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heloc.py

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+"""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=_ORIGINAL_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[features_types_per_config["risk"]]
+
+        if config == "risk":
+            return data
+        else:
+            raise ValueError(f"Unknown config: {config}")