|
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_worked_per_week", |
|
"marital_status", |
|
"native_country", |
|
"occupation", |
|
"race", |
|
"relationship", |
|
"is_male", |
|
"workclass", |
|
"over_threshold", |
|
] |
|
_ENCODING_DICS = { |
|
"is_male": { |
|
"Male": True, |
|
"Female": False |
|
} |
|
} |
|
_RACE_ENCODING = { |
|
"White": 0, |
|
"Black": 1, |
|
"Asian-Pac-Islander": 2, |
|
"Amer-Indian-Eskimo": 3, |
|
"Other": 4, |
|
} |
|
_EDUCATION_ENCODING = { |
|
"Preschool": 0, |
|
"1st-4th": 1, |
|
"5th-6th": 2, |
|
"7th-8th": 3, |
|
"9th": 4, |
|
"10th": 5, |
|
"11th": 6, |
|
"12th": 7, |
|
"HS-grad": 8, |
|
"Assoc-acdm": 9, |
|
"Assoc-voc": 10, |
|
"Some-college": 11, |
|
"Bachelors": 12, |
|
"Masters": 13, |
|
"Doctorate": 14, |
|
"Prof-school": 15 |
|
} |
|
|
|
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} |
|
}""" |
|
|
|
|
|
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 = { |
|
"encoding": { |
|
"feature": datasets.Value("string"), |
|
"original_value": datasets.Value("string"), |
|
"encoded_value": datasets.Value("int64"), |
|
}, |
|
"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_worked_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"), |
|
"is_male": datasets.Value("bool"), |
|
"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_worked_per_week": datasets.Value("int64"), |
|
"marital_status": datasets.Value("string"), |
|
"native_country": datasets.Value("string"), |
|
"occupation": datasets.Value("string"), |
|
"relationship": datasets.Value("string"), |
|
"is_male": datasets.Value("bool"), |
|
"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_worked_per_week": datasets.Value("int64"), |
|
"marital_status": datasets.Value("string"), |
|
"native_country": datasets.Value("string"), |
|
"occupation": datasets.Value("string"), |
|
"relationship": datasets.Value("string"), |
|
"is_male": datasets.Value("bool"), |
|
"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): |
|
|
|
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."), |
|
AdultConfig(name="encoding", |
|
description="Encoding dictionaries for discrete features.") |
|
] |
|
|
|
|
|
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"]}), |
|
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() |
|
|
|
for row_id, row in data.iterrows(): |
|
data_row = dict(row) |
|
|
|
yield row_id, data_row |
|
|
|
else: |
|
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 encodings(self): |
|
data = pandas.DataFrame([("education", original_value, encoded_value) |
|
for original_value, encoded_value in _EDUCATION_ENCODING.items()], |
|
columns=["feature", "original_value", "encoded_value"]) |
|
|
|
return data |
|
|
|
|
|
def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
|
data.drop("education", axis="columns", inplace=True) |
|
data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"}) |
|
|
|
data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight", |
|
"hours_per_week", "marital_status", "native_country", "occupation", |
|
"race", "relationship", "is_male", "workclass", "over_threshold"]] |
|
data.columns = _BASE_FEATURE_NAMES |
|
|
|
for feature in _ENCODING_DICS: |
|
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 data |
|
|
|
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] |
|
|
