v0.2.3 (May 13, 2023)
--------------------------

These are minor changes  in ``v0.2.2`` that shows fugs fixed, features and improvements.  

- |API change| Change performed in :func:`watex.utils.funcutils.assert_ratio`. The parameter ``as_percent`` does no longer 
  exist. It is depreated and modified to ``in_percent``.
  
- |Fix| Bug fixed in concatening sring argument passed to `titles` parameter in :func:`watex.view.plotLearningInspections`. Henceforth, 
  no need to put only single model in litteral string. However for a single model training, :func:`watex.view.plotLearningInspection`
  is prefered instead. Note the ``'s'`` at the end of the former function. 
  
- |Fix| Bug fixed in :func:`watex.utils.plot_logging` when  parameter :math:`X` is not passed as a DataFrame. Henceforth, a column 
  of random item  is generated and is used to build a temporray DataFrame for plotting. 

- |Feature| Add :func:`watex.datasets.load_mxs` load new datasets for performing the :term:`MXS`. `shuffle` parameter is needed to be 
  triggered to ``True`` for shuffling data before any splitting. By edfaultt it's set to ``False``. 

- |Feature| add transformers :class:`watex.transformers.KMeansFeaturizer` to transform numeric data into k-means cluster memberships. It runs k-means on the input data and converts 
  each data point into the ID of the closest cluster.

- |Feature| add :func:`watex.transformers.featurize_X` to transform predictor :math:`X` at once. It applies the K-Means featurization 
  approach. Refer to :class:`watex.transformers.KMeansFeaturizer` 

- |Feature| :func:`watex.utils.plotutils.plot_voronoi` plots the Voronoi diagram of the k-Means clusters overlaid with 
  the data.
  
- |Feature| new function :func:`watex.utils.plot_roc_curves` for visualizing the Receiving Operating Characterisctic (ROC) curves either 
  into a single plot ( used by default) or individual plot for each model by setting the parameter ``all=True``.