Metadata-Version: 2.1
Name: olymp
Version: 0.0.1b0
Summary: Benchmarking framework for noisy optimization and experiment planning
Home-page: https://github.com/aspuru-guzik-group/olympus
Author: Florian Hase, Matteo Aldeghi, Riley Hickman
License: UNKNOWN
Description: ## Olympus: a benchmarking framework for noisy optimization and experiment planning
        [![Build Status](https://travis-ci.com/FlorianHase/olympus.svg?token=bMWWqBdm3xytautMLsPK&branch=dev)](https://travis-ci.com/FlorianHase/olympus)
        [![codecov](https://codecov.io/gh/FlorianHase/olympus/branch/flo/graph/badge.svg?token=FyvePgBDQ5)](https://codecov.io/gh/FlorianHase/olympus)
        
        ``Olympus`` provides a consistent and easy-to-use **framework for benchmarking optimization algorithms**. With ``olympus`` you can:
        * Access a suite of **18 experiment planning algortihms** via a simple and consistent interface
        * Easily integrate custom optimization algorithms
        * Access **10 experimentally-derived benchmarks** emulated with probabilistic models, and **23 analytical test functions** for optimization
        * Easily integrate custom datasets, which can be used to train models for custom benchmarks
        
        You can find more details in the [documentation](https://florianhase.github.io/olympus/).
        
        ###  Installation
        ``Olympus`` can be installed with ``pip``:
        
        ```
        pip install olymp
        ```
        
        ### Dependencies
        The installation only requires:
        * ``python >= 3.6``
        * ``numpy``
        * ``pandas``
        
        Additional libraries are required to use specific modules and objects. ``Olympus`` will alert you about these requirements as you try access the related functionality.
        
        ###  Citation
        ``Olympus`` is research software. If you make use of it in scientific publications, please cite the following article:
        
        ```
        @misc{olympus,
              title={Olympus: a benchmarking framework for noisy optimization and experiment planning}, 
              author={Florian Häse and Matteo Aldeghi and Riley J. Hickman and Loïc M. Roch and Melodie Christensen and Elena Liles and Jason E. Hein and Alán Aspuru-Guzik},
              year={2020},
              eprint={2010.04153},
              archivePrefix={arXiv},
              primaryClass={stat.ML}
        }
        ```
        
        ###  License
        ``Olympus`` is distributed under an MIT License.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: planner
Provides-Extra: genetic
Provides-Extra: cma
Provides-Extra: deap
Provides-Extra: gpyopt
Provides-Extra: bayesian
Provides-Extra: hyperopt
Provides-Extra: matplotlib
Provides-Extra: plotter
Provides-Extra: pandas
Provides-Extra: dataset
Provides-Extra: phoenics
Provides-Extra: pyswarms
Provides-Extra: silence-tensorflow
Provides-Extra: emulator
Provides-Extra: seaborn
Provides-Extra: sqlalchemy
Provides-Extra: heuristic
Provides-Extra: snobfit
Provides-Extra: SQSnobFit
Provides-Extra: tensorflow
Provides-Extra: tensorflow-probability
Provides-Extra: all
