"""Fertility"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "season_of_sampling",
    "age_at_time_of_sampling",
    "has_had_childhood_diseases",
    "has_had_serious_trauma",
    "has_had_surgical_interventions",
    "has_had_high_fevers_in_the_past_year",
    "frequency_of_alcohol_consumption",
    "smoking_frequency",
    "number_of_sitting_hours_per_day",
    "has_fertility_issues"
]

_ENCODING_DICS = {
    "season_of_sampling" : {
        -1: "winter",
        -0.33: "spring",
        +0.33: "summer",
        +1: "fall",
    },
    "has_had_childhood_diseases" : {
        1: True,
        0: False
    },
    "has_had_serious_trauma" : {
        1: True,
        0: False
    },
    "has_had_surgical_interventions" : {
        1: True,
        0: False
    },
    "has_had_high_fevers_in_the_past_year" : {
        1: "no",
        0: "more than three months ago",
        -1: "less than three months ago"
    },
    "smoking_frequency" : {
        1: "daily",
        0: "occasionally",
        -1: "never"
    },
    "has_fertility_issues": {
        "N": 0,
        "O": 1
    }
}

DESCRIPTION = "Fertility dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Fertility"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Fertility")
_CITATION = """
@misc{misc_fertility_244,
  author       = {Gil,David & Girela,Jose},
  title        = {{Fertility}},
  year         = {2013},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5Z01Z}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/fertility/raw/main/fertility_Diagnosis.txt"
}
features_types_per_config = {
    "encoding": {
        "feature": datasets.Value("string"),
        "original_value": datasets.Value("string"),
        "encoded_value": datasets.Value("int64"),
    },
    "fertility": {
        "season_of_sampling": datasets.Value("string"),
        "age_at_time_of_sampling": datasets.Value("int8"),
        "has_had_childhood_diseases": datasets.Value("bool"),
        "has_had_serious_trauma": datasets.Value("bool"),
        "has_had_surgical_interventions": datasets.Value("bool"),
        "has_had_high_fevers_in_the_past_year": datasets.Value("string"),
        "frequency_of_alcohol_consumption": datasets.Value("float64"),
        "smoking_frequency": datasets.Value("string"),
        "number_of_sitting_hours_per_day": datasets.Value("float64"),
        "has_fertility_issues": 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 FertilityConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(FertilityConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Fertility(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "fertility"
    BUILDER_CONFIGS = [
        FertilityConfig(name="encoding",
                    description="Encoding dictionaries for discrete features."),
        FertilityConfig(name="fertility",
                    description="Fertility 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):
        if self.config.name == "encoding":
            data = self.encodings()

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

                yield row_id, data_row

        else:               
            data = pandas.read_csv(filepath)
            data = self.preprocess(data, config=self.config.name)

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

                yield row_id, data_row

    def encodings(self):
        data = [pandas.DataFrame([(feature, original_value, encoded_value)
                                   for original_value, encoded_value in d.items()],
                                 columns=["feature", "original_value", "encoded_value"])
                for feature, d in _ENCODING_DICS.items()]

        return data


    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data.columns = _BASE_FEATURE_NAMES
        
        for feature in _ENCODING_DICS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
        data.loc[:, "age_at_time_of_sampling"] = data["age_at_time_of_sampling"].apply(lambda x: 18 + x * 18)

        data = data[list(features_types_per_config[config].keys())]

        return data

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