File size: 5,574 Bytes
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 | """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],
use_rules: bool,
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,
use_rules=use_rules,
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,
"use_rules": use_rules,
"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 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("--no-rule-assist", action="store_true")
parser.add_argument("--no-constrained-bio", action="store_true")
args = parser.parse_args()
metrics = evaluate_cases(
model_dir=args.model_dir,
case_file=args.case_file,
tokenizer_variant=args.tokenizer,
max_length=args.max_length,
use_rules=not args.no_rule_assist,
constrain_bio=not args.no_constrained_bio,
)
print(
f"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("\nFailures:")
for failure in metrics["failures"]:
print(json.dumps(failure, ensure_ascii=False))
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()
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