Scikit-multilearn

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A new feature release:
- first python implementation of multi-label SVM (MLTSVM)
- a general multi-label embedding framework with several embedders supported (LNEMLC, CLEMS)
- balanced k-means clusterer from HOMER implemented
- wrapper for Keras model use in scikit-multilearn

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Fix a lot of bugs and generally improve stability, cross-platform functionality standard
and unit test coverage. This release has been tested with a large set of unit tests that
work across Windows

Also, new features:
- multi-label stratification algorithm and stratification quality measures
- a robust reorganization of label space division, alongside with a working stochastic blockmodel approach and new
underlying layer - graph builders that allow using graph models for dividing the label space based not just on
label co-occurence but on any kind of network relationships between labels you can come up with
- meka wrapper works fully cross-platform now, including windows 10
- multi-label data set downloading and load/save functionality brought in, like sklearn's dataset
- kNN models support sparse input
- MLARAM models support sparse input
- BSD-compatible label space partitioning via NetworkX
- dependence on GPL libraries made optional
- working predict_proba added for label space partitioning methods
- MLARAM moved to from neurofuzzy to adapt
- test coverage increased to 94%
- Classifier Chains allow specifying the chain order
- lots of documentation updates

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- a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
- support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
- support for label powerset based stratified kfold added
- graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
- bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

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- *kNN classifiers support sparse matrices properly
- support for the new model_selection API from scikit-learn
- extended graph-based label space clusteres to allow taking probability of a label occuring alone into consideration
- compatible with newest graphtool
- support the case when meka decides that an observation doesn't have any labels assigned
- HARAM classifier provided by Fernando Benitez from University of Konstanz
- predict_proba added to problem transformation classifiers
- ported to python 3

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- support for new multi-label classification methods:
- classsifier chains (CC)
- multi-label kNN methods: BRkNN and MLkNN
- all classifiers use sparse matrices internally
- a general network for clustering label space with a flat classifier
- the classifiers work with scikit pipelines / CVs

- interface to meka 1.9, meka can work as a scikit-ml classifier
- loading arff files to sparse matrices by default

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- support for new multi-label classification methods:
- classsifier chains (CC)
- multi-label kNN methods: BRkNN and MLkNN
- all classifiers use sparse matrices internally
- a general network for clustering label space with a flat classifier
- the classifiers work with scikit pipelines / CVs