scikit-learn
diff --git a/‎sklearn/ensemble/gradient_boosting.py
Lines changed: 15 additions & 10 deletions b/‎sklearn/ensemble/gradient_boosting.py
Lines changed: 15 additions & 10 deletions
diff --git a/‎sklearn/ensemble/tests/test_gradient_boosting.py
Lines changed: 30 additions & 0 deletions b/‎sklearn/ensemble/tests/test_gradient_boosting.py
Lines changed: 30 additions & 0 deletions
@@ -1478,20 +1478,25 @@ def fit(self, X, y, sample_weight=None, monitor=None):
                 raw_predictions = np.zeros(shape=(X.shape[0], self.loss_.K),
                                            dtype=np.float64)
             else:
-                try:
-                    self.init_.fit(X, y, sample_weight=sample_weight)
-                except TypeError:
-                    if sample_weight_is_none:
-                        self.init_.fit(X, y)
-                    else:
-                        raise ValueError(
-                            "The initial estimator {} does not support sample "
-                            "weights.".format(self.init_.__class__.__name__))
+                # XXX clean this once we have a support_sample_weight tag
+                if sample_weight_is_none:
+                    self.init_.fit(X, y)
+                else:
+                    msg = ("The initial estimator {} does not support sample "
+                           "weights.".format(self.init_.__class__.__name__))
+                    try:
+                        self.init_.fit(X, 
10000
y, sample_weight=sample_weight)
+                    except TypeError:  # regular estimator without SW support
+                        raise ValueError(msg)
+                    except ValueError as e:
+                        if 'not enough values to unpack' in str(e):  # pipeline
+                            raise ValueError(msg) from e
+                        else:  # regular estimator whose input checking failed
+                            raise
 
                 raw_predictions = \
                     self.loss_.get_init_raw_predictions(X, self.init_)
 
-
             begin_at_stage = 0
 
             # The rng state must be preserved if warm_start is True
 
@@ -39,6 +39,9 @@
 from sklearn.exceptions import DataConversionWarning
 from sklearn.exceptions import NotFittedError
 from sklearn.dummy import DummyClassifier, DummyRegressor
+from sklearn.pipeline import make_pipeline
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import NuSVR
 
 
 GRADIENT_BOOSTING_ESTIMATORS = [GradientBoostingClassifier,
@@ -1378,6 +1381,33 @@ def test_gradient_boosting_with_init(gb, dataset_maker, init_estimator):
         gb(init=init_est).fit(X, y, sample_weight=sample_weight)
 
 
+def test_gradient_boosting_with_init_pipeline():
+    # Check that the init estimator can be a pipeline (see issue #13466)
+
+    X, y = make_regression(random_state=0)
+    init = make_pipeline(LinearRegression())
+    gb = GradientBoostingRegressor(init=init)
+    gb.fit(X, y)  # pipeline without sample_weight works fine
+
+    with pytest.raises(
+            ValueError,
+            match='The initial estimator Pipeline does not support sample '
+                  'weights'):
+        gb.fit(X, y, sample_weight=np.ones(X.shape[0]))
+
+    # Passing sample_weight to a pipeline raises a ValueError. This test makes
+    # sure we make the distinction between ValueError raised by a pipeline that
+    # was passed sample_weight, and a ValueError raised by a regular estimator
+    # whose input checking failed.
+    with pytest.raises(
+            ValueError,
+            match='nu <= 0 or nu > 1'):
+        # Note that NuSVR properly supports sample_weight
+        init = NuSVR(gamma='auto', nu=1.5)
+        gb = GradientBoostingRegressor(init=init)
+        gb.fit(X, y, sample_weight=np.ones(X.shape[0]))
+
+
 @pytest.mark.parametrize('estimator, missing_method', [
     (GradientBoostingClassifier(init=LinearSVC()), 'predict_proba'),
     (GradientBoostingRegressor(init=OneHotEncoder()), 'predict')