0.8.7

Version 0.8.7

• New Added functionality for user extension of scikit-criteria with
  decorators for creating aggregation and transformation models using
  functions.

  >>> from skcriteria.extend import mkagg, mktransformer
  >>>
  >>> @mkagg
  >>> def MyAgg(**kwargs):
  >>>     # Implementation of the aggregation function
  >>>
  >>> @mkagg(foo=1)
  >>> def MyAggWithHyperparam(**kwargs):
  >>>     # Implementation of the aggregation function with
  >>>     # hyperparameter 'foo'
  >>>
  >>> @mktransformer
  >>> def MyTransformer(**kwargs):
  >>>     # Implementation of the transformation function
  >>>
  >>> @mktransformer(bar=2)
  >>> def MyTransformerWithHyperparam(**kwargs):
  >>>     # Implementation of the transformation function with
  >>>     # hyperparameter 'bar'

  These decorators enable the creation of aggregation and transformation
  classes based on provided functions, allowing users to
  define decision-making models with less flexibility than traditional
  inheritance-based models.

  For more information check the tutorial Extending Aggregation and Transformation Functions

• New Module: Introduced the skcriteria.testing module, exposing utility functions for for comparing objects created in Scikit-Criteria in a testing environment. These functions facilitate the comparison of instances of the DecisionMatrix, ResultABC, and RanksComparator classes.

  The assertion functions utilize pandas and numpy testing utilities for comparing matrices, series, and other attributes.

  Check the Reference for more information.

• New The API of the agg, pipeline, preprocessing, and extend modules has
  been cleaned up to prevent autocompletion with imports from other modules.
  The imported modules are still present, but they are excluded when attempting
  to autocomplete. This functionality is achieved thanks to the context manager
  skcriteria.utils.cmanagers.hidden().

• New All methods (agg and transformers) has a new get_method_name
  instance method.

• Drop Drop support for Python 3.8