FIX force pipeline steps to be list not a tuple (scikit-learn#9604)

sklearn/pipeline.py

@@ -17,7 +17,6 @@
 from .base import clone, TransformerMixin
 from .externals.joblib import Parallel, delayed, Memory
 from .externals import six
-from .utils import tosequence
 from .utils.metaestimators import if_delegate_has_method
 from .utils import Bunch
 
@@ -112,7 +111,7 @@ class Pipeline(_BaseComposition):
 
     def __init__(self, steps, memory=None):
         # shallow copy of steps
-        self.steps = tosequence(steps)
+        self.steps = list(steps)
         self._validate_steps()
         self.memory = memory
 
@@ -624,7 +623,7 @@ class FeatureUnion(_BaseComposition, TransformerMixin):
 
     """
     def __init__(self, transformer_list, n_jobs=1, transformer_weights=None):
-        self.transformer_list = tosequence(transformer_list)
+        self.transformer_list = list(transformer_list)
         self.n_jobs = n_jobs
         self.transformer_weights = transformer_weights
         self._validate_transformers()

sklearn/tests/test_pipeline.py

@@ -208,6 +208,18 @@ def test_pipeline_init():
     assert_equal(params, params2)
 
 
+def test_pipeline_init_tuple():
+    # Pipeline accepts steps as tuple
+    X = np.array([[1, 2]])
+    pipe = Pipeline((('transf', Transf()), ('clf', FitParamT())))
+    pipe.fit(X, y=None)
+    pipe.score(X)
+
+    pipe.set_params(transf=None)
+    pipe.fit(X, y=None)
+    pipe.score(X)
+
+
 def test_pipeline_methods_anova():
     # Test the various methods of the pipeline (anova).
     iris = load_iris()
@@ -425,6 +437,10 @@ def test_feature_union():
                         FeatureUnion,
                         [("transform", Transf()), ("no_transform", NoTrans())])
 
+    # test that init accepts tuples
+    fs = FeatureUnion((("svd", svd), ("select", select)))
+    fs.fit(X, y)
+
 
 def test_make_union():
     pca = PCA(svd_solver='full')

sklearn/utils/metaestimators.py

@@ -51,7 +51,7 @@ def _set_params(self, attr, **params):
 
     def _replace_estimator(self, attr, name, new_val):
         # assumes `name` is a valid estimator name
-        new_estimators = getattr(self, attr)[:]
+        new_estimators = list(getattr(self, attr))
         for i, (estimator_name, _) in enumerate(new_estimators):
             if estimator_name == name:
                 new_estimators[i] = (name, new_val)