Pushing the docs to dev/ for branch: master, commit b8e751abf1aaaf9340e7666a934c82535e96cfd5

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -145,7 +145,7 @@ def size_mb(docs):
     len(data_train.data), data_train_size_mb))
 print("%d documents - %0.3fMB (test set)" % (
     len(data_test.data), data_test_size_mb))
-print("%d categories" % len(categories))
+print("%d categories" % len(target_names))
 print()
 
 # split a training set and a test set

dev/_downloads/auto_examples_python.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import precision_recall_curve\nimport matplotlib.pyplot as plt\nfrom sklearn.utils.fixes 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\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))"
       ]
     },
     {

dev/_downloads/plot_document_classification_20newsgroups.ipynb

@@ -137,7 +137,7 @@
 # ................................
 from sklearn.metrics import precision_recall_curve
 import matplotlib.pyplot as plt
-from sklearn.utils.fixes import signature
+from inspect import signature
 
 precision, recall, _ = precision_recall_curve(y_test, y_score)