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https://github.com/scikit-multilearn/scikit-multilearn
A scikit-learn based module for multi-label et. al. classification
https://github.com/scikit-multilearn/scikit-multilearn
classification clustering label-prediction machine-learning multi-label partitioning scikit scikit-learn scikit-multilearn
Last synced: 2 days ago
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A scikit-learn based module for multi-label et. al. classification
- Host: GitHub
- URL: https://github.com/scikit-multilearn/scikit-multilearn
- Owner: scikit-multilearn
- License: bsd-2-clause
- Created: 2014-04-30T13:05:44.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-02-01T04:40:03.000Z (12 months ago)
- Last Synced: 2025-01-03T00:06:44.866Z (9 days ago)
- Topics: classification, clustering, label-prediction, machine-learning, multi-label, partitioning, scikit, scikit-learn, scikit-multilearn
- Language: Python
- Homepage: http://scikit.ml
- Size: 44.2 MB
- Stars: 923
- Watchers: 33
- Forks: 182
- Open Issues: 90
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
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README
# scikit-multilearn
[](https://badge.fury.io/py/scikit-multilearn)
[](https://opensource.org/licenses/BSD-2-Clause)
__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.
## Installation & Dependencies
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
```
In most cases requirements are installed when you install using `pip install scikit-multilearn` or run `python setup.py install`. There are also optional dependencies `pip install scikit-multilearn[gpl,keras,meka]` installs the GPL-incurring igraph for for igraph library based clusterers, keras for the keras classifiers and requirements for the meka bridge respectively.
To install `openNE`, run:
```bash
pip install 'openne @ git+https://github.com/thunlp/OpenNE.git@master#subdirectory=src'
```
Note that installing the GPL licensed graphtool, for graphtool based clusters, is complicated, and must be done manually, please see: [graphtool install instructions](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions)
## 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
}
```