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        "author": "Thijs van Ede",
        "author_email": "t.s.vanede@utwente.nl",
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            "License :: OSI Approved :: MIT License",
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        "description": "# info_gain\nImplementation of information gain algorithm. There seems to be a debate about how the information gain metric is defined. Whether to use the [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) or the [Mutual information](https://en.wikipedia.org/wiki/Mutual_information) as an algorithm to define information gain. This implementation uses the [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) as defined below:\n\n## Information gain definitions\n### Information gain calculation\nDefinition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).\nLet `Attr` be the set of all attributes and `Ex` the set of all training examples, `value(x, a)` with `x` in `Ex` defines the value of a specific example `x` for attribute `a` in `Attr`, `H` specifies the entropy. The `values(a)` function denotes the set of all possible values of attribute `a` in `Attr`. The information gain for an attribute `a` in `Attr` is defined as follows:\n\n![Information gain formula][ig]\n\n[ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif\n\n### Intrinsic value calculation\nDefinition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).\n\n![Intrinsic value calculation][iv]\n\n[iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif\n\n### Information gain ratio calculation\nDefinition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13).\n\n![Intrinsic value calculation][igr]\n\n[igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif\n\n## Installation\nTo install the package via pip use:\n```\npip install info_gain\n```\n\nTo clone the package from the git repository use:\n```\ngit clone https://github.com/Thijsvanede/info_gain.git\n```\n\n## Usage\nImport the `info_gain` module with:\n```\nfrom info_gain import info_gain\n```\nThe imported module has supports three methods:\n * `info_gain.info_gain(Ex, a)` to compute the information gain.\n * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value.\n * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio.\n\n### Example\n```python\nfrom info_gain import info_gain\n\n# Example of color to indicate whether something is fruit or vegatable\nproduce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant']\nfruit   = [ True  ,  True  ,  True  ,  True       ,  False    ]\ncolour  = ['green', 'green', 'red'  , 'red'       , 'purple'  ]\n\nig  = info_gain.info_gain(fruit, colour)\niv  = info_gain.intrinsic_value(fruit, colour)\nigr = info_gain.info_gain_ratio(fruit, colour)\n\nprint(ig, iv, igr)\n```\n\n\n",
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