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)
- Install the release version of
scorecardpyfrom PYPI with:
pip install scorecardpy - Install the latest version of
scorecardpyfrom github with:
pip install git+git://github.com/shichenxie/scorecardpy.git 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} )