| Name | scikit-multilearn JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | http://scikit.ml/ |
| Summary | Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem. |
| upload_time | 2018-12-10 16:24:47 |
| maintainer | |
| docs_url | None |
| author | Piotr Szymański |
| requires_python | |
| license | BSD |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# scikit-multilearn
[](https://badge.fury.io/py/scikit-multilearn)
[](https://opensource.org/licenses/BSD-2-Clause)
[](https://travis-ci.org/scikit-multilearn/scikit-multilearn)
[](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
}
```
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"description": "# scikit-multilearn\n\n[](https://badge.fury.io/py/scikit-multilearn)\n[](https://opensource.org/licenses/BSD-2-Clause)\n[](https://travis-ci.org/scikit-multilearn/scikit-multilearn)\n[](https://ci.appveyor.com/project/niedakh/scikit-multilearn/branch/master)\n\n__scikit-multilearn__ is a Python module capable of performing multi-label\nlearning tasks. It is built on-top of various scientific Python packages\n([numpy](http://www.numpy.org/), [scipy](https://www.scipy.org/)) and\nfollows a similar API to that of [scikit-learn](http://scikit-learn.org/).\n\n- __Website:__ [scikit.ml](http://scikit.ml)\n- __Documentation:__ [scikit-multilearn Documentation](http://scikit.ml/api/skmultilearn.html)\n\n\n## Features\n\n- __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).\n\n- __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.\n\n- __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.\n\n## Dependencies\n\nIn most cases you will want to follow the requirements defined in the requirements/*.txt files in the package. \n\n### Base dependencies\n```\nscipy\nnumpy\nfuture\nscikit-learn\nliac-arff # for loading ARFF files\nrequests # for dataset module\nnetworkx # for networkX base community detection clusterers\npython-louvain # for networkX base community detection clusterers\nkeras\n```\n\n### GPL-incurring dependencies for two clusterers\n```\npython-igraph # for igraph library based clusterers\npython-graphtool # for graphtool base clusterers\n```\n\nNote: Installing graphtool is complicated, please see: [graphtool install instructions](https://git.skewed.de/count0/graph-tool/wikis/installation-instructions)\n\n## Installation\n\nTo install scikit-multilearn, simply type the following command:\n\n```bash\n$ pip install scikit-multilearn\n```\n\nThis will install the latest release from the Python package index. If you\nwish to install the bleeding-edge version, then clone this repository and\nrun `setup.py`:\n\n```bash\n$ git clone https://github.com/scikit-multilearn/scikit-multilearn.git\n$ cd scikit-multilearn\n$ python setup.py\n```\n\n## Basic Usage\n\nBefore proceeding to classification, this library assumes that you have\na dataset with the following matrices:\n\n- `x_train`, `x_test`: training and test feature matrices of size `(n_samples, n_features)`\n- `y_train`, `y_test`: training and test label matrices of size `(n_samples, n_labels)`\n\nSuppose we wanted to use a problem-transformation method called Binary\nRelevance, which treats each label as a separate single-label classification\nproblem, to a Support-vector machine (SVM) classifier, we simply perform\nthe following tasks:\n\n```python\n# Import BinaryRelevance from skmultilearn\nfrom skmultilearn.problem_transform import BinaryRelevance\n\n# Import SVC classifier from sklearn\nfrom sklearn.svm import SVC\n\n# Setup the classifier\nclassifier = BinaryRelevance(classifier=SVC(), require_dense=[False,True])\n\n# Train\nclassifier.fit(X_train, y_train)\n\n# Predict\ny_pred = classifier.predict(X_test)\n```\n\nMore examples and use-cases can be seen in the \n[documentation](http://scikit.ml/api/classify.html). For using the MEKA\nwrapper, check this [link](http://scikit.ml/api/meka.html#mekawrapper).\n\n## Contributing\n\nThis project is open for contributions. Here are some of the ways for\nyou to contribute:\n\n- Bug reports/fix\n- Features requests\n- Use-case demonstrations\n- Documentation updates\n\nIn case you want to implement your own multi-label classifier, please \nread our [Developer's Guide](http://scikit.ml/api/base.html) to help\nyou integrate your implementation in our API.\n\nTo make a contribution, just fork this repository, push the changes\nin your fork, open up an issue, and make a Pull Request!\n\nWe're also available in Slack! Just go to our [slack group](https://scikit-ml.slack.com/).\n\n## Cite\n\nIf you used scikit-multilearn in your research or project, please\ncite [our work](https://arxiv.org/abs/1702.01460):\n\n```bibtex\n@ARTICLE{2017arXiv170201460S,\n author = {{Szyma{\\'n}ski}, P. and {Kajdanowicz}, T.},\n title = \"{A scikit-based Python environment for performing multi-label classification}\",\n journal = {ArXiv e-prints},\n archivePrefix = \"arXiv\",\n eprint = {1702.01460},\n year = 2017,\n month = feb\n}\n```\n\n\n",
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