Add xgboost example and upgrade to latest matplotlib version
Aurélien Geron
parent
d9fdb0542b
commit
581253b47a
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@ -31,9 +31,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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@ -107,9 +105,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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@ -136,8 +132,7 @@
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"\n",
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"voting_clf = VotingClassifier(\n",
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" estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n",
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" voting='hard')\n",
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"voting_clf.fit(X_train, y_train)"
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" voting='hard')"
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]
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},
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{
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@ -145,6 +140,15 @@
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"voting_clf.fit(X_train, y_train)"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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@ -156,7 +160,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -172,7 +176,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -193,10 +197,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.ensemble import BaggingClassifier\n",
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@ -211,7 +213,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -221,7 +223,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -233,10 +235,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib.colors import ListedColormap\n",
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@ -248,7 +248,7 @@
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" X_new = np.c_[x1.ravel(), x2.ravel()]\n",
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" y_pred = clf.predict(X_new).reshape(x1.shape)\n",
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" custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
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" plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap, linewidth=10)\n",
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" plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
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" if contour:\n",
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" custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
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" plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
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@ -261,7 +261,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -285,7 +285,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -296,7 +296,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -306,10 +306,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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@ -322,7 +320,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -331,7 +329,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -345,7 +343,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -354,7 +352,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -378,7 +376,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -391,7 +389,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -400,7 +398,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -418,10 +416,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import fetch_mldata\n",
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@ -430,7 +426,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -440,10 +436,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_digit(data):\n",
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@ -455,7 +449,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -477,7 +471,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -491,7 +485,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -500,7 +494,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -509,28 +503,28 @@
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"plt.figure(figsize=(11, 4))\n",
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"for subplot, learning_rate in ((121, 1), (122, 0.5)):\n",
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" sample_weights = np.ones(m)\n",
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" plt.subplot(subplot)\n",
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" for i in range(5):\n",
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" plt.subplot(subplot)\n",
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" svm_clf = SVC(kernel=\"rbf\", C=0.05, random_state=42)\n",
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" svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
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" y_pred = svm_clf.predict(X_train)\n",
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" sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
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" plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n",
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" plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n",
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" if subplot == 121:\n",
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" plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
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" plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
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" plt.text(-0.5, 0.10, \"3\", fontsize=14)\n",
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" plt.text(-0.4, 0.55, \"4\", fontsize=14)\n",
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" plt.text(-0.3, 0.90, \"5\", fontsize=14)\n",
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"\n",
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"plt.subplot(121)\n",
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"plt.text(-0.7, -0.65, \"1\", fontsize=14)\n",
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"plt.text(-0.6, -0.10, \"2\", fontsize=14)\n",
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"plt.text(-0.5, 0.10, \"3\", fontsize=14)\n",
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"plt.text(-0.4, 0.55, \"4\", fontsize=14)\n",
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"plt.text(-0.3, 0.90, \"5\", fontsize=14)\n",
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"save_fig(\"boosting_plot\")\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": 31,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -546,10 +540,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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@ -559,7 +551,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"tree_reg3.fit(X, y3)"
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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": 36,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"X_new = np.array([[0.8]])"
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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": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
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"X_new = np.array([[0.8]])"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred"
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"y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
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]
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},
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{
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred"
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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": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n",
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" x1 = np.linspace(axes[0], axes[1], 500)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 43,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"min_error = np.min(errors)"
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(gbrt.n_estimators)"
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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": 49,
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"metadata": {},
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"outputs": [],
|
||||
"source": [
|
||||
"print(\"Minimum validation MSE:\", min_val_error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using XGBoost"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" import xgboost\n",
|
||||
"except ImportError as ex:\n",
|
||||
" print(\"Error: the xgboost library is not installed.\")\n",
|
||||
" xgboost = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if xgboost is not None: # not shown in the book\n",
|
||||
" xgb_reg = xgboost.XGBRegressor(random_state=42)\n",
|
||||
" xgb_reg.fit(X_train, y_train)\n",
|
||||
" y_pred = xgb_reg.predict(X_val)\n",
|
||||
" val_error = mean_squared_error(y_val, y_pred)\n",
|
||||
" print(\"Validation MSE:\", val_error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if xgboost is not None: # not shown in the book\n",
|
||||
" xgb_reg.fit(X_train, y_train,\n",
|
||||
" eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n",
|
||||
" y_pred = xgb_reg.predict(X_val)\n",
|
||||
" val_error = mean_squared_error(y_val, y_pred)\n",
|
||||
" print(\"Validation MSE:\", val_error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%timeit GradientBoostingRegressor().fit(X_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
|
|
@ -830,9 +893,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue