Datasets:
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"""Heart"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"age"
"is_male"
"type_of_chest_pain"
"resting_blood_pressure"
"serum_cholesterol"
"fasting_blood_sugar"
"rest_electrocardiographic_type"
"maximum_heart_rate"
"has_exercise_induced_angina"
"depression_induced_by_exercise"
"slope_of_peak_exercise"
"number_of_major_vessels_colored_by_flourosopy"
"thal"
"has_hearth_disease"
]
DESCRIPTION = "Heart dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Heart"
_URLS = ("https://huggingface.co/datasets/mstz/heart/raw/heart.csv")
_CITATION = """
@misc{misc_heart_disease_45,
author = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.},
title = {{Heart Disease}},
year = {1988},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C52P4X}}
}"""
# Dataset info
urls_per_split = {
"cleveland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.cleveland.data"},
"hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"},
"switzerland": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.switzerland.data"},
"va": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.va.data"}
}
features_types_per_config = {
"cleveland": {
"age": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"type_of_chest_pain": datasets.Value("string"),
"resting_blood_pressure": datasets.Value("float32"),
"serum_cholesterol": datasets.Value("float32"),
"fasting_blood_sugar": datasets.Value("float32"),
"rest_electrocardiographic_type": datasets.Value("string"),
"maximum_heart_rate": datasets.Value("float32"),
"has_exercise_induced_angina": datasets.Value("bool"),
"depression_induced_by_exercise": datasets.Value("float32"),
"slope_of_peak_exercise": datasets.Value("float32"),
"number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
"thal": datasets.Value("float32"),
"has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"va": {
"age": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"type_of_chest_pain": datasets.Value("string"),
"resting_blood_pressure": datasets.Value("float32"),
"serum_cholesterol": datasets.Value("float32"),
"fasting_blood_sugar": datasets.Value("float32"),
"rest_electrocardiographic_type": datasets.Value("string"),
"maximum_heart_rate": datasets.Value("float32"),
"has_exercise_induced_angina": datasets.Value("bool"),
"depression_induced_by_exercise": datasets.Value("float32"),
"slope_of_peak_exercise": datasets.Value("float32"),
"number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
"thal": datasets.Value("float32"),
"has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"switzerland": {
"age": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"type_of_chest_pain": datasets.Value("string"),
"resting_blood_pressure": datasets.Value("float32"),
"serum_cholesterol": datasets.Value("float32"),
"fasting_blood_sugar": datasets.Value("float32"),
"rest_electrocardiographic_type": datasets.Value("string"),
"maximum_heart_rate": datasets.Value("float32"),
"has_exercise_induced_angina": datasets.Value("bool"),
"depression_induced_by_exercise": datasets.Value("float32"),
"slope_of_peak_exercise": datasets.Value("float32"),
"number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
"thal": datasets.Value("float32"),
"has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"hungary": {
"age": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"type_of_chest_pain": datasets.Value("string"),
"resting_blood_pressure": datasets.Value("float32"),
"serum_cholesterol": datasets.Value("float32"),
"fasting_blood_sugar": datasets.Value("float32"),
"rest_electrocardiographic_type": datasets.Value("string"),
"maximum_heart_rate": datasets.Value("float32"),
"has_exercise_induced_angina": datasets.Value("bool"),
"depression_induced_by_exercise": datasets.Value("float32"),
"slope_of_peak_exercise": datasets.Value("float32"),
"number_of_major_vessels_colored_by_flourosopy": datasets.Value("int16"),
"thal": datasets.Value("float32"),
"has_hearth_disease": 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 HeartConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(HeartConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Heart(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "cleveland"
BUILDER_CONFIGS = [
HeartConfig(name="cleveland",
description="Heart for binary classification, dataset."),
HeartConfig(name="va",
description="Heart for binary classification, va dataset."),
HeartConfig(name="switzerland",
description="Heart for binary classification, switzerland dataset."),
HeartConfig(name="hungary",
description="Heart for binary classification, hungary dataset.")
]
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)
print(downloads)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath)
data.columns = _BASE_FEATURE_NAMES
data = self.preprocess(data, config=self.config.name)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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