Delete sonar.py

sonar.py

@@ -1,72 +0,0 @@
-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