path: root/python-divik.spec

%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