Datasets:
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from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Pima dataset from the OpenML repository."
_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=37"
_URLS = ("https://www.openml.org/search?type=data&status=active&id=37")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/pima/raw/main/pima.csv"
}
features_types_per_config = {
"pima": {
"number_of_pregnancies": datasets.Value("int8"),
"plasma_glucose_concentration": datasets.Value("float64"),
"diastolic_blood_pressure": datasets.Value("float64"),
"triceps_thickness": datasets.Value("float64"),
"serum_insulin": datasets.Value("float64"),
"bmi": datasets.Value("float64"),
"diabetes_pedigree": datasets.Value("float64"),
"age": datasets.Value("float64"),
"has_diabetes": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class PimaConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(PimaConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Pima(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "pima"
BUILDER_CONFIGS = [
PimaConfig(name="pima",
description="Pima for binary classification."),
]
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)
data = data.rename(columns={"class": "has_diabetes"})
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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