updated to datasets 4.*

README.md

@@ -1,19 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: subscription
+  data_files:
+  - path: subscription/train.csv
+    split: train
+  default: true
+language: en
+license: unknown
+pretty_name: Bank
+size_categories: 1M<n<10M
 tags:
-- compas
 - tabular_classification
 - binary_classification
-- UCI
-pretty_name: Bank
-size_categories:
-- 1K<n<10K
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- encoding
-- subscription
 ---
 # Bank
 The [Bank dataset](https://archive.ics.uci.edu/ml/datasets/bank+marketing) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

bank.py

@@ -1,181 +0,0 @@
-"""Bank Dataset"""
-
-from typing import List
-from functools import partial
-
-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_level",
-    "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",
-    "successful_subscription"
-]
-_ENCODING_DICS = {
-    "education_level": {
-        "unknown": 0,
-        "primary": 1,
-        "secondary": 2,
-        "tertiary": 3
-    },
-    "has_personal_loan": {
-        "no": 0,
-        "yes": 1
-    },
-    "has_housing_loan": {
-        "no": 0,
-        "yes": 1
-    },
-    "has_defaulted": {
-        "no": 0,
-        "yes": 1
-    },
-    "successful_subscription": {
-        "no": 0,
-        "yes": 1
-    }
-}
-
-
-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_level": datasets.Value("int8"),
-        "has_defaulted": datasets.Value("bool"),
-        "account_balance": datasets.Value("int64"),
-        "has_housing_loan": datasets.Value("bool"),
-        "has_personal_loan": datasets.Value("bool"),
-        "month_of_last_contact": datasets.Value("string"),
-        "number_of_calls_in_ad_campaign": datasets.Value("int8"),
-        "days_since_last_contact_of_previous_campaign": datasets.Value("int16"),
-        "number_of_calls_before_this_campaign": datasets.Value("int16"),
-        "successful_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):
-        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 = DEFAULT_CONFIG) -> 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
-
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-        data = data.astype({
-            "has_defaulted": "bool",
-            "has_housing_loan": "bool",
-            "has_personal_loan": "bool"
-        })
-
-        return data
-       
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
-
-    def encoding_dics(self):
-        data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
-                for feature, d in _ENCODING_DICS.items()]
-        data = pandas.concat(data, axis="rows").reset_index()
-        data.drop("index", axis="columns", inplace=True)
-        data.columns = ["feature", "original_value", "encoded_value"]
-
-        return data