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australian_credit.py

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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)
+		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.features = features
+
+		return data