modAL 0.2.1
Pre-release
Pre-release
Release notes
New features
modAL.utils.combination.make_query_strategyfunction factory to make the implementation of custom query strategies easier.ActiveLearnerandCommitteemodels can be fitted using new data only by passingonly_new=Trueto their.teach()methods. This is useful when working with models where the fitting does not occur from scratch, for instance tensorflow or keras models.
Fixes
- Checks added to
modAL.utils.selection.weighted_random()to avoid division with zero. - ABC metaclassing now compatible with earlier Python versions (i.e. Python 2.7). Fixes #3 .
sklearn.utils.check_arraycalls removed frommodAL.models, performing checks now up to the estimator. As a consequence, images doesn't need to be flattened. Fixes #5 .BaseCommitteenow inherits fromsklearn.base.BaseEstimator.modAL.utils.combination.make_linear_combinationrewritten using genexps, resulting in performance increase.