| """NBFI: A Census Dataset""" |
|
|
| from typing import List |
| from functools import partial |
|
|
| import datasets |
|
|
| import pandas |
|
|
|
|
| VERSION = datasets.Version("1.0.0") |
| _ORIGINAL_FEATURE_NAMES = [ |
| "ID", |
| "Client_Income", |
| "Car_Owned", |
| "Bike_Owned", |
| "Active_Loan", |
| "House_Own", |
| "Child_Count", |
| "Credit_Amount", |
| "Loan_Annuity", |
| "Accompany_Client", |
| "Client_Income_Type", |
| "Client_Education", |
| "Client_Marital_Status", |
| "Client_Gender", |
| "Loan_Contract_Type", |
| "Client_Housing_Type", |
| "Population_Region_Relative", |
| "Age_Days", |
| "Employed_Days", |
| "Registration_Days", |
| "ID_Days", |
| "Own_House_Age", |
| "Mobile_Tag", |
| "Homephone_Tag", |
| "Workphone_Working", |
| "Client_Occupation", |
| "Client_Family_Members", |
| "Cleint_City_Rating", |
| "Application_Process_Day", |
| "Application_Process_Hour", |
| "Client_Permanent_Match_Tag", |
| "Client_Contact_Work_Tag", |
| "Type_Organization", |
| "Score_Source_1", |
| "Score_Source_2", |
| "Score_Source_3", |
| "Social_Circle_Default", |
| "Phone_Change", |
| "Credit_Bureau", |
| "Default" |
| ] |
| _BASE_FEATURE_NAMES = [ |
| "income", |
| "owns_a_car", |
| "owns_a_bike", |
| "has_an_active_loan", |
| "owns_a_house", |
| "nr_children", |
| "credit", |
| "loan_annuity", |
| "accompanied_by", |
| "income_type", |
| "education_level", |
| "marital_status", |
| "is_male", |
| "type_of_contract", |
| "type_of_housing", |
| "residence_density", |
| "age_in_days", |
| "consecutive_days_of_employment", |
| "nr_days_since_last_registration_change", |
| "nr_days_since_last_document_change", |
| "owned_a_house_for_nr_days", |
| "has_provided_a_mobile_number", |
| "has_provided_a_home_number", |
| "was_reachable_at_work", |
| "job", |
| "nr_family_members", |
| "city_rating", |
| "weekday_of_application", |
| "hour_of_application", |
| "same_residence_and_home", |
| "same_work_and_home", |
| "score_1", |
| "score_2", |
| "score_3", |
| "nr_defaults_in_social_circle", |
| "inquiries_in_last_year", |
| "has_defaulted" |
| ] |
| features_types_per_config = { |
| "income": datasets.Value("float32"), |
| "owns_a_car": datasets.Value("bool"), |
| "owns_a_bike": datasets.Value("bool"), |
| "has_an_active_loan": datasets.Value("bool"), |
| "owns_a_house": datasets.Value("bool"), |
| "nr_children": datasets.Value("int8"), |
| "credit": datasets.Value("float32"), |
| "loan_annuity": datasets.Value("float32"), |
| "accompanied_by": datasets.Value("string"), |
| "income_type": datasets.Value("string"), |
| "education_level": datasets.Value("float32"), |
| "marital_status": datasets.Value("float32"), |
| "is_male": datasets.Value("bool"), |
| "type_of_contract": datasets.Value("string"), |
| "type_of_housing": datasets.Value("string"), |
| "residence_density": datasets.Value("float32"), |
| "age_in_days": datasets.Value("int32"), |
| "consecutive_days_of_employment": datasets.Value("int16"), |
| "nr_days_since_last_registration_change": datasets.Value("int32"), |
| "nr_days_since_last_document_change": datasets.Value("int32"), |
| "owned_a_house_for_nr_days": datasets.Value("int32"), |
| "has_provided_a_mobile_number": datasets.Value("bool"), |
| "has_provided_a_home_number": datasets.Value("bool"), |
| "was_reachable_at_work": datasets.Value("bool"), |
| "job": datasets.Value("string"), |
| "nr_family_members": datasets.Value("int8"), |
| "city_rating": datasets.Value("int8"), |
| "weekday_of_application": datasets.Value("int8"), |
| "hour_of_application": datasets.Value("float32"), |
| "same_residence_and_home": datasets.Value("bool"), |
| "same_work_and_home": datasets.Value("bool"), |
| "score_1": datasets.Value("float32"), |
| "score_2": datasets.Value("float32"), |
| "score_3": datasets.Value("float32"), |
| "nr_defaults_in_social_circle": datasets.Value("int8"), |
| "inquiries_in_last_year": datasets.Value("float32"), |
| "has_defaulted": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
| } |
| _ENCODING_DICS = {} |
| _EDUCATION_ENCODING = { |
| "Junior secondary": 0, |
| "Secondary": 1, |
| "Graduation dropout": 2, |
| "Graduation": 2, |
| "Post": 4 |
| } |
|
|
| DESCRIPTION = "NBFI dataset from default prediction." |
| _HOMEPAGE = "https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset" |
| _URLS = ("https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset") |
| _CITATION = """""" |
|
|
| |
| urls_per_split = { |
| "train": "https://huggingface.co/datasets/mstz/nbfi/raw/main/Train_Dataset.csv", |
| "test": "https://huggingface.co/datasets/mstz/nbfi/raw/main/Test_Dataset.csv" |
| } |
|
|
| features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
| class NBFIConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(NBFIConfig, self).__init__(version=VERSION, **kwargs) |
| self.features = features_per_config[kwargs["name"]] |
|
|
|
|
| class NBFI(datasets.GeneratorBasedBuilder): |
| |
| DEFAULT_CONFIG = "default" |
| BUILDER_CONFIGS = [ |
| NBFIConfig(name="default", |
| description="NBFI for default binary classification.") |
| ] |
|
|
|
|
| def _info(self): |
| if self.config.name not in features_per_config: |
| raise ValueError(f"Unknown configuration: {self.config.name}") |
| |
| info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
| features=features_per_config[self.config.name]) |
|
|
| return info |
| |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
| downloads = dl_manager.download_and_extract(urls_per_split) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}), |
| ] |
| |
| def _generate_examples(self, filepath: str): |
| if self.config.name == "default": |
| data = pandas.read_csv(filepath) |
| data = self.preprocess(data, config=self.config.name) |
|
|
| for row_id, row in data.iterrows(): |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
| else: |
| raise ValueError(f"Unknown config: {self.config.name}") |
|
|
|
|
|
|
| def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
| data.drop("ID", axis="columns", inplace=True) |
| data.drop("Own_House_Age", axis="columns", inplace=True) |
| data.drop("Type_Organization", axis="columns", inplace=True) |
| data.drop("Phone_Change", axis="columns", inplace=True) |
|
|
| data = data[~data.Client_Education.isna()] |
| data = data[~data.Client_Marital_Status.isna()] |
| data = data[~data.Client_Gender.isna()] |
| data = data[~data.Loan_Contract_Type.isna()] |
| data = data[~data.Client_Housing_Type.isna()] |
| data = data[~data.Age_Days.isna()] |
| data = data[~data.Employed_Days.isna()] |
| data = data[~data.Registration_Days.isna()] |
| data = data[~data.ID_Days.isna()] |
| data = data[~data.Cleint_City_Rating.isna()] |
| data = data[~data.Application_Process_Day.isna()] |
| data = data[~data.Application_Process_Hour.isna()] |
| data = data[~data.Client_Permanent_Match_Tag.isna()] |
| data = data[~data.Client_Contact_Work_Tag.isna()] |
| data.columns = _BASE_FEATURE_NAMES |
|
|
| data.iloc[:, "owns_a_car"] = data["owns_a_car"].apply(bool) |
| data.iloc[:, "owns_a_bike"] = data["owns_a_bike"].apply(bool) |
| data.iloc[:, "has_an_active_loan"] = data["has_an_active_loan"].apply(bool) |
| data.iloc[:, "owns_a_house"] = data["owns_a_house"].apply(bool) |
| data.iloc[:, "is_male"] = data["is_male"].apply(bool) |
| data.iloc[:, "has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool) |
| data.iloc[:, "has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool) |
| data.iloc[:, "was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool) |
| data.iloc[:, "same_residence_and_home"] = data["same_residence_and_home"].apply(bool) |
| data.iloc[:, "same_work_and_home"] = data["same_work_and_home"].apply(bool) |
| |
| return data[list(features_types_per_config[config].keys())] |
|
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|
