File size: 6,763 Bytes
e63569d f712f4b 116c87c f712f4b e63569d f712f4b e63569d f712f4b e63569d f712f4b e63569d f712f4b 116c87c f712f4b 116c87c f712f4b e63569d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | """Evaluate parser checkpoints on fixed real-world filename cases."""
import argparse
import json
import os
from typing import Dict, List, Optional
import torch
from transformers import BertForTokenClassification
from config import Config
from inference import parse_filename
from tokenizer import load_tokenizer
DEFAULT_CASE_FILE = os.path.join("data", "parser_regression_cases.json")
def normalize_field_value(field: str, value) -> Optional[str]:
if value is None:
return None
if field in {"episode", "season"}:
try:
return str(int(value))
except (TypeError, ValueError):
return str(value).strip().lower()
text = str(value).strip()
if field in {"resolution", "source"}:
return text.lower().replace("_", "-")
return " ".join(text.lower().split())
def load_cases(path: str) -> List[Dict]:
with open(path, "r", encoding="utf-8") as f:
cases = json.load(f)
if not isinstance(cases, list):
raise ValueError(f"{path} must contain a JSON list")
return cases
def evaluate_cases(
model_dir: str,
case_file: str,
tokenizer_variant: Optional[str],
max_length: Optional[int],
constrain_bio: bool,
) -> Dict:
cfg = Config()
tokenizer = load_tokenizer(model_dir, tokenizer_variant)
model = BertForTokenClassification.from_pretrained(model_dir)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
id2label = {int(k): v for k, v in getattr(model.config, "id2label", cfg.id2label).items()}
resolved_max_length = max_length or int(getattr(model.config, "max_seq_length", 64))
cases = load_cases(case_file)
field_totals: Dict[str, int] = {}
field_correct: Dict[str, int] = {}
results = []
full_correct = 0
for case in cases:
expected = case.get("expected", {})
pred = parse_filename(
case["filename"],
model,
tokenizer,
id2label,
max_length=resolved_max_length,
debug=False,
constrain_bio=constrain_bio,
)
errors = {}
for field, expected_value in expected.items():
field_totals[field] = field_totals.get(field, 0) + 1
expected_norm = normalize_field_value(field, expected_value)
pred_norm = normalize_field_value(field, pred.get(field))
if expected_norm == pred_norm:
field_correct[field] = field_correct.get(field, 0) + 1
else:
errors[field] = {
"expected": expected_value,
"pred": pred.get(field),
}
if not errors:
full_correct += 1
results.append(
{
"id": case.get("id"),
"filename": case["filename"],
"ok": not errors,
"errors": errors,
"expected": expected,
"pred": {field: pred.get(field) for field in sorted(expected)},
}
)
field_accuracy = {
field: field_correct.get(field, 0) / total
for field, total in sorted(field_totals.items())
}
return {
"model_dir": model_dir,
"case_file": case_file,
"tokenizer_variant": getattr(tokenizer, "tokenizer_variant", "regex"),
"max_length": resolved_max_length,
"constrain_bio": constrain_bio,
"case_count": len(cases),
"full_correct": full_correct,
"full_accuracy": full_correct / len(cases) if cases else 0.0,
"field_correct": field_correct,
"field_total": field_totals,
"field_accuracy": field_accuracy,
"failures": [result for result in results if not result["ok"]],
"results": results,
}
def evaluate_case_modes(
model_dir: str,
case_file: str,
tokenizer_variant: Optional[str],
max_length: Optional[int],
) -> Dict:
modes = {
"model_only": {"constrain_bio": False},
"normalized_only": {"constrain_bio": True},
}
results = {
name: evaluate_cases(
model_dir=model_dir,
case_file=case_file,
tokenizer_variant=tokenizer_variant,
max_length=max_length,
constrain_bio=settings["constrain_bio"],
)
for name, settings in modes.items()
}
return {
"primary_metric": "normalized_only",
"modes": results,
}
def print_metrics(name: str, metrics: Dict) -> None:
print(
f"{name} full case accuracy: {metrics['full_correct']}/{metrics['case_count']} "
f"({metrics['full_accuracy']:.4f})"
)
for field, total in metrics["field_total"].items():
correct = metrics["field_correct"].get(field, 0)
print(f" {field}: {correct}/{total} ({correct / total:.4f})")
if metrics["failures"]:
print(f"\n{name} failures:")
for failure in metrics["failures"]:
print(json.dumps(failure, ensure_ascii=False))
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate parser on fixed filename regression cases")
parser.add_argument("--model-dir", required=True)
parser.add_argument("--case-file", default=DEFAULT_CASE_FILE)
parser.add_argument("--tokenizer", choices=["regex", "char"], default=None)
parser.add_argument("--max-length", type=int, default=None)
parser.add_argument("--output", default=None, help="Optional JSON output path")
parser.add_argument("--mode", choices=["all", "model-only", "normalized-only"], default="all")
parser.add_argument("--no-constrained-bio", action="store_true")
args = parser.parse_args()
if args.mode == "all" and not args.no_constrained_bio:
metrics = evaluate_case_modes(
model_dir=args.model_dir,
case_file=args.case_file,
tokenizer_variant=args.tokenizer,
max_length=args.max_length,
)
for name in ("model_only", "normalized_only"):
print_metrics(name, metrics["modes"][name])
print()
else:
constrain_bio = not args.no_constrained_bio and args.mode != "model-only"
metrics = evaluate_cases(
model_dir=args.model_dir,
case_file=args.case_file,
tokenizer_variant=args.tokenizer,
max_length=args.max_length,
constrain_bio=constrain_bio,
)
print_metrics(args.mode, metrics)
if args.output:
os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
with open(args.output, "w", encoding="utf-8") as f:
json.dump(metrics, f, ensure_ascii=False, indent=2)
if __name__ == "__main__":
main()
|