scikit-multilearn-ng

scikit-multilearn-ng is a Python module capable of performing multi-label learning tasks and is the follow-up to scikit-multilearn. It is built on-top of various scientific Python packages (numpy, scipy) and follows a similar API to that of scikit-learn.

More documentation:

• Website: 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.

• 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:

$ 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:

$ 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:

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

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:

# 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.

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!

Cite

If you used scikit-multilearn-ng in your research or project, please cite the original package scikit-multilearn:

@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
}