Add cluster bootstrap metrics, supplementary finite-time figure, teacher nested SEs

finite_time_supplement.py computes 95% cluster bootstrap intervals for
the predictor metrics (resampling forward initializations within each
cell) and renders the six-panel supplementary figure. The table
generator gains the teacher-task nested-SE fragment, computed from the
raw paired rows pulled from the experiment machine.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

9 files changed, 719 insertions, 0 deletions

diff --git a/outputs/aaai_supplementary/finite_time_supplement.pdf b/outputs/aaai_supplementary/finite_time_supplement.pdf
new file mode 100644
index 0000000..eced350
--- /dev/null
+++ b/outputs/aaai_supplementary/finite_time_supplement.pdf
diff --git a/outputs/aaai_supplementary/finite_time_supplement.png b/outputs/aaai_supplementary/finite_time_supplement.png
new file mode 100644
index 0000000..6731d61
--- /dev/null
+++ b/outputs/aaai_supplementary/finite_time_supplement.png
diff --git a/outputs/tables/finite_time_metrics_ci.tex b/outputs/tables/finite_time_metrics_ci.tex
new file mode 100644
index 0000000..b72f013
--- /dev/null
+++ b/outputs/tables/finite_time_metrics_ci.tex
@@ -0,0 +1,9 @@
+\begin{tabular}{@{}>{\raggedright\arraybackslash}p{2.4cm}ccc@{}}
+\toprule
+Predictor & MAE & Relative MAE & Correlation \\
+\midrule
+Frozen initialization operator & 0.06693 [0.05895, 0.07549] & 28.56\% [25.71, 31.68] & 0.97166 [0.95093, 0.98760] \\
+Linear extrapolation & 0.01043 [0.00890, 0.01205] & 4.45\% [3.82, 5.12] & 0.99492 [0.99259, 0.99691] \\
+Relinearization & 0.01394 [0.01254, 0.01544] & 5.95\% [5.45, 6.46] & 0.99830 [0.99747, 0.99902] \\
+\bottomrule
+\end{tabular}
diff --git a/outputs/tables/teacher_nested_se.tex b/outputs/tables/teacher_nested_se.tex
new file mode 100644
index 0000000..52e4e96
--- /dev/null
+++ b/outputs/tables/teacher_nested_se.tex
@@ -0,0 +1,14 @@
+\begin{tabular}{@{}rcc@{}}
+\toprule
+Width & Train difference & Test difference \\
+\midrule
+8 & \(0.2495\pm0.0097\) & \(0.1770\pm0.0483\) \\
+12 & \(0.1581\pm0.0089\) & \(0.0381\pm0.0202\) \\
+16 & \(0.1115\pm0.0108\) & \(-0.0380\pm0.0173\) \\
+24 & \(0.0803\pm0.0077\) & \(-0.0442\pm0.0454\) \\
+32 & \(0.0847\pm0.0039\) & \(-0.1202\pm0.0161\) \\
+48 & \(0.0485\pm0.0023\) & \(-0.0138\pm0.0328\) \\
+64 & \(0.0263\pm0.0005\) & \(0.0097\pm0.0078\) \\
+96 & \(0.0069\pm0.0006\) & \(0.0448\pm0.0197\) \\
+\bottomrule
+\end{tabular}
diff --git a/outputs/teacher_test_gap_sgd_T8000/runs.csv b/outputs/teacher_test_gap_sgd_T8000/runs.csv
new file mode 100644
index 0000000..4f45cb1
--- /dev/null
+++ b/outputs/teacher_test_gap_sgd_T8000/runs.csv
@@ -0,0 +1,217 @@
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diff --git a/outputs/teacher_test_gap_sgd_T8000/summary.json b/outputs/teacher_test_gap_sgd_T8000/summary.json
new file mode 100644
index 0000000..d9b9e7a
--- /dev/null
+++ b/outputs/teacher_test_gap_sgd_T8000/summary.json
@@ -0,0 +1,251 @@
+{
+  "config": {
+    "capacity_q": 0.01,
+    "data_seed": 7,
+    "device": "cpu",
+    "feedback_scale": "relu",
+    "feedback_seed_offset": 0,
+    "feedback_seeds": 8,
+    "init_seed_offset": 0,
+    "init_seeds": 3,
+    "input_dim": 16,
+    "jacobian_lambda_rel": 0.001,
+    "lr": 0.01,
+    "noise_std": 0.0,
+    "normalize_targets": true,
+    "optimizer": "sgd",
+    "outdir": "outputs/teacher_test_gap_sgd_T8000",
+    "output_dim": 4,
+    "plot": true,
+    "probe_samples": 32,
+    "skip_jacobian": true,
+    "steps": 8000,
+    "task": "mlp",
+    "teacher_hidden_layers": 2,
+    "teacher_rank": 4,
+    "teacher_width": 64,
+    "test_samples": 2048,
+    "torch_threads": 8,
+    "train_samples": 256,
+    "widths": [
+      8,
+      12,
+      16,
+      24,
+      32,
+      48,
+      64,
+      96
+    ]
+  },
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+      "fa_capacity_margin": 514,
+      "fa_constraint_rank": 9598,
+      "fa_test_mean": 0.9513155139710398,
+      "fa_test_std": 0.04816011708858036,
+      "fa_train_mean": 0.007945258532614088,
+      "fa_train_std": 0.001452885056156375,
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+      "train_gap_std": 0.0014660396836900331,
+      "width": 96
+    }
+  ]
+}
diff --git a/outputs/teacher_test_gap_sgd_T8000/width_summary.csv b/outputs/teacher_test_gap_sgd_T8000/width_summary.csv
new file mode 100644
index 0000000..cd0a1a0
--- /dev/null
+++ b/outputs/teacher_test_gap_sgd_T8000/width_summary.csv
@@ -0,0 +1,9 @@
+width,parameter_count,runs_bp,runs_fa,bp_test_mean,bp_test_std,bp_train_mean,bp_train_std,fa_test_mean,fa_test_std,fa_train_mean,fa_train_std,gap_mean,gap_std,train_gap_mean,train_gap_std,task_dimension,fa_constraint_rank,bp_capacity_margin,fa_capacity_margin,d_eff_mean,hard_rank_mean,fa_burden_nats,redundancy_score_mean

+8,224,3,24,0.8547195125219496,0.09416632052520177,0.48654225381273647,0.009430396910714468,1.03173780938202,0.07195063786750613,0.7360187817128564,0.08065577661619448,0.1770182968600705,0.09915053419946865,0.24947652790011995,0.07918404890030231,1024,94,-800,-894,0.0,0.0,1.3936644807238818,222.6063355192761

+12,384,3,24,0.8912211187592197,0.06296622353182581,0.35139158815529714,0.004244751408287705,0.9292951764049313,0.07525411172556047,0.5094820739240674,0.0707313348013235,0.03807405764571161,0.07715664669655001,0.15809048576877027,0.07029272672610735,1024,190,-640,-830,0.0,0.0,2.1683902917154665,381.83160970828453

+16,576,3,24,0.9192548641208319,0.033740239256328644,0.2520286029851933,0.007647223072501642,0.8812679121253054,0.07027921512298203,0.3635437201887566,0.047364665684149765,-0.037986951995526305,0.07361982827194134,0.11151511720356323,0.04520548886437488,1024,318,-448,-766,0.0,0.0,3.058119959290285,572.9418800407097

+24,1056,3,24,0.9293582800572858,0.07241895652383532,0.1396784759530465,0.01665120505504232,0.8851293442398639,0.06255425075132401,0.219954653180603,0.033287750843013345,-0.044228935817421745,0.09040086678358636,0.08027617722755646,0.034751538455168735,1024,670,32,-638,0.0,0.0,5.228037692481937,1050.7719623075182

+32,1664,3,24,1.0541338661341875,0.018275526958484174,0.0674924657026168,0.010299416214868514,0.9339004211933818,0.06403389508198398,0.1521629814467578,0.01750960740931603,-0.12023344494080564,0.06569708866945986,0.08467051574414101,0.015298364700276968,1024,1150,640,-510,0.0,0.0,7.958568352977123,1656.0414316470226

+48,3264,3,24,0.9791622527841524,0.047110478662005864,0.022625612139090635,0.004106878454242747,0.9653186953239867,0.05187486017852229,0.07109851792006452,0.007557098583336046,-0.013843557460165468,0.06974990528996139,0.048472905780973886,0.008244124666444821,1024,2494,2240,-254,0.0,0.0,15.199401552235027,3248.8005984477654

+64,5376,3,24,0.9653310456956622,0.03177320206769566,0.006939575153079867,0.0012012935377682527,0.9750088396534556,0.04179311994033258,0.033249367604793946,0.004644998390740636,0.009677793957793427,0.040482838262068055,0.02630979245171408,0.004635709869038379,1024,4350,4352,2,0.0,0.0,24.89249668727425,5351.107503312725

+96,11136,3,24,0.9064751809116146,0.05494609929229648,0.0010748642908555096,2.7736586821309024e-05,0.9513155139710398,0.04816011708858036,0.007945258532614088,0.001452885056156375,0.044840333059425225,0.05220001570680324,0.006870394241758578,0.0014660396836900331,1024,9598,10112,514,0.0,0.0,51.802320004200304,11084.1976799958

diff --git a/scripts/finite_time_supplement.py b/scripts/finite_time_supplement.py
new file mode 100644
index 0000000..cf70491
--- /dev/null
+++ b/scripts/finite_time_supplement.py
@@ -0,0 +1,189 @@
+#!/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()
diff --git a/scripts/generate_paper_tables.py b/scripts/generate_paper_tables.py
index ed66dae..95a67c8 100644
--- a/scripts/generate_paper_tables.py
+++ b/scripts/generate_paper_tables.py
@@ -26,6 +26,9 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--lr-dir", type=Path, default=Path("outputs/learning_rate_compensation")
     )
+    parser.add_argument(
+        "--teacher-dir", type=Path, default=Path("outputs/teacher_test_gap_sgd_T8000")
+    )
     parser.add_argument("--outdir", type=Path, default=Path("outputs/tables"))
     return parser.parse_args()
 
@@ -165,12 +168,39 @@ def learning_rate_table(lr_dir: Path, outdir: Path) -> None:
     write_fragment(outdir / "learning_rate_regimes.tex", lines)
 
 
+def teacher_table(teacher_dir: Path, outdir: Path) -> None:
+    rows = [r for r in read_rows(teacher_dir / "runs.csv") if r["run_type"] == "fa"]
+    by: dict[int, dict[int, dict[str, list[float]]]] = defaultdict(
+        lambda: defaultdict(lambda: defaultdict(list))
+    )
+    for row in rows:
+        cell = by[int(row["width"])][int(row["init_seed"])]
+        cell["train"].append(float(row["train_gap_to_bp"]))
+        cell["test"].append(float(row["test_gap_to_bp"]))
+    lines = [
+        r"\begin{tabular}{@{}rcc@{}}",
+        r"\toprule",
+        r"Width & Train difference & Test difference \\",
+        r"\midrule",
+    ]
+    for width, by_init in sorted(by.items()):
+        train_means = [statistics.mean(v["train"]) for _, v in sorted(by_init.items())]
+        test_means = [statistics.mean(v["test"]) for _, v in sorted(by_init.items())]
+        lines.append(
+            f"{width} & \\({statistics.mean(train_means):.4f}\\pm{sem(train_means):.4f}\\) & "
+            f"\\({statistics.mean(test_means):.4f}\\pm{sem(test_means):.4f}\\) \\\\"
+        )
+    lines += [r"\bottomrule", r"\end{tabular}"]
+    write_fragment(outdir / "teacher_nested_se.tex", lines)
+
+
 def main() -> None:
     args = parse_args()
     initialization_table(args.depth_dir, args.outdir)
     finite_time_table(args.depth_dir, args.outdir)
     cnn_table(args.cnn_dir, args.outdir)
     learning_rate_table(args.lr_dir, args.outdir)
+    teacher_table(args.teacher_dir, args.outdir)
 
 
 if __name__ == "__main__":