Version 0.6.2#
February 16, 2020
This is a bug-fix release to resolve some issues regarding the handling the input and the output format of the arrays.
Changelog#
Allow column vectors to be passed as targets. #673 by Christos Aridas.
Better input/output handling for pandas, numpy and plain lists. #681 by Christos Aridas.
Version 0.6.1#
December 7, 2019
This is a bug-fix release to primarily resolve some packaging issues in version 0.6.0. It also includes minor documentation improvements and some bug fixes.
Changelog#
Bug fixes#
Fix a bug in
imblearn.ensemble.BalancedRandomForestClassifierleading to a wrong number of samples used during fitting duemax_samplesand therefore a bad computation of the OOB score. #656 by Guillaume Lemaitre.
Version 0.6.0#
December 5, 2019
Changelog#
Changed models#
The following models might give some different sampling due to changes in scikit-learn:
The following samplers will give different results due to change linked to the random state internal usage:
Bug fixes#
imblearn.under_sampling.InstanceHardnessThresholdnow take into account therandom_stateand will give deterministic results. In addition,cross_val_predictis used to take advantage of the parallelism. #599 by Shihab Shahriar Khan.Fix a bug in
imblearn.ensemble.BalancedRandomForestClassifierleading to a wrong computation of the OOB score. #656 by Guillaume Lemaitre.
Maintenance#
Update imports from scikit-learn after that some modules have been privatize. The following import have been changed:
sklearn.ensemble._base._set_random_states,sklearn.ensemble._forest._parallel_build_trees,sklearn.metrics._classification._check_targets,sklearn.metrics._classification._prf_divide,sklearn.utils.Bunch,sklearn.utils._safe_indexing,sklearn.utils._testing.assert_allclose,sklearn.utils._testing.assert_array_equal,sklearn.utils._testing.SkipTest. #617 by Guillaume Lemaitre.Synchronize
imblearn.pipelinewithsklearn.pipeline. #620 by Guillaume Lemaitre.Synchronize
imblearn.ensemble.BalancedRandomForestClassifierand add parametersmax_samplesandccp_alpha. #621 by Guillaume Lemaitre.
Enhancement#
imblearn.under_sampling.RandomUnderSampling,imblearn.over_sampling.RandomOverSampling,imblearn.datasets.make_imbalanceaccepts Pandas DataFrame in and will output Pandas DataFrame. Similarly, it will accepts Pandas Series in and will output Pandas Series. #636 by Guillaume Lemaitre.imblearn.FunctionSampleraccepts a parametervalidateallowing to check or not the inputXandy. #637 by Guillaume Lemaitre.imblearn.under_sampling.RandomUnderSampler,imblearn.over_sampling.RandomOverSamplercan resample when non finite values are present inX. #643 by Guillaume Lemaitre.All samplers will output a Pandas DataFrame if a Pandas DataFrame was given as an input. #644 by Guillaume Lemaitre.
The samples generation in
imblearn.over_sampling.ADASYN,imblearn.over_sampling.SMOTE,imblearn.over_sampling.BorderlineSMOTE,imblearn.over_sampling.SVMSMOTE,imblearn.over_sampling.KMeansSMOTE,imblearn.over_sampling.SMOTENCis now vectorize with giving an additional speed-up whenXin sparse. #596 and #649 by Matt Eding.
Deprecation#
The following classes have been removed after 2 deprecation cycles:
ensemble.BalanceCascadeandensemble.EasyEnsemble. #617 by Guillaume Lemaitre.The following functions have been removed after 2 deprecation cycles:
utils.check_ratio. #617 by Guillaume Lemaitre.The parameter
ratioandreturn_indiceshas been removed from all samplers. #617 by Guillaume Lemaitre.The parameters
m_neighbors,out_step,kind,svm_estimatorhave been removed from theimblearn.over_sampling.SMOTE. #617 by Guillaume Lemaitre.