pycvi
The facts below are what the maker publishes about pycvi, checked and dated. Our editors have not written about it yet.
Plans and prices
From pycvi's own pricing page, checked 11 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| MIT-licensed package | Free | Python package; 12 internal clustering validity indices |
| PyCVI | Free | Open-source Python package · MIT license |
The facts
Each one read from pycvi's own pages, with the date it was checked.
What it does
- Additional capability
- PyCVI implements Variation of Information to measure distance between clusterings.checked 11 Oct 2026
- Bayesian analysis
- Nochecked 23 Sep 2026
- Clustering input
- It can generate clusterings when given a sklearn-like clustering class.checked 11 Oct 2026
- Clustering selection
- It computes index values and selects the best clustering based on the results.checked 11 Oct 2026
- Compatibility note
- The documentation warns that scikit-learn-extra 0.3.0 is incompatible with NumPy 2.0 and later, so users combining them must select a compatible NumPy version.checked 11 Oct 2026
- Custom indices
- Users can define custom CVIs and combine multiple CVIs to select a clustering by majority vote.checked 11 Oct 2026
- Dependency limitation
- The documentation warns that scikit-learn-extra 0.3.0 is incompatible with numpy versions 2.0.0 and later.checked 11 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- Example requirements
- Running example scripts requires extra dependencies, with matplotlib listed as an example dependency.checked 11 Oct 2026
- Indices
- It implements 12 internal clustering validity indices, including Silhouette, Dunn, and Davies-Bouldin.checked 11 Oct 2026
- Install
- The package can be installed with uv, Poetry, pip, or through pip in a conda environment.checked 11 Oct 2026
- Installation
- The project documents installation through uv, Poetry, pip, or pip within a Conda environment.checked 11 Oct 2026
- Integrations
- The documentation lists compatibility with scikit-learn, scikit-learn-extra, kmedoids, aeon, and sktime.checked 11 Oct 2026
- Intended users
- The project describes itself as a Python package for evaluating clustering, including time-series clustering workflows.checked 11 Oct 2026
- License
- The GitHub repository states that the project is licensed under the MIT license.checked 11 Oct 2026
- Purpose
- PyCVI evaluates precomputed clusterings and helps select the best one when datapoint labels or other external information are unavailable.checked 11 Oct 2026
- Regression models
- Nochecked 23 Sep 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Security
- The project documentation does not state security certifications or compliance claims.checked 11 Oct 2026
- Statistical tests
- Nochecked 23 Sep 2026
- Support
- The documentation directs users with issues to contact the maintainer or create a GitHub issue.checked 11 Oct 2026
- Time series
- PyCVI supports time-series distances and averages including DTW, MSM, DBA, and MBA.checked 11 Oct 2026
- Validity indices
- The package implements 12 internal clustering validity indices, including Silhouette, Dunn, and Davies-Bouldin.checked 11 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
