Rename '_split' to '_split_with_kernel'

AlexanderFabisch · AlexanderFabisch · commit 33994f024b4f · 2014-01-15T22:30:13.000+01:00
diff --git a/sklearn/cross_validation.py b/sklearn/cross_validation.py
@@ -1178,8 +1178,8 @@ def fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
 
     start_time = time.time()
 
-    X_train, y_train = _split(estimator, X, y, train)
-    X_test, y_test = _split(estimator, X, y, test, train)
+    X_train, y_train = _split_with_kernel(estimator, X, y, train)
+    X_test, y_test = _split_with_kernel(estimator, X, y, test, train)
     if y_train is None:
         estimator.fit(X_train, **fit_params)
     else:
@@ -1203,7 +1203,7 @@ def fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
     return ret
 
 
-def _split(estimator, X, y, indices, train_indices=None):
+def _split_with_kernel(estimator, X, y, indices, train_indices=None):
     """Create subset of dataset."""
     if hasattr(estimator, 'kernel') and callable(estimator.kernel):
         # cannot compute the kernel values with custom function
diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
@@ -13,7 +13,7 @@
 from ..base import clone
 from ..base import is_classifier
 from ..cross_validation import _check_cv as check_cv
-from ..cross_validation import _split, _score
+from ..cross_validation import _split_with_kernel, _score
 from .base import SelectorMixin
 from ..metrics.scorer import check_scoring
 
@@ -332,8 +332,9 @@ def fit(self, X, y):
 
         # Cross-validation
         for n, (train, test) in enumerate(cv):
-            X_train, y_train = _split(self.estimator, X, y, train)
-            X_test, y_test = _split(self.estimator, X, y, test, train)
+            X_train, y_train = _split_with_kernel(self.estimator, X, y, train)
+            X_test, y_test = _split_with_kernel(self.estimator, X, y, test,
+                                                train)
 
             # Compute a full ranking of the features
             ranking_ = rfe.fit(X_train, y_train).ranking_
diff --git a/sklearn/learning_curve.py b/sklearn/learning_curve.py
@@ -10,7 +10,7 @@
 from .cross_validation import _check_cv
 from .utils import check_arrays
 from .externals.joblib import Parallel, delayed
-from .cross_validation import _split, _score, fit_and_score
+from .cross_validation import _split_with_kernel, _score, fit_and_score
 from .metrics.scorer import check_scoring
 
 
@@ -205,10 +205,12 @@ def _incremental_fit_estimator(estimator, X, y, classes, train, test,
     train_scores, test_scores = [], []
     partitions = zip(train_sizes, np.split(train, train_sizes)[:-1])
     for n_train_samples, partial_train in partitions:
-        X_train, y_train = _split(estimator, X, y, train[:n_train_samples])
-        X_partial_train, y_partial_train = _split(estimator, X, y,
-                                                  partial_train)
-        X_test, y_test = _split(estimator, X, y, test, train[:n_train_samples])
+        X_train, y_train = _split_with_kernel(estimator, X, y,
+                                              train[:n_train_samples])
+        X_partial_train, y_partial_train = _split_with_kernel(estimator, X, y,
+                                                              partial_train)
+        X_test, y_test = _split_with_kernel(estimator, X, y, test,
+                                            train[:n_train_samples])
         if y_partial_train is None:
             estimator.partial_fit(X_partial_train, classes=classes)
         else:
