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"""Abalone.""" |
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from typing import List |
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from functools import partial |
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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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_ORIGINAL_FEATURE_NAMES = [ |
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"Sex", |
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"Length", |
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"Diameter", |
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"Height", |
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"Whole_weight", |
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"Shucked_weight", |
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"Viscera_weight", |
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"Shell_weight", |
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"Ring", |
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] |
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_BASE_FEATURE_NAMES = [ |
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"sex", |
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"length", |
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"diameter", |
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"height", |
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"whole_weight", |
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"shucked_weight", |
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"viscera_weight", |
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"shell_weight", |
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"number_of_rings", |
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] |
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DESCRIPTION = "Abalone dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Abalone" |
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_URLS = ("https://huggingface.co./datasets/mstz/abalone/raw/abalone.data") |
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_CITATION = """ |
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@misc{misc_abalone_1, |
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title = {{Abalone}}, |
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year = {1995}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C55C7W}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co./datasets/mstz/abalone/raw/main/abalone.data", |
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} |
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features_types_per_config = { |
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"abalone": { |
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"sex": datasets.Value("string"), |
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"length": datasets.Value("float64"), |
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"diameter": datasets.Value("float64"), |
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"height": datasets.Value("float64"), |
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"whole_weight": datasets.Value("float64"), |
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"shucked_weight": datasets.Value("float64"), |
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"viscera_weight": datasets.Value("float64"), |
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"shell_weight": datasets.Value("float64"), |
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"number_of_rings": datasets.Value("int8") |
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}, |
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"binary": { |
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"sex": datasets.Value("string"), |
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"length": datasets.Value("float64"), |
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"diameter": datasets.Value("float64"), |
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"height": datasets.Value("float64"), |
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"whole_weight": datasets.Value("float64"), |
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"shucked_weight": datasets.Value("float64"), |
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"viscera_weight": datasets.Value("float64"), |
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"shell_weight": datasets.Value("float64"), |
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"is_old": 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 AbaloneConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(AbaloneConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Abalone(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "abalone" |
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BUILDER_CONFIGS = [ |
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AbaloneConfig(name="abalone", description="Abalone for regression."), |
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AbaloneConfig(name="binary", description="Abalone 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 = _BASE_FEATURE_NAMES |
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if self.config.name == "binary": |
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data = data.rename(columns={"number_of_rings": "is_old"}) |
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data["is_old"] = data["is_old"].apply(lambda x: 1 if x > 9 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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