"""Heloc Dataset"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "RiskPerformance",
    "ExternalRiskEstimate",
    "MSinceOldestTradeOpen",
    "MSinceMostRecentTradeOpen",
    "AverageMInFile",
    "NumSatisfactoryTrades",
    "NumTrades60Ever2DerogPubRec",
    "NumTrades90Ever2DerogPubRec",
    "PercentTradesNeverDelq",
    "MSinceMostRecentDelq",
    "MaxDelq2PublicRecLast12M",
    "MaxDelqEver",
    "NumTotalTrades",
    "NumTradesOpeninLast12M",
    "PercentInstallTrades",
    "MSinceMostRecentInqexcl7days",
    "NumInqLast6M",
    "NumInqLast6Mexcl7days",
    "NetFractionRevolvingBurden",
    "NetFractionInstallBurden",
    "NumRevolvingTradesWBalance",
    "NumInstallTradesWBalance",
    "NumBank2NatlTradesWHighUtilization",
    "PercentTradesWBalance",
]
_BASE_FEATURE_NAMES = [
    "is_at_risk",
    "estimate_of_risk",
    "months_since_first_trade",
    "months_since_last_trade",
    "average_duration_of_resolution",
    "number_of_satisfactory_trades",
    "nr_trades_insolvent_for_over_60_days",
    "nr_trades_insolvent_for_over_90_days",
    "percentage_of_legal_trades",
    "months_since_last_illegal_trade",
    "maximum_illegal_trades_over_last_year",
    "maximum_illegal_trades",
    "nr_total_trades",
    "nr_trades_initiated_in_last_year",
    "percentage_of_installment_trades",
    "months_since_last_inquiry_not_recent",
    "nr_inquiries_in_last_6_months",
    "nr_inquiries_in_last_6_months_not_recent",
    "net_fraction_of_revolving_burden",
    "net_fraction_of_installment_burden",
    "nr_revolving_trades_with_balance",
    "nr_installment_trades_with_balance",
    "nr_banks_with_high_ratio",
    "percentage_trades_with_balance"
]

DESCRIPTION = "Heloc dataset for trade insolvency risk prediction."
_HOMEPAGE = "https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=ca01a"
_URLS = ("https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=ca01a")
_CITATION = """"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/heloc/raw/main/heloc.csv",
}
features_types_per_config = {
    "risk": {
        "estimate_of_risk": datasets.Value("int8"),
        "months_since_first_trade": datasets.Value("int32"),
        "months_since_last_trade": datasets.Value("int32"),
        "average_duration_of_resolution": datasets.Value("int32"),
        "number_of_satisfactory_trades": datasets.Value("int16"),
        "nr_trades_insolvent_for_over_60_days": datasets.Value("int16"),
        "nr_trades_insolvent_for_over_90_days": datasets.Value("int16"),
        "percentage_of_legal_trades": datasets.Value("int16"),
        "months_since_last_illegal_trade": datasets.Value("int32"),
        "maximum_illegal_trades_over_last_year": datasets.Value("int8"),
        "maximum_illegal_trades": datasets.Value("int16"),
        "nr_total_trades": datasets.Value("int16"),
        "nr_trades_initiated_in_last_year": datasets.Value("int16"),
        "percentage_of_installment_trades": datasets.Value("int16"),
        "months_since_last_inquiry_not_recent": datasets.Value("int16"),
        "nr_inquiries_in_last_6_months": datasets.Value("int16"),
        "nr_inquiries_in_last_6_months_not_recent": datasets.Value("int16"),
        "net_fraction_of_revolving_burden": datasets.Value("int32"),
        "net_fraction_of_installment_burden": datasets.Value("int32"),
        "nr_revolving_trades_with_balance": datasets.Value("int16"),
        "nr_installment_trades_with_balance": datasets.Value("int16"),
        "nr_banks_with_high_ratio": datasets.Value("int16"),
        "percentage_trades_with_balance": datasets.Value("int16"),
        "is_at_risk": 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 HelocConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(HelocConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Heloc(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "risk"
    BUILDER_CONFIGS = [
        HelocConfig(name="risk",
                     description="Binary classification of trade risk.")
    ]


    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)
        data.columns = _BASE_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
    
    def preprocess(self, data: pandas.DataFrame, config: str = "risk") -> pandas.DataFrame:
        data = data[list(features_types_per_config["risk"].keys())]

        data.loc[:, "is_at_risk"] = data.is_at_risk.apply(lambda x: 1 if x == "Bad" else 0)

        return data