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"""Acute_Inflammation""" |
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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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_BASE_FEATURE_NAMES = [ |
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"temperature", |
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"has_nausea", |
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"has_lumbar_pain", |
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"has_urine_pushing", |
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"has_micturition_pains", |
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"has_burnt_urethra", |
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"has_inflammed_bladder", |
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"has_nephritis_of_renal_pelvis" |
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] |
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DESCRIPTION = "Acute_Inflammation dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Acute_Inflammation" |
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_URLS = ("https://huggingface.co/datasets/mstz/acute_inflammation/raw/main/diagnosis.csv") |
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_CITATION = """ |
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@misc{misc_acute_inflammations_184, |
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author = {Czerniak,Jacek}, |
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title = {{Acute Inflammations}}, |
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year = {2009}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5V59S}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/acute_inflammation/raw/main/diagnosis.csv" |
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} |
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features_types_per_config = { |
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"inflammation": { |
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"temperature": datasets.Value("float64"), |
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"has_nausea": datasets.Value("bool"), |
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"has_lumbar_pain": datasets.Value("bool"), |
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"has_urine_pushing": datasets.Value("bool"), |
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"has_micturition_pains": datasets.Value("bool"), |
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"has_burnt_urethra": datasets.Value("bool"), |
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"has_inflammed_bladder": datasets.Value("bool"), |
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"has_nephritis_of_renal_pelvis": datasets.Value("int8") |
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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 Acute_InflammationConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(Acute_InflammationConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Acute_Inflammation(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "inflammation" |
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BUILDER_CONFIGS = [ |
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Acute_InflammationConfig(name="inflammation", |
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description="Binary classification of inflammation.") |
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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 = 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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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.columns = _BASE_FEATURE_NAMES |
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boolean_features = ["has_nausea", "has_lumbar_pain", "has_urine_pushing", |
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"has_micturition_pains", "has_burnt_urethra", "has_inflammed_bladder"] |
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for f in boolean_features: |
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data.loc[:, f] = data[f].apply(lambda x: True if x == "yes" else False) |
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data = data.astype({f: "bool" for f in boolean_features}) |
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return data |
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