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|
%global _empty_manifest_terminate_build 0
Name: python-scorecardpy
Version: 0.1.9.2
Release: 1
Summary: Credit Risk Scorecard
License: MIT License
URL: http://github.com/shichenxie/scorecardpy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/3e/18/8e3f675b9baaf027e84cb84f9bc3bb57381ab3d0b5241e089653d5d92784/scorecardpy-0.1.9.2.tar.gz
BuildArch: noarch
%description
# scorecardpy
[](https://pypi.python.org/pypi/scorecardpy)
[](https://pypi.python.org/pypi/scorecardpy)
[](http://pepy.tech/project/scorecardpy)
[](https://pepy.tech/project/scorecardpy/month)
This package is python version of R package [scorecard](https://github.com/ShichenXie/scorecard).
Its goal is to make the development of traditional credit risk scorecard model easier and efficient by providing functions for some common tasks.
- data partition (`split_df`)
- variable selection (`iv`, `var_filter`)
- weight of evidence (woe) binning (`woebin`, `woebin_plot`, `woebin_adj`, `woebin_ply`)
- scorecard scaling (`scorecard`, `scorecard_ply`)
- performance evaluation (`perf_eva`, `perf_psi`)
## Installation
- Install the release version of `scorecardpy` from [PYPI](https://pypi.org/project/scorecardpy/) with:
```
pip install scorecardpy
```
- Install the latest version of `scorecardpy` from [github](https://github.com/shichenxie/scorecardpy) with:
```
pip install git+git://github.com/shichenxie/scorecardpy.git
```
## Example
This is a basic example which shows you how to develop a common credit risk scorecard:
``` python
# Traditional Credit Scoring Using Logistic Regression
import scorecardpy as sc
# data prepare ------
# load germancredit data
dat = sc.germancredit()
# filter variable via missing rate, iv, identical value rate
dt_s = sc.var_filter(dat, y="creditability")
# breaking dt into train and test
train, test = sc.split_df(dt_s, 'creditability').values()
# woe binning ------
bins = sc.woebin(dt_s, y="creditability")
# sc.woebin_plot(bins)
# binning adjustment
# # adjust breaks interactively
# breaks_adj = sc.woebin_adj(dt_s, "creditability", bins)
# # or specify breaks manually
breaks_adj = {
'age.in.years': [26, 35, 40],
'other.debtors.or.guarantors': ["none", "co-applicant%,%guarantor"]
}
bins_adj = sc.woebin(dt_s, y="creditability", breaks_list=breaks_adj)
# converting train and test into woe values
train_woe = sc.woebin_ply(train, bins_adj)
test_woe = sc.woebin_ply(test, bins_adj)
y_train = train_woe.loc[:,'creditability']
X_train = train_woe.loc[:,train_woe.columns != 'creditability']
y_test = test_woe.loc[:,'creditability']
X_test = test_woe.loc[:,train_woe.columns != 'creditability']
# logistic regression ------
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(penalty='l1', C=0.9, solver='saga', n_jobs=-1)
lr.fit(X_train, y_train)
# lr.coef_
# lr.intercept_
# predicted proability
train_pred = lr.predict_proba(X_train)[:,1]
test_pred = lr.predict_proba(X_test)[:,1]
# performance ks & roc ------
train_perf = sc.perf_eva(y_train, train_pred, title = "train")
test_perf = sc.perf_eva(y_test, test_pred, title = "test")
# score ------
card = sc.scorecard(bins_adj, lr, X_train.columns)
# credit score
train_score = sc.scorecard_ply(train, card, print_step=0)
test_score = sc.scorecard_ply(test, card, print_step=0)
# psi
sc.perf_psi(
score = {'train':train_score, 'test':test_score},
label = {'train':y_train, 'test':y_test}
)
```
%package -n python3-scorecardpy
Summary: Credit Risk Scorecard
Provides: python-scorecardpy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-scorecardpy
# scorecardpy
[](https://pypi.python.org/pypi/scorecardpy)
[](https://pypi.python.org/pypi/scorecardpy)
[](http://pepy.tech/project/scorecardpy)
[](https://pepy.tech/project/scorecardpy/month)
This package is python version of R package [scorecard](https://github.com/ShichenXie/scorecard).
Its goal is to make the development of traditional credit risk scorecard model easier and efficient by providing functions for some common tasks.
- data partition (`split_df`)
- variable selection (`iv`, `var_filter`)
- weight of evidence (woe) binning (`woebin`, `woebin_plot`, `woebin_adj`, `woebin_ply`)
- scorecard scaling (`scorecard`, `scorecard_ply`)
- performance evaluation (`perf_eva`, `perf_psi`)
## Installation
- Install the release version of `scorecardpy` from [PYPI](https://pypi.org/project/scorecardpy/) with:
```
pip install scorecardpy
```
- Install the latest version of `scorecardpy` from [github](https://github.com/shichenxie/scorecardpy) with:
```
pip install git+git://github.com/shichenxie/scorecardpy.git
```
## Example
This is a basic example which shows you how to develop a common credit risk scorecard:
``` python
# Traditional Credit Scoring Using Logistic Regression
import scorecardpy as sc
# data prepare ------
# load germancredit data
dat = sc.germancredit()
# filter variable via missing rate, iv, identical value rate
dt_s = sc.var_filter(dat, y="creditability")
# breaking dt into train and test
train, test = sc.split_df(dt_s, 'creditability').values()
# woe binning ------
bins = sc.woebin(dt_s, y="creditability")
# sc.woebin_plot(bins)
# binning adjustment
# # adjust breaks interactively
# breaks_adj = sc.woebin_adj(dt_s, "creditability", bins)
# # or specify breaks manually
breaks_adj = {
'age.in.years': [26, 35, 40],
'other.debtors.or.guarantors': ["none", "co-applicant%,%guarantor"]
}
bins_adj = sc.woebin(dt_s, y="creditability", breaks_list=breaks_adj)
# converting train and test into woe values
train_woe = sc.woebin_ply(train, bins_adj)
test_woe = sc.woebin_ply(test, bins_adj)
y_train = train_woe.loc[:,'creditability']
X_train = train_woe.loc[:,train_woe.columns != 'creditability']
y_test = test_woe.loc[:,'creditability']
X_test = test_woe.loc[:,train_woe.columns != 'creditability']
# logistic regression ------
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(penalty='l1', C=0.9, solver='saga', n_jobs=-1)
lr.fit(X_train, y_train)
# lr.coef_
# lr.intercept_
# predicted proability
train_pred = lr.predict_proba(X_train)[:,1]
test_pred = lr.predict_proba(X_test)[:,1]
# performance ks & roc ------
train_perf = sc.perf_eva(y_train, train_pred, title = "train")
test_perf = sc.perf_eva(y_test, test_pred, title = "test")
# score ------
card = sc.scorecard(bins_adj, lr, X_train.columns)
# credit score
train_score = sc.scorecard_ply(train, card, print_step=0)
test_score = sc.scorecard_ply(test, card, print_step=0)
# psi
sc.perf_psi(
score = {'train':train_score, 'test':test_score},
label = {'train':y_train, 'test':y_test}
)
```
%package help
Summary: Development documents and examples for scorecardpy
Provides: python3-scorecardpy-doc
%description help
# scorecardpy
[](https://pypi.python.org/pypi/scorecardpy)
[](https://pypi.python.org/pypi/scorecardpy)
[](http://pepy.tech/project/scorecardpy)
[](https://pepy.tech/project/scorecardpy/month)
This package is python version of R package [scorecard](https://github.com/ShichenXie/scorecard).
Its goal is to make the development of traditional credit risk scorecard model easier and efficient by providing functions for some common tasks.
- data partition (`split_df`)
- variable selection (`iv`, `var_filter`)
- weight of evidence (woe) binning (`woebin`, `woebin_plot`, `woebin_adj`, `woebin_ply`)
- scorecard scaling (`scorecard`, `scorecard_ply`)
- performance evaluation (`perf_eva`, `perf_psi`)
## Installation
- Install the release version of `scorecardpy` from [PYPI](https://pypi.org/project/scorecardpy/) with:
```
pip install scorecardpy
```
- Install the latest version of `scorecardpy` from [github](https://github.com/shichenxie/scorecardpy) with:
```
pip install git+git://github.com/shichenxie/scorecardpy.git
```
## Example
This is a basic example which shows you how to develop a common credit risk scorecard:
``` python
# Traditional Credit Scoring Using Logistic Regression
import scorecardpy as sc
# data prepare ------
# load germancredit data
dat = sc.germancredit()
# filter variable via missing rate, iv, identical value rate
dt_s = sc.var_filter(dat, y="creditability")
# breaking dt into train and test
train, test = sc.split_df(dt_s, 'creditability').values()
# woe binning ------
bins = sc.woebin(dt_s, y="creditability")
# sc.woebin_plot(bins)
# binning adjustment
# # adjust breaks interactively
# breaks_adj = sc.woebin_adj(dt_s, "creditability", bins)
# # or specify breaks manually
breaks_adj = {
'age.in.years': [26, 35, 40],
'other.debtors.or.guarantors': ["none", "co-applicant%,%guarantor"]
}
bins_adj = sc.woebin(dt_s, y="creditability", breaks_list=breaks_adj)
# converting train and test into woe values
train_woe = sc.woebin_ply(train, bins_adj)
test_woe = sc.woebin_ply(test, bins_adj)
y_train = train_woe.loc[:,'creditability']
X_train = train_woe.loc[:,train_woe.columns != 'creditability']
y_test = test_woe.loc[:,'creditability']
X_test = test_woe.loc[:,train_woe.columns != 'creditability']
# logistic regression ------
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(penalty='l1', C=0.9, solver='saga', n_jobs=-1)
lr.fit(X_train, y_train)
# lr.coef_
# lr.intercept_
# predicted proability
train_pred = lr.predict_proba(X_train)[:,1]
test_pred = lr.predict_proba(X_test)[:,1]
# performance ks & roc ------
train_perf = sc.perf_eva(y_train, train_pred, title = "train")
test_perf = sc.perf_eva(y_test, test_pred, title = "test")
# score ------
card = sc.scorecard(bins_adj, lr, X_train.columns)
# credit score
train_score = sc.scorecard_ply(train, card, print_step=0)
test_score = sc.scorecard_ply(test, card, print_step=0)
# psi
sc.perf_psi(
score = {'train':train_score, 'test':test_score},
label = {'train':y_train, 'test':y_test}
)
```
%prep
%autosetup -n scorecardpy-0.1.9.2
%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-scorecardpy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.9.2-1
- Package Spec generated
|
