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#!/usr/bin/env python3
"""Extract the released Figure-5 protocol and endpoints from MATLAB objects."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import tempfile
import zipfile
import numpy as np
try:
from matio import load_from_mat
except ImportError as error: # pragma: no cover - environment guidance
raise SystemExit(
"Install the public `mat-io` package to decode MATLAB MCOS objects."
) from error
CONDITIONS = {
("taskcycle_L_18", 128.0, 0.0, 25.0): "standard",
("taskcycle_L_19", 32.0, 1.0, 200.0): "overclamp",
}
def scalar(properties: dict, name: str) -> float:
return float(np.asarray(properties[name]).reshape(-1)[0])
def final_classification_error(confusion: np.ndarray) -> float:
final = np.asarray(confusion, dtype=float)[:, :, -1]
total = float(np.sum(final))
if total <= 0.0:
raise ValueError("empty final confusion matrix")
return float(1.0 - np.trace(final) / total)
def extract_record(path: Path) -> dict | None:
properties = load_from_mat(
str(path), raw_data=True)["experiment"].properties
name = str(np.asarray(properties["Name"]).reshape(-1)[0])
if name not in {condition[0] for condition in CONDITIONS}:
return None
if not all(field in properties for field in ("ETA", "NOR", "ALF")):
return None
key = (
name,
scalar(properties, "ETA"),
scalar(properties, "NOR"),
scalar(properties, "ALF"),
)
method = CONDITIONS.get(key)
if method is None:
return None
node_multiplier = scalar(properties, "NODEMULT")
train = np.asarray(properties["TRAIN"], dtype=float)
inputs = train[:2] * node_multiplier
center = np.mean(inputs, axis=1)
diameter = 2.0 * float(np.mean(np.linalg.norm(
inputs - center[:, None], axis=0)))
horizontal = np.asarray(
properties["HorizontalCapacitors"], dtype=float)
vertical = np.asarray(
properties["VerticalCapacitors"], dtype=float)
gate_multiplier = scalar(properties, "GATEMULT")
initial_gates = np.concatenate((
horizontal[:, :, 0].reshape(-1),
vertical[:, :, 0].reshape(-1),
)) * gate_multiplier
final_gates = np.concatenate((
horizontal[:, :, -1].reshape(-1),
vertical[:, :, -1].reshape(-1),
)) * gate_multiplier
train_mse = np.asarray(properties["TrainMSE"], dtype=float)
return {
"source_file": path.name,
"method": method,
"experiment_name": name,
"input_diameter_v": diameter,
"inputs_v": inputs.tolist(),
"classes": np.asarray(
properties["TRAINCLASSES"], dtype=int).reshape(-1).tolist(),
"source_nodes_zero_indexed": np.asarray(
properties["SLOC"], dtype=int).tolist(),
"target_nodes_zero_indexed": np.asarray(
properties["TLOC"], dtype=int).tolist(),
"periodic_axes": np.asarray(
properties["ISPERIODIC"], dtype=int).reshape(-1).tolist(),
"initial_gates_v": initial_gates.tolist(),
"final_gates_v": final_gates.tolist(),
"final_classification_error": final_classification_error(
np.asarray(properties["TrainConfusion"])),
"final_hinge_loss_v2": float(train_mse.reshape(-1)[-1]),
"cumulative_learning_time_seconds": float(np.sum(
np.asarray(properties["LearnTimes"], dtype=float))) / 1e6,
"settings": {
"eta_over_129": scalar(properties, "ETA") / 129.0,
"alpha_microseconds": scalar(properties, "ALF"),
"normalized_alpha": bool(scalar(properties, "NOR")),
"hinge_buffer_millivolts": scalar(properties, "BUF"),
"one_hot_setting": scalar(properties, "HOT"),
"epochs": int(scalar(properties, "EPO")),
},
}
def summarize(records: list[dict]) -> list[dict]:
summaries = []
methods = sorted({record["method"] for record in records})
diameters = sorted({record["input_diameter_v"] for record in records})
for method in methods:
for diameter in diameters:
selected = [
record for record in records
if record["method"] == method
and abs(record["input_diameter_v"] - diameter) < 1e-12
]
errors = np.asarray([
record["final_classification_error"] for record in selected])
hinge = np.asarray([
record["final_hinge_loss_v2"] for record in selected])
summaries.append({
"method": method,
"input_diameter_v": diameter,
"trials": len(selected),
"mean_classification_error": float(np.mean(errors)),
"standard_error_classification_error": float(
np.std(errors, ddof=1) / np.sqrt(len(errors))),
"mean_hinge_loss_v2": float(np.mean(hinge)),
"standard_error_hinge_loss_v2": float(
np.std(hinge, ddof=1) / np.sqrt(len(hinge))),
})
return summaries
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--artifact-root", type=Path, required=True,
help="Extracted maguzj-imperfect-learning-physical-systems source tree")
parser.add_argument(
"--output", type=Path,
default=Path("results/physical_bias/dillavou_fig5_protocol.json"))
return parser.parse_args()
def main() -> None:
args = parse_args()
archive = args.artifact_root.resolve() / "big network" / "Experiments.zip"
if not archive.exists():
raise FileNotFoundError(archive)
records = []
with tempfile.TemporaryDirectory(prefix="dillavou_fig5_") as directory:
directory_path = Path(directory)
with zipfile.ZipFile(archive) as handle:
names = [
name for name in handle.namelist()
if name.startswith("Experiments/") and name.endswith(".mat")
]
for name in names:
path = directory_path / Path(name).name
path.write_bytes(handle.read(name))
record = extract_record(path)
if record is not None:
records.append(record)
records.sort(key=lambda record: (
record["method"],
record["input_diameter_v"],
record["classes"],
))
if len(records) != 80:
raise ValueError(f"expected 80 Figure-5 experiments, found {len(records)}")
class_patterns = {
tuple(record["classes"]) for record in records
if record["method"] == "standard"
and record["input_diameter_v"] == min(
item["input_diameter_v"] for item in records)
}
report = {
"analysis": "released_dillavou_figure5_protocol",
"provenance": {
"paper": "Dillavou et al., arXiv:2505.22887v2",
"zenodo_record": "15692914",
"release": "v1.0.1",
"source_revision": "71b8d724afc61d041bcfc1a0b2335dd88b3df62f",
},
"protocol_checks": {
"experiments": len(records),
"methods": sorted({record["method"] for record in records}),
"input_diameters_v": sorted({
record["input_diameter_v"] for record in records}),
"label_rotations": len(class_patterns),
"trials_per_method_diameter": 8,
"grid_shape": [4, 4],
"edges": 32,
},
"summary": summarize(records),
"experiments": records,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report["protocol_checks"], indent=2))
print(json.dumps(report["summary"], indent=2))
print(f"wrote {args.output}")
if __name__ == "__main__":
main()
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