Metadata-Version: 2.1
Name: alquitable
Version: 0.0.3
Summary: Keras-core based tools to enhance Alquimodelia
Home-page: https://github.com/alquimodelia/alquitable
License: LICENSE
Keywords: python,machine learning,modeling,keras
Author: João Santos
Author-email: jotaflame@gmail.com
Requires-Python: >=3.9,<4.0
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Provides-Extra: jax
Provides-Extra: tensorflow
Provides-Extra: torch
Requires-Dist: jax ; extra == "jax"
Requires-Dist: keras-core
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pytorch ; extra == "torch"
Requires-Dist: tensorflow ; extra == "tensorflow"
Project-URL: Bug Tracker, https://github.com/alquimodelia/alquitable/issues
Project-URL: Documentation, https://alquimodelia.github.io/alquitable/
Project-URL: Repository, https://github.com/alquimodelia/alquitable
Project-URL: Source Code, https://github.com/alquimodelia/alquitable
Description-Content-Type: text/markdown

# Alquitable

Alquitable is a Python package that provides a Keras-based set of tools to enhance Alquimodelia.

[![Python](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/)
[![Keras](https://img.shields.io/badge/keras-2.4.3-blue)](https://keras.io/)

It provides the loss function and callbacks to apply to keras models


## Usage

To use Alquitable, follow these steps:

```bash
    pip install alquitable
```

Since Aquitable is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow.
To change backend change the ```KERAS-BACKEND``` enviromental variable. Follow [this](https://keras.io/keras_core/#configuring-your-backend).

To get an arquiteture you only need to have a simple configuration and call the module:

```python
# Previous code and imports
...
from alquitable import losses, callbacks

# Based on forecat StackedCNN


loss_funtion=losses.weighted_loss
callback = callbacks.StopOnNanLoss

StackedCNN.compile(loss=loss_funtion)

StackedCNN.fit(
    ...
    callbacks=callback
)


```

## [Contribution](CONTRIBUTING.md)

Contributions to Alquitable are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.

## License

Alquitable is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the [LICENSE](LICENSE) file.

