Merge pull request #6104 from maniteja123/issue5715

[MRG+1] Enable pandas input to log_loss

sklearn/metrics/classification.py

@@ -1550,6 +1550,7 @@ def log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None):
     if T.shape[1] == 1:
         T = np.append(1 - T, T, axis=1)
 
+    y_pred = check_array(y_pred, ensure_2d=False)
     # Clipping
     Y = np.clip(y_pred, eps, 1 - eps)
 

sklearn/metrics/tests/test_classification.py

@@ -25,6 +25,7 @@
 from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import assert_not_equal
 from sklearn.utils.testing import ignore_warnings
+from sklearn.utils.mocking import MockDataFrame
 
 from sklearn.metrics import accuracy_score
 from sklearn.metrics import average_precision_score
@@ -53,6 +54,7 @@
 ###############################################################################
 # Utilities for testing
 
+
 def make_prediction(dataset=None, binary=False):
     """Make some classification predictions on a toy dataset using a SVC
 
@@ -1275,6 +1277,23 @@ def test_log_loss():
     assert_almost_equal(loss, 1.0383217, decimal=6)
 
 
+def test_log_loss_pandas_input():
+    # case when input is a pandas series and dataframe gh-5715
+    y_tr = np.array(["ham", "spam", "spam", "ham"])
+    y_pr = np.array([[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]])
+    types = [(MockDataFrame, MockDataFrame)]
+    try:
+        from pandas import Series, DataFrame
+        types.append((Series, DataFrame))
+    except ImportError:
+        pass
+    for TrueInputType, PredInputType in types:
+        # y_pred dataframe, y_true series
+        y_true, y_pred = TrueInputType(y_tr), PredInputType(y_pr)
+        loss = log_loss(y_true, y_pred)
+        assert_almost_equal(loss, 1.0383217, decimal=6)
+
+
 def test_brier_score_loss():
     # Check brier_score_loss function
     y_true = np.array([0, 1, 1, 0, 1, 1])