Multi-class Classification Baseline with commit message.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/apple/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package words to /Users/apple/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/apple/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "\n",
    "# funtions to handle labels\n",
    "from utils.handle_labels import get_tag_counts_and_labels\n",
    "from utils.handle_labels import drop_labels\n",
    "from utils.handle_labels import group_labels\n",
    "from utils.handle_labels import categories_count\n",
    "from utils.handle_labels import get_imbalance\n",
    "from utils.handle_labels import label_distribution\n",
    "from utils.handle_labels import number_of_labels\n",
    "from utils.message_preprocess import message_processing\n",
    "# plot untils funcion\n",
    "from utils.plot_utils import pie_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1922, 28)\n",
      "<class 'list'>\n",
      "['Testing', 'Bug fix']\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>project name</th>\n",
       "      <th>Commit ID</th>\n",
       "      <th>commit_message</th>\n",
       "      <th>Maintenance</th>\n",
       "      <th>Testing</th>\n",
       "      <th>Feature Add</th>\n",
       "      <th>Bug fix</th>\n",
       "      <th>Documentation</th>\n",
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       "      <th>Build</th>\n",
       "      <th>...</th>\n",
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       "      <th>Rename</th>\n",
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       "      <th>Initialization</th>\n",
       "      <th>Internationalization</th>\n",
       "      <th>Data</th>\n",
       "      <th>Module Add</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apache-avro</td>\n",
       "      <td>126e9769f45f978f42321c4fc465198982df482b</td>\n",
       "      <td>AVRO-906. Java: Fix so that ordering of schema...</td>\n",
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       "      <td>[Testing, Bug fix]</td>\n",
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       "      <th>1</th>\n",
       "      <td>apache-avro</td>\n",
       "      <td>2df0775d2f368b326e3ac6442ce4850e3fe62edc</td>\n",
       "      <td>AVRO-2003: Report specific location of schema ...</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>[Testing, Maintenance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apache-avro</td>\n",
       "      <td>a39e6deea243dcd9a09df8f67af724003dec1d02</td>\n",
       "      <td>AVRO-1099. Java: Fix JsonDecoder to permit flo...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[Testing, Bug fix]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apache-avro</td>\n",
       "      <td>2020c8a8cc19c58eaef2d9de75260e7341099038</td>\n",
       "      <td>AVRO-1046. Java: Fix ReflectDatumReader to be ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>[Testing, Maintenance, Bug fix]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apache-avro</td>\n",
       "      <td>fa0059c55a31813634188eb85e53b68e33644489</td>\n",
       "      <td>AVRO-607: Java: Make SpecificData schema cache...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>[Maintenance, Bug fix]</td>\n",
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       "  project name                                 Commit ID  \\\n",
       "0  apache-avro  126e9769f45f978f42321c4fc465198982df482b   \n",
       "1  apache-avro  2df0775d2f368b326e3ac6442ce4850e3fe62edc   \n",
       "2  apache-avro  a39e6deea243dcd9a09df8f67af724003dec1d02   \n",
       "3  apache-avro  2020c8a8cc19c58eaef2d9de75260e7341099038   \n",
       "4  apache-avro  fa0059c55a31813634188eb85e53b68e33644489   \n",
       "\n",
       "                                      commit_message  Maintenance  Testing  \\\n",
       "0  AVRO-906. Java: Fix so that ordering of schema...            0        1   \n",
       "1  AVRO-2003: Report specific location of schema ...            1        1   \n",
       "2  AVRO-1099. Java: Fix JsonDecoder to permit flo...            0        1   \n",
       "3  AVRO-1046. Java: Fix ReflectDatumReader to be ...            1        1   \n",
       "4  AVRO-607: Java: Make SpecificData schema cache...            1        0   \n",
       "\n",
       "   Feature Add  Bug fix  Documentation  Clean up  Build  ...  Module Remove  \\\n",
       "0            0        1              0         0      0  ...              0   \n",
       "1            0        0              0         0      0  ...              0   \n",
       "2            0        1              0         0      0  ...              0   \n",
       "3            0        1              0         0      0  ...              0   \n",
       "4            0        1              0         0      0  ...              0   \n",
       "\n",
       "   Module Move  Rename  Versioning  Merge  Initialization  \\\n",
       "0            0       0           0      0               0   \n",
       "1            0       0           0      0               0   \n",
       "2            0       0           0      0               0   \n",
       "3            0       0           0      0               0   \n",
       "4            0       0           0      0               0   \n",
       "\n",
       "   Internationalization  Data  Module Add                       categories  \n",
       "0                     0     0           0               [Testing, Bug fix]  \n",
       "1                     0     0           0           [Testing, Maintenance]  \n",
       "2                     0     0           0               [Testing, Bug fix]  \n",
       "3                     0     0           0  [Testing, Maintenance, Bug fix]  \n",
       "4                     0     0           0           [Maintenance, Bug fix]  \n",
       "\n",
       "[5 rows x 27 columns]"
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     "metadata": {},
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    }
   ],
   "source": [
    "df = pd.read_csv('data/commit_data_new.csv')\n",
    "print(df.shape)\n",
    "\n",
    "# convert string to list\n",
    "from ast import literal_eval\n",
    "\n",
    "df['categories'] = df['categories'].apply(lambda x: literal_eval(x))\n",
    "print(type(df['categories'].values[0]))\n",
    "print(df['categories'].values[0])\n",
    "df = df.drop(['Unnamed: 0'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tags Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maintenance : 891\n",
      "Feature Add : 330\n",
      "Bug fix : 266\n",
      "Documentation : 237\n",
      "Clean up : 192\n",
      "Refactoring : 111\n",
      "Indentation : 48\n",
      "Token Replace : 40\n",
      "Source Control : 30\n",
      "Cross : 24\n",
      "Legal : 18\n",
      "Debug : 10\n",
      "Module Remove : 6\n",
      "Module Move : 5\n",
      "Rename : 5\n",
      "Versioning : 4\n",
      "Merge : 3\n",
      "Initialization : 2\n",
      "Internationalization : 1\n",
      "Data : 1\n",
      "Module Add : 1\n",
      "1    1625\n",
      "2     226\n",
      "3      35\n",
      "4       7\n",
      "5       3\n",
      "dtype: int64\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrective : 266\n",
      "Adaptive : 239\n",
      "Perfective : 1130\n",
      "Implementation : 334\n",
      "Non_functional : 70\n",
      "Other : 34\n"
     ]
    }
   ],
   "source": [
    "# drop testing and build\n",
    "new_df = drop_labels(df, ['Testing', 'Build'])\n",
    "_ , target_col = get_tag_counts_and_labels(new_df)\n",
    "multi_count = number_of_labels(new_df, target_col)\n",
    "\n",
    "def group_labels_new(df, labels_to_group, new_label):\n",
    "    '''\n",
    "    Group some of labels\n",
    "\n",
    "    Args:\n",
    "        df - dataframe\n",
    "        labels_to_group -  List of labels you want to group\n",
    "        new_label -  string - new label name of grouped labels\n",
    "\n",
    "    Returns:\n",
    "        new_df - dataframe after grouped\n",
    "    '''\n",
    "    new_df = df.copy()\n",
    "\n",
    "    # generate new labels by group labels\n",
    "    def create_new_label(row, labels):\n",
    "        new_label = 0  # initialize new label\n",
    "        for label in labels:\n",
    "            if row[label] == 1:\n",
    "                new_label = 1  # if one of labels in grouped labels is 1 the new label is 1\n",
    "        return new_label\n",
    "\n",
    "    new_df[new_label] = df.apply(lambda row: create_new_label(row, labels_to_group), axis=1)\n",
    "\n",
    "    # generate list of new_categories\n",
    "\n",
    "    return new_df\n",
    "\n",
    "new_df = group_labels_new(new_df, ['Bug fix'], 'Corrective')\n",
    "new_df = group_labels_new(new_df, ['Internationalization', 'Documentation','Data'], 'Adaptive')\n",
    "new_df = group_labels_new(new_df, ['Clean up', 'Indentation','Maintenance','Module Move','Module Remove','Refactoring'], 'Perfective')\n",
    "new_df = group_labels_new(new_df, ['Initialization', 'Feature Add','Module Add','Internationalization'], 'Implementation')\n",
    "new_df = group_labels_new(new_df, ['Legal', 'Module Remove','Rename','Token Replace','Merge'], 'Non_functional')\n",
    "new_df = group_labels_new(new_df, ['Cross','Debug'], 'Other')\n",
    "\n",
    "target_col = ['Corrective','Adaptive','Perfective','Implementation','Non_functional','Other']\n",
    "multi_count = categories_count(new_df, target_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrective : 266\n",
      "Adaptive : 239\n",
      "Perfective : 1130\n",
      "Implementation : 334\n",
      "1    1635\n",
      "2     148\n",
      "0     101\n",
      "3      10\n",
      "4       2\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current number of samples: 1896\n",
      "number of samples after drop:1635\n"
     ]
    }
   ],
   "source": [
    "# Drop 'Non-functional' and 'Other'\n",
    "\n",
    "new_df = new_df.drop(['Non_functional','Other'],axis = 1)\n",
    "\n",
    "target_col = ['Corrective','Adaptive','Perfective','Implementation']\n",
    "multi_count = categories_count(new_df, target_col)\n",
    "\n",
    "# Drop rows with multiple labels\n",
    "target_col = ['Corrective','Adaptive','Perfective','Implementation']\n",
    "multi_count = number_of_labels(new_df, target_col)\n",
    "\n",
    "print('current number of samples: %d'%new_df.shape[0])\n",
    "new_df['label_sum'] = new_df[target_col].sum(axis=1)\n",
    "new_df = new_df[new_df['label_sum']==1].reset_index(drop=True)\n",
    "print('number of samples after drop:%d'%new_df.shape[0])\n",
    "new_df = new_df.drop(['label_sum'], axis = 1)\n",
    "\n",
    "# Drop target col\n",
    "# new_df = new_df[new_df['Corrective']!=1].reset_index(drop=True)\n",
    "# target_col = ['Adaptive','Perfective','Implementation']\n",
    "\n",
    "# new_df = new_df[new_df['Implementation']!=1].reset_index(drop=True)\n",
    "# target_col = ['Adaptive','Perfective','Corrective']\n",
    "\n",
    "# form target cols\n",
    "new_df = new_df.drop('categories', axis = 1)\n",
    "new_df['target_class'] = np.argmax(new_df[target_col].values, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Message Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['java fix order schema properti consist'\n",
      " 'report specif locat schema incompat close signedoffbi nandor kollar'\n",
      " 'java fix jsondecod permit float doubl read json valu without decim point int long read json valu decim point'\n",
      " 'fix specificdatumwrit write string schema silent convert object string contribut christoph taton'\n",
      " 'filteraggregatetransposerul lose condit cannot push'\n",
      " 'improv relmdpred deal rexliter'\n",
      " 'rule fail match miss link parent equival set'\n",
      " 'matchrecogn claus support one row per match row per match close apach calcit'\n",
      " 'relfieldtrimm trim sortrel collat trait set dont match'\n",
      " 'like must match period liter']\n"
     ]
    }
   ],
   "source": [
    "message_array = message_processing(new_df.commit_message, remove_noise=False)\n",
    "print(message_array[0:10])\n",
    "new_df.commit_message = message_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TD-IDF and Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse as sp_sparse\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score \n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from scipy.sparse import coo_matrix, hstack\n",
    "\n",
    "from skmultilearn.problem_transform import BinaryRelevance\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "def print_evaluation_scores(y_test, predicted):\n",
    "    \n",
    "    print('Accuracy:', accuracy_score(y_test, predicted))\n",
    "    print('F1-score macro:', f1_score(y_test, predicted, average='macro'))\n",
    "    print('F1-score micro:', f1_score(y_test, predicted, average='micro'))\n",
    "    print('F1-score weighted:', f1_score(y_test, predicted, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train shape :  (1471, 16974)\n",
      "X test shape :  (164, 16974)\n"
     ]
    }
   ],
   "source": [
    "def tfidf_features(X):\n",
    "    \"\"\"\n",
    "        X_train, X_val, X_test — samples        \n",
    "        return TF-IDF vectorized representation of each sample and vocabulary\n",
    "    \"\"\"\n",
    "    # Create TF-IDF vectorizer with a proper parameters choice\n",
    "    # Fit the vectorizer on the train set\n",
    "    # Transform the train, test, and val sets and return the result\n",
    "    \n",
    "    \n",
    "    tfidf_vectorizer = TfidfVectorizer(min_df=0, max_df=1.0, ngram_range=(1, 2),token_pattern='(\\S+)')\n",
    "    \n",
    "    X = tfidf_vectorizer.fit_transform(X)\n",
    "    \n",
    "    return X,tfidf_vectorizer.vocabulary_\n",
    "\n",
    "X = new_df['commit_message'].values\n",
    "y = new_df['target_class'].values\n",
    "X_tfidf, tfidf_vocab = tfidf_features(X)\n",
    "tfidf_reversed_vocab = {i:word for word,i in tfidf_vocab.items()}\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_tfidf, y, test_size=0.1, random_state=42)\n",
    "\n",
    "print('X train shape : ', X_train.shape)\n",
    "print('X test shape : ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "\n",
    "def kfold_evaluation(X, y, clf, params_grid, kfold = 10):\n",
    "    \n",
    "    skf = KFold(n_splits=kfold, random_state = 42, shuffle = True)\n",
    "    skf.get_n_splits(X, y)\n",
    "\n",
    "    eval_result = []\n",
    "    for train_index, test_index in skf.split(X, y):\n",
    "        print('========================================================')\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "\n",
    "        model = GridSearchCV(clf, params_grid, cv=5)\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # View the accuracy score\n",
    "        print('Best score for training data:', model.best_score_,\"\\n\") \n",
    "        print('Best parameters for training data:',model.best_params_,'\\n')\n",
    "\n",
    "        final_model = model.best_estimator_\n",
    "        y_pred = model.predict(X_test)\n",
    "        print_evaluation_scores(y_test, y_pred)\n",
    "\n",
    "        accu = accuracy_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred, average='macro')\n",
    "        eval_result.append((accu, f1))\n",
    "\n",
    "    # print average result\n",
    "    accu_result = [item[0] for item in eval_result]\n",
    "    f1_result = [item[1] for item in eval_result]\n",
    "    final_accu = sum(accu_result)/len(accu_result)\n",
    "    final_f1 = sum(f1_result)/len(f1_result)\n",
    "    print('\\n')\n",
    "    print('average accuracy Kfold:', final_accu)\n",
    "    print('average f1 Kfold',final_f1)\n",
    "    \n",
    "    return final_accu, final_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================================\n",
      "Best score for training data: 0.6553365057783821 \n",
      "\n",
      "Best parameters for training data: {'C': 2, 'multi_class': 'ovr', 'penalty': 'l1'} \n",
      "\n",
      "Accuracy: 0.6463414634146342\n",
      "F1-score macro: 0.4222514812376563\n",
      "F1-score micro: 0.6463414634146342\n",
      "F1-score weighted: 0.5660451355994798\n",
      "========================================================\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-d3a2f59579a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'saga'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mkfold_evaluation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_tfidf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-18-b0dabacf4f0c>\u001b[0m in \u001b[0;36mkfold_evaluation\u001b[0;34m(X, y, clf, params_grid, kfold)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;31m# View the accuracy score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    685\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1146\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    664\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    665\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 666\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    922\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    512\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1604\u001b[0m                       \u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_squared_sum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_squared_sum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1605\u001b[0m                       sample_weight=sample_weight)\n\u001b[0;32m-> 1606\u001b[0;31m             for class_, warm_start_coef_ in zip(classes_, warm_start_coef))\n\u001b[0m\u001b[1;32m   1607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1608\u001b[0m         \u001b[0mfold_coefs_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfold_coefs_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    935\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    831\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    832\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    834\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/venv/lib/python3.6/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    519\u001b[0m         AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m    520\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    522\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    523\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    425\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "# Create the parameter grid based on the results of random search \n",
    "params_grid = [{'penalty': ['l1'], \n",
    "                'C': [1,2,4,6,8,10,12,15,20], \n",
    "                'multi_class':['multinomial','ovr']}]\n",
    "clf = LogisticRegression(random_state=0,solver='saga',n_jobs=-1,max_iter=1000)\n",
    "\n",
    "kfold_evaluation(X_tfidf, y, clf, params_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'C': [1, 5, 10, 20, 50, 100, 200, 500, 1000, 5000],\n",
       "                          'decision_function_shape': ['ovo', 'ovr'],\n",
       "                          'gamma': [0.1, 0.01, 0.001, 0.0001, 1e-05],\n",
       "                          'kernel': ['rbf']},\n",
       "                         {'C': [1, 5, 10, 20, 50, 100, 200, 500, 1000, 5000],\n",
       "                          'decision_function_shape': ['ovo', 'ovr'],\n",
       "                          'kernel': ['linear']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "\n",
    "#Libraries to Build Ensemble Model : Random Forest Classifier \n",
    "# Create the parameter grid based on the results of random search \n",
    "params_grid = [{'kernel': ['rbf'], 'gamma': [1e-1, 1e-2, 1e-3, 1e-4, 1e-5], 'C': [1, 5, 10, 20, 50, 100, 200, 500, 1000, 5000], 'decision_function_shape':['ovo','ovr']},\n",
    "               {'kernel': ['linear'], 'C': [1, 5, 10, 20, 50, 100, 200, 500, 1000, 5000], 'decision_function_shape':['ovo','ovr']}]\n",
    "\n",
    "clf = SVC(random_state = 0)\n",
    "\n",
    "kfold_evaluation(X_tfidf, y, clf, params_grid)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
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 "nbformat_minor": 2
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