Metadata-Version: 2.4
Name: wrapshap
Version: 0.1.2
Summary: SHAP-based feature selection and exploration for high-dimensional data
Home-page: https://github.com/fefurger/wrapshap
Author: Félix Furger
Author-email: fefurger@hotmail.com
License: Apache-2.0
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.26.4
Requires-Dist: pandas>=2.2.2
Requires-Dist: shap>=0.45.0
Requires-Dist: scikit-learn>=1.4.2
Requires-Dist: scipy>=1.13.0
Requires-Dist: xgboost>=2.0.3
Requires-Dist: lightgbm>=4.6.0
Requires-Dist: seaborn>=0.13.2
Requires-Dist: matplotlib>=3.8.4
Provides-Extra: gpu
Requires-Dist: tensorflow<=2.20,>=2.16.1; extra == "gpu"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: provides-extra
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

This package allows you to perform SHAP-based feature selection and exploration for high-dimensional data, with both CPU and GPU support.

**Paper:** [Furger et al. (2026), *Wrapshap: a high dimension feature selection framework to explore biological systems through machine learning explanations*](https://www.nature.com/articles/s41540-026-00779-z).

## How to install Wrapshap

In order to install the CPU implementation of Wrapshap, simply run:

```bash
pip install wrapshap
```

If you can afford it, the GPU implementation is much faster and comes with additional methods. To install it instead, please run:

```bash
pip install "wrapshap[gpu]"
```

You should now be able to import wrapshap utilities.

## **Warning: If you are encountering import issues, please try using Python version 3.9**

### For the CPU implementation:

```python
from wrapshap import Wrapshap
```
or for the already wrapped models:

```python
from wrapshap.already_wrapped import WrappedBinaryClassifierXGB, WrappedRegressorXGB
```

### For the GPU implementation:

```python
from wrapshap.gpu_implementation import WrapshapGPU
```
or for the already wrapped models:

```python
from wrapshap.gpu_implementation.already_wrapped_gpu import WrappedRegressorXGB_GPU, WrappedBinaryClassifierXGB_GPU
```

For more details about how to use the package, please head over to our [GitHub repository](https://github.com/fefurger/wrapshap) for example notebooks.
