FunctionStudio 0.0.3

Wrapper around evotorch to simplify any optimization task

FunctionStudio

💡 Announcing the Development of a new Optimization Library

As an AI professional, I'm excited to share that I'm developing a new optimization library, planned for release later this year. This library aims to address key challenges in the field:

❓ Why another optimization library?

• Current tools like Scikit-learn, PyTorch, and TensorFlow offer limited support for multi-objective optimization. This library simplifies finding trade-off models for any combination of objectives, aimed to ease the use of multi-objective optimization in model development.
• Users will have the ability to choose whether to train models end-to-end or as separate modules. This feature is particularly useful for optimizing decisions composed of multiple sub-decisions.
• Built on a low-code language (DMN) for both the input and output layers, the library ensures interpretability, interoperability, and interchangeability of model chains.
• The library will support single-objective reinforcement learning, multi-objective reinforcement learning, supervised learning, and multi-objective supervised learning, all through a single, user-friendly interface.

To do

• Implement class called Function allowing to fit any multi-objective function by adding objectives.✅
• Implement supervised example case on how to use function class ✅
• Implement Reinforcement learning example for function ✅
• Implement CompositeFunction class ✅
• Implement reinforcement learning example for CompositeFunction class ✅
• Implement multi-objective reinforcement learning example ✅
• Implement the reporting module to visualize the found pareto front❌
• Implement the dmn input and output layer to convert function from and to dmn❌
• Setup pypi package, requirements file❌
• Make unit tests❌
• document classes in code❌