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#!/usr/bin/env python3
"""Cluster bootstrap metrics and the supplementary finite-time figure.
Both outputs are derived from outputs/aaai_depth_experiments/finite_time_rows.csv.
1. A LaTeX fragment with MAE, relative MAE, and correlation for the three
predictors, each with a 95% cluster bootstrap confidence interval that
resamples the four forward initializations within every architecture cell
(feedback draws stay nested inside their forward initialization).
2. A six-panel supplementary figure: measured cost against depth, the three
predictor calibration scatters, and signed residuals against depth and width.
"""
from __future__ import annotations
import argparse
import csv
from collections import defaultdict
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
PREDICTORS = [
("fixed_gap", "Frozen initialization operator", "frozen"),
("velocity_gap", "Linear extrapolation", "linear"),
("retangent_gap", "Relinearization", "relinearized"),
]
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--rows", type=Path, default=Path("outputs/aaai_depth_experiments/finite_time_rows.csv"))
parser.add_argument("--table-outdir", type=Path, default=Path("outputs/tables"))
parser.add_argument("--figure-outdir", type=Path, default=Path("outputs/aaai_supplementary"))
parser.add_argument("--bootstrap-samples", type=int, default=10_000)
parser.add_argument("--bootstrap-seed", type=int, default=2027)
return parser.parse_args()
def load(path: Path) -> list[dict[str, str]]:
with path.open() as handle:
return list(csv.DictReader(handle))
def metrics(measured: np.ndarray, predicted: np.ndarray) -> tuple[float, float, float]:
mae = float(np.mean(np.abs(predicted - measured)))
rmae = mae / float(np.mean(measured))
corr = float(np.corrcoef(predicted, measured)[0, 1])
return mae, rmae, corr
def bootstrap_table(rows: list[dict[str, str]], args: argparse.Namespace) -> None:
# cell -> init -> array of shape (8, 1 + predictors)
cells: dict[tuple[int, int], dict[int, list[list[float]]]] = defaultdict(lambda: defaultdict(list))
for row in rows:
cells[(int(row["depth"]), int(row["width"]))][int(row["init_seed"])].append(
[float(row["empirical_gap"])] + [float(row[f]) for f, _, _ in PREDICTORS]
)
stacked = [
np.asarray([by_init[i] for i in sorted(by_init)]) # (4, 8, 4)
for _, by_init in sorted(cells.items())
]
full = np.concatenate([cell.reshape(-1, cell.shape[-1]) for cell in stacked])
point = [metrics(full[:, 0], full[:, 1 + k]) for k in range(len(PREDICTORS))]
rng = np.random.default_rng(args.bootstrap_seed)
draws = np.empty((args.bootstrap_samples, len(PREDICTORS), 3))
n_init = stacked[0].shape[0]
for b in range(args.bootstrap_samples):
sample = np.concatenate(
[
cell[rng.integers(0, n_init, size=n_init)].reshape(-1, cell.shape[-1])
for cell in stacked
]
)
for k in range(len(PREDICTORS)):
draws[b, k] = metrics(sample[:, 0], sample[:, 1 + k])
lo, hi = np.percentile(draws, [2.5, 97.5], axis=0)
lines = [
r"\begin{tabular}{@{}>{\raggedright\arraybackslash}p{2.4cm}ccc@{}}",
r"\toprule",
r"Predictor & MAE & Relative MAE & Correlation \\",
r"\midrule",
]
for k, (_, label, _) in enumerate(PREDICTORS):
mae, rmae, corr = point[k]
lines.append(
f"{label} & {mae:.5f} [{lo[k,0]:.5f}, {hi[k,0]:.5f}] & "
f"{100*rmae:.2f}\\% [{100*lo[k,1]:.2f}, {100*hi[k,1]:.2f}] & "
f"{corr:.5f} [{lo[k,2]:.5f}, {hi[k,2]:.5f}] \\\\"
)
lines += [r"\bottomrule", r"\end{tabular}"]
args.table_outdir.mkdir(parents=True, exist_ok=True)
out = args.table_outdir / "finite_time_metrics_ci.tex"
out.write_text("\n".join(lines) + "\n")
print(f"wrote {out}")
def supplementary_figure(rows: list[dict[str, str]], args: argparse.Namespace) -> None:
depths = sorted({int(r["depth"]) for r in rows})
widths = sorted({int(r["width"]) for r in rows})
depth_color = {d: plt.cm.viridis(i / (len(depths) - 1)) for i, d in enumerate(depths)}
fig, axes = plt.subplots(2, 3, figsize=(14.0, 8.2), dpi=220)
ax = axes[0, 0]
markers = ["o", "s", "^"]
for width, marker in zip(widths, markers):
means, sems = [], []
for depth in depths:
cell = [r for r in rows if int(r["width"]) == width and int(r["depth"]) == depth]
init_means = [
np.mean([float(r["empirical_gap"]) for r in cell if int(r["init_seed"]) == seed])
for seed in sorted({int(r["init_seed"]) for r in cell})
]
means.append(np.mean(init_means))
sems.append(np.std(init_means, ddof=1) / np.sqrt(len(init_means)))
ax.errorbar(depths, means, yerr=sems, marker=marker, capsize=2, label=f"width {width}")
ax.set_xlabel("hidden-layer depth")
ax.set_ylabel("FA loss - BP loss")
ax.set_title("Measured cost by depth and width", fontsize=10)
ax.grid(alpha=0.16)
ax.legend(fontsize=7)
scatter_axes = [axes[0, 1], axes[0, 2], axes[1, 0]]
for (field, label, short), ax in zip(PREDICTORS, scatter_axes):
xs = np.asarray([float(r[field]) for r in rows])
ys = np.asarray([float(r["empirical_gap"]) for r in rows])
colors = [depth_color[int(r["depth"])] for r in rows]
ax.scatter(xs, ys, s=12, c=colors, alpha=0.6)
lo = min(xs.min(), ys.min())
hi = max(xs.max(), ys.max())
pad = 0.04 * (hi - lo)
ax.plot([lo - pad, hi + pad], [lo - pad, hi + pad], "k-", lw=1)
ax.set_xlabel("predicted gap")
ax.set_ylabel("measured gap")
ax.set_title(label, fontsize=10)
ax.grid(alpha=0.16)
residual_specs = [(axes[1, 1], "depth", "hidden-layer depth"), (axes[1, 2], "width", "hidden width")]
predictor_colors = ["#2f6f9f", "#c65f16", "#2b8a3e"]
offsets = [-0.18, 0.0, 0.18]
for ax, key, xlabel in residual_specs:
levels = depths if key == "depth" else widths
spacing = min(np.diff(levels)) if len(levels) > 1 else 1.0
for (field, label, short), color, off in zip(PREDICTORS, predictor_colors, offsets):
xs = np.asarray([float(r[key]) for r in rows]) + off * spacing
resid = np.asarray([float(r[field]) - float(r["empirical_gap"]) for r in rows])
ax.scatter(xs, resid, s=8, color=color, alpha=0.45, label=label)
ax.axhline(0.0, color="0.3", lw=1)
ax.set_xlabel(xlabel)
ax.set_ylabel("predicted minus measured")
ax.set_title(f"Signed residual by {key}", fontsize=10)
ax.grid(alpha=0.16)
if key == "depth":
ax.legend(fontsize=7)
for ax, letter in zip(axes.flat, "abcdef"):
ax.text(-0.14, 1.06, letter, transform=ax.transAxes, fontsize=12, fontweight="bold")
depth_handles = [
plt.Line2D([], [], marker="o", ls="", color=depth_color[d], label=f"depth {d}")
for d in depths
]
axes[0, 2].legend(handles=depth_handles, fontsize=6, ncols=2, loc="upper left")
fig.tight_layout(w_pad=2.0, h_pad=2.4)
args.figure_outdir.mkdir(parents=True, exist_ok=True)
path = args.figure_outdir / "finite_time_supplement.png"
fig.savefig(path, bbox_inches="tight")
fig.savefig(path.with_suffix(".pdf"), bbox_inches="tight")
plt.close(fig)
print(f"figure: {path}")
def main() -> None:
args = parse_args()
rows = load(args.rows)
bootstrap_table(rows, args)
supplementary_figure(rows, args)
if __name__ == "__main__":
main()
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