| """Promoters""" |
|
|
| from typing import List |
| from functools import partial |
|
|
| import datasets |
|
|
| import pandas |
|
|
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| DESCRIPTION = "Promoters dataset from the UCI ML repository." |
| _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Promoters" |
| _URLS = ("https://archive.ics.uci.edu/ml/datasets/Promoters") |
| _CITATION = """ |
| @misc{misc_molecular_biology_(promoter_gene_sequences)_67, |
| author = {Harley,C., Reynolds,R. & Noordewier,M.}, |
| title = {{Molecular Biology (Promoter Gene Sequences)}}, |
| year = {1990}, |
| howpublished = {UCI Machine Learning Repository}, |
| note = {{DOI}: \\url{10.24432/C5S01D}} |
| }""" |
|
|
| |
| urls_per_split = { |
| "train": "https://huggingface.co/datasets/mstz/promoters/raw/main/promoters.data" |
| } |
| features_types_per_config = { |
| "promoters": { |
| "seq_0": datasets.Value("string"), |
| "seq_1": datasets.Value("string"), |
| "seq_2": datasets.Value("string"), |
| "seq_3": datasets.Value("string"), |
| "seq_4": datasets.Value("string"), |
| "seq_5": datasets.Value("string"), |
| "seq_6": datasets.Value("string"), |
| "seq_7": datasets.Value("string"), |
| "seq_8": datasets.Value("string"), |
| "seq_9": datasets.Value("string"), |
| "seq_10": datasets.Value("string"), |
| "seq_11": datasets.Value("string"), |
| "seq_12": datasets.Value("string"), |
| "seq_13": datasets.Value("string"), |
| "seq_14": datasets.Value("string"), |
| "seq_15": datasets.Value("string"), |
| "seq_16": datasets.Value("string"), |
| "seq_17": datasets.Value("string"), |
| "seq_18": datasets.Value("string"), |
| "seq_19": datasets.Value("string"), |
| "seq_20": datasets.Value("string"), |
| "seq_21": datasets.Value("string"), |
| "seq_22": datasets.Value("string"), |
| "seq_23": datasets.Value("string"), |
| "seq_24": datasets.Value("string"), |
| "seq_25": datasets.Value("string"), |
| "seq_26": datasets.Value("string"), |
| "seq_27": datasets.Value("string"), |
| "seq_28": datasets.Value("string"), |
| "seq_29": datasets.Value("string"), |
| "seq_30": datasets.Value("string"), |
| "seq_31": datasets.Value("string"), |
| "seq_32": datasets.Value("string"), |
| "seq_33": datasets.Value("string"), |
| "seq_34": datasets.Value("string"), |
| "seq_35": datasets.Value("string"), |
| "seq_36": datasets.Value("string"), |
| "seq_37": datasets.Value("string"), |
| "seq_38": datasets.Value("string"), |
| "seq_39": datasets.Value("string"), |
| "seq_40": datasets.Value("string"), |
| "seq_41": datasets.Value("string"), |
| "seq_42": datasets.Value("string"), |
| "seq_43": datasets.Value("string"), |
| "seq_44": datasets.Value("string"), |
| "seq_45": datasets.Value("string"), |
| "seq_46": datasets.Value("string"), |
| "seq_47": datasets.Value("string"), |
| "seq_48": datasets.Value("string"), |
| "seq_49": datasets.Value("string"), |
| "seq_50": datasets.Value("string"), |
| "seq_51": datasets.Value("string"), |
| "seq_52": datasets.Value("string"), |
| "seq_53": datasets.Value("string"), |
| "seq_54": datasets.Value("string"), |
| "seq_55": datasets.Value("string"), |
| "seq_56": datasets.Value("string"), |
| "class": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
| } |
| } |
| features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
| class PromotersConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(PromotersConfig, self).__init__(version=VERSION, **kwargs) |
| self.features = features_per_config[kwargs["name"]] |
|
|
|
|
| class Promoters(datasets.GeneratorBasedBuilder): |
| |
| DEFAULT_CONFIG = "promoters" |
| BUILDER_CONFIGS = [ |
| PromotersConfig(name="promoters", |
| description="Promoters for binary classification.") |
| ] |
|
|
| def _info(self): |
| info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
| features=features_per_config[self.config.name]) |
|
|
| return info |
| |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
| downloads = dl_manager.download_and_extract(urls_per_split) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}) |
| ] |
| |
| def _generate_examples(self, filepath: str): |
| data = pandas.read_csv(filepath) |
|
|
| for row_id, row in data.iterrows(): |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
|
|
