Delete bank.py

bank.py

@@ -1,197 +0,0 @@
-"""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("int64"),
-        "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):
-        if self.config.name == "encoding":
-            data = self.encoding_dictionaries()
-        else:
-            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 = "subscription") -> 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)
-
-        # 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)
-        
-        data.columns = _BASE_FEATURE_NAMES
-        
-        data.loc[:, "successfull_subscription"] = data.successfull_subscription.apply(lambda x: 0 if x == "no" else 1)
-
-        if config == "subscription":
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")
-
-    def encoding_dictionaries(self):
-        education_dic, yes_no_dic = self.education_encoding_dic(), self.yes_no_encoding_dic()
-        education_data = [("education", education, code) for education, code in education_dic.items()]
-        loan_data = [("loan", loan, code) for loan, code in yes_no_dic.items()]
-        housing_data = [("housing", housing, code) for housing, code in yes_no_dic.items()]
-        default_data = [("default", default, code) for default, code in yes_no_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
-        }