Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments.

This repository provides the implementation for our work on Self-Healing Machine Learning, which explores methods for improving the robustness and reliability of machine learning models in dynamic and unpredictable environments. Our approach employs mechanisms to detect, diagnose, and mitigate issues such as data drift, corruption, and outliers, ensuring that models remain accurate and effective over time.

Important note. The codebase for H-LLM is intended to illustrate the key concepts in the paper. It is NOT intended to be used as a product, service, or outside of the intended viability studies. The reason is because these studies are viability studies, i.e. showing the fact that such adaptation is viable / possible.

Overview

The repository contains scripts and resources necessary to replicate experiments, generate results, and explore the self-healing functionality. The experiments cover various datasets, corruption types, noise levels, and outlier conditions, demonstrating how models can adapt to maintain performance in changing environments.

Prerequisites

To use this repository, you need an API key for querying an LLM if you plan to use the language model components. You can set this up by creating a Python file named openai_config.py within the src directory with the following structure:

def get_openai_config():
    openai_config = {
        "api_type": "azure",
        "api_base": api_base,
        "api_version": api_version,
        "api_key": api_key_main,
        "deployment_id": deployment_name,
        "deployment_id_ada": deployment_name_ada,
        "temperature": 0.0,
        "seed": 0
    }
    return openai_config

Replace the placeholders with your actual configuration values.

Getting Started

1. Install Dependencies

Ensure that you have Python 3.10 or later installed. Create a virtual environment and install the required packages by running:

``` python -m venv venv source venv/bin/activate # On macOS/Linux venv\Scripts\activate # On Windows pip install -r requirements.txt ```

2. Running the Project

• Run Experiments: Use the scripts in the experiments directory to replicate different studies and generate results. For example:

  python experiments/study_1.py
  

• Interactive Notebooks: Open and explore Jupyter notebooks in the notebooks directory to visualize results and understand different components:

  jupyter notebook notebooks/Example_usage.ipynb
  

3. Citing the paper

If you find this repository useful, please consider citing our work:

@article{rauba2024self,
  title={Self-healing machine learning: A framework for autonomous adaptation in real-world environments},
  author={Rauba, Paulius and Seedat, Nabeel and Kacprzyk, Krzysztof and van der Schaar, Mihaela},
  journal={Neural Information Processing Systems (NeurIPS) 2024},
  year={2024}
}