fixed bug for precision recall curve when all labels are negative

scikit-learn · Jul 12, 2017 · b0b9bca · b0b9bca
1 parent cb1b6c4
commit b0b9bca
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Showing 2 changed files with 16 additions and 13 deletions.
diff --git a/sklearn/metrics/ranking.py b/sklearn/metrics/ranking.py
@@ -417,7 +417,7 @@ def precision_recall_curve(y_true, probas_pred, pos_label=None,
                                              sample_weight=sample_weight)
 
     precision = tps / (tps + fps)
-    recall = tps / tps[-1]
+    recall = np.ones(tps.size) if tps[-1] == 0 else tps / tps[-1]
 
     # stop when full recall attained
     # and reverse the outputs so recall is decreasing

diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
@@ -309,15 +309,17 @@ def test_roc_curve_toydata():
     y_true = [0, 0]
     y_score = [0.25, 0.75]
     # assert UndefinedMetricWarning because of no positive sample in y_true
-    tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true, y_score)
+    tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve,
+                               y_true, y_score)
     assert_raises(ValueError, roc_auc_score, y_true, y_score)
     assert_array_almost_equal(tpr, [0., 0.5, 1.])
     assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan])
 
     y_true = [1, 1]
     y_score = [0.25, 0.75]
     # assert UndefinedMetricWarning because of no negative sample in y_true
-    tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true, y_score)
+    tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve,
+                               y_true, y_score)
     assert_raises(ValueError, roc_auc_score, y_true, y_score)
     assert_array_almost_equal(tpr, [np.nan, np.nan])
     assert_array_almost_equal(fpr, [0.5, 1.])
@@ -565,8 +567,9 @@ def test_precision_recall_curve_toydata():
 
         y_true = [0, 0]
         y_score = [0.25, 0.75]
-        assert_raises(Exception, precision_recall_curve, y_true, y_score)
-        assert_raises(Exception, average_precision_score, y_true, y_score)
+        p, r, _ = precision_recall_curve(y_true, y_score)
+        assert_array_equal(p, np.array([0.0, 1.0]))
+        assert_array_equal(r, np.array([1.0, 0.0]))
 
         y_true = [1, 1]
         y_score = [0.25, 0.75]
@@ -578,21 +581,21 @@ def test_precision_recall_curve_toydata():
         # Multi-label classification task
         y_true = np.array([[0, 1], [0, 1]])
         y_score = np.array([[0, 1], [0, 1]])
-        assert_raises(Exception, average_precision_score, y_true, y_score,
-                      average="macro")
-        assert_raises(Exception, average_precision_score, y_true, y_score,
-                      average="weighted")
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="macro"), 0.75)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="weighted"), 1.0)
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="samples"), 1.)
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="micro"), 1.)
 
         y_true = np.array([[0, 1], [0, 1]])
         y_score = np.array([[0, 1], [1, 0]])
-        assert_raises(Exception, average_precision_score, y_true, y_score,
-                      average="macro")
-        assert_raises(Exception, average_precision_score, y_true, y_score,
-                      average="weighted")
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="macro"), 0.75)
+        assert_almost_equal(average_precision_score(y_true, y_score,
+                            average="weighted"), 1.0)
         assert_almost_equal(average_precision_score(y_true, y_score,
                             average="samples"), 0.75)
         assert_almost_equal(average_precision_score(y_true, y_score,