6.a) Modélisation_1 et 2 (Bagging_stemming).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. IMPORTER LES LIBRAIRIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Zx5JYRmkwS2o"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "\n",
    "# Train_Test_Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Ré-échantillonnage\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Modélisation\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Performances\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# Enregistrer le modèle\n",
    "from sklearn import model_selection\n",
    "import joblib\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. IMPORTER LE FICHIER**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Stemming / TF-IDF / Réduction de dimension*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "id": "cxNQA9b5w9Gv",
    "outputId": "b446706d-0630-452b-88e1-fb3da5aa20fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "<class 'pandas.core.frame.DataFrame'>\n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_stem = pd.read_csv(\"4. Stemming_TF-IDF(Avec_Reduction).csv\").astype('float16')\n",
    "df_stem['y'] = df_stem['y'].astype('uint8')\n",
    "df_stem = df_stem.rename(columns={'1099' : 'ponctuation', '1100' : 'sentiment'})\n",
    "\n",
    "df_stem.info()\n",
    "display(df_stem.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **3. MODELISATION_1 (5 CLASSES)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1) Préparation des données*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "IS_Et_rCwZ0o"
   },
   "outputs": [],
   "source": [
    "# Target / Features\n",
    "X_stem = df_stem.drop('y', axis = 1).to_numpy()\n",
    "y_stem = df_stem['y'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train_Test_Split\n",
    "X_train_stem, X_test_stem, y_train_stem, y_test_stem = model_selection.train_test_split(X_stem, y_stem, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_stem = (55394, 1101)\n",
      "y_train_stem = (55394,)\n",
      "X_test_stem = (13849, 1101)\n",
      "y_test_stem = (13849,)\n"
     ]
    }
   ],
   "source": [
    "# Vérification shape\n",
    "print (\"X_train_stem =\", X_train_stem.shape)\n",
    "print (\"y_train_stem =\", y_train_stem.shape)\n",
    "print (\"X_test_stem =\", X_test_stem.shape)\n",
    "print (\"y_test_stem =\", y_test_stem.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes échantillon undersampled stemming : {1: 1406, 2: 1406, 3: 1406, 4: 1406, 5: 1406}\n"
     ]
    }
   ],
   "source": [
    "# Undersampling\n",
    "rUs = RandomUnderSampler(random_state = 42)\n",
    "X_ru_stem, y_ru_stem = rUs.fit_resample(X_train_stem, y_train_stem)\n",
    "print('Classes échantillon undersampled stemming :', dict(pd.Series(y_ru_stem).value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2) Modélisation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagging_model = BaggingClassifier(random_state=42)\n",
    "bagging_params = {'n_estimators': [10, 50, 100],\n",
    "                  'max_samples': [0.5, 1.0],\n",
    "                  'max_features': [0.5, 1.0]}\n",
    "\n",
    "# Initialisation de RandomizedSearchCV pour trouver les meilleurs hyperparamètres\n",
    "bagging_random_1 = RandomizedSearchCV(bagging_model, bagging_params, n_iter=4, cv=5, n_jobs=-1)\n",
    "bagging_random_1.fit(X_ru_stem, y_ru_stem)\n",
    "# Sélection du meilleur modèle obtenu après la recherche aléatoire\n",
    "best_bagging_model_1 = bagging_random_1.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*3) Performances*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score du modèle : 0.422413170626038\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          1       0.41      0.57      0.89      0.47      0.71      0.49      1647\n",
      "          2       0.08      0.32      0.89      0.13      0.53      0.27       405\n",
      "          3       0.13      0.28      0.89      0.18      0.50      0.24       764\n",
      "          4       0.24      0.29      0.78      0.26      0.48      0.22      2688\n",
      "          5       0.76      0.45      0.78      0.57      0.60      0.34      8345\n",
      "\n",
      "avg / total       0.56      0.42      0.80      0.46      0.58      0.33     13849\n",
      "\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Classe prédite</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <td>932</td>\n",
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       "      <td>166</td>\n",
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       "      <td>108</td>\n",
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       "      <td>349</td>\n",
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       "Classe prédite    1    2    3     4     5\n",
       "Classe réelle                            \n",
       "1               932  247  182   120   166\n",
       "2               108  128   72    51    46\n",
       "3               136  172  217   123   116\n",
       "4               292  349  379   783   885\n",
       "5               827  701  852  2175  3790"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_stem = best_bagging_model_1.predict(X_test_stem)\n",
    "\n",
    "print(\"Score du modèle :\", best_bagging_model_1.score(X_test_stem, y_test_stem))\n",
    "\n",
    "print(classification_report_imbalanced(y_test_stem, y_pred_stem))\n",
    "      \n",
    "confusion_matrix = pd.crosstab(y_test_stem, y_pred_stem, rownames=['Classe réelle'], colnames=['Classe prédite'])\n",
    "display(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score Bagging_1 = 0.46303819836220056\n",
      "f1_score par classes Baggin_1 = [0.47285642 0.12787213 0.17599351 0.26363636 0.56787534]\n"
     ]
    }
   ],
   "source": [
    "# f1_score du modèle Bagging_1\n",
    "print('f1_score Bagging_1 =', f1_score(y_test_stem, y_pred_stem, average='weighted'))\n",
    "\n",
    "# f1_score par classe du modèle Bagging_1\n",
    "BG_f1_stem_1 = f1_score(y_test_stem, y_pred_stem, average=None)\n",
    "print('f1_score par classes Baggin_1 =', BG_f1_stem_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*4) Enregistrement*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bagging_1.pkl']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(bagging_random_1, '6.a) bagging_1.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **4. MODELISATION_2 (BINAIRE)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Classification binaire\n",
    "# Classe 0 : [1, 2, 3]\n",
    "# Classe 1 : [4, 5]\n",
    "df_stem['y'] = df_stem['y'].replace((1, 2, 3, 4, 5), (0, 0, 0, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Target / Features\n",
    "X_stem = df_stem.drop('y', axis = 1).to_numpy()\n",
    "y_stem = df_stem['y'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train_Test_Split\n",
    "X_train_stem, X_test_stem, y_train_stem, y_test_stem = model_selection.train_test_split(X_stem, y_stem, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes échantillon undersampled stemming : {0: 11376, 1: 11376}\n"
     ]
    }
   ],
   "source": [
    "# Undersampling\n",
    "rUs = RandomUnderSampler(random_state = 42)\n",
    "X_ru_stem, y_ru_stem = rUs.fit_resample(X_train_stem, y_train_stem)\n",
    "print('Classes échantillon undersampled stemming :', dict(pd.Series(y_ru_stem).value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2) Modélisation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bagging_model = BaggingClassifier(random_state=42)\n",
    "bagging_params = {'n_estimators': [10, 50, 100],\n",
    "                  'max_samples': [0.5, 1.0],\n",
    "                  'max_features': [0.5, 1.0]}\n",
    "\n",
    "# Initialisation de RandomizedSearchCV pour trouver les meilleurs hyperparamètres\n",
    "bagging_random_2 = RandomizedSearchCV(bagging_model, bagging_params, n_iter=4, cv=5, n_jobs=-1)\n",
    "bagging_random_2.fit(X_ru_stem, y_ru_stem)\n",
    "# Sélection du meilleur modèle obtenu après la recherche aléatoire\n",
    "best_bagging_model_2 = bagging_random_2.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*3) Performances*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score du modèle : 0.7646761499025201\n",
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "          0       0.45      0.76      0.77      0.57      0.76      0.58      2816\n",
      "          1       0.93      0.77      0.76      0.84      0.76      0.58     11033\n",
      "\n",
      "avg / total       0.83      0.76      0.76      0.78      0.76      0.58     13849\n",
      "\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>Classe prédite</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classe réelle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2139</td>\n",
       "      <td>677</td>\n",
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       "      <th>1</th>\n",
       "      <td>2582</td>\n",
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      ],
      "text/plain": [
       "Classe prédite     0     1\n",
       "Classe réelle             \n",
       "0               2139   677\n",
       "1               2582  8451"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_stem = best_bagging_model_2.predict(X_test_stem)\n",
    "\n",
    "print(\"Score du modèle :\", best_bagging_model_2.score(X_test_stem, y_test_stem))\n",
    "\n",
    "print(classification_report_imbalanced(y_test_stem, y_pred_stem))\n",
    "      \n",
    "confusion_matrix = pd.crosstab(y_test_stem, y_pred_stem, rownames=['Classe réelle'], colnames=['Classe prédite'])\n",
    "display(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score Bagging_2 = 0.7832977643496211\n",
      "f1_score par classes Baggin_2 = [0.56759984 0.83835127]\n"
     ]
    }
   ],
   "source": [
    "# f1_score du modèle Bagging_2\n",
    "print('f1_score Bagging_2 =', f1_score(y_test_stem, y_pred_stem, average='weighted'))\n",
    "\n",
    "# f1_score par classe du modèle Bagging_2\n",
    "BG_f1_stem_2 = f1_score(y_test_stem, y_pred_stem, average=None)\n",
    "print('f1_score par classes Baggin_2 =', BG_f1_stem_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*4) Enregistrement du modèe*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bagging_2.pkl']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(best_bagging_model_2, '6.a) bagging_2.pkl')"
   ]
  }
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