DataPreprocessing.py

import numpy as np

def MajorityAndMinority(y):
    labels = np.unique(y)
    if len(np.where(y == labels[0])[0]) >= len(np.where(y == labels[1])[0]):
        majority_label = labels[0]
        majority_num = len(np.where(y == labels[0])[0])
        minority_label = labels[1]
        minority_num = y.shape[0] - majority_num
    else:
        majority_label = labels[1]
        majority_num = len(np.where(y == labels[1])[0])
        minority_label = labels[0]
        minority_num = y.shape[0] - majority_num
    return majority_label,majority_num,minority_label,minority_num

#binary
#ratio: majority'=(majority-minority)*ratio+minoirty
def RandomUnderSampling(X,y,ratio=0,random_seed=2,replace=False):
    np.random.seed(random_seed)
    majority_label, majority_num, minority_label, minority_num=MajorityAndMinority(y)
    majority_X=X[y==majority_label,:]
    minority_X=X[y==minority_label,:]
    random_index=np.random.choice(np.arange(majority_num),int(minority_num+(majority_num-minority_num)*ratio),replace=replace)
    majority_X=majority_X[random_index,:]
    final_X=np.concatenate((minority_X,majority_X),axis=0)
    final_y=np.ones(final_X.shape[0])
    final_y[0:minority_num]=final_y[0:minority_num] * minority_label
    final_y[minority_num:] = final_y[minority_num:] * majority_label

    return final_X,final_y