Repaired some of the damage caused by rebasing.

Michal Romaniuk · Michal Romaniuk · commit b29a54c25ffe · 2014-08-04T21:59:32.000+01:00
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -53,6 +53,7 @@ Enhancements
      parameter. This does not affect errors raised on re-fit. By 
 	 `Michal Romaniuk`_.
 
+
 Documentation improvements
 ..........................
 
diff --git a/sklearn/grid_search.py b/sklearn/grid_search.py
@@ -25,7 +25,7 @@
 from .externals.joblib import Parallel, delayed
 from .externals import six
 from .utils import check_random_state
-from .utils.validation import _num_samples, check_arrays
+from .utils.validation import _num_samples, indexable
 from .metrics.scorer import check_scoring
 
 
@@ -282,15 +282,13 @@ class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
     """Base class for hyper parameter search with cross-validation."""
 
     @abstractmethod
-    def __init__(self, estimator, scoring=None, loss_func=None,
-                 score_func=None, fit_params=None, n_jobs=1, iid=True,
+    def __init__(self, estimator, scoring=None,
+                 fit_params=None, n_jobs=1, iid=True,
                  refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs',
                  error_score='raise'):
 
         self.scoring = scoring
         self.estimator = estimator
-        self.loss_func = loss_func
-        self.score_func = score_func
         self.n_jobs = n_jobs
         self.fit_params = fit_params if fit_params is not None else {}
         self.iid = iid
@@ -349,20 +347,16 @@ def _fit(self, X, y, parameter_iterable):
 
         estimator = self.estimator
         cv = self.cv
-        self.scorer_ = check_scoring(self.estimator, scoring=self.scoring,
-                                     loss_func=self.loss_func,
-                                     score_func=self.score_func)
+        self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
 
         n_samples = _num_samples(X)
-        X, y = check_arrays(X, y, allow_lists=True, sparse_format='csr',
-                            allow_nans=True)
+        X, y = indexable(X, y)
 
         if y is not None:
             if len(y) != n_samples:
                 raise ValueError('Target variable (y) has a different number '
                                  'of samples (%i) than data (X: %i samples)'
                                  % (len(y), n_samples))
-            y = np.asarray(y)
         cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
 
         if self.verbose > 0:
@@ -525,14 +519,14 @@ class GridSearchCV(BaseSearchCV):
                          degree=..., gamma=..., kernel='rbf', max_iter=-1,
                          probability=False, random_state=None, shrinking=True,
                          tol=..., verbose=False),
-           fit_params={}, iid=..., loss_func=..., n_jobs=1, param_grid=...,
-           pre_dispatch=..., refit=..., score_func=..., scoring=...,
-           verbose=...)
+           fit_params={}, iid=..., n_jobs=1,
+           param_grid=..., pre_dispatch=..., refit=...,
+           scoring=..., verbose=...)
 
 
     Attributes
     ----------
-    `grid_scores_` : list of named tuples
+    grid_scores_ : list of named tuples
         Contains scores for all parameter combinations in param_grid.
         Each entry corresponds to one parameter setting.
         Each named tuple has the attributes:
@@ -542,18 +536,18 @@ class GridSearchCV(BaseSearchCV):
               cross-validation folds
             * ``cv_validation_scores``, the list of scores for each fold
 
-    `best_estimator_` : estimator
+    best_estimator_ : estimator
         Estimator that was chosen by the search, i.e. estimator
         which gave highest score (or smallest loss if specified)
         on the left out data.
 
-    `best_score_` : float
+    best_score_ : float
         Score of best_estimator on the left out data.
 
-    `best_params_` : dict
+    best_params_ : dict
         Parameter setting that gave the best results on the hold out data.
 
-    `scorer_` : function
+    scorer_ : function
         Scorer function used on the held out data to choose the best
         parameters for the model.
 
@@ -591,7 +585,7 @@ def __init__(self, estimator, param_grid, scoring=None, loss_func=None,
                  error_score='raise'):
 
         super(GridSearchCV, self).__init__(
-            estimator, scoring, loss_func, score_func, fit_params, n_jobs, iid,
+            estimator, scoring, fit_params, n_jobs, iid,
             refit, cv, verbose, pre_dispatch, error_score)
         self.param_grid = param_grid
         _check_param_grid(param_grid)
@@ -696,7 +690,7 @@ class RandomizedSearchCV(BaseSearchCV):
 
     Attributes
     ----------
-    `grid_scores_` : list of named tuples
+    grid_scores_ : list of named tuples
         Contains scores for all parameter combinations in param_grid.
         Each entry corresponds to one parameter setting.
         Each named tuple has the attributes:
@@ -706,15 +700,15 @@ class RandomizedSearchCV(BaseSearchCV):
               cross-validation folds
             * ``cv_validation_scores``, the list of scores for each fold
 
-    `best_estimator_` : estimator
+    best_estimator_ : estimator
         Estimator that was chosen by the search, i.e. estimator
         which gave highest score (or smallest loss if specified)
         on the left out data.
 
-    `best_score_` : float
+    best_score_ : float
         Score of best_estimator on the left out data.
 
-    `best_params_` : dict
+    best_params_ : dict
         Parameter setting that gave the best results on the hold out data.
 
     Notes
