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"""Breast Dataset""" |
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from typing import List |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_ORIGINAL_FEATURE_NAMES = [ |
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"id", |
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"clump_thickness", |
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"uniformity_of_cell_size", |
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"uniformity_of_cell_shape", |
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"marginal_adhesion", |
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"single_epithelial_cell_size", |
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"bare_nuclei", |
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"bland_chromatin", |
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"normal_nucleoli", |
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"mitoses", |
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"is_cancer" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"clump_thickness", |
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"uniformity_of_cell_size", |
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"uniformity_of_cell_shape", |
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"marginal_adhesion", |
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"single_epithelial_cell_size", |
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"bare_nuclei", |
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"bland_chromatin", |
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"normal_nucleoli", |
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"mitoses", |
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"is_cancer" |
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] |
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DESCRIPTION = "Breast dataset for cancer prediction." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29" |
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_URLS = ("https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data") |
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_CITATION = """ |
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@article{wolberg1990multisurface, |
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title={Multisurface method of pattern separation for medical diagnosis applied to breast cytology.}, |
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author={Wolberg, William H and Mangasarian, Olvi L}, |
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journal={Proceedings of the national academy of sciences}, |
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volume={87}, |
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number={23}, |
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pages={9193--9196}, |
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year={1990}, |
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publisher={National Acad Sciences} |
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} |
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""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/breast/raw/main/breast-cancer-wisconsin.data", |
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} |
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features_types_per_config = { |
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"cancer": { |
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"clump_thickness": datasets.Value("int8"), |
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"uniformity_of_cell_size": datasets.Value("int8"), |
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"uniformity_of_cell_shape": datasets.Value("int8"), |
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"marginal_adhesion": datasets.Value("int8"), |
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"single_epithelial_cell_size": datasets.Value("int8"), |
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"bare_nuclei": datasets.Value("int8"), |
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"bland_chromatin": datasets.Value("int8"), |
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"normal_nucleoli": datasets.Value("int8"), |
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"mitoses": datasets.Value("int8"), |
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"is_cancer": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class BreastConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(BreastConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Breast(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "cancer" |
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BUILDER_CONFIGS = [ |
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BreastConfig(name="cancer", |
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description="Encoding dictionaries for discrete features."), |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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if self.config.name == "cancer": |
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data = pandas.read_csv(filepath, header=None) |
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data.columns=_ORIGINAL_FEATURE_NAMES |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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else: |
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raise ValueError(f"Unknown config: {self.config.name}") |
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def preprocess(self, data: pandas.DataFrame, config: str = "cancer") -> pandas.DataFrame: |
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data.drop("id", axis="columns", inplace=True) |
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data = data[data.bare_nuclei != "?"] |
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for c in data.columns: |
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data.loc[:, c] = data[c].astype(int) |
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data.columns = _BASE_FEATURE_NAMES |
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data.loc[:, "is_cancer"] = data.is_cancer.apply(lambda x: 0 if x == 2 else 1) |
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if config == "cancer": |
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return data |
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else: |
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raise ValueError(f"Unknown config: {config}") |
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