Release History

Release notes for current and recent releases are detailed on this page.

Note

Legends for changelog entries

  • Major Feature: something big that you couldn’t do before.

  • Feature: something that you couldn’t do before.

  • Efficiency: an existing feature now may not require as much computation or memory.

  • Enhancement: a miscellaneous minor improvement.

  • Fix: something that previously didn’t work as documented – or according to reasonable expectations – should now work.

  • API Change: you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

Version 0.4.1

Sep 2019

  • Fix Fix bug in the calculation of Precision and Recall which affects the F1 and Geometric-mean scores. The bug was only triggered by a specific ordering of the class-values.

Version 0.4.0

Sep 2019

Version 0.3.0

May 2019

  • Feature Very Fast Decision Rules classifier rules.VFDR

  • Feature Online AdaC2 ensemble classifier meta.OnlineAdaC2

  • Feature Online Boosting ensemble classifier meta.OnlineBoosting

  • Feature Online CSB2 ensemble classifier meta.OnlineCSB2

  • Feature Online RUS Boost ensemble classifier meta.OnlineRUSBoost

  • Feature Online SMOTE Bagging ensemble classifier meta.OnlineSMOTEBagging

  • Feature Online Under Over Bagging ensemble classifier meta.OnlineUnderOverBagging

  • Enhancement Project documentation overhaul. Improved documentation layout to ease navigation. Documentation for multiple methods were revisited and corrections/extensions were included. Multiple typos fixed. A map of methods in scikit-multiflow is added to help users navigate the project.

  • Fix Update lazy.SAMKNN since changing a dictionary inside a loop now raises a RunTimeError (Python 3.7+).

  • Fix Fix bug in meta.LeverageBagging, use set(self.classes) instead of self.classes.

  • Fix Fix bug in meta.OzaBagging, now re-sampling is correctly calculated for each sample (instance). There was a corner case were this value was incorrectly calculated for batches of samples.

  • API Change Rename weight to sample_weight in partial_fit and fit abstract method (inherited by all estimators in scikit-multiflow)

  • API Change Rename cat_features_idx to cat_features and added new attribute name in data.DataStream

  • API Change Rename cat_features_idx to cat_features in data.FileStream

  • API Change Rename min_num_instances to min_instances in drift_detection.PageHinkley

  • API Change Rename categorical_list to nominal_attributes in lazy.KNN

  • API Change New base class core.BaseSKMObject for objects in scikit-multiflow. This class is based on sklearn.BaseEstimator for inter-framework compatibility and adds extra functionality relevant in the context of scikit-multiflow. Stream models (estimators) in are now created by extending core.BaseSKMObject and the corresponding task-specific mixin(s): core.ClassifierMixin, core.RegressorMixin, core.MetaEstimatorMixin, core.MultiOutputMixin

Version 0.2.0

April 2019

Version 0.1.4

April 2019

  • Feature Accuracy Weighted Ensemble classifier meta.AccuracyWeightedEnsemble

  • Enhancement Improvements and bug fixes in drift detection methods

  • Enhancement Improved examples/documentation for docker image

  • Enhancement Misc bug fixes

Version 0.1.3

March 2019

  • Feature Learn++.NSE ensemble classifier meta.LearnNSE

  • Feature Hoeffding Anytime Tree or Extremely Fast Decision Tree classifier trees.HATT

  • Efficiency Naive Bayes bayes.NaiveBayes

  • Efficiency Minor performance improvements

  • Enhancement Misc bug fixes

Version 0.1.2

February 2019

  • Feature Learn++ ensemble classifier meta.LearnPP

  • Efficiency Improve performance of evaluators when saving all predictions into a file

  • Enhancement Misc bug fixes

Version 0.1.0

January 2019

  • Initial release of scikit-multiflow.

  • From this point the package is available via PyPI.