"""Soybean Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {
	"class": {
		value: i for i, value in enumerate(["diaporthe_stem_canker",
											"charcoal_rot", "rhizoctonia_root_rot",
											"phytophthora_rot", "brown_stem_rot", "powdery_mildew",
											"downy_mildew", "brown_spot", "bacterial_blight",
       										"bacterial_pustule", "purple_seed_stain", "anthracnose",
       										"phyllosticta_leaf_spot", "alternarialeaf_spot",
       										"frog_eye_leaf_spot", "diaporthe_pod_&_stem_blight",
       										"cyst_nematode", "2_4_d_injury", "herbicide_injury"])
	}
}
_BASE_FEATURE_NAMES = [
	"date",
	"plant_stand",
	"precip",
	"temp",
	"hail",
	"crop_hist",
	"area_damaged",
	"severity",
	"seed_tmt",
	"germination",
	"plant_growth",
	"leaves",
	"leafspots_halo",
	"leafspots_marg",
	"leafspot_size",
	"leaf_shread",
	"leaf_malf",
	"leaf_mild",
	"stem",
	"lodging",
	"stem_cankers",
	"canker_lesion",
	"fruiting_bodies",
	"external decay",
	"mycelium",
	"int_discolor",
	"sclerotia",
	"fruit_pods",
	"fruit spots",
	"seed",
	"mold_growth",
	"seed_discolor",
	"seed_size",
	"shriveling",
	"roots",
	"class",
]

DESCRIPTION = "Soybean dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990")
_CITATION = """
@misc{misc_soybean_(large)_90,
  author       = {Michalski,R.S. & Chilausky,R.L.},
  title        = {{Soybean (Large)}},
  year         = {1988},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5JG6Z}}
}
"""

# Dataset info
urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/soybean/resolve/main/soybean.csv"
}
features_types_per_config = {
	"soybean": {
		"date": datasets.Value("string"),
		"plant_stand": datasets.Value("string"),
		"precip": datasets.Value("string"),
		"temp": datasets.Value("string"),
		"hail": datasets.Value("string"),
		"crop_hist": datasets.Value("string"),
		"area_damaged": datasets.Value("string"),
		"severity": datasets.Value("string"),
		"seed_tmt": datasets.Value("string"),
		"germination": datasets.Value("string"),
		"plant_growth": datasets.Value("string"),
		"leaves": datasets.Value("string"),
		"leafspots_halo": datasets.Value("string"),
		"leafspots_marg": datasets.Value("string"),
		"leafspot_size": datasets.Value("string"),
		"leaf_shread": datasets.Value("string"),
		"leaf_malf": datasets.Value("string"),
		"leaf_mild": datasets.Value("string"),
		"stem": datasets.Value("string"),
		"lodging": datasets.Value("string"),
		"stem_cankers": datasets.Value("string"),
		"canker_lesion": datasets.Value("string"),
		"fruiting_bodies": datasets.Value("string"),
		"external decay": datasets.Value("string"),
		"mycelium": datasets.Value("string"),
		"int_discolor": datasets.Value("string"),
		"sclerotia": datasets.Value("string"),
		"fruit_pods": datasets.Value("string"),
		"fruit spots": datasets.Value("string"),
		"seed": datasets.Value("string"),
		"mold_growth": datasets.Value("string"),
		"seed_discolor": datasets.Value("string"),
		"seed_size": datasets.Value("string"),
		"shriveling": datasets.Value("string"),
		"roots": datasets.Value("string"),
		"class": datasets.ClassLabel(num_classes=19,
									 names=["diaporthe_stem_canker",
											"charcoal_rot", "rhizoctonia_root_rot",
											"phytophthora_rot", "brown_stem_rot", "powdery_mildew",
											"downy_mildew", "brown_spot", "bacterial_blight",
       										"bacterial_pustule", "purple_seed_stain", "anthracnose",
       										"phyllosticta_leaf_spot", "alternarialeaf_spot",
       										"frog_eye_leaf_spot", "diaporthe_pod_&_stem_blight",
       										"cyst_nematode", "2_4_d_injury", "herbicide_injury"])
	}
}
for c in _ENCODING_DICS["class"].keys():
	features_types_per_config[c] = {
		"date": datasets.Value("string"),
		"plant_stand": datasets.Value("string"),
		"precip": datasets.Value("string"),
		"temp": datasets.Value("string"),
		"hail": datasets.Value("string"),
		"crop_hist": datasets.Value("string"),
		"area_damaged": datasets.Value("string"),
		"severity": datasets.Value("string"),
		"seed_tmt": datasets.Value("string"),
		"germination": datasets.Value("string"),
		"plant_growth": datasets.Value("string"),
		"leaves": datasets.Value("string"),
		"leafspots_halo": datasets.Value("string"),
		"leafspots_marg": datasets.Value("string"),
		"leafspot_size": datasets.Value("string"),
		"leaf_shread": datasets.Value("string"),
		"leaf_malf": datasets.Value("string"),
		"leaf_mild": datasets.Value("string"),
		"stem": datasets.Value("string"),
		"lodging": datasets.Value("string"),
		"stem_cankers": datasets.Value("string"),
		"canker_lesion": datasets.Value("string"),
		"fruiting_bodies": datasets.Value("string"),
		"external decay": datasets.Value("string"),
		"mycelium": datasets.Value("string"),
		"int_discolor": datasets.Value("string"),
		"sclerotia": datasets.Value("string"),
		"fruit_pods": datasets.Value("string"),
		"fruit spots": datasets.Value("string"),
		"seed": datasets.Value("string"),
		"mold_growth": datasets.Value("string"),
		"seed_discolor": datasets.Value("string"),
		"seed_size": datasets.Value("string"),
		"shriveling": datasets.Value("string"),
		"roots": datasets.Value("string"),
		"class": 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 SoybeanConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
		super(SoybeanConfig, self).__init__(version=VERSION, **kwargs)
		self.features = features_per_config[kwargs["name"]]


class Soybean(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "soybean"
	binary_configurations = [SoybeanConfig(name=c, description=f"Is this instance of class {c}?")
							 for c in _ENCODING_DICS["class"].keys()]
	BUILDER_CONFIGS = [SoybeanConfig(name="soybean", description="Soybean for binary classification.")]
	BUILDER_CONFIGS += binary_configurations


	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["class"] = data["class"].apply(lambda x: x.replace("-", "_"))

		for c in _ENCODING_DICS["class"].keys():
			if self.config.name == c:
				data["class"] = data["class"].apply(lambda x: 1 if x == c else 0)
				break

		for feature in _ENCODING_DICS:
			encoding_function = partial(self.encode, feature)
			data[feature] = data[feature].apply(encoding_function)
		
		data = data.rename(columns={"instance migration_code_change_in_msa": "migration_code_change_in_msa"})

				
		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}")