Merge pull request #4253 from Flimm/doc-precision_recall_fscore_support

GaelVaroquaux · GaelVaroquaux · commit 7ce32d7229c9 · 2015-08-30T13:00:17.000+01:00
[MRG + 1] Add example for precision_recall_fscore_support
diff --git a/sklearn/metrics/classification.py b/sklearn/metrics/classification.py
@@ -918,8 +918,8 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
     Examples
     --------
     >>> from sklearn.metrics import precision_recall_fscore_support
-    >>> y_true = np.array([0, 1, 2, 0, 1, 2])
-    >>> y_pred = np.array([0, 2, 1, 0, 0, 1])
+    >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
+    >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
     >>> precision_recall_fscore_support(y_true, y_pred, average='macro')
     ... # doctest: +ELLIPSIS
     (0.22..., 0.33..., 0.26..., None)
@@ -930,6 +930,16 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
     ... # doctest: +ELLIPSIS
     (0.22..., 0.33..., 0.26..., None)
 
+    It is possible to compute per-label precisions, recalls, F1-scores and
+    supports instead of averaging:
+    >>> precision_recall_fscore_support(y_true, y_pred, average=None,
+    ... labels=['pig', 'dog', 'cat'])
+    ... # doctest: +ELLIPSIS,+NORMALIZE_WHITESPACE
+    (array([ 0. ,  0. ,  0.66...]),
+     array([ 0.,  0.,  1.]),
+     array([ 0. ,  0. ,  0.8]),
+     array([2, 2, 2]))
+
     """
     average_options = (None, 'micro', 'macro', 'weighted', 'samples')
     if average not in average_options and average != 'binary':
