RELEASES

History of changes

1.1.1 (Latest)

  • Minor bug fix to ensure that all prediction calls can be carried.

1.1.0

  • Updating documentation introduction of the package

  • Added the chi square divergence in GEMINIs: gemclus.gemini.ChiSquareGEMINI

  • Changed gemclus.linear.KernelRIM implementation to directly depend on linear models.

  • Fixing compatibility with numpy>=2

1.0.0

  • Adding the kernelised version of RIM with: KernelRIM

  • Adding the dynamic version of paths for feature selection in sparse models. A simply argument dynamic=True activates the dynamic mode.

  • Possibility of passing custom kernels and metrics to sparse models. This is not compatible with the dynamic mode.

  • No need to specify any longer the full partition of the features in the groups arguments of the sparse models

  • New GEMINIs: HellingerGEMINI, TVGEMINI and KLGEMINI

  • Introducing generic models that can be combined with any GEMINI

    • gemclus.linear.LinearModel, gemclus.mlp.MLPModel, gemclus.nonparametric.CategoricalModel, gemclus.sparse.SparseLinearModel, gemclus.sparse.SparseMLPModel

    • The GEMINI parametrisation of DOUGLAS can now be done through string

    • The dedicated MMD and Wasserstein models remain and support custom kernel/metric parameters

  • Fusing GEMINIs into a single class per distance

    • gemclus.gemini.MMDOvA and gemclus.gemini.MMDOvO are now gemclus.gemini.MMDGEMINI

    • gemclus.gemini.WassersteinOvA and gemclus.gemini.WassersteinOvO are now gemclus.WassersteinGEMINI

    • Both the MMD and Wasserstein GEMINI now support custom kernel/metric parameters

  • Fixing a gradient mistake in the gemclus.MI

0.2.0

  • Adding a new sparse logistic regression model trained with mutual information instead of MMD GEMINI: gemclus.sparse.SparseLinearMI

  • Adding new package containing methods for unsupervised tree clustering with end-to-end training: gemclus.tree. The package features a CART-like algorithm for clustering named Kauri and a differentiable tree named Douglas

  • Experimental: A method for adding constraints of type must-link cannot-link to discriminative models: gemclus.add_mlcl_constraint

  • Minor fixes in documentation

  • Better compatibility with scikit learn 1.3.0 regarding parameter constraint check

0.1.1

  • Fixing the ABCMeta parameter validation problem for the draw_gmm method for retrocompatibility with Python 3.8.

  • Constraining the package to Python>=3.8 to respect the requirements of the package.

  • Minor fix on the get_selection method for the Linear sparse models to respect the 1d output shape of the array.

0.1.0

  • Isolating the definition of GEMINIs in a separate classes for external usages: gemini.MMDOvO, gemini.WassersteinOvA etc.

  • Adding the nonparametric models in package gemclus.nonparametric with 2 additional examples for its usage in graph node clustering.

  • Fixing control variables in the path method for spars models.

0.0.2

  • Adding the Gaussian+Student-t mixture dataset: gstm

  • Method for sampling multivariate student-t distributions: multivariate_student_t

  • Adding tests for data package

  • Adding the possibility of a precomputed kernel/distance passed to fit

  • Adding batch size parameters

  • Fixing zero division in sparse linear model proximal gradient