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https://github.com/scikit-multilearn-ng/scikit-multilearn-ng

A new maintained "successor" to scikit-multilearn, a scikit-learn based module for multi-label et. al. classification
https://github.com/scikit-multilearn-ng/scikit-multilearn-ng

classification clustering label-prediction machine-learning multi-label multi-label-classification partitioning scikit scikit-learn scikit-multilearn

Last synced: 24 days ago
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A new maintained "successor" to scikit-multilearn, a scikit-learn based module for multi-label et. al. classification

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README 

        
# scikit-multilearn-ng



[![PyPI version](https://badge.fury.io/py/scikit-multilearn-ng.svg)](https://badge.fury.io/py/scikit-multilearn-ng)

[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)



__scikit-multilearn-ng__ is a Python module capable of performing multi-label

learning tasks and is the follow-up to [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn).

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/).



More documentation:



- __Website:__ [https://scikit-multilearn-ng.github.io/scikit-multilearn-ng/](https://scikit-multilearn-ng.github.io/scikit-multilearn-ng/)



## 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-ng

```



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-ng/scikit-multilearn-ng.git

$ cd scikit-multilearn-ng

$ python setup.py

```



In most cases requirements are installed when you install using `pip install scikit-multilearn-ng` or run `python setup.py install`. There are also optional dependencies `pip install scikit-multilearn-ng[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



**Note: You should use the same import statement as previously with scikit-multilearn (`import skmultilearn`), after installation. This allows for quicker switching to this follow-up version.**



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](https://scikit-multilearn-ng.github.io/scikit-multilearn-ng/_static/example_usage.html).



## 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!



## Cite



If you used scikit-multilearn-ng in your research or project, please

cite [the original package scikit-multilearn](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

}

```