Rebuild dev docs at master=4bcfedb

Author: sklearn-ci

dev/_downloads/plot_roc.py

@@ -44,6 +44,7 @@
 from sklearn.cross_validation import train_test_split
 from sklearn.preprocessing import label_binarize
 from sklearn.multiclass import OneVsRestClassifier
+from scipy import interp
 
 # Import some data to play with
 iris = datasets.load_iris()
@@ -99,10 +100,23 @@
 # Plot ROC curves for the multiclass problem
 
 # Compute macro-average ROC curve and ROC area
-fpr["macro"] = np.mean([fpr[i] for i in range(n_classes)], axis=0)
-tpr["macro"] = np.mean([tpr[i] for i in range(n_classes)], axis=0)
+
+# First aggregate all false positive rates
+all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
+
+# Then interpolate all ROC curves at this points
+mean_tpr = np.zeros_like(all_fpr)
+for i in range(n_classes):
+    mean_tpr += interp(all_fpr, fpr[i], tpr[i])
+
+# Finally average it and compute AUC
+mean_tpr /= n_classes
+
+fpr["macro"] = all_fpr
+tpr["macro"] = mean_tpr
 roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
 
+# Plot all ROC curves
 plt.figure()
 plt.plot(fpr["micro"], tpr["micro"],
          label='micro-average ROC curve (area = {0:0.2f})'

dev/_images/plot_roc.png

@@ -4408,14 +4408,14 @@ Examples concerning model selection, mostly contained in the
 
 .. raw:: html
 
-    <div class="thumbnailContainer" tooltip="Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...">
+    <div class="thumbnailContainer" tooltip="On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset...">
 
 .. only:: html
 
-  .. figure:: model_selection/images/thumb/plot_roc.png
-    :target: ./model_selection/plot_roc.html
+  .. figure:: model_selection/images/thumb/plot_learning_curve.png
+    :target: ./model_selection/plot_learning_curve.html
 
-    :ref:`example_model_selection_plot_roc.py`
+    :ref:`example_model_selection_plot_learning_curve.py`
 
 
 .. raw:: html
@@ -4427,20 +4427,20 @@ Examples concerning model selection, mostly contained in the
 .. toctree::
    :hidden:
 
-   model_selection/plot_roc
+   model_selection/plot_learning_curve
 
 
 
 .. raw:: html
 
-    <div class="thumbnailContainer" tooltip="On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset...">
+    <div class="thumbnailContainer" tooltip="Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...">
 
 .. only:: html
 
-  .. figure:: model_selection/images/thumb/plot_learning_curve.png
-    :target: ./model_selection/plot_learning_curve.html
+  .. figure:: model_selection/images/thumb/plot_roc.png
+    :target: ./model_selection/plot_roc.html
 
-    :ref:`example_model_selection_plot_learning_curve.py`
+    :ref:`example_model_selection_plot_roc.py`
 
 
 .. raw:: html
@@ -4452,7 +4452,7 @@ Examples concerning model selection, mostly contained in the
 .. toctree::
    :hidden:
 
-   model_selection/plot_learning_curve
+   model_selection/plot_roc
 
 
 .. raw:: html

dev/_images/plot_roc1.png

@@ -44,6 +44,16 @@ label.
 .. rst-class:: horizontal
 
 
+    *
+
+      .. image:: images/plot_roc_001.png
+            :scale: 47
+
+    *
+
+      .. image:: images/plot_roc_002.png
+            :scale: 47
+
 
 
 
@@ -52,6 +62,6 @@ label.
 .. literalinclude:: plot_roc.py
     :lines: 38-
 
-**Total running time of the example:**  0.00 seconds
-( 0 minutes  0.00 seconds)
+**Total running time of the example:**  0.36 seconds
+( 0 minutes  0.36 seconds)
 

dev/_images/plot_roc_001.png

@@ -87,6 +87,9 @@ and tasks.
 - `mlxtend <https://github.com/rasbt/mlxtend>`_ Includes a number of additional
   estimators as well as model visualization utilities.
 
+- `kmodes <https://github.com/nicodv/kmodes>`_ k-modes clustering algorithm for categorical data, and
+  several of its variations.
+
 Statistical learning with Python
 --------------------------------
 Other packages useful for data analysis and machine learning.