File size: 7,039 Bytes
29f25be | 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 | """Classify frozen V1/V2 orders; preserve gold and candidate pools."""
import collections
import csv
import datetime
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
OUT = ROOT / "outputs/ime-eval/v2-error-audit-20261007"
LABELS = {
"C": "correct",
"H": "homophone_semantic",
"T": "technical_term",
"B": "compound_segmentation",
"I": "inflection_or_structure",
"P": "proper_name",
"S": "surface_reference_gap_pending",
"U": "context_underspecified",
}
def read_scores(relative):
return [
json.loads(line)
for line in (ROOT / "outputs/ime-eval" / relative / "scores.jsonl")
.read_text(encoding="utf-8")
.splitlines()
]
def length_band(query):
n = len(query)
return "<=16" if n <= 16 else "17-32" if n <= 32 else "33-64" if n <= 64 else ">64"
def main():
decisions = list(
csv.DictReader(
(ROOT / "scripts/benchmarks/v2-error-decisions.tsv").open(encoding="utf-8"),
delimiter="\t",
)
)
assert len({r["audit_id"] for r in decisions}) == len(decisions)
decisions = {r["audit_id"]: r for r in decisions}
all_rows = []
summary = {}
error_ids = set()
sources = [
("ajimee", "tiny-ja-v1-ajimee", "tiny-ja-v2.0-best-ajimee"),
("development_reviewed", "dev-label-audit-20261007/v1", "dev-label-audit-20261007/v2"),
]
for dataset, left, right in sources:
before = read_scores(left)
after = {r["id"]: r for r in read_scores(right)}
stats = {
model: {
"total": len(before),
"top1": 0,
"mean_top1_secondary": 0,
"stage": collections.Counter(),
"error_category": collections.Counter(),
"covered_error_category": collections.Counter(),
"reading_length": {},
"context": {},
"candidate_count": {},
}
for model in ("v1", "v2")
}
covered_total = 0
for i, a in enumerate(before, 1):
b = after[a["id"]]
gold = set(a["answers"])
assert a["answers"] == b["answers"] and a["orders"]["azookey"] == b["orders"]["azookey"]
covered = bool(gold.intersection(a["orders"]["azookey"]))
covered_total += covered
aid = ("A" if dataset == "ajimee" else "D") + f"{i:03}"
note = decisions.get(aid)
if any(row["orders"]["lm_context_sum"][0] not in gold for row in (a, b)):
error_ids.add(aid)
record = {
"audit_id": aid,
"dataset": dataset,
"id": a["id"],
"context": a["left_context"],
"query": a["query"],
"answers": a["answers"],
"candidate_coverage_exact": covered,
"candidate_count": len(a["orders"]["azookey"]),
"reading_length_band": length_band(a["query"]),
"note_zh": note["note_zh"] if note else "",
"models": {},
}
for name, row in (("v1", a), ("v2", b)):
top = row["orders"]["lm_context_sum"][0]
correct = top in gold
category = LABELS[note[name + "_category"]] if note else "correct"
assert (category == "correct") == correct, (aid, name, top)
stage = (
"correct"
if correct
else "candidate_missing_exact"
if not covered
else "covered_top1_miss"
)
model = stats[name]
model["top1"] += correct
model["mean_top1_secondary"] += (
row["orders"]["lm_context_mean_secondary"][0] in gold
)
model["stage"][stage] += 1
if not correct:
model["error_category"][category] += 1
if covered:
model["covered_error_category"][category] += 1
for dimension, key in [
("reading_length", length_band(row["query"])),
("context", "with_context" if row["left_context"] else "without_context"),
(
"candidate_count",
"1-5"
if len(row["orders"]["azookey"]) <= 5
else "6-10"
if len(row["orders"]["azookey"]) <= 10
else ">10",
),
]:
group = model[dimension].setdefault(key, {"total": 0, "top1": 0, "covered": 0})
group["total"] += 1
group["top1"] += correct
group["covered"] += covered
record["models"][name] = {
"top1": top,
"correct_exact": correct,
"stage": stage,
"category": category,
"annotation_status": "diagnostic_pending_adjudication"
if category in ("surface_reference_gap_pending", "context_underspecified")
else "reviewed_diagnostic",
}
all_rows.append(record)
summary[dataset] = {"cases": len(before), "covered_exact": covered_total, "models": stats}
assert set(decisions) == error_ids, "All error cases must be reviewed exactly once."
OUT.mkdir(parents=True, exist_ok=True)
(OUT / "case-classification.json").write_text(
json.dumps(all_rows, ensure_ascii=False, indent=2), encoding="utf-8"
)
report = {
"format": "v2_error_audit_v1",
"reviewed_error_union_cases": len(decisions),
"classified_total_cases": len(all_rows),
"summary": summary,
"label_changes_in_this_analysis": False,
"inference_rerun": False,
"limitations": [
"AI diagnosis; no external native adjudication",
"Candidate missing means exact frozen references absent; some are possible label gaps",
"Pending surface/ambiguity annotations do not alter primary scores",
"Length/context strata are descriptive, not causal",
],
"created_utc": datetime.datetime.now(datetime.timezone.utc).isoformat(),
}
(OUT / "summary.json").write_text(
json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8"
)
print(
json.dumps(
{
"reviewed_error_union_cases": len(decisions),
"classified_total_cases": len(all_rows),
"top1": {
dataset: {name: s["top1"] for name, s in data["models"].items()}
for dataset, data in summary.items()
},
},
ensure_ascii=False,
indent=2,
)
)
if __name__ == "__main__":
main()
|