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"""Bank Dataset""" |
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from typing import List |
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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", |
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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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"successfull_subscription" |
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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": datasets.Value("int8"), |
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"has_defaulted": datasets.Value("int8"), |
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"account_balance": datasets.Value("int64"), |
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"has_housing_loan": datasets.Value("int8"), |
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"has_personal_loan": datasets.Value("int8"), |
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"month_of_last_contact": datasets.Value("string"), |
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"number_of_calls_in_ad_campaign": datasets.Value("string"), |
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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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"successfull_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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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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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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return self.encoding_dictionaries() |
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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 = "income") -> 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.loc[:, "education"] = data.education.apply(self.encode_education) |
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data.loc[:, "loan"] = data.loan.apply(self.encode_yes_no) |
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data.loc[:, "housing"] = data.housing.apply(self.encode_yes_no) |
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data.loc[:, "default"] = data.default.apply(self.encode_yes_no) |
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data.columns = _BASE_FEATURE_NAMES |
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data.loc[:, "successfull_subscription"] = data.successfull_subscription.apply(lambda x: 0 if x == "no" else 1) |
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for f in _BASE_FEATURE_NAMES: |
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print(f, data[f].max(), data.dtypes[f]) |
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if config == "subscription": |
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return data |
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else: |
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raise ValueError(f"Unknown config: {config}") |
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def encoding_dictionaries(self): |
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education_dic, yes_no_dic = self.education_encoding_dic(), self.yes_no_encoding_dic() |
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education_data = [("education", education, code) for education, code in education_dic.items()] |
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loan_data = [("loan", loan, code) for loan, code in yes_no_dic.items()] |
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housing_data = [("housing", housing, code) for housing, code in yes_no_dic.items()] |
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default_data = [("default", default, code) for default, code in yes_no_dic.items()] |
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data = pandas.DataFrame(education_data + loan_data + housing_data + default_data, |
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columns=["feature", "original_value", "encoded_value"]) |
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print(data) |
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return data |
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def encode_education(self, education): |
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return self.education_encoding_dic()[education] |
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def decode_education(self, code): |
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return self.education_decoding_dic()[code] |
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def education_decoding_dic(self): |
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return { |
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0: "unknown", |
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1: "primary", |
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2: "secondary", |
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3: "tertiary" |
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} |
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def education_encoding_dic(self): |
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return { |
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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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def encode_yes_no(self, yes_no): |
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return self.yes_no_encoding_dic()[yes_no] |
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def decode_yes_no(self, code): |
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return self.yes_no_decoding_dic()[code] |
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def yes_no_decoding_dic(self): |
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return { |
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0: "no", |
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1: "yes" |
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} |
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def yes_no_encoding_dic(self): |
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return { |
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"no": 0, |
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"yes": 1 |
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} |
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