semvec/pypi-packages huggingface dataset:
khiops 11.0.0.2
Python library for the Khiops AutoML suite
Requires Python: >=3.8

# 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/
pygeomatch 0.0.2
A package to match geographical features
Requires Python: >=3.9

# pygeomatch

Python library for matching geospatial vector datasets.
For now, we only have surface matching algorithms but others will come soon.

# installation

## from source

```shell
git clone git@github.com:umrlastig/pygeomatch.git
cd pygeomatch
```

[Install uv](https://docs.astral.sh/uv/getting-started/installation/#installation-methods)

Install dependencies:
```shell
uv sync
```

```shell
source .venv/bin/activate
```

## with pip

```shell
pip install pygeomatch
```

# usage

You can now use the *pygeomatch* script to match your data.
You can see the different parameters with:
```shell
pygeomatch --help
```

For instance, with the test data in directory *data* using the *MCA* algorithm and saving the results in *test.gpkg*, you can use the following:
```shell
pygeomatch data/popRef.shp data/popComp.shp test.gpkg MCA
```

# Run tests

```shell
pytest -v --cov  --cov-report term-missing
```

## References

### Included matching algorithms

Ali, A. B. H. (2001). Qualité géométrique des entités géographiques surfaciques. Application à l'appariement et définition d'une typologie des écarts geométriques (Doctoral dissertation, université Gustave Eiffel).

Olteanu-Raimond, A. M., Mustiere, S., & Ruas, A. (2015). Knowledge formalization for vector data matching using belief theory. Journal of Spatial Information Science, (10), 21-46.

### More to be included

Du, H. (2015). Matching disparate geospatial datasets and validating matches using spatial logic (Doctoral dissertation, University of Nottingham).

Yan, Y., Sun, Y., Wang, S., Lu, Y., Hu, Y., & Lu, M. (2025). Research on Multi-Scale Vector Road-Matching Model Based on ISOD Descriptor. ISPRS International Journal of Geo-Information, 14(7), 280.

## Data

Ground-truth OSM/BDTopo centre Strasbourg (stage) [-> link BiblioDocs]