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# -*- coding: utf-8 -*-
"""
Parse ICBCBench LaTeX result tables and generate data/leaderboard.csv.
Reads:
- main_results.tex EN/ZH Objective/Subjective/Overall
- results_subset_en.tex EN Expert/Citation/Source details
- results_subset_zh.tex ZH Expert/Citation/Source details
- objective_public_CalibErr.tex All-language Accuracy / Calibration Error
"""
from __future__ import annotations
import re
import csv
from pathlib import Path
from collections import defaultdict
PROJECT_ROOT = Path(__file__).resolve().parent.parent
def parse_main_results(tex_file: Path) -> dict[str, dict]:
"""Parse main_results.tex and return {model: {objective_en, subjective_en, overall_en, objective_zh, subjective_zh, overall_zh}}."""
text = tex_file.read_text(encoding="utf-8")
rows = {}
for line in text.splitlines():
line = line.strip()
if not line or line.startswith("\\") or line.startswith("%"):
continue
# Match lines like:
# Gemini-deep-research & 50.00 & 64.77 & \underline{57.38} & 52.50 & \textbf{65.69} & \underline{59.09} \\
parts = [p.strip() for p in line.split("&")]
if len(parts) != 7:
continue
model = parts[0].strip()
values = []
for p in parts[1:]:
# Strip LaTeX formatting commands but keep their arguments
val = re.sub(r"\\(textbf|underline|rowcolor)\{([^}]*)\}", r"\2", p)
# Drop structural LaTeX commands and trailing \
val = re.sub(r"\\(multicolumn|cmidrule|toprule|midrule|bottomrule).*", "", val)
val = val.replace("\\", "").replace("{", "").replace("}", "").strip()
if val in ("", "--"):
values.append(None)
else:
try:
values.append(float(val))
except ValueError:
values.append(None)
if all(v is not None for v in values):
rows[model] = {
"objective_en": values[0],
"subjective_en": values[1],
"overall_en": values[2],
"objective_zh": values[3],
"subjective_zh": values[4],
"overall_zh": values[5],
}
return rows
def parse_subset(tex_file: Path) -> dict[str, dict]:
"""Parse results_subset_en.tex or results_subset_zh.tex.
Returns {model: {objective_text, objective_all, expert, citation, source, overall}}.
"""
text = tex_file.read_text(encoding="utf-8")
rows = {}
for line in text.splitlines():
line = line.strip()
if not line or line.startswith("\\") or line.startswith("%"):
continue
parts = [p.strip() for p in line.split("&")]
if len(parts) != 7:
continue
model = parts[0].strip()
values = []
for p in parts[1:]:
val = re.sub(r"\\(textbf|underline|rowcolor)\{([^}]*)\}", r"\2", p)
val = re.sub(r"\\(multicolumn|cmidrule|toprule|midrule|bottomrule).*", "", val)
val = val.replace("\\", "").replace("{", "").replace("}", "").strip()
if val in ("", "--"):
values.append(None)
else:
try:
values.append(float(val))
except ValueError:
values.append(None)
if values[-1] is not None: # overall is required
rows[model] = {
"objective_text": values[0],
"objective_all": values[1],
"expert": values[2],
"citation": values[3],
"source": values[4],
"overall": values[5],
}
return rows
def parse_calibration(tex_file: Path) -> dict[str, dict]:
"""Parse objective_public_CalibErr.tex. Returns {model: {accuracy, calibration_error}}."""
text = tex_file.read_text(encoding="utf-8")
rows = {}
for line in text.splitlines():
line = line.strip()
if not line or line.startswith("\\") or line.startswith("%"):
continue
parts = [p.strip() for p in line.split("&")]
if len(parts) != 3:
continue
model = parts[0].strip()
values = []
for p in parts[1:]:
val = p.replace("\\", "").strip()
if val in ("", "--"):
values.append(None)
else:
try:
values.append(float(val))
except ValueError:
values.append(None)
if all(v is not None for v in values):
rows[model] = {"accuracy": values[0], "calibration_error": values[1]}
return rows
def merge_scores(
main: dict[str, dict],
en_detail: dict[str, dict],
zh_detail: dict[str, dict],
calib: dict[str, dict],
) -> list[dict]:
models = sorted(main.keys())
results = []
for model in models:
m = main[model]
en = en_detail.get(model, {})
zh = zh_detail.get(model, {})
cal = calib.get(model, {})
# Aggregate citation / source from available detailed tables.
# Prefer EN values when both exist, otherwise use whichever is available.
citation = en.get("citation") if en.get("citation") is not None else zh.get("citation")
source = en.get("source") if en.get("source") is not None else zh.get("source")
# Expert score: average of EN and ZH expert if both exist.
experts = [v for v in [en.get("expert"), zh.get("expert")] if v is not None]
expert_avg = sum(experts) / len(experts) if experts else None
objective_avg = (m["objective_en"] + m["objective_zh"]) / 2
subjective_avg = (m["subjective_en"] + m["subjective_zh"]) / 2
overall = (m["overall_en"] + m["overall_zh"]) / 2
results.append({
"model": model,
"overall": overall,
"objective_en": m["objective_en"],
"objective_zh": m["objective_zh"],
"objective_avg": objective_avg,
"subjective_en": m["subjective_en"],
"subjective_zh": m["subjective_zh"],
"subjective_avg": subjective_avg,
"expert_avg": expert_avg,
"citation_score": citation,
"source_quality": source,
"rmsce": cal.get("calibration_error"),
})
# Sort by overall descending, then objective_avg, then subjective_avg.
results.sort(
key=lambda x: (x["overall"], x["objective_avg"], x["subjective_avg"]),
reverse=True,
)
return results
def write_leaderboard(results: list[dict], output_file: Path):
fieldnames = [
"model", "overall", "objective_en", "objective_zh", "objective_avg",
"subjective_en", "subjective_zh", "subjective_avg",
"expert_avg", "citation_score", "source_quality", "rmsce",
]
with open(output_file, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for r in results:
row = {k: r[k] for k in fieldnames}
for k in fieldnames:
if k == "model":
continue
val = row[k]
row[k] = f"{val:.2f}" if val is not None else "-"
writer.writerow(row)
print(f"Wrote {len(results)} models to {output_file}")
def main():
main_file = PROJECT_ROOT / "main_results.tex"
en_file = PROJECT_ROOT / "results_subset_en.tex"
zh_file = PROJECT_ROOT / "results_subset_zh.tex"
calib_file = PROJECT_ROOT / "objective_public_CalibErr.tex"
output_file = PROJECT_ROOT / "data" / "leaderboard.csv"
main = parse_main_results(main_file)
en_detail = parse_subset(en_file)
zh_detail = parse_subset(zh_file)
calib = parse_calibration(calib_file)
print(f"Parsed {len(main)} models from main results")
print(f"Parsed {len(en_detail)} EN detailed rows")
print(f"Parsed {len(zh_detail)} ZH detailed rows")
print(f"Parsed {len(calib)} calibration rows")
results = merge_scores(main, en_detail, zh_detail, calib)
write_leaderboard(results, output_file)
if __name__ == "__main__":
main()
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