model_selection.StratifiedKFold should not require the data array to simply return split indices. #7126
Description
When importing import sklearn.cross_validation I was prompted with a DepricationWarning
saying I should use sklearn.model_selection instead. To ensure my code is up to date, I switched to the new version, but then I encountered an odd behavior.
The place in my code where I was generating the cross validation indices does not have access to the data array. However, in the new version to simply generate the indices of the test/train split you must supply the entire dataset. Previously all that was needed was the labels.
Forcing the developer to specify a data array is a problem when you have large amounts of high dimensional data and you want to wait to load only the subset of it needed by the current cross validation run. Furthremore, I cannot think of a reason why X would be required by this process, nor can I see a reason in the scikit-learn code.
Here is a small piece of code demonstrating the issue.
import sklearn.cross_validation
import sklearn.model_selection
y = np.array([0, 0, 1, 1, 1, 0, 0, 1])
X = y.reshape(len(y), 1)
# In the old version all that is needed is the labels
skf_old = sklearn.cross_validation.StratifiedKFold(y, random_state=0)
indicies_old = list(skf_old)
# The new version seems to require a data array for some reason
skf_new = sklearn.model_selection.StratifiedKFold(random_state=0)
indicies_new = list(skf_new.split(X, y))
# Causes an error, but there is no reason why X must be specified
indicies_new2 = list(skf_new.split(None, y))
Even if it was nice to have the split signature contain an X for compatibility reasons, I think you should at least be able to specify X as None. However, if you try to set X=None it results in a type error.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-7995a67b2df2> in <module>()
----> 1 indicies_new2 = list(skf_new.split(None, y))
/home/joncrall/code/scikit-learn/sklearn/model_selection/_split.pyc in split(self, X, y, labels)
312 """
313 X, y, labels = indexable(X, y, labels)
--> 314 n_samples = _num_samples(X)
315 if self.n_folds > n_samples:
316 raise ValueError(
/home/joncrall/code/scikit-learn/sklearn/utils/validation.pyc in _num_samples(x)
120 else:
121 raise TypeError("Expected sequence or array-like, got %s" %
--> 122 type(x))
123 if hasattr(x, 'shape'):
124 if len(x.shape) == 0:
TypeError: Expected sequence or array-like, got <type 'None Type'>
It would be nice if there was either an alternative method like "split_indicies(y)" that generated the indices using only the labels, or if the developer was able to specify X=None when calling split.
Version Info:
Linux-3.13.0-92-generic-x86_64-with-Ubuntu-14.04-trusty
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2]
NumPy 1.11.1
SciPy 0.18.0
Scikit-Learn 0.18.dev0