Metadata-Version: 2.2
Name: model_tuner
Version: 0.0.25a0
Summary: A Python library for tuning machine learning models.
Author-email: "UCLA CTSI ML Team: Leonid Shpaner, Arthur Funnell, Panayiotis Petousis" <pp89@ucla.edu>
Project-URL: Author Website, https://www.mii.ucla.edu/
Project-URL: Documentation, https://uclamii.github.io/model_tuner
Project-URL: Zenodo Archive, https://zenodo.org/doi/10.5281/zenodo.12727322
Project-URL: Source Code, https://github.com/uclamii/model_tuner/
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: joblib==1.3.2
Requires-Dist: tqdm==4.66.4
Requires-Dist: catboost==1.2.7
Requires-Dist: numpy==1.21.4; python_version == "3.7"
Requires-Dist: pandas==1.1.5; python_version == "3.7"
Requires-Dist: scikit-learn==0.23.2; python_version == "3.7"
Requires-Dist: scipy==1.4.1; python_version == "3.7"
Requires-Dist: imbalanced-learn==0.7.0; python_version == "3.7"
Requires-Dist: scikit-optimize==0.8.1; python_version == "3.7"
Requires-Dist: xgboost==1.6.2; python_version == "3.7"
Requires-Dist: setuptools==75.1.0; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: wheel==0.44.0; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: numpy<2.0.0,>=1.19.5; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: pandas<2.2.3,>=1.3.5; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: scikit-learn<1.4.0,>=1.0.2; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: scipy<1.11,>=1.6.3; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: scikit-optimize==0.10.2; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: imbalanced-learn==0.12.4; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: xgboost==2.1.2; python_version >= "3.8" and python_version < "3.11"
Requires-Dist: setuptools==75.1.0; python_version >= "3.11"
Requires-Dist: wheel==0.44.0; python_version >= "3.11"
Requires-Dist: numpy<2.0.0,>=1.19.5; python_version >= "3.11"
Requires-Dist: pandas<2.2.2,>=1.3.5; python_version >= "3.11"
Requires-Dist: scikit-learn==1.5.1; python_version >= "3.11"
Requires-Dist: scipy==1.14.0; python_version >= "3.11"
Requires-Dist: imbalanced-learn==0.12.4; python_version >= "3.11"
Requires-Dist: scikit-optimize==0.10.2; python_version >= "3.11"
Requires-Dist: xgboost==2.1.2; python_version >= "3.11"

<br>

<img src="https://github.com/uclamii/model_tuner/blob/main/assets/modeltunersmaller.png?raw=true" width="250" style="border: none; outline: none; box-shadow: none;" oncontextmenu="return false;">

<br> 

[![Downloads](https://pepy.tech/badge/model_tuner)](https://pepy.tech/project/model_tuner) [![PyPI](https://img.shields.io/pypi/v/model_tuner.svg)](https://pypi.org/project/model_tuner/) [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.12727322.svg)](https://doi.org/10.5281/zenodo.12727322)


The `model_tuner` library is a versatile and powerful tool designed to facilitate the training, evaluation, and tuning of machine learning models. It supports various functionalities such as handling imbalanced data, applying different scaling and imputation techniques, calibrating models, and conducting cross-validation. This library is particularly useful for model selection, hyperparameter tuning, and ensuring optimal performance across different metrics.

## Prerequisites

Before installing `model_tuner`, ensure your system meets the following requirements:

## Python Version
`model_tuner` requires **Python 3.7 or higher**. Specific dependency versions vary depending on your Python version.

## Dependencies
The following dependencies will be automatically installed when you install `model_tuner` via pip:

### For Python 3.7:
- `joblib==1.3.2`
- `tqdm==4.66.4`
- `catboost==1.2.7`
- `numpy==1.21.4`
- `pandas==1.1.5`
- `scikit-learn==0.23.2`
- `scipy==1.4.1`
- `imbalanced-learn==0.7.0`
- `scikit-optimize==0.8.1`
- `xgboost==1.6.2`

### For Python 3.8 to 3.10:
- `joblib==1.3.2`
- `tqdm==4.66.4`
- `catboost==1.2.7`
- `setuptools==75.1.0`
- `wheel==0.44.0`
- `numpy>=1.19.5, <2.0.0`
- `pandas>=1.3.5, <2.2.3`
- `scikit-learn>=1.0.2, <1.4.0`
- `scipy>=1.6.3, <1.11`
- `imbalanced-learn==0.12.4`
- `scikit-optimize==0.10.2`
- `xgboost==2.1.2`

### For Python 3.11 and higher:
- `joblib==1.3.2`
- `tqdm==4.66.4`
- `catboost==1.2.7`
- `setuptools==75.1.0`
- `wheel==0.44.0`
- `numpy>=1.19.5, <2.0.0`
- `pandas>=1.3.5, <2.2.2`
- `scikit-learn==1.5.1`
- `scipy==1.14.0`
- `imbalanced-learn==0.12.4`
- `scikit-optimize==0.10.2`
- `xgboost==2.1.2`


## 💾 Installation

You can install `model_tuner` directly from PyPI:

```bash
pip install model_tuner
```

## 📄 Official Documentation

https://uclamii.github.io/model_tuner

## 🌐 Author Website

https://www.mii.ucla.edu/

## ⚖️ License

`model_tuner` is distributed under the Apache License. See [LICENSE](https://github.com/uclamii/model_tuner?tab=Apache-2.0-1-ov-file) for more information.

## 📚 Citing `model_tuner`

If you use `model_tuner` in your research or projects, please consider citing it.

```bibtex
@software{funnell_2024_12727322,
  author       = {Funnell, Arthur and
                  Shpaner, Leonid and
                  Petousis, Panayiotis},
  title        = {Model Tuner},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.0.25a},
  doi          = {10.5281/zenodo.12727322},
  url          = {https://doi.org/10.5281/zenodo.12727322}
}
```


## Support

If you have any questions or issues with `model_tuner`, please open an issue on this [GitHub repository](https://github.com/uclamii/model_tuner/).


## Acknowledgements

This work was supported by the UCLA Medical Informatics Institute (MII) and the Clinical and Translational Science Institute (CTSI). Special thanks to Dr. Alex Bui for his invaluable guidance and support, and to Panayiotis Petousis for his original contributions to this codebase.
