Merge branch 'pr/3395'

Author: larsmans

benchmarks/bench_multilabel_metrics.py

@@ -12,11 +12,13 @@
 import sys
 
 import matplotlib.pyplot as plt
+import scipy.sparse as sp
 import numpy as np
 
 from sklearn.datasets import make_multilabel_classification
 from sklearn.metrics import (f1_score, accuracy_score, hamming_loss,
                              jaccard_similarity_score)
+from sklearn.utils.testing import ignore_warnings
 
 
 METRICS = {
@@ -30,9 +32,12 @@
 FORMATS = {
     'sequences': lambda y: [list(np.flatnonzero(s)) for s in y],
     'dense': lambda y: y,
+    'csr': lambda y: sp.csr_matrix(y),
+    'csc': lambda y: sp.csc_matrix(y),
 }
 
 
+@ignore_warnings
 def benchmark(metrics=tuple(v for k, v in sorted(METRICS.items())),
               formats=tuple(v for k, v in sorted(FORMATS.items())),
               samples=1000, classes=4, density=.2,
@@ -109,7 +114,7 @@ def _tabulate(results, metrics, formats):
 
 
 def _plot(results, metrics, formats, title, x_ticks, x_label,
-          format_markers=('x', '|', 'o'),
+          format_markers=('x', '|', 'o', '+'),
           metric_colors=('c', 'm', 'y', 'k', 'g', 'r', 'b')):
     """
     Plot the results by metric, format and some other variable given by

doc/modules/model_evaluation.rst

@@ -260,7 +260,7 @@ where :math:`1(x)` is the `indicator function
 
 In the multilabel case with binary label indicators: ::
 
-  >>> accuracy_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.ones((2, 2)))
+  >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
   0.5
 
 .. topic:: Example:
@@ -373,7 +373,7 @@ where :math:`1(x)` is the `indicator function
 
 In the multilabel case with binary label indicators: ::
 
-  >>> hamming_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.zeros((2, 2)))
+  >>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2)))
   0.75
 
 .. note::
@@ -425,7 +425,7 @@ score is equal to the classification accuracy.
 
 In the multilabel case with binary label indicators: ::
 
-  >>> jaccard_similarity_score(np.array([[0.0, 1.0], [1.0, 1.0]]), np.ones((2, 2)))
+  >>> jaccard_similarity_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
   0.75
 
 .. _precision_recall_f_measure_metrics:
@@ -889,7 +889,7 @@ where :math:`1(x)` is the `indicator function
 
 In the multilabel case with binary label indicators: ::
 
-  >>> zero_one_loss(np.array([[0.0, 1.0], [1.0, 1.0]]), np.ones((2, 2)))
+  >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
   0.5