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#!/usr/bin/env python3
"""Summarize the closed Rain parameter-measurement development screen."""
from __future__ import annotations
import json
import math
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
RESULT_ROOT = ROOT / "results" / "ep_bias" / "s0"
FILES = {
"clean": "clean-e10-n10k-s1988.json",
"raw": "raw-e10-n10k-r0p1-s1988.json",
"same_rms_noise": "noise-e10-n10k-r0p1-s1988.json",
"oracle": "oracle-e10-n10k-r0p1-s1988.json",
"innovation_online": "innovation-e10-n10k-r0p1-p0p5-s1988.json",
"innovation_cal64_online": "innovation-cal64-e10-n10k-r0p1-p0p5-s1988.json",
"innovation_cal1000_frozen": (
"innovation-cal1000-frozen-e1-n10k-r0p1-p0p5-s1988.json"),
"constant_cal1000_frozen": (
"constant-cal1000-frozen-e1-n10k-r0p1-p0p5-s1988.json"),
}
def read(name: str) -> dict:
with (RESULT_ROOT / FILES[name]).open(encoding="utf-8") as handle:
return json.load(handle)
def finite_metric(metric: dict) -> bool:
values = (
metric.get("train_cost"), metric.get("test_cost"),
metric.get("train_accuracy"), metric.get("test_accuracy"),
)
return all(value is not None and math.isfinite(float(value)) for value in values)
def json_safe(value):
if isinstance(value, float) and not math.isfinite(value):
return None
if isinstance(value, dict):
return {key: json_safe(item) for key, item in value.items()}
if isinstance(value, list):
return [json_safe(item) for item in value]
return value
def main() -> None:
records = {name: read(name) for name in FILES}
rows = {}
for name, record in records.items():
metrics = record["metrics"]
rows[name] = {
"epochs_completed": len(metrics),
"all_metrics_finite": all(finite_metric(metric) for metric in metrics),
"final_test_accuracy": metrics[-1]["test_accuracy"],
"best_test_accuracy": max(metric["test_accuracy"] for metric in metrics),
"final_corrector": json_safe(metrics[-1].get("corrector", {})),
"protocol": record["protocol"],
}
report = {
"stage": "rain_ep_parameter_measurement_s0",
"status": "closed_negative_adapter_with_positive_bias_noise_control",
"rows": rows,
"observations": {
"raw_structured_bias_nonfinite": not rows["raw"]["all_metrics_finite"],
"same_rms_noise_remains_finite": rows["same_rms_noise"]["all_metrics_finite"],
"clean_final_test_accuracy": rows["clean"]["final_test_accuracy"],
"same_rms_noise_final_test_accuracy": rows[
"same_rms_noise"]["final_test_accuracy"],
"innovation_online_final_test_accuracy": rows[
"innovation_online"]["final_test_accuracy"],
"innovation_cal64_online_final_test_accuracy": rows[
"innovation_cal64_online"]["final_test_accuracy"],
"innovation_cal1000_frozen_first_epoch_test_accuracy": rows[
"innovation_cal1000_frozen"]["final_test_accuracy"],
"constant_cal1000_frozen_first_epoch_test_accuracy": rows[
"constant_cal1000_frozen"]["final_test_accuracy"],
},
"interpretation": (
"At bias ratio 0.1, fixed structured parameter-measurement bias "
"makes the raw run nonfinite while same-RMS zero-mean noise remains "
"trainable. The parameter-level affine predictor does not recover "
"clean learning under online, 64-batch warm-up, or matched 1000-batch "
"frozen calibration, so this adapter is closed rather than promoted."
),
"test_policy": "development_test_subset_observed_each_epoch",
}
output = RESULT_ROOT.parent / "s0_summary.json"
output.write_text(
json.dumps(report, indent=2, sort_keys=True, allow_nan=False) + "\n")
print(json.dumps(report, indent=2, sort_keys=True, allow_nan=False))
if __name__ == "__main__":
main()
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