Upload nbfi.py

nbfi.py

@@ -218,12 +218,15 @@ class NBFI(datasets.GeneratorBasedBuilder):
         data = data[~data.Application_Process_Hour.isna()]
         data = data[~data.Client_Permanent_Match_Tag.isna()]
         data = data[~data.Client_Contact_Work_Tag.isna()]
+        data = data[~data.Score_Source_1.isna()]
+        data = data[~data.Score_Source_2.isna()]
+        data = data[~data.Score_Source_3.isna()]
+        data = data[~data.Credit_Bureau.isna()]
         
         data.columns = _BASE_FEATURE_NAMES
 
         data["education_level"] = data["education_level"].apply(lambda x: _EDUCATION_ENCODING[x])
         data["is_male"] = data["is_male"].apply(lambda x: x == "M")
-        data = data.astype({"is_male": "bool"})
         data["owns_a_car"] = data["owns_a_car"].apply(bool)
         data["owns_a_bike"] = data["owns_a_bike"].apply(bool)
         data["has_an_active_loan"] = data["has_an_active_loan"].apply(bool)
@@ -235,7 +238,11 @@ class NBFI(datasets.GeneratorBasedBuilder):
         data["same_residence_and_home"] = data["same_residence_and_home"].apply(bool)
         data["same_work_and_home"] = data["same_work_and_home"].apply(bool)
         
-        print(data.head())
+        data = data.astype({
+            "is_male": "bool",
+            "nr_children": "int8"
+        })
+        print([f for f in data.columns if "M" in data[f]])
 
         return data[list(features_types_per_config[config].keys())]