| |
| """Cable clusters in the ACT encoder representation, ordered by cable diameter. |
| |
| Figure only -- titles and annotations are kept minimal so the plot can sit in a |
| slide or paper with its own caption. Numbers needed for that caption (held-out |
| R^2, per-class predicted diameter) are printed to stdout instead of drawn. |
| |
| left : unsupervised PCA of the 512-d encoder feature, on the two components |
| that actually carry cable identity (PC1/PC2 are dominated by arm pose) |
| right : ridge regression from the same features onto physical diameter, fitted |
| on training episodes -- if thickness is encoded as a quantity, 2.6 mm |
| lands between 1.7 and 4.5 without the ordering ever being supplied |
| |
| Episode-level split throughout: frames inside one episode are near-duplicates, |
| so a frame-level split would leak the answer. |
| """ |
| import argparse |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from sklearn.decomposition import PCA |
| from sklearn.linear_model import RidgeCV |
| from sklearn.preprocessing import StandardScaler |
| from sklearn.pipeline import make_pipeline |
|
|
| DIA = {"cable 1.70 sq": 1.7, "blue cable": 2.6, "cable 3.30 sq": 4.5} |
| NAME = {1.7: "1.7 mm", 2.6: "2.6 mm", 4.5: "4.5 mm"} |
| COLOR = {1.7: "#C05621", 2.6: "#2B6CB0", 4.5: "#2F855A"} |
|
|
|
|
| def sep_ratio(Z, y, k): |
| g = [Z[y == t, k] for t in sorted(set(y))] |
| return np.var([x.mean() for x in g]) / (np.mean([x.var() for x in g]) + 1e-12) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--cache", default="outputs/pca/cache_cable_feats.npz") |
| ap.add_argument("--out", default="outputs/pca/cable_diameter_clusters.png") |
| args = ap.parse_args() |
|
|
| d = np.load(args.cache, allow_pickle=True) |
| X, y, ep = d["feats"], d["y"], d["eps"] |
| dia = np.array([DIA[t] for t in y]) |
|
|
| rng = np.random.default_rng(0) |
| tr = np.zeros(len(y), bool) |
| for t in np.unique(y): |
| e = np.unique(ep[y == t]) |
| tr |= np.isin(ep, rng.choice(e, size=int(len(e) * 0.7), replace=False)) |
| te = ~tr |
|
|
| plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 12}) |
| fig, axes = plt.subplots(1, 2, figsize=(13, 5.6)) |
|
|
| Xc = X - X.mean(0) |
| p = PCA(n_components=30).fit(Xc) |
| Z = p.transform(Xc) |
| ratios = np.array([sep_ratio(Z, y, k) for k in range(30)]) |
| i, j = np.argsort(ratios)[::-1][:2] |
|
|
| for t in sorted(set(y), key=lambda s: DIA[s]): |
| m = te & (y == t) |
| axes[0].scatter(Z[m, i], Z[m, j], s=16, alpha=0.6, edgecolors="none", |
| c=COLOR[DIA[t]], label=NAME[DIA[t]]) |
| axes[0].set_xlabel(f"PC{i+1}") |
| axes[0].set_ylabel(f"PC{j+1}") |
| axes[0].legend(frameon=False) |
|
|
| reg = make_pipeline(StandardScaler(), RidgeCV(alphas=np.logspace(-2, 4, 25))).fit(X[tr], dia[tr]) |
| pred = reg.predict(X) |
| for t in sorted(set(y), key=lambda s: DIA[s]): |
| m = te & (y == t) |
| axes[1].scatter(pred[m], Z[m, j], s=16, alpha=0.6, edgecolors="none", |
| c=COLOR[DIA[t]], label=NAME[DIA[t]]) |
| for v in (1.7, 2.6, 4.5): |
| axes[1].axvline(v, color=COLOR[v], lw=1.1, ls="--", alpha=0.7) |
| axes[1].set_xlabel("predicted cable diameter [mm]") |
| axes[1].set_ylabel(f"PC{j+1}") |
| axes[1].set_xticks([1.7, 2.6, 4.5]) |
| axes[1].legend(frameon=False) |
|
|
| for ax in axes: |
| ax.spines[["top", "right"]].set_visible(False) |
| fig.tight_layout() |
| fig.savefig(args.out, dpi=160, bbox_inches="tight") |
|
|
| |
| print(f"wrote {args.out}\n") |
| print(f" left panel : PC{i+1} vs PC{j+1} " |
| f"({p.explained_variance_ratio_[i]:.1%} and {p.explained_variance_ratio_[j]:.1%} of variance)") |
| print(f" right panel : held-out R^2 = {reg.score(X[te], dia[te]):.2f}") |
| for t in sorted(set(y), key=lambda s: DIA[s]): |
| m = te & (y == t) |
| print(f" true {DIA[t]:>4} mm -> predicted {pred[m].mean():.2f} +- {pred[m].std():.2f} mm (n={m.sum()})") |
| print(f" frames: {te.sum()} held-out of {len(y)}; episode-level split") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
