Datasets:
Commit
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8dc6c87
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b127233
Update parquet files
Browse files- README.md +0 -2
- adult.py +0 -241
- adult_tr.csv +0 -0
- adult_ts.csv +0 -0
- income-no race/adult-test.parquet +3 -0
- income-no race/adult-train.parquet +3 -0
- income/adult-test.parquet +3 -0
- income/adult-train.parquet +3 -0
- race/adult-test.parquet +3 -0
- race/adult-train.parquet +3 -0
README.md
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# Adult
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Testing the dataset.
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adult.py
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"""Diva: A Fraud Detection Dataset"""
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from typing import List
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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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"threshold",
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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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# Dataset info
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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("string"),
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"workclass": datasets.Value("string"),
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"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("string"),
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"workclass": datasets.Value("string"),
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"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("string"),
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"workclass": datasets.Value("string"),
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"over_threshold": datasets.Value("string"),
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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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# dataset versions
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DEFAULT_CONFIG = "income"
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BUILDER_CONFIGS = [
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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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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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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", "threshold"]]
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data.columns = _BASE_FEATURE_NAMES
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# binarize features
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data.loc[:, "sex"] = data.sex.apply(self.encode_sex)
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if config == "income":
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return self.income_preprocessing(data)
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elif config == "income-no race":
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return self.income_norace_preprocessing(data)
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elif config =="race":
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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 income_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
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data = data[list(features_types_per_config["income"].keys())]
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return data
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def income_norace_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
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data = data[list(features_types_per_config["income-no race"].keys())]
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return data
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def race_preprocessing(self, data: pandas.DataFrame) -> pandas.DataFrame:
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features = list(features_types_per_config["race"].keys())
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features[features.index("over_threshold")] = "threshold"
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data.loc[:, "race"] = data.race.apply(self.encode_race)
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data = data[features]
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data.columns = ["age", "capital_gain", "capital_loss", "education", "final_weight",
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"hours_per_week", "marital_status", "native_country", "occupation", "relationship", "sex", "workclass", "over_threshold", "race"]
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return data
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def encode_race(self, race):
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return self.race_encoding_dic()[race]
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def decode_race(self, code):
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return self.race_decoding_dic()[code]
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def race_decoding_dic(self):
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return {
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0: "White",
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1: "Black",
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2: "Asian-Pac-Islander",
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3: "Amer-Indian-Eskimo",
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4: "Other",
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}
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def race_encoding_dic(self):
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return {
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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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def encode_sex(self, sex):
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return self.sex_encoding_dic()[sex]
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def decode_sex(self, code):
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return self.sex_decoding_dic()[code]
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def sex_encoding_dic(self):
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return {
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"Male": 0,
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"Female": 1
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}
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def sex_decoding_dic(self):
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return {
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0: "Male",
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1: "Female"
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}
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adult_tr.csv
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The diff for this file is too large to render.
See raw diff
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adult_ts.csv
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The diff for this file is too large to render.
See raw diff
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income-no race/adult-test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:953682b5689aad5109cbe3a437b8ce44c21fdf2e038317f69813f4c8829d1c92
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size 205111
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income-no race/adult-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d73d5529cc81d2b8e63c7a71514116a7afb0bf44af5c88c51b7206fcdbd52f01
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size 598338
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income/adult-test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:5decaf638c13bfbc856614e2c594d9d2add6bcc2b0f979acd4337a474053aea1
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size 212460
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income/adult-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:25ac33ca303f6dad6978854d5da5e60322927602a72f1a1a687d7463b684f739
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size 619511
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race/adult-test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:414e429181c8a27cbbd632aa62ab127fd32619dad5c1d326d4f766c14fa0a963
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size 212056
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race/adult-train.parquet
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version https://git-lfs.github.com/spec/v1
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size 618124
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