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

Below is my python implementation for creating balanced data copy. Assumptions: 1. target variable (y) is binary class (0 vs. 1) 2. 1 is the minority.

from numpy import unique
from numpy import random 

def balanced_sample_maker(X, y, random_seed=None):
    """ return a balanced data set by oversampling minority class 
        current version is developed on assumption that the positive
        class is the minority.

    Parameters:
    ===========
    X: {numpy.ndarrray}
    y: {numpy.ndarray}
    """
    uniq_levels = unique(y)
    uniq_counts = {level: sum(y == level) for level in uniq_levels}

    if not random_seed is None:
        random.seed(random_seed)

    # find observation index of each class levels
    groupby_levels = {}
    for ii, level in enumerate(uniq_levels):
        obs_idx = [idx for idx, val in enumerate(y) if val == level]
        groupby_levels[level] = obs_idx

    # oversampling on observations of positive label
    sample_size = uniq_counts[0]
    over_sample_idx = random.choice(groupby_levels[1], size=sample_size, replace=True).tolist()
    balanced_copy_idx = groupby_levels[0] + over_sample_idx
    random.shuffle(balanced_copy_idx)

    return X[balanced_copy_idx, :], y[balanced_copy_idx]