| |
| """Compute APM row-level metrics for model output files.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import re |
| from pathlib import Path |
| from typing import Iterable, Iterator, Optional, Tuple |
|
|
| from apm_metrics import compute_metrics, load_json_or_jsonl, write_json |
|
|
|
|
| NOISE_RE = re.compile(r"N\d+") |
| JSON_EXTENSIONS = {".json", ".jsonl"} |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument( |
| "--input-root", |
| type=Path, |
| required=True, |
| help="Root containing <model>/<noise>/results.jsonl outputs.", |
| ) |
| parser.add_argument( |
| "--output-root", |
| type=Path, |
| default=Path("metrics"), |
| help="Directory where metric JSON files will be written.", |
| ) |
| parser.add_argument( |
| "--model", |
| help="Model name to use when --input-root directly contains N*/ folders or is a file.", |
| ) |
| parser.add_argument( |
| "--noise", |
| help="Noise label to use when --input-root is a single file.", |
| ) |
| return parser.parse_args() |
|
|
|
|
| def is_json_data_file(path: Path) -> bool: |
| return path.suffix in JSON_EXTENSIONS and path.name not in {"metrics.json", "compiled.json"} |
|
|
|
|
| def preferred_files(noise_dir: Path) -> Iterable[Path]: |
| for preferred in ("results.jsonl", "results.json"): |
| path = noise_dir / preferred |
| if path.exists(): |
| return [path] |
| return sorted(path for path in noise_dir.iterdir() if path.is_file() and is_json_data_file(path)) |
|
|
|
|
| def direct_noise_dirs(root: Path) -> bool: |
| dirs = [path for path in root.iterdir() if path.is_dir()] |
| return bool(dirs) and all(NOISE_RE.fullmatch(path.name) for path in dirs) |
|
|
|
|
| def discover_inputs( |
| input_root: Path, |
| model_override: Optional[str], |
| noise_override: Optional[str], |
| ) -> Iterator[Tuple[str, str, Path, Path]]: |
| """Yield model, noise, input path, and output-relative metric path.""" |
|
|
| if input_root.is_file(): |
| if not model_override or not noise_override: |
| raise SystemExit("Single-file mode requires --model and --noise") |
| yield model_override, noise_override, input_root, Path(model_override) / noise_override / "metrics.json" |
| return |
|
|
| if direct_noise_dirs(input_root): |
| model = model_override or input_root.name |
| for noise_dir in sorted(path for path in input_root.iterdir() if path.is_dir()): |
| for file_path in preferred_files(noise_dir): |
| out_name = "metrics.json" if file_path.stem == "results" else f"{file_path.stem}_metrics.json" |
| yield model, noise_dir.name, file_path, Path(model) / noise_dir.name / out_name |
| return |
|
|
| for model_dir in sorted(path for path in input_root.iterdir() if path.is_dir()): |
| model = model_override or model_dir.name |
| for noise_dir in sorted(path for path in model_dir.iterdir() if path.is_dir()): |
| if noise_override and noise_dir.name != noise_override: |
| continue |
| for file_path in preferred_files(noise_dir): |
| out_name = "metrics.json" if file_path.stem == "results" else f"{file_path.stem}_metrics.json" |
| yield model, noise_dir.name, file_path, Path(model) / noise_dir.name / out_name |
|
|
|
|
| def evaluate_file(input_path: Path, output_path: Path, model: str, noise: str) -> Tuple[int, int]: |
| records = load_json_or_jsonl(input_path) |
| metrics = [] |
| skipped = 0 |
|
|
| for record in records: |
| row = compute_metrics(record, model=model, noise=noise) |
| if row is None: |
| skipped += 1 |
| continue |
| metrics.append(row) |
|
|
| write_json(metrics, output_path) |
| return len(metrics), skipped |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
|
|
| total_rows = 0 |
| total_skipped = 0 |
| total_files = 0 |
|
|
| for model, noise, input_path, rel_output_path in discover_inputs( |
| args.input_root, |
| args.model, |
| args.noise, |
| ): |
| output_path = args.output_root / rel_output_path |
| rows, skipped = evaluate_file(input_path, output_path, model=model, noise=noise) |
| total_rows += rows |
| total_skipped += skipped |
| total_files += 1 |
| print(f"{model}/{noise}: {rows} rows, {skipped} skipped -> {output_path}") |
|
|
| print( |
| f"Processed {total_files} files with {total_rows} metric rows " |
| f"and {total_skipped} skipped rows" |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
