initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
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hjnormey
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nautilus_dolphin/dvae/test_flint_hd_vae.py
Executable file
110
nautilus_dolphin/dvae/test_flint_hd_vae.py
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"""
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Unit test for FlintHDVAE inverse projection decoder.
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Criteria:
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1. No NaN during training
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2. z_var per dim > 0.01 after 20 epochs (no posterior collapse)
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3. Reconstruction MSE < 1.0 on held-out 20%
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"""
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import sys, os
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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sys.path.insert(0, os.path.dirname(__file__))
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sys.path.insert(0, r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict")
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import numpy as np
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from pathlib import Path
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HERE = Path(__file__).parent
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print("=" * 65)
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print("UNIT TEST: FlintHDVAE Inverse Projection Decoder")
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print("=" * 65)
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# ── Load T1 corpus ──────────────────────────────────────────────
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print("\nLoading 16K eigen corpus...")
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from corpus_builder import DolphinCorpus, OFF, T1 as T1_DIM
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corpus = DolphinCorpus.load(str(HERE / 'corpus_cache.npz'))
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idx_mask = corpus.mask[:, 1]
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X_e = corpus.X[idx_mask]
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T1 = X_e[:, OFF[1]:OFF[1] + T1_DIM].copy() # (16607, 20)
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N = len(T1)
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print(f" T1 shape: {T1.shape} dtype: {T1.dtype}")
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print(f" Any NaN in T1: {np.isnan(T1).any()}")
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print(f" T1 stats: min={T1.min():.4f} max={T1.max():.4f} std={T1.std():.4f}")
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# ── Train/val split (random 80/20 — avoids regime distribution shift) ────
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rng_split = np.random.RandomState(42)
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idx_all = rng_split.permutation(N)
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n_train = int(N * 0.8)
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X_train = T1[idx_all[:n_train]]
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X_val = T1[idx_all[n_train:]]
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print(f"\nTrain: {len(X_train)} Val: {len(X_val)}")
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# ── Instantiate model ────────────────────────────────────────────
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from flint_hd_vae import FlintHDVAE
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print("\nInstantiating FlintHDVAE(beta=0.1, use_flint_norm=False — plain z-score for decoder test)...")
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model = FlintHDVAE(input_dim=20, hd_dim=512, latent_dim=8, beta=0.1, seed=42,
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use_flint_norm=False)
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print(" OK")
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# ── Train 40 epochs ──────────────────────────────────────────────
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print("\nTraining 40 epochs on 80% T1 data (warmup first 30%)...")
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PASS = True
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try:
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model.fit(X_train, epochs=40, lr=1e-3, batch_size=256, verbose=True, warmup_frac=0.3)
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except Exception as ex:
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import traceback
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traceback.print_exc()
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PASS = False
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if PASS:
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# Check 1: No NaN in losses
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losses_arr = np.array(model.train_losses)
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nan_losses = np.isnan(losses_arr).sum()
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print(f"\n[CHECK 1] NaN in training losses: {nan_losses}")
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if nan_losses == 0:
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print(" PASS: No NaN detected")
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else:
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print(" FAIL: NaN losses detected!")
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PASS = False
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# Check 2: z_var per dim > 0.01 (use encode() which applies global MCDAIN)
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mu_s = model.encode(X_train[:1000]) # (1000, 8)
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var_per_dim = mu_s.var(axis=0) # (8,)
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min_var = var_per_dim.min()
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active_dims = (var_per_dim > 0.01).sum()
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print(f"\n[CHECK 2] z_var per dim: {var_per_dim.round(4)}")
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print(f" Min var: {min_var:.6f} Active dims (>0.01): {active_dims}/8")
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if active_dims >= 4: # at least half active
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print(f" PASS: {active_dims}/8 dims active (no posterior collapse)")
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elif active_dims >= 1:
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print(f" PARTIAL: Only {active_dims}/8 dims active — weak but not fully collapsed")
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else:
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print(f" FAIL: All dims collapsed (posterior collapse)")
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PASS = False
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# Check 3: Reconstruction MSE < 1.0 on val
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# reconstruct() returns (T1_hat, X_val_norm) — both in same normalised space
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T1_hat, X_val_norm = model.reconstruct(X_val)
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recon_mse = float(np.mean((T1_hat - X_val_norm) ** 2))
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print(f"\n[CHECK 3] Val reconstruction MSE: {recon_mse:.4f}")
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if recon_mse < 1.0:
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print(f" PASS: MSE={recon_mse:.4f} < 1.0")
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else:
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print(f" FAIL: MSE={recon_mse:.4f} >= 1.0 (decoder not learning)")
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PASS = False
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# Bonus: check encode output shape and values
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z_all = model.encode(X_val[:100])
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print(f"\n[BONUS] encode() output shape: {z_all.shape}")
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print(f" z range: [{z_all.min():.4f}, {z_all.max():.4f}] std: {z_all.std():.4f}")
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if np.isnan(z_all).any():
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print(" WARNING: NaN in encode() output!")
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print("\n" + "=" * 65)
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if PASS:
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print("OVERALL: PASS — FlintHDVAE inverse projection decoder functional")
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else:
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print("OVERALL: FAIL — see issues above")
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print("=" * 65)
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