<p align="center">
<img src=".github/images/niaclass_logo.png" alt="NiaClass" title="NiaClass"/>
</p>
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<p align="center">
<a href="#-installation">📦 Installation</a> •
<a href="#-functionalities">✨ Functionalities</a> •
<a href="#-examples">🚀 Examples</a> •
<a href="#-reference-papers">📝 Reference papers</a> •
<a href="#-license">🔑 License</a> •
<a href="#-cite-us">📄 Cite us</a>
</p>
NiaClass is a framework for solving classification tasks using nature-inspired algorithms. The framework is written fully in Python. Its goal is to find the best possible set of classification rules for the input data using the <a href="https://github.com/NiaOrg/NiaPy">NiaPy framework</a>, which is a popular Python collection of nature-inspired algorithms. The NiaClass classifier supports numerical and categorical features.
* **Free software:** MIT license
* **Documentation:** https://niaclass.readthedocs.io/en/latest
* **Python versions:** 3.7.x, 3.8.x, 3.9.x
<p align="center"><img src=".github/images/niaclass.png" alt="NiaClass" title="NiaClass"/></p>
## 📦 Installation
### pip3
To install NiaClass with pip3, use:
```sh
pip3 install niaclass
```
In case you would like to try out the latest pre-release version of the framework, install it using:
```sh
pip3 install niaclass --pre
```
### Fedora Linux
To install NiaClass on Fedora, use:
```sh
$ dnf install python-niaclass
```
## ✨ Functionalities
- Binary classification,
- Multi-class classification,
- Support for numerical and categorical features.
## 🚀 Examples
Usage examples can be found [here](examples).
## 📝 Reference papers
[1] Iztok Fister Jr., Iztok Fister, Dušan Fister, Grega Vrbančič, Vili Podgorelec. [On the potential of the nature-inspired algorithms for pure binary classification](http://www.iztok-jr-fister.eu/static/publications/267.pdf). In. Computational science - ICCS 2020 : 20th International Conference, Proceedings. Part V. Cham: Springer, pp. 18-28. Lecture notes in computer science, 12141, 2020
## 🔑 License
This package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.
## Disclaimer
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
## 📄 Cite us
Pečnik L., Fister I., Fister Jr. I. (2021) [NiaClass: Building Rule-Based Classification Models Using Nature-Inspired Algorithms](https://iztok-jr-fister.eu/static/publications/291.pdf). In: Tan Y., Shi Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science, vol 12690. Springer, Cham.
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"description": "<p align=\"center\">\n <img src=\".github/images/niaclass_logo.png\" alt=\"NiaClass\" title=\"NiaClass\"/>\n</p>\n\n<p align=\"center\">\n <a href=\"https://pypi.python.org/pypi/niaclass\">\n <img alt=\"PyPI Version\" src=\"https://img.shields.io/pypi/v/niaclass.svg\" />\n </a>\n <img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/niaclass.svg\" />\n <img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/niaclass.svg\" />\n <a href=\"https://github.com/lukapecnik/niaclass/blob/master/LICENSE\">\n <img alt=\"GitHub license\" src=\"https://img.shields.io/github/license/lukapecnik/niaclass.svg\">\n </a>\n</p>\n\n<p align=\"center\">\n <img alt=\"GitHub commit activity\" src=\"https://img.shields.io/github/commit-activity/w/lukapecnik/niaclass.svg\" />\n <a href=\"http://isitmaintained.com/project/lukapecnik/niaclass\">\n <img alt=\"Average time to resolve an issue\" src=\"http://isitmaintained.com/badge/resolution/lukapecnik/niaclass.svg\" />\n </a>\n <a href=\"http://isitmaintained.com/project/lukapecnik/niaclass\">\n <img alt=\"Percentage of issues still open\" src=\"http://isitmaintained.com/badge/open/lukapecnik/niaclass.svg\" />\n </a>\n <img alt=\"GitHub contributors\" src=\"https://img.shields.io/github/contributors/lukapecnik/niaclass.svg\" />\n</p>\n\n<p align=\"center\">\n <a href=\"#-installation\">\ud83d\udce6 Installation</a> \u2022\n <a href=\"#-functionalities\">\u2728 Functionalities</a> \u2022\n <a href=\"#-examples\">\ud83d\ude80 Examples</a> \u2022\n <a href=\"#-reference-papers\">\ud83d\udcdd Reference papers</a> \u2022\n <a href=\"#-license\">\ud83d\udd11 License</a> \u2022\n <a href=\"#-cite-us\">\ud83d\udcc4 Cite us</a>\n</p>\n\nNiaClass is a framework for solving classification tasks using nature-inspired algorithms. The framework is written fully in Python. Its goal is to find the best possible set of classification rules for the input data using the <a href=\"https://github.com/NiaOrg/NiaPy\">NiaPy framework</a>, which is a popular Python collection of nature-inspired algorithms. The NiaClass classifier supports numerical and categorical features.\n\n* **Free software:** MIT license\n* **Documentation:** https://niaclass.readthedocs.io/en/latest\n* **Python versions:** 3.7.x, 3.8.x, 3.9.x\n\n<p align=\"center\"><img src=\".github/images/niaclass.png\" alt=\"NiaClass\" title=\"NiaClass\"/></p>\n\n## \ud83d\udce6 Installation\n\n### pip3\n\nTo install NiaClass with pip3, use:\n\n```sh\npip3 install niaclass\n```\n\nIn case you would like to try out the latest pre-release version of the framework, install it using:\n\n```sh\npip3 install niaclass --pre\n```\n\n### Fedora Linux\n\nTo install NiaClass on Fedora, use:\n\n```sh\n$ dnf install python-niaclass\n```\n\n## \u2728 Functionalities\n\n- Binary classification,\n- Multi-class classification,\n- Support for numerical and categorical features.\n\n## \ud83d\ude80 Examples\n\nUsage examples can be found [here](examples).\n\n## \ud83d\udcdd Reference papers\n\n[1] Iztok Fister Jr., Iztok Fister, Du\u0161an Fister, Grega Vrban\u010di\u010d, Vili Podgorelec. [On the potential of the nature-inspired algorithms for pure binary classification](http://www.iztok-jr-fister.eu/static/publications/267.pdf). In. Computational science - ICCS 2020 : 20th International Conference, Proceedings. Part V. Cham: Springer, pp. 18-28. Lecture notes in computer science, 12141, 2020\n\n## \ud83d\udd11 License\n\nThis package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.\n\n## Disclaimer\n\nThis framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!\n\n## \ud83d\udcc4 Cite us\n\nPe\u010dnik L., Fister I., Fister Jr. I. (2021) [NiaClass: Building Rule-Based Classification Models Using Nature-Inspired Algorithms](https://iztok-jr-fister.eu/static/publications/291.pdf). In: Tan Y., Shi Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science, vol 12690. Springer, Cham.\n\n",
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