Datasets:
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"""German Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
"checking_account_status",
"account_life_in_months",
"credit_status",
"loan_purpose",
"current_credit",
"current_savings",
"employed_since",
"installment_rate_percentage",
"personal_status_and_sex",
"guarantors",
"years_living_in_current_residence",
"property",
"age",
"installment_plans",
"housing_status",
"nr_credit_accounts_in_bank",
"job_status",
"number_of_people_in_support",
"has_registered_phone_number",
"is_foreign",
"loan_granted",
]
_BASE_FEATURE_NAMES = [
"checking_account_status",
"account_life_in_months",
"credit_status",
"loan_purpose",
"current_credit",
"current_savings",
"employed_since",
"installment_rate_percentage",
"sex",
"marital_status",
"guarantors",
"years_living_in_current_residence",
"age",
"installment_plans",
"housing_status",
"nr_credit_accounts_in_bank",
"job_status",
"number_of_people_in_support",
"has_registered_phone_number",
"is_foreign",
"loan_granted"
]
_ENCODING_DICS = {
"is_foreign": {
"A201": 0,
"A202": 1
},
"has_registered_phone_number": {
"A191": 0,
"A192": 1
},
"job_status": {
"A171": 0,
"A172": 1,
"A173": 2,
"A174": 3
},
"housing_status": {
"A153": 0,
"A151": 1,
"A152": 2
},
"installment_plans": {
"A141": 0,
"A142": 1,
"A143": 2
},
"guarantors": {
"A101": 0,
"A102": 1,
"A103": 2
},
"marital_status": {
"A91": 0,
"A92": 0,
"A93": 1,
"A94": 2,
"A95": 1,
},
"sex": {
"A91": 0,
"A93": 0,
"A94": 0,
"A92": 1,
"A95": 1,
},
"employed_since": {
"A71": 0,
"A72": 1,
"A73": 2,
"A74": 3,
"A75": 4,
},
"current_savings": {
"A65": 0,
"A61": 1,
"A62": 2,
"A63": 3,
"A64": 4,
},
"credit_status": {
"A30": 0,
"A31": 1,
"A32": 2,
"A33": 3,
"A34": 4,
},
"checking_account_status": {
"A14": 0,
"A11": 1,
"A12": 2,
"A13": 3,
}
}
DESCRIPTION = "German dataset for cancer prediction."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/german/raw/main/german.data",
}
features_types_per_config = {
"encoding": {
"feature": datasets.Value("string"),
"original_value": datasets.Value("string"),
"encoded_value": datasets.Value("int8"),
},
"loan": {
"checking_account_status": datasets.Value("int8"),
"account_life_in_months": datasets.Value("int8"),
"credit_status": datasets.Value("int8"),
"loan_purpose": datasets.Value("string"),
"current_credit": datasets.Value("int32"),
"current_savings": datasets.Value("int8"),
"employed_since": datasets.Value("int8"),
"installment_rate_percentage": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"marital_status": datasets.Value("string"),
"guarantors": datasets.Value("int8"),
"years_living_in_current_residence": datasets.Value("int8"),
"age": datasets.Value("int8"),
"installment_plans": datasets.Value("string"),
"housing_status": datasets.Value("int8"),
"nr_credit_accounts_in_bank": datasets.Value("int8"),
"job_status": datasets.Value("int8"),
"number_of_people_in_support": datasets.Value("int8"),
"has_registered_phone_number": datasets.Value("int8"),
"is_foreign": datasets.Value("bool"),
"loan_granted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class GermanConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(GermanConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class German(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "loan"
BUILDER_CONFIGS = [
GermanConfig(name="encoding",
description="Encoding dictionaries for discrete features."),
GermanConfig(name="loan",
description="Binary classification of loan approval."),
]
def _info(self):
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"]}),
]
def _generate_examples(self, filepath: str):
if self.config.name == "encoding":
data = self.encoding_dics()
else:
data = pandas.read_csv(filepath, sep=" ", header=None)
data.columns=_ORIGINAL_FEATURE_NAMES
data = self.preprocess(data, config=self.config.name)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame, config: str = "loan") -> pandas.DataFrame:
for feature in _ENCODING_DICS:
if feature not in ["marital_status", "sex"]:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
encode_marital_status = partial(self.encode, "marital_status")
encode_sex = partial(self.encode, "sex")
data.loc[:, "marital_status"] = data.personal_status_and_sex.apply(encode_marital_status)
data.loc[:, "sex"] = data.personal_status_and_sex.apply(encode_sex)
data.loc[:, "loan_purpose"] = data.loan_purpose.apply(self.encode_loan_purpose)
data.loc[:, "loan_granted"] = data.loan_granted.apply(lambda x: x - 1)
data.drop("personal_status_and_sex", axis="columns", inplace=True)
data = data[_BASE_FEATURE_NAMES]
data = data.rename(columns={"sex": "is_male"})
data = data.astype({"is_foreign": "bool"})
data.loc[:, "is_foreign"] = data.is_foreign.apply(bool)
return data
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
def encoding_dics(self):
data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
for feature, d in _ENCODING_DICS.items()]
data = pandas.concat(data, axis="rows").reset_index()
data.drop("index", axis="columns", inplace=True)
data.columns = ["feature", "original_value", "encoded_value"]
return data
def encode_loan_purpose(self, code):
return {
"A40": "new car",
"A41": "used car",
"A42": "furniture/equipment",
"A43": "radio/television",
"A44": "domestic appliances",
"A45": "repairs",
"A46": "education",
"A47": "vacation ",
"A48": "retraining",
"A49": "business",
"A410": "others"
}[code]
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