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
|
"""Analyze full rescue results: per-cell rebound rates, Wilson CIs, McNemar."""
from __future__ import annotations
import json
import math
import statistics
from collections import defaultdict
from pathlib import Path
PATH = Path("/home/yurenh2/gap/analysis/rescue_results/rescue_30.jsonl")
def wilson_ci(k: int, n: int, z: float = 1.96) -> tuple:
if n == 0:
return (0.0, 0.0, 0.0)
p = k / n
denom = 1 + z * z / n
center = (p + z * z / (2 * n)) / denom
half = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / denom
return (p, max(0.0, center - half), min(1.0, center + half))
def mcnemar_p(b: int, c: int) -> float:
"""McNemar exact-ish p (binomial two-sided). b = treat A correct, B wrong;
c = treat A wrong, B correct. Returns p value testing b == c."""
n = b + c
if n == 0:
return 1.0
# Two-sided binomial test on min(b,c) ~ Bin(n, 0.5)
k = min(b, c)
# cumulative
cum = 0.0
for i in range(k + 1):
cum += math.comb(n, i) * (0.5 ** n)
p = min(1.0, 2 * cum)
return p
def main():
rows = [json.loads(l) for l in open(PATH)]
print(f"Loaded {len(rows)} rows")
# Quick sanity
from collections import Counter
print("Solve status:", Counter(r.get("solve_status") for r in rows))
print("Grade status:", Counter(r.get("grade_status") for r in rows))
# Per-cell counts
counts = defaultdict(lambda: {"total": 0, "correct": 0})
for r in rows:
if r.get("grade_status") != "success" and r.get("grade") not in ("CORRECT", "INCORRECT"):
# Treat solve failures / parse failures as INCORRECT (conservative)
pass
key = (r["model"], r["variant"], r["condition"])
counts[key]["total"] += 1
if r.get("grade") == "CORRECT":
counts[key]["correct"] += 1
# Aggregated by (variant, condition)
by_var_cond = defaultdict(lambda: {"total": 0, "correct": 0})
for (m, v, c), d in counts.items():
by_var_cond[(v, c)]["total"] += d["total"]
by_var_cond[(v, c)]["correct"] += d["correct"]
print("\n" + "=" * 90)
print("REBOUND RATE BY (VARIANT, CONDITION) [aggregated across 4 models]")
print("=" * 90)
print(f"{'Variant':<32} {'Condition':<14} {'k/n':>10} {'rate':>7} {'95% Wilson CI':>20}")
print("-" * 90)
variants_order = ["descriptive_long", "descriptive_long_confusing",
"descriptive_long_misleading", "garbled_string", "kernel_variant"]
conds_order = ["null", "canonical_T2", "own_T2"]
for v in variants_order:
for c in conds_order:
d = by_var_cond.get((v, c))
if not d:
continue
p, lo, hi = wilson_ci(d["correct"], d["total"])
print(f"{v:<32} {c:<14} {d['correct']:>4}/{d['total']:>4} "
f"{p*100:>5.1f}% [{lo*100:>5.1f}%, {hi*100:>5.1f}%]")
print()
# Per-model aggregated by (variant, condition)
print("\n" + "=" * 90)
print("REBOUND RATE PER (MODEL, VARIANT, CONDITION)")
print("=" * 90)
models_order = sorted({k[0] for k in counts})
print(f"{'Model':<22} {'Variant':<32} {'cond':<14} {'k/n':>10} {'rate':>7}")
for m in models_order:
for v in variants_order:
for c in conds_order:
d = counts.get((m, v, c))
if not d:
continue
p, lo, hi = wilson_ci(d["correct"], d["total"])
print(f" {m:<20} {v:<32} {c:<14} {d['correct']:>3}/{d['total']:>3} "
f"{p*100:>5.1f}%")
print()
# Paired McNemar test: same case, different conditions
# Pair canonical_T2 vs null, and own_T2 vs null
print("\n" + "=" * 90)
print("PAIRED MCNEMAR TESTS")
print("=" * 90)
case_grades = defaultdict(dict) # (model, variant, index) -> {cond: grade}
for r in rows:
case_grades[(r["model"], r["variant"], r["index"])][r["condition"]] = r.get("grade")
print("\ncanonical_T2 vs null:")
print(f" {'cell':<46} {'b (can-only)':>12} {'c (null-only)':>13} "
f"{'both-CORR':>10} {'both-INC':>10} {'McNemar p':>11}")
for m in models_order:
for v in variants_order:
b = c = both_corr = both_inc = 0
for k, grds in case_grades.items():
if k[0] != m or k[1] != v: continue
ca = grds.get("canonical_T2"); nu = grds.get("null")
if ca is None or nu is None: continue
if ca == "CORRECT" and nu == "INCORRECT": b += 1
elif ca == "INCORRECT" and nu == "CORRECT": c += 1
elif ca == "CORRECT" and nu == "CORRECT": both_corr += 1
elif ca == "INCORRECT" and nu == "INCORRECT": both_inc += 1
p = mcnemar_p(b, c)
print(f" {m+'/'+v:<46} {b:>12} {c:>13} {both_corr:>10} {both_inc:>10} {p:>11.3f}")
print("\nown_T2 vs null:")
print(f" {'cell':<46} {'b (own-only)':>12} {'c (null-only)':>13} "
f"{'both-CORR':>10} {'both-INC':>10} {'McNemar p':>11}")
for m in models_order:
for v in [vv for vv in variants_order if vv != "kernel_variant"]:
b = c = both_corr = both_inc = 0
for k, grds in case_grades.items():
if k[0] != m or k[1] != v: continue
ow = grds.get("own_T2"); nu = grds.get("null")
if ow is None or nu is None: continue
if ow == "CORRECT" and nu == "INCORRECT": b += 1
elif ow == "INCORRECT" and nu == "CORRECT": c += 1
elif ow == "CORRECT" and nu == "CORRECT": both_corr += 1
elif ow == "INCORRECT" and nu == "INCORRECT": both_inc += 1
p = mcnemar_p(b, c)
print(f" {m+'/'+v:<46} {b:>12} {c:>13} {both_corr:>10} {both_inc:>10} {p:>11.3f}")
print("\nown_T2 vs canonical_T2:")
print(f" {'cell':<46} {'b (own-only)':>12} {'c (can-only)':>13} "
f"{'both-CORR':>10} {'both-INC':>10} {'McNemar p':>11}")
for m in models_order:
for v in [vv for vv in variants_order if vv != "kernel_variant"]:
b = c = both_corr = both_inc = 0
for k, grds in case_grades.items():
if k[0] != m or k[1] != v: continue
ow = grds.get("own_T2"); ca = grds.get("canonical_T2")
if ow is None or ca is None: continue
if ow == "CORRECT" and ca == "INCORRECT": b += 1
elif ow == "INCORRECT" and ca == "CORRECT": c += 1
elif ow == "CORRECT" and ca == "CORRECT": both_corr += 1
elif ow == "INCORRECT" and ca == "INCORRECT": both_inc += 1
p = mcnemar_p(b, c)
print(f" {m+'/'+v:<46} {b:>12} {c:>13} {both_corr:>10} {both_inc:>10} {p:>11.3f}")
if __name__ == "__main__":
main()
|
