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6d1bbc7 | 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 | #!/usr/bin/env python3
"""Analyze L4 temporal accuracy for contamination detection.
Reads existing results.json files from results/llm/l4_*/ and reports
accuracy_pre_2023 vs accuracy_post_2024 gap per model/config.
A gap > 0.15 flags potential training data contamination.
"""
import json
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parent.parent
RESULTS_DIR = PROJECT_ROOT / "results" / "llm"
THRESHOLD = 0.15
def main():
rows = []
for run_dir in sorted(RESULTS_DIR.iterdir()):
if not run_dir.is_dir() or not run_dir.name.startswith("l4_"):
continue
results_file = run_dir / "results.json"
if not results_file.exists():
continue
with open(results_file) as f:
m = json.load(f)
name = run_dir.name
pre = m.get("accuracy_pre_2023")
post = m.get("accuracy_post_2024")
gap = round(pre - post, 4) if pre is not None and post is not None else None
flag = gap > THRESHOLD if gap is not None else None
rows.append({
"run": name,
"accuracy": m.get("accuracy"),
"mcc": m.get("mcc"),
"pre_2023": pre,
"post_2024": post,
"gap": gap,
"flag": flag,
})
if not rows:
print("No L4 results found.")
return
# Print table
print(f"{'Run':<55} {'Acc':>5} {'MCC':>6} {'Pre23':>6} {'Post24':>6} {'Gap':>6} {'Flag'}")
print("-" * 100)
for r in rows:
flag_str = "YES" if r["flag"] else ("NO" if r["flag"] is not None else "N/A")
print(
f"{r['run']:<55} "
f"{r['accuracy']:>5.3f} "
f"{r['mcc']:>6.3f} "
f"{r['pre_2023']:>6.3f} "
f"{r['post_2024']:>6.3f} "
f"{r['gap']:>6.3f} "
f"{flag_str}"
)
# Summary by model
from collections import defaultdict
model_gaps = defaultdict(list)
for r in rows:
# Extract model from run name
parts = r["run"][3:] # strip "l4_"
if parts.endswith("_zero-shot"):
model = parts[:-10]
elif parts.endswith("_3-shot"):
model = parts[:-7]
else:
model = parts
model = model.rsplit("_fs", 1)[0]
if r["gap"] is not None:
model_gaps[model].append(r["gap"])
print("\n--- Summary by Model ---")
print(f"{'Model':<40} {'Mean Gap':>8} {'Flag'}")
print("-" * 55)
for model, gaps in sorted(model_gaps.items()):
mean_gap = sum(gaps) / len(gaps)
flag = "YES" if mean_gap > THRESHOLD else "NO"
print(f"{model:<40} {mean_gap:>8.4f} {flag}")
print(f"\nThreshold: {THRESHOLD}")
flagged = sum(1 for r in rows if r["flag"])
print(f"Flagged runs: {flagged}/{len(rows)}")
if __name__ == "__main__":
main()
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