Package: yy-scikit-multilearn
scikit-multilearnscikit-multilearnis a Python module capable of performing multi-label
learning tasks. It is built on-top of various scientific Python packages
(numpy,scipy) and
follows a similar API to that ofscikit-learn.Website:scikit.mlDocumentation:scikit-multilearn DocumentationFeaturesNative Python implementation.A native Python implementation for a variety of multi-label classification algorithms. To see the list of all supported classifiers, check thislink.Interface to Meka.A Meka wrapper class is implemented for reference purposes and integration. This provides access to all methods available in MEKA, MULAN, and WEKA — the reference standard in the field.Builds upon giants!Team-up with the power of numpy and scikit. You can use scikit-learn's base classifiers as scikit-multilearn's classifiers. In addition, the two packages follow a similar API.DependenciesIn most cases you will want to follow the requirements defined in the requirements/*.txt files in the package.Base dependenciesscipy
numpy
future
scikit-learn
liac-arff # for loading ARFF files
requests # for dataset module
networkx # for networkX base community detection clusterers
python-louvain # for networkX base community detection clusterers
kerasGPL-incurring dependencies for two clustererspython-igraph # for igraph library based clusterers
python-graphtool # for graphtool base clusterersNote: Installing graphtool is complicated, please see:graphtool install instructionsInstallationTo install scikit-multilearn, simply type the following command:$pipinstallscikit-multilearnThis will install the latest release from the Python package index. If you
wish to install the bleeding-edge version, then clone this repository and
runsetup.py:$gitclonehttps://github.com/scikit-multilearn/scikit-multilearn.git
$cdscikit-multilearn
$pythonsetup.pyBasic UsageBefore proceeding to classification,  this library assumes that you have
a dataset with the following matrices:x_train,x_test: training and test feature matrices of size(n_samples, n_features)y_train,y_test: training and test label matrices of size(n_samples, n_labels)Suppose we wanted to use a problem-transformation method called Binary
Relevance, which treats each label as a separate single-label classification
problem, to a Support-vector machine (SVM) classifier, we simply perform
the following tasks:# Import BinaryRelevance from yyskmultilearnfromyyskmultilearn.problem_transformimportBinaryRelevance# Import SVC classifier from sklearnfromsklearn.svmimportSVC# Setup the classifierclassifier=BinaryRelevance(classifier=SVC(),require_dense=[False,True])# Trainclassifier.fit(X_train,y_train)# Predicty_pred=classifier.predict(X_test)More examples and use-cases can be seen in thedocumentation. For using the MEKA
wrapper, check thislink.ContributingThis project is open for contributions. Here are some of the ways for
you to contribute:Bug reports/fixFeatures requestsUse-case demonstrationsDocumentation updatesIn case you want to implement your own multi-label classifier, please
read ourDeveloper's Guideto help
you integrate your implementation in our API.To make a contribution, just fork this repository, push the changes
in your fork, open up an issue, and make a Pull Request!We're also available in Slack! Just go to ourslack group.CiteIf you used scikit-multilearn in your research or project, please
citeour work:@ARTICLE{2017arXiv170201460S,author={{Szyma{\'n}ski}, P. and {Kajdanowicz}, T.},title="{A scikit-based Python environment for performing multi-label classification}",journal={ArXiv e-prints},archivePrefix="arXiv",eprint={1702.01460},year=2017,month=feb}