[MRG+1] FIX precision/recall/f1-score for truncated range(n_labels) (#…

…10377)

doc/whats_new/v0.20.rst

@@ -256,6 +256,10 @@ Decomposition, manifold learning and clustering
 
 Metrics
 
+- Fixed a bug in :func:`metrics.precision_precision_recall_fscore_support`
+  when truncated `range(n_labels)` is passed as value for `labels`.
+  :issue:`10377` by :user:`Gaurav Dhingra <gxyd>`.
+
 - Fixed a bug due to floating point error in :func:`metrics.roc_auc_score` with
   non-integer sample weights. :issue:`9786` by :user:`Hanmin Qin <qinhanmin2014>`.
 

sklearn/metrics/classification.py

@@ -1072,6 +1072,7 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
                 raise ValueError('All labels must be in [0, n labels). '
                                  'Got %d < 0' % np.min(labels))
 
+        if n_labels is not None:
             y_true = y_true[:, labels[:n_labels]]
             y_pred = y_pred[:, labels[:n_labels]]
 

sklearn/metrics/tests/test_classification.py

@@ -197,6 +197,14 @@ def test_precision_recall_f_extra_labels():
         assert_raises(ValueError, recall_score, y_true_bin, y_pred_bin,
                       labels=np.arange(-1, 4), average=average)
 
+    # tests non-regression on issue #10307
+    y_true = np.array([[0, 1, 1], [1, 0, 0]])
+    y_pred = np.array([[1, 1, 1], [1, 0, 1]])
+    p, r, f, _ = precision_recall_fscore_support(y_true, y_pred,
+                                                 average='samples',
+                                                 labels=[0, 1])
+    assert_almost_equal(np.array([p, r, f]), np.array([3 / 4, 1, 5 / 6]))
+
 
 @ignore_warnings
 def test_precision_recall_f_ignored_labels():