Datasets:
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"""Heart Failure Dataset"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"age",
"has_anaemia",
"creatinine_phosphokinase_concentration_in_blood",
"has_diabetes",
"heart_ejection_fraction",
"has_high_blood_pressure",
"platelets_concentration_in_blood",
"serum_creatinine_concentration_in_blood",
"serum_sodium_concentration_in_blood",
"sex",
"is_smoker",
"days_in_study",
"is_dead"
]
DESCRIPTION = "Heart Failure dataset."
_HOMEPAGE = "https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures"
_URLS = ("https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/heart_failure/raw/main/heart_failure_clinical_records_dataset.csv",
}
features_types_per_config = {
"death": {
"age": datasets.Value("int8"),
"has_anaemia": datasets.Value("bool"),
"creatinine_phosphokinase_concentration_in_blood": datasets.Value("float64"),
"has_diabetes": datasets.Value("bool"),
"heart_ejection_fraction": datasets.Value("float64"),
"has_high_blood_pressure": datasets.Value("bool"),
"platelets_concentration_in_blood": datasets.Value("float64"),
"serum_creatinine_concentration_in_blood": datasets.Value("float64"),
"serum_sodium_concentration_in_blood": datasets.Value("float64"),
"is_male": datasets.Value("bool"),
"is_smoker": datasets.Value("bool"),
"days_in_study": datasets.Value("int64"),
"is_dead": 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 HeartFailureConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(HeartFailureConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class HeartFailure(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "death"
BUILDER_CONFIGS = [
HeartFailureConfig(name="death",
description="Binary classification, predict if the patient dies.")
]
def _info(self):
if self.config.name not in features_per_config:
raise ValueError(f"Unknown configuration: {self.config.name}")
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 = self.preprocess(data, config=self.config.name)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame, config: str = "death") -> pandas.DataFrame:
data.columns = _BASE_FEATURE_NAMES
data = data.rename(columns={"sex": "is_male"})
data = data.astype({"has_anaemia": "bool", "has_diabetes": "bool", "has_high_blood_pressure": "bool", "is_male": "bool",
"is_smoker": "bool"})
return data
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