Upload sonar.py

sonar.py

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+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