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README.md

@@ -6,13 +6,46 @@ tags:
 - fico
 - tabular_classification
 - binary_classification
-pretty_name: Compas
+pretty_name: Heloc
 size_categories:
-- 10K<n<100K
+- 1K<n<10K
 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.
+The [HELOC dataset](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=d157e) from FICO.
+Each entry in the dataset is a line of credit typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
+The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
+The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.
+
+# Configurations and tasks
+- `risk` Predict the risk of insolvency.
+
+# Features
+|**Feature**                                |**Type**|
+|-------------------------------------------|--------|
+|`estimate_of_risk`                         |`int8`  |
+|`months_since_first_trade`                 |`int32` |
+|`months_since_last_trade`                  |`int32` |
+|`average_duration_of_resolution`           |`int32` |
+|`number_of_satisfactory_trades`            |`int16` |
+|`nr_trades_insolvent_for_over_60_days`     |`int16` |
+|`nr_trades_insolvent_for_over_90_days`     |`int16` |
+|`percentage_of_legal_trades`               |`int16` |
+|`months_since_last_illegal_trade`          |`int32` |
+|`maximum_illegal_trades_over_last_year`    |`int8`  |
+|`maximum_illegal_trades`                   |`int16` |
+|`nr_total_trades`                          |`int16` |
+|`nr_trades_initiated_in_last_year`         |`int16` |
+|`percentage_of_installment_trades`         |`int16` |
+|`months_since_last_inquiry_not_recent`     |`int16` |
+|`nr_inquiries_in_last_6_months`            |`int16` |
+|`nr_inquiries_in_last_6_months_not_recent` |`int16` |
+|`net_fraction_of_revolving_burden`         |`int32` |
+|`net_fraction_of_installment_burden`       |`int32` |
+|`nr_revolving_trades_with_balance`         |`int16` |
+|`nr_installment_trades_with_balance`       |`int16` |
+|`nr_banks_with_high_ratio`                 |`int16` |
+|`percentage_trades_with_balance`           |`int16` |

heloc.py

@@ -136,21 +136,21 @@ class Heloc(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        data.columns = _BASE_FEATURE_NAMES
-        data = self.preprocess(data, config=self.config.name)
+        if self.config.name == "risk":
+            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)
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
 
-            yield row_id, data_row
+                yield row_id, data_row
+        else:
+            raise ValueError(f"Unknown config: {self.config.name}")
 
     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)
 
-        if config == "risk":
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")
+        return data