OKAPI: Genetic Programming for Ensemble Model Fusion

OKAPI is a Python library that uses genetic programming to evolve optimal ensembles of machine learning models for classification tasks. It combines the predictions of multiple models into a single, more accurate prediction by evolving tree structures representing different fusion strategies.

Key Features

• Tree-based representation: Models ensemble architectures as trees with models as leaves and fusion operations as nodes
• Evolutionary optimization: Uses genetic programming with crossover and mutation operations to find optimal fusion strategies
• Multiple fusion operations: Supports mean, min, max, weighted mean, and other operations to combine predictions
• Backend flexibility: Works with both NumPy and PyTorch backends
• Pareto optimization: Balances model complexity and performance for robust solutions

When to Use OKAPI

OKAPI is particularly useful when:

• You have multiple models predicting the same target
• You want to combine these models in a way that outperforms individual models
• You need interpretable fusion structures that show how models are combined
• You want to automatically discover which models are most useful for your task

Project Status

OKAPI is currently under active development. The core functionality is implemented, but the library may change significantly before the first stable release.

Documentation Structure

• API Reference: Detailed documentation for all classes and functions in the library
• Development: Information for contributors interested in helping with the development