Installation

Note: Python 3.8 is required!

pip install ceml

git clone https://github.com/andreArtelt/ceml.git
cd ceml

pip install -r requirements.txt

pip install .

Quick example

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

from ceml.sklearn import generate_counterfactual


if __name__ == "__main__":
    # Load data
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=4242)

    # Whitelist of features - list of features we can change/use when computing a counterfactual
    features_whitelist = None   # We can use all features

    # Create and fit model
    model = DecisionTreeClassifier(max_depth=3)
    model.fit(X_train, y_train)

    # Select data point for explaining its prediction
    x = X_test[1,:]
    print("Prediction on x: {0}".format(model.predict([x])))

    # Compute counterfactual
    print("\nCompute counterfactual ....")
    print(generate_counterfactual(model, x, y_target=0, features_whitelist=features_whitelist))

Documentation

Documentation is available on readthedocs:https://ceml.readthedocs.io/en/latest/

License

MIT license - See LICENSE

How to cite?

You can cite CEML by using the following BibTeX entry:

@misc{ceml,
        author = {André Artelt},
        title = {CEML: Counterfactuals for Explaining Machine Learning models - A Python toolbox},
        year = {2019 - 2023},
        publisher = {GitHub},
        journal = {GitHub repository},
        howpublished = {\url{https://www.github.com/andreArtelt/ceml}}
    }

Third party components

• numpy

• scipy

• jax

• cvxpy

• scikit-learn

• sklearn-lvq

• PyTorch

• tensorflow