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a06d854 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | #!/usr/bin/env python3
"""Merge DeepResearch evaluation shards and emit validated stratified metrics."""
from __future__ import annotations
import argparse
import hashlib
import json
import statistics
from collections import Counter
from pathlib import Path
from typing import Any, Callable
def read_jsonl(path: Path) -> list[dict[str, Any]]:
with path.open(encoding="utf-8") as handle:
return [json.loads(line) for line in handle if line.strip()]
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def score_metrics(rows: list[dict[str, Any]], key: str) -> dict[str, Any]:
scores = [float(row[key]) for row in rows if row.get(key) is not None]
return {
"questions": len(rows),
"scored": len(scores),
"correct": sum(scores),
"accuracy": statistics.fmean(scores) if scores else None,
}
def grouped_metrics(
rows: list[dict[str, Any]],
key: str,
group: Callable[[dict[str, Any]], Any],
) -> dict[str, Any]:
names = sorted({str(group(row)) for row in rows})
return {
name: score_metrics([row for row in rows if str(group(row)) == name], key)
for name in names
}
def difficulty_teacher(row: dict[str, Any]) -> str:
return f"D{row.get('difficulty')}/teacher_{str(row.get('teacher_correct')).lower()}"
def benchmark_cell(row: dict[str, Any]) -> str:
bucket = (row.get("benchmark") or {}).get("selection_bucket")
return f"{difficulty_teacher(row)}/{bucket}"
def calibration_tier(row: dict[str, Any]) -> str:
return str((row.get("benchmark") or {}).get("calibration_tier") or "unspecified")
def tier_bucket(row: dict[str, Any]) -> str:
benchmark = row.get("benchmark") or {}
return f"{calibration_tier(row)}/{benchmark.get('selection_bucket')}"
def build_summary(
rows: list[dict[str, Any]],
*,
input_files: list[Path],
expected_data: Path | None,
model_label: str,
) -> dict[str, Any]:
turns = [int(row["num_turns"]) for row in rows if row.get("num_turns") is not None]
active = [float(row["active_seconds"]) for row in rows if row.get("active_seconds") is not None]
result: dict[str, Any] = {
"questions": len(rows),
"model_label": model_label or None,
"input_files": [str(path) for path in input_files],
"expected_data": str(expected_data) if expected_data else None,
"expected_data_sha256": sha256(expected_data) if expected_data else None,
"agent_errors": sum(bool(row.get("error")) for row in rows),
"finish_rate": (
sum(row.get("stop_reason") == "finish" for row in rows) / len(rows)
if rows
else None
),
"mean_turns": statistics.fmean(turns) if turns else None,
"mean_active_seconds": statistics.fmean(active) if active else None,
"search_calls": sum(int((row.get("tool_stats") or {}).get("search", 0)) for row in rows),
"open_page_calls": sum(
int((row.get("tool_stats") or {}).get("open_page", 0)) for row in rows
),
"stop_reasons": dict(sorted(Counter(row.get("stop_reason") or "missing" for row in rows).items())),
"local_em": score_metrics(rows, "local_em"),
}
for score_key in ("judge_score",):
if any(row.get(score_key) is not None for row in rows):
result[score_key] = score_metrics(rows, score_key)
result[f"{score_key}_by_difficulty"] = grouped_metrics(
rows, score_key, lambda row: row.get("difficulty")
)
result[f"{score_key}_by_teacher_correct"] = grouped_metrics(
rows, score_key, lambda row: row.get("teacher_correct")
)
result[f"{score_key}_by_difficulty_teacher"] = grouped_metrics(
rows, score_key, difficulty_teacher
)
result[f"{score_key}_by_selection_bucket"] = grouped_metrics(
rows,
score_key,
lambda row: (row.get("benchmark") or {}).get("selection_bucket"),
)
result[f"{score_key}_by_calibration_tier"] = grouped_metrics(
rows, score_key, calibration_tier
)
result[f"{score_key}_by_calibration_tier_selection_bucket"] = grouped_metrics(
rows, score_key, tier_bucket
)
result[f"{score_key}_by_cell"] = grouped_metrics(
rows, score_key, benchmark_cell
)
result["local_em_by_difficulty"] = grouped_metrics(
rows, "local_em", lambda row: row.get("difficulty")
)
result["local_em_by_teacher_correct"] = grouped_metrics(
rows, "local_em", lambda row: row.get("teacher_correct")
)
result["local_em_by_difficulty_teacher"] = grouped_metrics(
rows, "local_em", difficulty_teacher
)
result["local_em_by_selection_bucket"] = grouped_metrics(
rows,
"local_em",
lambda row: (row.get("benchmark") or {}).get("selection_bucket"),
)
result["local_em_by_calibration_tier"] = grouped_metrics(
rows, "local_em", calibration_tier
)
result["local_em_by_calibration_tier_selection_bucket"] = grouped_metrics(
rows, "local_em", tier_bucket
)
result["local_em_by_cell"] = grouped_metrics(rows, "local_em", benchmark_cell)
result["judge_errors"] = sum(bool(row.get("judge_error")) for row in rows)
result["judge_remote_calls"] = sum(bool(row.get("judge_remote_called")) for row in rows)
return result
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--inputs", nargs="+", type=Path, required=True)
parser.add_argument("--out", type=Path, required=True)
parser.add_argument("--expected-data", type=Path)
parser.add_argument("--model-label", default="")
args = parser.parse_args()
rows = [row for path in args.inputs for row in read_jsonl(path)]
if not rows:
raise ValueError("no evaluation rows found")
indexes = [int(row["index"]) for row in rows]
duplicate_indexes = [index for index, count in Counter(indexes).items() if count > 1]
if duplicate_indexes:
raise ValueError(f"duplicate result indexes: {sorted(duplicate_indexes)}")
rows.sort(key=lambda row: int(row["index"]))
if args.expected_data:
expected = read_jsonl(args.expected_data)
expected_indexes = list(range(len(expected)))
if indexes := sorted(indexes):
if indexes != expected_indexes:
missing = sorted(set(expected_indexes) - set(indexes))
extra = sorted(set(indexes) - set(expected_indexes))
raise ValueError(f"result coverage mismatch: missing={missing} extra={extra}")
for index, (row, record) in enumerate(zip(rows, expected, strict=True)):
metadata = record.get("metadata") or {}
expected_qid = metadata.get("qid") or metadata.get("manifest_qid")
actual_qid = row.get("qid") or row.get("manifest_qid")
if actual_qid != expected_qid:
raise ValueError(
f"qid mismatch at index={index}: actual={actual_qid!r} expected={expected_qid!r}"
)
args.out.parent.mkdir(parents=True, exist_ok=True)
with args.out.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
summary = build_summary(
rows,
input_files=args.inputs,
expected_data=args.expected_data,
model_label=args.model_label,
)
summary["output"] = str(args.out)
summary["output_sha256"] = sha256(args.out)
summary_path = args.out.with_name(f"{args.out.stem}.summary.json")
summary_path.write_text(
json.dumps(summary, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
encoding="utf-8",
)
print("FINAL_AGGREGATE " + json.dumps(summary, sort_keys=True))
if __name__ == "__main__":
main()
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