"""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