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"""Isolet dataset.""" |
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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 = "Isolet dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Isolet" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/54/isolet") |
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_CITATION = """ |
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@misc{misc_isolet_54, |
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author = {Cole,Ron & Fanty,Mark}, |
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title = {{ISOLET}}, |
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year = {1994}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C51G69}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/isolet/resolve/main/isolet.zip" |
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} |
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features_types_per_config = { |
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"isolet": { |
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str(i): datasets.Value("float64") for i in range(617) |
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} |
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} |
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features_types_per_config["isolet"]["617"] = datasets.ClassLabel(num_classes=26) |
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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 IsoletConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(IsoletConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Isolet(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "isolet" |
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BUILDER_CONFIGS = [ |
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IsoletConfig(name="isolet", |
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description="Isolet for letter 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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print(filepath + "/isolet1+2+3+4.data") |
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data = pandas.read_csv(filepath + "/isolet1+2+3+4.data", 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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print(data.shape) |
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print(data.columns) |
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data.columns = [str(i) for i in range(618)] |
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return data.astype({"617": "int"}) |
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