https://github.com/viadee/javaanchoradapters

Getting the Anchors Explainer to work in Different Settings
https://github.com/viadee/javaanchoradapters

ai anchors explainability java machine-learning tool

Last synced: 4 months ago
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Getting the Anchors Explainer to work in Different Settings

• Host: GitHub
• URL: https://github.com/viadee/javaanchoradapters
• Owner: viadee
• License: bsd-3-clause
• Archived: true
• Created: 2018-11-21T12:43:30.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-10-06T09:32:14.000Z (over 1 year ago)
• Last Synced: 2025-08-04T07:27:43.116Z (6 months ago)
• Topics: ai, anchors, explainability, java, machine-learning, tool
• Language: Java
• Homepage:
• Size: 6.48 MB
• Stars: 5
• Watchers: 10
• Forks: 0
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# JavaAnchorAdapters

> *Adapter* [/əˈdaptə/] noun, a device for connecting pieces of equipment that cannot be connected directly.

This is a collection of tools that serve to make the [Java implementation of the Anchors algorithm](https://github.com/viadee/javaAnchorExplainer) more easy to use. The algorithm (as introduced Marco Tulio Ribeiro, 2018) is model-agnostic, but the nature of the dataset needs to be considered. 

This repository includes *methodological* aspects, i.e. default approaches on how to apply the algorithm to tabular data in typical use cases with tabular data (such as [bpmn.ai](https://github.com/viadee/bpmn.ai)), images or texts as well as *technical* aspects, such as running Anchors explanations on Apache Spark.

This project is to be considered research-in-progress.

# JavaAnchorAlgorithm Repository

For more information on Anchors and this implementation, see [main repository](https://github.com/viadee/javaAnchorExplainer).

# Exemplary Use / Tutorial

Examples of using the Anchors implementation and its various adapters are provided within the [XAI Examples](https://github.com/viadee/xai_examples) repository. 

Please refer to this project for tutorials and easy-to-run applications.

# Collaboration

The project is operated and further developed by the viadee Consulting AG in Münster, Westphalia. Results from theses at the WWU Münster and the FH Münster have been incorporated.

* Further theses are planned: Contact person is Dr. Frank Köhne from viadee.

    Community contributions to the project are welcome: Please open Github-Issues with suggestions (or PR), which we can then edit in the team. For general discussions please refer to the [main repository](https://github.com/viadee/javaAnchorExplainer).

*   We are looking for further partners who have interesting process data to refine our tooling as well as partners that are simply interested in a discussion about AI in the context of business process automation and explainability.