Datasets:
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"""ILPD"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "ILPD dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/ILPD"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/ILPD")
_CITATION = """
@misc{misc_ilpd_(indian_liver_patient_dataset)_225,
author = {Ramana,Bendi & Venkateswarlu,N.},
title = {{ILPD (Indian Liver Patient Dataset)}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5D02C}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/liver/raw/main/Indian%20Liver%20Patient%20Dataset%20(ILPD).csv"
}
features_types_per_config = {
"liver": {
"age": datasets.Value("int64"),
"is_male": datasets.Value("bool"),
"total_bilirubin": datasets.Value("float64"),
"direct_ribilubin": datasets.Value("float64"),
"alkaline_phosphotase": datasets.Value("int64"),
"alamine_aminotransferasi": datasets.Value("int64"),
"aspartate_aminotransferase": datasets.Value("int64"),
"total_proteins": datasets.Value("float64"),
"albumin": datasets.Value("float64"),
"albumin_to_globulin_ratio": 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 ILPDConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ILPDConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class ILPD(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "liver"
BUILDER_CONFIGS = [
ILPDConfig(name="liver",
description="ILPD 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).infer_objects()
data[["is_male"]].applymap(bool)
data.loc[:, "class"] = data["class"].apply(lambda x: x - 1)
data = data.astype({"is_male": "bool"})
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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