Cross_validation_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d94bde2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d23eb2ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x234b02e3850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "print(X.shape)\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4984fb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold, LeaveOneOut, ShuffleSplit, StratifiedKFold, GroupKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0d80d685",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93333333, 1.        , 0.96666667, 0.96666667, 0.93333333])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = KFold(5, shuffle=True) #saisir le nombre de split\n",
    "cross_val_score(KNeighborsClassifier(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "827281c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LeaveOneOut:cas particulier de KFold(si le nbr de split = le nbr d'échantillons )\n",
    "cv = LeaveOneOut() \n",
    "cross_val_score(KNeighborsClassifier(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4a5dae16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.96666667, 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ShuffleSplit (bonne alternative à KFold)\n",
    "cv = ShuffleSplit(4, test_size=0.2) #mélanger/découper X 4\n",
    "cross_val_score(KNeighborsClassifier(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "79a3018c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97368421, 0.94736842, 0.94594595, 1.        ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Stratified KFold (classes déséquilibrées)\n",
    "cv = StratifiedKFold(4)\n",
    "cross_val_score(KNeighborsClassifier(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "baa593b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 0.93333333, 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#GroupKFold (les données dépendent les unes des autres)\n",
    "cv = GroupKFold(5).get_n_splits(X, y, groups=X[:, 2]) #5 Splits(entrainement)\n",
    "cross_val_score(KNeighborsClassifier(), X, y, cv=cv)"
   ]
  }
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