automatic import of python-monotonic-binning

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+%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