Scikit-multilearn-ng

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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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General bug fixes and improvements including:
- Add sparse support for PCT
- Add .classes_ to classifiers to fix GridSearchCV and Pipeline compatibility

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Major enhancements and bug fixes include:
- First implementation of Predictive Clustering Trees (PCT)
- Probabilistic Classifier Chains (PCC) implementation for multi-label classification
- Fixed issue in iterative_train_test_split.

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Fixes a lot of bugs, improves stability and adds features, which include:
- Classification with heterogeneous features
- Structured GridSearchCV
- Combining Instance-Based Learning and Logistic Regression
- SMiLE algorithm for multi label with missing labels (which adds the skmultilearn.missing submodule)


scikit-multilearn Changelog
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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