from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")


DESCRIPTION = "Hayes efficiency dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/242/hayes+efficiency"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/hayes+method+choice")
_CITATION = """
@misc{misc_hayes_efficiency_242,
  author       = {Tsanas,Athanasios & Xifara,Angeliki},
  title        = {{Hayes efficiency}},
  year         = {2012},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C51307}}
}"""

# Dataset info
_BASE_FEATURE_NAMES = [
	"name",
	"hobby",
	"age",
	"educational_level",
	"marital_level",
	"class"
]
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/hayes_roth/raw/main/hayes.data"
}
features_types_per_config = {
	"hayes": {
		"hobby": datasets.Value("string"),
		"age": datasets.Value("int8"),
		"educational_level": datasets.Value("int8"),
		"marital_level": datasets.Value("string"),
		"class": datasets.ClassLabel(num_classes=3)
	},
	"hayes_1": {
		"hobby": datasets.Value("string"),
		"age": datasets.Value("int8"),
		"educational_level": datasets.Value("int8"),
		"marital_level": datasets.Value("string"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"hayes_2": {
		"hobby": datasets.Value("string"),
		"age": datasets.Value("int8"),
		"educational_level": datasets.Value("int8"),
		"marital_level": datasets.Value("string"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"hayes_3": {
		"hobby": datasets.Value("string"),
		"age": datasets.Value("int8"),
		"educational_level": datasets.Value("int8"),
		"marital_level": datasets.Value("string"),
		"class": datasets.ClassLabel(num_classes=2)
	}	
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class HayesConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(HayesConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Hayes(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "hayes"
	BUILDER_CONFIGS = [
		HayesConfig(name="hayes", description="Hayes dataset."),
		HayesConfig(name="hayes_1", description="Hayes for binary classification (is example of class 1?)."),
		HayesConfig(name="hayes_2", description="Hayes for binary classification (is example of class 2?)."),
		HayesConfig(name="hayes_3", description="Hayes for binary classification (is example of class 3?).")
	]


	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: pandas.DataFrame) -> pandas.DataFrame:
		data.columns = _BASE_FEATURE_NAMES
		data.drop("name", axis="columns", inplace=True)

		if self.config.name == "hayes_1":
			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
		elif self.config.name == "hayes_2":
			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
		elif self.config.name == "hayes_3":
			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
		else:
			data.loc[:, "class"] = data["class"].apply(lambda x: x - 1)


		return data