Datasets:
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from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Hayes efficiency dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/242/hayes+efficiency"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/hayes+method+choice")
_CITATION = """
@misc{misc_hayes_efficiency_242,
author = {Tsanas,Athanasios & Xifara,Angeliki},
title = {{Hayes efficiency}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C51307}}
}"""
# Dataset info
_BASE_FEATURE_NAMES = [
"name",
"hobby",
"age",
"educational_level",
"marital_level",
"class"
]
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/hayes_roth/raw/main/hayes.data"
}
features_types_per_config = {
"hayes": {
"hobby": datasets.Value("string"),
"age": datasets.Value("int8"),
"educational_level": datasets.Value("int8"),
"marital_level": datasets.Value("string"),
"class": datasets.ClassLabel(num_classes=3)
},
"hayes_1": {
"hobby": datasets.Value("string"),
"age": datasets.Value("int8"),
"educational_level": datasets.Value("int8"),
"marital_level": datasets.Value("string"),
"class": datasets.ClassLabel(num_classes=2)
},
"hayes_2": {
"hobby": datasets.Value("string"),
"age": datasets.Value("int8"),
"educational_level": datasets.Value("int8"),
"marital_level": datasets.Value("string"),
"class": datasets.ClassLabel(num_classes=2)
},
"hayes_3": {
"hobby": datasets.Value("string"),
"age": datasets.Value("int8"),
"educational_level": datasets.Value("int8"),
"marital_level": datasets.Value("string"),
"class": datasets.ClassLabel(num_classes=2)
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class HayesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(HayesConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Hayes(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "hayes"
BUILDER_CONFIGS = [
HayesConfig(name="hayes", description="Hayes dataset."),
HayesConfig(name="hayes_1", description="Hayes for binary classification (is example of class 1?)."),
HayesConfig(name="hayes_2", description="Hayes for binary classification (is example of class 2?)."),
HayesConfig(name="hayes_3", description="Hayes for binary classification (is example of class 3?).")
]
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"]})
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath, header=None)
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
data.columns = _BASE_FEATURE_NAMES
data.drop("name", axis="columns", inplace=True)
if self.config.name == "hayes_1":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
elif self.config.name == "hayes_2":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
elif self.config.name == "hayes_3":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
else:
data.loc[:, "class"] = data["class"].apply(lambda x: x - 1)
return data
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