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diff --git a/python-monotonic-binning.spec b/python-monotonic-binning.spec new file mode 100644 index 0000000..943c4b1 --- /dev/null +++ b/python-monotonic-binning.spec @@ -0,0 +1,210 @@ +%global _empty_manifest_terminate_build 0 +Name: python-monotonic-binning +Version: 0.0.1 +Release: 1 +Summary: Monotonic Variable Binning by WOE +License: MIT License +URL: https://github.com/jstephenj14/Monotonic-WOE-Binning-Algorithm +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c8/3f/af5dfe5546d0be72a528f3f8a174a9b21a88f6fd701f82de84c14ecc7928/monotonic_binning-0.0.1.tar.gz +BuildArch: noarch + + +%description +# Monotonic-WOE-Binning-Algorithm + +_This algorithm is based on the excellent paper by Mironchyk and Tchistiakov (2017) named "Monotone optimal binning algorithm for credit risk modeling"._ + +### How to use + +1. pip install monotonic_binning: `pip install -i https://test.pypi.org/simple/simple/ monotonic-binning` +2. Import monotonic_woe_binning: `from monotonic_binning import monotonic_woe_binning as bin` +3. Use `fit` and `transform` to bin variables for train and test datasets respectively + +### Demo Run Details + +The `demo_run.py` file available under `tests/` uses German credit card data from [Penn State's online course](https://online.stat.psu.edu/stat508/resource/analysis/gcd) and gives an overview of how to use the package. + +### Summary of Monotonic WOE + +The weight-of-evidence (WOE) method of evaluating strength of predictors is an understated one in the field of analytics. +While it is standard fare in credit risk modelling, it is under-utilized in other settings though its formulation makes it +generic enough for use in other domains too. The WOE method primarily aims to bin variables into buckets that deliver the most +information to a potential classification model. Quite often, WOE binning methods measure effectiveness of such bins using Information Value +or IV. For a more detailed introduction to WOE and IV, [this article](http://ucanalytics.com/blogs/information-value-and-weight-of-evidencebanking-case/) +is a useful read. + +In the world of credit risk modelling, regulatory oversight often requires that the variables that go into models +are split into bins + +- whose weight of evidence (WOE) values maintain a monotonic relationship with the 1/0 variable (loan default or not default for example.) +- are reasonably sized and large enough to be respresentative of population segments, and +- maximize the IV value of the given variable in the process of this binning. + +To exemplify the constraints such a problem, consider a simple dataset containing age and a default indicator (1 if defaulted, 0 if not). +The following is a possible scenario in which the variable is binned into three groups in such a manner that their WOE values decrease monotomically +as the ages of customers increase. + +<a href="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A"><img src="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A" style="width: 500px; max-width: 100%; height: auto" title="WOE Table" /></a> + +The WOE is derived in such a manner that as the WOE value increases, the default rate decreases. So we can infer +that younger customers are more likely to default in comparison to older customers. + +Arriving at the perfect bin cutoffs to meet all three requirements discussed earlier is a non-trivial exercise. Most statistical software +provide this type of optimal discretization of interval variables. R's [smbinning package](https://cran.r-project.org/web/packages/smbinning/smbinning.pdf) +and SAS' [proc transreg](https://statcompute.wordpress.com/2017/09/24/granular-monotonic-binning-in-sas/) are two such examples. To my knowledge, Python's solutions to this problem are fairly sparse. + +This package is an attempt to complement already exhaustive packages like [scorecardpy](https://github.com/ShichenXie/scorecardpy) with the capability to bin variables with monotonic WOE. + + + + +%package -n python3-monotonic-binning +Summary: Monotonic Variable Binning by WOE +Provides: python-monotonic-binning +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-monotonic-binning +# Monotonic-WOE-Binning-Algorithm + +_This algorithm is based on the excellent paper by Mironchyk and Tchistiakov (2017) named "Monotone optimal binning algorithm for credit risk modeling"._ + +### How to use + +1. pip install monotonic_binning: `pip install -i https://test.pypi.org/simple/simple/ monotonic-binning` +2. Import monotonic_woe_binning: `from monotonic_binning import monotonic_woe_binning as bin` +3. Use `fit` and `transform` to bin variables for train and test datasets respectively + +### Demo Run Details + +The `demo_run.py` file available under `tests/` uses German credit card data from [Penn State's online course](https://online.stat.psu.edu/stat508/resource/analysis/gcd) and gives an overview of how to use the package. + +### Summary of Monotonic WOE + +The weight-of-evidence (WOE) method of evaluating strength of predictors is an understated one in the field of analytics. +While it is standard fare in credit risk modelling, it is under-utilized in other settings though its formulation makes it +generic enough for use in other domains too. The WOE method primarily aims to bin variables into buckets that deliver the most +information to a potential classification model. Quite often, WOE binning methods measure effectiveness of such bins using Information Value +or IV. For a more detailed introduction to WOE and IV, [this article](http://ucanalytics.com/blogs/information-value-and-weight-of-evidencebanking-case/) +is a useful read. + +In the world of credit risk modelling, regulatory oversight often requires that the variables that go into models +are split into bins + +- whose weight of evidence (WOE) values maintain a monotonic relationship with the 1/0 variable (loan default or not default for example.) +- are reasonably sized and large enough to be respresentative of population segments, and +- maximize the IV value of the given variable in the process of this binning. + +To exemplify the constraints such a problem, consider a simple dataset containing age and a default indicator (1 if defaulted, 0 if not). +The following is a possible scenario in which the variable is binned into three groups in such a manner that their WOE values decrease monotomically +as the ages of customers increase. + +<a href="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A"><img src="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A" style="width: 500px; max-width: 100%; height: auto" title="WOE Table" /></a> + +The WOE is derived in such a manner that as the WOE value increases, the default rate decreases. So we can infer +that younger customers are more likely to default in comparison to older customers. + +Arriving at the perfect bin cutoffs to meet all three requirements discussed earlier is a non-trivial exercise. Most statistical software +provide this type of optimal discretization of interval variables. R's [smbinning package](https://cran.r-project.org/web/packages/smbinning/smbinning.pdf) +and SAS' [proc transreg](https://statcompute.wordpress.com/2017/09/24/granular-monotonic-binning-in-sas/) are two such examples. To my knowledge, Python's solutions to this problem are fairly sparse. + +This package is an attempt to complement already exhaustive packages like [scorecardpy](https://github.com/ShichenXie/scorecardpy) with the capability to bin variables with monotonic WOE. + + + + +%package help +Summary: Development documents and examples for monotonic-binning +Provides: python3-monotonic-binning-doc +%description help +# Monotonic-WOE-Binning-Algorithm + +_This algorithm is based on the excellent paper by Mironchyk and Tchistiakov (2017) named "Monotone optimal binning algorithm for credit risk modeling"._ + +### How to use + +1. pip install monotonic_binning: `pip install -i https://test.pypi.org/simple/simple/ monotonic-binning` +2. Import monotonic_woe_binning: `from monotonic_binning import monotonic_woe_binning as bin` +3. Use `fit` and `transform` to bin variables for train and test datasets respectively + +### Demo Run Details + +The `demo_run.py` file available under `tests/` uses German credit card data from [Penn State's online course](https://online.stat.psu.edu/stat508/resource/analysis/gcd) and gives an overview of how to use the package. + +### Summary of Monotonic WOE + +The weight-of-evidence (WOE) method of evaluating strength of predictors is an understated one in the field of analytics. +While it is standard fare in credit risk modelling, it is under-utilized in other settings though its formulation makes it +generic enough for use in other domains too. The WOE method primarily aims to bin variables into buckets that deliver the most +information to a potential classification model. Quite often, WOE binning methods measure effectiveness of such bins using Information Value +or IV. For a more detailed introduction to WOE and IV, [this article](http://ucanalytics.com/blogs/information-value-and-weight-of-evidencebanking-case/) +is a useful read. + +In the world of credit risk modelling, regulatory oversight often requires that the variables that go into models +are split into bins + +- whose weight of evidence (WOE) values maintain a monotonic relationship with the 1/0 variable (loan default or not default for example.) +- are reasonably sized and large enough to be respresentative of population segments, and +- maximize the IV value of the given variable in the process of this binning. + +To exemplify the constraints such a problem, consider a simple dataset containing age and a default indicator (1 if defaulted, 0 if not). +The following is a possible scenario in which the variable is binned into three groups in such a manner that their WOE values decrease monotomically +as the ages of customers increase. + +<a href="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A"><img src="https://drive.google.com/uc?export=view&id=10NHDsJQbZRgO3QQGK2dMkoAmzJxtQR_A" style="width: 500px; max-width: 100%; height: auto" title="WOE Table" /></a> + +The WOE is derived in such a manner that as the WOE value increases, the default rate decreases. So we can infer +that younger customers are more likely to default in comparison to older customers. + +Arriving at the perfect bin cutoffs to meet all three requirements discussed earlier is a non-trivial exercise. Most statistical software +provide this type of optimal discretization of interval variables. R's [smbinning package](https://cran.r-project.org/web/packages/smbinning/smbinning.pdf) +and SAS' [proc transreg](https://statcompute.wordpress.com/2017/09/24/granular-monotonic-binning-in-sas/) are two such examples. To my knowledge, Python's solutions to this problem are fairly sparse. + +This package is an attempt to complement already exhaustive packages like [scorecardpy](https://github.com/ShichenXie/scorecardpy) with the capability to bin variables with monotonic WOE. + + + + +%prep +%autosetup -n monotonic-binning-0.0.1 + +%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-monotonic-binning -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1-1 +- Package Spec generated |