%global _empty_manifest_terminate_build 0 Name: python-info-gain Version: 1.0.1 Release: 1 Summary: Information gain utilities License: MIT License URL: https://github.com/Thijsvanede/info_gain Source0: https://mirrors.aliyun.com/pypi/web/packages/74/da/b7ac47b517b47ca3f0bcf87a8ed3f17c2b1978c4df9f000e0ac577b2106e/info_gain-1.0.1.tar.gz BuildArch: noarch %description # info_gain Implementation 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: ## Information gain definitions ### Information gain calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). Let `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: ![Information gain formula][ig] [ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif ### Intrinsic value calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][iv] [iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif ### Information gain ratio calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][igr] [igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif ## Installation To install the package via pip use: ``` pip install info_gain ``` To clone the package from the git repository use: ``` git clone https://github.com/Thijsvanede/info_gain.git ``` ## Usage Import the `info_gain` module with: ``` from info_gain import info_gain ``` The imported module has supports three methods: * `info_gain.info_gain(Ex, a)` to compute the information gain. * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value. * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio. ### Example ```python from info_gain import info_gain # Example of color to indicate whether something is fruit or vegatable produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant'] fruit = [ True , True , True , True , False ] colour = ['green', 'green', 'red' , 'red' , 'purple' ] ig = info_gain.info_gain(fruit, colour) iv = info_gain.intrinsic_value(fruit, colour) igr = info_gain.info_gain_ratio(fruit, colour) print(ig, iv, igr) ``` %package -n python3-info-gain Summary: Information gain utilities Provides: python-info-gain BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-info-gain # info_gain Implementation 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: ## Information gain definitions ### Information gain calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). Let `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: ![Information gain formula][ig] [ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif ### Intrinsic value calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][iv] [iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif ### Information gain ratio calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][igr] [igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif ## Installation To install the package via pip use: ``` pip install info_gain ``` To clone the package from the git repository use: ``` git clone https://github.com/Thijsvanede/info_gain.git ``` ## Usage Import the `info_gain` module with: ``` from info_gain import info_gain ``` The imported module has supports three methods: * `info_gain.info_gain(Ex, a)` to compute the information gain. * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value. * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio. ### Example ```python from info_gain import info_gain # Example of color to indicate whether something is fruit or vegatable produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant'] fruit = [ True , True , True , True , False ] colour = ['green', 'green', 'red' , 'red' , 'purple' ] ig = info_gain.info_gain(fruit, colour) iv = info_gain.intrinsic_value(fruit, colour) igr = info_gain.info_gain_ratio(fruit, colour) print(ig, iv, igr) ``` %package help Summary: Development documents and examples for info-gain Provides: python3-info-gain-doc %description help # info_gain Implementation 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: ## Information gain definitions ### Information gain calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). Let `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: ![Information gain formula][ig] [ig]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_formula.gif ### Intrinsic value calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][iv] [iv]: https://github.com/Thijsvanede/info_gain/blob/master/images/intrinsic_value_formula.gif ### Information gain ratio calculation Definition from [information gain calculation](https://en.wikipedia.org/wiki/Information_gain_ratio) (retrieved 2018-07-13). ![Intrinsic value calculation][igr] [igr]: https://github.com/Thijsvanede/info_gain/blob/master/images/information_gain_ratio_formula.gif ## Installation To install the package via pip use: ``` pip install info_gain ``` To clone the package from the git repository use: ``` git clone https://github.com/Thijsvanede/info_gain.git ``` ## Usage Import the `info_gain` module with: ``` from info_gain import info_gain ``` The imported module has supports three methods: * `info_gain.info_gain(Ex, a)` to compute the information gain. * `info_gain.intrinsic_value(Ex, a)` to compute the intrinsic value. * `info_gain.info_gain_ratio(Ex, a)` to compute the information gain ratio. ### Example ```python from info_gain import info_gain # Example of color to indicate whether something is fruit or vegatable produce = ['apple', 'apple', 'apple', 'strawberry', 'eggplant'] fruit = [ True , True , True , True , False ] colour = ['green', 'green', 'red' , 'red' , 'purple' ] ig = info_gain.info_gain(fruit, colour) iv = info_gain.intrinsic_value(fruit, colour) igr = info_gain.info_gain_ratio(fruit, colour) print(ig, iv, igr) ``` %prep %autosetup -n info_gain-1.0.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-info-gain -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 1.0.1-1 - Package Spec generated