path: root/python-jmetalpy.spec

%global _empty_manifest_terminate_build 0
Name:		python-jmetalpy
Version:	1.6.0
Release:	1
Summary:	Python version of the jMetal framework
License:	MIT
URL:		https://github.com/jMetal/jMetalPy
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b7/29/ab3d41a6c5318ba8a438ad301e463ca1faa381c39f5c53871c86d3dfc504/jmetalpy-1.6.0.tar.gz
BuildArch:	noarch

Requires:	python3-tqdm
Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-plotly
Requires:	python3-matplotlib
Requires:	python3-scipy
Requires:	python3-statsmodels
Requires:	python3-dask[complete]
Requires:	python3-distributed
Requires:	python3-pyspark
Requires:	python3-isort
Requires:	python3-black
Requires:	python3-mypy
Requires:	python3-mockito
Requires:	python3-PyHamcrest
Requires:	python3-isort
Requires:	python3-black
Requires:	python3-mypy
Requires:	python3-dask[complete]
Requires:	python3-distributed
Requires:	python3-pyspark
Requires:	python3-mockito
Requires:	python3-PyHamcrest

%description
![jMetalPy](docs/source/jmetalpy.png)

[![CI](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml/badge.svg)](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)
[![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg)]()
[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.swevo.2019.100598-blue)](https://doi.org/10.1016/j.swevo.2019.100598)
[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg)]()
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598

### Table of Contents
- [Installation](#installation)
- [Usage](#hello-world-)
- [Features](#features)
- [Changelog](#changelog)
- [License](#license)

## Installation

You can install the latest version of jMetalPy with `pip`, 

```console
pip install jmetalpy  # or "jmetalpy[distributed]"
```

<details><summary><b>Notes on installing with <tt>pip</tt></b></summary>
<p>

jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/).

These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users):

```console
pip install jmetalpy
```

This is the equivalent of running: 

```console
pip install "jmetalpy[core]"
```

Other supported commands are listed next:

```console
pip install "jmetalpy[dev]"  # Install requirements for development
pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
pip install "jmetalpy[complete]"  # Install all requirements
```

</p>
</details>

## Hello, world! 👋

Examples of configuring and running all the included algorithms are located [in the documentation](https://jmetal.github.io/jMetalPy/multiobjective.algorithms.html).

```python
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)

algorithm.run()
```

We can then proceed to explore the results:

```python
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \ 
    print_variables_to_file

front = get_non_dominated_solutions(algorithm.get_result())

# save to files
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
```

Or visualize the Pareto front approximation produced by the algorithm:

```python
from jmetal.lab.visualization import Plot

plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
```

<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">

## Features
The current release of jMetalPy (v1.5.7) contains the following components:

* Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.
* Parallel computing based on Apache Spark and Dask.
* Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
* Encodings: real, binary, permutations.
* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
* Quality indicators: hypervolume, additive epsilon, GD, IGD.
* Pareto front approximation plotting in real-time, static or interactive.
* Experiment class for performing studies either alone or alongside [jMetal](https://github.com/jMetal/jMetal).
* Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

| ![Scatter plot 2D](docs/source/_static/2D.gif) | ![Scatter plot 3D](docs/source/_static/3D.gif) |
|-------------- | ----------------  |
| ![Parallel coordinates](docs/source/_static/p-c.gif) | ![Interactive chord plot](docs/source/_static/chordplot.gif) |

## Changelog

* [v1.6.0] Refactor class Problem, the single-objective genetic algorithm can solve constrained problems, performance improvements in NSGA-II, generation of Latex tables summarizing the results of the Wilcoxon rank sum test, added a notebook folder with examples.
* [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.
* [v1.5.6] Removed warnings when using Python 3.8.
* [v1.5.5] Minor bug fixes.
* [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
* [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)

## License

This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.


%package -n python3-jmetalpy
Summary:	Python version of the jMetal framework
Provides:	python-jmetalpy
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-jmetalpy
![jMetalPy](docs/source/jmetalpy.png)

[![CI](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml/badge.svg)](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)
[![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg)]()
[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.swevo.2019.100598-blue)](https://doi.org/10.1016/j.swevo.2019.100598)
[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg)]()
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598

### Table of Contents
- [Installation](#installation)
- [Usage](#hello-world-)
- [Features](#features)
- [Changelog](#changelog)
- [License](#license)

## Installation

You can install the latest version of jMetalPy with `pip`, 

```console
pip install jmetalpy  # or "jmetalpy[distributed]"
```

<details><summary><b>Notes on installing with <tt>pip</tt></b></summary>
<p>

jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/).

These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users):

```console
pip install jmetalpy
```

This is the equivalent of running: 

```console
pip install "jmetalpy[core]"
```

Other supported commands are listed next:

```console
pip install "jmetalpy[dev]"  # Install requirements for development
pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
pip install "jmetalpy[complete]"  # Install all requirements
```

</p>
</details>

## Hello, world! 👋

Examples of configuring and running all the included algorithms are located [in the documentation](https://jmetal.github.io/jMetalPy/multiobjective.algorithms.html).

```python
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)

algorithm.run()
```

We can then proceed to explore the results:

```python
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \ 
    print_variables_to_file

front = get_non_dominated_solutions(algorithm.get_result())

# save to files
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
```

Or visualize the Pareto front approximation produced by the algorithm:

```python
from jmetal.lab.visualization import Plot

plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
```

<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">

## Features
The current release of jMetalPy (v1.5.7) contains the following components:

* Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.
* Parallel computing based on Apache Spark and Dask.
* Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
* Encodings: real, binary, permutations.
* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
* Quality indicators: hypervolume, additive epsilon, GD, IGD.
* Pareto front approximation plotting in real-time, static or interactive.
* Experiment class for performing studies either alone or alongside [jMetal](https://github.com/jMetal/jMetal).
* Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

| ![Scatter plot 2D](docs/source/_static/2D.gif) | ![Scatter plot 3D](docs/source/_static/3D.gif) |
|-------------- | ----------------  |
| ![Parallel coordinates](docs/source/_static/p-c.gif) | ![Interactive chord plot](docs/source/_static/chordplot.gif) |

## Changelog

* [v1.6.0] Refactor class Problem, the single-objective genetic algorithm can solve constrained problems, performance improvements in NSGA-II, generation of Latex tables summarizing the results of the Wilcoxon rank sum test, added a notebook folder with examples.
* [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.
* [v1.5.6] Removed warnings when using Python 3.8.
* [v1.5.5] Minor bug fixes.
* [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
* [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)

## License

This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.


%package help
Summary:	Development documents and examples for jmetalpy
Provides:	python3-jmetalpy-doc
%description help
![jMetalPy](docs/source/jmetalpy.png)

[![CI](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml/badge.svg)](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)
[![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg)]()
[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.swevo.2019.100598-blue)](https://doi.org/10.1016/j.swevo.2019.100598)
[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg)]()
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598

### Table of Contents
- [Installation](#installation)
- [Usage](#hello-world-)
- [Features](#features)
- [Changelog](#changelog)
- [License](#license)

## Installation

You can install the latest version of jMetalPy with `pip`, 

```console
pip install jmetalpy  # or "jmetalpy[distributed]"
```

<details><summary><b>Notes on installing with <tt>pip</tt></b></summary>
<p>

jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/).

These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users):

```console
pip install jmetalpy
```

This is the equivalent of running: 

```console
pip install "jmetalpy[core]"
```

Other supported commands are listed next:

```console
pip install "jmetalpy[dev]"  # Install requirements for development
pip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computing
pip install "jmetalpy[complete]"  # Install all requirements
```

</p>
</details>

## Hello, world! 👋

Examples of configuring and running all the included algorithms are located [in the documentation](https://jmetal.github.io/jMetalPy/multiobjective.algorithms.html).

```python
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

algorithm = NSGAII(
    problem=problem,
    population_size=100,
    offspring_population_size=100,
    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
    crossover=SBXCrossover(probability=1.0, distribution_index=20),
    termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)

algorithm.run()
```

We can then proceed to explore the results:

```python
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \ 
    print_variables_to_file

front = get_non_dominated_solutions(algorithm.get_result())

# save to files
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
```

Or visualize the Pareto front approximation produced by the algorithm:

```python
from jmetal.lab.visualization import Plot

plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
```

<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">

## Features
The current release of jMetalPy (v1.5.7) contains the following components:

* Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.
* Parallel computing based on Apache Spark and Dask.
* Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
* Encodings: real, binary, permutations.
* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
* Quality indicators: hypervolume, additive epsilon, GD, IGD.
* Pareto front approximation plotting in real-time, static or interactive.
* Experiment class for performing studies either alone or alongside [jMetal](https://github.com/jMetal/jMetal).
* Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

| ![Scatter plot 2D](docs/source/_static/2D.gif) | ![Scatter plot 3D](docs/source/_static/3D.gif) |
|-------------- | ----------------  |
| ![Parallel coordinates](docs/source/_static/p-c.gif) | ![Interactive chord plot](docs/source/_static/chordplot.gif) |

## Changelog

* [v1.6.0] Refactor class Problem, the single-objective genetic algorithm can solve constrained problems, performance improvements in NSGA-II, generation of Latex tables summarizing the results of the Wilcoxon rank sum test, added a notebook folder with examples.
* [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.
* [v1.5.6] Removed warnings when using Python 3.8.
* [v1.5.5] Minor bug fixes.
* [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
* [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)

## License

This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.


%prep
%autosetup -n jmetalpy-1.6.0

%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-jmetalpy -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.6.0-1
- Package Spec generated