dash and training wip

utils/checkpoint_manager.py

@@ -14,11 +14,7 @@ from collections import defaultdict
 import torch
 import random
 
-try:
-    import wandb
-    WANDB_AVAILABLE = True
-except ImportError:
-    WANDB_AVAILABLE = False
+WANDB_AVAILABLE = False
 
 logger = logging.getLogger(__name__)
 
@@ -58,13 +54,13 @@ class CheckpointManager:
                  base_checkpoint_dir: str = "NN/models/saved",
                  max_checkpoints_per_model: int = 5,
                  metadata_file: str = "checkpoint_metadata.json",
-                 enable_wandb: bool = True):
+                 enable_wandb: bool = False):
         self.base_dir = Path(base_checkpoint_dir)
         self.base_dir.mkdir(parents=True, exist_ok=True)
         
         self.max_checkpoints = max_checkpoints_per_model
         self.metadata_file = self.base_dir / metadata_file
-        self.enable_wandb = enable_wandb and WANDB_AVAILABLE
+        self.enable_wandb = False
         
         self.checkpoints: Dict[str, List[CheckpointMetadata]] = defaultdict(list)
         self._load_metadata()
@@ -115,10 +111,7 @@ class CheckpointManager:
                 total_parameters=training_metadata.get('total_parameters') if training_metadata else None
             )
             
-            if self.enable_wandb and wandb.run is not None:
-                artifact_name = self._upload_to_wandb(checkpoint_path, metadata)
-                metadata.wandb_run_id = wandb.run.id
-                metadata.wandb_artifact_name = artifact_name
+            # W&B disabled
             
             self.checkpoints[model_name].append(metadata)
             self._rotate_checkpoints(model_name)
@@ -273,19 +266,7 @@ class CheckpointManager:
                 logger.error(f"Error removing rotated checkpoint {checkpoint.checkpoint_id}: {e}")
     
     def _upload_to_wandb(self, file_path: Path, metadata: CheckpointMetadata) -> Optional[str]:
-        try:
-            if not self.enable_wandb or wandb.run is None:
-                return None
-            
-            artifact_name = f"{metadata.model_name}_checkpoint"
-            artifact = wandb.Artifact(artifact_name, type="model")
-            artifact.add_file(str(file_path))
-            wandb.log_artifact(artifact)
-            
-            return artifact_name
-        except Exception as e:
-            logger.error(f"Error uploading to W&B: {e}")
-            return None
+        return None
     
     def _load_metadata(self):
         try:
@@ -404,6 +385,56 @@ class CheckpointManager:
                         logger.debug(f"Found legacy model file in subdirectory: {candidate_path}")
                         return candidate_path
         
+        # Extended search: scan common project model directories for best checkpoints
+        try:
+            # Attempt to infer project root from base_dir (NN/models/saved -> root)
+            project_root = base_dir.resolve().parent.parent.parent
+        except Exception:
+            project_root = Path(".").resolve()
+        additional_dirs = [
+            project_root / "models",
+            project_root / "models" / "archive",
+            project_root / "models" / "backtest",
+        ]
+
+        def _match_legacy_name(candidate: Path, model: str) -> bool:
+            name = candidate.name.lower()
+            model_keys = {
+                'dqn_agent': ['dqn', 'agent', 'policy'],
+                'enhanced_cnn': ['cnn', 'optimized_short_term'],
+                'extrema_trainer': ['supervised', 'extrema'],
+                'cob_rl': ['cob', 'rl', 'policy'],
+                'decision': ['decision', 'transformer']
+            }.get(model, [model])
+            return any(k in name for k in model_keys)
+
+        candidates: List[Path] = []
+        for adir in additional_dirs:
+            if not adir.exists():
+                continue
+            try:
+                for pt in adir.rglob('*.pt'):
+                    # Prefer files that indicate "best" and match model hints
+                    lname = pt.name.lower()
+                    if 'best' in lname and _match_legacy_name(pt, model_name):
+                        candidates.append(pt)
+                # Do not add generic fallbacks to avoid mismatched model types
+            except Exception:
+                # Ignore directory traversal issues
+                pass
+
+        if candidates:
+            # Pick the most recently modified candidate
+            try:
+                best = max(candidates, key=lambda p: p.stat().st_mtime)
+                logger.debug(f"Found legacy model file in project models dir: {best}")
+                return best
+            except Exception:
+                # If stat fails, just return the first one deterministically
+                candidates.sort()
+                logger.debug(f"Found legacy model file in project models dir: {candidates[0]}")
+                return candidates[0]
+
         return None
     
     def _create_legacy_metadata(self, model_name: str, file_path: Path) -> CheckpointMetadata: