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"""Bank Dataset""" |
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from typing import List |
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from functools import partial |
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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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"age", |
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"job", |
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"marital", |
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"education", |
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"default", |
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"balance", |
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"housing", |
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"loan", |
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"contact", |
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"day", |
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"month", |
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"duration", |
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"campaign", |
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"pdays", |
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"previous", |
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"poutcome", |
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"y" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"age", |
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"job", |
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"marital_status", |
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"education_level", |
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"has_defaulted", |
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"account_balance", |
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"has_housing_loan", |
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"has_personal_loan", |
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"month_of_last_contact", |
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"number_of_calls_in_ad_campaign", |
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"days_since_last_contact_of_previous_campaign", |
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"number_of_calls_before_this_campaign", |
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"successful_subscription" |
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] |
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_ENCODING_DICS = { |
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"education_level": { |
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"unknown": 0, |
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"primary": 1, |
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"secondary": 2, |
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"tertiary": 3 |
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}, |
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"has_personal_loan": { |
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"no": 0, |
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"yes": 1 |
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}, |
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"has_housing_loan": { |
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"no": 0, |
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"yes": 1 |
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}, |
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"has_defaulted": { |
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"no": 0, |
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"yes": 1 |
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}, |
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"successful_subscription": { |
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"no": 0, |
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"yes": 1 |
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} |
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} |
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DESCRIPTION = "Bank dataset for subscription prediction." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/bank+marketing" |
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_URLS = ("https://huggingface.co/datasets/mstz/bank/raw/main/bank-full.csv") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/bank/raw/main/bank-full.csv", |
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} |
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features_types_per_config = { |
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"encoding": { |
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"feature": datasets.Value("string"), |
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"original_value": datasets.Value("string"), |
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"encoded_value": datasets.Value("int8"), |
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}, |
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"subscription": { |
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"age": datasets.Value("int64"), |
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"job": datasets.Value("string"), |
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"marital_status": datasets.Value("string"), |
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"education_level": datasets.Value("int8"), |
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"has_defaulted": datasets.Value("bool"), |
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"account_balance": datasets.Value("int64"), |
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"has_housing_loan": datasets.Value("bool"), |
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"has_personal_loan": datasets.Value("bool"), |
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"month_of_last_contact": datasets.Value("string"), |
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"number_of_calls_in_ad_campaign": datasets.Value("int8"), |
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"days_since_last_contact_of_previous_campaign": datasets.Value("int16"), |
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"number_of_calls_before_this_campaign": datasets.Value("int16"), |
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"successful_subscription": 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 BankConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(BankConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Bank(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "subscription" |
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BUILDER_CONFIGS = [ |
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BankConfig(name="encoding", |
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description="Encoding dictionaries for discrete features."), |
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BankConfig(name="subscription", |
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description="Bank binary classification for client subscription."), |
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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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if self.config.name == "encoding": |
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data = self.encoding_dictionaries() |
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else: |
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data = pandas.read_csv(filepath, sep=";") |
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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 = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.drop("day", axis="columns", inplace=True) |
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data.drop("contact", axis="columns", inplace=True) |
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data.drop("duration", axis="columns", inplace=True) |
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data.drop("poutcome", axis="columns", inplace=True) |
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data.columns = _BASE_FEATURE_NAMES |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data.loc[:, feature] = data[feature].apply(encoding_function) |
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data = data.astype({ |
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"has_defaulted": "bool", |
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"has_housing_loan": "bool", |
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"has_personal_loan": "bool" |
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}) |
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return data |
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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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def encoding_dics(self): |
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data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()]) |
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for feature, d in _ENCODING_DICS.items()] |
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data = pandas.concat(data, axis="rows").reset_index() |
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data.drop("index", axis="columns", inplace=True) |
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data.columns = ["feature", "original_value", "encoded_value"] |
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return data |
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