"""PostOperative Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

_ENCODING_DICS = {
    "internal_temperature": {
        "low": 0,
        "mid": 1,
        "high": 2
    },
    "surface_temperature": {
        "low": 0,
        "mid": 1,
        "high": 2
    },
    "oxigen_saturation": {
        "good": 0,
        "excellent": 1
    },
    "blood_pressure": {
        "low": 0,
        "mid": 1,
        "high": 2
    },
    "is_surface_temperature_stable": {
        "stable": True,
        "unstable": False,
    },
    "is_internal_temperature_stable": {
        "stable": 2,
        "mod-stable": 1,
        "unstable": 0,
    },
    "is_blood_pressure_stable": {
        "stable": 2,
        "mod-stable": 1,
        "unstable": 0,
    }
}

DESCRIPTION = "PostOperative dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification")
_CITATION = """
@misc{misc_post-operative_patient_82,
  author       = {Summers,Sharon & Woolery,Linda},
  title        = {{Post-Operative Patient}},
  year         = {1993},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5DG6Q}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/post_operative/raw/main/post_operative.data"
}
features_types_per_config = {
    "post_operative": {
        "internal_temperature": datasets.Value("int8"),
        "surface_temperature": datasets.Value("int8"),
        "oxigen_saturation": datasets.Value("int8"),
        "blood_pressure": datasets.Value("int8"),
        "is_surface_temperature_stable": datasets.Value("bool"),
        "is_internal_temperature_stable": datasets.Value("int8"),
        "is_blood_pressure_stable": datasets.Value("int8"),
        "perceived_comfort": datasets.Value("int8"),
        "decision": datasets.ClassLabel(num_classes=3, names=("discharge", "hospital floor", "intensive care")),
    },
    "post_operative_binary": {
		"internal_temperature": datasets.Value("int8"),
        "surface_temperature": datasets.Value("int8"),
        "oxigen_saturation": datasets.Value("int8"),
        "blood_pressure": datasets.Value("int8"),
        "is_surface_temperature_stable": datasets.Value("bool"),
        "is_internal_temperature_stable": datasets.Value("int8"),
        "is_blood_pressure_stable": datasets.Value("int8"),
        "perceived_comfort": datasets.Value("int8"),
        "decision": datasets.ClassLabel(num_classes=2, names=("discharge", "don't discharge")),
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class PostOperativeConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(PostOperativeConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class PostOperative(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "post_operative"
    BUILDER_CONFIGS = [
        PostOperativeConfig(name="post_operative",
                   description="PostOperative for regression."),
        PostOperativeConfig(name="post_operative_binary",
                   description="PostOperative 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 == "post_operative_binary":
            data["decision"] = data["decision"].apply(lambda x: 1 if x >= 1 else 0)

        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
        
        data = data.reset_index()
        data.drop("index", axis="columns", inplace=True)
        data = data[data.perceived_comfort != "?"]
                
        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}")