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README.md

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-# Adult
-Testing the dataset.

adult.py

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-"""Diva: A Fraud Detection Dataset"""
-
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_ORIGINAL_FEATURE_NAMES = [
-    "age",
-    "workclass",
-    "final_weight",
-    "education",
-    "education-num",
-    "marital_status",
-    "occupation",
-    "relationship",
-    "race",
-    "sex",
-    "capital_gain",
-    "capital_loss",
-    "hours_per_week",
-    "native_country",
-    "threshold"
-]
-_BASE_FEATURE_NAMES = [
-    "age",
-    "capital_gain",
-    "capital_loss",
-    "education",
-    "final_weight",
-    "hours_per_week",
-    "marital_status",
-    "native_country",
-    "occupation",
-    "race",
-    "relationship",
-    "sex",
-    "workclass",
-    "threshold",
-]
-DESCRIPTION = "Adult dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Adult"
-_URLS = ("https://huggingface.co/datasets/mstz/adult/raw/adult.csv")
-_CITATION = """
-@inproceedings{DBLP:conf/kdd/Kohavi96,
-  author    = {Ron Kohavi},
-  editor    = {Evangelos Simoudis and
-               Jiawei Han and
-               Usama M. Fayyad},
-  title     = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
-               Hybrid},
-  booktitle = {Proceedings of the Second International Conference on Knowledge Discovery
-               and Data Mining (KDD-96), Portland, Oregon, {USA}},
-  pages     = {202--207},
-  publisher = {{AAAI} Press},
-  year      = {1996},
-  url       = {http://www.aaai.org/Library/KDD/1996/kdd96-033.php},
-  timestamp = {Mon, 05 Jun 2017 13:20:21 +0200},
-  biburl    = {https://dblp.org/rec/conf/kdd/Kohavi96.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/adult/raw/main/adult_tr.csv", 
-    "test": "https://huggingface.co/datasets/mstz/adult/raw/main/adult_ts.csv"
-}
-features_types_per_config = {
-    "income": {"age": datasets.Value("int64"),
-               "capital_gain": datasets.Value("float64"),
-               "capital_loss": datasets.Value("float64"),
-               "education": datasets.Value("int8"),
-               "final_weight": datasets.Value("int64"),
-               "hours_per_week": datasets.Value("int64"),
-               "marital_status": datasets.Value("string"),
-               "native_country": datasets.Value("string"),
-               "occupation": datasets.Value("string"),
-               "race": datasets.Value("string"),
-               "relationship": datasets.Value("string"),
-               "sex": datasets.Value("string"),
-               "workclass": datasets.Value("string"),
-               "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
-    "income-no race": {"age": datasets.Value("int64"),
-                       "capital_gain": datasets.Value("float64"),
-                       "capital_loss": datasets.Value("float64"),
-                       "education": datasets.Value("int64"),
-                       "final_weight": datasets.Value("int64"),
-                       "hours_per_week": datasets.Value("int64"),
-                       "marital_status": datasets.Value("string"),
-                       "native_country": datasets.Value("string"),
-                       "occupation": datasets.Value("string"),
-                       "relationship": datasets.Value("string"),
-                       "sex": datasets.Value("string"),
-                       "workclass": datasets.Value("string"),
-                       "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))},
-    "race": {"age": datasets.Value("int64"),
-             "capital_gain": datasets.Value("float64"),
-             "capital_loss": datasets.Value("float64"),
-             "education": datasets.Value("int64"),
-             "final_weight": datasets.Value("int64"),
-             "hours_per_week": datasets.Value("int64"),
-             "marital_status": datasets.Value("string"),
-             "native_country": datasets.Value("string"),
-             "occupation": datasets.Value("string"),
-             "relationship": datasets.Value("string"),
-             "sex": datasets.Value("string"),
-             "workclass": datasets.Value("string"),
-             "over_threshold": datasets.Value("string"),
-             "race": datasets.ClassLabel(num_classes=5, names=["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"])}
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class AdultConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(AdultConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Adult(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "income"
-    BUILDER_CONFIGS = [
-        AdultConfig(name="income",
-                    description="Adult for income threshold binary classification."),
-        AdultConfig(name="income-no race",
-                    description="Adult for income threshold binary classification, race excluded from features."),
-        AdultConfig(name="race", 
-                    description="Adult for race (multiclass) classification."),
-    ]
-
-
-    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"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}),
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        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("education", axis="columns", inplace=True)
-        data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
-                     "hours_per_week", "marital_status", "native_country", "occupation",
-                     "race", "relationship", "sex", "workclass", "threshold"]]
-        data.columns = _BASE_FEATURE_NAMES
-
-        # binarize features
-        data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
-        
-        if config == "income":
-            return self.income_preprocessing(data)
-        elif config == "income-no race":
-            return self.income_norace_preprocessing(data)
-        elif config =="race":
-            return self.race_preprocessing(data)
-        else:
-            raise ValueError(f"Unknown config: {config}")
-    
-    def income_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[list(features_types_per_config["income"].keys())]
-
-        return data
-        
-    def income_norace_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        data = data[list(features_types_per_config["income-no race"].keys())]
-
-        return data
-    
-    def race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
-        features = list(features_types_per_config["race"].keys())
-        features[features.index("over_threshold")] = "threshold"
-        data.loc[:, "race"] = data.race.apply(self.encode_race)
-        data = data[features]
-        data.columns = ["age", "capital_gain", "capital_loss", "education", "final_weight",
-                        "hours_per_week", "marital_status", "native_country", "occupation", "relationship", "sex", "workclass", "over_threshold", "race"]
-
-        return data
-
-    def encode_race(self, race):
-        return self.race_encoding_dic()[race]
-
-    def decode_race(self, code):
-        return self.race_decoding_dic()[code]
-    
-    def race_decoding_dic(self):
-        return {
-            0: "White",
-            1: "Black",
-            2: "Asian-Pac-Islander",
-            3: "Amer-Indian-Eskimo",
-            4: "Other",
-        }
-
-    def race_encoding_dic(self):
-        return {
-            "White": 0,
-            "Black": 1,
-            "Asian-Pac-Islander": 2,
-            "Amer-Indian-Eskimo": 3,
-            "Other": 4,
-        }
-
-    def encode_sex(self, sex):
-        return self.sex_encoding_dic()[sex]
-
-    def decode_sex(self, code):
-        return self.sex_decoding_dic()[code]
-
-    def sex_encoding_dic(self):
-        return {
-            "Male": 0,
-            "Female": 1
-        }
-    
-    def sex_decoding_dic(self):
-        return {
-            0: "Male",
-            1: "Female"
-        }

income-no race/adult-test.parquet

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income-no race/adult-train.parquet

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income/adult-test.parquet

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income/adult-train.parquet

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race/adult-test.parquet

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race/adult-train.parquet

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