from typing import List import datasets import pandas VERSION = datasets.Version("1.0.0") DESCRIPTION = "Pol dataset from the OpenML repository." _HOMEPAGE = "https://www.openml.org/search?type=data&sort=runs&id=722&status=active" _URLS = ("https://www.openml.org/search?type=data&sort=runs&id=722&status=active") _CITATION = """""" # Dataset info urls_per_split = { "train": "https://huggingface.co/datasets/mstz/pol/raw/main/pol.csv" } features_types_per_config = { "pol": { "f1": datasets.Value("int64"), "f2": datasets.Value("int64"), "f3": datasets.Value("int64"), "f4": datasets.Value("int64"), "f5": datasets.Value("int64"), "f6": datasets.Value("int64"), "f7": datasets.Value("int64"), "f8": datasets.Value("int64"), "f9": datasets.Value("int64"), "f10": datasets.Value("int64"), "f11": datasets.Value("int64"), "f12": datasets.Value("int64"), "f13": datasets.Value("int64"), "f14": datasets.Value("int64"), "f15": datasets.Value("int64"), "f16": datasets.Value("int64"), "f17": datasets.Value("int64"), "f18": datasets.Value("int64"), "f19": datasets.Value("int64"), "f20": datasets.Value("int64"), "f21": datasets.Value("int64"), "f22": datasets.Value("int64"), "f23": datasets.Value("int64"), "f24": datasets.Value("int64"), "f25": datasets.Value("int64"), "f26": datasets.Value("int64"), "f27": datasets.Value("int64"), "f28": datasets.Value("int64"), "f29": datasets.Value("int64"), "f30": datasets.Value("int64"), "f31": datasets.Value("int64"), "f32": datasets.Value("int64"), "f33": datasets.Value("int64"), "f34": datasets.Value("int64"), "f35": datasets.Value("int64"), "f36": datasets.Value("int64"), "f37": datasets.Value("int64"), "f38": datasets.Value("int64"), "f39": datasets.Value("int64"), "f40": datasets.Value("int64"), "f41": datasets.Value("int64"), "f42": datasets.Value("int64"), "f43": datasets.Value("int64"), "f44": datasets.Value("int64"), "f45": datasets.Value("int64"), "f46": datasets.Value("int64"), "f47": datasets.Value("int64"), "f48": datasets.Value("int64"), "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 ElectricityConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(ElectricityConfig, self).__init__(version=VERSION, **kwargs) self.features = features_per_config[kwargs["name"]] class Electricity(datasets.GeneratorBasedBuilder): # dataset versions DEFAULT_CONFIG = "pol" BUILDER_CONFIGS = [ ElectricityConfig(name="pol", description="Electricity 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