Initial script to load embeddings from geoparquet file

Experimenting with ArrowBasedBuilder, mostly with help from https://discuss.huggingface.co/t/how-to-tweak-a-dataset-without-a-loading-script/43533/5

clay_vector_embeddings.py

@@ -0,0 +1,70 @@
+"""
+Custom HuggingFace dataset loading script for GeoParquet files.
+
+References:
+- https://huggingface.co/docs/datasets/v2.15.0/en/dataset_script
+- https://github.com/huggingface/datasets/blob/2.15.0/templates/new_dataset_script.py
+- https://huggingface.co/docs/datasets/v2.15.0/en/package_reference/builder_classes
+- https://huggingface.co/docs/datasets/v2.15.0/en/about_dataset_load
+- https://discuss.huggingface.co/t/how-to-tweak-a-dataset-without-a-loading-script/43533/5
+"""
+import datasets
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+
+_URLS = {"32VLM": " 32VLM_v01.gpq"}
+_MGRS_TILES = ["32VLM"]
+
+
+class ClayVectorEmbeddings(datasets.ArrowBasedBuilder):
+    """Clay Vector Embeddings in GeoParquet format."""
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'MGRS_TILE')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=name,
+            version=datasets.Version(version="0.0.1"),
+            description=f"Clay vector embeddings for MGRS tile {name}",
+        )
+        for name in _MGRS_TILES
+    ]
+
+    # DEFAULT_CONFIG_NAME = "32VLM"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description="Clay Vector Embeddings in GeoParquet format.",
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "source_url": datasets.Value(dtype="string"),
+                    "date": datasets.Value(dtype="date32"),
+                    "embeddings": datasets.Value("string"),
+                    "geometry": datasets.Value("binary"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager: datasets.download.DownloadManager):
+        files = _URLS[self.config.name]
+        downloaded_files = dl_manager.download(files)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.ALL,
+                # These kwargs will be passed to _generate_tables
+                gen_kwargs={"filepaths": downloaded_files},
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_tables(self, filepaths: list[str] = ["32VLM_v01.gpq"]):
+        for file_idx, filepath in enumerate(filepaths):
+            with open(filepath, mode="rb") as f:
+                parquet_file = pq.ParquetFile(source=filepath)
+                for batch_idx, record_batch in enumerate(parquet_file.iter_batches()):
+                    pa_table = pa.Table.from_batches([record_batch])
+                    yield f"{file_idx_batch_idx}", pa_table