Stratified Train/Test-split in scikit-learn

Solution 1:

[update for 0.17]

See the docs of sklearn.model_selection.train_test_split:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    stratify=y, 
                                                    test_size=0.25)

[/update for 0.17]

There is a pull request here. But you can simply do train, test = next(iter(StratifiedKFold(...))) and use the train and test indices if you want.

Solution 2:

TL;DR : Use StratifiedShuffleSplit with test_size=0.25

Scikit-learn provides two modules for Stratified Splitting:

StratifiedKFold : This module is useful as a direct k-fold cross-validation operator: as in it will set up n_folds training/testing sets such that classes are equally balanced in both.

Heres some code(directly from above documentation)

>>> skf = cross_validation.StratifiedKFold(y, n_folds=2) #2-fold cross validation
>>> len(skf)
2
>>> for train_index, test_index in skf:
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
...    #fit and predict with X_train/test. Use accuracy metrics to check validation performance

StratifiedShuffleSplit : This module creates a single training/testing set having equally balanced(stratified) classes. Essentially this is what you want with the n_iter=1. You can mention the test-size here same as in train_test_split

Code:

>>> sss = StratifiedShuffleSplit(y, n_iter=1, test_size=0.5, random_state=0)
>>> len(sss)
1
>>> for train_index, test_index in sss:
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
>>> # fit and predict with your classifier using the above X/y train/test

Solution 3:

You can simply do it with train_test_split() method available in Scikit learn:

from sklearn.model_selection import train_test_split 
train, test = train_test_split(X, test_size=0.25, stratify=X['YOUR_COLUMN_LABEL'])

I have also prepared a short GitHub Gist which shows how stratify option works:

https://gist.github.com/SHi-ON/63839f3a3647051a180cb03af0f7d0d9

Solution 4:

Here's an example for continuous/regression data (until this issue on GitHub is resolved).

min = np.amin(y)
max = np.amax(y)

# 5 bins may be too few for larger datasets.
bins     = np.linspace(start=min, stop=max, num=5)
y_binned = np.digitize(y, bins, right=True)

X_train, X_test, y_train, y_test = train_test_split(
    X, 
    y, 
    stratify=y_binned
)

Where start is min and stop is max of your continuous target.
If you don't set right=True then it will more or less make your max value a separate bin and your split will always fail because too few samples will be in that extra bin.

Solution 5:

In addition to the accepted answer by @Andreas Mueller, just want to add that as @tangy mentioned above:

StratifiedShuffleSplit most closely resembles train_test_split(stratify = y) with added features of:

stratify by default
by specifying n_splits, it repeatedly splits the data