[MRG+1] Added unit test for adding classes_ property to GridSearchCV, f…

…ixes scikit-learn#6298 (scikit-learn#7661)

* Fix issue scikit-learn#6298
Adds a "classes_" property to BaseSearchCV

* Added test to ensure classes_ property is added to gridSearch correctly

* Fixed formatting

* Added test to ensure gridSearchCV with a regressor does not have a classes_ attribute

* Fixed whitespace issues

* Combined tests for the new GridSearchSV.classes_ property into a single test.

* Removed trailing whitespace

* Added what's new for pull request scikit-learn#7661

* Fixed formatting of update in what's new

doc/whats_new.rst

@@ -19,6 +19,11 @@ New features
 Enhancements
 ............
 
+ - Added ``classes_`` attribute to :class:`model_selection.GridSearchCV`
+ that matches the ``classes_`` attribute of ``best_estimator_``. (`#7661
+ <https://github.com/scikit-learn/scikit-learn/pull/7661>`_) by `Alyssa
+ Batula`_ and `Dylan Werner-Meier`_.
+
  - The ``min_weight_fraction_leaf`` constraint in tree construction is now
  more efficient, taking a fast path to declare a node a leaf if its weight
  is less than 2 * the minimum. Note that the constructed tree will be

sklearn/grid_search.py

@@ -387,6 +387,10 @@ def __init__(self, estimator, scoring=None,
  def _estimator_type(self):
  return self.estimator._estimator_type
 
+ @property
+ def classes_(self):
+ return self.best_estimator_.classes_
+
  def score(self, X, y=None):
  """Returns the score on the given data, if the estimator has been refit.
 
@@ -688,7 +692,7 @@ class GridSearchCV(BaseSearchCV):
  - An iterable yielding train/test splits.
 
  For integer/None inputs, if the estimator is a classifier and ``y`` is
- either binary or multiclass, 
+ either binary or multiclass,
  :class:`sklearn.model_selection.StratifiedKFold` is used. In all
  other cases, :class:`sklearn.model_selection.KFold` is used.
 
@@ -900,7 +904,7 @@ class RandomizedSearchCV(BaseSearchCV):
  - An iterable yielding train/test splits.
 
  For integer/None inputs, if the estimator is a classifier and ``y`` is
- either binary or multiclass, 
+ either binary or multiclass,
  :class:`sklearn.model_selection.StratifiedKFold` is used. In all
  other cases, :class:`sklearn.model_selection.KFold` is used.
 

sklearn/tests/test_grid_search.py

@@ -42,6 +42,7 @@
 from sklearn.metrics import f1_score
 from sklearn.metrics import make_scorer
 from sklearn.metrics import roc_auc_score
+from sklearn.linear_model import Ridge
 
 from sklearn.exceptions import ChangedBehaviorWarning
 from sklearn.exceptions import FitFailedWarning
@@ -785,3 +786,20 @@ def test_parameters_sampler_replacement():
  sampler = ParameterSampler(params_distribution, n_iter=7)
  samples = list(sampler)
  assert_equal(len(samples), 7)
+
+
+def test_classes__property():
+ # Test that classes_ property matches best_esimator_.classes_
+ X = np.arange(100).reshape(10, 10)
+ y = np.array([0] * 5 + [1] * 5)
+ Cs = [.1, 1, 10]
+
+ grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})
+ grid_search.fit(X, y)
+ assert_array_equal(grid_search.best_estimator_.classes_,
+ grid_search.classes_)
+
+ # Test that regressors do not have a classes_ attribute
+ grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})
+ grid_search.fit(X, y)
+ assert_false(hasattr(grid_search, 'classes_'))