@@ -10,4 +10,5 @@ pandas==1.5.3
scikit-learn==1.1.3
pyhive==0.7.0
thrift==0.21.0
-thrift-sasl==0.4.3
+thrift-sasl==0.4.3
+seaborn==0.13.2
@@ -5,8 +5,10 @@ scorecardpy==0.1.9.7
dataframe_image==0.1.14
matplotlib==3.3.4
numpy==1.19.5
-pandas==1.1.1
+pandas==1.1.5
scikit-learn==0.24.2
+seaborn==0.11.2
+contextvars==2.4
@@ -17,6 +17,10 @@ if __name__ == "__main__":
# 加载数据
dat = sc.germancredit()
+ dat_columns = dat.columns.tolist()
+ dat_columns = [c.replace(".","_") for c in dat_columns]
+ dat.columns = dat_columns
+
dat["creditability"] = dat["creditability"].apply(lambda x: 1 if x == "bad" else 0)
data = DataSplitEntity(train_data=dat[:709], val_data=None, test_data=dat[709:])