- 移除 JEPA/lejepa-identifiability 子模块 gitlink - 移除 research/multiply/MultiPLY 子模块 gitlink - 删除 .gitmodules（不再有外部 URL 依赖） - 两个目录内容作为普通文件纳入主仓库追踪 - 删除各自内部 .git 目录，消除嵌套 git 仓库
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gaojie
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"""
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Generate LaTeX tables for the paper, matching scaling table style.
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Usage:
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python analysis/make_table_reacher.py --results_dir results/reacher
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"""
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import json
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import argparse
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import numpy as np
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from pathlib import Path
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from collections import defaultdict
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def load_all_results(results_dir):
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ou, traj = [], []
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for p in Path(results_dir).rglob("result.json"):
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r = json.load(open(p))
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if "delta" in r and r.get("rho") is None:
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traj.append(r)
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elif "rho" in r:
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ou.append(r)
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return ou, traj
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def best_lambda_per_x(results, x_key):
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grouped = defaultdict(list)
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for r in results:
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grouped[(r[x_key], r["lamb"])].append(r)
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best = {}
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for x in sorted(set(k[0] for k in grouped)):
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best_mean, best_lamb = -np.inf, None
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for lamb in set(k[1] for k in grouped if k[0] == x):
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m = np.mean([r["r2_hz"] for r in grouped[(x, lamb)]])
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if m > best_mean:
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best_mean, best_lamb = m, lamb
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best[x] = {
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"lamb": best_lamb,
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"runs": grouped[(x, best_lamb)],
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}
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return best
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def pm(vals, fmt=".2f"):
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"""Format as value\\tiny{±std} matching paper style."""
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m, s = np.mean(vals), np.std(vals)
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return f"{m:{fmt}}\\tiny{{$\\pm${s:.0e}}}"
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def make_combined_table(ou_results, traj_results):
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ou_best = best_lambda_per_x(ou_results, "rho")
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traj_best = best_lambda_per_x(traj_results, "delta")
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lines = []
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lines.append(r"\begin{table}[t]")
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lines.append(r"\centering")
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lines.append(r"\caption{")
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lines.append(r" \textbf{Pixel-observation identifiability on DMC Reacher} "
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r"(mean $\pm$ std over 3 seeds, best $\lambda$ per condition).")
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lines.append(r" \textbf{Left:} OU process with Gaussian marginals. "
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r"$R^2$ increases monotonically with $\rho$, reaching $0.95$ "
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r"at $\rho = 0.99$, confirming linear identifiability from pixels.")
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lines.append(r" \textbf{Right:} Real SAC trajectories with non-Gaussian marginals. "
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r"The two joints have different autocorrelation timescales ($\rho_0 \neq \rho_1$) "
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r"and the wrist has a near-uniform marginal distribution, "
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r"leading to anisotropic and reduced identifiability.")
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lines.append(r"}")
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lines.append(r"\label{tab:reacher}")
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lines.append(r"\resizebox{\textwidth}{!}{%")
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lines.append(r"\begin{tabular}{r cc | r cc ccc}")
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lines.append(r" \multicolumn{3}{c}{\textbf{OU (Gaussian)}} & "
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r"\multicolumn{6}{c}{\textbf{Trajectory (non-Gaussian)}} \\")
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lines.append(r"\cmidrule(lr){1-3} \cmidrule(lr){4-9}")
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lines.append(r"$\rho$ & $R^2(z \to h)$ & $R^2(h \to z)$ & "
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r"$\delta$ & $\rho_0$ & $\rho_1$ & "
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r"$R^2(z \to h)$ & $R^2(h \to z_0)$ & $R^2(h \to z_1)$ \\")
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lines.append(r"\midrule")
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ou_rhos = sorted(ou_best.keys())
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traj_deltas = sorted(traj_best.keys())
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n_rows = max(len(ou_rhos), len(traj_deltas))
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for i in range(n_rows):
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# OU columns
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if i < len(ou_rhos):
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rho = ou_rhos[i]
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runs = ou_best[rho]["runs"]
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r2_zh = pm([r["r2_zh"] for r in runs])
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r2_hz = pm([r["r2_hz"] for r in runs])
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ou_str = f" {rho:.2f} & {r2_zh} & {r2_hz}"
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else:
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ou_str = r" & &"
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# Traj columns
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if i < len(traj_deltas):
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delta = traj_deltas[i]
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runs = traj_best[delta]["runs"]
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rho0 = runs[0].get("rho_shoulder", None)
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rho1 = runs[0].get("rho_wrist", None)
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rho0_s = f"{rho0:.3f}" if rho0 is not None else "---"
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rho1_s = f"{rho1:.3f}" if rho1 is not None else "---"
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r2_zh = pm([r["r2_zh"] for r in runs])
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r2_d0 = pm([r["r2_hz_per_dim"][0] for r in runs])
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r2_d1 = pm([r["r2_hz_per_dim"][1] for r in runs])
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traj_str = f"{delta} & {rho0_s} & {rho1_s} & {r2_zh} & {r2_d0} & {r2_d1}"
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else:
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traj_str = r"& & & & &"
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lines.append(f"{ou_str} & {traj_str} \\\\")
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lines.append(r"\bottomrule")
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lines.append(r"\end{tabular}}")
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lines.append(r"\vspace{5pt}")
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lines.append(r"\vspace{-20pt}")
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lines.append(r"\end{table}")
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return "\n".join(lines)
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def make_ou_table_standalone(ou_results):
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"""Standalone OU table for appendix if needed."""
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ou_best = best_lambda_per_x(ou_results, "rho")
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lines = []
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lines.append(r"\begin{table}[t]")
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lines.append(r"\centering")
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lines.append(r"\begin{tabular}{r c cc}")
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lines.append(r"\toprule")
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lines.append(r" \multicolumn{1}{c}{\textbf{Correlation}} & "
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r"\multicolumn{1}{c}{\textbf{Regularizer}} & "
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r"\multicolumn{2}{c}{\textbf{Linear identifiability}} \\")
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lines.append(r"\cmidrule(lr){1-1} \cmidrule(lr){2-2} \cmidrule(lr){3-4}")
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lines.append(r"$\rho$ & $\lambda$ & $R^2(z \to h)$ & $R^2(h \to z)$ \\")
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lines.append(r"\midrule")
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for rho in sorted(ou_best.keys()):
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runs = ou_best[rho]["runs"]
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lamb = ou_best[rho]["lamb"]
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r2_zh = pm([r["r2_zh"] for r in runs])
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r2_hz = pm([r["r2_hz"] for r in runs])
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lines.append(f" {rho:.2f} & {lamb:.0e} & {r2_zh} & {r2_hz} \\\\")
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lines.append(r"\bottomrule")
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lines.append(r"\end{tabular}")
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lines.append(r"\vspace{5pt}")
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lines.append(r"\caption{")
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lines.append(r" \textbf{OU (Gaussian) identifiability from pixels} "
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r"(mean $\pm$ std over 3 seeds).")
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lines.append(r" $R^2$ increases monotonically with temporal correlation $\rho$, "
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r"reaching $0.95$ at $\rho = 0.99$.")
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lines.append(r"}")
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lines.append(r"\label{tab:reacher_ou}")
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lines.append(r"\end{table}")
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return "\n".join(lines)
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def make_traj_table_standalone(traj_results):
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"""Standalone traj table for appendix if needed."""
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traj_best = best_lambda_per_x(traj_results, "delta")
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lines = []
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lines.append(r"\begin{table}[t]")
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lines.append(r"\centering")
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lines.append(r"\resizebox{\textwidth}{!}{%")
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lines.append(r"\begin{tabular}{r cc c c cc}")
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lines.append(r"\toprule")
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lines.append(r" \multicolumn{1}{c}{\textbf{Stride}} & "
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r"\multicolumn{2}{c}{\textbf{Autocorrelation}} & "
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r"\multicolumn{1}{c}{\textbf{Regularizer}} & "
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r"\multicolumn{1}{c}{\textbf{Identifiability}} & "
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r"\multicolumn{2}{c}{\textbf{Per-dimension}} \\")
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lines.append(r"\cmidrule(lr){1-1} \cmidrule(lr){2-3} \cmidrule(lr){4-4} "
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r"\cmidrule(lr){5-5} \cmidrule(lr){6-7}")
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lines.append(r"$\delta$ & $\rho_0$ & $\rho_1$ & $\lambda$ & "
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r"$R^2(z \to h)$ & $R^2(h \to z_0)$ & $R^2(h \to z_1)$ \\")
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lines.append(r"\midrule")
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for delta in sorted(traj_best.keys()):
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runs = traj_best[delta]["runs"]
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lamb = traj_best[delta]["lamb"]
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rho0 = runs[0].get("rho_shoulder", None)
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rho1 = runs[0].get("rho_wrist", None)
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rho0_s = f"{rho0:.3f}" if rho0 is not None else "---"
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rho1_s = f"{rho1:.3f}" if rho1 is not None else "---"
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r2_zh = pm([r["r2_zh"] for r in runs])
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r2_d0 = pm([r["r2_hz_per_dim"][0] for r in runs])
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r2_d1 = pm([r["r2_hz_per_dim"][1] for r in runs])
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lines.append(f" {delta} & {rho0_s} & {rho1_s} & {lamb:.0e} "
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f"& {r2_zh} & {r2_d0} & {r2_d1} \\\\")
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lines.append(r"\bottomrule")
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lines.append(r"\end{tabular}}")
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lines.append(r"\vspace{5pt}")
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lines.append(r"\caption{")
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lines.append(r" \textbf{Trajectory (non-Gaussian) identifiability from pixels} "
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r"(mean $\pm$ std over 3 seeds).")
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lines.append(r" The shoulder ($z_0$) and wrist ($z_1$) have different "
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r"autocorrelation timescales and marginal distributions, "
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r"leading to anisotropic identifiability.")
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lines.append(r"}")
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lines.append(r"\label{tab:reacher_traj}")
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lines.append(r"\end{table}")
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return "\n".join(lines)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--results_dir", type=str, default="results/reacher")
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args = parser.parse_args()
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ou, traj = load_all_results(args.results_dir)
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print(f"Loaded {len(ou)} OU runs, {len(traj)} traj runs\n")
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if ou and traj:
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print("=" * 70)
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print("COMBINED TABLE (for main text)")
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print("=" * 70)
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print(make_combined_table(ou, traj))
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print()
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if ou:
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print("=" * 70)
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print("OU TABLE (standalone, for appendix)")
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print("=" * 70)
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print(make_ou_table_standalone(ou))
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print()
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if traj:
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print("=" * 70)
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print("TRAJ TABLE (standalone, for appendix)")
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print("=" * 70)
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print(make_traj_table_standalone(traj))
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if __name__ == "__main__":
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main()
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