feat: add fixed-pool scoliosis partition helper
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from __future__ import annotations
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import json
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import random
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from collections import Counter
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from pathlib import Path
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from typing import TypedDict, cast
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import click
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class Partition(TypedDict):
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TRAIN_SET: list[str]
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TEST_SET: list[str]
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def infer_pid_label(dataset_root: Path, pid: str) -> str:
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pid_root = dataset_root / pid
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if not pid_root.exists():
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raise FileNotFoundError(f"PID root not found under dataset root: {pid_root}")
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label_dirs = sorted([entry.name.lower() for entry in pid_root.iterdir() if entry.is_dir()])
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if len(label_dirs) != 1:
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raise ValueError(f"Expected exactly one class dir for pid {pid}, got {label_dirs}")
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label = label_dirs[0]
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if label not in {"positive", "neutral", "negative"}:
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raise ValueError(f"Unexpected label directory for pid {pid}: {label}")
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return label
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@click.command()
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@click.option(
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"--base-partition",
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type=click.Path(path_type=Path, exists=True, dir_okay=False),
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required=True,
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help="Path to the source partition JSON, e.g. datasets/Scoliosis1K/Scoliosis1K_118.json",
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)
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@click.option(
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"--dataset-root",
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type=click.Path(path_type=Path, exists=True, file_okay=False),
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required=True,
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help="Dataset root used to infer each pid class label, e.g. /mnt/public/data/Scoliosis1K/Scoliosis1K-sil-pkl",
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)
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@click.option(
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"--negative-multiplier",
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type=int,
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required=True,
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help="Target negative count as a multiple of the positive/neutral count, e.g. 2 for 1:1:2",
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)
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@click.option(
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"--output-path",
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type=click.Path(path_type=Path, dir_okay=False),
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required=True,
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help="Path to write the derived partition JSON.",
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)
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@click.option(
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"--seed",
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type=int,
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default=118,
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show_default=True,
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help="Random seed used when downsampling negatives.",
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)
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def main(
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base_partition: Path,
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dataset_root: Path,
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negative_multiplier: int,
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output_path: Path,
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seed: int,
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) -> None:
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with base_partition.open("r", encoding="utf-8") as handle:
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partition = cast(Partition, json.load(handle))
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train_ids = list(partition["TRAIN_SET"])
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test_ids = list(partition["TEST_SET"])
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train_by_label: dict[str, list[str]] = {"positive": [], "neutral": [], "negative": []}
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for pid in train_ids:
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label = infer_pid_label(dataset_root, pid)
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train_by_label[label].append(pid)
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pos_count = len(train_by_label["positive"])
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neu_count = len(train_by_label["neutral"])
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neg_count = len(train_by_label["negative"])
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if pos_count != neu_count:
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raise ValueError(
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"This helper assumes equal positive/neutral train counts so that only "
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+ f"negative downsampling changes the ratio. Got positive={pos_count}, neutral={neu_count}."
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)
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target_negative_count = negative_multiplier * pos_count
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if target_negative_count > neg_count:
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raise ValueError(
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f"Requested {target_negative_count} negatives but only {neg_count} are available "
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+ f"in base partition {base_partition}."
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)
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rng = random.Random(seed)
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sampled_negatives = sorted(rng.sample(train_by_label["negative"], target_negative_count))
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derived_train = (
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sorted(train_by_label["positive"])
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+ sorted(train_by_label["neutral"])
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+ sampled_negatives
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)
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derived_partition = {
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"TRAIN_SET": derived_train,
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"TEST_SET": test_ids,
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}
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with output_path.open("w", encoding="utf-8") as handle:
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json.dump(derived_partition, handle, indent=2)
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_ = handle.write("\n")
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train_counts = Counter(infer_pid_label(dataset_root, pid) for pid in derived_train)
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test_counts = Counter(infer_pid_label(dataset_root, pid) for pid in test_ids)
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click.echo(f"wrote {output_path}")
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click.echo(f"train_counts={dict(train_counts)}")
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click.echo(f"test_counts={dict(test_counts)}")
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if __name__ == "__main__":
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main()
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