Version 1.3.0#

  • Support for nested parameter searches

    • BPt.Model and BPt.Ensemble now support a param_search parameter.

    • The parameter param_search accepts a BPt.ParamSearch object, and turns the model or ensemble into a nested search object.

  • Initial support for passing nested BPt.ModelPipeline
  • Better support for stacking ensembles

    • Stacking ensembles are ported from scikit-learn’s StackingClassifier and StackingRegressor.

    • The 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 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 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 BPt.Model

    • Initial support for an experimental view method for the 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 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.

    • BPt.Model and BPt.Ensemble now support an experimental argument for specifying a target transformation.

  • Introduce new 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