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