Datasets:
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"""Australian_credit"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Australian_credit dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Australian_credit"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Australian_credit")
_CITATION = """
@misc{misc_statlog_(australian_credit_approval)_143,
author = {Quinlan,Ross},
title = {{Statlog (Australian Credit Approval)}},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C59012}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/australian_credit/raw/main/australian.dat"
}
features_types_per_config = {
"australian_credit": {
"feature_1": datasets.Value("bool"),
"feature_2": datasets.Value("float64"),
"feature_3": datasets.Value("float64"),
"feature_4": datasets.Value("string"),
"feature_5": datasets.Value("string"),
"feature_6": datasets.Value("string"),
"feature_7": datasets.Value("float64"),
"feature_8": datasets.Value("string"),
"feature_9": datasets.Value("string"),
"feature_10": datasets.Value("float64"),
"feature_11": datasets.Value("string"),
"feature_12": datasets.Value("string"),
"feature_13": datasets.Value("float64"),
"feature_14": datasets.Value("float64"),
"is_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 AustralianCreditConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(AustralianCreditConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class AustralianCredit(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "australian_credit"
BUILDER_CONFIGS = [
AustralianCreditConfig(name="australian_credit",
description="Australian_credit for binary classification.")
]
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):
data = pandas.read_csv(filepath, header=None, sep=" ")
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data):
features = list(features_types_per_config[self.config.name])
data.columns = features
return data
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