"""Bank Dataset"""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "age",
    "job",
    "marital",
    "education",
    "default",
    "balance",
    "housing",
    "loan",
    "contact",
    "day",
    "month",
    "duration",
    "campaign",
    "pdays",
    "previous",
    "poutcome",
    "y"
]
_BASE_FEATURE_NAMES = [
    "age",
    "job",
    "marital_status",
    "education_level",
    "has_defaulted",
    "account_balance",
    "has_housing_loan",
    "has_personal_loan",
    "month_of_last_contact",
    "number_of_calls_in_ad_campaign",
    "days_since_last_contact_of_previous_campaign",
    "number_of_calls_before_this_campaign",
    "successful_subscription"
]
_ENCODING_DICS = {
    "education_level": {
        "unknown": 0,
        "primary": 1,
        "secondary": 2,
        "tertiary": 3
    },
    "has_personal_loan": {
        "no": 0,
        "yes": 1
    },
    "has_housing_loan": {
        "no": 0,
        "yes": 1
    },
    "has_defaulted": {
        "no": 0,
        "yes": 1
    },
    "successful_subscription": {
        "no": 0,
        "yes": 1
    }
}


DESCRIPTION = "Bank dataset for subscription prediction."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/bank+marketing"
_URLS = ("https://huggingface.co./datasets/mstz/bank/raw/main/bank-full.csv")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co./datasets/mstz/bank/raw/main/bank-full.csv",
}
features_types_per_config = {
    "encoding": {
        "feature": datasets.Value("string"),
        "original_value": datasets.Value("string"),
        "encoded_value":  datasets.Value("int8"),
    },

    "subscription": {
        "age": datasets.Value("int64"),
        "job": datasets.Value("string"),
        "marital_status": datasets.Value("string"),
        "education_level": datasets.Value("int8"),
        "has_defaulted": datasets.Value("bool"),
        "account_balance": datasets.Value("int64"),
        "has_housing_loan": datasets.Value("bool"),
        "has_personal_loan": datasets.Value("bool"),
        "month_of_last_contact": datasets.Value("string"),
        "number_of_calls_in_ad_campaign": datasets.Value("int8"),
        "days_since_last_contact_of_previous_campaign": datasets.Value("int16"),
        "number_of_calls_before_this_campaign": datasets.Value("int16"),
        "successful_subscription": 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 BankConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(BankConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Bank(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "subscription"
    BUILDER_CONFIGS = [
        BankConfig(name="encoding",
                   description="Encoding dictionaries for discrete features."),
        BankConfig(name="subscription",
                   description="Bank binary classification for client subscription."),
    ]


    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.encoding_dictionaries()
        else:
            data = pandas.read_csv(filepath, sep=";")
            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 preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
        data.drop("day", axis="columns", inplace=True)
        data.drop("contact", axis="columns", inplace=True)
        data.drop("duration", axis="columns", inplace=True)
        data.drop("poutcome", axis="columns", inplace=True)

        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 = data.astype({
            "has_defaulted": "bool",
            "has_housing_loan": "bool",
            "has_personal_loan": "bool"
        })

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

    def encoding_dics(self):
        data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
                for feature, d in _ENCODING_DICS.items()]
        data = pandas.concat(data, axis="rows").reset_index()
        data.drop("index", axis="columns", inplace=True)
        data.columns = ["feature", "original_value", "encoded_value"]

        return data