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CHANGELOG.md

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Changelog

Version 0.8.1 (March 27, 2024)

  • Minor compatibility update for Bokeh plotting.

Version 0.8.0 (August 1, 2023)

API changes:

  • The first argument in Clustergram.fit() is now X instead of data.

Enhancements:

  • API: follow scikit-learn API conventions (#59)
  • ENH: ensure that x ticks are only integers (#58)
  • ENH: format time in verbose mode

Version 0.7.0 (January 15, 2023)

Enhancements:

  • ENH: allow weighting by a custom principal component (#35)

Compatibility notes:

  • clustergram now requires Python 3.8
  • RAPIDS.AI implementation has been tested with version 22.12.

Minor notes:

  • examples dictionary has been removed. Refer to the notebooks in the documentation.

Version 0.6.0 (November 21, 2021)

Enhancements:

  • ENH: optionally measure BIC during GMM (#21)

Bug fixes:

  • BUG: cuML non-weighted plot fix (#25)

Version 0.5.1 (May 24, 2021)

Fix for from_data method with non-default indices.

Bugs:

  • BUG: cluster centers empty due to index mismatch (#19)

Version 0.5.0 (May 11, 2021)

clustergram now supports interactive plotting using a new .bokeh() method based on BokehJS. It can be handy for exploration of larger and more complex clustergrams or those with significant outliers.

Enhancements:

  • ENH: support interactive bokeh plots (#14)

  • ENH: skip k=1 in K-Means implementations (#18)

  • documentation restructuring

Version 0.4.0 (April 27, 2021)

Spring comes with native hierarchical clustering and the ability to create clustergam from a manual input.

Enhancements:

  • ENH: support hierarchical clustering using scipy (#11)
  • ENH: from_data and from_centers methods (#12)

Version 0.3.0 (January 31, 2021)

API changes:

  • pca_weighted is now keyword of Clustergram.plot() not __init__.

Enhancements:

  • Support MiniBatchKMeans (scikit-learn)
  • Custom __repr__
  • Expose cluster labels obtained during the loop
  • Expose cluster centers
  • Silhouette score
  • Calinski and Harabasz score
  • Davies-Bouldin score

Version 0.2.0 (December 21, 2020)

Version 0.2.0 brings support of Gaussian Mixture Models (using scikit-learn) and few minor changes.

Enhancements:

  • Gaussian Mixture Model support (#4)
  • Verbosity - Clustergram now indicates the progress
  • Additional arguments can be passed to the PCA object

Bug fixes:

  • BUG: avoid LinAlgError: singular matrix