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"""PostOperative Dataset""" |
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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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_ENCODING_DICS = { |
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"internal_temperature": { |
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"low": 0, |
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"mid": 1, |
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"high": 2 |
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}, |
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"surface_temperature": { |
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"low": 0, |
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"mid": 1, |
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"high": 2 |
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}, |
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"oxigen_saturation": { |
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"good": 0, |
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"excellent": 1 |
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}, |
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"blood_pressure": { |
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"low": 0, |
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"mid": 1, |
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"high": 2 |
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}, |
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"is_surface_temperature_stable": { |
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"stable": True, |
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"unstable": False, |
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}, |
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"is_internal_temperature_stable": { |
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"stable": 2, |
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"mod-stable": 1, |
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"unstable": 0, |
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}, |
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"is_blood_pressure_stable": { |
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"stable": 2, |
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"mod-stable": 1, |
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"unstable": 0, |
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} |
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} |
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DESCRIPTION = "PostOperative dataset." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification") |
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_CITATION = """ |
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@misc{misc_post-operative_patient_82, |
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author = {Summers,Sharon & Woolery,Linda}, |
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title = {{Post-Operative Patient}}, |
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year = {1993}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5DG6Q}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/post_operative/raw/main/post_operative.data" |
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} |
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features_types_per_config = { |
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"post_operative": { |
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"internal_temperature": datasets.Value("int8"), |
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"surface_temperature": datasets.Value("int8"), |
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"oxigen_saturation": datasets.Value("int8"), |
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"blood_pressure": datasets.Value("int8"), |
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"is_surface_temperature_stable": datasets.Value("bool"), |
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"is_internal_temperature_stable": datasets.Value("int8"), |
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"is_blood_pressure_stable": datasets.Value("int8"), |
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"perceived_comfort": datasets.Value("int8"), |
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"decision": datasets.ClassLabel(num_classes=3, names=("discharge", "hospital floor", "intensive care")), |
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}, |
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"post_operative_binary": { |
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"internal_temperature": datasets.Value("int8"), |
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"surface_temperature": datasets.Value("int8"), |
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"oxigen_saturation": datasets.Value("int8"), |
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"blood_pressure": datasets.Value("int8"), |
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"is_surface_temperature_stable": datasets.Value("bool"), |
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"is_internal_temperature_stable": datasets.Value("int8"), |
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"is_blood_pressure_stable": datasets.Value("int8"), |
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"perceived_comfort": datasets.Value("int8"), |
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"decision": datasets.ClassLabel(num_classes=2, names=("discharge", "don't discharge")), |
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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 PostOperativeConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(PostOperativeConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class PostOperative(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "post_operative" |
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BUILDER_CONFIGS = [ |
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PostOperativeConfig(name="post_operative", |
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description="PostOperative for regression."), |
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PostOperativeConfig(name="post_operative_binary", |
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description="PostOperative 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) |
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data = self.preprocess(data) |
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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 preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame: |
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if self.config.name == "post_operative_binary": |
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data["decision"] = data["decision"].apply(lambda x: 1 if x >= 1 else 0) |
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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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data = data.reset_index() |
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data.drop("index", axis="columns", inplace=True) |
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data = data[data.perceived_comfort != "?"] |
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return data[list(features_types_per_config[self.config.name].keys())] |
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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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