Improve a few figures (e.g., add missing labels, share axes, etc.)
Aurélien Geron
parent
23b6366c39
commit
7e6489f8a4
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@ -249,13 +249,14 @@
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"deep_tree_clf1.fit(Xm, ym)\n",
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"deep_tree_clf2.fit(Xm, ym)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
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"fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
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"plt.sca(axes[0])\n",
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"plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
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"plt.title(\"No restrictions\", fontsize=16)\n",
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"plt.subplot(122)\n",
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"plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
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"plt.sca(axes[1])\n",
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"plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
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"plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
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"plt.ylabel(\"\")\n",
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"\n",
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"save_fig(\"min_samples_leaf_plot\")\n",
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"plt.show()"
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@ -299,11 +300,12 @@
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"tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
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"tree_clf_sr.fit(Xsr, ys)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
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"plt.sca(axes[0])\n",
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"plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
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"plt.subplot(122)\n",
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"plt.sca(axes[1])\n",
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"plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
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"plt.ylabel(\"\")\n",
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"\n",
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"save_fig(\"sensitivity_to_rotation_plot\")\n",
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"plt.show()"
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@ -365,8 +367,8 @@
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" plt.plot(X, y, \"b.\")\n",
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" plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
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"plt.sca(axes[0])\n",
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"plot_regression_predictions(tree_reg1, X, y)\n",
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"for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
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" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
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@ -376,7 +378,7 @@
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"plt.legend(loc=\"upper center\", fontsize=18)\n",
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"plt.title(\"max_depth=2\", fontsize=14)\n",
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"\n",
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"plt.subplot(122)\n",
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"plt.sca(axes[1])\n",
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"plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
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"for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
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" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
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@ -428,9 +430,9 @@
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"y_pred1 = tree_reg1.predict(x1)\n",
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"y_pred2 = tree_reg2.predict(x1)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
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"\n",
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"plt.subplot(121)\n",
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"plt.sca(axes[0])\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
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"plt.axis([0, 1, -0.2, 1.1])\n",
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@ -439,7 +441,7 @@
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"plt.legend(loc=\"upper center\", fontsize=18)\n",
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"plt.title(\"No restrictions\", fontsize=14)\n",
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"\n",
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"plt.subplot(122)\n",
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"plt.sca(axes[1])\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
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"plt.axis([0, 1, -0.2, 1.1])\n",
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@ -720,7 +722,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.4"
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},
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"nav_menu": {
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"height": "309px",
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@ -260,7 +260,7 @@
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"source": [
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"from matplotlib.colors import ListedColormap\n",
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"\n",
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"def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):\n",
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"def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):\n",
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" x1s = np.linspace(axes[0], axes[1], 100)\n",
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" x2s = np.linspace(axes[2], axes[3], 100)\n",
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" x1, x2 = np.meshgrid(x1s, x2s)\n",
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@ -284,13 +284,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(11,4))\n",
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"plt.subplot(121)\n",
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"fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
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"plt.sca(axes[0])\n",
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"plot_decision_boundary(tree_clf, X, y)\n",
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"plt.title(\"Decision Tree\", fontsize=14)\n",
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"plt.subplot(122)\n",
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"plt.sca(axes[1])\n",
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"plot_decision_boundary(bag_clf, X, y)\n",
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"plt.title(\"Decision Trees with Bagging\", fontsize=14)\n",
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"plt.ylabel(\"\")\n",
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"save_fig(\"decision_tree_without_and_with_bagging_plot\")\n",
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"plt.show()"
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]
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@ -381,7 +382,7 @@
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" tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
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" indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
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" tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
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" plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
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" plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n",
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"\n",
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"plt.show()"
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]
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@ -521,10 +522,10 @@
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"source": [
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"m = len(X_train)\n",
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"\n",
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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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"fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
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"for subplot, learning_rate in ((0, 1), (1, 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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" plt.sca(axes[subplot])\n",
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" for i in range(5):\n",
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" svm_clf = SVC(kernel=\"rbf\", C=0.05, gamma=\"scale\", random_state=42)\n",
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" svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
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@ -532,12 +533,14 @@
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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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" if subplot == 0:\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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" else:\n",
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" plt.ylabel(\"\")\n",
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"\n",
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"save_fig(\"boosting_plot\")\n",
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"plt.show()"
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@ -715,15 +718,18 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(11,4))\n",
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"fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
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"\n",
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"plt.subplot(121)\n",
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"plt.sca(axes[0])\n",
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"plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\n",
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"plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n",
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"plt.xlabel(\"$x_1$\", fontsize=16)\n",
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"plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
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"\n",
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"plt.subplot(122)\n",
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"plt.sca(axes[1])\n",
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"plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
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"plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n",
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"plt.xlabel(\"$x_1$\", fontsize=16)\n",
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"\n",
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"save_fig(\"gbrt_learning_rate_plot\")\n",
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"plt.show()"
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@ -774,7 +780,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(11, 4))\n",
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"plt.figure(figsize=(10, 4))\n",
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"\n",
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"plt.subplot(121)\n",
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"plt.plot(errors, \"b.-\")\n",
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@ -784,11 +790,14 @@
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"plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n",
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"plt.axis([0, 120, 0, 0.01])\n",
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"plt.xlabel(\"Number of trees\")\n",
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"plt.ylabel(\"Error\", fontsize=16)\n",
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"plt.title(\"Validation error\", fontsize=14)\n",
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"\n",
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"plt.subplot(122)\n",
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"plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n",
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"plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n",
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"plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n",
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"plt.xlabel(\"$x_1$\", fontsize=16)\n",
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"\n",
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"save_fig(\"early_stopping_gbrt_plot\")\n",
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"plt.show()"
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@ -1200,7 +1209,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"That's a significant improvement, and it's much better than each of the individual classifiers."
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"Nope, hard voting wins in this case."
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]
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},
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{
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@ -1216,6 +1225,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"voting_clf.voting = \"hard\"\n",
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"voting_clf.score(X_test, y_test)"
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]
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},
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@ -1232,7 +1242,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The voting classifier reduced the error rate from about 4.0% for our best model (the `MLPClassifier`) to just 3.1%. That's about 22.5% less errors, not bad!"
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"The voting classifier only very slightly reduced the error rate of the best model in this case."
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]
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},
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{
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@ -1346,8 +1356,15 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This stacking ensemble does not perform as well as the soft voting classifier we trained earlier, it's just as good as the best individual classifier."
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"This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier."
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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": null,
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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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"metadata": {
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@ -1366,7 +1383,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.7.4"
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},
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"nav_menu": {
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"height": "252px",
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