| |
| """t-SNE of four cable sizes, testing whether 3.00 sq lands between blue and 3.30. |
| |
| The claim under test is an ordering one, so it is checked numerically rather than |
| by eye: a diameter/size axis is regressed on held-out episodes and the class means |
| along it are compared. t-SNE distances are not metric, so the map alone cannot |
| support a "lies between" statement -- the axis can. |
| |
| Confound worth stating: 3.00 sq comes from a different dataset than the other |
| three. Anything session-specific (lighting, gel state, day) travels with that |
| label. The ordering test is the guard: a session artefact has no reason to place |
| 3.00 sq *between* two other sizes rather than off to one side. |
| """ |
| 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.manifold import TSNE |
| from sklearn.pipeline import make_pipeline |
| from sklearn.preprocessing import StandardScaler |
|
|
| |
| SQ = {"1.70 sq": 1.70, "blue (2.6 mm)": 2.30, "3.00 sq": 3.00, "3.30 sq": 3.30} |
| COLOR = {"1.70 sq": "#C05621", "blue (2.6 mm)": "#2B6CB0", |
| "3.00 sq": "#805AD5", "3.30 sq": "#2F855A"} |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--cache", default="outputs/pca/cache/cache_4sizes.npz") |
| ap.add_argument("--outdir", default="outputs/pca/4sizes") |
| ap.add_argument("--perplexity", type=float, default=30.0) |
| ap.add_argument("--blue-sq", type=float, default=2.30, |
| help="cross-section assigned to the blue cable for the ordering test") |
| args = ap.parse_args() |
| SQ["blue (2.6 mm)"] = args.blue_sq |
|
|
| import os |
| os.makedirs(args.outdir, exist_ok=True) |
| d = np.load(args.cache, allow_pickle=True) |
| X, lab, ep = d["feats"], d["label"], d["eps"] |
| order = sorted(set(lab), key=lambda s: SQ[s]) |
|
|
| rng = np.random.default_rng(0) |
| tr = np.zeros(len(lab), bool) |
| for t in np.unique(lab): |
| e = np.unique(ep[lab == t]) |
| tr |= np.isin(ep, rng.choice(e, size=int(len(e) * 0.7), replace=False)) |
| te = ~tr |
|
|
| Xc = X - X.mean(0) |
| Z = PCA(n_components=30).fit_transform(Xc) |
| emb = TSNE(n_components=2, perplexity=args.perplexity, init="pca", |
| random_state=0).fit_transform(Z) |
|
|
| plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 12}) |
|
|
| |
| fig, ax = plt.subplots(figsize=(7.6, 6.8)) |
| for t in order: |
| m = te & (lab == t) |
| ax.scatter(emb[m, 0], emb[m, 1], s=16, alpha=0.65, edgecolors="none", |
| c=COLOR[t], label=t) |
| ax.set_xlabel("t-SNE 1"); ax.set_ylabel("t-SNE 2") |
| ax.set_title(f"t-SNE of four cable sizes (perplexity {args.perplexity:g})", fontsize=13) |
| ax.legend(frameon=False, title="cable") |
| ax.spines[["top", "right"]].set_visible(False) |
| fig.tight_layout(); fig.savefig(f"{args.outdir}/tsne_4sizes.png", dpi=160, bbox_inches="tight") |
|
|
| |
| sq = np.array([SQ[t] for t in lab]) |
| reg = make_pipeline(StandardScaler(), RidgeCV(alphas=np.logspace(-2, 4, 25))).fit(X[tr], sq[tr]) |
| pred = reg.predict(X) |
| r2 = reg.score(X[te], sq[te]) |
|
|
| fig, ax = plt.subplots(figsize=(8.4, 6.0)) |
| for t in order: |
| m = te & (lab == t) |
| ax.scatter(pred[m], Z[m, 1], s=16, alpha=0.6, edgecolors="none", c=COLOR[t], label=t) |
| ax.axvline(pred[m].mean(), color=COLOR[t], lw=1.3, ls="--", alpha=0.85) |
| ax.set_xlabel("predicted conductor cross-section [sq mm]") |
| ax.set_ylabel("PC2") |
| ax.set_title(f"Size axis — held-out $R^2$ = {r2:.2f}", fontsize=13) |
| ax.legend(frameon=False, title="cable") |
| ax.spines[["top", "right"]].set_visible(False) |
| fig.tight_layout(); fig.savefig(f"{args.outdir}/size_axis_4sizes.png", dpi=160, bbox_inches="tight") |
|
|
| |
| cent = {t: X[te & (lab == t)].mean(0) for t in order} |
| def dist(a, b): return float(np.linalg.norm(cent[a] - cent[b])) |
|
|
| print(f"wrote {args.outdir}/tsne_4sizes.png") |
| print(f"wrote {args.outdir}/size_axis_4sizes.png\n") |
| print(f" size axis held-out R^2 = {r2:.2f}") |
| print(" mean position on the size axis (held-out frames):") |
| means = {} |
| for t in order: |
| m = te & (lab == t) |
| means[t] = pred[m].mean() |
| print(f" {t:14s} true {SQ[t]:.2f} -> {pred[m].mean():.2f} +- {pred[m].std():.2f}") |
| b, t300, t330 = means["blue (2.6 mm)"], means["3.00 sq"], means["3.30 sq"] |
| print(f"\n 3.00 sq between blue and 3.30 sq on this axis: " |
| f"{'YES' if b < t300 < t330 or t330 < t300 < b else 'NO'}") |
| print("\n centroid distances in the 512-d feature space:") |
| print(f" blue <-> 3.00 {dist('blue (2.6 mm)', '3.00 sq'):.2f}") |
| print(f" 3.00 <-> 3.30 {dist('3.00 sq', '3.30 sq'):.2f}") |
| print(f" blue <-> 3.30 {dist('blue (2.6 mm)', '3.30 sq'):.2f} " |
| f"(should be the largest if 3.00 sits between)") |
| print(f" 1.70 <-> blue {dist('1.70 sq', 'blue (2.6 mm)'):.2f}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
