.. _whatsnew_130:

Version 1.3.0
-----------------

- Support for nested parameter searches
    - :class:`BPt.Model` and :class:`BPt.Ensemble` now support a param_search parameter.
    - The parameter param_search accepts a :class:`BPt.ParamSearch` object, and turns the model or ensemble into a nested search object.

- Initial support for passing nested :class:`BPt.ModelPipeline`
    - Now can pass nested :class:`BPt.ModelPipeline` if wrapped in a :class:`BPt.Model`
    - Warning: there are still cases which will not work.

- Better support for stacking ensembles
    - Stacking ensembles are ported from scikit-learn's StackingClassifier and StackingRegressor.
    - The :class:`Ensemble` object can now support the arguments base_model and cv_splits.
    - The parameter, base_model allows passing in BPt compatible models to act as the final_estimator in stacking.
    - cv_splits allows passing a new input class :class:`BPt.CV_Splits` which in the context of stacking, allows for custom CV behavior to train the base estimators.

- Add experimental auto data type to loading targets
    - You can now pass 'a' or 'auto' when loading targets to the data_type parameter to specify that the data type should be automatically inferred.

- Change input parameter CV to cv
    - In order to be more compatible with other libraries and intuitive, now CV always refers to classes and cv an input parameter.

- New Loky multi-processing support
    - Changed to the new default mp_context.
    - Loky is a python library https://pypi.org/project/loky/ with better multiprocessing support than python's default.

- New Dask multi-processing support
    - Experimental support for dask multiprocessing

- Fixed how n_jobs propagates in complex model pipelines
    - New parameter in :class:`BPt.Ensemble` n_jobs_type, which allows more controls over how n_jobs are spread out in the context of Ensembles.

- Fixed bug with RandomParcels
    - The RandomParcels object can be imported through from BPt.extensions import RandomParcels
    - A previous bug would allow some vertex labelled as medial wall, to be mislabeled, this has been fixed.
    
- Add view to :class:`BPt.Model`
    - Initial support for an experimental `view` method for the :class:`BPt.Model` class.

- Improve the outputted results from Evaluate and Test
    - Default feature importance to calculate is now None.
    - Added more optional parameters here.
    - Added new returned single metric.
    - Optional parameter for returning the trained model(s).

- Add default case for :class:`BPt.ProblemSpec`
    - Now with default detecting of problem type, can optionally not specify a problem spec in Evaluate or Test.

- Add default problem type
    - Now if no target_type is specified, a default type will be set based on the type of the loaded target.

- New default scorers
    - The default scorers have changed, now provides multiple scorers for each type by default

- Speed up working with Data Files
    - Some improved performance in loading Data Files

- Separate caching for transformers and loaders
    - Loaders and Transformers can now be cached via a cache_loc parameter.

- Added experimental support for target transformation
    - In some cases it is useful to allow nested transformations to the target variable.
    - :class:`BPt.Model` and :class:`BPt.Ensemble` now support an experimental argument for specifying a target transformation.

- Introduce new :class:`BPt.Values_Subset`
    - In addition, added better description of `subjects` as a parameter type, with more universal behavior.

- Large amounts of internal refactoring
    - From docstrings, to structure of code, big amounts of re-factoring.

- Name change from ABCD_ML to BPt
    - Along with this change, the import of the ML object changed.

- New support for k bins encoding when loading targets
    - When loading targets, you may now specify a k-bins encoding scheme directly.

- Renamed metric to scorer
    - The argument metric has been renamed to scorer
    - The scorers accepted have also been re-defined to more closely align with scikit-learn's scorers.

- Added support for categorical encoders and the categorical encoder library
    - The new encouraged way to perform categorical encoding is by specifying transformers, via added options from the categorical encoders library.

- New, now all parameter objects can accept scope as an argument
    - In previous versions, input objects differed in which could accept a `scope` argument, now all can.

- New ML verbosity options
    - Some new ML verbosity options

- Support latest scikit-learn version
    - Backend changes allowing full compat. with latest scikit-learn versions.

- Add more print information
    - In an effort to make more of the library behavior transparent, more verbose print info has been added by default.

- Removed ML class eval and test scores
    - Depreciated the class wide eval and test scores previously stored in ML object