adult.py

"""Adult: A Census Dataset"""

from typing import List
from functools import partial

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",
    "over_threshold",
]
_ENCODINGS = {
    "sex": {
            "Male": 0,
            "Female": 1
        }
}
_RACE_ENCODING = {
    "White": 0,
    "Black": 1,
    "Asian-Pac-Islander": 2,
    "Amer-Indian-Eskimo": 3,
    "Other": 4,
}

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("int8"),
               "workclass": datasets.Value("string"),
               "over_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("int8"),
                       "workclass": datasets.Value("string"),
                       "over_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("int8"),
             "workclass": datasets.Value("string"),
             "over_threshold": datasets.Value("int8"),
             "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="encoding",
                    description="Encoding dictionaries."),
        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):
        if self.config.name == "encoding":
            data = self.encodings()
        elif self.config.name in ["income", "income-no race", "race"]:               
            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
        else:
            raise ValueError(f"Unknown config: {self.config.name}")

    def encodings(self):
        data = [pandas.DataFrame([(feature, original_value, encoded_value)
                                   for original_value, encoded_value in d.items()],
                                 columns=["feature", "original_value", "encoded_value"])
                for feature in _ENCODINGS]
        data.append(pandas.DataFrame([("race", original_value, encoded_value)
                                       for original_value, encoded_value in _RACE_ENCODING.items()],
                    columns=["feature", "original_value", "encoded_value"]))
        data = pandas.concat(data, axis="rows").reset_index()
        data.drop("index", axis="columns", inplace=True)

        return data


    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", "over_threshold"]]
        data.columns = _BASE_FEATURE_NAMES

        for feature in _ENCODINGS:
            encoding_function = partial(self.encode, feature)
            data.loc[:, feature] = data[feature].apply(encoding_function)
        
        if config == "income":
            return data[list(features_types_per_config["income"].keys())]
        elif config == "income-no race":
            return data[list(features_types_per_config["income-no race"].keys())]
        elif config =="race":
            data.loc[:, "race"] = data.race.apply(self.encode_race)
            data = data[list(features_types_per_config["race"].keys())]

            return self.race_preprocessing(data)
        else:
            raise ValueError(f"Unknown config: {config}")
    
    def encode(self, feature, value):
        if feature in _ENCODING_DICS:
            return _ENCODING_DICS[feature][value]
        raise ValueError(f"Unknown feature: {feature}")
    
    def encode_race(self, race):
        return _RACE_ENCODING[race]