#!/usr/bin/env python3 """Run EffB2 prediction QC for paired diffusion augmentation and print confidence.""" from __future__ import annotations import argparse import csv import subprocess import sys from collections import defaultdict from pathlib import Path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Run EffB2 QC prediction and print confidence summary.") checkpoint_group = parser.add_mutually_exclusive_group(required=True) checkpoint_group.add_argument("--checkpoint", type=Path, help="Path to one classifier best.pt checkpoint.") checkpoint_group.add_argument( "--checkpoint-dir", type=Path, help="Run directory containing fold_*/best.pt; all folds are ensembled for QC.", ) parser.add_argument("--output-dir", type=Path, default=Path("Stable_diffusion_augmentation/out_minority_pairs")) parser.add_argument("--batch-size", type=int, default=16) parser.add_argument("--image-size", type=int, default=384) parser.add_argument("--num-workers", type=int, default=0) parser.add_argument("--python", default=sys.executable, help="Python executable to use for prediction.") parser.add_argument( "--predict-script", type=Path, default=None, help="Path to predict_milk10k_effb2_dual_metadata.py. Defaults to auto-detect from repo root.", ) parser.add_argument( "--summary-script", type=Path, default=None, help="Path to summarize_effb2_qc.py. Defaults to this script's folder.", ) parser.add_argument("--print-misses", type=int, default=20, help="Number of wrong-target rows to print.") return parser.parse_args() def run_command(cmd: list[str]) -> None: print("Running:") print(" " + " ".join(cmd)) subprocess.run(cmd, check=True) def default_repo_root() -> Path: return Path(__file__).resolve().parents[1] def resolve_script(path: Path | None, default_path: Path, label: str) -> Path: script = (path or default_path).expanduser().resolve() if not script.exists(): raise FileNotFoundError(f"{label} not found: {script}") return script def read_rows(path: Path) -> list[dict[str, str]]: with path.open(newline="") as f: return list(csv.DictReader(f)) def as_float(value: str) -> float: try: return float(value) except (TypeError, ValueError): return 0.0 def print_confidence_summary(summary_path: Path, print_misses: int) -> None: rows = read_rows(summary_path) if not rows: print("No QC rows found.") return correct = [row for row in rows if row["is_target_predicted"] == "True"] by_class: dict[str, list[dict[str, str]]] = defaultdict(list) for row in rows: by_class[row["target_class"]].append(row) print("") print("EffB2 QC confidence summary") print(f" Total synthetic pairs: {len(rows)}") print(f" Target predicted: {len(correct)}/{len(rows)} ({len(correct) / len(rows):.1%})") for class_name in sorted(by_class): class_rows = by_class[class_name] class_correct = [row for row in class_rows if row["is_target_predicted"] == "True"] avg_conf = sum(as_float(row["confidence"]) for row in class_rows) / len(class_rows) avg_target_prob = sum(as_float(row["target_class_probability"]) for row in class_rows) / len(class_rows) pred_counts: dict[str, int] = defaultdict(int) for row in class_rows: pred_counts[row["label_pred"]] += 1 top_preds = ", ".join(f"{label}:{count}" for label, count in sorted(pred_counts.items(), key=lambda item: (-item[1], item[0]))[:5]) print( f" {class_name}: target_predicted={len(class_correct)}/{len(class_rows)} " f"({len(class_correct) / len(class_rows):.1%}), " f"avg_confidence={avg_conf:.4f}, avg_target_prob={avg_target_prob:.4f}, " f"top_preds=[{top_preds}]" ) misses = [row for row in rows if row["is_target_predicted"] != "True"] misses.sort(key=lambda row: as_float(row["target_class_probability"])) if misses and print_misses > 0: print("") print(f"Lowest target-probability misses, first {min(print_misses, len(misses))}:") for row in misses[:print_misses]: print( f" {row['synthetic_lesion_id']}: target={row['target_class']} " f"pred={row['label_pred']} conf={as_float(row['confidence']):.4f} " f"target_prob={as_float(row['target_class_probability']):.4f}" ) if any(row.get("source_lesion_id") for row in rows): print("") print("Worst source lesions by target probability:") by_source: dict[str, list[dict[str, str]]] = defaultdict(list) for row in rows: by_source[row.get("source_lesion_id", "")].append(row) source_stats = [] for source_lesion_id, source_rows in by_source.items(): avg_target_prob = sum(as_float(row["target_class_probability"]) for row in source_rows) / len(source_rows) target_predicted = sum(1 for row in source_rows if row["is_target_predicted"] == "True") source_stats.append((avg_target_prob, source_lesion_id, target_predicted, len(source_rows))) for avg_target_prob, source_lesion_id, target_predicted, total in sorted(source_stats)[:10]: print( f" {source_lesion_id}: target_predicted={target_predicted}/{total} " f"({target_predicted / total:.1%}), avg_target_prob={avg_target_prob:.4f}" ) def main() -> None: args = parse_args() output_dir = args.output_dir.expanduser().resolve() checkpoint = args.checkpoint.expanduser().resolve() if args.checkpoint else None checkpoint_dir = args.checkpoint_dir.expanduser().resolve() if args.checkpoint_dir else None manifest = output_dir / "paired_augmentation_manifest.csv" metadata_csv = output_dir / "metadata_for_prediction.csv" groundtruth_csv = output_dir / "groundtruth_for_prediction.csv" input_dir = output_dir / "prediction_input" predictions = output_dir / "effb2_qc_predictions.csv" summary = output_dir / "effb2_qc_summary.csv" repo_root = default_repo_root() predict_script = resolve_script(args.predict_script, repo_root / "predict_milk10k_effb2_dual_metadata.py", "Predict script") summary_script = resolve_script( args.summary_script, Path(__file__).resolve().parent / "summarize_effb2_qc.py", "Summary script", ) for path in (checkpoint or checkpoint_dir, manifest, metadata_csv, groundtruth_csv, input_dir): if not path.exists(): raise FileNotFoundError(f"Required QC input not found: {path}") predict_command = [ args.python, str(predict_script), ] if checkpoint is not None: predict_command.extend(["--checkpoint", str(checkpoint)]) else: predict_command.extend(["--checkpoint-dir", str(checkpoint_dir)]) predict_command.extend([ "--input-dir", str(input_dir), "--metadata-csv", str(metadata_csv), "--groundtruth-csv", str(groundtruth_csv), "--output", str(predictions), "--include-debug-columns", "--batch-size", str(args.batch_size), "--image-size", str(args.image_size), "--num-workers", str(args.num_workers), ]) run_command(predict_command) run_command( [ args.python, str(summary_script), "--manifest", str(manifest), "--predictions", str(predictions), "--output", str(summary), ] ) print_confidence_summary(summary, args.print_misses) print("") print(f"Predictions CSV: {predictions}") print(f"QC summary CSV: {summary}") if __name__ == "__main__": main()