Datasets:
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"""Breast Dataset"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
"id",
"clump thickness",
"uniformity of cell size",
"uniformity of cell shape",
"marginal adhesion",
"single epithelial cell size",
"bare nuclei",
"bland chromatin",
"normal nucleoli",
"mitoses",
"is_cancer"
]
_BASE_FEATURE_NAMES = [
"clump thickness",
"uniformity of cell size",
"uniformity of cell shape",
"marginal adhesion",
"single epithelial cell size",
"bare nuclei",
"bland chromatin",
"normal nucleoli",
"mitoses",
"is_cancer"
]
DESCRIPTION = "Breast dataset for cancer prediction."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29"
_URLS = ("https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data")
_CITATION = """
@article{wolberg1990multisurface,
title={Multisurface method of pattern separation for medical diagnosis applied to breast cytology.},
author={Wolberg, William H and Mangasarian, Olvi L},
journal={Proceedings of the national academy of sciences},
volume={87},
number={23},
pages={9193--9196},
year={1990},
publisher={National Acad Sciences}
}
"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data",
}
features_types_per_config = {
"encoding": {
"feature": datasets.Value("string"),
"original_value": datasets.Value("string"),
"encoded_value": datasets.Value("int8"),
},
"cancer": {
"clump thickness": datasets.Value("int8"),
"uniformity of cell size": datasets.Value("int8"),
"uniformity of cell shape": datasets.Value("int8"),
"marginal adhesion": datasets.Value("int8"),
"single epithelial cell size": datasets.Value("int8"),
"bare nuclei": datasets.Value("int8"),
"bland chromatin": datasets.Value("int8"),
"normal nucleoli": datasets.Value("int8"),
"mitoses": datasets.Value("int8"),
"is_cancer": 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 BreastConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(BreastConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Breast(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "cancer"
BUILDER_CONFIGS = [
BreastConfig(name="cancer",
description="Encoding dictionaries for discrete features."),
]
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, header=None)
data.columns=_ORIGINAL_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
def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
data.drop("id", axis="columns", inplace=True)
data.columns = _BASE_FEATURE_NAMES
data.cancer.apply(lambda x: 0 if x == 2 else 1)
if config == "cancer":
return data
else:
raise ValueError(f"Unknown config: {config}")
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