from typing import List import datasets import pandas import gzip VERSION = datasets.Version("1.0.0") DESCRIPTION = "Sonar dataset from the UCI ML repository." _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/31/sonar" _URLS = ("https://archive-beta.ics.uci.edu/dataset/31/sonar") _CITATION = """""" # Dataset info urls_per_split = { "train": "https://huggingface.co./datasets/mstz/sonar/raw/main/sonar.all-data" } features_types_per_config = { "sonar": {str(i): datasets.Value("float32") for i in range(60)} } features_types_per_config["sonar"]["is_rock"] = datasets.ClassLabel(num_classes=2) features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} class SonarConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(SonarConfig, self).__init__(version=VERSION, **kwargs) self.features = features_per_config[kwargs["name"]] class Sonar(datasets.GeneratorBasedBuilder): # dataset versions DEFAULT_CONFIG = "sonar" BUILDER_CONFIGS = [ SonarConfig(name="sonar", description="Sonar for binary classification.") ] def _info(self): if self.config.name not in features_per_config: raise ValueError(f"Unknown configuration: {self.config.name}") 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, header=None) data.columns = [str(i) for i in range(60)] + ["is_rock"] data = self.preprocess(data, config=self.config.name) for row_id, row in data.iterrows(): data_row = dict(row) yield row_id, data_row def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: data.loc[:, "is_rock"] = data["is_rock"].apply(lambda x: 1 if x == "R" else 0) return data