Skip to content
Scorecard Development in python, 评分卡
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.vscode pdo Nov 17, 2018
scorecardpy Update condition_fun.py May 6, 2019
.DS_Store pdo Nov 17, 2018
.gitignore #11 Apr 23, 2019
LICENSE version Oct 28, 2018
MANIFEST.in version Oct 28, 2018
NEWS.md save breaks Jan 23, 2019
README.md version Oct 28, 2018
setup.py version Oct 28, 2018

README.md

scorecardpy

PyPI release Downloads PyPI version

This package is python version of R package 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 with:
pip install scorecardpy
  • Install the latest version of scorecardpy from github 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:

# 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}
)
You can’t perform that action at this time.