modAL 0.3.1

Release notes

The new release of modAL is here! This is a milestone in its evolution, because it has just received its first contributions from the open source community! :) Thanks for @dataframing and @nikolay-bushkov for their work! Hoping to see many more contributions from the community, because modAL still has a long way to go! :)

New features

• Ranked batch mode queries by @dataframing. With this query strategy, several instances can be queried for labeling, which alleviates a lot of problems in uncertainty sampling. For details, see Ranked batch mode learning by Cardoso et al.
• Sparse matrix support by @nikolay-bushkov. From now, if the estimator can handle sparse matrices, you can use them to fit the active learning models!
• Cold start support has been added to all the models. This means that now learner.query() can be used without training the model first.

Changes

• The documentation has gone under a major refactoring thanks to @nikolay-bushkov! Type annotations have been added and the docstrings were refactored to follow Google style docstrings. The website has been changed accordingly. Instead of GitHub pages, ReadTheDocs are used and the old website is merged with the API reference. Regarding the examples, Jupyter notebooks were added by @dataframing.
  For details, check it out at https://modAL-python.github.io/!
• .query() methods changed for BaseLearner and BaseCommittee to allow more general arguments for query strategies. Now it can accept any argument as long as the query_strategy function supports it.
• .score() method was added for Committee. Fixes #6.
• The modAL.density module was refactored using functions from sklearn.metrics.pairwise. This resulted in a major increase in performance as well as a more sustainable codebase for the module.

Bugfixes

• 1D array handling issues fixed, numpy.vstack calls replaced with numpy.concatenate.
  Fixes #15.
• np.sum(generator) calls were replaced with np.sum(np.from_iter(generator)) because deprecation of the original one.