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"""Ionosphere""" |
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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 = "Ionosphere dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Ionosphere" |
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_URLS = ("https://huggingface.co/datasets/mstz/ionosphere/raw/ionosphere.data") |
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_CITATION = """ |
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@misc{misc_ionosphere_52, |
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author = {Sigillito,V., Wing,S., Hutton,L. & Baker,K.}, |
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title = {{Ionosphere}}, |
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year = {1989}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5W01B}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/ionosphere/raw/main/ionosphere.data" |
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} |
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features_types_per_config = { |
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"ionosphere": { |
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"signal_0": datasets.Value("float64"), |
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"signal_1": datasets.Value("float64"), |
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"signal_2": datasets.Value("float64"), |
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"signal_3": datasets.Value("float64"), |
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"signal_4": datasets.Value("float64"), |
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"signal_5": datasets.Value("float64"), |
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"signal_6": datasets.Value("float64"), |
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"signal_7": datasets.Value("float64"), |
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"signal_8": datasets.Value("float64"), |
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"signal_9": datasets.Value("float64"), |
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"signal_10": datasets.Value("float64"), |
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"signal_11": datasets.Value("float64"), |
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"signal_12": datasets.Value("float64"), |
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"signal_13": datasets.Value("float64"), |
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"signal_14": datasets.Value("float64"), |
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"signal_15": datasets.Value("float64"), |
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"signal_16": datasets.Value("float64"), |
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"signal_17": datasets.Value("float64"), |
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"signal_18": datasets.Value("float64"), |
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"signal_19": datasets.Value("float64"), |
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"signal_20": datasets.Value("float64"), |
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"signal_21": datasets.Value("float64"), |
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"signal_22": datasets.Value("float64"), |
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"signal_23": datasets.Value("float64"), |
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"signal_24": datasets.Value("float64"), |
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"signal_25": datasets.Value("float64"), |
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"signal_26": datasets.Value("float64"), |
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"signal_27": datasets.Value("float64"), |
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"signal_28": datasets.Value("float64"), |
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"signal_29": datasets.Value("float64"), |
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"signal_30": datasets.Value("float64"), |
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"signal_31": datasets.Value("float64"), |
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"signal_32": datasets.Value("float64"), |
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"signal_33": datasets.Value("float64"), |
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"class": datasets.ClassLabel(num_classes=2) |
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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 IonosphereConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(IonosphereConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Ionosphere(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "ionosphere" |
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BUILDER_CONFIGS = [ |
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IonosphereConfig(name="ionosphere", |
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description="Ionosphere 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 = [f"signal_{i}" for i in range(data.shape[1] - 1)] + ["class"] |
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data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == "g" else 0) |
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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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