"""Mammography"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "rads",
    "age",
    "shape",
    "margin",
    "density",
    "is_severe"
]
_BASE_FEATURE_NAMES = [
    "age",
    "shape",
    "margin",
    "density",
    "is_severe"
]
_ENCODING_DICS = {
    "shape": {
        1: "round",
        2: "oval",
        3: "lobular",
        4: "irregular",
    },
    "margin": {
        1: "circumbscribed",
        2: "microlobulated",
        3: "obscured",
        4: "ill-defined",
        5: "spiculated",
    },
    "density": {
        1: "high",
        2: "iso",
        3: "low",
        4: "fat-containing",
        5: "spiculated",
    },
}

DESCRIPTION = "Mammography dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Mammography"
_URLS = ("https://huggingface.co/datasets/mstz/mammography/raw/mammography_masses.data")
_CITATION = """
@misc{misc_mammographic_mass_161,
  author       = {Elter,Matthias},
  title        = {{Mammographic Mass}},
  year         = {2007},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C53K6Z}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/mammography/raw/main/mammographic_masses.data"
}
features_types_per_config = {
    "mammography": {
        
        "over_threshold": 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 MammographyConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(MammographyConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Mammography(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "mammography"
    BUILDER_CONFIGS = [       
        MammographyConfig(name="mammography",
                          description="Mammography 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, header=None)
        data.columns = _ORIGINAL_FEATURE_NAMES
        
        data.drop("rads", axis="columns", inplace=True)
        data = data[(data.age != "?") & (data.shape != "?") & (data.margin != "?") & (data.density != "?")]
        data = data.infer_objects()
        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row


    def encode(self, feature, value):
        if feature in _ENCODING_DICS:
            return _ENCODING_DICS[feature][value]
        raise ValueError(f"Unknown feature: {feature}")