"""Bank Dataset"""

from typing import List

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",
    "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",
    "successfull_subscription"
]

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": datasets.Value("int8"),
        "has_defaulted": datasets.Value("int8"),
        "account_balance": datasets.Value("int64"),
        "has_housing_loan": datasets.Value("int8"),
        "has_personal_loan": datasets.Value("int8"),
        "month_of_last_contact": datasets.Value("string"),
        "number_of_calls_in_ad_campaign": datasets.Value("string"),
        "days_since_last_contact_of_previous_campaign": datasets.Value("int16"),
        "number_of_calls_before_this_campaign": datasets.Value("int16"),
        "successfull_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):
        if self.config.name not in features_per_config:
            raise ValueError(f"Unknown configuration: {self.config.name}")
        
        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":
            return self.encoding_dictionaries()

        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 = "income") -> 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)

        # discretize features
        data.loc[:, "education"] = data.education.apply(self.encode_education)
        data.loc[:, "loan"] = data.loan.apply(self.encode_yes_no)
        data.loc[:, "housing"] = data.housing.apply(self.encode_yes_no)
        data.loc[:, "default"] = data.default.apply(self.encode_yes_no)
        
        data.columns = _BASE_FEATURE_NAMES
        
        data.loc[:, "successfull_subscription"] = data.successfull_subscription.apply(lambda x: 0 if x == "no" else 1)

        for f in _BASE_FEATURE_NAMES:
            print(f, data[f].max(), data.dtypes[f])

        if config == "subscription":
            return data
        else:
            raise ValueError(f"Unknown config: {config}")

    def encoding_dictionaries(self):
        education_dic, yes_no_dic = self.education_encoding_dic(), self.yes_no_encoding_dic()
        education_data = [("education", education, code) for education, code in education_dic.items()]
        loan_data = [("loan", loan, code) for loan, code in yes_no_dic.items()]
        housing_data = [("housing", housing, code) for housing, code in yes_no_dic.items()]
        default_data = [("default", default, code) for default, code in yes_no_dic.items()]
        data = pandas.DataFrame(education_data, loan_data + housing_data + default_data,
                                columns=["feature", "original_value", "encoded_value"])
        
        return data

    def encode_education(self, education):
        return self.education_encoding_dic()[education]

    def decode_education(self, code):
        return self.education_decoding_dic()[code]
    
    def education_decoding_dic(self):
        return {
            0: "unknown",
            1: "primary",
            2: "secondary",
            3: "tertiary"
        }

    def education_encoding_dic(self):
        return {
            "unknown": 0,
            "primary": 1,
            "secondary": 2,
            "tertiary": 3
        }
        
    def encode_yes_no(self, yes_no):
        return self.yes_no_encoding_dic()[yes_no]

    def decode_yes_no(self, code):
        return self.yes_no_decoding_dic()[code]
    
    def yes_no_decoding_dic(self):
        return {
            0: "no",
            1: "yes"
        }

    def yes_no_encoding_dic(self):
        return {
            "no": 0,
            "yes": 1
        }