Metadata-Version: 2.1
Name: teex
Version: 0.1a1
Summary: A Toolbox for the Evaluation of Explanations
Home-page: https://github.com/chus-chus/teex
Author: Jesus Antonanzas
Author-email: jesus.maria.antonanzas@estudiantat.upc.edu
License: UNKNOWN
Project-URL: Documentation, https://teex.readthedocs.io
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
License-File: LICENSE

# teex

A Python **T**oolbox for the **E**valuation of machine learning **Ex**planations.

This project aims to provide a simple way of evaluating all kinds of individual black box explanations. Moreover, it contains a collection
of easy-to-access datasets with available ground truth explanations.

## Installation

The teex package is on [PyPI](https://pypi.org/project/teex/). To install it, simply run

```shell
pip install teex
```

Note that Python >= 3.5 is required.

## Tutorials and API

The full API documentation can be found on [Read The Docs](https://teex.readthedocs.io).

Here are some sample notebooks on basic usages and examples:

- [Generating image data with g.t. saliency map explanations](https://github.com/chus-chus/teex/blob/main/docs/_demos/notebooks/gen_saliency_maps_seneca.ipynb)

### Datasets

To use a dataset, simply search the one you want in the API documentation and:

```python
from teex import datasets

data = datasets.Kahikatea()
X, y, explanations = data[:100]
```

## Contributing

Before contributing to teex, please take a moment to read the [manual]((https://github.com/chus-chus/teex/blob/main/CONTRIBUTING.md)).


