model_selection/tests/test_split.py

21 from sklearn.model_selection import KFold
79     kf = KFold(n_splits)
163         KFold(),
224     (ValueError, next, KFold(4).split(X1))
261         KFold(0)
263         KFold(1)
276         KFold(1.5)
278         KFold(2.0)
290         KFold(n_splits=4, shuffle=None)
296     kf = KFold(3)
302     kf = KFold(3)
306     assert 5 == KFold(5).get_n_splits(X2)
313     splits = KFold(2).split(X2[:-1])
322     splits = KFold(2).split(X2)
371         list(StratifiedKFold(3).split(X, y)), list(KFold(3).split(X, y))
436         kf = KFold(5).split(X=np.ones(i))
465     kf = KFold(3)
466     kf2 = KFold(3, shuffle=True, random_state=0)
467     kf3 = KFold(3, shuffle=True, random_state=1)
486 @pytest.mark.parametrize("kfold", [KFold, StratifiedKFold, StratifiedGroupKFold])
555     cv = KFold(n_splits=n_splits, shuffle=False)
564     cv = KFold(n_splits, shuffle=True, random_state=0)
568     cv = KFold(n_splits, shuffle=True, random_state=1)
918     for i, (train_ind, test_ind) in enumerate(KFold(5, shuffle=True).split(X)):
1415     np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
1438         == next(KFold(3).split(X, y_multiclass_2d))[0]
1446     np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
1450     np.testing.assert_equal(list(KFold(3).split(X)), list(cv.split(X)))
1457     kf_iter = KFold().split(X, y)
1467     kf_randomized_iter = KFold(shuffle=True, random_state=0).split(X, y)
1862 @pytest.mark.parametrize("Klass", (KFold, StratifiedKFold, StratifiedGroupKFold))
1872         (KFold(), True),
1873         (KFold(shuffle=True, random_state=123), True),
1889         (KFold(shuffle=True, random_state=None), False),
1890         (KFold(shuffle=True, random_state=None), False),