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            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3.6",
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        "description": "# Kondo\n\n[![Build Status](https://travis-ci.com/activatedgeek/kondo.svg?branch=master)](https://travis-ci.com/activatedgeek/kondo)\n[![PyPI version](https://badge.fury.io/py/kondo.svg)](https://pypi.org/project/kondo/)\n![Alpha](https://img.shields.io/badge/status-beta-orange.svg)\n[![Python 3.6](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/release/python-360/)\n\n\n![Marie Kondo Spark Joy](https://i.imgflip.com/2zdobd.jpg)\n\nThe name is inspired by [Marie Kondo](https://konmari.com)'s tidying adventures.\n\nThrow away experiments that don't spark joy with this tiny module.\n\n## Installation\n\n### PyPI\n\n```\npip install kondo\n```\n\n### Source\n\n```\npip install git+https://github.com/activatedgeek/kondo.git@master\n```\n\n**NOTE**: Prefer pinning to a reference than the master branch for unintended updates.\n\n## Minimal Usage Example\n\n* Create new `Experiment` class\n  ```python\n  from kondo import Spec, Experiment, RandIntType, ChoiceType\n\n  class MyExp(Experiment):\n    def __init__(self, foo=100, bar='c', **kwargs):\n      super().__init__(**kwargs)\n      self.foo = foo\n      self.bar = bar\n\n    def run(self):\n      print('Running experiment with foo={}, bar=\"{}\".'.format(self.foo, self.bar))\n\n    @staticmethod\n    def spec_list():\n      return [\n        Spec(\n          group='random',\n          params=dict(\n            foo=RandIntType(low=10, high=100),\n            bar=ChoiceType(['a', 'b', 'c'])\n          ),\n          n_trials=3,\n        ),\n        Spec(\n          group='fixed_foo',\n          params=dict(\n            foo=200,\n            bar=ChoiceType(['a', 'b', 'c'])\n          ),\n          n_trials=3,\n        )\n      ]\n  ```\n  Make sure to capture all keyword arguments to the super class using `**kwargs`\n  as above.\n\n* Create `Hyperparameter` spec\n  ```python\n  from kondo import HParams\n\n  hparams = HParams(MyExp)\n  ```\n  `HParams` class automagically recognizes all the possible parameters to the\n  experiment specified as arguments to the constructor with default values. The\n  `spec` can be any key value pairs (and can include constant values which will\n  remain common across all trials).\n\n  Other types available can be seen in [param_types.py](./kondo/param_types.py).\n\n* Generate trials and create a new experiment each time\n  ```python\n  for _, trial in hparams.trials():\n    exp = MyExp(**trial)\n    exp.run()\n  ```\n\n  A sample output for these three trials with randomly selected values for `foo`\n  and `bar` is shown below. Each line represents the dictionary sent in to the\n  constructor of the `MyExp` class.\n\n  ```shell\n  Running experiment with foo=93, bar=\"b\".\n  Running experiment with foo=30, bar=\"c\".\n  Running experiment with foo=75, bar=\"c\".\n  ...\n  ```\n\n* You can also generate trials from only a subset of groups by using the `groups` argument as\n  ```python\n  for _, trial in hparams.trials(groups=['fixed_foo']):\n    # ... same as earlier\n  ```\n  `ignore_groups` is a similar argument with the filtering out effect.\n\nNow, you can keep tuning the spec during your hyperparameter search and *throw\naway the ones that don't spark joy*!.\n\nThe full example file is available at [basic.py](./examples/basic.py).\n\n## Advanced Usage\n\nSee [Experiment](./kondo/experiment.py) object for other important attributes.\nThe only thing to care about are `@property` annotated methods, especially the\nones for logging.\n\nThe [tests](./kondo/tests) directory is a good place to discover all possibilities.\n\n## License\n\nApache 2.0",
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