examples/test_load_subsets.py

#!/usr/bin/env python3
"""Load and validate the four Hugging Face Dataset subsets in this repo."""

from __future__ import annotations

import argparse
from pathlib import Path

from datasets import Dataset, load_dataset


SUBSETS = (
    "depth_frames",
    "radar_frames",
    "radar_depth_joined",
    "radar_video_grouped",
)

EXPECTED_COLUMNS = {
    "depth_frames": {
        "relative_path",
        "recording_id",
        "sequence_name",
        "activity",
        "subject",
        "frame_index",
        "local_frame_index",
    },
    "radar_frames": {
        "relative_path",
        "recording_id",
        "sequence_name",
        "activity",
        "subject",
        "snaplength",
        "format",
        "win_size",
        "win_stride",
        "frame_index",
        "range_bin_index",
        "entrophy",
    },
    "radar_video_grouped": {
        "recording_id",
        "subject",
        "activity",
        "range_bin_index",
        "radar_sequence_name",
        "radar_snaplength",
        "radar_format",
        "radar_win_size",
        "radar_win_stride",
        "frame_count",
        "frame_indexes",
        "radar_paths",
        "radar_entrophies",
        "depth_paths",
        "depth_sequence_names",
        "depth_local_frame_indexes",
    },
    "radar_depth_joined": {
        "recording_id",
        "subject",
        "activity",
        "frame_index",
        "radar_path",
        "radar_sequence_name",
        "radar_snaplength",
        "radar_format",
        "radar_win_size",
        "radar_win_stride",
        "radar_range_bin_index",
        "radar_entrophy",
        "depth_paths",
        "depth_sequence_names",
        "depth_local_frame_indexes",
    },
}


def load_subset(repo_path: Path, subset: str) -> Dataset:
    """Load one local config's train split."""
    dataset = load_dataset(str(repo_path), subset, split="train")
    missing = EXPECTED_COLUMNS[subset] - set(dataset.column_names)
    if missing:
        raise AssertionError(f"{subset} is missing columns: {sorted(missing)}")
    return dataset


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--repo",
        default="./",
        help="Local dataset repository path (default: ./)",
    )
    args = parser.parse_args()

    repo = Path(args.repo).expanduser().resolve()
    if not (repo / "README.md").is_file():
        raise FileNotFoundError(f"Dataset README.md not found under: {repo}")

    loaded = {}
    for subset in SUBSETS:
        dataset = load_subset(repo, subset)
        loaded[subset] = dataset
        print(f"\n[{subset}]")
        print(f"rows: {len(dataset)}")
        print(f"columns: {dataset.column_names}")
        if len(dataset):
            print(f"first row: {dataset[0]}")

    radar_frame_count = len(loaded["radar_frames"])
    joined_frame_count = len(loaded["radar_depth_joined"])
    grouped_frame_count = sum(loaded["radar_video_grouped"]["frame_count"])
    assert joined_frame_count == radar_frame_count, (
        "Joined row-count validation failed: "
        f"radar_frames={radar_frame_count}, radar_depth_joined={joined_frame_count}"
    )
    assert grouped_frame_count == radar_frame_count, (
        "Radar frame-count validation failed: "
        f"radar_frames={radar_frame_count}, grouped_frames={grouped_frame_count}"
    )

    print("\nAll four subsets loaded successfully.")
    print(
        "Radar frame-count validation passed: "
        f"grouped frames = radar frames = {radar_frame_count}"
    )


if __name__ == "__main__":
    main()