Datasets:
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"""Haberman"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Haberman dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Haberman"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Haberman")
_CITATION = """
@misc{misc_haberman's_survival_43,
author = {Haberman,S.},
title = {{Haberman's Survival}},
year = {1999},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5XK51}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/haberman/raw/main/haberman.data"
}
features_types_per_config = {
"survival": {
"age": datasets.Value("int32"),
"year_of_operation": datasets.Value("int32"),
"number_of_axillary_nodes": datasets.Value("int32"),
"has_survived_5_years": 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 HabermanConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(HabermanConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Haberman(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "survival"
BUILDER_CONFIGS = [
HabermanConfig(name="survival",
description="Haberman 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
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