from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "id",
    "clump_thickness",
    "uniformity_of_cell_size",
    "uniformity_of_cell_shape",
    "marginal_adhesion",
    "single_epithelial_cell_size",
    "bare_nuclei",
    "bland_chromatin",
    "normal_nucleoli",
    "mitoses",
    "is_cancer"
]
_BASE_FEATURE_NAMES = [
    "clump_thickness",
    "uniformity_of_cell_size",
    "uniformity_of_cell_shape",
    "marginal_adhesion",
    "single_epithelial_cell_size",
    "bare_nuclei",
    "bland_chromatin",
    "normal_nucleoli",
    "mitoses",
    "is_cancer"
]

DESCRIPTION = "Breast dataset for cancer prediction."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29"
_URLS = ("https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data")
_CITATION = """
@article{wolberg1990multisurface,
  title={Multisurface method of pattern separation for medical diagnosis applied to breast cytology.},
  author={Wolberg, William H and Mangasarian, Olvi L},
  journal={Proceedings of the national academy of sciences},
  volume={87},
  number={23},
  pages={9193--9196},
  year={1990},
  publisher={National Acad Sciences}
}
"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data",
}
features_types_per_config = {
    "cancer": {
        "clump_thickness": datasets.Value("int8"),
        "uniformity_of_cell_size": datasets.Value("int8"),
        "uniformity_of_cell_shape": datasets.Value("int8"),
        "marginal_adhesion": datasets.Value("int8"),
        "single_epithelial_cell_size": datasets.Value("int8"),
        "bare_nuclei": datasets.Value("int8"),
        "bland_chromatin": datasets.Value("int8"),
        "normal_nucleoli": datasets.Value("int8"),
        "mitoses": datasets.Value("int8"),
        "is_cancer": 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 BreastConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(BreastConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Breast(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "cancer"
    BUILDER_CONFIGS = [
        BreastConfig(name="cancer",
                   description="Breast cancer 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):
        if self.config.name == "cancer":
            data = pandas.read_csv(filepath, header=None)
            data.columns=_ORIGINAL_FEATURE_NAMES

            data = self.preprocess(data, config=self.config.name)

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

                yield row_id, data_row
        else:
            raise ValueError(f"Unknown config: {self.config.name}")

    def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
        data.drop("id", axis="columns", inplace=True)

        data = data[data.bare_nuclei != "?"]
        data = data.astype({f: int for f in data.columns})
        
        data.columns = _BASE_FEATURE_NAMES
        data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1)
        

        return data