Description

train_test_split splits the single sample data such that train part has 0 samples and test has that sample. Also this behaviour is not affected by setting the test_size to any value.

Steps/Code to Reproduce

import numpy as np
from sklearn.model_selection import train_test_split

data = np.random.normal(0, 1, [1, 100])
print(A.shape)
#Output:  (1, 100)

data_train, data_test = train_test_split(data)
print(data_train.shape, data_test.shape)
#Output:  ((0, 100), (1, 100))

Expected Results

I am not sure of expected results as this seems like an unintended usage. But still think that at-least a warning (if not error) should be given when splitting.

Versions

Linux-3.16.0-77-generic-x86_64-with-Ubuntu-14.04-trusty
('Python', '2.7.6 (default, Nov 23 2017, 15:49:48) \n[GCC 4.8.4]')
('NumPy', '1.14.2')
('SciPy', '1.0.1')
('Scikit-Learn', '0.19.1')

I am sorry if its a duplicate. I tried searching for similar issues but could not find (even though I thought that this would have been discussed somewhere).