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README.md

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+---
+language:
+- en
+tags:
+- compas
+- tabular_classification
+- binary_classification
+pretty_name: Bank
+size_categories:
+- 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:
+- encoding
+- subscription
+---
+# Bank
+The [Bank dataset](https://archive.ics.uci.edu/ml/datasets/bank+marketing) is cool.

bank.py

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+"""Bank Dataset"""
+
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_ORIGINAL_FEATURE_NAMES = [
+    "age",
+    "job",
+    "marital",
+    "education",
+    "default",
+    "balance",
+    "housing",
+    "loan",
+    "contact",
+    "day",
+    "month",
+    "duration",
+    "campaign",
+    "pdays",
+    "previous",
+    "poutcome",
+    "y"
+]
+_BASE_FEATURE_NAMES = [
+    "age",
+    "job",
+    "marital_status",
+    "education",
+    "has_defaulted",
+    "account_balance",
+    "has_housing_loan",
+    "has_personal_loan",
+    "month_of_last_contact",
+    "number_of_calls_in_ad_campaign",
+    "days_since_last_contact_of_previous_campaign",
+    "number_of_calls_before_this_campaign",
+    "successfull_subscription"
+]
+
+DESCRIPTION = "Bank dataset for subscription prediction."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/bank+marketing"
+_URLS = ("https://huggingface.co/datasets/mstz/bank/raw/main/bank-full.csv")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/bank/raw/main/bank-full.csv",
+}
+features_types_per_config = {
+    "encoding": {
+        "feature": datasets.Value("string"),
+        "original_value": datasets.Value("string"),
+        "encoded_value":  datasets.Value("int8"),
+    },
+
+    "subscription": {
+        "age": datasets.Value("int64"),
+        "job": datasets.Value("string"),
+        "marital_status": datasets.Value("string"),
+        "education": datasets.Value("int8"),
+        "has_defaulted": datasets.Value("int8"),
+        "account_balance": datasets.Value("int16"),
+        "has_housing_loan": datasets.Value("int8"),
+        "has_personal_loan": datasets.Value("int8"),
+        "month_of_last_contact": datasets.Value("string"),
+        "number_of_calls_in_ad_campaign": datasets.Value("string"),
+        "days_since_last_contact_of_previous_campaign": datasets.Value("int16"),
+        "number_of_calls_before_this_campaign": datasets.Value("int16"),
+        "successfull_subscription": 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 BankConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(BankConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Bank(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "subscription"
+    BUILDER_CONFIGS = [
+        BankConfig(name="encoding",
+                   description="Encoding dictionaries for discrete features."),
+        BankConfig(name="subscription",
+                   description="Bank binary classification for client subscription."),
+    ]
+
+
+    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, sep=";")
+        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 = "income") -> pandas.DataFrame:
+        data.drop("day", axis="columns", inplace=True)
+        data.drop("contact", axis="columns", inplace=True)
+        data.drop("duration", axis="columns", inplace=True)
+        data.drop("poutcome", axis="columns", inplace=True)
+
+        data.columns = _BASE_FEATURE_NAMES
+
+        # discretize features
+        data.loc[:, "education"] = data.education.apply(self.encode_education)
+        data.loc[:, "loan"] = data.loan.apply(self.encode_yes_no)
+        data.loc[:, "housing"] = data.housing.apply(self.encode_yes_no)
+        data.loc[:, "default"] = data.default.apply(self.encode_yes_no)
+        
+        if config == "encoding":
+            return self.encoding_dictionaries()
+        elif config == "subscription":
+            return data
+        else:
+            raise ValueError(f"Unknown config: {config}")
+
+    def encoding_dictionaries(self):
+        education_dic, binary_dic = self.education_encoding_dic(), self.binary_encoding_dic()
+        education_data = [("education", education, code) for education, code in education_dic.items()]
+        loan_data = [("loan", loan, code) for loan, code in binary_dic.items()]
+        housing_data = [("housing", housing, code) for housing, code in binary_dic.items()]
+        default_data = [("default", default, code) for default, code in binary_dic.items()]
+        data = pandas.DataFrame(education_data, loan_data + housing_data + default_data,
+                                columns=["feature", "original_value", "encoded_value"])
+        
+        return data
+
+    def encode_education(self, education):
+        return self.education_encoding_dic()[education]
+
+    def decode_education(self, code):
+        return self.education_decoding_dic()[code]
+    
+    def education_decoding_dic(self):
+        return {
+            0: "unknown",
+            1: "primary",
+            2: "secondary",
+            3: "tertiary"
+        }
+
+    def education_encoding_dic(self):
+        return {
+            "unknown": 0,
+            "primary": 1,
+            "secondary": 2,
+            "tertiary": 3
+        }
+        
+    def encode_yes_no(self, yes_no):
+        return self.yes_no_encoding_dic()[yes_no]
+
+    def decode_yes_no(self, code):
+        return self.yes_no_decoding_dic()[code]
+    
+    def yes_no_decoding_dic(self):
+        return {
+            0: "no",
+            1: "yes"
+        }
+
+    def yes_no_encoding_dic(self):
+        return {
+            "no": 0,
+            "yes": 1
+        }