feat: Add h5folder dataset loader for HDF5 support

src/datasets/packaged_modules/h5folder/h5folder.py

@@ -0,0 +1,66 @@
+import datasets
+import h5py
+import os
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Load datasets stored in HDF5 (.h5) format.
+This loader expects a specific structure within the HDF5 file: each feature (column) should be a top-level dataset.
+All datasets should have the same length (number of rows).
+"""
+
+_HOMEPAGE = "https://www.h5py.org/"
+
+_CITATION = """"""
+
+class H5FolderConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class H5Folder(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = H5FolderConfig
+
+    def _info(self):
+        # NOTE: You can later modify features to be dynamic if needed
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "id": datasets.Value("int32"),
+                "data": datasets.Sequence(datasets.Value("float32")),  # or other shape
+                "label": datasets.ClassLabel(names=["class_0", "class_1"]),
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_path = os.path.join(self.config.data_dir or dl_manager.manual_dir, "data.h5")
+
+        if not os.path.exists(data_path):
+            raise FileNotFoundError(f"Expected HDF5 file at {data_path}")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_path},
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples from the HDF5 file."""
+        logger.info(f"Opening HDF5 file: {filepath}")
+        with h5py.File(filepath, "r") as f:
+            # Assume structure: /data, /label, /id or similar
+            data = f["data"]
+            labels = f["label"]
+            ids = f["id"] if "id" in f else range(len(data))
+
+            for idx in range(len(data)):
+                yield idx, {
+                    "id": int(ids[idx]) if "id" in f else idx,
+                    "data": data[idx].tolist(),  # Convert from numpy array
+                    "label": int(labels[idx]),
+                }