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"""Heloc Dataset""" |
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from typing import List |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_ORIGINAL_FEATURE_NAMES = [ |
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"RiskPerformance", |
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"ExternalRiskEstimate", |
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"MSinceOldestTradeOpen", |
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"MSinceMostRecentTradeOpen", |
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"AverageMInFile", |
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"NumSatisfactoryTrades", |
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"NumTrades60Ever2DerogPubRec", |
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"NumTrades90Ever2DerogPubRec", |
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"PercentTradesNeverDelq", |
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"MSinceMostRecentDelq", |
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"MaxDelq2PublicRecLast12M", |
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"MaxDelqEver", |
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"NumTotalTrades", |
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"NumTradesOpeninLast12M", |
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"PercentInstallTrades", |
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"MSinceMostRecentInqexcl7days", |
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"NumInqLast6M", |
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"NumInqLast6Mexcl7days", |
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"NetFractionRevolvingBurden", |
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"NetFractionInstallBurden", |
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"NumRevolvingTradesWBalance", |
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"NumInstallTradesWBalance", |
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"NumBank2NatlTradesWHighUtilization", |
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"PercentTradesWBalance", |
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] |
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_BASE_FEATURE_NAMES = [ |
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"is_at_risk", |
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"estimate_of_risk", |
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"months_since_first_trade", |
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"months_since_last_trade", |
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"average_duration_of_resolution", |
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"number_of_satisfactory_trades", |
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"nr_trades_insolvent_for_over_60_days", |
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"nr_trades_insolvent_for_over_90_days", |
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"percentage_of_legal_trades", |
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"months_since_last_illegal_trade", |
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"maximum_illegal_trades_over_last_year", |
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"maximum_illegal_trades", |
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"nr_total_trades", |
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"nr_trades_initiated_in_last_year", |
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"percentage_of_installment_trades", |
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"months_since_last_inquiry_not_recent", |
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"nr_inquiries_in_last_6_months", |
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"nr_inquiries_in_last_6_months_not_recent", |
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"net_fraction_of_revolving_burden", |
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"net_fraction_of_installment_burden", |
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"nr_revolving_trades_with_balance", |
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"nr_installment_trades_with_balance", |
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"nr_banks_with_high_ratio", |
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"percentage_trades_with_balance" |
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] |
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DESCRIPTION = "Heloc dataset for trade insolvency risk prediction." |
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_HOMEPAGE = "https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=ca01a" |
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_URLS = ("https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=ca01a") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/heloc/raw/main/heloc.csv", |
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} |
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features_types_per_config = { |
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"risk": { |
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"estimate_of_risk": datasets.Value("int8"), |
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"months_since_first_trade": datasets.Value("int32"), |
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"months_since_last_trade": datasets.Value("int32"), |
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"average_duration_of_resolution": datasets.Value("int32"), |
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"number_of_satisfactory_trades": datasets.Value("int16"), |
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"nr_trades_insolvent_for_over_60_days": datasets.Value("int16"), |
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"nr_trades_insolvent_for_over_90_days": datasets.Value("int16"), |
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"percentage_of_legal_trades": datasets.Value("int16"), |
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"months_since_last_illegal_trade": datasets.Value("int32"), |
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"maximum_illegal_trades_over_last_year": datasets.Value("int8"), |
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"maximum_illegal_trades": datasets.Value("int16"), |
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"nr_total_trades": datasets.Value("int16"), |
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"nr_trades_initiated_in_last_year": datasets.Value("int16"), |
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"percentage_of_installment_trades": datasets.Value("int16"), |
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"months_since_last_inquiry_not_recent": datasets.Value("int16"), |
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"nr_inquiries_in_last_6_months": datasets.Value("int16"), |
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"nr_inquiries_in_last_6_months_not_recent": datasets.Value("int16"), |
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"net_fraction_of_revolving_burden": datasets.Value("int32"), |
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"net_fraction_of_installment_burden": datasets.Value("int32"), |
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"nr_revolving_trades_with_balance": datasets.Value("int16"), |
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"nr_installment_trades_with_balance": datasets.Value("int16"), |
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"nr_banks_with_high_ratio": datasets.Value("int16"), |
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"percentage_trades_with_balance": datasets.Value("int16"), |
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"is_at_risk": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class HelocConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(HelocConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Heloc(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "risk" |
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BUILDER_CONFIGS = [ |
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HelocConfig(name="risk", |
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description="Binary classification of trade risk.") |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath) |
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data.columns = _BASE_FEATURE_NAMES |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data: pandas.DataFrame, config: str = "risk") -> pandas.DataFrame: |
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data = data[list(features_types_per_config["risk"].keys())] |
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data.loc[:, "is_at_risk"] = data.is_at_risk.apply(lambda x: 1 if x == "Bad" else 0) |
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return data |
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