Pushing the docs to dev/ for branch: master, commit 968252dfb4c252e252614ae8736cc6b1ebdde9ee

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -134,25 +134,12 @@
 # Plot the Precision-Recall curve
 # ................................
 from sklearn.metrics import precision_recall_curve
+from sklearn.metrics import plot_precision_recall_curve
 import matplotlib.pyplot as plt
-from inspect import signature
 
-precision, recall, _ = precision_recall_curve(y_test, y_score)
-
-# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument
-step_kwargs = ({'step': 'post'}
-               if 'step' in signature(plt.fill_between).parameters
-               else {})
-plt.step(recall, precision, color='b', alpha=0.2,
-         where='post')
-plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)
-
-plt.xlabel('Recall')
-plt.ylabel('Precision')
-plt.ylim([0.0, 1.05])
-plt.xlim([0.0, 1.0])
-plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(
-          average_precision))
+disp = plot_precision_recall_curve(classifier, X_test, y_test)
+disp.ax_.set_title('2-class Precision-Recall curve: '
+                   'AP={0:0.2f}'.format(average_precision))
 
 ###############################################################################
 # In multi-label settings
@@ -212,10 +199,7 @@
 #
 
 plt.figure()
-plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,
-         where='post')
-plt.fill_between(recall["micro"], precision["micro"], alpha=0.2, color='b',
-                 **step_kwargs)
+plt.step(recall['micro'], precision['micro'], where='post')
 
 plt.xlabel('Recall')
 plt.ylabel('Precision')

dev/_downloads/83c5d8e143d12d2f86db0941ce3ebf3d/plot_precision_recall.py

@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import precision_recall_curve\nimport matplotlib.pyplot as plt\nfrom inspect import signature\n\nprecision, recall, _ = precision_recall_curve(y_test, y_score)\n\n# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument\nstep_kwargs = ({'step': 'post'}\n               if 'step' in signature(plt.fill_between).parameters\n               else {})\nplt.step(recall, precision, color='b', alpha=0.2,\n         where='post')\nplt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)\n\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.ylim([0.0, 1.05])\nplt.xlim([0.0, 1.0])\nplt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n          average_precision))"
+        "from sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import plot_precision_recall_curve\nimport matplotlib.pyplot as plt\n\ndisp = plot_precision_recall_curve(classifier, X_test, y_test)\ndisp.ax_.set_title('2-class Precision-Recall curve: '\n                   'AP={0:0.2f}'.format(average_precision))"
       ]
     },
     {
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "plt.figure()\nplt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,\n         where='post')\nplt.fill_between(recall[\"micro\"], precision[\"micro\"], alpha=0.2, color='b',\n                 **step_kwargs)\n\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.ylim([0.0, 1.05])\nplt.xlim([0.0, 1.0])\nplt.title(\n    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n    .format(average_precision[\"micro\"]))"
+        "plt.figure()\nplt.step(recall['micro'], precision['micro'], where='post')\n\nplt.xlabel('Recall')\nplt.ylabel('Precision')\nplt.ylim([0.0, 1.05])\nplt.xlim([0.0, 1.0])\nplt.title(\n    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n    .format(average_precision[\"micro\"]))"
       ]
     },
     {