Datasets:
File size: 4,179 Bytes
73025cd 12f6038 73025cd 12f6038 73025cd cfc8a2f 73025cd 12f6038 73025cd e28185c 73025cd e28185c 73025cd e28185c 73025cd e28185c 73025cd 12f6038 3a9740d 73025cd f3ba2de 73025cd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
"""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 = {
"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):
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):
if self.config.name == "cancer":
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
else:
raise ValueError(f"Unknown config: {self.config.name}")
def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame:
data.drop("id", axis="columns", inplace=True)
data = data[data.bare_nuclei != "?"]
for c in data.columns:
data.loc[:, c] = data[c].astype(int)
data.columns = _BASE_FEATURE_NAMES
data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1)
if config == "cancer":
return data
else:
raise ValueError(f"Unknown config: {config}")
|