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@@ -0,0 +1 @@ +/info_gain-1.0.1.tar.gz diff --git a/python-info-gain.spec b/python-info-gain.spec new file mode 100644 index 0000000..434fa1c --- /dev/null +++ b/python-info-gain.spec @@ -0,0 +1,264 @@ +%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.nju.edu.cn/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 +* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1 +- Package Spec generated @@ -0,0 +1 @@ +81965db77e37d4a9d181a3a9ba47836f info_gain-1.0.1.tar.gz |