How to get a non-shuffled train_test_split in sklearn

Question

If I want a random train/test split, I use the sklearn helper function:

In [1]: from sklearn.model_selection import train_test_split
   ...: train_test_split         


        

Answer 1

Use numpy.split:

import numpy as np
data = np.array([1,2,3,4,5,6])

np.split(data, [4])           # modify the index here to specify where to split the array
# [array([1, 2, 3, 4]), array([5, 6])]

In case you want to split by a percentage, you can calculate the split index from the shape of data:

data = np.array([1,2,3,4,5,6])
p = 0.6

idx = int(p * data.shape[0]) + 1      # since the percentage may end up to be a fractional 
                                      # number, modify this as you need, usually shouldn't
                                      # affect much if data is large
np.split(data, [idx])
# [array([1, 2, 3, 4]), array([5, 6])]

Answer 2

I'm not adding much to Psidom's answer except an easy to copy paste function:

def non_shuffling_train_test_split(X, y, test_size=0.2):
    i = int((1 - test_size) * X.shape[0]) + 1
    X_train, X_test = np.split(X, [i])
    y_train, y_test = np.split(y, [i])
    return X_train, X_test, y_train, y_test

Update: At some point this feature became built in, so now you can do:

from sklearn.model_selection import train_test_split
train_test_split(X, y, test_size=0.2, shuffle=False)

Answer 3

All you need to do is to set the shuffle parameter to False and stratify parameter to None:

    In [49]: train_test_split([1,2,3,4,5,6],shuffle = False, stratify = None)
    Out[49]: [[1, 2, 3, 4], [5, 6]]