keshik

Oversample class imbalanced tabular data by Denoising Diffusion Probabilistic Model (DDPM)

Usage Guide

Installation:

pip install keshik

The APIs are very simple to use. This example shows the usage :

from keshik.oversampler import Diffusion_Oversampler

totalX,totalY=Diffusion_Oversampler(X_train,y_train,n_iteration=40,categorical_columns=['SEX','EDUCATION','MARRIAGE','PAY_0','PAY_2','PAY_3','PAY_4','PAY_5','PAY_6'])

Method : Diffusion_Oversampler

Input:

• X_train : Dataframe of Feature Variables
• y_train : Series of binary labels
• n_iteration: number of epochs to train the DDPM model
• categorical_columns : list of categorical columns
• cat_handle_mode : ['prediction','roundoff'] (default : 'prediction')
• oversampling_mode : ['byMatrix','byRows','byColumns'] (default : 'byMatrix')

Returns :

• A dataframe with original features
• a numpy array (the binary labels)

Applications:

• Credit Card Fraud Detection
• Network Intrusion Detection
• And Other similar binary class imbalanced tabular data problems