- [Pairwise Metrics and Visualization](#Pairwise Metrics and Visualization)
Additional Info
Evaluation and visualization tools for pairwise measures. The motivation for this repo is to evaluate clustering algorithms. However, amongst other use cases, this code-base is relevant in problems that involve sample pairs and distance matrices.
SKLearn is complete with many metrics, which include submodules for clustering and pairwise. However, this code-base sets out to compliment this with pairwise metrics determined for cluster (or class) assignments of arbitrary assignments.
Measure performance for cluster assignments (i.e., pseudo-labels) provided ground-truth labels.
Various pair-wise metrics allow for in depth analysis of clustering algorithms.
Powerful visualizations generated on-the-fly provide quick and systematic way to analyze and communicate results.
Visualizations tools and demos included.
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Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
This to do. Unless specified, order is arbitrary.
- Update LICENSE
- Complete README
- Utility functions
- Visualization Tools
- Make metrics a class
- inherent visualization tools
- Demos and notebooks
- Tests
- Choose interface (i.e., python package) and create skeleton code-base
- Unit tests
- Type checking
- Inputs
- Outputs
- All tests
- Add script for auto-testing
- Decide on interface/ technology for this
- Create material providing/ setting relevant configurations
- Add pre-commit for git (i.e., run tests before commit, and only do if tests PASS)
- Contributing Guidelines (create)
See changelog.md