There is already a description here of how to do stratified train/test split in scikit via train_test_split (Stratified Train/Test-split in scikit-learn) and a description of how to random train/validation/test split via np.split (How to split data into 3 sets (train, validation and test)?). But what about doing stratified train/validation/test split.
The closest approximation that comes to mind for doing stratified (on class label) train/validation/test split is as follows, but I suspect there's a better way that can perhaps achieve this in one function call or in a more accurate way:
Let's say we want to do a 60/20/20 train/validation/test split, then my current approach is to first do 60/40 stratified split, then do a 50/50 stratifeid split on that first 40 as to ultimately get a 60/20/20 stratified split.
from sklearn.cross_validation import train_test_split
SEED = 2000
x_train, x_validation_and_test, y_train, y_validation_and_test = train_test_split(x, y, test_size=.4, random_state=SEED)
x_validation, x_test, y_validation, y_test = train_test_split(x_validation_and_test, y_validation_and_test, test_size=.5, random_state=SEED)
Please get back if my approach is correct and/or if you have a better approach.
Thank you