In Dev

  • New Dataset class

    • The new Dataset class is designed to eventually replace the old system for data loading.

  • Full refactor of feature importance plotting

    • The plotting interface will eventually be moved fully to within the feature importance object as return as a result.

Release 1.3.6

  • Removed cache option from Model_Pipeline

    • Use cache_loc parameter instead in each individual piece for more flexibility.

  • New search_only_params param

    • In the Param_Search object, there is now a parameter for search_only_params.

    • This parameter allows some advanced behavior, w.r.t. to only passing params when searching for params.

Release 1.3.5

  • GridSearchCV support

    • Added new abstract BPtSearchCV class.

    • Added in if search_type = ‘grid’ will try and convert parameters to grid search compatible, and use on the backend sklearn’s GridSearchCV.

    • n_jobs will propegate correctly.

  • Replaced LGBM with BPtLBGM

    • Replaces LGBMRegressor and Classifier with BPtLGBMRegressor and BPtLGBMClassifier.

    • These classes act as wrappers which automatically pass categorical features to the LGBM fit.

    • These classes also allow setting parameters ‘early_stopping_rounds’ and ‘eval_split’.

  • Update Nevergrad version

    • Update to nevergrad version 0.4.2.post5

    • Warning: The list of avaliable search types may be a little out of date.

  • New CV_Split class

    • This can be used for passing single splits

  • New parameter fix_n_wrapper_jobs for Loader

    • This parameter allows setting a fixed number of jobs for the Loading Wrapper.

    • In the future a better system for fixing n_jobs may be added.

  • Fix/Change internal representation for Scope Models + Transformers

    • Introduce new internal classes for ScopeModel + ScopeTransformer.

    • These classes fixed a few existing bugs, and should make behavior moving forward more consistent.

  • Fix bug with Loader transform_df

    • Fixed a bug with the transform_df function for Loaders.

    • This resulted in a error with computing feature importances for data loaded with a Loader.

  • Better pipeline names

    • When using sklearn verbose, or inspecting models, a few names have been changed to look better / be more informative.

Release 1.3.4

  • Added support for pandas >= 1

    • Previously didn’t support latest pandas.

  • Add sklearn OneHotEncoder

    • Previously used category_encoders, use scikit-learn’s instead for better and more reliable performance.

    • This object can be accessed as a transformer under ‘one hot encoder’.

  • Added initial support for in-place FIs

    • Moving from plotting via ML to plotting from the Feature Importance object itself

    • Only fully supports global right now.

  • Allow transformers to be skipped if out of scope

    • Previously would cause error.

Release 1.3.3

  • Fixed bug with problem type

    • There was an error which was mistakenly setting categorical problem type instead of regression.

  • Fixed internal bug with mapping

    • Effected Transformer.

  • Added base_dtype option

    • Evaluate and Test now have base dtype options, which allow changing dtype of data

    • Changed default data dtype from float64 to float32, should provide general speed ups

Release 1.3.1 and 1.3.2

  • New AutoGluon option

    • Can now specify the auto machine learning package AutoGluon as a BPt.Model

  • New SurfMaps extension loader

    • Added new extension Loader BPt.SurfMaps

  • only_fold parameter

    • New optional parameter in Evaluate for running only one fold.

  • Better support for scikit-learns VotingClassifier and VotingRegressor

    • Similar to Stacking update from 1.3, but for voting classifier + regressor.

  • More support for nested pipelines

    • Can now have nested pipelines propagate their parameter distributions to a parameter search in the top level pipeline.

  • Bug Fix with CV

    • Fixed rare bug with CV expecting pandas Series, added support for passing numpy array.

Release 1.3

  • Support for nested parameter searches

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

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

  • Initial support for passing nested BPt.Model_Pipeline
  • 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 intuative, 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 propegates 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.Problem_Spec

    • 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

  • Seperate 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