https://github.com/hdmf-dev/hdmf-ai

A schema and API for storing the results from AI/ML workflows
https://github.com/hdmf-dev/hdmf-ai

artificial-intelligence data-standards hdmf machine-learning python reproducible-research

Last synced: 4 months ago
JSON representation

A schema and API for storing the results from AI/ML workflows

• Host: GitHub
• URL: https://github.com/hdmf-dev/hdmf-ai
• Owner: hdmf-dev
• License: other
• Created: 2020-10-16T18:43:21.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2025-01-07T22:57:06.000Z (5 months ago)
• Last Synced: 2025-01-15T23:41:43.345Z (4 months ago)
• Topics: artificial-intelligence, data-standards, hdmf, machine-learning, python, reproducible-research
• Language: Jupyter Notebook
• Homepage:
• Size: 376 KB
• Stars: 1
• Watchers: 6
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: docs/CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
# HDMF-AI - an HDMF schema and API for AI/ML workflows

`HDMF-AI` is a schema and Python API for storing the common results of AI algorithms in a standardized way within the [Hierarchical Data Modeling Framework (HDMF)](https://hdmf.readthedocs.io/en/stable/).

`HDMF-AI` is designed to be flexible and extensible, allowing users to store a range of AI and machine learning results and metadata, such as from classification, regression, and clustering. These results are stored in the `ResultsTable` data type, which extends the `DynamicTable` data type within the base HDMF schema. The `ResultsTable` schema represents each data sample as a row and includes columns for storing model outputs and information about the AI/ML workflow, such as which data were used for training, validation, and testing.

By leveraging existing HDMF tools and standards, `HDMF-AI` provides a scalable and extensible framework for storing AI results in an accessible, standardized way that is compatible with other HDMF-based data formats, such as [Neurodata Without Borders (NWB)](https://nwb-overview.readthedocs.io/), a popular data standard for neurophysiology, and [HDMF-Seq](https://github.com/exabiome/deep-taxon), a format for storing taxonomic and genomic sequence data. By enabling standardized co-storage of data and AI results, `HDMF-AI` may enhance the reproducibility and explainability of AI for science.

![UML diagram of the HDMF-AI schema. Data types with orange headers are introduced by HDMF-AI. Data types with blue headers are defined in HDMF. Fields colored in gray are optional.](paper/schema.png)

## Installation

```bash

pip install hdmf-ai

```

## Usage

For example usage, see `example_usage.ipynb`.