"""PostOperative Dataset""" from typing import List from functools import partial import datasets import pandas VERSION = datasets.Version("1.0.0") _ENCODING_DICS = { "internal_temperature": { "low": 0, "mid": 1, "high": 2 }, "surface_temperature": { "low": 0, "mid": 1, "high": 2 }, "oxigen_saturation": { "good": 0, "excellent": 1 }, "blood_pressure": { "low": 0, "mid": 1, "high": 2 }, "is_surface_temperature_stable": { "stable": True, "unstable": False, }, "is_internal_temperature_stable": { "stable": 2, "mod-stable": 1, "unstable": 0, }, "is_blood_pressure_stable": { "stable": 2, "mod-stable": 1, "unstable": 0, } } DESCRIPTION = "PostOperative dataset." _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification" _URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification") _CITATION = """ @misc{misc_post-operative_patient_82, author = {Summers,Sharon & Woolery,Linda}, title = {{Post-Operative Patient}}, year = {1993}, howpublished = {UCI Machine Learning Repository}, note = {{DOI}: \\url{10.24432/C5DG6Q}} }""" # Dataset info urls_per_split = { "train": "https://huggingface.co/datasets/mstz/post_operative/raw/main/post_operative.data" } features_types_per_config = { "post_operative": { "internal_temperature": datasets.Value("int8"), "surface_temperature": datasets.Value("int8"), "oxigen_saturation": datasets.Value("int8"), "blood_pressure": datasets.Value("int8"), "is_surface_temperature_stable": datasets.Value("bool"), "is_internal_temperature_stable": datasets.Value("int8"), "is_blood_pressure_stable": datasets.Value("int8"), "perceived_comfort": datasets.Value("int8"), "decision": datasets.ClassLabel(num_classes=3, names=("discharge", "hospital floor", "intensive care")), }, "post_operative_binary": { "internal_temperature": datasets.Value("int8"), "surface_temperature": datasets.Value("int8"), "oxigen_saturation": datasets.Value("int8"), "blood_pressure": datasets.Value("int8"), "is_surface_temperature_stable": datasets.Value("bool"), "is_internal_temperature_stable": datasets.Value("int8"), "is_blood_pressure_stable": datasets.Value("int8"), "perceived_comfort": datasets.Value("int8"), "decision": datasets.ClassLabel(num_classes=2, names=("discharge", "don't discharge")), } } features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} class PostOperativeConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(PostOperativeConfig, self).__init__(version=VERSION, **kwargs) self.features = features_per_config[kwargs["name"]] class PostOperative(datasets.GeneratorBasedBuilder): # dataset versions DEFAULT_CONFIG = "post_operative" BUILDER_CONFIGS = [ PostOperativeConfig(name="post_operative", description="PostOperative for regression."), PostOperativeConfig(name="post_operative_binary", description="PostOperative for binary classification.") ] 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): data = pandas.read_csv(filepath) data = self.preprocess(data) for row_id, row in data.iterrows(): data_row = dict(row) yield row_id, data_row def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame: if self.config.name == "post_operative_binary": data["decision"] = data["decision"].apply(lambda x: 1 if x >= 1 else 0) for feature in _ENCODING_DICS: encoding_function = partial(self.encode, feature) data.loc[:, feature] = data[feature].apply(encoding_function) data = data.reset_index() data.drop("index", axis="columns", inplace=True) data = data[data.perceived_comfort != "?"] return data[list(features_types_per_config[self.config.name].keys())] def encode(self, feature, value): if feature in _ENCODING_DICS: return _ENCODING_DICS[feature][value] raise ValueError(f"Unknown feature: {feature}")