275 lines
92 KiB
Text
275 lines
92 KiB
Text
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# TP 3 SVM\n",
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"On se propose de faire de la modélisation par SVM sur des problématiques de discrimination, en utilisant la bibliothèque scikit-learn.\n",
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"\n",
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"On veut : \n",
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"- Traiter un problème de discrimination linéairement séparable\n",
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"- Traiter un problème non linéairement séparable (SVM à noyaux)\n",
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"- Traiter le problème de la discrimination de chiffres manuscrits"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Problème linéairement séparable"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 120,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.svm import SVC"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 121,
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"metadata": {},
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"outputs": [],
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"source": [
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"def genere_ex_1(n1=100, n2=50, mu1=[0,3], mu2=[3,0], sd1=0.15, sd2=0.2):\n",
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" \"\"\" Génération de point\n",
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" \"\"\"\n",
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" X = np.concatenate((np.random.multivariate_normal(mu1, np.diagflat(sd1*np.ones(2)), n1),\n",
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" np.random.multivariate_normal(mu2, np.diagflat(sd2*np.ones(2)),n2)))\n",
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"\n",
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" Y = np.concatenate((np.ones((n1,1)), -1*np.ones((n2,1))))[:,0]\n",
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" return X,Y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 122,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_data_hyperplan(X, Y, classifier, name):\n",
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" \"\"\" Affichage du classifieur \n",
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" \"\"\"\n",
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" w = classifier.coef_[0]\n",
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" b = classifier.intercept_[0]\n",
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" a = -w[0] / w[1]\n",
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" xx = np.linspace(min(X[:,0]), max(X[:,0]))\n",
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" yy = a * xx - b/w[1]\n",
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"\n",
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" color = ['red' if c >= 0 else 'blue' for c in Y]\n",
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" plt.scatter(X[:,0], X[:,1], color=color)\n",
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"\n",
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" plt.plot(xx,yy, color='black')\n",
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" plt.plot(xx, yy+1/w[1], color='green')\n",
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" plt.plot(xx, yy-1/w[1], color='green')\n",
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" plt.xlabel(\"x1\")\n",
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" plt.ylabel(\"x2\")\n",
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" plt.title(\"Classe 1 (red), Classe -1 (blue)\")\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 123,
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"metadata": {},
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"outputs": [],
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"source": [
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"def main(X,Y):\n",
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" classifier = SVC(kernel='linear', probability=True)\n",
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" classifier = classifier.fit(X, Y)\n",
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"\n",
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" plot_data_hyperplan(X, Y, classifier, 'Graph_SVM_linear')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 135,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"X, Y = genere_ex_1() \n",
|
|
"main(X, Y)\n"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Problème non linéairement séparable\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 136,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.model_selection import GridSearchCV"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 137,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def genere_ex_2(n=300, mu=[0,0], std=0.25, delta=0.2):\n",
|
|
" \"\"\" Génération de données de nuage gaussien centré en 0 et traversé par une fonction polynomiale de degré 3\n",
|
|
" \"\"\"\n",
|
|
" X = np.random.multivariate_normal(mu, np.diagflat(std*np.ones(2)), n)\n",
|
|
" Y = np.zeros((X.shape[0]))\n",
|
|
"\n",
|
|
" for i in range(X.shape[0]):\n",
|
|
" x = X[i,0]\n",
|
|
" y = X[i,1]\n",
|
|
" if y < x*(x-1)*(x+1):\n",
|
|
" Y[i] = -1\n",
|
|
" X[i,1] = X[i,1] - delta\n",
|
|
" else:\n",
|
|
" Y[i] = 1\n",
|
|
" X[i,1] = X[i,1] + delta\n",
|
|
" return X,Y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 138,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def plot(X,Y,classifier, nameFig):\n",
|
|
" \"\"\" Visualisation\n",
|
|
" \"\"\"\n",
|
|
" minx1 = min(X[:,0])\n",
|
|
" maxx1 = max(X[:,0])\n",
|
|
" minx2 = min(X[:,1])\n",
|
|
" maxx2 = max(X[:,1])\n",
|
|
"\n",
|
|
" xx = np.linspace(minx1, maxx1, 100)\n",
|
|
" yy = np.linspace(minx2, maxx2, 100).T\n",
|
|
" xx, yy = np.meshgrid(xx,yy)\n",
|
|
" Xfull = np.c_[xx.ravel(), yy.ravel()]\n",
|
|
"\n",
|
|
" probas = classifier.predict_proba(Xfull)\n",
|
|
" Z = classifier.decision_function(Xfull)\n",
|
|
"\n",
|
|
" k = 1\n",
|
|
" plt.title(\"Class %d\" %k)\n",
|
|
" imshow_handle = plt.imshow(probas[:, k].reshape((100,100)), extent=(minx1, maxx1, minx2, maxx2), origin='lower')\n",
|
|
" \n",
|
|
" classPos = Y>=0\n",
|
|
" classNeg = Y<0\n",
|
|
"\n",
|
|
" plt.contour(xx, yy, Z.reshape((100,100)), [-1,0,1], colors=['blue', 'black', 'red'])\n",
|
|
" plt.scatter(X[classPos, 0], X[classPos, 1], marker='o', c='r', edgecolors='k')\n",
|
|
" plt.scatter(X[classNeg, 0], X[classNeg, 1], marker='o', c='b', edgecolors='k')\n",
|
|
"\n",
|
|
" ax = plt.axes([0.8, 0.15, 0.05, 0.7])\n",
|
|
"\n",
|
|
" plt.title('Probability')\n",
|
|
" plt.colorbar(imshow_handle, cax=ax, orientation='vertical')\n",
|
|
" \n",
|
|
" plt.savefig(nameFig+'.jpg', dpi=300)\n",
|
|
" plt.show()\n",
|
|
" plt.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 139,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def main(X,Y,nameFig = 'GridSearchCV'):\n",
|
|
" \"\"\" Création du modèle avec la recherche du meilleur paramétrage de GridSearchCV \n",
|
|
" \"\"\"\n",
|
|
" parameters = {'kernel':('poly', 'poly'), 'C':[0.1,0.5, 1, 10], 'degree':[3,5], 'coef0':[0, 0.1, 0.5, 1, 10]}\n",
|
|
" svc = SVC(probability=True)\n",
|
|
" classifier = GridSearchCV(svc, parameters)\n",
|
|
" classifier = classifier.fit(X,Y)\n",
|
|
" print(classifier.best_params_)\n",
|
|
"\n",
|
|
" plot(X,Y, classifier=classifier, nameFig=nameFig)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 144,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'C': 10, 'coef0': 0.5, 'degree': 3, 'kernel': 'poly'}\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"X,Y = genere_ex_2()\n",
|
|
"main(X,Y)"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.9.4"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|