| |
| """PCA of the ACT encoder space, computed separately before and after contact. |
| |
| Each phase gets its own PCA: the dominant directions differ between reaching and |
| manipulation, so a shared basis would misrepresent both. Contact onset is |
| detected per episode from finger torque, so "pre" is genuinely before the |
| GelSight touches anything. |
| |
| Top row is the default PC1/PC2 view, bottom row the first three components in |
| 3-D. Accuracies are from a probe on exactly the axes drawn, held-out episodes. |
| """ |
| import argparse |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from mpl_toolkits.mplot3d import Axes3D |
| from sklearn.decomposition import PCA |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.pipeline import make_pipeline |
| from sklearn.preprocessing import StandardScaler |
|
|
| 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"} |
| TITLE = {"pre": "pre-contact (GelSight untouched)", "post": "post-contact (cable in hand)"} |
|
|
|
|
| def acc(Z, y, tr, cols): |
| c = make_pipeline(StandardScaler(), LogisticRegression(max_iter=3000)) |
| c.fit(Z[tr][:, cols], y[tr]) |
| return c.score(Z[~tr][:, cols], y[~tr]) |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--cache", default="outputs/pca/cache_cable_contact.npz") |
| ap.add_argument("--out", default="outputs/pca/cable_contact_pca.png") |
| args = ap.parse_args() |
|
|
| d = np.load(args.cache, allow_pickle=True) |
| X, y, ep, ph = d["feats"], d["y"], d["eps"], d["phase"] |
| 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)) |
| order = sorted(set(y), key=lambda s: DIA[s]) |
|
|
| plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 11}) |
| fig = plt.figure(figsize=(13, 11)) |
|
|
| for col, pname in enumerate(["pre", "post"]): |
| w = ph == pname |
| Xp, yp, trp = X[w], y[w], tr[w] |
| Xc = Xp - Xp.mean(0) |
| p = PCA(n_components=10).fit(Xc) |
| Z, evr = p.transform(Xc), p.explained_variance_ratio_ |
| tep = ~trp |
|
|
| a2 = acc(Z, yp, trp, [0, 1]) |
| ax = fig.add_subplot(2, 2, col + 1) |
| for t in order: |
| m = tep & (yp == t) |
| ax.scatter(Z[m, 0], Z[m, 1], s=16, alpha=0.65, edgecolors="none", |
| c=COLOR[DIA[t]], label=NAME[DIA[t]]) |
| ax.set_xlabel(f"PC1 ({evr[0]:.1%})"); ax.set_ylabel(f"PC2 ({evr[1]:.1%})") |
| ax.set_title(f"{TITLE[pname]}\nPCA PC1 vs PC2 — {a2:.1%} (chance 33.3%)", fontsize=12) |
| ax.spines[["top", "right"]].set_visible(False) |
| if col == 0: |
| ax.legend(frameon=False, title="cable diameter") |
|
|
| a3 = acc(Z, yp, trp, [0, 1, 2]) |
| ax = fig.add_subplot(2, 2, col + 3, projection="3d") |
| for t in order: |
| m = tep & (yp == t) |
| ax.scatter(Z[m, 0], Z[m, 1], Z[m, 2], s=11, alpha=0.55, edgecolors="none", |
| c=COLOR[DIA[t]], label=NAME[DIA[t]]) |
| ax.set_xlabel("PC1"); ax.set_ylabel("PC2"); ax.set_zlabel("PC3") |
| ax.set_title(f"3-D PCA (PC1-PC3) — {a3:.1%}", fontsize=12) |
| ax.view_init(elev=18, azim=-60) |
|
|
| print(f" {pname:4s} PC1/PC2 {a2:.1%} PC1-PC3 {a3:.1%} " |
| f"variance PC1 {evr[0]:.1%} PC2 {evr[1]:.1%} PC3 {evr[2]:.1%}", flush=True) |
|
|
| fig.tight_layout(); fig.savefig(args.out, dpi=150, bbox_inches="tight") |
| print(f"\nwrote {args.out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
