train_test_split.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:27:45.562692Z",
     "start_time": "2020-10-11T17:27:45.426775Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:26:13.257887Z",
     "start_time": "2020-10-11T17:26:13.253540Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:26:33.458394Z",
     "start_time": "2020-10-11T17:26:33.449198Z"
    }
   },
   "outputs": [],
   "source": [
    "iris=load_iris()\n",
    "X=iris.data\n",
    "y=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:26:40.799729Z",
     "start_time": "2020-10-11T17:26:40.790045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:28:41.185112Z",
     "start_time": "2020-10-11T17:28:40.995288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fdda06cd850>"
      ]
     },
     "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": [
    "plt.scatter(X[:,0],X[:,1],c=y,alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:29:14.964139Z",
     "start_time": "2020-10-11T17:29:14.959754Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:33:28.758900Z",
     "start_time": "2020-10-11T17:33:28.754257Z"
    }
   },
   "outputs": [],
   "source": [
    "Xtrain,Xtest,ytrain,ytest=train_test_split(X,y,test_size=0.2,random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:33:29.294010Z",
     "start_time": "2020-10-11T17:33:29.260394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:33:29.919904Z",
     "start_time": "2020-10-11T17:33:29.910946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:33:30.727059Z",
     "start_time": "2020-10-11T17:33:30.517892Z"
    }
   },
   "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"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.scatter(Xtrain[:,0],Xtrain[:,1],c=ytrain,alpha=0.8)\n",
    "plt.title('Train set')\n",
    "plt.subplot(122)\n",
    "plt.scatter(Xtest[:,0],Xtest[:,1],c=ytest,alpha=0.8)\n",
    "plt.title('Test set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:34:05.025397Z",
     "start_time": "2020-10-11T17:34:05.022543Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:35:29.518044Z",
     "start_time": "2020-10-11T17:35:29.493926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=1)\n",
    "model.fit(Xtrain,ytrain)\n",
    "model.score(Xtrain,ytrain)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:36:07.601677Z",
     "start_time": "2020-10-11T17:36:07.590023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(Xtest,ytest)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:38:45.695545Z",
     "start_time": "2020-10-11T17:38:45.681132Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score :  0.975\n",
      "test score :  0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "#regardez ce qui se passe si on change le nombre de voisins\n",
    "model=KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(Xtrain,ytrain)\n",
    "print('train score : ', model.score(Xtrain,ytrain))\n",
    "print('test score : ',model.score(Xtest,ytest)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:39:05.730165Z",
     "start_time": "2020-10-11T17:39:05.713576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score :  0.9833333333333333\n",
      "test score :  0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "#regardez ce qui se passe si on change le nombre de voisins\n",
    "model=KNeighborsClassifier(n_neighbors=6)\n",
    "model.fit(Xtrain,ytrain)\n",
    "print('train score : ', model.score(Xtrain,ytrain))\n",
    "print('test score : ',model.score(Xtest,ytest)) "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "mais attention, maintenant on a pris toutes les données et donc son évaluation est faussée"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:51:33.497283Z",
     "start_time": "2020-10-11T17:51:33.494213Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:52:29.199335Z",
     "start_time": "2020-10-11T17:52:29.154698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 1.        , 0.95833333, 0.95833333])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:52:42.804624Z",
     "start_time": "2020-10-11T17:52:42.775287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333334"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:53:04.100637Z",
     "start_time": "2020-10-11T17:53:04.073467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(1),Xtrain,ytrain,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:53:09.103834Z",
     "start_time": "2020-10-11T17:53:09.069598Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666668"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(2),Xtrain,ytrain,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:53:16.080277Z",
     "start_time": "2020-10-11T17:53:16.032814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(3),Xtrain,ytrain,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:53:21.987672Z",
     "start_time": "2020-10-11T17:53:21.916021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666668"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(KNeighborsClassifier(4),Xtrain,ytrain,cv=5,scoring='accuracy').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:54:52.769388Z",
     "start_time": "2020-10-11T17:54:52.094120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdda4d68050>]"
      ]
     },
     "execution_count": 36,
     "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": [
    "val_score=[]\n",
    "for k in range(1,50):\n",
    "    score=cross_val_score(KNeighborsClassifier(k),Xtrain,ytrain,cv=5).mean()\n",
    "    val_score.append(score)\n",
    "plt.plot(val_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:55:31.867768Z",
     "start_time": "2020-10-11T17:55:31.861649Z"
    }
   },
   "source": [
    "## boucle initule car il existe : VALIDATION CURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:56:18.117294Z",
     "start_time": "2020-10-11T17:56:18.114392Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:57:33.991227Z",
     "start_time": "2020-10-11T17:57:32.429512Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jmb/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass param_name=n_neighbors, param_range=[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48\n",
      " 49] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model=KNeighborsClassifier()\n",
    "k=np.arange(1,50)\n",
    "\n",
    "train_score,val_score=validation_curve(model,Xtrain,ytrain, 'n_neighbors',k,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:57:55.826124Z",
     "start_time": "2020-10-11T17:57:55.814894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 5)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_score.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T17:58:12.055557Z",
     "start_time": "2020-10-11T17:58:12.028309Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        , 0.95833333, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 1.        , 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 0.95833333, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 1.        , 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 0.95833333, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 0.95833333, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 1.        , 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 1.        , 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 0.95833333, 0.95833333, 0.95833333],\n",
       "       [1.        , 1.        , 1.        , 0.95833333, 0.95833333],\n",
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       "       [1.        , 0.91666667, 1.        , 0.95833333, 0.95833333],\n",
       "       [1.        , 0.95833333, 1.        , 0.95833333, 0.95833333],\n",
       "       [1.        , 0.95833333, 1.        , 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.91666667, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.95833333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.91666667, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.83333333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.875     , 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.83333333, 0.91666667, 0.95833333, 0.95833333],\n",
       "       [0.95833333, 0.83333333, 0.91666667, 0.95833333, 0.91666667],\n",
       "       [0.95833333, 0.83333333, 0.91666667, 1.        , 0.95833333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.91666667],\n",
       "       [0.95833333, 0.79166667, 0.91666667, 1.        , 0.91666667],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 0.95833333, 0.91666667],\n",
       "       [0.91666667, 0.83333333, 0.91666667, 0.95833333, 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 0.95833333, 0.875     ],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.875     ],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.875     ],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 0.95833333, 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 1.        , 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 0.95833333, 0.875     ],\n",
       "       [0.875     , 0.79166667, 0.91666667, 0.95833333, 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.875     , 0.95833333, 0.83333333],\n",
       "       [0.875     , 0.83333333, 0.875     , 1.        , 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.875     , 0.95833333, 0.83333333],\n",
       "       [0.91666667, 0.83333333, 0.91666667, 0.95833333, 0.83333333],\n",
       "       [0.91666667, 0.79166667, 0.91666667, 0.95833333, 0.875     ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:26:18.092653Z",
     "start_time": "2020-10-11T18:26:18.087055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.975     , 0.96666667, 0.975     , 0.96666667, 0.98333333,\n",
       "       0.95833333, 0.96666667, 0.96666667, 0.98333333, 0.975     ,\n",
       "       0.98333333, 0.975     , 0.98333333, 0.975     , 0.96666667,\n",
       "       0.975     , 0.975     , 0.95      , 0.95833333, 0.95      ,\n",
       "       0.95      , 0.95      , 0.95      , 0.95      , 0.94166667,\n",
       "       0.925     , 0.93333333, 0.925     , 0.91666667, 0.93333333,\n",
       "       0.90833333, 0.91666667, 0.9       , 0.89166667, 0.89166667,\n",
       "       0.9       , 0.9       , 0.89166667, 0.88333333, 0.89166667,\n",
       "       0.89166667, 0.89166667, 0.89166667, 0.875     , 0.875     ,\n",
       "       0.88333333, 0.875     , 0.89166667, 0.89166667])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_score.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:28:43.775283Z",
     "start_time": "2020-10-11T18:28:43.559438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdda52b3850>"
      ]
     },
     "execution_count": 47,
     "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": [
    "plt.plot(k, val_score.mean(axis=1),label='validation')\n",
    "plt.plot(k, train_score.mean(axis=1),label='train')\n",
    "plt.ylabel('score')\n",
    "plt.xlabel('nombre de voisins')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ces graphes permettent de voirsi on a de l'overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## il existe de multiples hyperparamètres dans chaque modèle => gridsearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:31:37.561235Z",
     "start_time": "2020-10-11T18:31:37.558024Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:01.769482Z",
     "start_time": "2020-10-11T18:37:01.766839Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid={'n_neighbors':np.arange(1,20),'metric':['euclidean','manhattan','minkowski']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:02.543739Z",
     "start_time": "2020-10-11T18:37:02.527850Z"
    }
   },
   "outputs": [],
   "source": [
    "grid=GridSearchCV(KNeighborsClassifier(),param_grid,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:03.775011Z",
     "start_time": "2020-10-11T18:37:03.166731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'metric': ['euclidean', 'manhattan', 'minkowski'],\n",
       "                         'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " grid.fit(Xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:05.272903Z",
     "start_time": "2020-10-11T18:37:05.267222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333334"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:06.640025Z",
     "start_time": "2020-10-11T18:37:06.634303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'euclidean', 'n_neighbors': 5}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:08.150866Z",
     "start_time": "2020-10-11T18:37:08.146147Z"
    }
   },
   "outputs": [],
   "source": [
    "model=grid.best_estimator_ #pour sauver le modèle le meilleur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:37:09.088061Z",
     "start_time": "2020-10-11T18:37:09.069126Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(Xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:38:11.234847Z",
     "start_time": "2020-10-11T18:38:11.224730Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:38:35.185045Z",
     "start_time": "2020-10-11T18:38:35.174805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  0,  0],\n",
       "       [ 0,  9,  2],\n",
       "       [ 0,  0, 11]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest,model.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## learning curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:40:40.709688Z",
     "start_time": "2020-10-11T18:40:40.694721Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:42:21.025893Z",
     "start_time": "2020-10-11T18:42:21.020304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2, 0.4, 0.6, 0.8, 1. ])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0.2,1.0,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:45:31.351978Z",
     "start_time": "2020-10-11T18:45:30.902390Z"
    }
   },
   "outputs": [],
   "source": [
    "N,train_score,val_score=learning_curve(model,Xtrain, ytrain, train_sizes=np.linspace(0.1,1.0,20),cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:45:31.570097Z",
     "start_time": "2020-10-11T18:45:31.562992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 14, 18, 23, 27, 32, 36, 41, 45, 50, 55, 59, 64, 68, 73, 77, 82,\n",
       "       86, 91, 96])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T18:45:32.736482Z",
     "start_time": "2020-10-11T18:45:32.552785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdda5351e10>"
      ]
     },
     "execution_count": 77,
     "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": [
    "plt.plot(N,train_score.mean(axis=1),label='train')\n",
    "plt.plot(N,val_score.mean(axis=1),label='validation')\n",
    "plt.xlabel('train_sizes')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## les différentes techniques de Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:01:53.228588Z",
     "start_time": "2020-10-11T19:01:53.207260Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:03:50.155284Z",
     "start_time": "2020-10-11T19:03:50.116081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.95833333, 1.        , 0.95833333, 0.95833333])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=KFold(5)  #nombre de découpes\n",
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:05:05.855498Z",
     "start_time": "2020-10-11T19:05:05.852442Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:05:40.604325Z",
     "start_time": "2020-10-11T19:05:40.331246Z"
    }
   },
   "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., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1.])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=LeaveOneOut()\n",
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=cv)\n",
    "#on n'obtien que des 1 ou des 0 normal puisque la validation se fait surnune seule donnée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:09:50.057557Z",
     "start_time": "2020-10-11T19:09:50.053766Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:10:59.539657Z",
     "start_time": "2020-10-11T19:10:59.520774Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.95833333, 1.        , 1.        ])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=ShuffleSplit(4,test_size=0.2) #nombre de découpage et proportion du jeu de validation\n",
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:14:27.762556Z",
     "start_time": "2020-10-11T19:14:27.755253Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:14:49.456417Z",
     "start_time": "2020-10-11T19:14:49.436678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 1.        , 0.93333333])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=StratifiedKFold(4)\n",
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:18:28.966919Z",
     "start_time": "2020-10-11T19:18:28.951734Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GroupKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-11T19:19:42.077619Z",
     "start_time": "2020-10-11T19:19:42.043925Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 1.        , 0.95833333, 0.95833333])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=GroupKFold(5).get_n_splits(Xtrain,ytrain,groups=X[:,0])\n",
    "cross_val_score(KNeighborsClassifier(),Xtrain,ytrain,cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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