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028b945 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | #!/usr/bin/env python3
"""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()
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