Get started

Installation

Go to the PyPI site to download and install the package.

Or use the command :

pip install kuplift

Quick start

Code example :

import kuplift as kp
import pandas as pd

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

# Make sure the dtype of all categorical variables is object
df = df.astype({"some_categorical_variable": object})

variables = list(df.columns[:-2])  # Last two columns are treatment and target columns

# Univariate variable transformation
ue = kp.UnivariateEncoding()
encoded_data = ue.fit_transform(df[variables], df["treatment"], df["target"])

# Univariate variable transformation optimized through the use of the C++ implementation of umodl
ue = kp.OptimizedUnivariateEncoding()
encoded_data = ue.fit_transform(df[variables], df["treatment"], df["target"])

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

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

# Uplift Bayesian Random Forest
forest = kp.BayesianRandomForest(n_trees=4)
forest.fit(df[variables], df["treatment"], df["target"])
preds = forest.predict(df[variables])

License

See the LICENSE.md file of this repository or in the -info directory of the python package installation (you can find it with pip show -f kuplift)

Credits

kuplift has been developed at Orange Labs.

Current contributors:

Mina Rafla

Nicolas Voisine