Project details for scikit multilearn

Logo scikit multilearn 0.0.5

by niedakh - February 25, 2017, 03:51:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

view (4 today), download ( 2 today ), 3 subscriptions

Description:

scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python implementations of popular multi-label classification methods alongside novel framework for label space partitioning and division. It includes graph-based community detection methods that utilize the powerful igraph library for extracting label dependency information. In addition its code is well test covered and follows PEP8. Source code and documentation can be downloaded from http://scikit.ml and also via pip. The library follows scikit's BSD licencing scheme.

Changes to previous version:
  • 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
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Arff, Numpy, Scipy
Tags: Machine Learning, Large Datasets, Multi Label
Archive: download here

Other available revisons

Version Changelog Date
0.0.5
  • 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
February 25, 2017, 03:51:59
0.0.4

*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

February 15, 2017, 21:11:40
0.0.3

Initial Announcement on mloss.org.

June 15, 2016, 19:28:32

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.