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        "author": "Leo Cazenille",
        "author_email": "leo.cazenille@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Environment :: Console",
            "Intended Audience :: Education",
            "Intended Audience :: Other Audience",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Visualization"
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        "description": "Package `qdpy` implements recent Quality-Diversity algorithms: Map-Elites, CVT-Map-Elites, NSLC, SAIL, etc. \nQD algorithms can be accessed directly, but `qdpy` also includes building blocks that can be easily assembled together to build your own QD algorithms. It can be used with parallelism mechanisms and in distributed environments.\n\nThis package requires Python 3.6+.\n\n`qdpy` includes the following features:\n * Generic support for diverse Containers: Grids, Novelty-Archives, Populations, etc\n * Optimisation algorithms for QD: random search methods, quasi-random methods, evolutionary algorithms\n * Support for multi-objective optimisation methods\n * Possible to use optimisation methods not designed for QD, such as [CMA-ES](https://arxiv.org/pdf/1604.00772.pdf)\n * Parallelisation of evaluations, using parallelism libraries, such as multiprocessing, concurrent.futures or [SCOOP](https://github.com/soravux/scoop)\n * Easy integration with the popular [DEAP](https://github.com/DEAP/deap) evolutionary computation framework \n\nInstall\n=======\n`qdpy` requires Python 3.6+. It can be installed with:\n    pip3 install qdpy\n\n`qdpy` includes optional features that need extra packages to be installed:\n * `cma` for CMA-ES support\n * `deap` to integrate with the DEAP library\n * `tables` to output results files in the HDF5 format\n * `tqdm` to display a progress bar showing optimisation progress\n * `colorama` to add colours to pretty-printed outputs\n\nYou can install `qdpy` and all of these optional dependencies with:\n    pip3 install qdpy[all]\n\nThe latest version can be installed from the GitLab repository:\n    pip3 install git+https://gitlab.com/leo.cazenille/qdpy.git@master\n\nExample\n=======\nFrom a python shell::\n\n    from qdpy import algorithms, containers, benchmarks, plots\n\n    # Create container and algorithm. Here we use MAP-Elites, by illuminating a Grid container by evolution.\n    grid = containers.Grid(shape=(64,64), max_items_per_bin=1, fitness_domain=((0., 1.),), features_domain=((0., 1.), (0., 1.)))\n    algo = algorithms.RandomSearchMutPolyBounded(grid, budget=60000, batch_size=500,\n            dimension=3, optimisation_task=\"maximisation\")\n\n    # Create a logger to pretty-print everything and generate output data files\n    logger = algorithms.AlgorithmLogger(algo)\n\n    # Define evaluation function\n    eval_fn = algorithms.partial(benchmarks.illumination_rastrigin_normalised,\n            nb_features = len(grid.shape))\n\n    # Run illumination process !\n    best = algo.optimise(eval_fn)\n\n    # Print results info\n    print(algo.summary())\n\n    # Plot the results\n    plots.default_plots_grid(logger)\n\n    print(\"All results are available in the '%s' pickle file.\" % logger.final_filename)\n\n\nUsage, Documentation\n====================\nPlease to go the GitLab repository main page (https://gitlab.com/leo.cazenille/qdpy) and the documentation main page (https://leo.cazenille.gitlab.io/qdpy/).\n\n\n:Author: Leo Cazenille, 2018-*\n\n:License: LGPLv3, see LICENSE file.\n\n\n\n",
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