diff --git a/TP1_prog1.py.ipynb b/TP1_prog1.py.ipynb index 70c562e..68bbd00 100644 --- a/TP1_prog1.py.ipynb +++ b/TP1_prog1.py.ipynb @@ -36,6 +36,95 @@ "plt.show()\n", "print(\"Classe : \", mnist.target[0])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cb9b8da", + "metadata": {}, + "outputs": [], + "source": [ + "# mnist = fetch_openml('mnist_784')\n", + "mnist = fetch_openml('mnist_784',as_frame=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40907bdc", + "metadata": {}, + "outputs": [], + "source": [ + "# print(mnist)\n", + "# print (mnist.data)\n", + "# print (mnist.target)\n", + "# len(mnist.data)\n", + "# help(len)\n", + "# print (mnist.data.shape)\n", + "# print (mnist.target.shape)\n", + "# mnist.data[0]\n", + "# mnist.data[0][1]\n", + "# mnist.data[:,1]\n", + "# mnist.data[:100]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f4dff3a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7i37sEnO15HHj7bJAELyDDgiCsgNBUHYgCMoOBEHZgSAoOxAEZQeC+D+ypTV9clByEAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classe : 5\n" + ] + } + ], + "source": [ + "# from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "images = mnist.data.reshape((-1, 28, 28))\n", + "plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n", + "plt.show()\n", + "print(\"Classe : \", mnist.target[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38f52261", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import model_selection\n", + "from sklearn import neighbors\n", + "\n", + "data = mnist.data\n", + "target = mnist.target\n", + "\n", + "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.7)\n", + "\n", + "n_neighbors = 5\n", + "clf = neighbors.KNeighborsClassifier(n_neighbors)\n", + "clf.fit(xtrain, ytrain)\n", + "clf.predict(xtest)\n", + "clf.predict_proba(xtest)\n", + "print(clf.score(xtest, ytest))" + ] } ], "metadata": { diff --git a/TP1_prog2.py.ipynb b/TP1_prog2.py.ipynb index ce7d5e5..780b9ae 100644 --- a/TP1_prog2.py.ipynb +++ b/TP1_prog2.py.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "id": "530f620c", "metadata": {}, "outputs": [], @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "id": "eb2c4496", "metadata": {}, "outputs": [ @@ -33,74 +33,74 @@ " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]]\n", - "Etiquettes : ['3' '6' '3' ... '0' '1' '2']\n", - "Prédiction : ['4' '1' '6' '7' '4' '0' '4' '5' '6' '3' '3' '0' '0' '4' '7' '0' '8' '9'\n", - " '6' '0' '8' '8' '1' '7' '7' '9' '7' '5' '9' '7' '0' '8' '5' '8' '7' '0'\n", - " '0' '1' '3' '3' '3' '4' '1' '5' '8' '6' '9' '5' '4' '1' '3' '0' '3' '4'\n", - " '3' '6' '2' '5' '2' '4' '2' '8' '6' '1' '6' '0' '2' '9' '2' '7' '3' '4'\n", - " '2' '6' '7' '9' '0' '0' '0' '2' '7' '6' '4' '8' '4' '1' '9' '2' '3' '9'\n", - " '5' '1' '1' '8' '9' '8' '6' '4' '3' '1' '6' '6' '2' '1' '8' '7' '5' '2'\n", - " '7' '0' '6' '6' '7' '2' '4' '3' '2' '3' '0' '4' '7' '9' '0' '0' '7' '9'\n", - " '7' '7' '2' '6' '4' '6' '0' '6' '8' '3' '1' '4' '6' '7' '3' '1' '9' '2'\n", - " '1' '2' '3' '0' '4' '5' '5' '5' '3' '3' '9' '9' '1' '0' '5' '1' '2' '3'\n", - " '1' '6' '1' '7' '2' '4' '8' '4' '1' '6' '2' '9' '4' '4' '2' '1' '8' '8'\n", - " '6' '2' '5' '8' '6' '7' '6' '9' '3' '3' '9' '4' '5' '0' '5' '4' '0' '0'\n", - " '4' '6' '3' '3' '6' '9' '3' '5' '8' '2' '9' '2' '5' '1' '9' '1' '9' '6'\n", - " '3' '5' '4' '9' '6' '7' '1' '0' '1' '0' '9' '6' '8' '4' '9' '2' '2' '5'\n", - " '0' '7' '3' '1' '6' '1' '3' '1' '9' '7' '4' '6' '8' '0' '3' '8' '5' '1'\n", - " '7' '7' '3' '1' '1' '4' '0' '8' '1' '6' '4' '2' '5' '1' '3' '1' '3' '0'\n", - " '9' '7' '2' '6' '2' '5' '4' '7' '0' '4' '9' '8' '2' '1' '4' '9' '1' '0'\n", - " '7' '3' '7' '1' '4' '8' '2' '0' '6' '7' '6' '6' '3' '5' '2' '4' '1' '0'\n", - " '9' '9' '9' '7' '2' '4' '0' '7' '7' '4' '8' '6' '8' '7' '7' '9' '6' '4'\n", - " '6' '4' '2' '4' '4' '1' '5' '1' '4' '4' '9' '8' '7' '4' '3' '0' '1' '2'\n", - " '7' '9' '4' '7' '7' '1' '7' '5' '7' '6' '4' '1' '9' '6' '2' '2' '1' '3'\n", - " '7' '0' '6' '3' '9' '6' '0' '7' '3' '9' '4' '5' '0' '5' '4' '1' '7' '0'\n", - " '7' '7' '5' '1' '9' '3' '2' '3' '7' '2' '1' '0' '5' '8' '5' '5' '3' '7'\n", - " '7' '4' '5' '2' '9' '9' '2' '2' '3' '5' '1' '8' '6' '2' '3' '7' '9' '8'\n", - " '4' '3' '9' '4' '0' '4' '1' '8' '9' '0' '3' '2' '5' '1' '7' '0' '7' '3'\n", - " '5' '6' '8' '5' '6' '1' '6' '9' '4' '1' '7' '1' '8' '1' '3' '7' '8' '6'\n", - " '1' '1' '7' '0' '5' '2' '7' '4' '4' '8' '4' '2' '7' '2' '9' '2' '8' '7'\n", - " '7' '9' '7' '6' '4' '0' '1' '6' '8' '4' '4' '6' '9' '6' '3' '6' '4' '9'\n", - " '8' '5' '2' '2' '7' '0' '7' '9' '7' '2' '7' '0' '5' '4' '8' '6' '6' '3'\n", - " '1' '5' '1' '5' '9' '7' '3' '4' '6' '5' '1' '9' '6' '8' '4' '5' '5' '2'\n", - " '1' '3' '4' '3' '6' '1' '6' '9' '0' '2' '1' '5' '8' '6' '7' '0' '1' '3'\n", - " '7' '6' '5' '6' '4' '0' '8' '1' '9' '0' '4' '2' '1' '5' '2' '0' '6' '6'\n", - " '4' '8' '0' '2' '7' '5' '9' '3' '3' '6' '3' '3' '0' '2' '8' '6' '6' '5'\n", - " '5' '0' '1' '2' '6' '6' '7' '1' '1' '9' '3' '8' '8' '2' '4' '7' '5' '5'\n", - " '7' '5' '2' '1' '1' '1' '6' '0' '4' '8' '4' '1' '8' '3' '4' '4' '9' '3'\n", - " '7' '6' '3' '5' '7' '5' '4' '7' '1' '7' '5' '4' '7' '9' '4' '8' '6' '9'\n", - " '1' '2' '8' '5' '4' '8' '3' '1' '5' '7' '3' '2' '1' '4' '4' '1' '2' '1'\n", - " '2' '1' '7' '3' '2' '1' '0' '7' '6' '7' '2' '5' '2' '5' '7' '3' '7' '2'\n", - " '9' '1' '4' '3' '3' '7' '6' '8' '5' '1' '2' '8' '2' '0' '3' '1' '8' '4'\n", - " '5' '4' '9' '1' '7' '2' '4' '9' '4' '9' '2' '8' '5' '8' '9' '7' '4' '7'\n", - " '9' '4' '9' '5' '2' '7' '4' '5' '5' '1' '5' '0' '9' '5' '2' '6' '9' '7'\n", - " '3' '8' '1' '9' '6' '6' '5' '3' '1' '2' '8' '0' '5' '9' '3' '3' '5' '3'\n", - " '5' '1' '6' '3' '0' '1' '3' '0' '7' '6' '2' '7' '9' '9' '7' '4' '6' '4'\n", - " '7' '3' '1' '9' '9' '7' '2' '9' '4' '5' '0' '1' '4' '1' '7' '6' '0' '7'\n", - " '5' '2' '6' '4' '8' '5' '3' '7' '9' '4' '3' '1' '9' '2' '2' '8' '5' '7'\n", - " '1' '9' '4' '3' '2' '4' '2' '6' '9' '1' '1' '0' '7' '7' '3' '7' '8' '9'\n", - " '6' '6' '9' '3' '7' '7' '6' '6' '3' '7' '3' '3' '6' '0' '3' '1' '0' '0'\n", - " '8' '1' '3' '5' '7' '7' '9' '3' '9' '3' '1' '7' '2' '3' '6' '7' '0' '4'\n", - " '9' '3' '3' '1' '8' '9' '0' '3' '9' '1' '7' '1' '8' '4' '7' '8' '4' '1'\n", - " '5' '4' '7' '1' '1' '8' '3' '7' '8' '3' '1' '7' '4' '3' '1' '2' '7' '5'\n", - " '7' '9' '5' '9' '5' '4' '7' '4' '0' '4' '2' '4' '2' '1' '7' '9' '3' '0'\n", - " '1' '7' '8' '0' '2' '8' '7' '1' '8' '4' '1' '6' '9' '9' '9' '3' '7' '1'\n", - " '2' '4' '5' '9' '7' '2' '1' '6' '7' '4' '5' '9' '7' '7' '9' '8' '5' '2'\n", - " '5' '4' '0' '1' '9' '8' '2' '2' '9' '7' '3' '5' '2' '1' '4' '6' '7' '3'\n", - " '1' '1' '1' '8' '6' '0' '8' '0' '1' '6' '6' '7' '1' '4' '8' '0' '8' '6'\n", - " '2' '6' '2' '8' '7' '9' '1' '9' '2' '1' '9' '2' '5' '4' '5' '5' '1' '0'\n", - " '6' '3' '8' '5' '0' '2' '6' '8' '7' '2']\n", - "Probabilités : [[0. 0.4 0. ... 0. 0. 0.1]\n", - " [0. 1. 0. ... 0. 0. 0. ]\n", - " [0. 0. 0. ... 0. 0. 0. ]\n", - " ...\n", - " [0. 0. 0. ... 0.1 0.5 0.3]\n", + "Etiquettes : ['1' '3' '4' ... '5' '1' '2']\n", + "Prédiction : ['6' '7' '1' '4' '2' '7' '6' '6' '4' '9' '8' '4' '0' '0' '6' '8' '5' '0'\n", + " '9' '6' '5' '0' '7' '7' '0' '7' '6' '1' '0' '1' '6' '6' '5' '8' '5' '6'\n", + " '6' '5' '0' '7' '7' '5' '2' '7' '3' '2' '2' '6' '0' '0' '5' '8' '2' '4'\n", + " '1' '0' '9' '6' '3' '7' '6' '3' '9' '4' '0' '0' '8' '8' '0' '6' '7' '1'\n", + " '8' '3' '1' '6' '9' '1' '8' '0' '2' '0' '4' '5' '9' '3' '4' '3' '6' '3'\n", + " '2' '3' '8' '0' '8' '6' '1' '7' '3' '8' '4' '2' '0' '7' '9' '4' '0' '2'\n", + " '2' '0' '2' '2' '3' '0' '0' '0' '6' '8' '2' '4' '3' '7' '2' '6' '8' '4'\n", + " '3' '8' '8' '0' '4' '6' '1' '0' '4' '6' '6' '0' '0' '6' '1' '6' '5' '5'\n", + " '1' '5' '8' '2' '6' '4' '7' '5' '3' '2' '5' '8' '5' '2' '2' '3' '0' '3'\n", + " '6' '1' '4' '8' '1' '7' '7' '5' '9' '1' '3' '5' '0' '7' '8' '6' '5' '0'\n", + " '6' '6' '8' '5' '9' '5' '3' '9' '7' '4' '9' '0' '1' '5' '3' '3' '6' '1'\n", + " '1' '1' '8' '7' '7' '1' '7' '4' '1' '1' '3' '8' '4' '4' '3' '9' '8' '4'\n", + " '0' '4' '4' '9' '6' '0' '6' '0' '3' '8' '8' '0' '9' '1' '4' '4' '2' '1'\n", + " '5' '7' '5' '0' '7' '6' '0' '4' '5' '7' '5' '9' '4' '3' '4' '4' '0' '5'\n", + " '0' '0' '1' '9' '1' '7' '3' '4' '6' '0' '5' '9' '6' '1' '1' '5' '6' '5'\n", + " '2' '9' '4' '3' '4' '1' '0' '0' '4' '2' '1' '7' '1' '4' '1' '3' '9' '2'\n", + " '0' '8' '7' '7' '4' '4' '7' '1' '8' '7' '1' '4' '6' '9' '2' '7' '1' '4'\n", + " '5' '1' '1' '4' '2' '7' '3' '8' '5' '8' '3' '3' '4' '7' '2' '1' '4' '9'\n", + " '9' '4' '7' '9' '3' '4' '9' '7' '1' '0' '7' '7' '3' '8' '4' '6' '1' '3'\n", + " '5' '5' '4' '9' '6' '0' '1' '1' '0' '0' '0' '3' '2' '7' '9' '8' '0' '3'\n", + " '6' '1' '9' '4' '0' '1' '0' '0' '1' '6' '9' '6' '3' '8' '2' '5' '9' '5'\n", + " '1' '3' '7' '0' '9' '3' '2' '6' '8' '5' '1' '5' '4' '1' '4' '1' '1' '3'\n", + " '1' '5' '7' '2' '3' '2' '6' '1' '2' '6' '3' '8' '7' '3' '3' '9' '8' '0'\n", + " '4' '3' '7' '7' '9' '3' '9' '8' '7' '8' '0' '4' '8' '8' '0' '4' '1' '5'\n", + " '1' '2' '1' '3' '5' '4' '9' '8' '1' '3' '1' '5' '8' '4' '8' '2' '9' '8'\n", + " '2' '3' '6' '3' '5' '2' '4' '0' '1' '0' '1' '8' '9' '9' '6' '2' '4' '1'\n", + " '5' '6' '7' '7' '1' '5' '0' '2' '6' '5' '0' '3' '2' '8' '8' '9' '7' '9'\n", + " '4' '4' '1' '9' '7' '8' '2' '1' '9' '6' '2' '4' '8' '7' '8' '9' '9' '4'\n", + " '6' '9' '9' '5' '6' '9' '9' '8' '5' '5' '6' '4' '6' '8' '8' '7' '6' '0'\n", + " '0' '9' '2' '3' '7' '7' '1' '5' '9' '1' '9' '9' '1' '4' '1' '9' '6' '9'\n", + " '0' '9' '4' '6' '1' '0' '7' '0' '8' '9' '7' '3' '8' '2' '3' '0' '2' '8'\n", + " '3' '1' '7' '0' '2' '1' '0' '4' '2' '0' '8' '1' '5' '2' '4' '5' '0' '9'\n", + " '8' '1' '3' '9' '8' '7' '2' '4' '6' '2' '3' '9' '1' '8' '2' '1' '9' '0'\n", + " '2' '4' '0' '9' '1' '4' '1' '3' '2' '4' '9' '5' '0' '2' '2' '1' '1' '7'\n", + " '6' '8' '4' '9' '7' '7' '9' '4' '2' '3' '8' '1' '3' '5' '7' '9' '2' '0'\n", + " '4' '8' '1' '6' '1' '7' '9' '6' '3' '6' '0' '0' '4' '7' '1' '1' '1' '4'\n", + " '5' '6' '6' '1' '7' '6' '1' '7' '6' '1' '1' '2' '0' '8' '6' '1' '4' '3'\n", + " '3' '6' '8' '7' '1' '1' '1' '4' '3' '3' '2' '6' '3' '3' '8' '8' '3' '1'\n", + " '8' '6' '6' '8' '8' '9' '6' '7' '6' '7' '8' '9' '1' '8' '3' '9' '5' '0'\n", + " '6' '6' '9' '3' '1' '2' '5' '5' '0' '9' '5' '9' '0' '0' '6' '1' '8' '5'\n", + " '0' '2' '2' '8' '3' '9' '7' '2' '7' '6' '2' '8' '6' '8' '8' '0' '2' '0'\n", + " '6' '2' '7' '7' '3' '7' '2' '7' '1' '7' '9' '3' '4' '7' '7' '9' '9' '2'\n", + " '5' '8' '3' '7' '7' '2' '1' '7' '1' '1' '9' '9' '3' '0' '9' '4' '9' '0'\n", + " '7' '6' '7' '7' '7' '7' '9' '7' '8' '1' '1' '6' '2' '6' '3' '8' '2' '8'\n", + " '1' '5' '7' '0' '8' '3' '2' '7' '5' '1' '5' '3' '5' '2' '1' '7' '6' '0'\n", + " '2' '6' '3' '2' '6' '0' '6' '2' '3' '9' '8' '6' '4' '9' '1' '3' '0' '4'\n", + " '2' '3' '8' '1' '9' '0' '3' '5' '4' '5' '3' '2' '5' '0' '1' '1' '8' '3'\n", + " '5' '6' '2' '1' '9' '3' '0' '4' '5' '9' '7' '2' '2' '1' '2' '1' '1' '5'\n", + " '0' '9' '3' '7' '1' '9' '6' '5' '1' '6' '0' '1' '1' '6' '5' '8' '2' '2'\n", + " '1' '8' '9' '7' '6' '8' '4' '5' '2' '3' '0' '7' '6' '0' '6' '6' '6' '0'\n", + " '8' '8' '3' '4' '0' '9' '7' '5' '1' '1' '1' '4' '6' '7' '9' '6' '3' '9'\n", + " '3' '9' '1' '9' '6' '4' '5' '4' '7' '0' '1' '9' '4' '8' '4' '6' '1' '8'\n", + " '5' '6' '5' '1' '2' '7' '9' '5' '8' '0' '8' '8' '3' '2' '9' '4' '4' '8'\n", + " '3' '0' '6' '5' '9' '7' '0' '0' '9' '7' '0' '3' '2' '1' '0' '5' '6' '4'\n", + " '0' '4' '6' '9' '3' '0' '4' '1' '5' '6' '3' '6' '9' '1' '5' '6' '3' '0'\n", + " '1' '6' '1' '0' '6' '2' '1' '7' '1' '9']\n", + "Probabilités : [[0. 0. 0. ... 0. 0. 0. ]\n", " [0. 0. 0. ... 1. 0. 0. ]\n", - " [0. 0. 1. ... 0. 0. 0. ]]\n", + " [0. 1. 0. ... 0. 0. 0. ]\n", + " ...\n", + " [0. 0. 0. ... 1. 0. 0. ]\n", + " [0. 0.4 0. ... 0.1 0. 0.3]\n", + " [0. 0. 0. ... 0.1 0. 0.9]]\n", "Classe image 4 : 9\n", - "Classe prédite image 4 : 7\n", - "Score échantillon de test : 0.922\n", - "Score données apprentissage : 0.9395\n" + "Classe prédite image 4 : 4\n", + "Score échantillon de test : 0.912\n", + "Score données apprentissage : 0.94325\n" ] } ], @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "id": "90db6e29", "metadata": {}, "outputs": [ @@ -153,8 +153,8 @@ " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]]\n", - "Etiquettes : ['8' '3' '7' ... '5' '9' '1']\n", - "[0.924, 0.91, 0.938, 0.932, 0.932, 0.928, 0.918, 0.93, 0.94, 0.914, 0.938, 0.946, 0.94, 0.952, 0.95, 0.932, 0.934, 0.938, 0.92, 0.916, 0.92, 0.942, 0.944, 0.926, 0.918, 0.928, 0.95, 0.932, 0.936, 0.94, 0.94, 0.954, 0.91, 0.942, 0.928, 0.928, 0.93, 0.91, 0.944, 0.956, 0.928, 0.94, 0.938, 0.942, 0.914, 0.922, 0.916, 0.93, 0.938, 0.934, 0.936, 0.946, 0.922, 0.938, 0.926, 0.922, 0.92, 0.904, 0.938, 0.922, 0.92, 0.924, 0.904, 0.934, 0.924, 0.952, 0.928, 0.936, 0.934, 0.922, 0.926, 0.922, 0.926, 0.922, 0.92, 0.934, 0.922, 0.912, 0.95, 0.918, 0.946, 0.92, 0.928, 0.914, 0.928, 0.924, 0.91, 0.92, 0.934, 0.936, 0.898, 0.914, 0.92, 0.928, 0.92, 0.92, 0.93, 0.944, 0.924, 0.934, 0.922, 0.926, 0.93, 0.924, 0.922, 0.898, 0.924, 0.916, 0.942, 0.898, 0.93, 0.908, 0.928, 0.91, 0.93, 0.95, 0.938, 0.89, 0.932, 0.898, 0.924, 0.902, 0.894, 0.912, 0.922, 0.932, 0.932, 0.924, 0.924, 0.932]\n" + "Etiquettes : ['9' '9' '8' ... '9' '4' '6']\n", + "[0.92, 0.922, 0.93, 0.966, 0.924, 0.922, 0.922, 0.896, 0.92, 0.91, 0.916, 0.94, 0.938, 0.938, 0.926, 0.936, 0.932, 0.932, 0.934, 0.938, 0.922, 0.934, 0.96, 0.926, 0.942, 0.934, 0.908, 0.926, 0.92, 0.936, 0.932, 0.924, 0.922, 0.938, 0.938, 0.916, 0.932, 0.96, 0.942, 0.922, 0.926, 0.938, 0.936, 0.924, 0.938, 0.946, 0.922, 0.928, 0.912, 0.908, 0.916, 0.932, 0.932, 0.93, 0.92, 0.928, 0.908, 0.932, 0.918, 0.938, 0.92, 0.93, 0.938, 0.924, 0.924, 0.932, 0.916, 0.916, 0.934, 0.928, 0.924, 0.94, 0.942, 0.926, 0.924, 0.912, 0.93, 0.906, 0.894, 0.922, 0.924, 0.912, 0.906, 0.942, 0.95, 0.924, 0.926, 0.92, 0.92, 0.9, 0.918, 0.908, 0.93, 0.942, 0.916, 0.934, 0.916, 0.92, 0.91, 0.918, 0.93, 0.918, 0.916, 0.894, 0.934, 0.926, 0.934, 0.91, 0.9, 0.914, 0.928, 0.918, 0.924, 0.916, 0.908, 0.904, 0.922, 0.912, 0.92, 0.914, 0.926, 0.906, 0.902, 0.914, 0.9, 0.936, 0.906, 0.942, 0.922, 0.906]\n" ] } ], @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 5, "id": "bf91b914", "metadata": {}, "outputs": [ @@ -212,20 +212,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2 : 0.9266\n", - "3 : 0.9366\n", - "4 : 0.9336\n", - "5 : 0.9341999999999999\n", - "6 : 0.9297777777777778\n", - "7 : 0.9275555555555557\n", - "8 : 0.9273333333333333\n", - "9 : 0.926888888888889\n", - "10 : 0.9264444444444445\n", - "11 : 0.9204444444444445\n", - "12 : 0.9277777777777779\n", - "13 : 0.918\n", - "14 : 0.922222222222222\n", - "15 : 0.9193333333333334\n" + "2 : 0.9232000000000001\n", + "3 : 0.933\n", + "4 : 0.9308\n", + "5 : 0.9326000000000001\n", + "6 : 0.9300000000000002\n", + "7 : 0.922888888888889\n", + "8 : 0.9266666666666666\n", + "9 : 0.9273333333333333\n", + "10 : 0.9206666666666666\n", + "11 : 0.9208888888888889\n", + "12 : 0.9197777777777778\n", + "13 : 0.9175555555555555\n", + "14 : 0.9162222222222223\n", + "15 : 0.9148888888888889\n" ] } ], @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 11, "id": "cc24e898", "metadata": {}, "outputs": [ @@ -252,19 +252,41 @@ " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]\n", " [0. 0. 0. ... 0. 0. 0.]]\n", - "Etiquettes : ['3' '0' '3' ... '4' '0' '6']\n", - "3 : 0.8908571428571429\n", - "4 : 0.893\n", - "5 : 0.92\n", - "6 : 0.9105\n", - "7 : 0.9326666666666666\n", - "8 : 0.926\n", - "9 : 0.946\n" + "Etiquettes : ['0' '0' '5' ... '9' '8' '6']\n", + "Temps d'entraînement : 0.002\n", + "Temps de prédiction : 0.338\n", + "Temps total : 0.34\n", + "Temps d'entraînement : 0.003\n", + "Temps de prédiction : 0.31\n", + "Temps total : 0.313\n", + "Temps d'entraînement : 0.002\n", + "Temps de prédiction : 0.328\n", + "Temps total : 0.33\n", + "Temps d'entraînement : 0.003\n", + "Temps de prédiction : 0.305\n", + "Temps total : 0.308\n", + "Temps d'entraînement : 0.003\n", + "Temps de prédiction : 0.254\n", + "Temps total : 0.257\n", + "Temps d'entraînement : 0.003\n", + "Temps de prédiction : 0.244\n", + "Temps total : 0.247\n", + "Temps d'entraînement : 0.004\n", + "Temps de prédiction : 0.203\n", + "Temps total : 0.207\n", + "3 : 0.9045714285714286\n", + "4 : 0.91\n", + "5 : 0.9168\n", + "6 : 0.925\n", + "7 : 0.934\n", + "8 : 0.922\n", + "9 : 0.952\n" ] } ], "source": [ "from sklearn.model_selection import KFold\n", + "import time\n", "\n", "rand_indexes = np.random.randint(70000, size=5000)\n", "\n", @@ -280,15 +302,19 @@ "\n", "for j in range (3, 10):\n", " xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=(j/10))\n", - "\n", - "\n", - " \n", - "\n", + " \n", + " t1 = round(time.time(),3)\n", " clf = neighbors.KNeighborsClassifier(n_neighbors=3,p = 2, n_jobs=-1)\n", " # On entraîne l'algorithme sur xtrain et ytrain\n", " clf.fit(xtrain, ytrain)\n", + " t2 = round(time.time(),3)\n", " # On prédit sur xtest\n", " pred = clf.predict(xtest)\n", + " t3 = round(time.time(),3)\n", + " \n", + " print(\"Temps d'entraînement : \", round(t2-t1,3))\n", + " print(\"Temps de prédiction : \", round(t3-t2,3))\n", + " print(\"Temps total : \", round(t3-t1,3))\n", "# print(\"Prédiction : \", pred)\n", " # Probabilités des prédictions sur xtest\n", " pred_proba = clf.predict_proba(xtest)\n", @@ -309,6 +335,14 @@ " print (n, \" : \", i)\n", " n += 1" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbb5eda6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/TP2_prog1.py.ipynb b/TP2_prog1.py.ipynb new file mode 100644 index 0000000..af6c8e7 --- /dev/null +++ b/TP2_prog1.py.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "id": "3eb7a65b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classe : 5\n" + ] + } + ], + "source": [ + "from sklearn.datasets import fetch_openml\n", + "import sklearn\n", + "import matplotlib.pyplot as plt\n", + "mnist = fetch_openml('mnist_784',as_frame=False)\n", + "images = mnist.data.reshape((-1, 28, 28))\n", + "plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n", + "plt.show()\n", + "print(\"Classe : \", mnist.target[0])\n", + "\n", + "from sklearn import model_selection\n", + "from sklearn import neural_network\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3b1a54ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Score échantillon de test : 0.9461904761904761\n", + "Classe image 4 : 1\n", + "Classe prédite image 4 : 1\n", + "Précision pour chaque classe : [0.98069307 0.96283925 0.9399428 0.92090657 0.95114943 0.94722671\n", + " 0.9645803 0.95956383 0.9084195 0.9247724 ]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/insa/anaconda/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n", + "# xtest dataset de prédiction et ytest étiquettes de xtest\n", + "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(mnist.data, mnist.target,train_size=0.7)\n", + "\n", + "#Entraîne le classifier\n", + "clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50))\n", + "clf.fit(xtrain, ytrain)\n", + "\n", + "#Prédiction sur le jeu de tests\n", + "pred = clf.predict(xtest)\n", + "#print(\"Prédiction : \", pred)\n", + "# Probabilités des prédictions sur xtest\n", + "pred_proba = clf.predict_proba(xtest)\n", + "#print(\"Probabilités : \", pred_proba)\n", + "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n", + "score = clf.score(xtest, ytest)\n", + "print(\"Score échantillon de test : \", score)\n", + "\n", + "#Infos image 4\n", + "print(\"Classe image 4 : \", ytest[3])\n", + "print(\"Classe prédite image 4 : \", pred[3])\n", + "\n", + "#Calcul de la précision avec metrics.precision_score\n", + "print(\"Précision pour chaque classe : \", metrics.precision_score(ytest, pred,average=None))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6068ca09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Précision pour chaque classe :\n", + " [[1981 2 18 8 6 12 6 0 29 8]\n", + " [ 0 2306 9 5 4 3 0 4 12 2]\n", + " [ 6 20 1972 31 19 2 16 23 52 3]\n", + " [ 1 6 17 1991 0 20 0 14 24 12]\n", + " [ 3 8 5 3 1986 0 11 9 6 40]\n", + " [ 3 1 1 59 5 1759 20 7 20 17]\n", + " [ 7 4 5 0 6 24 1988 0 8 0]\n", + " [ 1 14 44 14 5 3 1 2112 9 25]\n", + " [ 4 28 24 21 11 21 19 8 1845 50]\n", + " [ 14 6 3 30 46 13 0 24 26 1930]]\n" + ] + } + ], + "source": [ + "print(\"Précision pour chaque classe :\\n\", metrics.confusion_matrix(ytest, pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a4a5485", + "metadata": {}, + "outputs": [], + "source": [ + "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n", + "# xtest dataset de prédiction et ytest étiquettes de xtest\n", + "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(mnist.data, mnist.target,train_size=0.7)\n", + "\n", + "list_scores = []\n", + "\n", + "for i in range(1, 101):\n", + " #Entraîne le classifier\n", + " clf = neural_network.MLPClassifier(random_state=1, max_iter=300, hidden_layer_sizes=(50))\n", + " clf.fit(xtrain, ytrain)\n", + "\n", + " #Prédiction sur le jeu de tests\n", + " pred = clf.predict(xtest)\n", + " #print(\"Prédiction : \", pred)\n", + " # Probabilités des prédictions sur xtest\n", + " pred_proba = clf.predict_proba(xtest)\n", + " #print(\"Probabilités : \", pred_proba)\n", + " # On calcule le score obtenu sur xtest avec les étiquettes ytest\n", + " score = clf.score(xtest, ytest)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}