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0.15.0 - 2022-01-29

active

  • Created this module dedicated to online active learning.
  • Added active.EntropySampler.

base

  • Fixed an issue where an estimator that has attribute a pipeline could not be cloned.
  • Added a base.DriftAndWarningDetector to clarify the difference between drift detectors that have a warning_detected property and those that don't.
  • Added MultiLabelClassifier.
  • Added MultiTargetRegressor.
  • Added drift.BinaryDriftDetector.
  • Added drift.BinaryDriftAndWarningDetector.

conf

  • Introduced this new module to perform conformal predictions.
  • Added a conf.Interval dataclass to represent predictive intervals.
  • Added conf.RegressionJackknife.

datasets

  • Removed unnecessary Numpy usage in the synth submodule.
  • Changed np.random.RandomState to np.random.default_rng where necessary.

drift

  • Added drift.DriftRetrainingClassifier.
  • Renamed drift.PeriodicTrigger to drift.DummyDriftDetector to clarify it is a naive baseline.
  • Created a binary submodule to organize all drift detectors which only apply to binary inputs.

ensemble

  • Added ensemble.ADWINBoostingClassifier.
  • Added ensemble.BOLEClassifier.

evaluate

  • evaluate.progressive_val_score and evaluate.iter_progressive_val_score will now also produce a report once the last sample has been processed, in addition to every print_every steps.

feature_extraction

  • feature_extraction.BagOfWords now outputs a dictionary, and not a collections.Counter.

forest

  • Created this new module to host all models based on an ensemble of decision trees.
  • Moved ensemble.AdaptiveRandomForestClassifier to forest.ARFClassifier.
  • Moved ensemble.AdaptiveRandomForestRegressor to forest.ARFRegressor.
  • Added forest.AMFClassifier.
  • Added forest.OXTRegressor.

linear_model

  • Renamed use_dist to with_dist in linear_model.BayesianLinearRegression's predict_one method.

multiclass

  • Added a coding_method method to multiclass.OCC to control how the codes are randomly generated.

multioutput

  • Added MultiClassEncoder to convert multi-label tasks into multi-class problems.

preprocessing

  • Renamed alpha to fading_factor in preprocessing.AdaptiveStandardScaler.

rules

  • Renamed alpha to fading_factor in rules.AMRules.

sketch

  • Renamed alpha to fading_factor in sketch.HeavyHitters.

stats

  • Renamed alpha to fading_factor in stats.Entropy.
  • Renamed alpha to fading_factor in stats.EWMean.
  • Renamed alpha to fading_factor in stats.EWVar.

stream

  • Upgraded stream.iter_sql to SQLAlchemy 2.0.

tree

  • Remove LabelCombinationHoeffdingTreeClassifier. New code should use multioutput.MulticlassEncoder instead.

utils

  • Removed artifacts from the merger.