scikit-learn
diff --git a/‎sklearn/base.py
Lines changed: 17 additions & 18 deletions b/‎sklearn/base.py
Lines changed: 17 additions & 18 deletions
diff --git a/‎sklearn/tests/test_base.py
Lines changed: 18 additions & 0 deletions b/‎sklearn/tests/test_base.py
Lines changed: 18 additions & 0 deletions
diff --git a/‎sklearn/tests/test_pipeline.py
Lines changed: 13 additions & 2 deletions b/‎sklearn/tests/test_pipeline.py
Lines changed: 13 additions & 2 deletions
@@ -5,6 +5,7 @@
 
 import copy
 import warnings
+from collections import defaultdict
 
 import numpy as np
 from scipy import sparse
@@ -248,26 +249,24 @@ def set_params(self, **params):
             # Simple optimization to gain speed (inspect is slow)
             return self
         valid_params = self.get_params(deep=True)
-        for key, value in six.iteritems(params):
-            split = key.split('__', 1)
-            if len(split) > 1:
-                # nested objects case
-                name, sub_name = split
-                if name not in valid_params:
-                    raise ValueError('Invalid parameter %s for estimator %s. '
-                                     'Check the list of available parameters '
-                                     'with `estimator.get_params().keys()`.' %
-                                     (name, self))
-                sub_object = valid_params[name]
-                sub_object.set_params(**{sub_name: value})
+
+        nested_params = defaultdict(dict)  # grouped by prefix
+        for key, value in params.items():
+            key, delim, sub_key = key.partition('__')
+            if key not in valid_params:
+                raise ValueError('Invalid parameter %s for estimator %s. '
+                                 'Check the list of available parameters '
+                                 'with `estimator.get_params().keys()`.' %
+                                 (key, self))
+
+            if delim:
+                nested_params[key][sub_key] = value
             else:
-                # simple objects case
-                if key not in valid_params:
-                    raise ValueError('Invalid parameter %s for estimator %s. '
-                                     'Check the list of available parameters '
-                                     'with `estimator.get_params().keys()`.' %
-                                     (key, self.__class__.__name__))
                 setattr(self, key, value)
+
+        for key, sub_params in nested_params.items():
+            valid_params[key].set_params(**sub_params)
+
         return self
 
     def __repr__(self):
 
@@ -228,6 +228,24 @@ def test_set_params():
     #               bad__stupid_param=True)
 
 
+def test_set_params_passes_all_parameters():
+    # Make sure all parameters are passed together to set_params
+    # of nested estimator. Regression test for #9944
+
+    class TestDecisionTree(DecisionTreeClassifier):
+        def set_params(self, **kwargs):
+            super(TestDecisionTree, self).set_params(**kwargs)
+            # expected_kwargs is in test scope
+            assert kwargs == expected_kwargs
+            return self
+
+    expected_kwargs = {'max_depth': 5, 'min_samples_leaf': 2}
+    for est in [Pipeline([('estimator', TestDecisionTree())]),
+                GridSearchCV(TestDecisionTree(), {})]:
+        est.set_params(estimator__max_depth=5,
+                       estimator__min_samples_leaf=2)
+
+
 def test_score_sample_weight():
 
     rng = np.random.RandomState(0)
 
@@ -24,10 +24,11 @@
 from sklearn.base import clone, BaseEstimator
 from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline, make_union
 from sklearn.svm import SVC
-from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import LogisticRegression, Lasso
 from sklearn.linear_model import LinearRegression
 from sklearn.cluster import KMeans
 from sklearn.feature_selection import SelectKBest, f_classif
+from sklearn.dummy import DummyRegressor
 from sklearn.decomposition import PCA, TruncatedSVD
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
@@ -289,7 +290,7 @@ def test_pipeline_raise_set_params_error():
                  'with `estimator.get_params().keys()`.')
 
     assert_raise_message(ValueError,
-                         error_msg % ('fake', 'Pipeline'),
+                         error_msg % ('fake', pipe),
                          pipe.set_params,
                          fake='nope')
 
@@ -863,6 +864,16 @@ def test_step_name_validation():
                                  [[1]], [1])
 
 
+def test_set_params_nested_pipeline():
+    estimator = Pipeline([
+        ('a', Pipeline([
+            ('b', DummyRegressor())
+        ]))
+    ])
+    estimator.set_params(a__b__alpha=0.001, a__b=Lasso())
+    estimator.set_params(a__steps=[('b', LogisticRegression())], a__b__C=5)
+
+
 def test_pipeline_wrong_memory():
     # Test that an error is raised when memory is not a string or a Memory
     # instance