This is the repository of the Khiops Python Library for the Khiops AutoML suite.

Khiops is a robust AutoML suite for constructing supervised models (classifiers, regressors and encoders) and unsupervised models (coclusterings). With this package you can use Khiops via Python in two ways:

• with the module khiops.core: To use Khiops in its native way (Khiops dictionary files + tabular data files as input)
• with the module khiops.sklearn: To use Khiops with Scikit-Learn estimator objects (Pandas dataframes or NumPy arrays as input)

Using conda

conda create -n khiops-env conda activate khiops-env conda install -c conda-forge khiops

Using pip under Linux (in a bash shell)

python -m venv khiops-venv source khiops-venv/bin/activate pip install -U khiops

Using pip under Windows (in a powershell shell)

python -m venv khiops-venv khiops-venv\Scripts\activate pip install -U khiops

Other installation methods are documented at the Khiops website.

• Python (>=3.8)
• Pandas (>=0.25.3)
• Scikit-Learn (>=0.22.2)

The API Docs for the Khiops Python library are available here. Other documentation (algorithms, installation etc.) can be found on the Khiops site.

The library itself is documented with docstrings: for example, to obtain help on the KhiopsClassifier estimator and on the train_predictor function, respectively, you can use:

from khiops.sklearn import KhiopsClassifier help(KhiopsClassifier) from khiops import core as kh help(kh.train_predictor)

The Khiops Python library is distributed under the BSD 3-Clause-clear License, the text of which is available at https://spdx.org/licenses/BSD-3-Clause-Clear.html or see the LICENSE.md for more details.

The Khiops Python library is currently developed at Orange Innovation by the Khiops Team: khiops.team@orange.com .