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

Project description

# scikit-multilearn

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__scikit-multilearn__ is 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 of [scikit-learn](

- __Website:__ [](
- __Documentation:__ [scikit-multilearn Documentation](

## Features

- __Native Python implementation.__ A native Python implementation for a variety of multi-label classification algorithms. To see the list of all supported classifiers, check this [link](

- __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.

## Dependencies

In most cases you will want to follow the requirements defined in the requirements/*.txt files in the package.

### Base dependencies
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

### GPL-incurring dependencies for two clusterers
python-igraph # for igraph library based clusterers
python-graphtool # for graphtool base clusterers

Note: Installing graphtool is complicated, please see: [graphtool install instructions](

## Installation

To install scikit-multilearn, simply type the following command:

$ pip install scikit-multilearn

This will install the latest release from the Python package index. If you
wish to install the bleeding-edge version, then clone this repository and
run ``:

$ git clone
$ cd scikit-multilearn
$ python

## Basic Usage

Before 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 skmultilearn
from skmultilearn.problem_transform import BinaryRelevance

# Import SVC classifier from sklearn
from sklearn.svm import SVC

# Setup the classifier
classifier = BinaryRelevance(classifier=SVC(), require_dense=[False,True])

# Train, y_train)

# Predict
y_pred = classifier.predict(X_test)

More examples and use-cases can be seen in the
[documentation]( For using the MEKA
wrapper, check this [link](

## Contributing

This project is open for contributions. Here are some of the ways for
you to contribute:

- Bug reports/fix
- Features requests
- Use-case demonstrations
- Documentation updates

In case you want to implement your own multi-label classifier, please
read our [Developer's Guide]( to 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 our [slack group](

## Cite

If you used scikit-multilearn in your research or project, please
cite [our work](

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

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