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f791e67 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | #!/usr/bin/env python3
"""Standalone fail-closed checker for the Space's retained Claim 1–3 evidence.
This intentionally uses only Python's standard library and files reachable
from the Space entrypoint. It recomputes the row-level ONNX and independent
CodeNet correlations, and integrity-checks the retained APPS/KBSS summary.
"""
from __future__ import annotations
import csv
import hashlib
import json
import math
import statistics
from collections import defaultdict
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
EVIDENCE = ROOT / "evidence/cumulative"
EXPECTED_HASHES = {
"full_n512.csv":
"3016dfb245deb56b3d0d3d06f902471bc415f80196d61ecf4d49eb773f1edd76",
"full_gpu_n200.csv":
"7843f75c38b9ee0bf3f15d796f151808fc01a526c704f429049016d57538f390",
"table3_results.json":
"173dbce239ca51269d3b231dd851b7fb299dffc82d2b0eede75f61ae6a835dc2",
}
EXPECTED_ACCURACY = {
"NASBench101": 0.4065993610622034,
"ENAS": 0.24946117751369043,
"NASNet": 0.20673752343866375,
}
EXPECTED_LANGUAGES = {
"C++", "Python", "Java", "C", "Ruby", "C#", "Rust", "Go", "Haskell",
"Kotlin", "JavaScript", "PHP", "D", "Scala", "OCaml", "Perl", "Fortran",
}
def sha256(path: Path) -> str:
return hashlib.sha256(path.read_bytes()).hexdigest()
def ranks(values: list[float]) -> list[float]:
"""Average ranks for ties, equivalent to scipy.stats.rankdata."""
order = sorted(range(len(values)), key=values.__getitem__)
output = [0.0] * len(values)
start = 0
while start < len(order):
end = start + 1
while end < len(order) and values[order[end]] == values[order[start]]:
end += 1
rank = (start + 1 + end) / 2
for index in order[start:end]:
output[index] = rank
start = end
return output
def spearman(xs: list[float], ys: list[float]) -> float:
rx, ry = ranks(xs), ranks(ys)
mx, my = statistics.fmean(rx), statistics.fmean(ry)
numerator = sum((x - mx) * (y - my) for x, y in zip(rx, ry))
denominator = math.sqrt(
sum((x - mx) ** 2 for x in rx) * sum((y - my) ** 2 for y in ry)
)
if denominator == 0:
raise AssertionError("undefined Spearman correlation")
return numerator / denominator
def check_hashes() -> dict[str, str]:
observed = {}
for filename, expected in EXPECTED_HASHES.items():
path = EVIDENCE / filename
if not path.is_file():
raise AssertionError(f"missing retained artifact: {filename}")
observed[filename] = sha256(path)
if observed[filename] != expected:
raise AssertionError(f"SHA-256 mismatch: {filename}")
return observed
def check_accuracy() -> dict:
grouped: dict[str, list[tuple[float, float]]] = defaultdict(list)
identifiers = set()
with (EVIDENCE / "full_n512.csv").open(newline="") as handle:
for row in csv.DictReader(handle):
identifier = row["identifier"]
if identifier in identifiers:
raise AssertionError(f"duplicate identifier: {identifier}")
identifiers.add(identifier)
draws = [float(row[f"draw_{index}"]) for index in range(8)]
prediction = float(row["prediction"])
if not math.isclose(
prediction, statistics.median(draws), abs_tol=1e-12
):
raise AssertionError(f"prediction is not median: {identifier}")
grouped[row["space"]].append((float(row["target"]), prediction))
if set(grouped) != set(EXPECTED_ACCURACY):
raise AssertionError("accuracy spaces changed")
observed = {}
for space, expected in EXPECTED_ACCURACY.items():
pairs = grouped[space]
if len(pairs) != 512:
raise AssertionError(f"{space}: expected 512 rows")
xs, ys = map(list, zip(*pairs))
rho = spearman(xs, ys)
if not math.isclose(rho, expected, abs_tol=1e-12):
raise AssertionError(f"{space}: Spearman changed: {rho}")
observed[space] = rho
return {"rows": len(identifiers), "raw_draws": len(identifiers) * 8,
"spearman": observed}
def check_codenet() -> dict:
grouped: dict[str, list[tuple[float, float]]] = defaultdict(list)
with (EVIDENCE / "full_gpu_n200.csv").open(newline="") as handle:
for row in csv.DictReader(handle):
grouped[row["language"]].append(
(float(row["y_true"]), float(row["y_pred"]))
)
if set(grouped) != EXPECTED_LANGUAGES:
raise AssertionError("CodeNet language set changed")
observed = {}
for language, pairs in grouped.items():
if len(pairs) != 200:
raise AssertionError(f"{language}: expected 200 rows")
xs, ys = map(list, zip(*pairs))
observed[language] = spearman(xs, ys)
average = statistics.fmean(observed.values())
if not math.isclose(average, 0.5234034026121069, abs_tol=1e-12):
raise AssertionError(f"CodeNet mean changed: {average}")
return {"languages": len(observed), "rows_per_language": 200,
"average_spearman": average, "per_language": observed}
def check_table3() -> dict:
summary = json.loads((EVIDENCE / "table3_results.json").read_text())["table3"]
apps, kbss = summary["APPS"], summary["KBSS"]
if apps["n"] != 512 or not math.isclose(
apps["spearman"], 0.9268067718469594, abs_tol=1e-15
):
raise AssertionError("APPS retained summary changed")
if kbss["n"] != 512 or not math.isclose(
kbss["spearman"], 0.5352789637599933, abs_tol=1e-15
):
raise AssertionError("KBSS retained summary changed")
return {"APPS": apps, "KBSS": kbss}
def verify() -> dict:
return {
"status": "PASS",
"scope": (
"row-level recomputation for ONNX accuracy and independent CodeNet; "
"hash and summary integrity for APPS/KBSS"
),
"sha256": check_hashes(),
"claim_1": check_accuracy(),
"claim_2": check_table3(),
"claim_3": check_codenet(),
"limitation": (
"The judged repository did not retain paper-scale APPS/KBSS rows; "
"this checker cannot independently recompute those two correlations."
),
}
if __name__ == "__main__":
print("SPACE_CUMULATIVE_RESULT " + json.dumps(verify(), sort_keys=True))
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