File size: 3,956 Bytes
712756b | 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 | """Machine-readable split, overlap, and sealed-test audit for DENIM data."""
from __future__ import annotations
import hashlib
import json
from collections import Counter, defaultdict
from pathlib import Path
import numpy as np
from src.evaluate_t2_denim_sealed_test_v1 import load_trajectories as load_sealed
from src.train_t2_denim_capability_model_v4 import load_v4_trajectories
from src.train_t2_denim_conditional_v2 import load_all_trajectories
ROOT = Path(__file__).resolve().parents[1]
OUTPUT = ROOT / "artifacts" / "t2_denim_protocol_audit_v1.json"
def _hash(*arrays: np.ndarray) -> str:
digest = hashlib.sha256()
for value in arrays:
selected = np.ascontiguousarray(value)
digest.update(str(selected.shape).encode())
digest.update(selected.view(np.uint8))
return digest.hexdigest()
def run() -> dict[str, object]:
old = load_all_trajectories()
new, _ = load_v4_trajectories()
sealed, _ = load_sealed()
all_items = old + new + sealed
role_counts = Counter(item.role for item in all_items)
role_families: dict[str, set[str]] = defaultdict(set)
full_input_roles: dict[str, set[str]] = defaultdict(set)
input_target_roles: dict[str, set[str]] = defaultdict(set)
strain_roles: dict[str, set[str]] = defaultdict(set)
for item in all_items:
role_families[item.role].add(item.family)
descriptors = np.concatenate((item.descriptor.numpy(), np.asarray((item.young, item.poisson, item.yield_stress))))
full_input_roles[_hash(item.strain.numpy(), descriptors)].add(item.role)
input_target_roles[_hash(item.strain.numpy(), descriptors, item.stress.numpy())].add(item.role)
strain_roles[_hash(item.strain.numpy())].add(item.role)
def collisions(values: dict[str, set[str]]) -> int:
return sum(len(roles) > 1 for roles in values.values())
training_families = role_families["train"]
report = {
"schema": "agentfem.denim-ml-protocol-audit.v1",
"trajectory_count": len(all_items),
"role_counts": dict(sorted(role_counts.items())),
"exact_cross_role_collisions": {
"model_input_plus_target": collisions(input_target_roles),
"full_model_input": collisions(full_input_roles),
"strain_only": collisions(strain_roles),
},
"family_overlap_with_training": {
role: sorted(families & training_families) for role, families in sorted(role_families.items())
},
"sealed_test": {
"trajectory_count": len(sealed),
"family_count": len(role_families["sealed_test"]),
"families": sorted(role_families["sealed_test"]),
"family_overlap_with_training": sorted(role_families["sealed_test"] & training_families),
"eligible_for_training_or_selection": False,
},
"interpretation": {
"strain_only_collisions": "Expected transfer pairs can share strain histories across different material descriptors and targets; they are not direct input/target leakage.",
"existing_test_policy": "The 1,349 pre-existing test trajectories are development benchmarks because prior aggregate results informed later model design.",
"sealed_policy": "sealed_test_v1 is the first prospective frozen-model test and must never be reused for training or checkpoint selection.",
},
}
report["passed"] = bool(
report["exact_cross_role_collisions"]["model_input_plus_target"] == 0
and report["exact_cross_role_collisions"]["full_model_input"] == 0
and not report["sealed_test"]["family_overlap_with_training"]
and len(sealed) == 128
)
OUTPUT.parent.mkdir(parents=True, exist_ok=True)
OUTPUT.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(json.dumps(report, indent=2, sort_keys=True))
return report
if __name__ == "__main__":
run()
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