Update src/active_learning/al_loop.py

src/active_learning/al_loop.py

@@ -1,19 +1,3 @@
-"""
-al_loop.py — Pool-based active learning for transporter–compound discovery.
-
-Strategies implemented
-----------------------
-  random    : baseline random acquisition
-  uncertainty : select pairs with highest predictive entropy
-  diversity   : select pairs maximally different from already-labeled set
-  causal      : bias acquisition toward causally-ranked transporters
-  hybrid      : 0.5 * uncertainty + 0.5 * causal weight
-
-Usage
------
-  python scripts/run_pipeline.py --task al --cfg env/config.yaml
-"""
-
 from pathlib import Path
 
 import numpy as np
@@ -28,7 +12,6 @@ from ..atlas.model_mlp import AtlasMLP
 from ..utils.io import load_cfg, set_seed, save_json
 
 
-# ── Internal training helper ──────────────────────────────────────────────────
 
 def _train_model(ds: PairDataset, lr: float, epochs: int, batch_size: int,
                  device: str) -> AtlasMLP:
@@ -53,8 +36,6 @@ def _predict_proba(model: AtlasMLP, ds: PairDataset, batch_size: int,
     return np.concatenate(probs)
 
 
-# ── Acquisition strategies ────────────────────────────────────────────────────
-
 def _scores_uncertainty(model, pool_ds, batch_size, device):
     """Predictive entropy: max at p=0.5."""
     p = _predict_proba(model, pool_ds, batch_size, device)
@@ -76,11 +57,10 @@ def _scores_diversity(model, pool_ds, labeled_ds, batch_size, device):
     pool_emb    = _embeddings(pool_ds)
     labeled_emb = _embeddings(labeled_ds)
 
-    # Cosine distance to nearest labeled point
     pool_n    = pool_emb    / (np.linalg.norm(pool_emb,    axis=1, keepdims=True) + 1e-9)
     labeled_n = labeled_emb / (np.linalg.norm(labeled_emb, axis=1, keepdims=True) + 1e-9)
-    sims = pool_n @ labeled_n.T          # (n_pool, n_labeled)
-    return 1.0 - sims.max(axis=1)        # higher = more diverse
+    sims = pool_n @ labeled_n.T         
+    return 1.0 - sims.max(axis=1)       
 
 
 def _scores_causal(pool_ds, causal_effects: dict) -> np.ndarray:
@@ -95,11 +75,10 @@ def _scores_causal(pool_ds, causal_effects: dict) -> np.ndarray:
     return weights / (weights.max() + 1e-9)
 
 
-# ── Main AL loop ──────────────────────────────────────────────────────────────
 
 def run_active_learning(
     cfg_path: str = "env/config.yaml",
-    strategy: str = "uncertainty",          # random | uncertainty | diversity | causal | hybrid
+    strategy: str = "uncertainty",          
     causal_csv: str = "results/causal_effects.csv",
 ) -> dict:
     """
@@ -121,13 +100,11 @@ def run_active_learning(
     n        = len(full_ds.pairs)
     rng      = np.random.default_rng(tr_cfg["seed"])
 
-    # Load causal weights if needed
     causal_effects = {}
     if strategy in ("causal", "hybrid") and Path(causal_csv).exists():
         df_c = pd.read_csv(causal_csv)
         causal_effects = dict(zip(df_c["gene"], df_c["ATE"].abs()))
 
-    # Warm start
     init_k    = int(al_cfg["init_frac"] * n)
     acquire_k = int(al_cfg["acquire_per_iter"] * n)
     labeled   = set(rng.choice(n, size=init_k, replace=False).tolist())
@@ -145,7 +122,6 @@ def run_active_learning(
         model = _train_model(ds_labeled, tr_cfg["lr"], epochs=8,
                              batch_size=tr_cfg["batch_size"], device=device)
 
-        # ── Score pool ────────────────────────────────────────────────────────
         if strategy == "random":
             scores = rng.random(len(pool_list))
         elif strategy == "uncertainty":
@@ -161,14 +137,12 @@ def run_active_learning(
         else:
             raise ValueError(f"Unknown strategy: {strategy!r}")
 
-        # ── Acquire top-k ─────────────────────────────────────────────────────
         acquire_k_actual = min(acquire_k, len(pool_list))
         top_local = np.argsort(scores)[::-1][:acquire_k_actual]
         newly_labeled = {pool_list[i] for i in top_local}
         labeled |= newly_labeled
         pool    -= newly_labeled
 
-        # ── Evaluate on held-out pool ─────────────────────────────────────────
         hold_size = min(int(0.2 * n), len(pool))
         if hold_size > 0:
             hold_idx = rng.choice(sorted(pool), size=hold_size, replace=False)