maskani-moh
diff --git a/‎doc/whats_new.rst
Lines changed: 9 additions & 3 deletions b/‎doc/whats_new.rst
Lines changed: 9 additions & 3 deletions
diff --git a/‎sklearn/ensemble/gradient_boosting.py
Lines changed: 2 additions & 2 deletions b/‎sklearn/ensemble/gradient_boosting.py
Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
Lines changed: 11 additions & 0 deletions b/‎sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py
Lines changed: 11 additions & 0 deletions
@@ -98,7 +98,7 @@ Enhancements
    - Added ability to set ``n_jobs`` parameter to :func:`pipeline.make_union`.
      A ``TypeError`` will be raised for any other kwargs. :issue:`8028`
      by :user:`Alexander Booth <alexandercbooth>`.
-     
+
    - Added type checking to the ``accept_sparse`` parameter in
      :mod:`sklearn.utils.validation` methods. This parameter now accepts only
      boolean, string, or list/tuple of strings. ``accept_sparse=None`` is deprecated
@@ -140,12 +140,18 @@ Bug fixes
      where the ``perplexity`` method was returning incorrect results because
      the ``transform`` method returns normalized document topic distributions
      as of version 0.18. :issue:`7954` by :user:`Gary Foreman <garyForeman>`.
-     
+
    - Fix a bug where :class:`sklearn.ensemble.GradientBoostingClassifier` and
      :class:`sklearn.ensemble.GradientBoostingRegressor` ignored the
      ``min_impurity_split`` parameter.
      :issue:`8006` by :user:`Sebastian Pölsterl <sebp>`.
 
+   - Fix a bug where
+     :class:`sklearn.ensemble.gradient_boosting.QuantileLossFunction` computed
+     negative errors for negative values of ``ytrue - ypred`` leading to
+     wrong values when calling ``__call__``.
+
 API changes summary
 -------------------
 
@@ -154,7 +160,7 @@ API changes summary
      ensemble estimators (deriving from :class:`ensemble.BaseEnsemble`)
      now only have ``self.estimators_`` available after ``fit``.
      :issue:`7464` by `Lars Buitinck`_ and `Loic Esteve`_.
-     
+
    - Deprecate the ``doc_topic_distr`` argument of the ``perplexity`` method
      in :class:`sklearn.decomposition.LatentDirichletAllocation` because the
      user no longer has access to the unnormalized document topic distribution
 
@@ -418,10 +418,10 @@ def __call__(self, y, pred, sample_weight=None):
 
         mask = y > pred
         if sample_weight is None:
-            loss = (alpha * diff[mask].sum() +
+            loss = (alpha * diff[mask].sum() -
                     (1.0 - alpha) * diff[~mask].sum()) / y.shape[0]
         else:
-            loss = ((alpha * np.sum(sample_weight[mask] * diff[mask]) +
+            loss = ((alpha * np.sum(sample_weight[mask] * diff[mask]) -
                     (1.0 - alpha) * np.sum(sample_weight[~mask] * diff[~mask])) /
                     sample_weight.sum())
         return loss
 
@@ -15,6 +15,7 @@
 from sklearn.ensemble.gradient_boosting import RegressionLossFunction
 from sklearn.ensemble.gradient_boosting import LOSS_FUNCTIONS
 from sklearn.ensemble.gradient_boosting import _weighted_percentile
+from sklearn.ensemble.gradient_boosting import QuantileLossFunction
 
 
 def test_binomial_deviance():
@@ -141,6 +142,16 @@ def test_weighted_percentile_zero_weight():
     assert score == 1.0
 
 
+def test_quantile_loss_function():
+    # Non regression test for the QuantileLossFunction object
+    # There was a sign problem when evaluating the function
+    # for negative values of 'ytrue - ypred'
+    x = np.asarray([-1.0, 0.0, 1.0])
+    y_found = QuantileLossFunction(1, 0.9)(x, np.zeros_like(x))
+    y_expected = np.asarray([0.1, 0.0, 0.9]).mean()
+    np.testing.assert_allclose(y_found, y_expected)
+
+
 def test_sample_weight_deviance():
     # Test if deviance supports sample weights.
     rng = check_random_state(13)