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"""Planning: A Census 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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_BASE_FEATURE_NAMES = [ |
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"V1", |
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"V2", |
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"V3", |
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"V4", |
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"V5", |
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"V6", |
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"V7", |
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"V8", |
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"V9", |
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"V10", |
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"V11", |
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"V12", |
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"class" |
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] |
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DESCRIPTION = "Planning dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Planning" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Planning") |
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_CITATION = """ |
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@misc{misc_planning_relax_230, |
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author = {Bhatt,Rajen}, |
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title = {{Planning Relax}}, |
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year = {2012}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5T023}} |
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}""" |
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urls_per_split = { |
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"planning": {"train": "https://huggingface.co/datasets/mstz/planning/raw/main/plrx.csv"} |
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} |
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features_types_per_config = { |
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"planning": { |
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"V1": datasets.Value("float64"), |
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"V2": datasets.Value("float64"), |
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"V3": datasets.Value("float64"), |
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"V4": datasets.Value("float64"), |
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"V5": datasets.Value("float64"), |
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"V6": datasets.Value("float64"), |
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"V7": datasets.Value("float64"), |
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"V8": datasets.Value("float64"), |
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"V9": datasets.Value("float64"), |
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"V10": datasets.Value("float64"), |
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"V11": datasets.Value("float64"), |
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"V12": datasets.Value("float64"), |
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"class": 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 PlanningConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(PlanningConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Planning(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "planning" |
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BUILDER_CONFIGS = [ |
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PlanningConfig(name="planning", |
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description="Planning 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) |
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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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