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"""Spect""" |
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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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DESCRIPTION = "Spect dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Spect" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Spect") |
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_CITATION = """ |
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@misc{misc_spect_heart_95, |
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author = {Cios,Krzysztof, Kurgan,Lukasz & Goodenday,Lucy}, |
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title = {{SPECT Heart}}, |
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year = {2001}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5P304}} |
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}""" |
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urls_per_split = { |
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"spect": { |
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"train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.train", |
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"test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.test" |
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}, |
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"spectf": { |
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"train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.train", |
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"test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.test" |
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} |
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} |
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features_types_per_config = { |
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"spect": { |
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"feature_0": datasets.Value("bool"), |
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"feature_1": datasets.Value("bool"), |
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"feature_2": datasets.Value("bool"), |
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"feature_3": datasets.Value("bool"), |
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"feature_4": datasets.Value("bool"), |
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"feature_5": datasets.Value("bool"), |
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"feature_6": datasets.Value("bool"), |
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"feature_7": datasets.Value("bool"), |
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"feature_8": datasets.Value("bool"), |
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"feature_9": datasets.Value("bool"), |
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"feature_10": datasets.Value("bool"), |
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"feature_11": datasets.Value("bool"), |
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"feature_12": datasets.Value("bool"), |
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"feature_13": datasets.Value("bool"), |
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"feature_14": datasets.Value("bool"), |
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"feature_15": datasets.Value("bool"), |
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"feature_16": datasets.Value("bool"), |
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"feature_17": datasets.Value("bool"), |
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"feature_18": datasets.Value("bool"), |
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"feature_19": datasets.Value("bool"), |
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"feature_20": datasets.Value("bool"), |
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"feature_21": datasets.Value("bool"), |
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"is_emitted": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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}, |
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"spectf": { |
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"F1R": datasets.Value("int8"), |
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"F1S": datasets.Value("int8"), |
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"F2R": datasets.Value("int8"), |
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"F2S": datasets.Value("int8"), |
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"F3R": datasets.Value("int8"), |
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"F3S": datasets.Value("int8"), |
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"F4R": datasets.Value("int8"), |
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"F4S": datasets.Value("int8"), |
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"F5R": datasets.Value("int8"), |
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"F5S": datasets.Value("int8"), |
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"F6R": datasets.Value("int8"), |
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"F6S": datasets.Value("int8"), |
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"F7R": datasets.Value("int8"), |
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"F7S": datasets.Value("int8"), |
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"F8R": datasets.Value("int8"), |
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"F8S": datasets.Value("int8"), |
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"F9R": datasets.Value("int8"), |
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"F9S": datasets.Value("int8"), |
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"F10R": datasets.Value("int8"), |
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"F10S": datasets.Value("int8"), |
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"F11R": datasets.Value("int8"), |
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"F11S": datasets.Value("int8"), |
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"F12R": datasets.Value("int8"), |
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"F12S": datasets.Value("int8"), |
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"F13R": datasets.Value("int8"), |
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"F13S": datasets.Value("int8"), |
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"F14R": datasets.Value("int8"), |
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"F14S": datasets.Value("int8"), |
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"F15R": datasets.Value("int8"), |
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"F15S": datasets.Value("int8"), |
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"F16R": datasets.Value("int8"), |
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"F16S": datasets.Value("int8"), |
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"F17R": datasets.Value("int8"), |
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"F17S": datasets.Value("int8"), |
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"F18R": datasets.Value("int8"), |
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"F18S": datasets.Value("int8"), |
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"F19R": datasets.Value("int8"), |
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"F19S": datasets.Value("int8"), |
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"F20R": datasets.Value("int8"), |
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"F20S": datasets.Value("int8"), |
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"F21R": datasets.Value("int8"), |
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"F21S": datasets.Value("int8"), |
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"F22R": datasets.Value("int8"), |
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"F22S": datasets.Value("int8"), |
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"is_emitted": 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 SpectConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(SpectConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Spect(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "spect" |
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BUILDER_CONFIGS = [ |
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SpectConfig(name="spect", |
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description="Spect for binary classification."), |
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SpectConfig(name="spectf", |
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description="Spectf 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[self.config.name]["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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features = list(features_types_per_config[self.config.name]) |
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base_features = [features[-1]] + features[:-1] |
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data.columns = base_features |
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data = data[features] |
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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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