275 lines
89 KiB
Text
275 lines
89 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": 145,
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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": 146,
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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": 147,
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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": 148,
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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": 149,
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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": 150,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.model_selection import GridSearchCV"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 151,
|
|
"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": 152,
|
|
"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": 153,
|
|
"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": 154,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'C': 1, '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 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|