"""Visualize motivation evidence for CTA, given a feature .npz dumped by scripts/analysis/dump_cta_features.py. Produces three figures: 1) ``hist_.pdf`` for key in {l_av, l_va, asym} Per-class histogram + KDE. The asym histogram is THE figure that directly demonstrates the detection signal. 2) ``scatter_lva_lav.pdf`` L_AV vs L_VA scatter plot. Shows whether fake samples collapse onto a near-diagonal manifold (i.e. V is fully determined by A under the generator), while real samples stay off-diagonal. 3) ``tsne_residual.pdf`` 2-up: t-SNE of the V-prediction residual (left) and A-prediction residual (right), colored by real/fake. Lets you see which residual direction carries more class-separating information. The script avoids any non-stdlib dependency beyond numpy / matplotlib / sklearn (already required by the project for compute_extra_metrics.py). Usage ----- python3 scripts/analysis/visualize_motivation.py \\ --npz outputs/analysis/cta_features_oursval.npz \\ --out-dir outputs/analysis/figs_oursval # subsample t-SNE to speed up (default 2000) python3 scripts/analysis/visualize_motivation.py \\ --npz <...>.npz --out-dir <...>/figs --tsne-n 1000 """ from __future__ import annotations import argparse from pathlib import Path import matplotlib.pyplot as plt import numpy as np def _palette(): return {0: "#2E86C1", 1: "#E74C3C"} # blue=real, red=fake def _label_name(y: int) -> str: return "real" if y == 0 else "fake" # --------------------------------------------------------------------------- # 1) Histograms # --------------------------------------------------------------------------- def plot_histogram(values, labels, out_path, title, xlabel, bins=60): fig, ax = plt.subplots(figsize=(6.0, 4.0)) pal = _palette() lo = float(np.percentile(values, 0.5)) hi = float(np.percentile(values, 99.5)) edges = np.linspace(lo, hi, bins + 1) for y in (0, 1): v = values[labels == y] if len(v) == 0: continue ax.hist(v, bins=edges, density=True, alpha=0.55, color=pal[y], edgecolor="white", linewidth=0.4, label=f"{_label_name(y)} (n={len(v)}, mean={v.mean():.4f})") ax.axvline(0.0, color="0.4", linestyle="--", linewidth=0.8, alpha=0.7) ax.set_xlabel(xlabel) ax.set_ylabel("density") ax.set_title(title) ax.legend(frameon=False, loc="best") ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) fig.tight_layout() fig.savefig(out_path, dpi=200) fig.savefig(out_path.with_suffix(".png"), dpi=200) plt.close(fig) print(f"[viz] wrote {out_path}") # --------------------------------------------------------------------------- # 2) L_AV vs L_VA scatter # --------------------------------------------------------------------------- def plot_scatter(l_av, l_va, labels, out_path, max_points_per_class=1500): fig, ax = plt.subplots(figsize=(5.5, 5.0)) pal = _palette() rng = np.random.default_rng(0) for y in (1, 0): mask = labels == y x = l_av[mask] z = l_va[mask] if len(x) > max_points_per_class: idx = rng.choice(len(x), size=max_points_per_class, replace=False) x = x[idx]; z = z[idx] ax.scatter(x, z, s=8, c=pal[y], alpha=0.45, edgecolors="none", label=f"{_label_name(y)} (n={mask.sum()})") lo = float(min(l_av.min(), l_va.min())) hi = float(max(l_av.max(), l_va.max())) pad = (hi - lo) * 0.05 ax.plot([lo - pad, hi + pad], [lo - pad, hi + pad], color="0.3", linestyle="--", linewidth=0.8, alpha=0.8, label="y = x") ax.set_xlim(lo - pad, hi + pad) ax.set_ylim(lo - pad, hi + pad) ax.set_xlabel(r"$L_{A \to V}$ (A->V prediction MSE)") ax.set_ylabel(r"$L_{V \to A}$ (V->A prediction MSE)") ax.set_title("Per-sample prediction-loss asymmetry") ax.legend(frameon=False, loc="best") ax.set_aspect("equal", adjustable="box") ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) fig.tight_layout() fig.savefig(out_path, dpi=200) fig.savefig(out_path.with_suffix(".png"), dpi=200) plt.close(fig) print(f"[viz] wrote {out_path}") # --------------------------------------------------------------------------- # 3) Residual t-SNE # --------------------------------------------------------------------------- def _tsne(x, n, seed=0): if len(x) > n: rng = np.random.default_rng(seed) idx = rng.choice(len(x), size=n, replace=False) x = x[idx] else: idx = np.arange(len(x)) try: from sklearn.manifold import TSNE except ImportError as e: raise SystemExit( "[viz] sklearn is required for t-SNE. Install: pip install scikit-learn" ) from e tsne = TSNE( n_components=2, perplexity=min(30, max(5, len(x) // 50)), init="pca", learning_rate="auto", random_state=seed, ) return tsne.fit_transform(x), idx def plot_residual_tsne(r_av, r_va, labels, out_path, n=2000): fig, axes = plt.subplots(1, 2, figsize=(11, 5)) pal = _palette() for ax, x, name in [(axes[0], r_av, r"$r_{A \to V}$ (V token residual)"), (axes[1], r_va, r"$r_{V \to A}$ (A token residual)")]: emb, idx = _tsne(x, n=n) y = labels[idx] for cls in (1, 0): m = y == cls ax.scatter(emb[m, 0], emb[m, 1], s=10, c=pal[cls], alpha=0.55, edgecolors="none", label=f"{_label_name(cls)} ({m.sum()})") ax.set_title(name) ax.set_xticks([]); ax.set_yticks([]) for s in ax.spines.values(): s.set_visible(False) ax.legend(frameon=False, loc="best") fig.suptitle("t-SNE of cross-modal prediction residuals", fontsize=11) fig.tight_layout() fig.savefig(out_path, dpi=200) fig.savefig(out_path.with_suffix(".png"), dpi=200) plt.close(fig) print(f"[viz] wrote {out_path}") # --------------------------------------------------------------------------- def write_per_generator_summary(asym, labels, generators, out_path): rows = [("group", "n", "asym_mean", "asym_median", "asym_std")] rows.append(("real (all)", int((labels == 0).sum()), float(asym[labels == 0].mean()), float(np.median(asym[labels == 0])), float(asym[labels == 0].std()))) fakes = labels == 1 if fakes.any(): for g in sorted(set(generators[fakes].tolist())): sel = fakes & (generators == g) v = asym[sel] rows.append((f"fake/{g}", int(sel.sum()), float(v.mean()), float(np.median(v)), float(v.std()))) rows.append(("fake (all)", int(fakes.sum()), float(asym[fakes].mean()), float(np.median(asym[fakes])), float(asym[fakes].std()))) with open(out_path, "w") as f: f.write(",".join(map(str, rows[0])) + "\n") for r in rows[1:]: f.write(f"{r[0]},{r[1]},{r[2]:.6f},{r[3]:.6f},{r[4]:.6f}\n") print(f"[viz] wrote {out_path}") def main(): p = argparse.ArgumentParser(description=__doc__) p.add_argument("--npz", required=True, help="Path to dump_cta_features.py output.") p.add_argument("--out-dir", required=True, help="Directory for figures + summary csv.") p.add_argument("--tsne-n", type=int, default=2000, help="t-SNE subsample size.") args = p.parse_args() npz_path = Path(args.npz).resolve() out_dir = Path(args.out_dir).resolve() out_dir.mkdir(parents=True, exist_ok=True) print(f"[viz] loading {npz_path}") data = np.load(npz_path, allow_pickle=True) l_av = data["l_av"].astype(np.float64) l_va = data["l_va"].astype(np.float64) asym = data["asym"].astype(np.float64) label = data["label"].astype(np.int64) gen = data["generator"].astype(object) r_av = data["r_av"].astype(np.float32) r_va = data["r_va"].astype(np.float32) meta = data["meta"].item() if "meta" in data.files else {} print(f"[viz] n={len(l_av)} reals={(label==0).sum()} fakes={(label==1).sum()}") if meta: print(f"[viz] meta: {meta}") plot_histogram(l_av, label, out_dir / "hist_l_av.pdf", r"Distribution of $L_{A \to V}$", r"$L_{A \to V}$") plot_histogram(l_va, label, out_dir / "hist_l_va.pdf", r"Distribution of $L_{V \to A}$", r"$L_{V \to A}$") plot_histogram(asym, label, out_dir / "hist_asym.pdf", r"Asymmetry score $s = L_{V \to A} - L_{A \to V}$", r"$s_\mathrm{asym}$") plot_scatter(l_av, l_va, label, out_dir / "scatter_lva_lav.pdf") plot_residual_tsne(r_av, r_va, label, out_dir / "tsne_residual.pdf", n=args.tsne_n) write_per_generator_summary(asym, label, gen, out_dir / "per_generator_asym.csv") print("[viz] DONE.") return 0 if __name__ == "__main__": raise SystemExit(main())