"""Yeast Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {
	"class": {
		"MIT": 0,
		"NUC": 1,
		"CYT": 2,
		"ME1": 3,
		"EXC": 4,
		"ME2": 5,
		"ME3": 6,
		"VAC": 7,
		"POX": 8,
		"ERL": 9
	}
}

DESCRIPTION = "Yeast dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/110/yeast"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/110/yeast")
_CITATION = """
@misc{misc_yeast_110,
	author       = {Nakai,Kenta},
	title        = {{Yeast}},
	year         = {1996},
	howpublished = {UCI Machine Learning Repository},
	note         = {{DOI}: \\url{10.24432/C5KG68}}
}
"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co./datasets/mstz/yeast/raw/main/yeast.csv"
}
features_types_per_config = {
	"yeast": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=10)
	},
	"yeast_0": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_1": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_2": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_3": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_4": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_5": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_6": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_7": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_8": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"yeast_9": {
		"mcg": datasets.Value("float64"),
		"gvh": datasets.Value("float64"),
		"alm": datasets.Value("float64"),
		"mit": datasets.Value("float64"),
		"erl": datasets.Value("bool"),
		"pox": datasets.Value("float64"),
		"vac": datasets.Value("float64"),
		"nuc": datasets.Value("float64"),
		"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 YeastConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(YeastConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Yeast(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "yeast"
	BUILDER_CONFIGS = [
		YeastConfig(name="yeast", description="Yeast for multiclass classification."),
		YeastConfig(name="yeast_0", description="Yeast for binary classification."),
		YeastConfig(name="yeast_1", description="Yeast for binary classification."),
		YeastConfig(name="yeast_2", description="Yeast for binary classification."),
		YeastConfig(name="yeast_3", description="Yeast for binary classification."),
		YeastConfig(name="yeast_4", description="Yeast for binary classification."),
		YeastConfig(name="yeast_5", description="Yeast for binary classification."),
		YeastConfig(name="yeast_6", description="Yeast for binary classification."),
		YeastConfig(name="yeast_7", description="Yeast for binary classification."),
		YeastConfig(name="yeast_8", description="Yeast for binary classification."),
		YeastConfig(name="yeast_9", description="Yeast 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)
		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:
		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data.loc[:, feature] = data[feature].apply(encoding_function)

		data["erl"] = data["erl"].apply(lambda x: True if x == 1 else False)
		data = data.astype({"erl": "bool"})

		if self.config.name == "yeast_0":
			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
		elif self.config.name == "yeast_1":
			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
		elif self.config.name == "yeast_2":
			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
		elif self.config.name == "yeast_3":
			data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
		elif self.config.name == "yeast_4":
			data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
		elif self.config.name == "yeast_5":
			data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
		elif self.config.name == "yeast_6":
			data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
		elif self.config.name == "yeast_7":
			data["class"] = data["class"].apply(lambda x: 1 if x == 7 else 0)
		elif self.config.name == "yeast_8":
			data["class"] = data["class"].apply(lambda x: 1 if x == 8 else 0)
		elif self.config.name == "yeast_9":
			data["class"] = data["class"].apply(lambda x: 1 if x == 9 else 0)
				
		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}")