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"""Promoters""" |
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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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DESCRIPTION = "Promoters dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Promoters" |
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_URLS = ("https://archive.ics.uci.edu/ml/datasets/Promoters") |
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_CITATION = """ |
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@misc{misc_molecular_biology_(promoter_gene_sequences)_67, |
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author = {Harley,C., Reynolds,R. & Noordewier,M.}, |
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title = {{Molecular Biology (Promoter Gene Sequences)}}, |
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year = {1990}, |
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howpublished = {UCI Machine Learning Repository}, |
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note = {{DOI}: \\url{10.24432/C5S01D}} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/promoters/raw/main/promoters.data" |
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} |
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features_types_per_config = { |
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"promoters": { |
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"seq_0": datasets.Value("string"), |
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"seq_1": datasets.Value("string"), |
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"seq_2": datasets.Value("string"), |
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"seq_3": datasets.Value("string"), |
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"seq_4": datasets.Value("string"), |
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"seq_5": datasets.Value("string"), |
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"seq_6": datasets.Value("string"), |
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"seq_7": datasets.Value("string"), |
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"seq_8": datasets.Value("string"), |
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"seq_9": datasets.Value("string"), |
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"seq_10": datasets.Value("string"), |
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"seq_11": datasets.Value("string"), |
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"seq_12": datasets.Value("string"), |
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"seq_13": datasets.Value("string"), |
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"seq_14": datasets.Value("string"), |
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"seq_15": datasets.Value("string"), |
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"seq_16": datasets.Value("string"), |
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"seq_17": datasets.Value("string"), |
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"seq_18": datasets.Value("string"), |
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"seq_19": datasets.Value("string"), |
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"seq_20": datasets.Value("string"), |
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"seq_21": datasets.Value("string"), |
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"seq_22": datasets.Value("string"), |
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"seq_23": datasets.Value("string"), |
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"seq_24": datasets.Value("string"), |
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"seq_25": datasets.Value("string"), |
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"seq_26": datasets.Value("string"), |
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"seq_27": datasets.Value("string"), |
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"seq_28": datasets.Value("string"), |
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"seq_29": datasets.Value("string"), |
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"seq_30": datasets.Value("string"), |
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"seq_31": datasets.Value("string"), |
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"seq_32": datasets.Value("string"), |
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"seq_33": datasets.Value("string"), |
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"seq_34": datasets.Value("string"), |
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"seq_35": datasets.Value("string"), |
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"seq_36": datasets.Value("string"), |
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"seq_37": datasets.Value("string"), |
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"seq_38": datasets.Value("string"), |
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"seq_39": datasets.Value("string"), |
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"seq_40": datasets.Value("string"), |
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"seq_41": datasets.Value("string"), |
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"seq_42": datasets.Value("string"), |
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"seq_43": datasets.Value("string"), |
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"seq_44": datasets.Value("string"), |
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"seq_45": datasets.Value("string"), |
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"seq_46": datasets.Value("string"), |
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"seq_47": datasets.Value("string"), |
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"seq_48": datasets.Value("string"), |
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"seq_49": datasets.Value("string"), |
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"seq_50": datasets.Value("string"), |
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"seq_51": datasets.Value("string"), |
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"seq_52": datasets.Value("string"), |
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"seq_53": datasets.Value("string"), |
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"seq_54": datasets.Value("string"), |
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"seq_55": datasets.Value("string"), |
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"seq_56": datasets.Value("string"), |
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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 PromotersConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(PromotersConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Promoters(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "promoters" |
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BUILDER_CONFIGS = [ |
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PromotersConfig(name="promoters", |
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description="Promoters 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) |
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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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