model-NB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "t = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous cherchons à prédire la positivité / négativité d'un tweet à l'aide de la **classification bayésienne naïve**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Outils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment140(stop_words, lemmatization, negation, nb_data):\n",
    "    file = \"train\"\n",
    "    if stop_words:\n",
    "        file += \"_stop\"\n",
    "    if lemmatization:\n",
    "        file += \"_lemm\"\n",
    "    if negation:\n",
    "        file += \"_neg\"\n",
    "    return pd.read_pickle(os.path.join(\"data\", \"sentiment140\", file + \".bz2\")).sample(nb_data, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Modelisation():\n",
    "    def __init__(self, X, y, vectorizer, model, scaling=True):\n",
    "        X = vectorizer.fit_transform(X)\n",
    "\n",
    "        if scaling:\n",
    "            scaler = StandardScaler(with_mean=False)\n",
    "            X = scaler.fit_transform(X)\n",
    "        \n",
    "        if isinstance(model, GaussianNB):\n",
    "            X = X.toarray()\n",
    "\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state=1234)\n",
    "\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "        sc_accuracy = metrics.accuracy_score(y_pred, y_test)    \n",
    "        sc_balanced_accuracy = metrics.balanced_accuracy_score(y_pred, y_test)\n",
    "        sc_roc_auc = metrics.roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
    "        probs = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "        metrics_score = {'accuracy': sc_accuracy, 'balanced_accuracy': sc_balanced_accuracy, 'roc_auc': sc_roc_auc}\n",
    "\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = X_train, X_test, y_train, y_test\n",
    "        self.model = model\n",
    "        self.probs = probs\n",
    "        self.metrics_score = metrics_score\n",
    "    \n",
    "    def get_data(self):\n",
    "        return self.X_train, self.X_test, self.y_train, self.y_test\n",
    "    \n",
    "    def show_conf_matrix(self):\n",
    "        metrics.plot_confusion_matrix(self.model, self.X_test, self.y_test, cmap='Blues')\n",
    "        plt.show()\n",
    "\n",
    "        print(f\"Accuracy : {self.metrics_score['accuracy']:.4f}\")\n",
    "        print(f\"Balanced accuracy : {self.metrics_score['balanced_accuracy']:.4f}\")\n",
    "        print(f\"ROC AUC : {self.metrics_score['roc_auc']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparaison(X, y, N, model, scaling=True, show_graph=True):\n",
    "    \"\"\"\n",
    "    Compraison des modèles par rapport à la courbe ROC\n",
    "    N correspond au nombre de features\n",
    "    \"\"\"\n",
    "    table = []\n",
    "    \n",
    "    m = Modelisation(X, y, CountVectorizer(max_features = N), model)\n",
    "    fpr, tpr, _ = metrics.roc_curve(m.y_test, m.probs, pos_label=\"Positive\")\n",
    "    if show_graph: plt.plot(fpr, tpr, label=f\"CountVectorizer {N}\")\n",
    "    table.append(m.metrics_score)\n",
    "\n",
    "    m = Modelisation(X, y, TfidfVectorizer(max_features = N), model)\n",
    "    fpr, tpr, _ = metrics.roc_curve(m.y_test, m.probs, pos_label=\"Positive\")\n",
    "    if show_graph: plt.plot(fpr, tpr, label=f\"TfidfVectorizer {N}\")\n",
    "    table.append(m.metrics_score)\n",
    "\n",
    "    m = Modelisation(X, y, CountVectorizer(max_features = N, ngram_range=(1,2)), model)\n",
    "    fpr, tpr, _ = metrics.roc_curve(m.y_test, m.probs, pos_label=\"Positive\")\n",
    "    if show_graph: plt.plot(fpr, tpr, label=f\"CountVectorizer {N} ngram 2\")\n",
    "    table.append(m.metrics_score)\n",
    "\n",
    "    m = Modelisation(X, y, TfidfVectorizer(max_features = N, ngram_range=(1,2)), model)\n",
    "    fpr, tpr, _ = metrics.roc_curve(m.y_test, m.probs, pos_label=\"Positive\")\n",
    "    if show_graph: plt.plot(fpr, tpr, label=f\"TfidfVectorizer {N} ngram 2\")\n",
    "    table.append(m.metrics_score)\n",
    "    \n",
    "    if show_graph:\n",
    "        plt.plot([0, 1], [0, 1], \"r-\")\n",
    "        plt.plot([0, 0, 1], [0, 1, 1], 'b-')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    \n",
    "    df = pd.DataFrame(table, index=[f\"CountVectorizer {N}\", f\"TfidfVectorizer {N}\", f\"CountVectorizer {N} ngram 2\", f\"TfidfVectorizer {N} ngram 2\"])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def influence_preprocessing(N, model, show_graph=False, show_table=False):\n",
    "    tables = []\n",
    "    for stop_words in [False, True]:\n",
    "        for lemmatization in [False, True]:\n",
    "            for negation in [False, True]:\n",
    "                print(f\"stop_words: {stop_words}, lemmatization: {lemmatization}, negation: {negation}\")\n",
    "                df_temp = sentiment140(stop_words, lemmatization, negation, NB_DATA)\n",
    "                X = df_temp.text.to_list()\n",
    "                y = df_temp.sentiment.to_list()            \n",
    "                df_metrics = comparaison(X, y, N, model, show_graph=show_graph)\n",
    "                if show_table:\n",
    "                    display(df_metrics)\n",
    "                df_metrics['stop_words'] = stop_words\n",
    "                df_metrics['lemmatization'] = lemmatization\n",
    "                df_metrics['negation'] = negation\n",
    "                tables.append(df_metrics)\n",
    "    return pd.concat(tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def influence_features(X, y, max_features_list, model, scaling=True):\n",
    "    table = []\n",
    "    for max_features in max_features_list:\n",
    "        m = Modelisation(X, y, CountVectorizer(max_features=max_features), model)\n",
    "        metrics_score = m.metrics_score\n",
    "        metrics_score['features'] = max_features\n",
    "        metrics_score['method'] = \"CountVectorizer\"\n",
    "        table.append(metrics_score)\n",
    "\n",
    "        m = Modelisation(X, y, TfidfVectorizer(max_features=max_features), model)\n",
    "        metrics_score = m.metrics_score\n",
    "        metrics_score['features'] = max_features\n",
    "        metrics_score['method'] = \"TfidfVectorizer\"\n",
    "        table.append(metrics_score)\n",
    "\n",
    "        m = Modelisation(X, y, CountVectorizer(max_features=max_features, ngram_range=(1, 2)), model)\n",
    "        metrics_score = m.metrics_score\n",
    "        metrics_score['features'] = max_features\n",
    "        metrics_score['method'] = \"CountVectorizer ngram 2\"\n",
    "        table.append(metrics_score)\n",
    "\n",
    "        m = Modelisation(X, y, TfidfVectorizer(max_features=max_features, ngram_range=(1, 2)), model)\n",
    "        metrics_score = m.metrics_score\n",
    "        metrics_score['features'] = max_features\n",
    "        metrics_score['method'] = \"TfidfVectorizer ngram 2\"\n",
    "        table.append(metrics_score)\n",
    "\n",
    "    df = pd.DataFrame(table)\n",
    "\n",
    "    methods = list(set(df.method))\n",
    "    scores = list(df.columns[:-2])\n",
    "    fig, axes = plt.subplots(1, len(scores), figsize=(20, 6))\n",
    "    for i_score in range(len(scores)):\n",
    "        for j_method in range(len(methods)):\n",
    "            df[df.method == methods[j_method]].plot(x='features', y=scores[i_score], label=methods[j_method], ax=axes[i_score])\n",
    "        axes[i_score].set(xlabel='Nombre de features')\n",
    "        axes[i_score].legend()\n",
    "        axes[i_score].set_title(scores[i_score])\n",
    "    plt.show()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Chargement d'une base Sentiment140 préprocessée "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_DATA = 100000\n",
    "STOP_WORDS = False\n",
    "LEMMATIZATION = False\n",
    "NEGATION = False\n",
    "df = sentiment140(STOP_WORDS, LEMMATIZATION, NEGATION, NB_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>query_string</th>\n",
       "      <th>user</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>599410</th>\n",
       "      <td>Negative</td>\n",
       "      <td>2220663113</td>\n",
       "      <td>Thu Jun 18 03:39:49 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>gemgembam</td>\n",
       "      <td>open itunes will not open naooo the silence is...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549856</th>\n",
       "      <td>Negative</td>\n",
       "      <td>2202759103</td>\n",
       "      <td>Tue Jun 16 22:21:43 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>twistofcolour</td>\n",
       "      <td>the sun is already gone for the day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363048</th>\n",
       "      <td>Positive</td>\n",
       "      <td>2049441111</td>\n",
       "      <td>Fri Jun 05 16:53:10 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>Niico1e</td>\n",
       "      <td>hiya this is my st tweet i am so tired though ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558776</th>\n",
       "      <td>Negative</td>\n",
       "      <td>2204830292</td>\n",
       "      <td>Wed Jun 17 03:14:38 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>MeriEsteban</td>\n",
       "      <td>i have the bac today at p m i do not know if i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582398</th>\n",
       "      <td>Positive</td>\n",
       "      <td>2190306594</td>\n",
       "      <td>Tue Jun 16 02:21:03 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>MeghanMagnolia</td>\n",
       "      <td>as far as i remember you were not half bad ine...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353343</th>\n",
       "      <td>Positive</td>\n",
       "      <td>2046706853</td>\n",
       "      <td>Fri Jun 05 12:34:26 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>WahooPooh</td>\n",
       "      <td>as tempting as that is i am just waiting on my...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586354</th>\n",
       "      <td>Negative</td>\n",
       "      <td>2215937155</td>\n",
       "      <td>Wed Jun 17 18:49:10 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>amandarie</td>\n",
       "      <td>cool how i have to be stuck at home i want to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860355</th>\n",
       "      <td>Positive</td>\n",
       "      <td>1676343245</td>\n",
       "      <td>Fri May 01 21:46:07 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>MatchesMalone</td>\n",
       "      <td>oh and followfriday my writing partner because...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521668</th>\n",
       "      <td>Negative</td>\n",
       "      <td>2192486836</td>\n",
       "      <td>Tue Jun 16 07:06:35 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>hockeyfan45214</td>\n",
       "      <td>you would think if i continue to block someone...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243888</th>\n",
       "      <td>Negative</td>\n",
       "      <td>1981647618</td>\n",
       "      <td>Sun May 31 09:35:54 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>ilovemytroops</td>\n",
       "      <td>nevaeh buchanan missing friends of her mother ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sentiment          id                          date query_string  \\\n",
       "599410   Negative  2220663113  Thu Jun 18 03:39:49 PDT 2009     NO_QUERY   \n",
       "549856   Negative  2202759103  Tue Jun 16 22:21:43 PDT 2009     NO_QUERY   \n",
       "1363048  Positive  2049441111  Fri Jun 05 16:53:10 PDT 2009     NO_QUERY   \n",
       "558776   Negative  2204830292  Wed Jun 17 03:14:38 PDT 2009     NO_QUERY   \n",
       "1582398  Positive  2190306594  Tue Jun 16 02:21:03 PDT 2009     NO_QUERY   \n",
       "1353343  Positive  2046706853  Fri Jun 05 12:34:26 PDT 2009     NO_QUERY   \n",
       "586354   Negative  2215937155  Wed Jun 17 18:49:10 PDT 2009     NO_QUERY   \n",
       "860355   Positive  1676343245  Fri May 01 21:46:07 PDT 2009     NO_QUERY   \n",
       "521668   Negative  2192486836  Tue Jun 16 07:06:35 PDT 2009     NO_QUERY   \n",
       "243888   Negative  1981647618  Sun May 31 09:35:54 PDT 2009     NO_QUERY   \n",
       "\n",
       "                   user                                               text  \n",
       "599410        gemgembam  open itunes will not open naooo the silence is...  \n",
       "549856    twistofcolour                the sun is already gone for the day  \n",
       "1363048         Niico1e  hiya this is my st tweet i am so tired though ...  \n",
       "558776      MeriEsteban  i have the bac today at p m i do not know if i...  \n",
       "1582398  MeghanMagnolia  as far as i remember you were not half bad ine...  \n",
       "1353343       WahooPooh  as tempting as that is i am just waiting on my...  \n",
       "586354        amandarie  cool how i have to be stuck at home i want to ...  \n",
       "860355    MatchesMalone  oh and followfriday my writing partner because...  \n",
       "521668   hockeyfan45214  you would think if i continue to block someone...  \n",
       "243888    ilovemytroops  nevaeh buchanan missing friends of her mother ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.text.to_list()\n",
    "y = df.sentiment.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Comparaison entre CountVectorizer et TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 19 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69125</td>\n",
       "      <td>0.699269</td>\n",
       "      <td>0.756736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70365</td>\n",
       "      <td>0.705249</td>\n",
       "      <td>0.755654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70110</td>\n",
       "      <td>0.704653</td>\n",
       "      <td>0.768165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71075</td>\n",
       "      <td>0.710815</td>\n",
       "      <td>0.766956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc\n",
       "CountVectorizer 500           0.69125           0.699269  0.756736\n",
       "TfidfVectorizer 500           0.70365           0.705249  0.755654\n",
       "CountVectorizer 500 ngram 2   0.70110           0.704653  0.768165\n",
       "TfidfVectorizer 500 ngram 2   0.71075           0.710815  0.766956"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "comparaison(X, y, 500, GaussianNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 41s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 5000</th>\n",
       "      <td>0.66075</td>\n",
       "      <td>0.669049</td>\n",
       "      <td>0.670408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 5000</th>\n",
       "      <td>0.68800</td>\n",
       "      <td>0.689589</td>\n",
       "      <td>0.701881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 5000 ngram 2</th>\n",
       "      <td>0.72825</td>\n",
       "      <td>0.731327</td>\n",
       "      <td>0.749425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 5000 ngram 2</th>\n",
       "      <td>0.74120</td>\n",
       "      <td>0.741487</td>\n",
       "      <td>0.766212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              accuracy  balanced_accuracy   roc_auc\n",
       "CountVectorizer 5000           0.66075           0.669049  0.670408\n",
       "TfidfVectorizer 5000           0.68800           0.689589  0.701881\n",
       "CountVectorizer 5000 ngram 2   0.72825           0.731327  0.749425\n",
       "TfidfVectorizer 5000 ngram 2   0.74120           0.741487  0.766212"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "comparaison(X, y, 5000, GaussianNB())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A priori le TfidfVectorizer 5000 ngram 2 est le plus approprié."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Comparaison des méthodes de pré-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_words: False, lemmatization: False, negation: False\n",
      "stop_words: False, lemmatization: False, negation: True\n",
      "stop_words: False, lemmatization: True, negation: False\n",
      "stop_words: False, lemmatization: True, negation: True\n",
      "stop_words: True, lemmatization: False, negation: False\n",
      "stop_words: True, lemmatization: False, negation: True\n",
      "stop_words: True, lemmatization: True, negation: False\n",
      "stop_words: True, lemmatization: True, negation: True\n",
      "Wall time: 3min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_metrics = influence_preprocessing(500, GaussianNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>stop_words</th>\n",
       "      <th>lemmatization</th>\n",
       "      <th>negation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71530</td>\n",
       "      <td>0.715305</td>\n",
       "      <td>0.771489</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70725</td>\n",
       "      <td>0.709766</td>\n",
       "      <td>0.770466</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70110</td>\n",
       "      <td>0.704653</td>\n",
       "      <td>0.768165</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70215</td>\n",
       "      <td>0.706521</td>\n",
       "      <td>0.767446</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71075</td>\n",
       "      <td>0.710815</td>\n",
       "      <td>0.766956</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71105</td>\n",
       "      <td>0.711581</td>\n",
       "      <td>0.766681</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69900</td>\n",
       "      <td>0.706051</td>\n",
       "      <td>0.765082</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69540</td>\n",
       "      <td>0.699806</td>\n",
       "      <td>0.762888</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70780</td>\n",
       "      <td>0.709840</td>\n",
       "      <td>0.762250</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70485</td>\n",
       "      <td>0.705140</td>\n",
       "      <td>0.762156</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70615</td>\n",
       "      <td>0.709386</td>\n",
       "      <td>0.760543</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70465</td>\n",
       "      <td>0.711610</td>\n",
       "      <td>0.760463</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69455</td>\n",
       "      <td>0.704395</td>\n",
       "      <td>0.760087</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70410</td>\n",
       "      <td>0.707367</td>\n",
       "      <td>0.758752</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.70090</td>\n",
       "      <td>0.707507</td>\n",
       "      <td>0.757425</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69125</td>\n",
       "      <td>0.699269</td>\n",
       "      <td>0.756736</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69290</td>\n",
       "      <td>0.701654</td>\n",
       "      <td>0.756177</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70385</td>\n",
       "      <td>0.707014</td>\n",
       "      <td>0.755825</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70365</td>\n",
       "      <td>0.705249</td>\n",
       "      <td>0.755654</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70010</td>\n",
       "      <td>0.707895</td>\n",
       "      <td>0.755525</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69240</td>\n",
       "      <td>0.702942</td>\n",
       "      <td>0.755478</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69305</td>\n",
       "      <td>0.697078</td>\n",
       "      <td>0.755009</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70480</td>\n",
       "      <td>0.707817</td>\n",
       "      <td>0.754647</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70090</td>\n",
       "      <td>0.701091</td>\n",
       "      <td>0.754403</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.69870</td>\n",
       "      <td>0.703525</td>\n",
       "      <td>0.753099</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69435</td>\n",
       "      <td>0.704024</td>\n",
       "      <td>0.752912</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.69945</td>\n",
       "      <td>0.705531</td>\n",
       "      <td>0.752700</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70190</td>\n",
       "      <td>0.705879</td>\n",
       "      <td>0.751009</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.68840</td>\n",
       "      <td>0.696732</td>\n",
       "      <td>0.747984</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.69735</td>\n",
       "      <td>0.700955</td>\n",
       "      <td>0.744731</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69005</td>\n",
       "      <td>0.693151</td>\n",
       "      <td>0.743161</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.68675</td>\n",
       "      <td>0.686793</td>\n",
       "      <td>0.742557</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc  \\\n",
       "TfidfVectorizer 500 ngram 2   0.71530           0.715305  0.771489   \n",
       "CountVectorizer 500 ngram 2   0.70725           0.709766  0.770466   \n",
       "CountVectorizer 500 ngram 2   0.70110           0.704653  0.768165   \n",
       "CountVectorizer 500 ngram 2   0.70215           0.706521  0.767446   \n",
       "TfidfVectorizer 500 ngram 2   0.71075           0.710815  0.766956   \n",
       "TfidfVectorizer 500 ngram 2   0.71105           0.711581  0.766681   \n",
       "CountVectorizer 500           0.69900           0.706051  0.765082   \n",
       "CountVectorizer 500 ngram 2   0.69540           0.699806  0.762888   \n",
       "TfidfVectorizer 500           0.70780           0.709840  0.762250   \n",
       "TfidfVectorizer 500 ngram 2   0.70485           0.705140  0.762156   \n",
       "TfidfVectorizer 500           0.70615           0.709386  0.760543   \n",
       "CountVectorizer 500 ngram 2   0.70465           0.711610  0.760463   \n",
       "CountVectorizer 500           0.69455           0.704395  0.760087   \n",
       "TfidfVectorizer 500 ngram 2   0.70410           0.707367  0.758752   \n",
       "CountVectorizer 500           0.70090           0.707507  0.757425   \n",
       "CountVectorizer 500           0.69125           0.699269  0.756736   \n",
       "CountVectorizer 500 ngram 2   0.69290           0.701654  0.756177   \n",
       "TfidfVectorizer 500           0.70385           0.707014  0.755825   \n",
       "TfidfVectorizer 500           0.70365           0.705249  0.755654   \n",
       "CountVectorizer 500 ngram 2   0.70010           0.707895  0.755525   \n",
       "CountVectorizer 500           0.69240           0.702942  0.755478   \n",
       "CountVectorizer 500           0.69305           0.697078  0.755009   \n",
       "TfidfVectorizer 500 ngram 2   0.70480           0.707817  0.754647   \n",
       "TfidfVectorizer 500           0.70090           0.701091  0.754403   \n",
       "TfidfVectorizer 500 ngram 2   0.69870           0.703525  0.753099   \n",
       "CountVectorizer 500 ngram 2   0.69435           0.704024  0.752912   \n",
       "TfidfVectorizer 500           0.69945           0.705531  0.752700   \n",
       "TfidfVectorizer 500 ngram 2   0.70190           0.705879  0.751009   \n",
       "CountVectorizer 500           0.68840           0.696732  0.747984   \n",
       "TfidfVectorizer 500           0.69735           0.700955  0.744731   \n",
       "CountVectorizer 500           0.69005           0.693151  0.743161   \n",
       "TfidfVectorizer 500           0.68675           0.686793  0.742557   \n",
       "\n",
       "                             stop_words  lemmatization  negation  \n",
       "TfidfVectorizer 500 ngram 2       False           True     False  \n",
       "CountVectorizer 500 ngram 2       False           True     False  \n",
       "CountVectorizer 500 ngram 2       False          False     False  \n",
       "CountVectorizer 500 ngram 2       False           True      True  \n",
       "TfidfVectorizer 500 ngram 2       False          False     False  \n",
       "TfidfVectorizer 500 ngram 2       False           True      True  \n",
       "CountVectorizer 500               False           True      True  \n",
       "CountVectorizer 500 ngram 2       False          False      True  \n",
       "TfidfVectorizer 500               False           True      True  \n",
       "TfidfVectorizer 500 ngram 2       False          False      True  \n",
       "TfidfVectorizer 500               False          False      True  \n",
       "CountVectorizer 500 ngram 2        True           True      True  \n",
       "CountVectorizer 500               False          False      True  \n",
       "TfidfVectorizer 500 ngram 2        True           True      True  \n",
       "CountVectorizer 500                True           True      True  \n",
       "CountVectorizer 500               False          False     False  \n",
       "CountVectorizer 500 ngram 2        True          False      True  \n",
       "TfidfVectorizer 500                True           True      True  \n",
       "TfidfVectorizer 500               False          False     False  \n",
       "CountVectorizer 500 ngram 2        True           True     False  \n",
       "CountVectorizer 500                True          False      True  \n",
       "CountVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500 ngram 2        True           True     False  \n",
       "TfidfVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500 ngram 2        True          False      True  \n",
       "CountVectorizer 500 ngram 2        True          False     False  \n",
       "TfidfVectorizer 500                True          False      True  \n",
       "TfidfVectorizer 500 ngram 2        True          False     False  \n",
       "CountVectorizer 500                True          False     False  \n",
       "TfidfVectorizer 500                True          False     False  \n",
       "CountVectorizer 500                True           True     False  \n",
       "TfidfVectorizer 500                True           True     False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics.sort_values(by=\"roc_auc\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.707507</td>\n",
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       "      <td>0.70385</td>\n",
       "      <td>0.707014</td>\n",
       "      <td>0.755825</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70215</td>\n",
       "      <td>0.706521</td>\n",
       "      <td>0.767446</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69900</td>\n",
       "      <td>0.706051</td>\n",
       "      <td>0.765082</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70190</td>\n",
       "      <td>0.705879</td>\n",
       "      <td>0.751009</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.69945</td>\n",
       "      <td>0.705531</td>\n",
       "      <td>0.752700</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70365</td>\n",
       "      <td>0.705249</td>\n",
       "      <td>0.755654</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70485</td>\n",
       "      <td>0.705140</td>\n",
       "      <td>0.762156</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70110</td>\n",
       "      <td>0.704653</td>\n",
       "      <td>0.768165</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69455</td>\n",
       "      <td>0.704395</td>\n",
       "      <td>0.760087</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69435</td>\n",
       "      <td>0.704024</td>\n",
       "      <td>0.752912</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.69870</td>\n",
       "      <td>0.703525</td>\n",
       "      <td>0.753099</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69240</td>\n",
       "      <td>0.702942</td>\n",
       "      <td>0.755478</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69290</td>\n",
       "      <td>0.701654</td>\n",
       "      <td>0.756177</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70090</td>\n",
       "      <td>0.701091</td>\n",
       "      <td>0.754403</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.69735</td>\n",
       "      <td>0.700955</td>\n",
       "      <td>0.744731</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.69540</td>\n",
       "      <td>0.699806</td>\n",
       "      <td>0.762888</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69125</td>\n",
       "      <td>0.699269</td>\n",
       "      <td>0.756736</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69305</td>\n",
       "      <td>0.697078</td>\n",
       "      <td>0.755009</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.68840</td>\n",
       "      <td>0.696732</td>\n",
       "      <td>0.747984</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.69005</td>\n",
       "      <td>0.693151</td>\n",
       "      <td>0.743161</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.68675</td>\n",
       "      <td>0.686793</td>\n",
       "      <td>0.742557</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc  \\\n",
       "TfidfVectorizer 500 ngram 2   0.71530           0.715305  0.771489   \n",
       "CountVectorizer 500 ngram 2   0.70465           0.711610  0.760463   \n",
       "TfidfVectorizer 500 ngram 2   0.71105           0.711581  0.766681   \n",
       "TfidfVectorizer 500 ngram 2   0.71075           0.710815  0.766956   \n",
       "TfidfVectorizer 500           0.70780           0.709840  0.762250   \n",
       "CountVectorizer 500 ngram 2   0.70725           0.709766  0.770466   \n",
       "TfidfVectorizer 500           0.70615           0.709386  0.760543   \n",
       "CountVectorizer 500 ngram 2   0.70010           0.707895  0.755525   \n",
       "TfidfVectorizer 500 ngram 2   0.70480           0.707817  0.754647   \n",
       "CountVectorizer 500           0.70090           0.707507  0.757425   \n",
       "TfidfVectorizer 500 ngram 2   0.70410           0.707367  0.758752   \n",
       "TfidfVectorizer 500           0.70385           0.707014  0.755825   \n",
       "CountVectorizer 500 ngram 2   0.70215           0.706521  0.767446   \n",
       "CountVectorizer 500           0.69900           0.706051  0.765082   \n",
       "TfidfVectorizer 500 ngram 2   0.70190           0.705879  0.751009   \n",
       "TfidfVectorizer 500           0.69945           0.705531  0.752700   \n",
       "TfidfVectorizer 500           0.70365           0.705249  0.755654   \n",
       "TfidfVectorizer 500 ngram 2   0.70485           0.705140  0.762156   \n",
       "CountVectorizer 500 ngram 2   0.70110           0.704653  0.768165   \n",
       "CountVectorizer 500           0.69455           0.704395  0.760087   \n",
       "CountVectorizer 500 ngram 2   0.69435           0.704024  0.752912   \n",
       "TfidfVectorizer 500 ngram 2   0.69870           0.703525  0.753099   \n",
       "CountVectorizer 500           0.69240           0.702942  0.755478   \n",
       "CountVectorizer 500 ngram 2   0.69290           0.701654  0.756177   \n",
       "TfidfVectorizer 500           0.70090           0.701091  0.754403   \n",
       "TfidfVectorizer 500           0.69735           0.700955  0.744731   \n",
       "CountVectorizer 500 ngram 2   0.69540           0.699806  0.762888   \n",
       "CountVectorizer 500           0.69125           0.699269  0.756736   \n",
       "CountVectorizer 500           0.69305           0.697078  0.755009   \n",
       "CountVectorizer 500           0.68840           0.696732  0.747984   \n",
       "CountVectorizer 500           0.69005           0.693151  0.743161   \n",
       "TfidfVectorizer 500           0.68675           0.686793  0.742557   \n",
       "\n",
       "                             stop_words  lemmatization  negation  \n",
       "TfidfVectorizer 500 ngram 2       False           True     False  \n",
       "CountVectorizer 500 ngram 2        True           True      True  \n",
       "TfidfVectorizer 500 ngram 2       False           True      True  \n",
       "TfidfVectorizer 500 ngram 2       False          False     False  \n",
       "TfidfVectorizer 500               False           True      True  \n",
       "CountVectorizer 500 ngram 2       False           True     False  \n",
       "TfidfVectorizer 500               False          False      True  \n",
       "CountVectorizer 500 ngram 2        True           True     False  \n",
       "TfidfVectorizer 500 ngram 2        True           True     False  \n",
       "CountVectorizer 500                True           True      True  \n",
       "TfidfVectorizer 500 ngram 2        True           True      True  \n",
       "TfidfVectorizer 500                True           True      True  \n",
       "CountVectorizer 500 ngram 2       False           True      True  \n",
       "CountVectorizer 500               False           True      True  \n",
       "TfidfVectorizer 500 ngram 2        True          False     False  \n",
       "TfidfVectorizer 500                True          False      True  \n",
       "TfidfVectorizer 500               False          False     False  \n",
       "TfidfVectorizer 500 ngram 2       False          False      True  \n",
       "CountVectorizer 500 ngram 2       False          False     False  \n",
       "CountVectorizer 500               False          False      True  \n",
       "CountVectorizer 500 ngram 2        True          False     False  \n",
       "TfidfVectorizer 500 ngram 2        True          False      True  \n",
       "CountVectorizer 500                True          False      True  \n",
       "CountVectorizer 500 ngram 2        True          False      True  \n",
       "TfidfVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500                True          False     False  \n",
       "CountVectorizer 500 ngram 2       False          False      True  \n",
       "CountVectorizer 500               False          False     False  \n",
       "CountVectorizer 500               False           True     False  \n",
       "CountVectorizer 500                True          False     False  \n",
       "CountVectorizer 500                True           True     False  \n",
       "TfidfVectorizer 500                True           True     False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics.sort_values(by=\"balanced_accuracy\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La combinaison optimale semble correspondre à `stop_words=False, lemmatization=True, negation=False` pour ce nombre de features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Comparaison par rapport au nombre de features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "max_features_list = np.logspace(2.3, 3.7, 6, endpoint=True, dtype=int)\n",
    "_ = influence_features(X, y, max_features_list, GaussianNB())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Multinomial NB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Comparaison entre CountVectorizer et TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12.4 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.72280</td>\n",
       "      <td>0.722881</td>\n",
       "      <td>0.791750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.72280</td>\n",
       "      <td>0.722901</td>\n",
       "      <td>0.798565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.72180</td>\n",
       "      <td>0.721814</td>\n",
       "      <td>0.791805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71965</td>\n",
       "      <td>0.719675</td>\n",
       "      <td>0.797992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc\n",
       "CountVectorizer 500           0.72280           0.722881  0.791750\n",
       "TfidfVectorizer 500           0.72280           0.722901  0.798565\n",
       "CountVectorizer 500 ngram 2   0.72180           0.721814  0.791805\n",
       "TfidfVectorizer 500 ngram 2   0.71965           0.719675  0.797992"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "comparaison(X, y, 500, MultinomialNB(alpha=1)) #par défaut alpha=1 : Laplace smoothing parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12.8 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 5000</th>\n",
       "      <td>0.72340</td>\n",
       "      <td>0.723435</td>\n",
       "      <td>0.766215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 5000</th>\n",
       "      <td>0.72070</td>\n",
       "      <td>0.720702</td>\n",
       "      <td>0.771534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 5000 ngram 2</th>\n",
       "      <td>0.76060</td>\n",
       "      <td>0.760601</td>\n",
       "      <td>0.819857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 5000 ngram 2</th>\n",
       "      <td>0.75845</td>\n",
       "      <td>0.758453</td>\n",
       "      <td>0.824121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              accuracy  balanced_accuracy   roc_auc\n",
       "CountVectorizer 5000           0.72340           0.723435  0.766215\n",
       "TfidfVectorizer 5000           0.72070           0.720702  0.771534\n",
       "CountVectorizer 5000 ngram 2   0.76060           0.760601  0.819857\n",
       "TfidfVectorizer 5000 ngram 2   0.75845           0.758453  0.824121"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "comparaison(X, y, 5000, MultinomialNB(alpha=1)) #par défaut alpha=1 : Laplace smoothing parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A priori le CountVectorizer (avec ngram) semble le plus approprié."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Comparaison des méthodes de pré-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_words: False, lemmatization: False, negation: False\n",
      "stop_words: False, lemmatization: False, negation: True\n",
      "stop_words: False, lemmatization: True, negation: False\n",
      "stop_words: False, lemmatization: True, negation: True\n",
      "stop_words: True, lemmatization: False, negation: False\n",
      "stop_words: True, lemmatization: False, negation: True\n",
      "stop_words: True, lemmatization: True, negation: False\n",
      "stop_words: True, lemmatization: True, negation: True\n",
      "Wall time: 2min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_metrics = influence_preprocessing(500, MultinomialNB(alpha=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71450</td>\n",
       "      <td>0.716001</td>\n",
       "      <td>0.792781</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>CountVectorizer 500</th>\n",
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       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.72180</td>\n",
       "      <td>0.721814</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.72280</td>\n",
       "      <td>0.722881</td>\n",
       "      <td>0.791750</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.72310</td>\n",
       "      <td>0.723099</td>\n",
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       "      <td>False</td>\n",
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       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.72060</td>\n",
       "      <td>0.720619</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>CountVectorizer 500 ngram 2</th>\n",
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       "      <th>TfidfVectorizer 500</th>\n",
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       "      <td>0.710298</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.71635</td>\n",
       "      <td>0.717526</td>\n",
       "      <td>0.788207</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71125</td>\n",
       "      <td>0.713159</td>\n",
       "      <td>0.787471</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71985</td>\n",
       "      <td>0.721021</td>\n",
       "      <td>0.786949</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70630</td>\n",
       "      <td>0.708845</td>\n",
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       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70775</td>\n",
       "      <td>0.709032</td>\n",
       "      <td>0.784644</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71480</td>\n",
       "      <td>0.714799</td>\n",
       "      <td>0.784580</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71180</td>\n",
       "      <td>0.713222</td>\n",
       "      <td>0.782798</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70790</td>\n",
       "      <td>0.710099</td>\n",
       "      <td>0.782268</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70755</td>\n",
       "      <td>0.707988</td>\n",
       "      <td>0.781829</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.71015</td>\n",
       "      <td>0.711061</td>\n",
       "      <td>0.781076</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.70905</td>\n",
       "      <td>0.709330</td>\n",
       "      <td>0.777934</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc  \\\n",
       "TfidfVectorizer 500 ngram 2   0.72705           0.727059  0.803151   \n",
       "TfidfVectorizer 500           0.72495           0.725205  0.802768   \n",
       "TfidfVectorizer 500           0.72240           0.722755  0.798747   \n",
       "TfidfVectorizer 500           0.72280           0.722901  0.798565   \n",
       "TfidfVectorizer 500 ngram 2   0.71965           0.719675  0.797992   \n",
       "TfidfVectorizer 500           0.72075           0.720766  0.797682   \n",
       "TfidfVectorizer 500 ngram 2   0.72220           0.722236  0.797219   \n",
       "CountVectorizer 500           0.72635           0.726533  0.797031   \n",
       "CountVectorizer 500 ngram 2   0.72720           0.727232  0.795446   \n",
       "TfidfVectorizer 500 ngram 2   0.71450           0.716001  0.792781   \n",
       "CountVectorizer 500           0.72255           0.722817  0.792202   \n",
       "TfidfVectorizer 500 ngram 2   0.71750           0.718743  0.792025   \n",
       "TfidfVectorizer 500           0.71320           0.714484  0.791980   \n",
       "CountVectorizer 500 ngram 2   0.72180           0.721814  0.791805   \n",
       "CountVectorizer 500           0.72280           0.722881  0.791750   \n",
       "CountVectorizer 500           0.72310           0.723099  0.791715   \n",
       "TfidfVectorizer 500 ngram 2   0.71210           0.712107  0.790650   \n",
       "CountVectorizer 500 ngram 2   0.72060           0.720619  0.790331   \n",
       "CountVectorizer 500 ngram 2   0.71725           0.718654  0.788958   \n",
       "TfidfVectorizer 500           0.70720           0.710298  0.788406   \n",
       "CountVectorizer 500           0.71635           0.717526  0.788207   \n",
       "TfidfVectorizer 500 ngram 2   0.71125           0.713159  0.787471   \n",
       "CountVectorizer 500 ngram 2   0.71985           0.721021  0.786949   \n",
       "TfidfVectorizer 500 ngram 2   0.70630           0.708845  0.785576   \n",
       "CountVectorizer 500           0.70965           0.712321  0.784950   \n",
       "TfidfVectorizer 500           0.70775           0.709032  0.784644   \n",
       "CountVectorizer 500 ngram 2   0.71480           0.714799  0.784580   \n",
       "CountVectorizer 500 ngram 2   0.71180           0.713222  0.782798   \n",
       "CountVectorizer 500 ngram 2   0.70790           0.710099  0.782268   \n",
       "TfidfVectorizer 500           0.70755           0.707988  0.781829   \n",
       "CountVectorizer 500           0.71015           0.711061  0.781076   \n",
       "CountVectorizer 500           0.70905           0.709330  0.777934   \n",
       "\n",
       "                             stop_words  lemmatization  negation  \n",
       "TfidfVectorizer 500 ngram 2       False           True     False  \n",
       "TfidfVectorizer 500               False           True      True  \n",
       "TfidfVectorizer 500               False          False      True  \n",
       "TfidfVectorizer 500               False          False     False  \n",
       "TfidfVectorizer 500 ngram 2       False          False     False  \n",
       "TfidfVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500 ngram 2       False           True      True  \n",
       "CountVectorizer 500               False           True      True  \n",
       "CountVectorizer 500 ngram 2       False           True     False  \n",
       "TfidfVectorizer 500 ngram 2        True           True      True  \n",
       "CountVectorizer 500               False          False      True  \n",
       "TfidfVectorizer 500 ngram 2        True           True     False  \n",
       "TfidfVectorizer 500                True           True      True  \n",
       "CountVectorizer 500 ngram 2       False          False     False  \n",
       "CountVectorizer 500               False          False     False  \n",
       "CountVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500 ngram 2       False          False      True  \n",
       "CountVectorizer 500 ngram 2       False           True      True  \n",
       "CountVectorizer 500 ngram 2        True           True      True  \n",
       "TfidfVectorizer 500                True          False      True  \n",
       "CountVectorizer 500                True           True      True  \n",
       "TfidfVectorizer 500 ngram 2        True          False     False  \n",
       "CountVectorizer 500 ngram 2        True           True     False  \n",
       "TfidfVectorizer 500 ngram 2        True          False      True  \n",
       "CountVectorizer 500                True          False      True  \n",
       "TfidfVectorizer 500                True          False     False  \n",
       "CountVectorizer 500 ngram 2       False          False      True  \n",
       "CountVectorizer 500 ngram 2        True          False     False  \n",
       "CountVectorizer 500 ngram 2        True          False      True  \n",
       "TfidfVectorizer 500                True           True     False  \n",
       "CountVectorizer 500                True          False     False  \n",
       "CountVectorizer 500                True           True     False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics.sort_values(by=\"roc_auc\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>accuracy</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.72240</td>\n",
       "      <td>0.722755</td>\n",
       "      <td>0.798747</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.72220</td>\n",
       "      <td>0.722236</td>\n",
       "      <td>0.797219</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.72180</td>\n",
       "      <td>0.721814</td>\n",
       "      <td>0.791805</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71985</td>\n",
       "      <td>0.721021</td>\n",
       "      <td>0.786949</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.72075</td>\n",
       "      <td>0.720766</td>\n",
       "      <td>0.797682</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.72060</td>\n",
       "      <td>0.720619</td>\n",
       "      <td>0.790331</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71965</td>\n",
       "      <td>0.719675</td>\n",
       "      <td>0.797992</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71750</td>\n",
       "      <td>0.718743</td>\n",
       "      <td>0.792025</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71725</td>\n",
       "      <td>0.718654</td>\n",
       "      <td>0.788958</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.71635</td>\n",
       "      <td>0.717526</td>\n",
       "      <td>0.788207</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71450</td>\n",
       "      <td>0.716001</td>\n",
       "      <td>0.792781</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71480</td>\n",
       "      <td>0.714799</td>\n",
       "      <td>0.784580</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.71320</td>\n",
       "      <td>0.714484</td>\n",
       "      <td>0.791980</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.71180</td>\n",
       "      <td>0.713222</td>\n",
       "      <td>0.782798</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71125</td>\n",
       "      <td>0.713159</td>\n",
       "      <td>0.787471</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.70965</td>\n",
       "      <td>0.712321</td>\n",
       "      <td>0.784950</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.71210</td>\n",
       "      <td>0.712107</td>\n",
       "      <td>0.790650</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.71015</td>\n",
       "      <td>0.711061</td>\n",
       "      <td>0.781076</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70720</td>\n",
       "      <td>0.710298</td>\n",
       "      <td>0.788406</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500 ngram 2</th>\n",
       "      <td>0.70790</td>\n",
       "      <td>0.710099</td>\n",
       "      <td>0.782268</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountVectorizer 500</th>\n",
       "      <td>0.70905</td>\n",
       "      <td>0.709330</td>\n",
       "      <td>0.777934</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70775</td>\n",
       "      <td>0.709032</td>\n",
       "      <td>0.784644</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500 ngram 2</th>\n",
       "      <td>0.70630</td>\n",
       "      <td>0.708845</td>\n",
       "      <td>0.785576</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TfidfVectorizer 500</th>\n",
       "      <td>0.70755</td>\n",
       "      <td>0.707988</td>\n",
       "      <td>0.781829</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             accuracy  balanced_accuracy   roc_auc  \\\n",
       "CountVectorizer 500 ngram 2   0.72720           0.727232  0.795446   \n",
       "TfidfVectorizer 500 ngram 2   0.72705           0.727059  0.803151   \n",
       "CountVectorizer 500           0.72635           0.726533  0.797031   \n",
       "TfidfVectorizer 500           0.72495           0.725205  0.802768   \n",
       "CountVectorizer 500           0.72310           0.723099  0.791715   \n",
       "TfidfVectorizer 500           0.72280           0.722901  0.798565   \n",
       "CountVectorizer 500           0.72280           0.722881  0.791750   \n",
       "CountVectorizer 500           0.72255           0.722817  0.792202   \n",
       "TfidfVectorizer 500           0.72240           0.722755  0.798747   \n",
       "TfidfVectorizer 500 ngram 2   0.72220           0.722236  0.797219   \n",
       "CountVectorizer 500 ngram 2   0.72180           0.721814  0.791805   \n",
       "CountVectorizer 500 ngram 2   0.71985           0.721021  0.786949   \n",
       "TfidfVectorizer 500           0.72075           0.720766  0.797682   \n",
       "CountVectorizer 500 ngram 2   0.72060           0.720619  0.790331   \n",
       "TfidfVectorizer 500 ngram 2   0.71965           0.719675  0.797992   \n",
       "TfidfVectorizer 500 ngram 2   0.71750           0.718743  0.792025   \n",
       "CountVectorizer 500 ngram 2   0.71725           0.718654  0.788958   \n",
       "CountVectorizer 500           0.71635           0.717526  0.788207   \n",
       "TfidfVectorizer 500 ngram 2   0.71450           0.716001  0.792781   \n",
       "CountVectorizer 500 ngram 2   0.71480           0.714799  0.784580   \n",
       "TfidfVectorizer 500           0.71320           0.714484  0.791980   \n",
       "CountVectorizer 500 ngram 2   0.71180           0.713222  0.782798   \n",
       "TfidfVectorizer 500 ngram 2   0.71125           0.713159  0.787471   \n",
       "CountVectorizer 500           0.70965           0.712321  0.784950   \n",
       "TfidfVectorizer 500 ngram 2   0.71210           0.712107  0.790650   \n",
       "CountVectorizer 500           0.71015           0.711061  0.781076   \n",
       "TfidfVectorizer 500           0.70720           0.710298  0.788406   \n",
       "CountVectorizer 500 ngram 2   0.70790           0.710099  0.782268   \n",
       "CountVectorizer 500           0.70905           0.709330  0.777934   \n",
       "TfidfVectorizer 500           0.70775           0.709032  0.784644   \n",
       "TfidfVectorizer 500 ngram 2   0.70630           0.708845  0.785576   \n",
       "TfidfVectorizer 500           0.70755           0.707988  0.781829   \n",
       "\n",
       "                             stop_words  lemmatization  negation  \n",
       "CountVectorizer 500 ngram 2       False           True     False  \n",
       "TfidfVectorizer 500 ngram 2       False           True     False  \n",
       "CountVectorizer 500               False           True      True  \n",
       "TfidfVectorizer 500               False           True      True  \n",
       "CountVectorizer 500               False           True     False  \n",
       "TfidfVectorizer 500               False          False     False  \n",
       "CountVectorizer 500               False          False     False  \n",
       "CountVectorizer 500               False          False      True  \n",
       "TfidfVectorizer 500               False          False      True  \n",
       "TfidfVectorizer 500 ngram 2       False           True      True  \n",
       "CountVectorizer 500 ngram 2       False          False     False  \n",
       "CountVectorizer 500 ngram 2        True           True     False  \n",
       "TfidfVectorizer 500               False           True     False  \n",
       "CountVectorizer 500 ngram 2       False           True      True  \n",
       "TfidfVectorizer 500 ngram 2       False          False     False  \n",
       "TfidfVectorizer 500 ngram 2        True           True     False  \n",
       "CountVectorizer 500 ngram 2        True           True      True  \n",
       "CountVectorizer 500                True           True      True  \n",
       "TfidfVectorizer 500 ngram 2        True           True      True  \n",
       "CountVectorizer 500 ngram 2       False          False      True  \n",
       "TfidfVectorizer 500                True           True      True  \n",
       "CountVectorizer 500 ngram 2        True          False     False  \n",
       "TfidfVectorizer 500 ngram 2        True          False     False  \n",
       "CountVectorizer 500                True          False      True  \n",
       "TfidfVectorizer 500 ngram 2       False          False      True  \n",
       "CountVectorizer 500                True          False     False  \n",
       "TfidfVectorizer 500                True          False      True  \n",
       "CountVectorizer 500 ngram 2        True          False      True  \n",
       "CountVectorizer 500                True           True     False  \n",
       "TfidfVectorizer 500                True          False     False  \n",
       "TfidfVectorizer 500 ngram 2        True          False      True  \n",
       "TfidfVectorizer 500                True           True     False  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics.sort_values(by=\"balanced_accuracy\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La combinaison optimale semble correspondre à `stop_words=False, lemmatization=True, negation=False` pour ce nombre de features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Comparaison par rapport au nombre de features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "_ = influence_features(X, y, max_features_list, MultinomialNB(alpha=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4. Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au vu des études précédentes, on fixe `LEMMATIZATION=True` avec le modèle `MultinomialNB`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_DATA = 100000\n",
    "STOP_WORDS = False\n",
    "LEMMATIZATION = True\n",
    "NEGATION = False\n",
    "df = sentiment140(STOP_WORDS, LEMMATIZATION, NEGATION, NB_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.text.to_list()\n",
    "y = df.sentiment.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Accuracy : 0.836\n",
      "Best Balanced Accuracy Through Grid Search : 0.776\n",
      "Best Parameters :  {'clf__alpha': 1, 'vect__max_features': 40097, 'vect__ngram_range': (1, 2)}\n",
      "Wall time: 21min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# CountVectorizer\n",
    "text_clf = Pipeline([('vect', CountVectorizer()), ('clf', MultinomialNB())])\n",
    "nb_max_words = len(CountVectorizer().fit(X_train).get_feature_names())\n",
    "\n",
    "parameters = {'vect__max_features' : np.logspace(2.3, np.log10(nb_max_words), 6, endpoint=True, dtype=int),\n",
    "          'vect__ngram_range' : [(1,1),(1,2)],\n",
    "          'clf__alpha': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.5, 0.75, 1]\n",
    "}\n",
    "\n",
    "grid_clf = GridSearchCV(text_clf, parameters, scoring='balanced_accuracy', cv=5)\n",
    "_ = grid_clf.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print('Balanced Accuracy : %.3f' % grid_clf.best_estimator_.score(X_train, y_train))\n",
    "print('Best Balanced Accuracy Through Grid Search : %.3f' % grid_clf.best_score_)\n",
    "print('Best Parameters : ', grid_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7160\n",
      "Balanced accuracy : 0.7161\n",
      "ROC AUC : 0.7405\n",
      "Wall time: 4.58 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "m = Modelisation(X, y, CountVectorizer(max_features=40097, ngram_range=(1,2)), MultinomialNB(alpha=1))\n",
    "m.show_conf_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced Accuracy : 0.841\n",
      "Best Balanced Accuracy Through Grid Search : 0.777\n",
      "Best Parameters :  {'clf__alpha': 1, 'idf__max_features': 40097, 'idf__ngram_range': (1, 2)}\n",
      "Wall time: 20min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#Tfidfvectorizer\n",
    "text_clf = Pipeline([('idf', TfidfVectorizer()), ('clf', MultinomialNB())])\n",
    "nb_max_words = len(TfidfVectorizer().fit(X_train).get_feature_names())\n",
    "\n",
    "parameters = {'idf__max_features' : np.logspace(2.3, np.log10(nb_max_words), 6, endpoint=True, dtype=int),\n",
    "           'idf__ngram_range' : [(1,1),(1,2)],\n",
    "          'clf__alpha': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2, 0.5, 0.75, 1]\n",
    "}\n",
    "\n",
    "grid_clf = GridSearchCV(text_clf, parameters, scoring='balanced_accuracy', cv=5)\n",
    "_ = grid_clf.fit(X_train, y_train)\n",
    "\n",
    "print('Balanced Accuracy : %.3f' % grid_clf.best_estimator_.score(X_train, y_train))\n",
    "print('Best Balanced Accuracy Through Grid Search : %.3f' % grid_clf.best_score_)\n",
    "print('Best Parameters : ', grid_clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.7156\n",
      "Balanced accuracy : 0.7156\n",
      "ROC AUC : 0.7438\n",
      "Wall time: 4.44 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "m = Modelisation(X, y, TfidfVectorizer(max_features=40097, ngram_range=(1,2)), MultinomialNB(alpha=1))\n",
    "m.show_conf_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temps d'exécution total : 01:25:48\n"
     ]
    }
   ],
   "source": [
    "print(f\"Temps d'exécution total : {time.strftime('%H:%M:%S', time.gmtime(time.time()-t))}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}