"""Sydt Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}
_BASE_FEATURE_NAMES = [
	"salary",
	"commission",
	"age",
	"education",
	"car",
	"zip",
	"housevalue",
	"yearsowned",
	"loan",
	"class",
]

DESCRIPTION = "Sydt dataset."
_HOMEPAGE = ""
_URLS = ("")
_CITATION = """"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/sydt/resolve/main/sydt.csv"
}
features_types_per_config = {
	"sydt": {
		"salary": datasets.Value("int64"),
		"commission": datasets.Value("int64"),
		"age": datasets.Value("int64"),
		"education": datasets.Value("int64"),
		"car": datasets.Value("int64"),
		"zip": datasets.Value("string"),
		"housevalue": datasets.Value("int64"),
		"yearsowned": datasets.Value("int64"),
		"loan": datasets.Value("int64"),
		"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 SydtConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(SydtConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Sydt(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "sydt"
	BUILDER_CONFIGS = [SydtConfig(name="sydt", description="Sydt 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: pandas.DataFrame) -> pandas.DataFrame:
		data.columns = _BASE_FEATURE_NAMES
		data = data[~data["class"].isna()]
		data["class"] = data["class"].apply(lambda x: x - 1)

		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data[feature] = data[feature].apply(encoding_function)
				
		return data[list(features_types_per_config[self.config.name].keys())]

	def encode(self, feature, value):
		if feature in _ENCODING_DICS:
			return _ENCODING_DICS[feature][value]
		raise ValueError(f"Unknown feature: {feature}")