Pushing the docs to dev/ for branch: master, commit 0cae688f251e1b21bbc9d6ecca66aa6987fe88a9

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom itertools import cycle\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn.multiclass import OneVsRestClassifier\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\n# setup plot details\ncolors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])\nlw = 2\n\n# Binarize the output\ny = label_binarize(y, classes=[0, 1, 2])\nn_classes = y.shape[1]\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nn_samples, n_features = X.shape\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# Split into training and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\n                                                    random_state=random_state)\n\n# Run classifier\nclassifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n                                 random_state=random_state))\ny_score = classifier.fit(X_train, y_train).decision_function(X_test)\n\n# Compute Precision-Recall and plot curve\nprecision = dict()\nrecall = dict()\naverage_precision = dict()\nfor i in range(n_classes):\n    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],\n                                                        y_score[:, i])\n    average_precision[i] = average_precision_score(y_test[:, i], y_score[:, i])\n\n# Compute micro-average ROC curve and ROC area\nprecision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(y_test.ravel(),\n    y_score.ravel())\naverage_precision[\"micro\"] = average_precision_score(y_test, y_score,\n                                                     average=\"micro\")\n\n\n# Plot Precision-Recall curve\nplt.clf()\nplt.plot(recall[0], precision[0], lw=lw, color='navy',\n         label='Precision-Recall curve')\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.ylim([0.0, 1.05])\nplt.xlim([0.0, 1.0])\nplt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\nplt.legend(loc=\"lower left\")\nplt.show()\n\n# Plot Precision-Recall curve for each class\nplt.clf()\nplt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=lw,\n         label='micro-average Precision-recall curve (area = {0:0.2f})'\n               ''.format(average_precision[\"micro\"]))\nfor i, color in zip(range(n_classes), colors):\n    plt.plot(recall[i], precision[i], color=color, lw=lw,\n             label='Precision-recall curve of class {0} (area = {1:0.2f})'\n                   ''.format(i, average_precision[i]))\n\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.title('Extension of Precision-Recall curve to multi-class')\nplt.legend(loc=\"lower right\")\nplt.show()"
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom itertools import cycle\n\nfrom sklearn import svm, datasets\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn.multiclass import OneVsRestClassifier\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\n\n# setup plot details\ncolors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])\nlw = 2\n\n# Binarize the output\ny = label_binarize(y, classes=[0, 1, 2])\nn_classes = y.shape[1]\n\n# Add noisy features\nrandom_state = np.random.RandomState(0)\nn_samples, n_features = X.shape\nX = np.c_[X, random_state.randn(n_samples, 200 * n_features)]\n\n# Split into training and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\n                                                    random_state=random_state)\n\n# Run classifier\nclassifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,\n                                 random_state=random_state))\ny_score = classifier.fit(X_train, y_train).decision_function(X_test)\n\n# Compute Precision-Recall and plot curve\nprecision = dict()\nrecall = dict()\naverage_precision = dict()\nfor i in range(n_classes):\n    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i],\n                                                        y_score[:, i])\n    average_precision[i] = average_precision_score(y_test[:, i], y_score[:, i])\n\n# Compute micro-average ROC curve and ROC area\nprecision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(y_test.ravel(),\n    y_score.ravel())\naverage_precision[\"micro\"] = average_precision_score(y_test, y_score,\n                                                     average=\"micro\")\n\n\n# Plot Precision-Recall curve\nplt.clf()\nplt.plot(recall[0], precision[0], lw=lw, color='navy',\n         label='Precision-Recall curve')\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.ylim([0.0, 1.05])\nplt.xlim([0.0, 1.0])\nplt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[0]))\nplt.legend(loc=\"lower left\")\nplt.show()\n\n# Plot Precision-Recall curve for each class and iso-f1 curves\nplt.clf()\nf_scores = np.linspace(0.2, 0.8, num=4)\nlines = []\nlabels = []\nfor f_score in f_scores:\n    x = np.linspace(0.01, 1)\n    y = f_score * x / (2 * x - f_score)\n    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)\n    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))\n\nlines.append(l)\nlabels.append('iso-f1 curves')\nl, = plt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=lw)\nlines.append(l)\nlabels.append('micro-average Precision-recall curve (area = {0:0.2f})'\n              ''.format(average_precision[\"micro\"]))\nfor i, color in zip(range(n_classes), colors):\n    l, = plt.plot(recall[i], precision[i], color=color, lw=lw)\n    lines.append(l)\n    labels.append('Precision-recall curve of class {0} (area = {1:0.2f})'\n                  ''.format(i, average_precision[i]))\n\nfig = plt.gcf()\nfig.set_size_inches(7, 7)\nfig.subplots_adjust(bottom=0.25)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.title('Extension of Precision-Recall curve to multi-class')\nplt.figlegend(lines, labels, loc='lower center')\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -139,20 +139,36 @@
 plt.legend(loc="lower left")
 plt.show()
 
-# Plot Precision-Recall curve for each class
+# Plot Precision-Recall curve for each class and iso-f1 curves
 plt.clf()
-plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw,
-         label='micro-average Precision-recall curve (area = {0:0.2f})'
-               ''.format(average_precision["micro"]))
+f_scores = np.linspace(0.2, 0.8, num=4)
+lines = []
+labels = []
+for f_score in f_scores:
+    x = np.linspace(0.01, 1)
+    y = f_score * x / (2 * x - f_score)
+    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)
+    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))
+
+lines.append(l)
+labels.append('iso-f1 curves')
+l, = plt.plot(recall["micro"], precision["micro"], color='gold', lw=lw)
+lines.append(l)
+labels.append('micro-average Precision-recall curve (area = {0:0.2f})'
+              ''.format(average_precision["micro"]))
 for i, color in zip(range(n_classes), colors):
-    plt.plot(recall[i], precision[i], color=color, lw=lw,
-             label='Precision-recall curve of class {0} (area = {1:0.2f})'
-                   ''.format(i, average_precision[i]))
-
+    l, = plt.plot(recall[i], precision[i], color=color, lw=lw)
+    lines.append(l)
+    labels.append('Precision-recall curve of class {0} (area = {1:0.2f})'
+                  ''.format(i, average_precision[i]))
+
+fig = plt.gcf()
+fig.set_size_inches(7, 7)
+fig.subplots_adjust(bottom=0.25)
 plt.xlim([0.0, 1.0])
 plt.ylim([0.0, 1.05])
 plt.xlabel('Recall')
 plt.ylabel('Precision')
 plt.title('Extension of Precision-Recall curve to multi-class')
-plt.legend(loc="lower right")
+plt.figlegend(lines, labels, loc='lower center')
 plt.show()