partie_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PARTIE 3 : Régularisation du modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous entraînons un modèle de régression régularisé pour combattre l'overfitting que l'on a avec un modèle de base."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Store</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1572117.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.61</td>\n",
       "      <td>3.045</td>\n",
       "      <td>214.777523</td>\n",
       "      <td>6.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1807545.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.38</td>\n",
       "      <td>3.435</td>\n",
       "      <td>128.616064</td>\n",
       "      <td>7.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1644470.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.89</td>\n",
       "      <td>2.759</td>\n",
       "      <td>212.412888</td>\n",
       "      <td>7.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1857533.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.77</td>\n",
       "      <td>2.756</td>\n",
       "      <td>126.160226</td>\n",
       "      <td>7.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>695396.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.80</td>\n",
       "      <td>4.069</td>\n",
       "      <td>134.855161</td>\n",
       "      <td>7.658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Day  Month  Year  Weekly_Sales  Holiday_Flag  Temperature  \\\n",
       "0    6.0   18      2  2011    1572117.54           0.0        59.61   \n",
       "1   13.0   25      3  2011    1807545.43           0.0        42.38   \n",
       "2    6.0   28      5  2010    1644470.66           0.0        78.89   \n",
       "3    4.0   28      5  2010    1857533.70           0.0        60.77   \n",
       "4   15.0    3      6  2011     695396.19           0.0        69.80   \n",
       "\n",
       "   Fuel_Price         CPI  Unemployment  \n",
       "0       3.045  214.777523         6.858  \n",
       "1       3.435  128.616064         7.470  \n",
       "2       2.759  212.412888         7.092  \n",
       "3       2.756  126.160226         7.896  \n",
       "4       4.069  134.855161         7.658  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./src/dataset_for_ml.csv')\n",
    "dataset.drop(['Date'], axis=1, inplace=True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessings avec Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Separating labels from features...\n",
      "...Done\n",
      "\n",
      "y:\n",
      "   Weekly_Sales\n",
      "0    1572117.54\n",
      "1    1807545.43\n",
      "2    1644470.66\n",
      "3    1857533.70\n",
      "4     695396.19\n",
      "\n",
      "X:\n",
      "   Store  Day  Month  Year  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
      "0    6.0   18      2  2011           0.0        59.61       3.045  214.777523   \n",
      "1   13.0   25      3  2011           0.0        42.38       3.435  128.616064   \n",
      "2    6.0   28      5  2010           0.0        78.89       2.759  212.412888   \n",
      "3    4.0   28      5  2010           0.0        60.77       2.756  126.160226   \n",
      "4   15.0    3      6  2011           0.0        69.80       4.069  134.855161   \n",
      "\n",
      "   Unemployment  \n",
      "0         6.858  \n",
      "1         7.470  \n",
      "2         7.092  \n",
      "3         7.896  \n",
      "4         7.658  \n",
      "\n",
      "Dividing into train set and test set...\n",
      "...Done\n",
      "\n",
      "Performing preprocessings on train set...\n",
      "     Store  Day  Month  Year  Holiday_Flag  Temperature  Fuel_Price  \\\n",
      "109    7.0   25      5  2012           0.0        50.60       3.804   \n",
      "13     6.0   30      4  2010           0.0        68.91       2.780   \n",
      "63    15.0   16      3  2012           0.0        47.42       4.021   \n",
      "71     9.0    9      7  2010           0.0        78.51       2.642   \n",
      "26     7.0   10      2  2012           1.0        18.79       3.103   \n",
      "\n",
      "            CPI  Unemployment  \n",
      "109  197.588605         8.090  \n",
      "13   211.894272         7.092  \n",
      "63   137.584387         7.943  \n",
      "71   214.656430         6.442  \n",
      "26   196.919506         8.256  \n",
      "...Done\n",
      "[[ 1.00479387 -0.4864211   1.29732633 -0.62993689  1.15793519  0.48636272\n",
      "   0.8273496   0.          0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [ 1.60182688 -0.80175616 -1.11749892  0.44730295 -1.2052199   0.85457633\n",
      "  -0.25140508  0.          0.          0.          0.          1.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [-0.06986557 -1.11709121  1.29732633 -0.81702715  1.65872099 -1.05808595\n",
      "   0.66845488  0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          1.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [-0.90571179  0.14424902 -1.11749892  1.01210374 -1.52369197  0.92567151\n",
      "  -0.95400082  0.          0.          0.          0.          0.\n",
      "   0.          0.          1.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [-0.78630519 -1.43242627  1.29732633 -2.50142784 -0.45981063  0.46914078\n",
      "   1.00678175  0.          0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          1.        ]]\n",
      "\n",
      "Performing preprocessings on test set:\n",
      "    Store  Day  Month  Year  Holiday_Flag  Temperature  Fuel_Price  \\\n",
      "86   18.0   28      5  2010           0.0        69.12       2.906   \n",
      "85    8.0   12      2  2010           1.0        33.34       2.548   \n",
      "94    9.0   18      6  2010           0.0        82.99       2.637   \n",
      "8     8.0   19      8  2011           0.0        82.92       3.554   \n",
      "89    2.0   19      2  2010           0.0        39.69       2.514   \n",
      "\n",
      "           CPI  Unemployment  \n",
      "86  132.293936         7.650  \n",
      "85  214.621419         6.299  \n",
      "94  215.016648         6.384  \n",
      "8   219.070197         6.425  \n",
      "89  210.945160         7.650  \n",
      "...Done\n",
      "[[ 1.36301368 -0.4864211  -1.11749892  0.45965797 -0.91444105 -1.19425689\n",
      "   0.35174634  0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          1.          0.\n",
      "   0.          0.        ]\n",
      " [-0.54749198 -1.43242627 -1.11749892 -1.64540165 -1.74062223  0.92477036\n",
      "  -1.10857189  0.          0.          0.          0.          0.\n",
      "   0.          1.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          1.        ]\n",
      " [ 0.16894764 -0.17108604 -1.11749892  1.27567744 -1.53523082  0.93494317\n",
      "  -1.01669398  0.          0.          0.          0.          0.\n",
      "   0.          0.          1.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [ 0.28835424  0.45958407  0.08991371  1.2715591   0.58099303  1.03927746\n",
      "  -0.9723764   0.          0.          0.          0.          0.\n",
      "   0.          1.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]\n",
      " [ 0.28835424 -1.43242627 -1.11749892 -1.27180946 -1.81908636  0.83014714\n",
      "   0.35174634  1.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.        ]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Seperate target variable y from features X:\n",
    "target_variable = ['Weekly_Sales']\n",
    "print('Separating labels from features...')\n",
    "y = dataset.loc[:,target_variable]\n",
    "X = dataset.drop(target_variable, axis = 1)\n",
    "print('...Done')\n",
    "print()\n",
    "print('y:')\n",
    "print(y.head())\n",
    "print()\n",
    "print('X:')\n",
    "print(X.head())\n",
    "print()\n",
    "\n",
    "# Divide dataset into train set and test set:\n",
    "print('Dividing into train set and test set...')\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "print('...Done')\n",
    "print()\n",
    "\n",
    "# Distinguish numeric and categorical features:\n",
    "numeric_features = ['Day', 'Month', 'Year', 'Temperature', 'Fuel_Price', 'CPI', 'Unemployment']\n",
    "categorical_features = ['Store', 'Holiday_Flag']\n",
    "\n",
    "# Create pipeline for numeric features:\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "# Create pipeline for categorical features:\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('encoder', OneHotEncoder(drop='first'))\n",
    "])\n",
    "\n",
    "# Use ColumnTransformer to make a preprocessor object that describes all the treatments to be done:\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Preprocessings on train set:\n",
    "print('Performing preprocessings on train set...')\n",
    "print(X_train.head())\n",
    "X_train = preprocessor.fit_transform(X_train)\n",
    "print('...Done')\n",
    "print(X_train[0:5])\n",
    "print()\n",
    "\n",
    "# Preprocessings on test set:\n",
    "print('Performing preprocessings on test set:')\n",
    "print(X_test.head())\n",
    "X_test = preprocessor.transform(X_test)\n",
    "print('...Done')\n",
    "print(X_test[0:5])\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "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>Store</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2011</td>\n",
       "      <td>1572117.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.61</td>\n",
       "      <td>3.045</td>\n",
       "      <td>214.777523</td>\n",
       "      <td>6.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2011</td>\n",
       "      <td>1807545.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.38</td>\n",
       "      <td>3.435</td>\n",
       "      <td>128.616064</td>\n",
       "      <td>7.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1644470.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.89</td>\n",
       "      <td>2.759</td>\n",
       "      <td>212.412888</td>\n",
       "      <td>7.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1857533.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.77</td>\n",
       "      <td>2.756</td>\n",
       "      <td>126.160226</td>\n",
       "      <td>7.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>695396.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.80</td>\n",
       "      <td>4.069</td>\n",
       "      <td>134.855161</td>\n",
       "      <td>7.658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store  Day  Month  Year  Weekly_Sales  Holiday_Flag  Temperature  \\\n",
       "0    6.0   18      2  2011    1572117.54           0.0        59.61   \n",
       "1   13.0   25      3  2011    1807545.43           0.0        42.38   \n",
       "2    6.0   28      5  2010    1644470.66           0.0        78.89   \n",
       "3    4.0   28      5  2010    1857533.70           0.0        60.77   \n",
       "4   15.0    3      6  2011     695396.19           0.0        69.80   \n",
       "\n",
       "   Fuel_Price         CPI  Unemployment  \n",
       "0       3.045  214.777523         6.858  \n",
       "1       3.435  128.616064         7.470  \n",
       "2       2.759  212.412888         7.092  \n",
       "3       2.756  126.160226         7.896  \n",
       "4       4.069  134.855161         7.658  "
      ]
     },
     "execution_count": 540,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A. Modèle de régression Ridge\n",
    "\n",
    "Nous essayons ici le modèle de régression linéaire régularisé Ridge.\n",
    "\n",
    "Selon la documentation, le modèle Ridge est adapté lorsque nous avons des variables fortement corrélées entre elles. \n",
    "\n",
    "C'est le cas des variables Month avec Temperature, ou encore des variables Fuel_Price et Year.\n",
    "\n",
    "(Voir la matrice de corrélation dans le notebook partie_1.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recherche du meilleur alpha\n",
    "\n",
    "Nous recherchons le meilleur hyperparamètre de régularisation alpha pour notre modèle de régression linéaire Ridge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5]"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphas = [i / 100 for i in range(105, 155, 5)]\n",
    "print(len(alphas))\n",
    "alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1.05}\n",
      "0.792742666765531\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'alpha': alphas}\n",
    "ridge = Ridge()\n",
    "\n",
    "best_ridge = GridSearchCV(estimator=ridge, param_grid=param_grid, cv=5)\n",
    "best_ridge.fit(X_train, y_train)\n",
    "\n",
    "print(best_ridge.best_params_)\n",
    "print(best_ridge.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RIDGE, R2 score on training set :  0.9277751177126579\n",
      "RIDGE, R2 score on test set :  0.9250674465901909\n"
     ]
    }
   ],
   "source": [
    "print(\"RIDGE, R2 score on train set : \", best_ridge.score(X_train, y_train))\n",
    "print(\"RIDGE, R2 score on test set : \", best_ridge.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Résultats & performance\n",
    "\n",
    "Avec une valeur alpha de 1.05, on obtient un R2 score de 0.9277 et de 0.9250 sur le train set et le test set.\n",
    "\n",
    "--> Grâce à la régularisation nous avons réussi à minimiser l'écart entre le R2 score en phase de train et de test, tout en conservant une valeur élevée et la plus proche de 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importance des features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the names and coefficients of the features:\n",
    "feature_names_out = preprocessor.get_feature_names_out()\n",
    "feature_names = [fname for fname in feature_names_out]\n",
    "coefs = pd.DataFrame(index = feature_names, data = best_ridge.best_estimator_.coef_.transpose(), columns=[\"coefficients\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_coefs = feature_importance[feature_importance['coefficients']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute abs() and sort values:\n",
    "feature_importance_abs = abs(coefs).sort_values(by='coefficients', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "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>cat__Store_3.0</th>\n",
       "      <th>cat__Store_5.0</th>\n",
       "      <th>cat__Store_7.0</th>\n",
       "      <th>cat__Store_4.0</th>\n",
       "      <th>cat__Store_9.0</th>\n",
       "      <th>cat__Store_16.0</th>\n",
       "      <th>cat__Store_14.0</th>\n",
       "      <th>cat__Store_15.0</th>\n",
       "      <th>cat__Store_13.0</th>\n",
       "      <th>cat__Store_2.0</th>\n",
       "      <th>...</th>\n",
       "      <th>cat__Store_11.0</th>\n",
       "      <th>num__Month</th>\n",
       "      <th>num__Temperature</th>\n",
       "      <th>num__Unemployment</th>\n",
       "      <th>num__Day</th>\n",
       "      <th>num__CPI</th>\n",
       "      <th>num__Year</th>\n",
       "      <th>num__Fuel_Price</th>\n",
       "      <th>cat__Store_19.0</th>\n",
       "      <th>cat__Holiday_Flag_1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coefficients</th>\n",
       "      <td>812626.244852</td>\n",
       "      <td>806541.116966</td>\n",
       "      <td>723944.313109</td>\n",
       "      <td>682916.326195</td>\n",
       "      <td>594121.552512</td>\n",
       "      <td>593707.712621</td>\n",
       "      <td>584652.981227</td>\n",
       "      <td>547302.587453</td>\n",
       "      <td>522918.107469</td>\n",
       "      <td>515016.691722</td>\n",
       "      <td>...</td>\n",
       "      <td>191758.698132</td>\n",
       "      <td>64438.118174</td>\n",
       "      <td>52766.709411</td>\n",
       "      <td>46360.71976</td>\n",
       "      <td>41016.019184</td>\n",
       "      <td>37458.378135</td>\n",
       "      <td>25203.894326</td>\n",
       "      <td>22245.652215</td>\n",
       "      <td>5319.130511</td>\n",
       "      <td>1545.060013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cat__Store_3.0  cat__Store_5.0  cat__Store_7.0  cat__Store_4.0  \\\n",
       "coefficients   812626.244852   806541.116966   723944.313109   682916.326195   \n",
       "\n",
       "              cat__Store_9.0  cat__Store_16.0  cat__Store_14.0  \\\n",
       "coefficients   594121.552512    593707.712621    584652.981227   \n",
       "\n",
       "              cat__Store_15.0  cat__Store_13.0  cat__Store_2.0  ...  \\\n",
       "coefficients    547302.587453    522918.107469   515016.691722  ...   \n",
       "\n",
       "              cat__Store_11.0    num__Month  num__Temperature  \\\n",
       "coefficients    191758.698132  64438.118174      52766.709411   \n",
       "\n",
       "              num__Unemployment      num__Day      num__CPI     num__Year  \\\n",
       "coefficients        46360.71976  41016.019184  37458.378135  25203.894326   \n",
       "\n",
       "              num__Fuel_Price  cat__Store_19.0  cat__Holiday_Flag_1.0  \n",
       "coefficients     22245.652215      5319.130511            1545.060013  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 773,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_abs = feature_importance_abs.transpose()\n",
    "feature_importance_abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot coefficients (in absolute value):\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "colors = ['deeppink' if y in negative_coefs.index else 'limegreen' for y in feature_importance_abs.columns]\n",
    "sns.set(rc={'figure.figsize':(10, 3.5)}, style='white')\n",
    "sns.barplot(data = feature_importance_abs,orient = 'v', palette = colors, width = .9, edgecolor = 'black')\n",
    "plt.title('Features importance in the Ridge Model', fontsize=12.5, pad=20)\n",
    "plt.xlabel('Colors : pink for negative coefficients / green for positive coefficients', fontsize=11)\n",
    "plt.ylabel('Coefficients (absolute value)', fontsize=10)\n",
    "plt.xticks(fontsize=9, fontweight='bold', rotation=45, ha='right')\n",
    "plt.xticks(feature_importance_abs.columns, fontsize=9, fontweight='bold', rotation=45, ha=\"right\")\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B. Modèle de régression Lasso\n",
    "\n",
    "Nous testons un modèle de régression linéaire régularisé Lasso.\n",
    "\n",
    "L'étude de l'importance des features nous a montré que les prédictions sont calculées à partir de 26 features différentes.\n",
    "\n",
    "Ce nombre de features est important alors que nous avons uniquement 118 observations dans notre jeu de données.\n",
    "\n",
    "Lasso permettra de simplifier le modèle en supprimant certaines features en y appliquant un poids de 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recherche du meilleur alpha\n",
    "\n",
    "Nous recherchons le meilleur hyperparamètre de régularisation alpha pour notre modèle de régression linéaire Lasso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3000,\n",
       " 3100,\n",
       " 3200,\n",
       " 3300,\n",
       " 3400,\n",
       " 3500,\n",
       " 3600,\n",
       " 3700,\n",
       " 3800,\n",
       " 3900,\n",
       " 4000,\n",
       " 4100,\n",
       " 4200,\n",
       " 4300,\n",
       " 4400,\n",
       " 4500,\n",
       " 4600,\n",
       " 4700,\n",
       " 4800,\n",
       " 4900]"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphas = [i for i in range(3000, 5000, 100)]\n",
    "print(len(alphas))\n",
    "alphas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 3000}\n",
      "0.8883991233063891\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'alpha': alphas}\n",
    "lasso = Lasso()\n",
    "\n",
    "best_lasso = GridSearchCV(estimator=lasso, param_grid=param_grid, cv=5)\n",
    "best_lasso.fit(X_train, y_train)\n",
    "\n",
    "print(best_lasso.best_params_)\n",
    "print(best_lasso.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO, R2 score on train set :  0.959923796786412\n",
      "LASSO, R2 score on test set :  0.9541634407734857\n"
     ]
    }
   ],
   "source": [
    "print(\"LASSO, R2 score on train set : \", best_lasso.score(X_train, y_train))\n",
    "print(\"LASSO, R2 score on test set : \", best_lasso.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Résultats & performance\n",
    "\n",
    "Avec une valeur alpha de 3000, on obtient un R2 score de 0.9599 et de 0.9541 sur le train set et le test set.\n",
    "\n",
    "--> Grâce à la régularisation nous avons réussi à minimiser l'écart entre le R2 score en phase de train et de test, tout en conservant une valeur élevée et la plus proche de 1.\n",
    "\n",
    "--> Nous obtenons un meilleur modèle de prédictions avec le Lasso plutôt que le Ridge avec un R2 score de 0.95 contre 0.92."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importance des features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the names and coefficients of the features:\n",
    "feature_names_out = preprocessor.get_feature_names_out()\n",
    "feature_names = [fname for fname in feature_names_out]\n",
    "coefs = pd.DataFrame(index = feature_names, data = best_lasso.best_estimator_.coef_.transpose(), columns=[\"coefficients\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [],
   "source": [
    "negative_coefs = feature_importance[feature_importance['coefficients']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute abs() and sort values:\n",
    "feature_importance_abs = abs(coefs).sort_values(by='coefficients', ascending=False)\n",
    "feature_importance_abs_drop = feature_importance_abs[feature_importance_abs['coefficients'] <= 0]\n",
    "feature_importance_abs_remain = feature_importance_abs[feature_importance_abs['coefficients'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le modèle Lasso a éliminé 5 features : Umemployment, Year, Holiday_Flag et les magasins n°11 et 19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "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>coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cat__Store_11.0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num__Unemployment</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat__Store_19.0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num__Year</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat__Holiday_Flag_1.0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       coefficients\n",
       "cat__Store_11.0                 0.0\n",
       "num__Unemployment               0.0\n",
       "cat__Store_19.0                 0.0\n",
       "num__Year                       0.0\n",
       "cat__Holiday_Flag_1.0           0.0"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_abs_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "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>cat__Store_5.0</th>\n",
       "      <th>cat__Store_3.0</th>\n",
       "      <th>cat__Store_9.0</th>\n",
       "      <th>cat__Store_16.0</th>\n",
       "      <th>cat__Store_7.0</th>\n",
       "      <th>cat__Store_15.0</th>\n",
       "      <th>cat__Store_4.0</th>\n",
       "      <th>cat__Store_14.0</th>\n",
       "      <th>cat__Store_17.0</th>\n",
       "      <th>cat__Store_8.0</th>\n",
       "      <th>...</th>\n",
       "      <th>cat__Store_2.0</th>\n",
       "      <th>cat__Store_10.0</th>\n",
       "      <th>cat__Store_20.0</th>\n",
       "      <th>cat__Store_18.0</th>\n",
       "      <th>num__Month</th>\n",
       "      <th>num__Temperature</th>\n",
       "      <th>num__Day</th>\n",
       "      <th>cat__Store_6.0</th>\n",
       "      <th>num__CPI</th>\n",
       "      <th>num__Fuel_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>coefficients</th>\n",
       "      <td>1.127095e+06</td>\n",
       "      <td>1.070332e+06</td>\n",
       "      <td>927278.336339</td>\n",
       "      <td>900584.118015</td>\n",
       "      <td>899811.062594</td>\n",
       "      <td>671171.429264</td>\n",
       "      <td>663408.606465</td>\n",
       "      <td>585951.946835</td>\n",
       "      <td>568299.800003</td>\n",
       "      <td>548015.036705</td>\n",
       "      <td>...</td>\n",
       "      <td>424963.87429</td>\n",
       "      <td>347336.878735</td>\n",
       "      <td>288175.320169</td>\n",
       "      <td>287697.626241</td>\n",
       "      <td>61409.117017</td>\n",
       "      <td>44706.183567</td>\n",
       "      <td>35677.000785</td>\n",
       "      <td>35621.461785</td>\n",
       "      <td>30209.326462</td>\n",
       "      <td>10954.264948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cat__Store_5.0  cat__Store_3.0  cat__Store_9.0  cat__Store_16.0  \\\n",
       "coefficients    1.127095e+06    1.070332e+06   927278.336339    900584.118015   \n",
       "\n",
       "              cat__Store_7.0  cat__Store_15.0  cat__Store_4.0  \\\n",
       "coefficients   899811.062594    671171.429264   663408.606465   \n",
       "\n",
       "              cat__Store_14.0  cat__Store_17.0  cat__Store_8.0  ...  \\\n",
       "coefficients    585951.946835    568299.800003   548015.036705  ...   \n",
       "\n",
       "              cat__Store_2.0  cat__Store_10.0  cat__Store_20.0  \\\n",
       "coefficients    424963.87429    347336.878735    288175.320169   \n",
       "\n",
       "              cat__Store_18.0    num__Month  num__Temperature      num__Day  \\\n",
       "coefficients    287697.626241  61409.117017      44706.183567  35677.000785   \n",
       "\n",
       "              cat__Store_6.0      num__CPI  num__Fuel_Price  \n",
       "coefficients    35621.461785  30209.326462     10954.264948  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_abs_remain = feature_importance_abs_remain.transpose()\n",
    "feature_importance_abs_remain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot coefficients (in absolute value):\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "colors = ['deeppink' if y in negative_coefs.index else 'limegreen' for y in feature_importance_abs_remain.columns]\n",
    "sns.set(rc={'figure.figsize':(10, 3.5)}, style='white')\n",
    "sns.barplot(data = feature_importance_abs_remain,orient = 'v', palette = colors, width = .9, edgecolor = 'black')\n",
    "plt.title('Features importance in the Lasso Model', fontsize=12.5, pad=20)\n",
    "plt.xlabel('Colors : pink for negative coefficients / green for positive coefficients', fontsize=11)\n",
    "plt.ylabel('Coefficients (absolute value)', fontsize=10)\n",
    "plt.xticks(fontsize=9, fontweight='bold', rotation=45, ha='right')\n",
    "plt.xticks(feature_importance_abs_remain.columns, fontsize=9, fontweight='bold', rotation=45, ha=\"right\")\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Résultats de l'analyse : \n",
    "\n",
    "    - L'analyse des données en partie 1 avait tout d'abord mis en avant que les ventes hebdomadaires étaient très variables d'un magasin à l'autre, et également d'un mois à l'autre.\n",
    "\n",
    "    - Les nuages de points ne nous ont pas permis de déceler de relation corrélée entre les ventes hebdomadaires et les indicateurs économiques.\n",
    "\n",
    "    - La matrice de corrélation a suggéré une corrélation positive entre les ventes hebdomadaires et le taux de chômage, et une corrélation négative avec l'index d'inflation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Résultats des modèles : \n",
    "\n",
    "    - Le modèle de prédiction Lasso est celui qui obtient les meilleures performances : il réalise peu d'erreurs et ne sur-apprend pas.\n",
    "\n",
    "    - Il confirme l'importance des magasins pour calculer les prévisions de ventes (sauf pour le magasin n°6 dont le coefficient est seulement de 35 000).\n",
    "    \n",
    "    - La variable Month est conservée mais son importance est réduite par rapport au modèle de base (coefficient de 60 000 contre 75 000).\n",
    "\n",
    "    - Il infirme l'importance du taux de chômage pour calculer les prévisions de ventes. Le modèle supprime cette variable et ne la prend pas en compte dans ses prédictions. \n",
    "    \n",
    "    - Enfin, le poids de l'index d'inflation est réduit de plus de 10 fois : le coefficient vaut 30 000 dans le modèle Lasso contre 310 000 dans le modèle de base.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}