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f87cdbe | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | #!/usr/bin/env python3
"""Fresh native/full-label and exact protection runs for FzP6XZGG4d.
The native policy audit exhausts all 2^16 collaborative sets for selected
ImageNet-16H/VGG19 cells and compares the exact finite optimum with every
two-threshold policy induced by the 16 posterior scores. The other two
audits add disjoint exact conformal-rank and CUP-Online regimes. No stored
result artifact is read as evidence.
"""
from __future__ import annotations
import argparse
import importlib.util
import json
import sys
from fractions import Fraction
from pathlib import Path
import numpy as np
import pandas as pd
def load_reproduce(root: Path):
spec = importlib.util.spec_from_file_location("humanai_reproduce", root / "reproduce.py")
if spec is None or spec.loader is None:
raise RuntimeError("cannot load reproduce.py")
module = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
return module
def load_helpers(root: Path):
sys.path.insert(0, str(root / "code"))
from offline_helpers import AIModel, HumanExpert, Imagenet16HPaths, get_common_image_names
return AIModel, HumanExpert, Imagenet16HPaths, get_common_image_names
def native_exhaustive(root: Path, data_root: Path) -> dict:
AIModel, HumanExpert, Imagenet16HPaths, get_common_image_names = load_helpers(root)
paths = Imagenet16HPaths.from_data_root(data_root)
true = pd.read_csv(paths.true_labels_csv, index_col="image_name")
masks = np.arange(1 << 16, dtype=np.uint32)
bit_weights = (1 << np.arange(16, dtype=np.uint32))
popcount = np.zeros(1 << 16, dtype=np.int16)
for bit in range(16):
popcount += ((masks >> bit) & 1).astype(np.int16)
rows = []
for noise in (80, 95, 110, 125):
ai = AIModel(paths, noise_level=noise, model_name="vgg19")
human = HumanExpert(paths, noise_level=noise)
images = get_common_image_names(ai, human, true)
# Evenly spaced cells cover all noise conditions without selecting a
# convenient contiguous image block.
chosen = [images[int(i)] for i in np.linspace(0, len(images) - 1, 8)]
for image in chosen:
p = ai.get_prob(image).to_numpy(dtype=float)
human_set = set(human.predict_set(image, strategy="topk2"))
inside = np.array([label in human_set for label in ai.labels], dtype=bool)
in_mass = float(p[inside].sum())
out_mass = float(p[~inside].sum())
selected = ((masks[:, None] & bit_weights[None, :]) != 0)
harm = ((selected[:, inside] == 0) * p[inside]).sum(axis=1) / in_mass
complementarity = (selected[:, ~inside] * p[~inside]).sum(axis=1) / out_mass
feasible = (harm < 0.2 - 1e-12) & (complementarity >= 0.75 - 1e-12)
if not feasible.any():
raise AssertionError(f"no feasible native policy for {noise}/{image}")
exact_min = int(popcount[feasible].min())
unique_in = sorted(set(p[inside]), reverse=True)
unique_out = sorted(set(p[~inside]), reverse=True)
threshold_masks = []
for q_in in [None, *unique_in]:
for q_out in [None, *unique_out]:
keep = np.array([
((q_in is not None and value >= q_in) if inside[idx]
else (q_out is not None and value >= q_out))
for idx, value in enumerate(p)
])
threshold_masks.append(int(np.dot(keep.astype(np.uint32), bit_weights)))
threshold_masks = np.array(sorted(set(threshold_masks)), dtype=np.uint32)
threshold_ok = feasible[threshold_masks]
if not threshold_ok.any():
raise AssertionError(f"no feasible threshold policy for {noise}/{image}")
threshold_min = int(popcount[threshold_masks[threshold_ok]].min())
chosen_index = int(np.flatnonzero(feasible & (popcount == exact_min))[0])
rows.append({
"noise": noise,
"image": image,
"labels": 16,
"policies_exhausted": int(1 << 16),
"feasible_policies": int(feasible.sum()),
"exact_min_size": exact_min,
"two_threshold_min_size": threshold_min,
"gap": threshold_min - exact_min,
"harm_at_best": float(harm[chosen_index]),
"complementarity_at_best": float(complementarity[chosen_index]),
})
return {
"cells": rows,
"cell_count": len(rows),
"policies_exhausted": sum(row["policies_exhausted"] for row in rows),
"zero_gap_cells": sum(row["gap"] == 0 for row in rows),
"max_gap": max(row["gap"] for row in rows),
"max_feasible_policy_count": max(row["feasible_policies"] for row in rows),
}
def rank_protection() -> dict:
rows = []
for group, alpha in (("human_in_epsilon", Fraction(1, 5)), ("human_out_delta", Fraction(1, 4))):
for n in (3, 5, 11, 23, 47, 127, 255, 511):
target = (1 - alpha) * (n + 1)
k = min(n + 1, target.numerator // target.denominator + int(target.numerator % target.denominator != 0))
coverage = Fraction(k, n + 1)
lower = 1 - alpha
upper = lower + Fraction(1, n + 1)
rows.append({
"group": group,
"n": n,
"rank": k,
"coverage": f"{coverage.numerator}/{coverage.denominator}",
"lower_holds": coverage >= lower,
"strict_upper_holds": coverage < upper,
})
return {"rows": rows, "cells": len(rows), "all_bounds_hold": all(r["lower_holds"] and r["strict_upper_holds"] for r in rows)}
def online_protection(reproduce) -> dict:
rows = []
for eta, epsilon, delta in ((Fraction(1, 7), Fraction(1, 8), Fraction(1, 5)),
(Fraction(1, 9), Fraction(1, 6), Fraction(1, 4))):
for length in (600, 1800, 6000):
for kind in ("abrupt", "alternating", "chirp", "human_adaptation"):
row = reproduce.run_online(kind, length, eta, epsilon, delta)
row.update({"eta": str(eta), "epsilon": str(epsilon), "delta": str(delta)})
rows.append(row)
return {
"rows": rows,
"cells": len(rows),
"all_bounds_hold": all(r["in_bound_holds"] and r["out_bound_holds"] and
r["telescope_in_exact"] and r["telescope_out_exact"] for r in rows),
}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--repo", type=Path, required=True)
ap.add_argument("--data-root", type=Path, required=True)
ap.add_argument("--output", type=Path, required=True)
args = ap.parse_args()
reproduce = load_reproduce(args.repo)
result = {
"native_exhaustive": native_exhaustive(args.repo, args.data_root),
"exact_rank_protection": rank_protection(),
"online_protection": online_protection(reproduce),
}
if result["native_exhaustive"]["zero_gap_cells"] != result["native_exhaustive"]["cell_count"]:
raise AssertionError("a native exhaustive cell did not have a two-threshold optimum")
if not result["exact_rank_protection"]["all_bounds_hold"] or not result["online_protection"]["all_bounds_hold"]:
raise AssertionError("protection gate failed")
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(json.dumps({
"native_cells": result["native_exhaustive"]["cell_count"],
"native_policies": result["native_exhaustive"]["policies_exhausted"],
"native_zero_gap": result["native_exhaustive"]["zero_gap_cells"],
"rank_cells": result["exact_rank_protection"]["cells"],
"online_cells": result["online_protection"]["cells"],
}, sort_keys=True))
if __name__ == "__main__":
main()
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