Remove _score utility function.

sklearn/cross_validation.py

@@ -1184,9 +1184,9 @@ def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
         estimator.fit(X_train, **fit_params)
     else:
         estimator.fit(X_train, y_train, **fit_params)
-    test_score = _score(estimator, X_test, y_test, scorer)
+    test_score = scorer(estimator, X_test, y_test)
     if return_train_score:
-        train_score = _score(estimator, X_train, y_train, scorer)
+        train_score = scorer(estimator, X_train, y_train)
 
     scoring_time = time.time() - start_time
 
@@ -1235,18 +1235,6 @@ def _safe_split(estimator, X, y, indices, train_indices=None):
     return X_subset, y_subset
 
 
-def _score(estimator, X_test, y_test, scorer):
-    """Compute the score of an estimator on a given test set."""
-    if y_test is None:
-        score = scorer(estimator, X_test)
-    else:
-        score = scorer(estimator, X_test, y_test)
-    if not isinstance(score, numbers.Number):
-        raise ValueError("scoring must return a number, got %s (%s) instead."
-                         % (str(score), type(score)))
-    return score
-
-
 def _permutation_test_score(estimator, X, y, cv, scorer):
     """Auxiliary function for permutation_test_score"""
     avg_score = []

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 _safe_split, _score
+from ..cross_validation import _safe_split
 from .base import SelectorMixin
 from ..metrics.scorer import check_scoring
 
@@ -342,7 +342,7 @@ def fit(self, X, y):
                 mask = np.where(ranking_ <= k + 1)[0]
                 estimator = clone(self.estimator)
                 estimator.fit(X_train[:, mask], y_train)
-                score = _score(estimator, X_test[:, mask], y_test, scorer)
+                score = scorer(estimator, X_test[:, mask], y_test)
 
                 if self.verbose > 0:
                     print("Finished fold with %d / %d feature ranks, score=%f"

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 _safe_split, _score, _fit_and_score
+from .cross_validation import _safe_split, _fit_and_score
 from .metrics.scorer import check_scoring
 
 
@@ -204,18 +204,22 @@ def _incremental_fit_estimator(estimator, X, y, classes, train, test,
     """Train estimator on training subsets incrementally and compute scores."""
     train_scores, test_scores = [], []
     partitions = zip(train_sizes, np.split(train, train_sizes)[:-1])
+
     for n_train_samples, partial_train in partitions:
         train_subset = train[:n_train_samples]
         X_train, y_train = _safe_split(estimator, X, y, train_subset)
         X_partial_train, y_partial_train = _safe_split(estimator, X, y,
                                                               partial_train)
         X_test, y_test = _safe_split(estimator, X, y, test, train_subset)
+
         if y_partial_train is None:
             estimator.partial_fit(X_partial_train, classes=classes)
         else:
             estimator.partial_fit(X_partial_train, y_partial_train,
                                   classes=classes)
-        train_scores.append(_score(estimator, X_train, y_train, scorer))
-        test_scores.append(_score(estimator, X_test, y_test, scorer))
+
+        train_scores.append(scorer(estimator, X_train, y_train))
+        test_scores.append(scorer(estimator, X_test, y_test))
+
     return np.array((train_scores, test_scores)).T
 

sklearn/metrics/scorer.py

@@ -20,6 +20,7 @@
 
 from abc import ABCMeta, abstractmethod
 from warnings import warn
+import numbers
 
 import numpy as np
 
@@ -127,6 +128,10 @@ def _evaluate_scorers(estimator, X, y, scorers):
         else:
             score = scorer.score_func(y, y_pred, **scorer.kwargs)
 
+        if not isinstance(score, numbers.Number):
+            raise ValueError("scoring must return a number, got %s (%s)"
+                             " instead." % (str(score), type(score)))
+
         sign = 1 if scorer.greater_is_better else -1
         scores.append(sign * score)
 
@@ -146,9 +151,12 @@ def get_scorer(scoring):
     return scorer
 
 
-def _passthrough_scorer(estimator, *args, **kwargs):
+def _default_scorer(estimator, X, y, *args, **kwargs):
     """Function that wraps estimator.score"""
-    return estimator.score(*args, **kwargs)
+    if y is None:
+        return estimator.score(X, *args, **kwargs)
+    else:
+        return estimator.score(X, y, *args, **kwargs)
 
 
 def check_scoring(estimator, scoring=None, allow_none=False, loss_func=None,
@@ -204,7 +212,7 @@ def check_scoring(estimator, scoring=None, allow_none=False, loss_func=None,
             scorer = get_scorer(scoring)
         return scorer
     elif hasattr(estimator, 'score'):
-        return _passthrough_scorer
+        return _default_scorer
     elif not has_scoring:
         if allow_none:
             return None