Research-Stack/.atlas/score.py
allaun 180bf43cce
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fix(optimize): add TARGET fallback + enrich score feedback for GEPA
benchmark.sh: fall back to  when ATLAS_OPTIMIZE_TARGET unset
score.py: track find_optimal_crossing and SA direct as separate
  scorecard examples with diagnostic context for reflector
gate.sh: add positional-arg fallback for ATLAS_OPTIMIZE_TARGET

Build: N/A (config & Python shims only)
2026-07-07 00:49:07 -05:00

73 lines
2.2 KiB
Python
Executable file

#!/usr/bin/env python3
import argparse, json, os, re, sys
METRICS = {
"find_optimal_crossing": False,
"SA direct": False,
}
def parse(text):
results = {}
for name, higher in METRICS.items():
pat = re.compile(re.escape(name) + r"\s*[:=]?\s*([+-]?[0-9]*\.?[0-9]+)")
for m in pat.finditer(text):
raw = m.group(0).strip()
val = float(m.group(1))
line_before = text[max(0, m.start()-80):m.start()].split("\n")[-1].strip()
results[name] = (val, higher, raw, line_before)
return results
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--stdout", required=True)
args = ap.parse_args()
text = open(args.stdout, encoding="utf-8", errors="replace").read()
parsed = parse(text)
if "find_optimal_crossing" not in parsed:
sys.stderr.write(f"[score] could not find metric in output.\n")
sys.exit(3)
primary_name = "find_optimal_crossing"
primary_val, _, primary_raw, ctx = parsed[primary_name]
direction = "lower is better"
examples = [{
"id": primary_name,
"score": primary_val,
"pass": True,
"feedback": f"{primary_raw} | context: {ctx}" if ctx else primary_raw,
}]
if "SA direct" in parsed:
sa_val, _, sa_raw, sa_ctx = parsed["SA direct"]
examples.append({
"id": "SA direct",
"score": sa_val,
"pass": sa_val < 100,
"feedback": f"{sa_raw} | context: {sa_ctx}" if sa_ctx else sa_raw,
})
diag_lines = []
for line in text.strip().split("\n"):
stripped = line.strip()
if "find_optimal_crossing" in stripped or "SA direct" in stripped:
diag_lines.append(stripped)
diagnostic = "; ".join(diag_lines) if diag_lines else primary_raw
result = {
"score": primary_val,
"examples": examples,
"feedback": f"find_optimal_crossing={primary_val} ({direction}) | {diagnostic}",
}
out = os.environ.get("ATLAS_OPTIMIZE_RESULT")
payload = json.dumps(result)
if out:
with open(out, "w") as fh:
fh.write(payload)
else:
sys.stdout.write(payload)
if __name__ == "__main__":
main()