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        "author": "Vesa Ojalehto",
        "author_email": "vesa.ojalehto@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
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            "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
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        "description": "# DESDEO README #\n\n<p align=\"center\">\n<a href=\"https://badge.fury.io/py/desdeo\"><img src=\"https://badge.fury.io/py/desdeo.svg\" alt=\"Available on PyPI\" height=\"18\"></a>\n<a href=\"https://desdeo.readthedocs.io/en/latest/?badge=latest\"><img alt=\"Documentation Status\" src=\"https://readthedocs.org/projects/desdeo/badge/?version=latest\"></a>\n<a href=\"https://travis-ci.com/industrial-optimization-group/DESDEO\"><img alt=\"Build Status\" src=\"https://travis-ci.com/industrial-optimization-group/DESDEO.svg?branch=master\"></a>\n<a href=\"https://github.com/ambv/black\"><img alt=\"Code style: black\" src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"></a>\n</p>\n\nDESDEO is a free and open source Python-based framework for developing and\nexperimenting with interactive multiobjective optimization.\n\n[Documentation is available.](https://desdeo.readthedocs.io/en/latest/)\n\n[Background and publications available on the University of Jyv\u00e4skyl\u00e4 Research Group in Industrial Optimization web pages.](https://desdeo.it.jyu.fi)\n\n## Try in your browser ##\n\nYou can try a guided example problem in your browser: [choose how to deal with\nriver pollution using\nNIMBUS](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-vis/master?filepath=desdeo_notebooks%2Fnimbus-river-pollution.ipynb).\nYou can also [browse the other\nexamples](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-vis/master?filepath=desdeo_notebooks).\n\n## What is interactive multiobjective optimization? ##\n\nThere exist many methods to solve [multiobjective\noptimization](https://en.wikipedia.org/wiki/Multi-objective_optimization)\nproblems. Methods which introduce some preference information into the solution\nprocess are commonly known as multiple criteria decision making methods. When\nusing so called [interactive\nmethods](https://en.wikipedia.org/wiki/Multi-objective_optimization#Interactive_methods),\nthe decision maker (DM) takes an active part in an iterative solution process\nby expressing preference information at several iterations. According to the\ngiven preferences, the solution process is updated at each iteration and one or\nseveral new solutions are generated. This iterative process continues until the\nDM is sufficiently satisfied with one of the solutions found.\n\nMany interactive methods have been proposed and they differ from each other\ne.g. in the way preferences are expressed and how the preferences are utilized\nwhen new solutions. The aim of the DESDEO is to implement aspects common for\ndifferent interactive methods, as well as provide framework for developing and\nimplementing new methods.\n\n## Installation ##\n\n### From conda-forge using Conda ###\n\nThis is the **recommended installation method**, especially for those who are\nnewer to Python. First download and install the [Anaconda Python\ndistribution](https://www.anaconda.com/download/).\n\nNext, run the following commands in a terminal:\n\n    conda config --add channels conda-forge\n    conda install desdeo desdeo-vis\n\nNote: if you prefer not to install the full Anaconda distribution, you can\ninstall [miniconda](https://conda.io/miniconda.html) instead.\n\n### From PyPI using pip ###\n\nAssuming you have Pip and Python 3 installed, you can [install desdeo from\nPyPI](https://pypi.org/project/desdeo/) by running the following command in\na terminal:\n\n    pip install desdeo[vis]\n\nThis installs desdeo *and*\n[desdeo-vis](https://github.com/industrial-optimization-group/desdeo-vis),\nwhich you will also want in most cases.\n\n## Getting started with example problems ##\n\nTo proceed with this section, you must [first install Jupyter\nnotebook](http://jupyter.org/install). If you're using Anaconda, you already\nhave it!\n\nYou can copy the example notebooks to the current directory by running:\n\n    python -m desdeo_notebooks\n\nYou can then open them using Jupyter notebook by running:\n\n    jupyter notebook\n\nAfter trying out the examples, the next step is to [read the full\ndocumentation.](https://desdeo.readthedocs.io/en/latest/)\n\n## Development ##\n\n### Set-up ###\n\nYou should install the git pre-commit hook so that code formatting is kept consistent automatically. This is configured using the pre-commit utility. See [the installation instructions](https://pre-commit.com/#install).\n\nIf you are using pipenv for development, you can install desdeo and its\ndependencies after obtaining a git checkout like so:\n\n    pipenv install -e .[docs,dev,vis]\n\n### Tests ###\n\nTests use pytest. After installing pytest you can run:\n\n    pytest tests\n\n### Release process ###\n\n1. Make a release commit in which the version is incremented in setup.py and an entry added to HISTORY.md\n\n2. Make a git tag of this commit with `git tag v$VERSION`\n\n3. Push -- including the tags with `git push --tags`\n\n4. Upload to PyPI with `python setup.py sdist bdist_wheel` and `twine upload dist/*`\n\n\n\n# Documentation #\n\nThe full documentation is located at https://desdeo.readthedocs.io/en/latest/\n\nInformation about the academic project, including publications is available at http://desdeo.it.jyu.fi\n\n# History #\n\n## 0.2.1 (2018-09-10) ##\n\n* Documentation improvements\n* Add porcelain for specifying problems declaratively and convert toy problems\n\n## 0.2.0 (2018-08-28) ##\n\n* Documentation improvements\n\n## 0.1.5 (2018-08-27) ##\n\n* Specify prompt-toolkit as <2 to avoid Jupyter-console incompatibility\n* Documentation improvements\n\n## 0.1.4 (2018-07-16) ##\n\n* Add CylinderProblem\n* Add background documentation\n* Add desdeo-vis as extra dependency\n\n## 0.1.3 (2018-06-18) ##\n\n* Fix PyPI package\n\n## 0.1.2 (2018-06-13) ##\n\n* Improvements to automatically generated documentation\n* Move river pollution example into `desdeo.problem.toy` module\n* Allow solutions as well as objective functions to be obtained by adding ResultSet class\n* Improvements to NIMBUS\n  * Add missing NIMBUS scalarising functions\n  * Add method to generate in-between solutions for NIMBUS\n* Add RangeEstimator module for finding the nadir/ideal with a payoff table\n\n## 0.1.1 (2018-05-21) ##\n\n* Convert package description to use Markdown\n\n## 0.1.0 (2018-04-25) ##\n\n* First release\n\n\n",
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