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3c41fb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #!/usr/bin/env python3
"""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 # noqa: F401
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()
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