https://github.com/khiopsml/khiops-python
The Python library of the Khiops AutoML suite
https://github.com/khiopsml/khiops-python
auto-feature-engineering automatic-machine-learning automl data-science machine-learning python supervised-learning unsupervised-learning
Last synced: 1 day ago
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The Python library of the Khiops AutoML suite
- Host: GitHub
- URL: https://github.com/khiopsml/khiops-python
- Owner: KhiopsML
- License: bsd-3-clause-clear
- Created: 2023-06-23T13:47:54.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2026-06-29T18:54:45.000Z (3 days ago)
- Last Synced: 2026-06-29T20:07:06.626Z (3 days ago)
- Topics: auto-feature-engineering, automatic-machine-learning, automl, data-science, machine-learning, python, supervised-learning, unsupervised-learning
- Language: Python
- Homepage: https://khiops.org
- Size: 3.43 MB
- Stars: 19
- Watchers: 3
- Forks: 2
- Open Issues: 84
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
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README
# Khiops Python Library
This is the repository of the **Khiops Python Library** for the [Khiops AutoML suite][khiops].
## Description
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)
## Installation
Using `conda`
```bash
conda create -n khiops-env
conda activate khiops-env
conda install -c conda-forge khiops
```
Using `pip` under Linux (in a `bash` shell)
```bash
python -m venv khiops-venv
source khiops-venv/bin/activate
pip install -U khiops
```
Using `pip` under Windows (in a `powershell` shell)
```shell
python -m venv khiops-venv
khiops-venv\Scripts\activate
pip install -U khiops
```
Other installation methods are documented at the [Khiops website][khiops-install].
### Requirements
- [Python][python] (>=3.10)
- [Pandas][pandas] (>=2.3.3)
- [Scikit-Learn][sklearn] (>=1.7.2)
[pandas]: https://pandas.pydata.org
[sklearn]: https://scikit-learn.org/stable
## Documentation
The API Docs for the Khiops Python library are available [here][khiops-api-docs]. Other
documentation (algorithms, installation etc.) can be found on the [Khiops site][khiops].
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:
```python
from khiops.sklearn import KhiopsClassifier
help(KhiopsClassifier)
from khiops import core as kh
help(kh.train_predictor)
```
## License
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](./LICENSE.md) for more
details.
## Credits
The Khiops Python library is currently developed at [Orange Innovation][o-innov] by the Khiops
Team: khiops.team@orange.com .
[khiops]: https://khiops.org
[khiops-install]: https://khiops.org/setup
[khiops-api-docs]: https://khiopsml.github.io/khiops-python
[python]: https://www.python.org
[pandas]: https://pandas.pydata.org
[sklearn]: https://scikit-learn.org/stable
[boto3]: https://github.com/boto/boto3
[gcs]: https://github.com/googleapis/python-storage
[o-innov]: https://hellofuture.orange.com/en/