Datasets:
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"""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",
"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 = {
"default": {
"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"),
"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 Grad": 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 = """"""
# Dataset info
urls_per_split = {
"train": "https://gist.githubusercontent.com/msetzu/6c83dc3b7092d428ae2f08dc91e1020c/raw/9fc3171b293d0dc29963357450308eb4c7e3a15b/Train_Dataset.csv",
"test": "https://gist.githubusercontent.com/msetzu/f0032b855008f579299d7ad78d9dd9c2/raw/ba42badeb10b505cb283bdb16d3de581ffe7a332/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):
# dataset versions
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 = 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["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)
data["owns_a_house"] = data["owns_a_house"].apply(bool)
data["is_male"] = data["is_male"].apply(bool)
data["has_provided_a_mobile_number"] = data["has_provided_a_mobile_number"].apply(bool)
data["has_provided_a_home_number"] = data["has_provided_a_home_number"].apply(bool)
data["was_reachable_at_work"] = data["was_reachable_at_work"].apply(bool)
data["same_residence_and_home"] = data["same_residence_and_home"].apply(bool)
data["same_work_and_home"] = data["same_work_and_home"].apply(bool)
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())]
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