Changelog

Version 0.7

  • New method: ELECTRE2.

  • New preprocessin strategy: A new way to transform from minimization to maximization criteria: NegateMinimize() which reverses the sign of the values of the criteria to be minimized (useful for not breaking distance relations in methods like TOPSIS). Additionally the previous we rename the MinimizeToMaximize() transformer to InvertMinimize().

  • Now the RankingResult, support repeated/tied rankings and some were implemented to deal with these cases.

    • RankingResult.has_ties_ to see if there are tied values.

    • RankingResult.ties_ to see how often values are repeated.

    • RankingResult.untided_rank_ to get a ranking with no repeated values.

      repeated values.

  • KernelResult now implements several new properties:

    • kernel_alternatives_ to know which alternatives are in the kernel.

    • kernel_size_ to know the number of alternatives in the kernel.

    • kernel_where_ was replaced by kernel_where_ to standardize the api.

Version 0.6

  • Support for Python 3.10.

  • All the objects of the project are now immutable by design, and can only be mutated troughs the object.copy() method.

  • Dominance analysis tools (DecisionMatrix.dominance).

  • The method DecisionMatrix.describe() was deprecated and will be removed in version 1.0.

  • New statistics functionalities DecisionMatrix.stats accessor.

  • The accessors are now cached in the DecisionMatrix.

  • Tutorial for dominance and satisfaction analysis.

  • TOPSIS now support hyper-parameters to select different metrics.

  • Generalize the idea of accessors in scikit-criteria througth a common framework (skcriteria.utils.accabc module).

  • New deprecation mechanism through the

  • skcriteria.utils.decorators.deprecated decorator.

Version 0.5

In this version scikit-criteria was rewritten from scratch. Among other things:

  • The model implementation API was simplified.

  • The Data object was removed in favor of DecisionMatrix which implements many more useful features for MCDA.

  • Plots were completely re-implemented using Seaborn.

  • Coverage was increased to 100%.

  • Pipelines concept was added (Thanks to Scikit-learn).

  • New documentation. The quick start is totally rewritten!

Full Changelog: https://github.com/quatrope/scikit-criteria/commits/0.5

Version 0.2

First OO stable version.

Version 0.1

Only functions.