaFCLjuSUI6ekZGBll/qacsXIPK7ce0Th+avOXfDbzR34+dD4Ho8/fshtKz+734MvcvHmTyMhIGjX6Zwm9nZ0dNWvWJDQ0lE6dOhEaGoq9vb06MQBo1KgRRkZGHDt2jPfee0+rz9LbnIPmzZszevRoVq5cqa8mc9X8OTN5p0493NzcSExMZPcvOzl14jgLg5dTpIhTtpMQXV3dKVqsmAGizVumTp7Irl92Mmf+IqwsrYiJjgbA2sYGc3NzEhIS6NurB8nJSUyd/g2JCQkkJiQA4ODoWOCW4/2blZUVpZ+bm2FuYYG9nT2lvctw9+4d9u7eRa13auPg4MD9+/dZtWIZ5mZm1K5T7wWtvrmSnjwh4t4/+6lERd7jxtUr2Nja4uTixnudujJj4udUqFKdSlVr8OfxI5w4EsJXc5YCmUsdQ/bvxrdmbWxs7bl94yorFs6kQpXqeJbKfo7Mm+KXkHOM6tmUOxEPuXg9gqrlijHoowas2XYUgPjEZEJOXmXqkLYkJacRFhFLXd/SdGn5NqNmZQ6p3n8Qn+0kxDsRD7kd/iBXryen6bPjILuh9PHjxzNhwgSd2omMjATAxcVFo9zFxUV9LDIyEmdnZ43jhQoVwtHRUV1HG3pLDjZv3oyjo6O+mst1sbGxjPtyFDHR0Vjb2ODtXZaFwcup9c6Ll02JTD9sXA9Az24fa5RP+moabd5rx6WLFzh39i8AWjZvrFHnlz37KVpUEqwXMTM14/SfJ1n/3Rri4uIoXLgw1XxrsGLNehwLFzZ0eLnu2pWLjB3aW/3+24WzAGjQtBWDR0+kVt2G9B32BT9+v5Ll877BvXgJRk36Bp/K1QAoZGLC2VPH2Ll5HclJSRRxdsGvXkM6fvyJQa4nNw37ehPjP23J3C8+wMnBmojox6zY/AdTl+5S1+n6+bdMGtiGVVMDcbC1JCwilgkLd7JsU/abIL3J9DmskN1Qel7vPVUpim7bWFSrVk1jQqKiKERGRhIdHc2iRYvo3bv3f5z9YnlxWCG/MDbKp2tl8oC09IyXVxIvdDf2zV8CmFOqtxhl6BDytaTTC3K0/YjH+hu2c7N7tTl1KpVKY7XCjRs3KFWqFKdPn6Zq1arqev7+/lStWpW5c+fy7bffMnz4cB4+fKg+/vTpU8zNzdm0aVPODSu0adNGIzkwMjLCycmJ+vXrU65cOV2bE0IIIfKcvPhMBC8vL1xdXdm/f786OYiLi+PYsWP065e546efnx+PHj3i1KlT+PpmrtA5cOAAGRkZ1KxZU+vP0jk50HWMRAghhMh3DJQbJCQkcO3aNfX7mzdvcubMGRwdHfHw8GDIkCF89dVXeHt7q5cyuru7q3sXypcvT7NmzejVqxfBwcGkpaUxYMAAOnXqpPVKBXiF5MDY2JiIiIgsEx4ePHiAs7Mz6enpujYphBBC5CmG6jc4efKkehdiQD1XITAwkFWrVvHZZ5+RmJhI7969efToEXXq1GH37t3qPQ4Avv/+ewYMGMC7776r3gRp3jzdnmOj85wDIyOjbGdDhoeHU6pUKZKSXm0MUuYcvDqZc/DqZM7B65E5B69O5hy8npyec3A/Lk1vbbnY5r89XbTuOXiWdahUKpYvX66xDWN6ejohISEy50AIIcQbIb8+E0FftE4OZs+eDWSuTggODtZYm25qaoqnpyfBwcEvOl0IIYTIN/LihMTcpHVycPPmTQAaNGjAli1bcHBwyLGghBBCCGE4Ok9IPHgw6+NPhRBCiDdKwe44wEjXE9q3b8/XX3+dpTwoKIgOHTroJSghhBDCkFR6fOVHOicHISEhtGjRIkt58+bNCQkJ0UtQQgghhDAcnYcVEhISMDXNuhWkiYkJcXFxeglKCCGEMKSCvlpB556DSpUqsXHjxizlGzZswMfHRy9BCSGEEIak0uP/8iOdew7Gjh1Lu3btuH79Og0bNgRg//79rF+/nk2bNuk9QCGEEELkLp2Tg1atWrFt2zamTp3K5s2bsbCwoHLlyuzbtw9/f/+ciFEIIYTIVQV9WEHn5AAgICCAgICALOXnz5+nYsWKrx2UEEIIIQxH5zkHz4uPj2fp0qW8/fbbVKlSRR8xCSGEEAalUunvlR+9cnIQEhJC165dcXNzY8aMGTRs2JCjR4/qMzYhhBBCGIBOwwqRkZGsWrWKFStWEBcXR8eOHUlJSWHbtm2yUkEIIcQbI7+uMtAXrXsOWrVqRdmyZTl79ixz5swhPDyc+fPn52RsQgghhEEU9GEFrXsOdu3axaBBg+jXrx/e3t45GZMQQgghDEjrnoPDhw8THx+Pr68vNWvWZMGCBcTExORkbEIIIYRByLMVtFSrVi2WLVtGREQEffr0YcOGDbi7u5ORkcHevXuJj4/PyTiFEEKI3FPAswOdVytYWVnRo0cPDh8+zLlz5xg+fDjTp0/H2dmZ1q1b50SMQgghhMhFr7XPQdmyZQkKCuLu3busX79eXzEJIYQQBiXPVtADY2Nj2rZtS9u2bfXRnBBCCGFQ+XWVgb689g6JQgghhHiz6KXnQAghhHiTFPCOA0kOhBBCiCwKeHYgyYEQQgjxnPw6kVBfZM6BEEIIITRIz4EQQgjxnIK+WkGlKIpi6CDyspSUFKZNm8bo0aMxMzMzdDj5jty/Vyf37tXJvXs9cv+EJAcvERcXh52dHY8fP8bW1tbQ4eQ7cv9endy7Vyf37vXI/RMy50AIIYQQGiQ5EEIIIYQGSQ6EEEIIoUGSg5cwMzNj/PjxMinnFcn9e3Vy716d3LvXI/dPyIREIYQQQmiQngMhhBBCaJDkQAghhBAaJDkQQgghhAZJDoQQQgihocAmB926daNt27bq9/Xr12fIkCG5Hsdvv/2GSqXi0aNHuf7Zr0Pu36uTe/fq5N69Hrl/Qlt5Kjno1q0bKpUKlUqFqakppUuXZtKkSTx9+jTHP3vLli1MnjxZq7q5/Y29dOlS6tevj62t7X9+rty/rGJjYxk4cCBly5bFwsICDw8PBg0axOPHjzXqyb3LXp8+fShVqhQWFhY4OTnRpk0bLl++rFFH7t1/UxSF5s2bo1Kp2LZtW5bjcv+yV79+ffV9efbq27dvrny2yINPZWzWrBkrV64kJSWFX375hf79+2NiYsLo0aOz1E1NTcXU1FQvn+vo6KiXdnLCkydPaNasGc2aNcv2Pvyb3D9N4eHhhIeHM2PGDHx8fLh9+zZ9+/YlPDyczZs3a9SVe5eVr68vXbp0wcPDg9jYWCZMmECTJk24efMmxsbG6npy715szpw5qF7yiD+5f9nr1asXkyZNUr+3tLQ0YDQFS57qOYDMzTdcXV0pUaIE/fr1o1GjRmzfvh34p0tsypQpuLu7U7ZsWQDu3LlDx44dsbe3x9HRkTZt2nDr1i11m+np6QwbNgx7e3sKFy7MZ599xvPbOzzfvZaSksKoUaMoXrw4ZmZmlC5dmhUrVnDr1i0aNGgAgIODAyqVim7dugGQkZHBtGnT8PLywsLCgipVqmT5BfTLL79QpkwZLCwsaNCggUacLzJkyBA+//xzatWqJfdPx/tXsWJFfvzxR1q1akWpUqVo2LAhU6ZMYceOHVn+MpN7l1Xv3r2pV68enp6eVK9ena+++oo7d+5kOVfuXfbOnDnDzJkz+fbbb/+znty/7FlaWuLq6qp+yUOgck+eSw6eZ2FhQWpqqvr9/v37uXLlCnv37mXnzp2kpaXRtGlTbGxs+P333/njjz+wtramWbNm6vNmzpzJqlWr+Pbbbzl8+DCxsbFs3br1Pz+3a9eurF+/nnnz5nHp0iWWLFmCtbU1xYsX58cffwTgypUrREREMHfuXACmTZvGmjVrCA4O5sKFCwwdOpSPPvqIQ4cOAZn/mNu1a0erVq04c+YMn3zyCZ9//nlO3DY1uX9ZPXvSXKFC/91xJvdOU2JiIitXrsTLy4vixYvLvXuJJ0+e8OGHH7Jw4UJcXV1fWl/uX1bff/89RYoUoWLFiowePZonT57odB/Fa1DykMDAQKVNmzaKoihKRkaGsnfvXsXMzEwZMWKE+riLi4uSkpKiPmft2rVK2bJllYyMDHVZSkqKYmFhofz666+KoiiKm5ubEhQUpD6elpamFCtWTP1ZiqIo/v7+yuDBgxVFUZQrV64ogLJ3795s4zx48KACKA8fPlSXJScnK5aWlsqRI0c06vbs2VPp3LmzoiiKMnr0aMXHx0fj+KhRo7K09SLZfe6/yf17uejoaMXDw0P54osvNMrl3r3YwoULFSsrKwVQypYtq1y7dk3juNy77PXu3Vvp2bOn+j2gbN26NUs9uX/ZW7JkibJ7927l7NmzynfffacULVpUee+9915YX+hXnptzsHPnTqytrUlLSyMjI4MPP/yQCRMmqI9XqlRJY7ztr7/+4tq1a9jY2Gi0k5yczPXr13n8+DERERHUrFlTfaxQoULUqFEjSxfbM2fOnMHY2Bh/f3+t47527RpPnjyhcePGGuWpqalUq1YNgEuXLmnEAeDn56f1Z2hD7t+LxcXFERAQgI+Pj8Y9eUbuXfa6dOlC48aNiYiIYMaMGXTs2JE//vgDc3NzdR25d5q2b9/OgQMHOH36tFZxyP3Lqnfv3uqvK1WqhJubG++++y7Xr1+nVKlSWscoXk2eSw4aNGjA4sWLMTU1xd3dPUvXr5WVlcb7hIQEfH19+f7777O05eTk9EoxWFhY6HxOQkICAD///DNFixbVOJabDy+R+5e9+Ph4mjVrho2NDVu3bsXExCRLHbl32bOzs8POzg5vb29q1aqFg4MDW7dupXPnzuo6cu80HThwgOvXr2Nvb69R3r59e+rWrctvv/2mUS737+WeJRjXrl2T5CAX5LnkwMrKitKlS2tdv3r16mzcuBFnZ+cXTlZxc3Pj2LFj1KtXD4CnT59y6tQpqlevnm39SpUqkZGRwaFDh2jUqFGW488y+PT0dHWZj48PZmZmhIWFvTDzLl++vHqS0TNHjx59+UXqQO5fVnFxcTRt2hQzMzO2b9+u8Rfvv8m9ezlFUVAUhZSUFI1yuXeaPv/8cz755JMs8c2ePZtWrVplqS/37+XOnDmjvi6R8/L8hMSX6dKlC0WKFKFNmzb8/vvv3Lx5k99++41BgwZx9+5dAAYPHsz06dPZtm0bly9f5tNPP/3Ptbqenp4EBgbSo0cPtm3bpm7zhx9+AKBEiRKoVCp27txJdHQ0CQkJ2NjYMGLECIYOHcrq1au5fv06f/75J/Pnz2f16tUA9O3bl6tXrzJy5EiuXLnCunXrWLVq1UuvMTIykjNnznDt2jUAzp07x5kzZ4iNjX29m8ebf//i4uJo0qQJiYmJrFixgri4OCIjI4mMjNT4ISf3LqsbN24wbdo0Tp06RVhYGEeOHKFDhw5YWFjQokULuXf/wdXVlYoVK2q8ADw8PPDy8nqte1cQ7t/169eZPHkyp06d4tatW2zfvp2uXbtSr149Kleu/Nr3T2jBgPMdsvj3xBxdjkdERChdu3ZVihQpopiZmSklS5ZUevXqpTx+/FhRlMyJOIMHD1ZsbW0Ve3t7ZdiwYUrXrl1fODFHURQlKSlJGTp0qOLm5qaYmpoqpUuXVr799lv18UmTJimurq6KSqVSAgMDFUXJnEw0Z84cpWzZsoqJiYni5OSkNG3aVDl06JD6vB07diilS5dWzMzMlLp16yrffvvtSyfmjB8/XgGyvFauXCn37yX379kkquxeN2/elHv3H/fu3r17SvPmzRVnZ2fFxMREKVasmPLhhx8qly9f1ureFOR7lx20mJCYnYJ4/8LCwpR69eopjo6OipmZmVK6dGll5MiR6msTOU+lKC+YnSKEEEKIAinfDysIIYQQQr8kORBCCCGEBkkOhBBCCKFBkgMhhBBCaJDkQAghhBAaJDkQQgghhAZJDoQQQgihQZIDIYQQQmiQ5EAIIYQQGiQ5EEIIIYQGSQ6EEEIIoeF/g+8/lVkjVrAAAAAASUVORK5CYII=",
      "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(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\": [15000, 35000],\n",
    "    \"transformer__vect_reviewText__max_features\": [10000, 15000],\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",
    "accu = accuracy_score(y_test, y_pred_svc)\n",
    "accuracy_arr.append(accu)\n",
    "print(\"accuracy: \", accu)\n",
    "\n",
    "# ROC curve & AUC Score\n",
    "y_pred_prob_svc = grid_svc.predict_proba(X_test)\n",
    "label_binarizer = LabelBinarizer().fit(y_train)\n",
    "y_onehot_test = label_binarizer.transform(y_test)\n",
    "\n",
    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
    "for i in range(5):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_onehot_test[:, i], y_pred_prob_svc[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    print(f\"auc score for Y = {i+1}: \", roc_auc[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(\n",
    "    y_onehot_test.ravel(), y_pred_prob_svc.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "print(\"auc score average: \", roc_auc[\"micro\"])\n",
    "auc_arr.append(roc_auc[\"micro\"])\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_svc)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted 1', 'Predicted 2',\n",
    "                         'Predicted 3', 'Predicted 4', 'Predicted 5'],\n",
    "            yticklabels=['Actual 1', 'Actual 2', 'Actual 3', 'Actual 4', 'Actual 5'])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the ROC curve for each class\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr[0], tpr[0], color='orange', lw=lw,\n",
    "         label='Y Label 1 (area = %0.2f)' % roc_auc[0])\n",
    "plt.plot(fpr[1], tpr[1], color='green', lw=lw,\n",
    "         label='Y Label 2 (area = %0.2f)' % roc_auc[1])\n",
    "plt.plot(fpr[2], tpr[2], color='blue', lw=lw,\n",
    "         label='Y Label 3 (area = %0.2f)' % roc_auc[2])\n",
    "plt.plot(fpr[3], tpr[3], color='cyan', lw=lw,\n",
    "         label='Y Label 4 (area = %0.2f)' % roc_auc[3])\n",
    "plt.plot(fpr[4], tpr[4], color='purple', lw=lw,\n",
    "         label='Y Label 5 (area = %0.2f)' % roc_auc[4])\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], color='black', lw=lw, label='Average')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"LinearSVC\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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=0.588 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=0.587 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=0.593 total time=   2.9s\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=0.590 total time=   3.0s\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=0.591 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=0.590 total time=   3.0s\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=0.586 total time=   3.0s\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=0.591 total time=   3.0s\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=0.593 total time=   3.0s\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=0.595 total time=   3.0s\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=0.582 total time=   3.1s\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=0.585 total time=   3.1s\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=0.591 total time=   3.0s\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=0.590 total time=   3.0s\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=0.598 total time=   3.0s\n",
      "[CV 1/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.585 total time=   3.1s\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=0.581 total time=   3.1s\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=0.589 total time=   3.1s\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=0.596 total time=   3.1s\n",
      "[CV 5/5; 4/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 4/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.597 total time=   3.1s\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=0.575 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=0.575 total time=   2.7s\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=0.582 total time=   2.7s\n",
      "[CV 4/5; 5/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 5/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   2.7s\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=0.583 total time=   2.7s\n",
      "[CV 1/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.575 total time=   2.7s\n",
      "[CV 2/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.578 total time=   2.7s\n",
      "[CV 3/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.7s\n",
      "[CV 4/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.592 total time=   2.7s\n",
      "[CV 5/5; 6/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 6/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.7s\n",
      "[CV 1/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.8s\n",
      "[CV 2/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.576 total time=   2.8s\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=0.577 total time=   2.8s\n",
      "[CV 4/5; 7/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 7/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 total time=   2.8s\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=0.577 total time=   2.8s\n",
      "[CV 1/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.580 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=0.584 total time=   2.8s\n",
      "[CV 3/5; 8/32] START classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 8/32] END classifier__estimator__alpha=0.001, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.582 total time=   2.8s\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=0.592 total time=   2.8s\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=0.580 total time=   2.8s\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=0.501 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=0.499 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=0.521 total time=   2.6s\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=0.524 total time=   2.6s\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=0.518 total time=   2.6s\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=0.500 total time=   2.6s\n",
      "[CV 2/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.503 total time=   2.6s\n",
      "[CV 3/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.523 total time=   2.6s\n",
      "[CV 4/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.517 total time=   2.6s\n",
      "[CV 5/5; 10/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 10/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.512 total time=   2.6s\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=0.496 total time=   2.7s\n",
      "[CV 2/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.501 total time=   2.7s\n",
      "[CV 3/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.516 total time=   2.7s\n",
      "[CV 4/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.519 total time=   2.7s\n",
      "[CV 5/5; 11/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 11/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.517 total time=   2.7s\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=0.500 total time=   2.7s\n",
      "[CV 2/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.499 total time=   2.7s\n",
      "[CV 3/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.511 total time=   2.7s\n",
      "[CV 4/5; 12/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 12/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.514 total time=   2.7s\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=0.506 total time=   2.7s\n",
      "[CV 1/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.583 total time=   2.5s\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=0.583 total time=   2.5s\n",
      "[CV 3/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.5s\n",
      "[CV 4/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.600 total time=   2.6s\n",
      "[CV 5/5; 13/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 13/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.595 total time=   2.5s\n",
      "[CV 1/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.579 total time=   2.5s\n",
      "[CV 2/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.588 total time=   2.5s\n",
      "[CV 3/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.589 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=0.598 total time=   2.6s\n",
      "[CV 5/5; 14/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 14/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.6s\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=0.579 total time=   2.6s\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=0.588 total time=   2.6s\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=0.590 total time=   2.6s\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=0.598 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=0.587 total time=   2.6s\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=0.575 total time=   2.6s\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=0.585 total time=   2.6s\n",
      "[CV 3/5; 16/32] START classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 16/32] END classifier__estimator__alpha=0.01, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 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=0.592 total time=   2.8s\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=0.588 total time=   2.6s\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=0.356 total time=   2.5s\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=0.348 total time=   2.5s\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=0.361 total time=   2.5s\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=0.360 total time=   2.6s\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=0.353 total time=   2.5s\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=0.354 total time=   2.5s\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=0.345 total time=   2.5s\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=0.359 total time=   2.6s\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=0.357 total time=   2.5s\n",
      "[CV 5/5; 18/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 18/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.353 total time=   2.6s\n",
      "[CV 1/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.353 total time=   2.5s\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=0.342 total time=   2.5s\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=0.356 total time=   2.6s\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=0.357 total time=   2.6s\n",
      "[CV 5/5; 19/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 19/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.349 total time=   2.6s\n",
      "[CV 1/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.353 total time=   2.6s\n",
      "[CV 2/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.343 total time=   2.6s\n",
      "[CV 3/5; 20/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 20/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.353 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=0.355 total time=   2.6s\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=0.347 total time=   2.6s\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=0.584 total time=   2.5s\n",
      "[CV 2/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.586 total time=   2.5s\n",
      "[CV 3/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.593 total time=   2.5s\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=0.594 total time=   2.5s\n",
      "[CV 5/5; 21/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 21/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.587 total time=   2.5s\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=0.579 total time=   2.5s\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=0.574 total time=   2.5s\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=0.592 total time=   2.5s\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=0.597 total time=   2.5s\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=0.588 total time=   2.5s\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=0.587 total time=   2.5s\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=0.578 total time=   2.5s\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=0.592 total time=   2.5s\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=0.592 total time=   2.6s\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=0.599 total time=   2.5s\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=0.580 total time=   2.5s\n",
      "[CV 2/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.584 total time=   2.5s\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=0.582 total time=   2.6s\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=0.599 total time=   2.6s\n",
      "[CV 5/5; 24/32] START classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 24/32] END classifier__estimator__alpha=0.1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.590 total time=   2.6s\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=0.130 total time=   2.5s\n",
      "[CV 2/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.129 total time=   2.6s\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=0.132 total time=   2.6s\n",
      "[CV 4/5; 25/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 25/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.111 total time=   2.6s\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=0.133 total time=   2.5s\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=0.130 total time=   2.6s\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=0.129 total time=   2.8s\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=0.131 total time=   2.6s\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=0.110 total time=   2.6s\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=0.132 total time=   2.6s\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=0.131 total time=   2.6s\n",
      "[CV 2/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.127 total time=   2.6s\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=0.130 total time=   2.6s\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=0.112 total time=   2.6s\n",
      "[CV 5/5; 27/32] START classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 27/32] END classifier__estimator__alpha=1, classifier__estimator__loss=hinge, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.133 total time=   2.6s\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=0.128 total time=   2.6s\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=0.120 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=0.129 total time=   2.6s\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=0.112 total time=   2.6s\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=0.133 total time=   2.6s\n",
      "[CV 1/5; 29/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 29/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=5000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.554 total time=   2.5s\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=0.541 total time=   2.5s\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=0.507 total time=   2.5s\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=0.565 total time=   2.5s\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=0.538 total time=   2.5s\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=0.514 total time=   2.5s\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=0.518 total time=   2.5s\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=0.559 total time=   2.5s\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=0.519 total time=   2.5s\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=0.560 total time=   2.5s\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=0.528 total time=   2.5s\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=0.512 total time=   2.5s\n",
      "[CV 3/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.510 total time=   2.5s\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=0.555 total time=   2.6s\n",
      "[CV 5/5; 31/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 31/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=10000, transformer__vect_summary__ngram_range=(1, 2);, score=0.532 total time=   2.5s\n",
      "[CV 1/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 1/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.518 total time=   2.6s\n",
      "[CV 2/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 2/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.541 total time=   2.5s\n",
      "[CV 3/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 3/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.550 total time=   2.6s\n",
      "[CV 4/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 4/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.515 total time=   2.6s\n",
      "[CV 5/5; 32/32] START classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2)\n",
      "[CV 5/5; 32/32] END classifier__estimator__alpha=1, classifier__estimator__loss=perceptron, transformer__vect_reviewText__max_features=10000, transformer__vect_reviewText__ngram_range=(1, 2), transformer__vect_summary__max_features=32000, transformer__vect_summary__ngram_range=(1, 2);, score=0.542 total time=   2.6s\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n",
      "Best: 0.591123 using {'classifier__estimator__alpha': 0.001, 'classifier__estimator__loss': 'hinge', 'transformer__vect_reviewText__max_features': 5000, 'transformer__vect_reviewText__ngram_range': (1, 2), 'transformer__vect_summary__max_features': 32000, 'transformer__vect_summary__ngram_range': (1, 2)}\n",
      "f1_score:  0.5886156811535134\n",
      "accuracy:  0.5907845152449469\n",
      "auc score for Y = 1:  0.9182169246996279\n",
      "auc score for Y = 2:  0.8162562148677823\n",
      "auc score for Y = 3:  0.8413264101087936\n",
      "auc score for Y = 4:  0.8609829942901012\n",
      "auc score for Y = 5:  0.9299305178933603\n",
      "auc score average:  0.8790634281592491\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",
    "accu = accuracy_score(y_test, y_pred_sgd)\n",
    "accuracy_arr.append(accu)\n",
    "print(\"accuracy: \", accu)\n",
    "\n",
    "# ROC curve & AUC Score\n",
    "y_pred_prob_sgd = grid_sgd.predict_proba(X_test)\n",
    "label_binarizer = LabelBinarizer().fit(y_train)\n",
    "y_onehot_test = label_binarizer.transform(y_test)\n",
    "\n",
    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
    "for i in range(5):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_onehot_test[:, i], y_pred_prob_sgd[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    print(f\"auc score for Y = {i+1}: \", roc_auc[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(\n",
    "    y_onehot_test.ravel(), y_pred_prob_sgd.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "print(\"auc score average: \", roc_auc[\"micro\"])\n",
    "auc_arr.append(roc_auc[\"micro\"])\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_sgd)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['Predicted 1', 'Predicted 2',\n",
    "                         'Predicted 3', 'Predicted 4', 'Predicted 5'],\n",
    "            yticklabels=['Actual 1', 'Actual 2', 'Actual 3', 'Actual 4', 'Actual 5'])\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the ROC curve for each class\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr[0], tpr[0], color='orange', lw=lw,\n",
    "         label='Y Label 1 (area = %0.2f)' % roc_auc[0])\n",
    "plt.plot(fpr[1], tpr[1], color='green', lw=lw,\n",
    "         label='Y Label 2 (area = %0.2f)' % roc_auc[1])\n",
    "plt.plot(fpr[2], tpr[2], color='blue', lw=lw,\n",
    "         label='Y Label 3 (area = %0.2f)' % roc_auc[2])\n",
    "plt.plot(fpr[3], tpr[3], color='cyan', lw=lw,\n",
    "         label='Y Label 4 (area = %0.2f)' % roc_auc[3])\n",
    "plt.plot(fpr[4], tpr[4], color='purple', lw=lw,\n",
    "         label='Y Label 5 (area = %0.2f)' % roc_auc[4])\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"], color='black', lw=lw, label='Average')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "model_arr.append(\"Stochastic Gradient classifier\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>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>MultinomialNB</td>\n",
       "      <td>0.887109</td>\n",
       "      <td>0.602023</td>\n",
       "      <td>0.601062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.888729</td>\n",
       "      <td>0.600205</td>\n",
       "      <td>0.602604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.887789</td>\n",
       "      <td>0.602928</td>\n",
       "      <td>0.606201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stochastic Gradient classifier</td>\n",
       "      <td>0.879063</td>\n",
       "      <td>0.588616</td>\n",
       "      <td>0.590785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model       auc  f1 score  Accuracy\n",
       "0                   MultinomialNB  0.887109  0.602023  0.601062\n",
       "1             Logistic Regression  0.888729  0.600205  0.602604\n",
       "2                       LinearSVC  0.887789  0.602928  0.606201\n",
       "3  Stochastic Gradient classifier  0.879063  0.588616  0.590785"
      ]
     },
     "execution_count": 179,
     "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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