| |
| """script for the NEO 1 month dataset to split the data in train (or train+missing) and val set. Creates symlinks.""" |
| from pathlib import Path |
| import os |
| from argparse import ArgumentParser, Namespace |
| from loggez import loggez_logger as logger |
|
|
| TRAIN_SET_CUTOFF = "2014-08" |
| TEST_SET_CUTOFF = "2017-02" |
|
|
| def _get_data(data_dir: Path): |
| representations = [x.name for x in data_dir.iterdir() if x.is_dir()] |
| data, date_to_representations = {}, {} |
| for representation in representations: |
| data[representation] = [f.stem for f in (data_dir / representation).iterdir() if f.suffix == ".npz"] |
| assert len(data[representation]) > 0, f"No files found for representation '{representation}'" |
| for item in data[representation]: |
| date_to_representations.setdefault(item, []).append(representation) |
| return {k: v for k, v in sorted(date_to_representations.items(), key=lambda item: item[0])} |
|
|
| def _total(data: dict[str, list[str]]) -> int: |
| return sum(len(v) for v in data.values()) |
|
|
| def get_args() -> Namespace: |
| """cli args""" |
| parser = ArgumentParser() |
| parser.add_argument("data_dir", type=lambda p: Path(p).absolute()) |
| parser.add_argument("--output_path", "-o", required=True, type=lambda p: Path(p).absolute()) |
| parser.add_argument("--mode", choices=["train_set", "test_set", "train_set_plus_missing"], required=True) |
| args = parser.parse_args() |
| assert args.data_dir.exists(), f"'{args.data_dir}' doesn't exist" |
| assert not (pth := (args.output_path / args.mode)).exists(), f"'{pth}' already exists, remove first" |
| return args |
|
|
| def main(args: Namespace): |
| """main fn""" |
| data = _get_data(args.data_dir) |
| logger.info(f"Read raw data from '{args.data_dir}' across {len(data)} modalities (total npz: {_total(data)})") |
|
|
| if args.mode == "train_set": |
| max_values = max([len(v) for v in data.values()]) |
| data_filtered = {k: v for k, v in data.items() if k <= TRAIN_SET_CUTOFF and len(v) == max_values} |
| elif args.mode == "train_set_plus_missing": |
| max_values = max([len(v) for v in data.values()]) |
| data_filtered = {k: v for k, v in data.items() if k <= TRAIN_SET_CUTOFF} |
| else: |
| max_values = max([len(v) for v in data.values()]) |
| data_filtered = {k: v for k, v in data.items() if k > TRAIN_SET_CUTOFF and k <= TEST_SET_CUTOFF |
| and len(v) == max_values} |
| logger.info(f"Filtered data using mode='{args.mode}' (total npz: {_total(data_filtered)})") |
|
|
| out_dir: Path = args.output_path / args.mode |
| out_dir.mkdir(exist_ok=False, parents=True) |
|
|
| for date_stem, modalities in data_filtered.items(): |
| for modality in modalities: |
| in_file: Path = args.data_dir / modality / f"{date_stem}.npz" |
| out_file: Path = out_dir / modality / f"{date_stem}.npz" |
| out_file.parent.mkdir(parents=True, exist_ok=True) |
| os.symlink(os.path.relpath(in_file, out_file.parent), out_file) |
|
|
| logger.info(f"Created symlinks at: '{out_dir}'") |
|
|
| if __name__ == "__main__": |
| main(get_args()) |
|
|
