NiaAML’s documentation!

NiaAML is an automated machine learning Python framework based on nature-inspired algorithms for optimization. The name comes from the automated machine learning method of the same name [1]. Its goal is to efficiently compose the best possible classification pipeline for the given task using components on the input. The components are divided into three groups: feature seletion algorithms, feature transformation algorithms and classifiers. The framework uses nature-inspired algorithms for optimization to choose the best set of components for the classification pipeline on the output and optimize their parameters. We use NiaPy framework for the optimization process which is a popular Python collection of nature-inspired algorithms. The NiaAML framework is easy to use and customize or expand to suit your needs.

• Free software: MIT license

• Github repository: https://github.com/lukapecnik/NiaAML

• Python versions: 3.6.x, 3.7.x, 3.8.x

The main documentation is organized into a couple of sections:

• User Documentation

• Developer Documentation

• About

User Documentation

• Getting Started
  • Basic example
  • Components
    • Classifiers
    • Feature Selection Algorithms
    • Feature Transformation Algorithms
    • Fitness Functions based on
    • Categorical Feature Encoders
    • Feature Imputers
    • Optimization Algorithms
  • Optimization Process And Parameter Tuning

Developer Documentation

• Changelog
  • 1.1.10 (2022-08-17)
  • 1.1.9 (2022-05-25)
  • 1.1.8 (2022-05-24)
  • 1.1.7 (2022-02-21)
  • 1.1.6 (2021-06-27)
  • 1.1.5 (2021-06-01)
  • 1.1.4 (2021-05-24)
  • 1.1.3 (2021-05-23)
  • 1.1.2 (2021-05-19)
  • 1.1.1 (2021-03-09)
  • 1.1.1rc2 (2020-12-22)
  • 1.1.1rc1 (2020-12-22)
  • 1.1.0 (2020-12-16)
  • 1.0.0rc7 (2020-12-14)
  • 1.0.0rc6 (2020-12-12)
  • 1.0.0rc5 (2020-12-11)
  • 1.0.0rc4 (2020-12-10)
  • 1.0.0rc3 (2020-12-10)
  • 1.0.0rc2 (2020-12-08)
  • 1.0.0rc1 (2020-12-06)
  • 0.1.4 (2020-12-05)
  • 0.1.3 (2020-12-04)
  • 0.1.3a1 (2020-12-01)
  • 0.1.2 (2020-11-30)
  • 0.1.2a1 (2020-11-29)
  • 0.1.1 (2020-11-28)
  • 0.1.0 (2020-11-27)
• Installation
  • Setup development environment
• Testing
• Documentation
• API
  • niaaml
  • niaaml.data
  • niaaml.classifiers
  • niaaml.preprocessing
  • niaaml.fitness

About

• About
  • Licence
  • Disclaimer
• Contributing to NiaAML
  • Code of Conduct
  • How Can I Contribute?
    • Reporting Bugs
    • Suggesting Enhancements
    • Pull requests
• Contributor Covenant Code of Conduct
  • Our Pledge
  • Our Standards
  • Enforcement Responsibilities
  • Scope
  • Enforcement
  • Enforcement Guidelines
    • 1. Correction
    • 2. Warning
    • 3. Temporary Ban
    • 4. Permanent Ban
  • Attribution

References

[1] Iztok Fister Jr., Milan Zorman, Dušan Fister, Iztok Fister. Continuous optimizers for automatic design and evaluation of classification pipelines. In: Frontier applications of nature inspired computation. Springer tracts in nature-inspired computing, pp.281-301, 2020.