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74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
"""
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Scaling plots: R² and orthogonality vs latent dimension N.
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Usage:
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python analysis/plot_scaling.py --results_dir results/scaling/ --out figures/
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"""
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import argparse, os, glob, json
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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def load_results(results_dir):
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rows = []
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for path in sorted(glob.glob(os.path.join(results_dir, "*.json"))):
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with open(path) as f:
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r = json.load(f)
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rows.append({k: r.get(k) for k in [
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"N", "seed", "r2_zx", "r2_xz", "r2_zh", "r2_hz",
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"orth_err", "orth_err_normalized", "final_loss",
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"final_align", "final_sigreg", "final_whiten",
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"epsilon", "delta", "approx_bound", "procrustes_mse",
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]})
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return pd.DataFrame(rows)
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def main():
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p = argparse.ArgumentParser()
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p.add_argument("--results_dir", default="results/scaling/")
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p.add_argument("--out", default="figures/")
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args = p.parse_args()
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os.makedirs(args.out, exist_ok=True)
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df = load_results(args.results_dir)
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if len(df) == 0:
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print("No results."); return
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summary = df.groupby("N").agg(
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r2_xz_mean=("r2_xz", "mean"), r2_xz_std=("r2_xz", "std"),
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r2_hz_mean=("r2_hz", "mean"), r2_hz_std=("r2_hz", "std"),
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orth_mean=("orth_err_normalized", "mean"), orth_std=("orth_err_normalized", "std"),
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).reset_index()
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dims = summary["N"].values
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fig, axes = plt.subplots(1, 2, figsize=(8, 3.5))
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ax = axes[0]
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ax.errorbar(dims, summary["r2_xz_mean"], yerr=summary["r2_xz_std"],
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fmt="o-", capsize=3, color="gray", label=r"Probe: $g(z) \to z$")
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ax.errorbar(dims, summary["r2_hz_mean"], yerr=summary["r2_hz_std"],
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fmt="s-", capsize=3, label=r"Probe: $f \circ g(z) \to z$")
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ax.set_xscale("log", base=2)
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ax.set_xlabel("Latent dimension $N$"); ax.set_ylabel(r"Linearity [$R^2$]")
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ax.set_title("Latent Recovery"); ax.set_ylim(-0.05, 1.05)
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ax.set_xticks(dims); ax.legend(); ax.grid(alpha=0.3)
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ax = axes[1]
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ax.errorbar(dims, summary["orth_mean"], yerr=summary["orth_std"],
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fmt="D-", capsize=3, color="tab:green")
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ax.set_xscale("log", base=2)
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ax.set_xlabel("Latent dimension $N$")
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ax.set_ylabel(r"$\|A^\top A - I\|_F / \sqrt{N}$")
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ax.set_title("Orthogonality Error"); ax.set_xticks(dims); ax.grid(alpha=0.3)
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fig.tight_layout()
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fig.savefig(os.path.join(args.out, "fig_scaling.pdf"), bbox_inches="tight")
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print("Saved fig_scaling.pdf")
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plt.close()
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if __name__ == "__main__":
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main()
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