Counterfactuals for explaining machine learning models - A Python toolbox
Project description
CEML is a Python toolbox for computing counterfactuals. Counterfactuals can be used to explain the predictions of machine learing models.
It supports many common machine learning frameworks:
scikit-learn (1.3.1)
PyTorch (2.0.1)
Keras & Tensorflow (2.13.1)
Furthermore, CEML is easy to use and can be extended very easily. See the following user guide for more information on how to use and extend CEML.
Installation
Note: Python 3.8 is required!
PyPI
pip install ceml
Note: The package hosted on PyPI uses the cpu only. If you want to use the gpu, you have to install CEML manually - see next section.
Git
Download or clone the repository:
git clone https://github.com/andreArtelt/ceml.git
cd ceml
Install all requirements (listed in requirements.txt):
pip install -r requirements.txt
Note: If you want to use a gpu/tpu, you have to install the gpu version of jax, tensorflow and pytorch manually. Do not use pip install -r requirements.txt.
Install the toolbox itself:
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}} }
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