| |
| """Full-512-d linear probe on the cached encoder features. |
| |
| The figure scripts probe only the 2-3 PCA axes they draw, which understates what |
| the representation holds. This probes the whole feature vector, which is the |
| number to quote when asking "does the encoder know which cable this is". |
| |
| Episode-level split: frames inside one episode are near-duplicates, so a |
| frame-level split would leak the answer. |
| """ |
| import argparse |
| import numpy as np |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.pipeline import make_pipeline |
| from sklearn.preprocessing import StandardScaler |
|
|
| ap = argparse.ArgumentParser() |
| ap.add_argument("--cache", default="cache/cache_cable_feats.npz") |
| ap.add_argument("--label-key", default="y") |
| a = ap.parse_args() |
|
|
| d = np.load(a.cache, allow_pickle=True) |
| X, y, ep = d["feats"], d[a.label_key], 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)) |
|
|
| clf = make_pipeline(StandardScaler(), LogisticRegression(max_iter=5000)) |
| clf.fit(X[tr], y[tr]) |
| print(f" classes : {list(np.unique(y))}") |
| print(f" frames : {tr.sum()} train / {(~tr).sum()} held out (episode-level split)") |
| print(f" probe accuracy on the full 512-d feature : {clf.score(X[~tr], y[~tr]):.1%}" |
| f" (chance {1 / len(np.unique(y)):.1%})") |
|
|
