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"""Two follow-up analyses (zero API):
1. Per-model self-correction success rate: P(correct | SC) vs P(correct | no SC)
2. Difficulty-stratified surface vs kernel dichotomy
"""
from __future__ import annotations
import json
import sys
import statistics
from pathlib import Path
from collections import defaultdict
THIS_DIR = Path(__file__).resolve().parent
sys.path.insert(0, str(THIS_DIR))
from structural_overlap import find_variant_file, load_problems, RESULTS_DIR, SURFACE_VARIANTS
from self_correction import has_self_correction
# ----------------- 1. SC success rate per model -----------------
def sc_success_rate():
base = RESULTS_DIR
models = sorted([d.name for d in base.iterdir() if d.is_dir()])
print("=" * 80)
print("PER-MODEL SELF-CORRECTION SUCCESS RATE")
print("(does an SC attempt improve probability of being correct?)")
print("=" * 80)
print()
rows = []
for m in models:
mdir = base / m
# Aggregate over all variants
n_sc_correct = 0
n_sc_total = 0
n_nosc_correct = 0
n_nosc_total = 0
for v in ["original"] + SURFACE_VARIANTS + ["kernel_variant"]:
vp = find_variant_file(mdir, v)
if not vp: continue
for p in load_problems(vp):
text = (p.get("solve") or {}).get("solution") or ""
if not text: continue
correct = p.get("correct")
if correct is None: continue
if has_self_correction(text):
n_sc_total += 1
if correct: n_sc_correct += 1
else:
n_nosc_total += 1
if correct: n_nosc_correct += 1
if n_sc_total < 5 or n_nosc_total < 5:
continue
p_sc = n_sc_correct / n_sc_total
p_nosc = n_nosc_correct / n_nosc_total
delta = p_sc - p_nosc
# Wilson 95% CI on each rate
rows.append({
"model": m,
"sc_n": n_sc_total, "sc_correct": n_sc_correct, "p_sc": p_sc,
"nosc_n": n_nosc_total, "nosc_correct": n_nosc_correct, "p_nosc": p_nosc,
"delta": delta,
})
rows.sort(key=lambda r: -r["sc_n"])
print(f"{'Model':<22} {'#SC trials':>11} {'P(corr|SC)':>12} {'P(corr|noSC)':>13} {'Δ':>9}")
print("-" * 75)
for r in rows:
print(f"{r['model']:<22} {r['sc_n']:>11} "
f"{r['p_sc']*100:>10.1f}% {r['p_nosc']*100:>11.1f}% "
f"{r['delta']*100:>+7.1f}pp")
json.dump(rows, open(THIS_DIR / "sc_success_per_model.json", "w"), indent=2)
return rows
# ----------------- 2. Difficulty stratified dichotomy -----------------
DATASET_DIR = Path("/home/yurenh2/gap/putnam-bench-anon/dataset")
def load_difficulty_metadata():
"""Per-problem difficulty assignment using year/section/index heuristic.
Per the paper's existing exposition, we derive Easy/Medium/Hard from the
problem index (1-2 = Easy, 3-4 = Medium, 5-6 = Hard, 7-8 = extra-hard tail)
because the dataset's `difficulty` field is heterogeneous.
"""
out = {}
for f in sorted(DATASET_DIR.glob("*.json")):
d = json.load(open(f))
idx = d.get("index")
if not idx: continue
# Extract problem number from "YEAR-PART-NUM"
parts = idx.split("-")
if len(parts) != 3: continue
try:
num = int(parts[2])
except ValueError:
continue
if num <= 2: bucket = "Easy"
elif num <= 4: bucket = "Medium"
elif num <= 6: bucket = "Hard"
else: bucket = "ExtraHard"
out[idx] = bucket
return out
def difficulty_stratified_dichotomy():
print("\n\n" + "=" * 80)
print("DIFFICULTY-STRATIFIED ACCURACY (mean across 18 models)")
print("Easy/Medium/Hard buckets defined by problem index 1-2/3-4/5-6")
print("=" * 80)
print()
diff = load_difficulty_metadata()
base = RESULTS_DIR
models = sorted([d.name for d in base.iterdir() if d.is_dir()])
# buckets[(model, variant, difficulty)] = (n, n_correct)
cells = defaultdict(lambda: [0, 0])
for m in models:
mdir = base / m
for v in ["original"] + SURFACE_VARIANTS + ["kernel_variant"]:
vp = find_variant_file(mdir, v)
if not vp: continue
for p in load_problems(vp):
idx = p.get("index")
correct = p.get("correct")
if idx is None or correct is None: continue
bucket = diff.get(idx, "Unknown")
cells[(m, v, bucket)][0] += 1
if correct: cells[(m, v, bucket)][1] += 1
# Aggregate per (variant, difficulty) by averaging per-model rates
print(f"{'Variant':<24} {'Easy':>8} {'Medium':>8} {'Hard':>8} {'XHard':>8}")
print("-" * 60)
for v in ["original"] + SURFACE_VARIANTS + ["kernel_variant"]:
row = {}
for bucket in ["Easy", "Medium", "Hard", "ExtraHard"]:
rates = []
for m in models:
n, c = cells.get((m, v, bucket), [0, 0])
if n >= 5:
rates.append(c / n)
row[bucket] = statistics.fmean(rates) * 100 if rates else None
print(f"{v:<24} "
f"{row['Easy']:>7.1f}% " if row['Easy'] is not None else f"{v:<24} {'-':>8}",
end="")
for bucket in ["Medium", "Hard", "ExtraHard"]:
print(f"{row[bucket]:>7.1f}% " if row[bucket] is not None else f"{'-':>8}", end="")
print()
# Compute Δ_orig→KV per difficulty bucket
print(f"\n--- Δ original → KV per difficulty bucket ---")
for bucket in ["Easy", "Medium", "Hard", "ExtraHard"]:
orig_rates = []
kv_rates = []
for m in models:
no, co = cells.get((m, "original", bucket), [0, 0])
nk, ck = cells.get((m, "kernel_variant", bucket), [0, 0])
if no >= 5 and nk >= 5:
orig_rates.append(co / no)
kv_rates.append(ck / nk)
if orig_rates:
mo = statistics.fmean(orig_rates) * 100
mk = statistics.fmean(kv_rates) * 100
print(f" {bucket:<10} orig={mo:5.1f}% kv={mk:5.1f}% Δ={mk-mo:+.1f}pp")
# Compute Δ_orig→GS per difficulty bucket
print(f"\n--- Δ original → GS (surface, hardest renamer) per difficulty bucket ---")
for bucket in ["Easy", "Medium", "Hard", "ExtraHard"]:
orig_rates = []
gs_rates = []
for m in models:
no, co = cells.get((m, "original", bucket), [0, 0])
ng, cg = cells.get((m, "garbled_string", bucket), [0, 0])
if no >= 5 and ng >= 5:
orig_rates.append(co / no)
gs_rates.append(cg / ng)
if orig_rates:
mo = statistics.fmean(orig_rates) * 100
mg = statistics.fmean(gs_rates) * 100
print(f" {bucket:<10} orig={mo:5.1f}% GS={mg:5.1f}% Δ={mg-mo:+.1f}pp")
def main():
sc_success_rate()
difficulty_stratified_dichotomy()
if __name__ == "__main__":
main()
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