automatic import of python-divik

3 files changed, 571 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..88c25b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/divik-3.2.2.tar.gz
diff --git a/python-divik.spec b/python-divik.spec
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--- /dev/null
+++ b/python-divik.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-divik
+Version:	3.2.2
+Release:	1
+Summary:	Divisive iK-means algorithm implementation
+License:	Apache-2.0
+URL:		https://github.com/gmrukwa/divik
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ee/5f/5955b3a724fa5d93a532c8d1af8535dd6377a4897c3e5f7fb1f352d8e3ba/divik-3.2.2.tar.gz
+
+Requires:	python3-dask-distance
+Requires:	python3-dask[dataframe]
+Requires:	python3-gin-config
+Requires:	python3-h5py
+Requires:	python3-importlib-metadata
+Requires:	python3-joblib
+Requires:	python3-kneed
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-polyaxon
+Requires:	python3-scikit-image
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-tqdm
+
+%description
+[![CodeFactor](https://www.codefactor.io/repository/github/gmrukwa/divik/badge)](https://www.codefactor.io/repository/github/gmrukwa/divik)
+[![Maintainability](https://api.codeclimate.com/v1/badges/4cf5d42d0a0076c38445/maintainability)](https://codeclimate.com/github/gmrukwa/divik/maintainability)
+![](https://github.com/gmrukwa/divik/workflows/Build%20and%20push%20deployment%20images/badge.svg)
+![](https://github.com/gmrukwa/divik/workflows/Run%20unit%20tests/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/divik/badge/?version=latest)](https://divik.readthedocs.io/en/latest/?badge=latest)
+
+# divik
+
+Python implementation of Divisive iK-means (DiviK) algorithm.
+
+## Tools within this package
+
+- Clustering at your command line with fit-clusters
+- Set of algorithm implementations for unsupervised analyses
+  - Clustering
+    - DiviK - hands-free clustering method with built-in feature selection
+    - K-Means with Dunn method for selecting the number of clusters
+    - K-Means with GAP index for selecting the number of clusters
+    - Modular K-Means implementation with custom distance metrics and initializations
+  - Feature extraction
+    - PCA with knee-based components selection
+    - Locally Adjusted RBF Spectral Embedding
+  - Feature selection
+    - EXIMS
+    - Gaussian Mixture Model based data-driven feature selection
+      - High Abundance And Variance Selector - allows you to select highly variant features above noise level, based on GMM-decomposition
+    - Outlier based Selector
+      - Outlier Abundance And Variance Selector - allows you to select highly variant features above noise level, based on outlier detection
+    - Percentage based Selector - allows you to select highly variant features above noise level with your predefined thresholds for each
+  - Sampling
+    - StratifiedSampler - generates samples of fixed number of rows from given dataset
+    - UniformPCASampler - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data
+    - UniformSampler - generates samples of random observations within boundaries of an original dataset
+
+## Installation
+
+### Docker
+
+The recommended way to use this software is through
+[Docker](https://www.docker.com/). This is the most convenient way, if you want
+to use `divik` application.
+
+To install latest stable version use:
+
+```bash
+docker pull gmrukwa/divik
+```
+
+### Python package
+
+Prerequisites for installation of base package:
+
+- Python 3.7 / 3.8 / 3.9
+- compiler capable of compiling the native C code and OpenMP support
+
+#### Installation of OpenMP for Ubuntu / Debian
+
+You should have it already installed with GCC compiler, but if somehow
+not, try the following:
+
+```bash
+sudo apt-get install libgomp1
+```
+
+#### Installation of OpenMP for Mac
+
+OpenMP is available as part of LLVM. You may need to install it with conda:
+
+```bash
+conda install -c conda-forge "compilers>=1.0.4,!=1.1.0" llvm-openmp
+```
+
+#### Installation of dependencied on Mac
+
+You may see messages that some dependencies are invalid for the platform.
+It is a [known bug](https://github.com/actions/setup-python/issues/469),
+with [a workaround](https://github.com/actions/setup-python/issues/469#issuecomment-1192522949).
+
+Use:
+
+```bash
+SYSTEM_VERSION_COMPAT=0 pip install divik
+```
+
+#### DiviK Installation
+
+Having prerequisites installed, one can install latest base version of the
+package:
+
+```bash
+pip install divik
+```
+
+If you want to have compatibility with
+[`gin-config`](https://github.com/google/gin-config), you can install
+necessary extras with:
+
+```bash
+pip install divik[gin]
+```
+
+**Note:** Remember about `\` before `[` and `]` in `zsh` shell.
+
+You can install all extras with:
+
+```bash
+pip install divik[all]
+```
+
+## High-Volume Data Considerations
+
+If you are using DiviK to run the analysis that could fail to fit RAM of your
+computer, consider disabling the default parallelism and switch to
+[dask](https://dask.org/). It's easy to achieve through configuration:
+
+- set all parameters named `n_jobs` to `1`;
+- set all parameters named `allow_dask` to `True`.
+
+**Note:** Never set `n_jobs>1` and `allow_dask=True` at the same time, the
+computations will freeze due to how `multiprocessing` and `dask` handle
+parallelism.
+
+## Known Issues
+
+### Segmentation Fault
+
+It can happen if the he `gamred_native` package (part of `divik` package) was
+compiled with different numpy ABI than scikit-learn. This could happen if you
+used different set of compilers than the developers of the scikit-learn
+package.
+
+In such a case, a handler is defined to display the stack trace. If the trace
+comes from `_matlab_legacy.py`, the most probably this is the issue.
+
+To resolve the issue, consider following the installation instructions once
+again. The exact versions get updated to avoid the issue.
+
+## Contributing
+
+Contribution guide will be developed soon.
+
+Format the code with:
+
+```bash
+isort -m 3 --fgw 3 --tc .
+black -t py36 .
+```
+
+## References
+
+This software is part of contribution made by [Data Mining Group of Silesian
+University of Technology](http://www.zaed.polsl.pl/), rest of which is
+published [here](https://github.com/ZAEDPolSl).
+
+- [Mrukwa, G. and Polanska, J., 2020. DiviK: Divisive intelligent K-means for
+hands-free unsupervised clustering in biological big data. *arXiv preprint
+arXiv:2009.10706.*][1]
+
+[1]: https://arxiv.org/abs/2009.10706
+
+
+
+%package -n python3-divik
+Summary:	Divisive iK-means algorithm implementation
+Provides:	python-divik
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-divik
+[![CodeFactor](https://www.codefactor.io/repository/github/gmrukwa/divik/badge)](https://www.codefactor.io/repository/github/gmrukwa/divik)
+[![Maintainability](https://api.codeclimate.com/v1/badges/4cf5d42d0a0076c38445/maintainability)](https://codeclimate.com/github/gmrukwa/divik/maintainability)
+![](https://github.com/gmrukwa/divik/workflows/Build%20and%20push%20deployment%20images/badge.svg)
+![](https://github.com/gmrukwa/divik/workflows/Run%20unit%20tests/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/divik/badge/?version=latest)](https://divik.readthedocs.io/en/latest/?badge=latest)
+
+# divik
+
+Python implementation of Divisive iK-means (DiviK) algorithm.
+
+## Tools within this package
+
+- Clustering at your command line with fit-clusters
+- Set of algorithm implementations for unsupervised analyses
+  - Clustering
+    - DiviK - hands-free clustering method with built-in feature selection
+    - K-Means with Dunn method for selecting the number of clusters
+    - K-Means with GAP index for selecting the number of clusters
+    - Modular K-Means implementation with custom distance metrics and initializations
+  - Feature extraction
+    - PCA with knee-based components selection
+    - Locally Adjusted RBF Spectral Embedding
+  - Feature selection
+    - EXIMS
+    - Gaussian Mixture Model based data-driven feature selection
+      - High Abundance And Variance Selector - allows you to select highly variant features above noise level, based on GMM-decomposition
+    - Outlier based Selector
+      - Outlier Abundance And Variance Selector - allows you to select highly variant features above noise level, based on outlier detection
+    - Percentage based Selector - allows you to select highly variant features above noise level with your predefined thresholds for each
+  - Sampling
+    - StratifiedSampler - generates samples of fixed number of rows from given dataset
+    - UniformPCASampler - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data
+    - UniformSampler - generates samples of random observations within boundaries of an original dataset
+
+## Installation
+
+### Docker
+
+The recommended way to use this software is through
+[Docker](https://www.docker.com/). This is the most convenient way, if you want
+to use `divik` application.
+
+To install latest stable version use:
+
+```bash
+docker pull gmrukwa/divik
+```
+
+### Python package
+
+Prerequisites for installation of base package:
+
+- Python 3.7 / 3.8 / 3.9
+- compiler capable of compiling the native C code and OpenMP support
+
+#### Installation of OpenMP for Ubuntu / Debian
+
+You should have it already installed with GCC compiler, but if somehow
+not, try the following:
+
+```bash
+sudo apt-get install libgomp1
+```
+
+#### Installation of OpenMP for Mac
+
+OpenMP is available as part of LLVM. You may need to install it with conda:
+
+```bash
+conda install -c conda-forge "compilers>=1.0.4,!=1.1.0" llvm-openmp
+```
+
+#### Installation of dependencied on Mac
+
+You may see messages that some dependencies are invalid for the platform.
+It is a [known bug](https://github.com/actions/setup-python/issues/469),
+with [a workaround](https://github.com/actions/setup-python/issues/469#issuecomment-1192522949).
+
+Use:
+
+```bash
+SYSTEM_VERSION_COMPAT=0 pip install divik
+```
+
+#### DiviK Installation
+
+Having prerequisites installed, one can install latest base version of the
+package:
+
+```bash
+pip install divik
+```
+
+If you want to have compatibility with
+[`gin-config`](https://github.com/google/gin-config), you can install
+necessary extras with:
+
+```bash
+pip install divik[gin]
+```
+
+**Note:** Remember about `\` before `[` and `]` in `zsh` shell.
+
+You can install all extras with:
+
+```bash
+pip install divik[all]
+```
+
+## High-Volume Data Considerations
+
+If you are using DiviK to run the analysis that could fail to fit RAM of your
+computer, consider disabling the default parallelism and switch to
+[dask](https://dask.org/). It's easy to achieve through configuration:
+
+- set all parameters named `n_jobs` to `1`;
+- set all parameters named `allow_dask` to `True`.
+
+**Note:** Never set `n_jobs>1` and `allow_dask=True` at the same time, the
+computations will freeze due to how `multiprocessing` and `dask` handle
+parallelism.
+
+## Known Issues
+
+### Segmentation Fault
+
+It can happen if the he `gamred_native` package (part of `divik` package) was
+compiled with different numpy ABI than scikit-learn. This could happen if you
+used different set of compilers than the developers of the scikit-learn
+package.
+
+In such a case, a handler is defined to display the stack trace. If the trace
+comes from `_matlab_legacy.py`, the most probably this is the issue.
+
+To resolve the issue, consider following the installation instructions once
+again. The exact versions get updated to avoid the issue.
+
+## Contributing
+
+Contribution guide will be developed soon.
+
+Format the code with:
+
+```bash
+isort -m 3 --fgw 3 --tc .
+black -t py36 .
+```
+
+## References
+
+This software is part of contribution made by [Data Mining Group of Silesian
+University of Technology](http://www.zaed.polsl.pl/), rest of which is
+published [here](https://github.com/ZAEDPolSl).
+
+- [Mrukwa, G. and Polanska, J., 2020. DiviK: Divisive intelligent K-means for
+hands-free unsupervised clustering in biological big data. *arXiv preprint
+arXiv:2009.10706.*][1]
+
+[1]: https://arxiv.org/abs/2009.10706
+
+
+
+%package help
+Summary:	Development documents and examples for divik
+Provides:	python3-divik-doc
+%description help
+[![CodeFactor](https://www.codefactor.io/repository/github/gmrukwa/divik/badge)](https://www.codefactor.io/repository/github/gmrukwa/divik)
+[![Maintainability](https://api.codeclimate.com/v1/badges/4cf5d42d0a0076c38445/maintainability)](https://codeclimate.com/github/gmrukwa/divik/maintainability)
+![](https://github.com/gmrukwa/divik/workflows/Build%20and%20push%20deployment%20images/badge.svg)
+![](https://github.com/gmrukwa/divik/workflows/Run%20unit%20tests/badge.svg)
+[![Documentation Status](https://readthedocs.org/projects/divik/badge/?version=latest)](https://divik.readthedocs.io/en/latest/?badge=latest)
+
+# divik
+
+Python implementation of Divisive iK-means (DiviK) algorithm.
+
+## Tools within this package
+
+- Clustering at your command line with fit-clusters
+- Set of algorithm implementations for unsupervised analyses
+  - Clustering
+    - DiviK - hands-free clustering method with built-in feature selection
+    - K-Means with Dunn method for selecting the number of clusters
+    - K-Means with GAP index for selecting the number of clusters
+    - Modular K-Means implementation with custom distance metrics and initializations
+  - Feature extraction
+    - PCA with knee-based components selection
+    - Locally Adjusted RBF Spectral Embedding
+  - Feature selection
+    - EXIMS
+    - Gaussian Mixture Model based data-driven feature selection
+      - High Abundance And Variance Selector - allows you to select highly variant features above noise level, based on GMM-decomposition
+    - Outlier based Selector
+      - Outlier Abundance And Variance Selector - allows you to select highly variant features above noise level, based on outlier detection
+    - Percentage based Selector - allows you to select highly variant features above noise level with your predefined thresholds for each
+  - Sampling
+    - StratifiedSampler - generates samples of fixed number of rows from given dataset
+    - UniformPCASampler - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data
+    - UniformSampler - generates samples of random observations within boundaries of an original dataset
+
+## Installation
+
+### Docker
+
+The recommended way to use this software is through
+[Docker](https://www.docker.com/). This is the most convenient way, if you want
+to use `divik` application.
+
+To install latest stable version use:
+
+```bash
+docker pull gmrukwa/divik
+```
+
+### Python package
+
+Prerequisites for installation of base package:
+
+- Python 3.7 / 3.8 / 3.9
+- compiler capable of compiling the native C code and OpenMP support
+
+#### Installation of OpenMP for Ubuntu / Debian
+
+You should have it already installed with GCC compiler, but if somehow
+not, try the following:
+
+```bash
+sudo apt-get install libgomp1
+```
+
+#### Installation of OpenMP for Mac
+
+OpenMP is available as part of LLVM. You may need to install it with conda:
+
+```bash
+conda install -c conda-forge "compilers>=1.0.4,!=1.1.0" llvm-openmp
+```
+
+#### Installation of dependencied on Mac
+
+You may see messages that some dependencies are invalid for the platform.
+It is a [known bug](https://github.com/actions/setup-python/issues/469),
+with [a workaround](https://github.com/actions/setup-python/issues/469#issuecomment-1192522949).
+
+Use:
+
+```bash
+SYSTEM_VERSION_COMPAT=0 pip install divik
+```
+
+#### DiviK Installation
+
+Having prerequisites installed, one can install latest base version of the
+package:
+
+```bash
+pip install divik
+```
+
+If you want to have compatibility with
+[`gin-config`](https://github.com/google/gin-config), you can install
+necessary extras with:
+
+```bash
+pip install divik[gin]
+```
+
+**Note:** Remember about `\` before `[` and `]` in `zsh` shell.
+
+You can install all extras with:
+
+```bash
+pip install divik[all]
+```
+
+## High-Volume Data Considerations
+
+If you are using DiviK to run the analysis that could fail to fit RAM of your
+computer, consider disabling the default parallelism and switch to
+[dask](https://dask.org/). It's easy to achieve through configuration:
+
+- set all parameters named `n_jobs` to `1`;
+- set all parameters named `allow_dask` to `True`.
+
+**Note:** Never set `n_jobs>1` and `allow_dask=True` at the same time, the
+computations will freeze due to how `multiprocessing` and `dask` handle
+parallelism.
+
+## Known Issues
+
+### Segmentation Fault
+
+It can happen if the he `gamred_native` package (part of `divik` package) was
+compiled with different numpy ABI than scikit-learn. This could happen if you
+used different set of compilers than the developers of the scikit-learn
+package.
+
+In such a case, a handler is defined to display the stack trace. If the trace
+comes from `_matlab_legacy.py`, the most probably this is the issue.
+
+To resolve the issue, consider following the installation instructions once
+again. The exact versions get updated to avoid the issue.
+
+## Contributing
+
+Contribution guide will be developed soon.
+
+Format the code with:
+
+```bash
+isort -m 3 --fgw 3 --tc .
+black -t py36 .
+```
+
+## References
+
+This software is part of contribution made by [Data Mining Group of Silesian
+University of Technology](http://www.zaed.polsl.pl/), rest of which is
+published [here](https://github.com/ZAEDPolSl).
+
+- [Mrukwa, G. and Polanska, J., 2020. DiviK: Divisive intelligent K-means for
+hands-free unsupervised clustering in biological big data. *arXiv preprint
+arXiv:2009.10706.*][1]
+
+[1]: https://arxiv.org/abs/2009.10706
+
+
+
+%prep
+%autosetup -n divik-3.2.2
+
+%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-divik -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 3.2.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..84fa042
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f662d419598db682fcc9f960c4eac15f  divik-3.2.2.tar.gz