Update files to use _BaseComposition, fix conflicting files

yl565 · yl565 · commit 8881395b261a · 2016-10-10T16:48:14.000-04:00
diff --git a/sklearn/ensemble/voting_classifier.py b/sklearn/ensemble/voting_classifier.py
@@ -167,7 +167,6 @@ def fit(self, X, y, sample_weight=None):
                 delayed(_parallel_fit_estimator)(clone(clf), X, transformed_y,
                                                  sample_weight)
                 for _, clf in self.estimators if clf is not None)
-
         return self
 
     @property
@@ -210,7 +209,6 @@ def predict(self, X):
                                       arr=predictions.astype('int'))
 
         maj = self.le_.inverse_transform(maj)
-
         return maj
 
     def _collect_probas(self, X):
diff --git a/sklearn/utils/metaestimators.py b/sklearn/utils/metaestimators.py
@@ -3,6 +3,8 @@
 #         Andreas Mueller
 # License: BSD
 
+import numpy as np
+from ..utils import safe_indexing
 from operator import attrgetter
 from functools import update_wrapper
 from ..externals import six
@@ -120,3 +122,38 @@ def if_delegate_has_method(delegate):
 
     return lambda fn: _IffHasAttrDescriptor(fn, delegate,
                                             attribute_name=fn.__name__)
+
+
+def _safe_split(estimator, X, y, indices, train_indices=None):
+    """Create subset of dataset and properly handle kernels."""
+    from ..gaussian_process.kernels import Kernel as GPKernel
+
+    if (hasattr(estimator, 'kernel') and callable(estimator.kernel) and
+            not isinstance(estimator.kernel, GPKernel)):
+        # cannot compute the kernel values with custom function
+        raise ValueError("Cannot use a custom kernel function. "
+                         "Precompute the kernel matrix instead.")
+
+    if not hasattr(X, "shape"):
+        if getattr(estimator, "_pairwise", False):
+            raise ValueError("Precomputed kernels or affinity matrices have "
+                             "to be passed as arrays or sparse matrices.")
+        X_subset = [X[index] for index in indices]
+    else:
+        if getattr(estimator, "_pairwise", False):
+            # X is a precomputed square kernel matrix
+            if X.shape[0] != X.shape[1]:
+                raise ValueError("X should be a square kernel matrix")
+            if train_indices is None:
+                X_subset = X[np.ix_(indices, indices)]
+            else:
+                X_subset = X[np.ix_(indices, train_indices)]
+        else:
+            X_subset = safe_indexing(X, indices)
+
+    if y is not None:
+        y_subset = safe_indexing(y, indices)
+    else:
+        y_subset = None
+
+    return X_subset, y_subset
