Skip to main content

Credit Risk Scorecard

Project description

scorecardpy

PyPI version PyPI release Downloads Downloads

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}
)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scorecardpy-0.1.9.7.tar.gz (58.1 kB view details)

Uploaded Source

File details

Details for the file scorecardpy-0.1.9.7.tar.gz.

File metadata

  • Download URL: scorecardpy-0.1.9.7.tar.gz
  • Upload date:
  • Size: 58.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.6

File hashes

Hashes for scorecardpy-0.1.9.7.tar.gz
Algorithm Hash digest
SHA256 a81c7e6f3bf5f10a87b61af73b25f1fc8bc5acbadf5d9e38c3addb02df128d03
MD5 459dd45355dad6194debd4b8b317baf7
BLAKE2b-256 565a7bae50d0875192a86d2153535fa221596c9f0c83b86dd7c25b005aa37de9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page