Update parquet files

README.md

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----
-language:
-- en
-tags:
-- heloc
-- fico
-- tabular_classification
-- binary_classification
-pretty_name: Compas
-size_categories:
-- 10K<n<100K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
-- tabular-classification
-configs:
-- risk
----
-# HELOC
-The [HELOC dataset](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=d157e) is cool.

heloc.py

@@ -1,156 +0,0 @@
-"""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 cancer 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):
-        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):
-        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 = "cancer") -> 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)
-
-        if config == "risk":
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")

risk/heloc-train.parquet

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