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from typing import List |
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from functools import partial |
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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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"rads", |
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"age", |
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"lesion_shape", |
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"margin", |
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"density", |
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"is_severe" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"age", |
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"lesion_shape", |
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"margin", |
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"density", |
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"is_severe" |
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] |
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_ENCODING_DICS = { |
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"lesion_shape": { |
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"1": "round", |
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"2": "oval", |
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"3": "lobular", |
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"4": "irregular", |
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}, |
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"margin": { |
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"1": "circumbscribed", |
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"2": "microlobulated", |
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"3": "obscured", |
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"4": "ill-defined", |
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"5": "spiculated", |
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}, |
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"density": { |
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"1": "high", |
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"2": "iso", |
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"3": "low", |
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"4": "fat-containing", |
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"5": "spiculated", |
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} |
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} |
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DESCRIPTION = "Mammography dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Mammography" |
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_URLS = ("https://huggingface.co/datasets/mstz/mammography/raw/mammography_masses.data") |
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_CITATION = """ |
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@misc{misc_mammographic_mass_161, |
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author = {Elter,Matthias}, |
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title = {{Mammographic Mass}}, |
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year = {2007}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C53K6Z}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/mammography/raw/main/mammographic_masses.data" |
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} |
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features_types_per_config = { |
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"mammography": { |
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"age": datasets.Value("int8"), |
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"lesion_shape": datasets.Value("string"), |
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"margin": datasets.Value("string"), |
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"density": datasets.Value("string"), |
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"is_severe": 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 MammographyConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(MammographyConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Mammography(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "mammography" |
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BUILDER_CONFIGS = [ |
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MammographyConfig(name="mammography", |
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description="Mammography for binary classification.") |
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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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data = pandas.read_csv(filepath, header=None) |
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data.columns = _ORIGINAL_FEATURE_NAMES |
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data.drop("rads", axis="columns", inplace=True) |
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data = data[data.age != "?"] |
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data = data[data.lesion_shape != "?"] |
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data = data[data.margin != "?"] |
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data = data[data.density != "?"] |
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data = data.infer_objects() |
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for feature in _ENCODING_DICS: |
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encoding_function = partial(self.encode, feature) |
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data.loc[:, feature] = data[feature].apply(encoding_function) |
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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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def encode(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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