"""WineOrigin Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {}

DESCRIPTION = "WineOrigin dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2")
_CITATION = """
@misc{misc_wine_origin_database_generator_(version_2)_108,
  author       = {Breiman,L. & Stone,C.J.},
  title        = {{Waveform Database Generator (Version 2)}},
  year         = {1988},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C56014}}
}
"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/wine_origin/raw/main/wine.data"
}
features_types_per_config = {
	"wine_origin": {
		"malic_acid": datasets.Value("float64"),
		"ash": datasets.Value("float64"),
		"alcalinity_of_ash": datasets.Value("float64"),
		"magnesium": datasets.Value("float64"),
		"phenols": datasets.Value("float64"),
		"flavanoids": datasets.Value("float64"),
		"nonflavanoid_phenols": datasets.Value("float64"),
		"proanthocyanins": datasets.Value("float64"),
		"color_intensity": datasets.Value("float64"),
		"hue": datasets.Value("float64"),
		"diluted_wines": datasets.Value("float64"),
		"proline": datasets.Value("float64"),
		"unknown": datasets.Value("int64"),
		"class": datasets.ClassLabel(num_classes=3)
	},
	"wine_origin_0": {
		"malic_acid": datasets.Value("float64"),
		"ash": datasets.Value("float64"),
		"alcalinity_of_ash": datasets.Value("float64"),
		"magnesium": datasets.Value("float64"),
		"phenols": datasets.Value("float64"),
		"flavanoids": datasets.Value("float64"),
		"nonflavanoid_phenols": datasets.Value("float64"),
		"proanthocyanins": datasets.Value("float64"),
		"color_intensity": datasets.Value("float64"),
		"hue": datasets.Value("float64"),
		"diluted_wines": datasets.Value("float64"),
		"proline": datasets.Value("float64"),
		"unknown": datasets.Value("int64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"wine_origin_1": {
		"malic_acid": datasets.Value("float64"),
		"ash": datasets.Value("float64"),
		"alcalinity_of_ash": datasets.Value("float64"),
		"magnesium": datasets.Value("float64"),
		"phenols": datasets.Value("float64"),
		"flavanoids": datasets.Value("float64"),
		"nonflavanoid_phenols": datasets.Value("float64"),
		"proanthocyanins": datasets.Value("float64"),
		"color_intensity": datasets.Value("float64"),
		"hue": datasets.Value("float64"),
		"diluted_wines": datasets.Value("float64"),
		"proline": datasets.Value("float64"),
		"unknown": datasets.Value("int64"),
		"class": datasets.ClassLabel(num_classes=2)
	},
	"wine_origin_2": {
		"malic_acid": datasets.Value("float64"),
		"ash": datasets.Value("float64"),
		"alcalinity_of_ash": datasets.Value("float64"),
		"magnesium": datasets.Value("float64"),
		"phenols": datasets.Value("float64"),
		"flavanoids": datasets.Value("float64"),
		"nonflavanoid_phenols": datasets.Value("float64"),
		"proanthocyanins": datasets.Value("float64"),
		"color_intensity": datasets.Value("float64"),
		"hue": datasets.Value("float64"),
		"diluted_wines": datasets.Value("float64"),
		"proline": datasets.Value("float64"),
		"unknown": 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 WineOriginConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(WineOriginConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class WineOrigin(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "wine_origin"
	BUILDER_CONFIGS = [
		WineOriginConfig(name="wine_origin", description="WineOrigin for multiclass classification."),
		WineOriginConfig(name="wine_origin_0", description="WineOrigin for binary classification."),
		WineOriginConfig(name="wine_origin_1", description="WineOrigin for binary classification."),
		WineOriginConfig(name="wine_origin_2", description="WineOrigin 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:
		data["class"] = data["class"].apply(lambda x: x - 1)

		if self.config.name == "wine_origin_0":
			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
		elif self.config.name == "wine_origin_1":
			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
		elif self.config.name == "wine_origin_2":
			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)

		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data.loc[:, 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}")