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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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"color", |
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"size", |
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"act", |
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"age", |
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"is_inflated" |
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] |
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DESCRIPTION = "Balloons dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Balloons" |
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_URLS = ("https://huggingface.co/datasets/mstz/balloons/raw/balloons.csv") |
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_CITATION = """ |
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@misc{misc_balloons_13, |
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title = {{Balloons}}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5BP4D}} |
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}""" |
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urls_per_split = { |
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"adult_or_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/adult+stretch.data"}, |
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"adult_and_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/adult-stretch.data"}, |
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"yellow_and_small": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/yellow-small.data"}, |
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"yellow_and_small_or_adult_and_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/yellow-small+adult-stretch.data"} |
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} |
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features_types_per_config = { |
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"adult_or_stretch": { |
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"color": datasets.Value("string"), |
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"size": datasets.Value("string"), |
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"act": datasets.Value("string"), |
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"age": datasets.Value("string"), |
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"is_inflated": datasets.ClassLabel(num_classes=2) |
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}, |
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"adult_and_stretch": { |
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"color": datasets.Value("string"), |
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"size": datasets.Value("string"), |
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"act": datasets.Value("string"), |
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"age": datasets.Value("string"), |
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"is_inflated": datasets.ClassLabel(num_classes=2) |
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}, |
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"yellow_and_small": { |
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"color": datasets.Value("string"), |
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"size": datasets.Value("string"), |
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"act": datasets.Value("string"), |
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"age": datasets.Value("string"), |
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"is_inflated": datasets.ClassLabel(num_classes=2) |
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}, |
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"yellow_and_small_or_adult_and_stretch": { |
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"color": datasets.Value("string"), |
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"size": datasets.Value("string"), |
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"act": datasets.Value("string"), |
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"age": datasets.Value("string"), |
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"is_inflated": 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 BalloonsConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(BalloonsConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Balloons(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "adult_or_stretch" |
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BUILDER_CONFIGS = [ |
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BalloonsConfig(name="adult_or_stretch", |
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description="Binary classification, balloons are inflated if age == adult or act == stretch."), |
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BalloonsConfig(name="adult_and_stretch", |
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description="Binary classification, balloons are inflated if age == adult and act == stretch."), |
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BalloonsConfig(name="yellow_and_small", |
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description="Binary classification, balloons are inflated if color == yellow and size == small."), |
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BalloonsConfig(name="yellow_and_small_or_adult_and_stretch", |
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description="Binary classification, balloons are inflated if color == yellow and size == small or age == adult and act == stretch.") |
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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_per_config = {config: dl_manager.download_and_extract(urls_per_split) for config in urls_per_split} |
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print(downloads_per_config) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads_per_config[self.config.name][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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data.columns = _BASE_FEATURE_NAMES |
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data.loc[:, "is_inflated"] = data.is_inflated.apply(lambda x: 1 if x == "T" 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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