Metadata-Version: 2.4
Name: khiops
Version: 11.0.0.2
Summary: Python library for the Khiops AutoML suite
Maintainer-email: The Khiops Team <khiops.team@orange.com>
License-Expression: BSD-3-Clause-Clear
Project-URL: Homepage, https://khiops.org
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: pandas<=2.3.3,>=0.25.3
Requires-Dist: scikit-learn<=1.7.2,>=0.22.2
Provides-Extra: s3
Requires-Dist: boto3<=1.35.69,>=1.17.39; extra == "s3"
Provides-Extra: gcs
Requires-Dist: google-cloud-storage>=1.37.0; extra == "gcs"
Dynamic: license-file

# 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] (>=3.8)
- [Pandas][pandas] (>=0.25.3)
- [Scikit-Learn][sklearn] (>=0.22.2)

[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/
