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        "author": "Sean Robertson",
        "author_email": "sdrobert@cs.toronto.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "# pydrobert-gpyopt\nUtilities to streamline GPyOpt interfaces for ML\n\n## How to use\nGPyOpt is incredibly powerful, but a tad clunky. This lightweight package\nprovides two utilities in ``pydrobert.gpyopt`` to make things easier. The\nfirst, ``GPyOptObjectiveWrapper``, wraps a function for use in GPyOpt. The\nsecond, ``bayesopt``, takes a wrapper instance and a\n``BayesianOptimizationParams`` instance and handles the optimization loop.\nHere's an example:\n\n``` python\ndef foo(a, d, b, **kwargs):\n    r = a ** d + b\n    weirdness = kwargs['weirdness']\n    if weirdness == 'flip':\n        r *= -1\n    elif weirdness == 'null':\n        r = 0\n    return r\nwrapped = GPyOptObjectiveWrapper(foo)\nwrapped.set_fixed_parameter('b', 1.)  # 'b' will always be 1\nwrapped.set_variable_parameter('a', 'continuous', (-1., 1.))  # a is real\n                                                              # btw [-1,1] inc\nwrapped.set_variable_parameter('d', 'discrete', (0, 3))  # d is an int\n                                                         # btw [0, 3] inc\nwrapped.add_parameter('weirdness')  # we can add new parameters as dynamic\n                                    # keyword args if the method has a **\n                                    # parameter\nwrapped.set_variable_parameter(  # weirness one of the elements in the list\n    'weirdness', 'categorical', ('flip', 'null', None))\nparams = BayesianOptimizationParams(\n  seed=1,  # setting this makes the bayesian optimization deterministic\n           # (assuming foo is deterministic)\n  log_after_iters=5,\n)\nbest = bayesopt(wrapper, params, 'hist.csv')\n```\n\nIf you provide a history file to read/write from, optimization can be\nresumed after unexpected interrupts. There are a lot of options to ``bayesopt``\nthat are listed in ``BayesianOptimizationParams``.\n\n## Installation\n\nGPyOpt currently does not have a Conda build, so pydrobert-gpyopt is available\nvia PyPI and source install.\n\n``` bash\npip install pydrobert-gpyopt\n```\n\n\n",
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        "keywords": "",
        "license": "Apache 2.0",
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        "name": "pydrobert-gpyopt",
        "package_url": "https://pypi.org/project/pydrobert-gpyopt/",
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        "summary": "Utilities to streamline GPyOpt interfaces for ML",
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