fix error when calculating log loss

scikit-learn-contrib · May 4, 2024 · d1a73ac · d1a73ac
1 parent f6fe948
commit d1a73ac
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diff --git a/hiclass/metrics.py b/hiclass/metrics.py
@@ -393,7 +393,7 @@ def log_loss(
         for level in range(make_leveled(y_true).shape[1]):
             scores.append(_log_loss(classifier, y_true, y_prob[level], level))
         return _aggregate_scores(scores, agg)
-    return _multiclass_brier_score(classifier, y_true, y_prob, level)
+    return _log_loss(classifier, y_true, y_prob, level)
 
 
 def expected_calibration_error(

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
@@ -478,8 +478,8 @@ def test_log_loss_single_level(uncertainty_data_multi_level):
 
     log_loss_1 = log_loss(classifier, y_true, prob, level=1)
     log_loss_2 = log_loss(classifier, y_true, [prob], level=1)
-    assert math.isclose(log_loss_1, 0.48793, abs_tol=1e-4)
-    assert math.isclose(log_loss_2, 0.48793, abs_tol=1e-4)
+    assert math.isclose(log_loss_1, 0.81349, abs_tol=1e-4)
+    assert math.isclose(log_loss_2, 0.81348, abs_tol=1e-4)
 
 
 def test_local_expected_calibration_error(uncertainty_data):