0.9.0 - 2021-11-30

• Wheels for Python 3.6 have been dropped.
• Wheels for Python 3.9 have been added.

anomaly

• Moved api.anomaly.base.AnomalyDetector to anomaly.AnomalyDetector.
• Implemented anomaly.ConstantThresholder.
• Implemented anomaly.QuantileThresholder.
• Implemented api.anomaly.OneClassSVM.

base

• Renamed base.WrapperMixin to base.Wrapper.
• Introduced base.WrapperEnsemble.
• Clarified the difference between a base.typing.Dataset and a base.typing.Stream. A Stream is an instance of a Dataset and is stateful. A Dataset is stateless. It's essentially the same difference between an Iterable and an Iterator in the Python standard library.

compat

• Added compat.PyTorch2RiverClassifier
• Implemented median absolute deviation in stats.MAD.
• Refactored compat.PyTorch2RiverRegressor
• Fixed an issue where some statistics could not be printed if they had not seen any data yet.

compose

• You can now use a list as a shorthand to build a TransformerUnion.
• Fixed a visualization issue when using a pipeline with multiple feature unions.
• The prejudiced terms blacklist and whitelist have both been renamed to keys.
• Removed learn_unsupervised parameter from pipeline methods.
• Implemented compose.TransformerProduct.

datasets

• Added datasets.Keystroke.

ensemble

• Bug fixes in ensemble.SRPClassifier and ensemble.SRPRegressor.
• Some estimators have been moved into the ensemble module.

feature_extraction

• Implemented feature_extraction.Lagger.
• Implemented feature_extraction.TargetLagger.

meta

This module has been deleted.

• Move meta.PredClipper to the preprocessing module.
• Removed meta.BoxCoxRegressor.
• Moved meta.TargetTransformRegressor to compose.TargetTransformRegressor.
• Moved meta.TargetStandardScaler to preprocessing.TargetStandardScaler.

model_selection

• This new module replaces the expert module.
• Implemented model_selection.GreedyRegressor.
• Added ModelSelector base class.

optim

• optim.Adam and optim.RMSProp now work with utils.VectorDicts as well as numpy.ndarrays.
• Added optim.losses.Huber.

preprocessing

• Enabled preprocessing.OneHotEncoder to one-hot encode values that are list or sets.

reco

• Added a debug_one method to reco.FMRegressor.

selection

• This new module replaces the expert module.
• Implemented selection.GreedyExpertRegressor.

stats

• Fixed an issue where some statistics could not be printed if they had not seen any data yet.
• Implemented median absolute deviation in stats.MAD.
• The stats.Mean and stats.Var implementations have been made more numerically stable.

time_series

• time_series.Detrender and time_series.GroupDetrender have been removed as they overlap with preprocessing.TargetStandardScaler.
• Implemented a time_series.evaluate method, which performs progressive validation for time series scenarios.
• Implemented time_series.HorizonMetric class to evaluate the performance of a forecasting model at each time step along a horizon.
• Implemented time_series.HoltWinters.

utils

• Moved model_selection.expand_param_grid to utils.expand_param_grid.
• Added utils.poisson.
• Added the utils.log_method_calls context manager.
• Added the utils.warm_up_mode context manager.
• Added the utils.pure_inference_model context manager.