1. Introduction

MLPro is a comprehensive and integrative middleware framework for standardized machine learning (ML) applications in Python. The objective is to provide processes and templates for a wide range of relevant ML sub-areas without having to forego the use of already established and proven ML frameworks such as Scikit-learn, TensorFlow, PyTorch, Optuna, etc. Rather, the latter is seamlessly integrated into the process landscapes of MLPro. By using MLPro, researchers, developers, engineers, and students can focus on their essential core tasks without having to worry about the integration/interaction of different frameworks or having to re-implement existing algorithms. MLPro is architecturally designed for extensibility and recombinability, which in particular enables the creation of hybrid ML applications across different learning paradigms.

1.1. Key Features

The most important key features of MLPro are…

1. Sub-Frameworks for various ML topics
2. Powerful substructure of overarching basic functions
3. Extensive pool of examples
4. Integration of proven 3rd party packages

5. Open source, open design

1.2. Architecture

MLPro consists of a continuously growing number of sub-frameworks covering different areas of machine learning. These include one or more fundamental process models (e.g. the Markovian Decision Process in reinforcement learning) and appropriate service and template classes. Furthermore, each sub-framework contains a specific pool of reusable classes for algorithms, data sources, training subjects, etc. Numerous sample programs for self-study complete the scope.

The sub-frameworks mentioned are in turn based on an overarching layer of basic functions. This is a common and obvious approach. What is special about MLPro, however, is the scope and internal structure of this base layer. A spectrum of elementary functions for logging and plotting through multitasking and numerics to the basics of machine learning is covered in a hierarchy of sub-layers that build on one another. This is also the key to the far-reaching recombinability of higher functions of MLPro. In fact, with each new feature, we consider how deeply we can incorporate it into MLPro. The deeper the level, the more universal the usability, and thus the range within MLPro.


1.2.1. Standardized Machine Learning

A special feature of MLPro is that machine learning standards are already defined in the basic functions. Templates for adaptive models and their hyperparameters as well as for executable ML scenarios are introduced in the top layer of MLPro-BF. Furthermore, standards for training and hyperparameter tuning are defined. These basic ML elements are reused and specifically extended in all higher sub-frameworks. On the one hand, this facilitates the creation of new sub-frameworks and, on the other hand, the recombination of higher functions from MLPro in your hybrid ML applications.

Learn more: Basics of Machine Learning

1.2.2. Example Pool

Numerous executable example programs (we call them “howtos”) illustrate the essential functions of MLPro. They are also used for validation and are therefore an integral part of our automatic unit tests. With this, we ensure two things: the operability of all howtos and thus also the operability of the demonstrated functionalities (tdd - test-driven development).

Learn more: Example Pool

1.2.3. Third Party Support

MLPro integrates an increasing number of selected ML packages into its process landscapes. This is done at different levels of MLPro using so-called wrapper classes that are compatible with the corresponding MLPro classes.

Learn more: Wrappers

1.2.4. Real-World Applications in Focus

MLPro was designed not only for simulations but for use in real-world applications. To this end, various basic functions have been implemented that make diagnostics easier and make optimal use of the available system resources. These are for example

  • Detailed logging

  • Precise time management of simulated and real processes on a microsecond time scale

  • Creation of detailed training data files (ASCII/CSV)

  • Multithreading/multiprocessing

In addition, powerful templates for state-based systems are provided. They allow the standardized implementation of your systems, which can then be controlled, for example, by adaptive controllers based on reinforcement learning or game theory. Furthermore, a wrapper for the popular physics engine MuJoCo is provided through MLPro-Int-MuJoCo, which can be used for the simulation and visualization of externally designed system models. The MLPro templates are also prepared for connection to industrial components like controllers, sensors, and actuators.

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1.3. Development

MLPro is developed at the South Westphalia University of Applied Sciences, Germany in the Department for Electrical Power Engineering in the Lab for Automation Technology and Learning Systems and is freely available to all interested users from research and development as industry and economy.

The development team consistently applies the following principles:

  • Quality first

    We aim to provide ML functionalities at the highest possible level. We put these up for discussion in scientific publications. Open feedback and suggestions for improvement are always welcome.

  • Design first

    In MLPro, new functions are not created in the code editor but in a class diagram. We provide the latter in the API Reference. A colour system documents the respective development status.

  • Clean Code Paradigm

    We firmly believe that a clearly structured and legible source code has a significant influence on both the acceptance and the life cycle of software. Anyone who opens any source code of MLPro knows immediately what we mean :-)

1.3.1. Customer Extensions

Of course, frameworks like MLPro are made to reuse their functions in own applications. That’s why we put a lot of effort into design and documentation to create powerful and understandable templates and related example programs. The following notes are intended to help software developers to interpret and use them correctly.

There are essentially three types of classes in the MLPro framework:

  • Property classes

    These are classes that standardize certain properties and pass them on to child classes through inheritance. These classes are primarily found in the lower layers of MLPro and are not intended for direct use in your own applications. Nevertheless, they can of course be used in your own classes to maintain compatibility and integrity with MLPro. Examples can be found in Basic Functions, Layer 0 among others.

  • Process classes

    These classes provide higher level application functions such as training or running a model. They are primarily found in the higher sub-frameworks for machine learning. Customer extensions are not provided here.

  • Template classes

    In order to be able to implement your own algorithms, models, data objects, systems, etc. in compliance with the MLPro standards, numerous template classes are provided on different levels. These in turn contain special custom methods that are intended for your own adjustments. These are explicitly identified in the API Reference both in the description of the classes and methods and in the associated class diagram. It should be noted here that custom methods are often inherited from parent classes (e.g. property classes). It is therefore recommended to follow the inheritance lines of template classes.

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