kuplift 0.0.5

A User Parameter-free Bayesian Framework for Uplift Modeling

kuplift package

kuplift is a Python package that provides a series of uplift modeling methods based on recent research work. kuplift allows users to easily use the following algorithms:

1. Encoding data using a discretization method for treatment effect (uplift) modeling called UMODL.

2. Variable selection for uplift modeling with UMODL-FS.

3. Learning a Bayesian decision tree model for uplift modeling with UB-DT.

4. Learning a random forest model for uplift modeling with UB-RF.

How to install:

pip install kuplift

User Guide:

import kuplift as kp
import pandas as pd

df = pd.read_csv("dataname.csv")

# Univariate variable transformation:
ue = kp.UnivariateEncoding()
encoded_data = ue.fit_transform(df[column_names], df["treatment"], df["outcome"])

# Feature selection
fs = kp.FeatureSelection()
important_vars = fs.filter(df[column_names], df["treatment"], df["outcome"])

# Uplift Bayesian Decision Tree
tree = kp.BayesianDecisionTree(df[column_names], df["treatment"], df["outcome"])
tree.fit()
preds = tree.predict(df[column_names])

# Uplift Bayesian Random Forest
forest = kp.BayesianRandomForest(df[column_names], df["treatment"], df["outcome"])
forest.fit()
preds = forest.predict(df[column_names])

Documentation:

Refer to the documentation at https://udata-orange.github.io/kuplift/

Credits: kuplift has been developed at Orange Labs.

Current contributors:

Mina Rafla

Nicolas Voisine

References:

Rafla, M., Voisine, N., Crémilleux, B., & Boullé, M. (2022, September). A non-parametric bayesian approach for uplift discretization and feature selection. ECML PKDD 2022

Rafla, M., Voisine, N., & Crémilleux, B. (2023, May). Parameter-free Bayesian decision trees for uplift modeling. PAKDD 2023