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mlpro

MLPro - The Integrative Middleware Framework for Standardized Machine Learning in Python

https://github.com/fhswf/mlpro

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization fhswf has institutional domain (fh-swf.de)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.7%) to scientific vocabulary

Keywords

engineering framework game-theory machine-learning middleware online-machine-learning python3 reinforcement-learning science-research scientific-computing supervised-learning
Last synced: 6 months ago · JSON representation

Repository

MLPro - The Integrative Middleware Framework for Standardized Machine Learning in Python

Basic Info
Statistics
  • Stars: 17
  • Watchers: 3
  • Forks: 3
  • Open Issues: 135
  • Releases: 33
Topics
engineering framework game-theory machine-learning middleware online-machine-learning python3 reinforcement-learning science-research scientific-computing supervised-learning
Created over 4 years ago · Last pushed 6 months ago
Metadata Files
Readme Contributing License Citation Zenodo

README.md

CI Documentation Status PyPI version PyPI Total Downloads PyPI Last Month Downloads DOI

MLPro - The integrative middleware framework for standardized machine learning in Python

MLPro is a modular, open-source middleware framework for standardized machine learning tasks in Python. It seamlessly integrates reinforcement learning, game theory, and online learning into flexible, recombinable workflows. With its rigorous design, scientific validity, and ready-to-use process models, MLPro accelerates research, development, and education. Whether for hybrid ML applications or real-time adaptive systems, MLPro is the right choice due to its transparency, reusability, and professional quality.

Key Features

a) Open, modular, and extensible architecture

  • Overarching software infrastructure (mathematics, data management and plotting, UI framework, logging, ...)
  • Fundamental ML classes for adaptive models and their training and hyperparameter tuning

b) Growing number of dedicated sub-frameworks

  • MLPro-BF: Powerful substructure with numerous cross-sectional functions
  • MLPro-RL: Reinforcement learning
  • MLPro-GT: Game theory
  • MLPro-OA: Online machine learning
  • MLPro-SL: Supervised learning

c) Online documentation (learn more)

d) Example pool (learn more)

e) Extension hub (learn more)

Development

  • Consequent object-oriented design and programming (OOD/OOP)
  • Quality assurance by test-driven development
  • Agile CI/CD approach with automated test and deployment
  • Clean code paradigm

Project and Team

Project MLPro was started in 2021 by the Group for Automation Technology and Learning Systems at the South Westphalia University of Applied Sciences, Germany.

Contributors.

How to contribute

If you want to contribute, please read CONTRIBUTING.md

Owner

  • Name: Fachhochschule Südwestfalen
  • Login: fhswf
  • Kind: organization
  • Location: Iserlohn, Germany

Wir geben Impulse!

GitHub Events

Total
  • Create event: 36
  • Release event: 9
  • Issues event: 236
  • Watch event: 11
  • Delete event: 26
  • Member event: 4
  • Issue comment event: 31
  • Push event: 601
  • Pull request review event: 13
  • Pull request event: 71
  • Fork event: 2
Last Year
  • Create event: 36
  • Release event: 9
  • Issues event: 236
  • Watch event: 11
  • Delete event: 26
  • Member event: 4
  • Issue comment event: 31
  • Push event: 601
  • Pull request review event: 13
  • Pull request event: 71
  • Fork event: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 99
  • Total pull requests: 31
  • Average time to close issues: 5 months
  • Average time to close pull requests: 21 days
  • Total issue authors: 5
  • Total pull request authors: 4
  • Average comments per issue: 0.38
  • Average comments per pull request: 0.13
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 87
  • Pull requests: 31
  • Average time to close issues: 24 days
  • Average time to close pull requests: 21 days
  • Issue authors: 4
  • Pull request authors: 4
  • Average comments per issue: 0.1
  • Average comments per pull request: 0.13
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • detlefarend (188)
  • steveyuwono (15)
  • mlpro-admin (14)
  • hsdwolfo (2)
  • syamrajsatheesh (2)
  • rizkydiprasetya (1)
Pull Request Authors
  • detlefarend (72)
  • steveyuwono (16)
  • mlpro-admin (13)
  • syamrajsatheesh (5)
  • laxmikantbaheti (4)
  • rizkydiprasetya (1)
Top Labels
Issue Labels
enhancement (104) next release (99) quality (61) OA (59) BF (59) admin (41) bug (39) refactoring (36) pending-extension (12) WR (12) documentation (11) RL (5) research (2) SL (2) external (2) GT (2) review (1) v2.3.0 (1) v2.2.0 (1) v1.4.0 (1) v1.0.0 (1) v1.3.1 (1) parked (1)
Pull Request Labels
OA (40) BF (24) enhancement (12) documentation (11) RL (8) next release (8) bug (7) admin (7) quality (7) refactoring (5) GT (4) WR (4) SL (1) pending-extension (1)

Dependencies

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doc/rtd/requirements.txt pypi
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pyproject.toml pypi
requirements.txt pypi
  • PySide6 >=6.8.1
  • dill >=0.3.9
  • matplotlib >=3.10.0
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  • pandas >=2.1.3
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  • torch *
src/setup.py pypi