"""Nursery Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {
	"is_family_financially_stable": {
		"convenient": True,
		"inconvenient": False
	}
}

DESCRIPTION = "Nursery dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/69/molecular+biology+nursery+junction+gene+sequences"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/69/molecular+biology+nursery+junction+gene+sequences")
_CITATION = """
@misc{misc_nursery_76,
  author       = {Rajkovic,Vladislav},
  title        = {{Nursery}},
  year         = {1997},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5P88W}}
}
"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/nursery/raw/main/nursery.data"
}
features_types_per_config = {
    "nursery": {
		"parents_attitude": datasets.Value("string"),
		"current_nursery_status": datasets.Value("string"),
		"form": datasets.Value("string"),
		"number_of_children": datasets.Value("int8"),
		"housing_status": datasets.Value("string"),
		"is_family_financially_stable": datasets.Value("bool"),
		"social_status": datasets.Value("string"),
		"health_status": datasets.Value("string"),
        "recommendation": datasets.ClassLabel(num_classes=5, names=("not recommended", "recommended", "priority recommendation",
																	"highly recommended", "specifically recommended"))
    },
	"nursery_binary": {
		"parents_attitude": datasets.Value("string"),
		"current_nursery_status": datasets.Value("string"),
		"form": datasets.Value("string"),
		"number_of_children": datasets.Value("int8"),
		"housing_status": datasets.Value("string"),
		"is_family_financially_stable": datasets.Value("bool"),
		"social_status": datasets.Value("string"),
		"health_status": datasets.Value("string"),
        "recommendation": 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 NurseryConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(NurseryConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Nursery(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "nursery"
    BUILDER_CONFIGS = [
        NurseryConfig(name="nursery",
                   description="Nursery for multiclass classification."),
        NurseryConfig(name="nursery_binary",
                   description="Nursery 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:
        if self.config.name == "nursery_binary":
            data["recommendation"] = data["recommendation"].apply(lambda x: 1 if x > 0 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}")