Fix VAE sampling: enable reparameterization by default, return mu for inference

[Copilot]

Encoder defaulted to sampling=False, causing the encoder to return the raw hidden state h as z instead of the reparameterized sample — making every VAE a plain autoencoder silently.

Root cause

forward() returned (mu, logvar, h) when sampling=False (wrong: h ≠ mu). The build_encoder factory never passed sampling=True. Result: KL loss computed against mu/logvar but decoder received h.

Changes

• encoder.py: Default sampling=True. When sampling=False, return (mu, logvar, mu) — the third element is now always the correct decode input (reparameterized z or deterministic mu).
• base_vae.py: build_encoder() accepts and forwards sampling=True. encode() returns mu for stable deterministic embeddings. SimpleEncoder likewise returns mu.
• vae.py: VAE.__init__ exposes sampling: bool = True, passed through to build_encoder. Serialized in to_dict/from_dict.
• commands/model.py: Added --sampling/--no-sampling flag to train-vae (default True). encode command now uses result[0] (mu) instead of result[2] for inference output.

# Before: z was the raw hidden state h — not a VAE sample
enc = Encoder(feature_dim=64, latent_dim=16)  # sampling=False default
mu, logvar, z = enc(x)
assert torch.allclose(z, mu)  # False! z was h, not even mu

# After: sampling=True by default; sampling=False gives z=mu (deterministic AE)
enc = Encoder(feature_dim=64, latent_dim=16)  # sampling=True
mu, logvar, z = enc(x)
# z = mu + eps*std  ✓

enc_det = Encoder(feature_dim=64, latent_dim=16, sampling=False)
mu, logvar, z = enc_det(x)
assert torch.allclose(z, mu)  # True ✓

Tests

Four new tests in test_encoder.py cover: sampling enabled produces stochastic z, disabled gives z == mu, default is True, and forward() always returns a 3-tuple.

[github-actions]

☂️ Python Coverage

current status: ❌

Overall Coverage

Lines	Covered	Coverage	Threshold	Status
2602	1255	48%	30%	🟢

New Files

No new covered files...

Modified Files

File	Coverage	Status
src/embkit/commands/model.py	35%	🔴
src/embkit/models/vae/base_vae.py	67%	🔴
src/embkit/models/vae/encoder.py	94%	🟢
src/embkit/models/vae/vae.py	37%	🔴
TOTAL	58%	🔴

updated for commit: 970fbc2 by action🐍

[kbcoulter]

I didn't see this on the coverage report, but the tests themselves are working.

[kbcoulter]

The new tests do not seem to appear in the coverage report (missing rather than excluded), but everything runs as expected and the math looks correct.