StratifiedKFold does not raise error when number of samples for all the classes is less than n_folds #6177
Description
We should raise an error instead of a warning (As pointed out by @MechCoder here)
import numpy as np
from sklearn.cross_validation import StratifiedKFold as OldSKF
from sklearn.model_selection import StratifiedKFold
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y = np.array([3, 3, -1, -1, 2, 2])
for tr, te in OldSKF(y, n_folds=3):
print tr, te[1 3 5] [0 2 4]
[0 2 4] [1 3 5]
[0 1 2 3 4 5] []for tr, te in StratifiedKFold(n_folds=6).split(X, y):
print tr, te[1 3 5] [0 2 4]
[0 2 4] [1 3 5]
[0 1 2 3 4 5] []
[0 1 2 3 4 5] []
[0 1 2 3 4 5] []
[0 1 2 3 4 5] []To be tagged "Easy", "Need Contributor"