rm CV class tests

sklearn/ensemble/tests/test_gradient_boosting.py

@@ -14,8 +14,6 @@ from sklearn.utils import check_random_state
 
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.ensemble import GradientBoostingRegressor
-from sklearn.ensemble import GradientBoostingClassifierCV
-from sklearn.ensemble import GradientBoostingRegressorCV
 
 from sklearn import datasets
 
@@ -418,52 +416,3 @@ def test_mem_layout():
     clf.fit(X, y_)
     assert_array_equal(clf.predict(T), true_result)
     assert_equal(100, len(clf.estimators_))
-
-
-def test_cv_attr():
-    """Test attribute access for CV classes. """
-    clf = GradientBoostingClassifierCV(max_estimators=100,
-                                       min_samples_leaf=3)
-    clf.fit(X, y)
-
-    assert clf.min_samples_leaf == 3 == clf._model.min_samples_leaf
-    clf.min_samples_leaf = 2
-    assert clf.min_samples_leaf == 2 == clf._model.min_samples_leaf
-
-
-def test_cv_clf():
-    """Test GradientBoostingClassifierCV n_estimators selection. """
-    X, y = datasets.make_hastie_10_2(n_samples=1000, random_state=1)
-
-    max_estimators = 50
-
-    clf = GradientBoostingClassifierCV(max_estimators=max_estimators)
-    clf.fit(X, y)
-    # max_estimators very small so it chooses all of them
-    assert clf.n_estimators == max_estimators
-
-    clf_prime = GradientBoostingClassifier(
-        n_estimators=max_estimators).fit(X, y)
-
-    assert_array_equal(clf_prime.train_score_, clf.train_score_)
-    assert clf.cv_score_.shape[0] == 5  # default 5-fold CV
-    assert clf.cv_score_.shape[1] == max_estimators
-
-
-def test_cv_reg():
-    """Test GradientBoostingRegressorCV n_estimators selection. """
-    X, y = datasets.make_friedman1(n_samples=1000, random_state=1)
-
-    max_estimators = 50
-
-    clf = GradientBoostingRegressorCV(max_estimators=max_estimators)
-    clf.fit(X, y)
-    # max_estimators very small so it chooses all of them
-    assert clf.n_estimators == max_estimators
-
-    clf_prime = GradientBoostingRegressor(
-        n_estimators=max_estimators).fit(X, y)
-
-    assert_array_equal(clf_prime.train_score_, clf.train_score_)
-    assert clf.cv_score_.shape[0] == 5  # default 5-fold CV
-    assert clf.cv_score_.shape[1] == max_estimators