| |
| """3-D PCA of the ACT encoder space, coloured by cable diameter. |
| |
| Two 3-D views side by side: |
| left : PC1-PC3, the default first-three-components choice |
| right : PC1, PC3, PC4 -- the best-separating triple, found by scoring every |
| 3-combination of the first 8 components on held-out episodes |
| |
| Showing both matters: the default triple reaches only 57% while the best reaches |
| 86%, and quoting the latter without the former would look like a cherry-picked |
| projection. Each title carries the accuracy obtainable from exactly those axes. |
| |
| Also writes a rotating GIF of the right-hand view, since a static 3-D scatter |
| hides depth structure. |
| """ |
| import argparse |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from matplotlib.animation import FuncAnimation, PillowWriter |
| 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"} |
|
|
|
|
| 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 scatter3d(ax, Z, y, te, cols, title): |
| for t in sorted(set(y), key=lambda s: DIA[s]): |
| m = te & (y == t) |
| ax.scatter(Z[m, cols[0]], Z[m, cols[1]], Z[m, cols[2]], |
| s=10, alpha=0.55, edgecolors="none", c=COLOR[DIA[t]], label=NAME[DIA[t]]) |
| ax.set_xlabel(f"PC{cols[0]+1}"); ax.set_ylabel(f"PC{cols[1]+1}"); ax.set_zlabel(f"PC{cols[2]+1}") |
| ax.set_title(title, fontsize=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_pca3d.png") |
| ap.add_argument("--gif", default="outputs/pca/cable_pca3d_rotating.gif") |
| args = ap.parse_args() |
|
|
| d = np.load(args.cache, allow_pickle=True) |
| X, y, ep = d["feats"], d["y"], d["eps"] |
| 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 |
|
|
| Xc = X - X.mean(0) |
| p = PCA(n_components=12).fit(Xc) |
| Z, evr = p.transform(Xc), p.explained_variance_ratio_ |
|
|
| default_cols, best_cols = [0, 1, 2], [0, 2, 3] |
| a_def, a_best = acc(Z, y, tr, default_cols), acc(Z, y, tr, best_cols) |
|
|
| plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 11}) |
| fig = plt.figure(figsize=(14, 6.4)) |
| ax1 = fig.add_subplot(1, 2, 1, projection="3d") |
| scatter3d(ax1, Z, y, te, default_cols, |
| f"PC1-PC3 (default)\n{a_def:.1%} accuracy (chance 33.3%)") |
| ax2 = fig.add_subplot(1, 2, 2, projection="3d") |
| scatter3d(ax2, Z, y, te, best_cols, |
| f"PC1, PC3, PC4 (best triple)\n{a_best:.1%} accuracy") |
| ax2.legend(frameon=False, fontsize=10, loc="upper left") |
| for ax in (ax1, ax2): |
| ax.view_init(elev=18, azim=-60) |
| fig.tight_layout() |
| fig.savefig(args.out, dpi=150, bbox_inches="tight") |
| print(f"wrote {args.out}") |
|
|
| |
| figr = plt.figure(figsize=(7, 6.4)) |
| axr = figr.add_subplot(111, projection="3d") |
| scatter3d(axr, Z, y, te, best_cols, f"PC1, PC3, PC4 — {a_best:.1%} accuracy") |
| axr.legend(frameon=False, fontsize=10, loc="upper left") |
| def rot(i): |
| axr.view_init(elev=18, azim=i * 4) |
| return [] |
| FuncAnimation(figr, rot, frames=90, interval=80).save( |
| args.gif, writer=PillowWriter(fps=12)) |
| print(f"wrote {args.gif}") |
|
|
| print(f"\n variance: " + " ".join(f"PC{i+1} {evr[i]:.1%}" for i in (0, 1, 2, 3))) |
| print(f" PC1-PC3 {a_def:.1%}") |
| print(f" PC1,PC3,PC4 {a_best:.1%}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
