Skip to main content

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

[![PyPI version](https://badge.fury.io/py/scikit-multilearn.svg)](https://badge.fury.io/py/scikit-multilearn)
[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
[![Build Status Linux and OSX](https://travis-ci.org/scikit-multilearn/scikit-multilearn.svg?branch=master)](https://travis-ci.org/scikit-multilearn/scikit-multilearn)
[![Build Status Windows](https://ci.appveyor.com/api/projects/status/vd4k18u1lp5btaql/branch/master?svg=true)](https://ci.appveyor.com/project/niedakh/scikit-multilearn/branch/master)

__scikit-multilearn__ is a Python module capable of performing multi-label
learning tasks. It is built on-top of various scientific Python packages
([numpy](http://www.numpy.org/), [scipy](https://www.scipy.org/)) and
follows a similar API to that of [scikit-learn](http://scikit-learn.org/).

- __Website:__ [scikit.ml](http://scikit.ml)
- __Documentation:__ [scikit-multilearn Documentation](http://scikit.ml/api/skmultilearn.html)


## 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](http://scikit.ml/#classifiers).

- __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
```
scipy
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
keras
```

### 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](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions)

## Installation

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

```bash
$ 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 `setup.py`:

```bash
$ git clone https://github.com/scikit-multilearn/scikit-multilearn.git
$ cd scikit-multilearn
$ python setup.py
```

## 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:

```python
# 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
classifier.fit(X_train, y_train)

# Predict
y_pred = classifier.predict(X_test)
```

More examples and use-cases can be seen in the
[documentation](http://scikit.ml/api/classify.html). For using the MEKA
wrapper, check this [link](http://scikit.ml/api/meka.html#mekawrapper).

## 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](http://scikit.ml/api/base.html) 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](https://scikit-ml.slack.com/).

## Cite

If you used scikit-multilearn in your research or project, please
cite [our work](https://arxiv.org/abs/1702.01460):

```bibtex
@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
}
```


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-multilearn-0.2.0.linux-x86_64.tar.gz (113.7 kB view hashes)

Uploaded Source

Built Distributions

scikit_multilearn-0.2.0-py3-none-any.whl (89.4 kB view hashes)

Uploaded Python 3

scikit_multilearn-0.2.0-py2-none-any.whl (89.4 kB view hashes)

Uploaded Python 2

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page