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diff --git a/python-scikit-multilearn.spec b/python-scikit-multilearn.spec new file mode 100644 index 0000000..79dcf67 --- /dev/null +++ b/python-scikit-multilearn.spec @@ -0,0 +1,486 @@ +%global _empty_manifest_terminate_build 0 +Name: python-scikit-multilearn +Version: 0.2.0 +Release: 1 +Summary: Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem. +License: BSD +URL: http://scikit.ml/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fc/57/4c8951d3613c1cd569910bc3ddd5b3a755ad383297a12004eb2d61eefc06/scikit-multilearn-0.2.0.linux-x86_64.tar.gz +BuildArch: noarch + + +%description +# 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 +} +``` + + + + +%package -n python3-scikit-multilearn +Summary: Scikit-multilearn is a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem. +Provides: python-scikit-multilearn +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-scikit-multilearn +# 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 +} +``` + + + + +%package help +Summary: Development documents and examples for scikit-multilearn +Provides: python3-scikit-multilearn-doc +%description help +# 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 +} +``` + + + + +%prep +%autosetup -n scikit-multilearn-0.2.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-scikit-multilearn -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.0-1 +- Package Spec generated |