|
"""Pima""" |
|
|
|
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 = """""" |
|
|
|
|
|
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"), |
|
"class": 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): |
|
|
|
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) |
|
|
|
for row_id, row in data.iterrows(): |
|
data_row = dict(row) |
|
|
|
yield row_id, data_row |
|
|