scikit-multilearn API Reference
- Classifiers and tools

scikit-multilearn API Reference¶

Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem.

Classifiers and tools¶

Algorithm Adaptation approaches¶

The skmultilearn.adapt module implements algorithm adaptation approaches to multi-label classification.

Algorithm adaptation methods for multi-label classification concentrate on adapting single-label classification algorithms to the multi-label case usually by changes in cost/decision functions.

Currently the following algorithm adaptation classification schemes are available in scikit-multilearn:

Classifier	Description
`BRkNNaClassifier`	a Binary Relevance kNN classifier that assigns a label if at least half of the neighbors are also classified with the label
`BRkNNbClassifier`	a Binary Relevance kNN classifier that assigns top m labels of neighbors with m - average number of labels assigned to neighbors
`MLkNN`	a multi-label adapted kNN classifier with bayesian prior corrections
`MLARAM`	a multi-Label Hierarchical ARAM Neural Network
`MLTSVM`	twin multi-Label Support Vector Machines

Problem Transformation approaches¶

The skmultilearn.problem_transform module provides classifiers that follow the problem transformation approaches to multi-label classification.

The problem transformation approach to multi-label classification converts multi-label problems to single-label problems: single-class or multi-class.

Classifier	Description
`BinaryRelevance`	treats each label as a separate single-class classification problem
`ClassifierChain`	treats each label as a part of a conditioned chain of single-class classification problems
`LabelPowerset`	treats each label combination as a separate class with one multi-class classification problem

Multi-label embeddings¶

The skmultilearn.embedding module provides implementations of label space embedding methods and a general embedding based classifier.

Name	Description
`CLEMS`	Cost-Sensitive Label Embedding with Multidimensional Scaling
`OpenNetworkEmbedder`	Label Network Embedding for Multilabel Classification
`SKLearnEmbedder`	Wrapper for scikit-learn embedders
`EmbeddingClassifier`	A general embedding-based classifier

Ensembles of classifiers¶

The skmultilearn.ensemble module implements ensemble classification schemes that construct an ensemble of base multi-label classifiers.

Currently the following ensemble classification schemes are available in scikit-multilearn:

Classifier name	Description
`RakelD`	Distinct RAndom k-labELsets multi-label classifier
`RakelO`	Overlapping RAndom k-labELsets multi-label classifier.
`LabelSpacePartitioningClassifier`	a label space partitioning classifier that trains a classifier per label subspace as clustered using methods from `skmultilearn.cluster`.
`MajorityVotingClassifier`	a label space division classifier that trains a classifier per label subspace as clustered using methods from `skmultilearn.cluster` and assign labels if the majority of classifiers that contain the label agree on the assignment.

Label Space Clusterers¶

The skmultilearn.cluster module gathers label space clustering methods.

Name	Description
`FixedLabelSpaceClusterer`	Return a predefined fixed clustering, usually driven by expert knowledge
`MatrixLabelSpaceClusterer`	Cluster the label space using a scikit-compatible matrix-based clusterer
`GraphToolLabelGraphClusterer`	Fits a Stochastic Block Model to the Label Graph and infers the communities
`StochasticBlockModel`	A Stochastic Blockmodel class
`IGraphLabelGraphClusterer`	Clusters label space using igraph community detection
`RandomLabelSpaceClusterer`	Randomly divides label space into equally-sized clusters
`NetworkXLabelGraphClusterer`	Cluster label space with NetworkX community detection

External classifiers¶

The skmultilearn.ext provides wrappers for other multi-label classification libraries. Currently it provides a wrapper for:

Currently the available classes include:

Name	Description
`Meka`	Wrapper for the Multilabel Extension to WEKA - MEKA library
`Keras`	Wrapper for the Python Deep Learning library - KERAS
`download_meka()`	Helper function for installing MEKA

Model selection and data manipulation¶

The skmultilearn.model_selection module provides implementations multi-label stratification methods useful for parameter estimation.

Currently the available modules include:

Name	Description
`iterative_stratification`	Iterative stratification
`measures`	Stratification quality measures package