Scikit-learn balanced subsampling

Question

I\'m trying to create N balanced random subsamples of my large unbalanced dataset. Is there a way to do this simply with scikit-learn / pandas or do I have to implement it m

Answer 1

Here is my solution, which can be tightly integrated in an existing sklearn pipeline:

from sklearn.model_selection import RepeatedKFold
import numpy as np


class DownsampledRepeatedKFold(RepeatedKFold):

    def split(self, X, y=None, groups=None):
        for i in range(self.n_repeats):
            np.random.seed()
            # get index of major class (negative)
            idxs_class0 = np.argwhere(y == 0).ravel()
            # get index of minor class (positive)
            idxs_class1 = np.argwhere(y == 1).ravel()
            # get length of minor class
            len_minor = len(idxs_class1)
            # subsample of major class of size minor class
            idxs_class0_downsampled = np.random.choice(idxs_class0, size=len_minor)
            original_indx_downsampled = np.hstack((idxs_class0_downsampled, idxs_class1))
            np.random.shuffle(original_indx_downsampled)
            splits = list(self.cv(n_splits=self.n_splits, shuffle=True).split(original_indx_downsampled))

            for train_index, test_index in splits:
                yield original_indx_downsampled[train_index], original_indx_downsampled[test_index]

    def __init__(self, n_splits=5, n_repeats=10, random_state=None):
        self.n_splits = n_splits
         super(DownsampledRepeatedKFold, self).__init__(
        n_splits=n_splits, n_repeats=n_repeats, random_state=random_state
    )

Use it as usual:

    for train_index, test_index in DownsampledRepeatedKFold(n_splits=5, n_repeats=10).split(X, y):
         X_train, X_test = X[train_index], X[test_index]
         y_train, y_test = y[train_index], y[test_index]