unify model names

NN/models/cob_rl_model.py

@@ -15,11 +15,19 @@ Architecture:
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import numpy as np
 import logging
 from typing import Dict, List, Optional, Tuple, Any
 from abc import ABC, abstractmethod
 
+# Try to import numpy, but provide fallback if not available
+try:
+    import numpy as np
+    HAS_NUMPY = True
+except ImportError:
+    np = None
+    HAS_NUMPY = False
+    logging.warning("NumPy not available - COB RL model will have limited functionality")
+
 from .model_interfaces import ModelInterface
 
 logger = logging.getLogger(__name__)
@@ -164,45 +172,54 @@ class MassiveRLNetwork(nn.Module):
             'features': x  # Hidden features for analysis
         }
     
-    def predict(self, cob_features: np.ndarray) -> Dict[str, Any]:
+    def predict(self, cob_features) -> Dict[str, Any]:
         """
         High-level prediction method for COB features
-        
+
         Args:
-            cob_features: COB features as numpy array [input_size]
-            
+            cob_features: COB features as tensor or numpy array [input_size]
+
         Returns:
             Dict containing prediction results
         """
         self.eval()
         with torch.no_grad():
             # Convert to tensor and add batch dimension
-            if isinstance(cob_features, np.ndarray):
+            if HAS_NUMPY and isinstance(cob_features, np.ndarray):
                 x = torch.from_numpy(cob_features).float()
-            else:
+            elif isinstance(cob_features, torch.Tensor):
                 x = cob_features.float()
-            
+            else:
+                # Try to convert from list or other format
+                x = torch.tensor(cob_features, dtype=torch.float32)
+
             if x.dim() == 1:
                 x = x.unsqueeze(0)  # Add batch dimension
-            
+
             # Move to device
             device = next(self.parameters()).device
             x = x.to(device)
-            
+
             # Forward pass
             outputs = self.forward(x)
-            
+
             # Process outputs
             price_probs = F.softmax(outputs['price_logits'], dim=1)
             predicted_direction = torch.argmax(price_probs, dim=1).item()
             confidence = outputs['confidence'].item()
             value = outputs['value'].item()
-            
+
+            # Convert probabilities to list (works with or without numpy)
+            if HAS_NUMPY:
+                probabilities = price_probs.cpu().numpy()[0].tolist()
+            else:
+                probabilities = price_probs.cpu().tolist()[0]
+
             return {
                 'predicted_direction': predicted_direction,  # 0=DOWN, 1=SIDEWAYS, 2=UP
                 'confidence': confidence,
                 'value': value,
-                'probabilities': price_probs.cpu().numpy()[0],
+                'probabilities': probabilities,
                 'direction_text': ['DOWN', 'SIDEWAYS', 'UP'][predicted_direction]
             }
     
@@ -250,36 +267,45 @@ class COBRLModelInterface(ModelInterface):
         
         logger.info(f"COB RL Model Interface initialized on {self.device}")
         
-    def predict(self, cob_features: np.ndarray) -> Dict[str, Any]:
+    def predict(self, cob_features) -> Dict[str, Any]:
         """Make prediction using the model"""
         self.model.eval()
         with torch.no_grad():
             # Convert to tensor and add batch dimension
-            if isinstance(cob_features, np.ndarray):
+            if HAS_NUMPY and isinstance(cob_features, np.ndarray):
                 x = torch.from_numpy(cob_features).float()
-            else:
+            elif isinstance(cob_features, torch.Tensor):
                 x = cob_features.float()
-            
+            else:
+                # Try to convert from list or other format
+                x = torch.tensor(cob_features, dtype=torch.float32)
+
             if x.dim() == 1:
                 x = x.unsqueeze(0)  # Add batch dimension
-            
+
             # Move to device
             x = x.to(self.device)
-            
+
             # Forward pass
             outputs = self.model(x)
-            
+
             # Process outputs
             price_probs = F.softmax(outputs['price_logits'], dim=1)
             predicted_direction = torch.argmax(price_probs, dim=1).item()
             confidence = outputs['confidence'].item()
             value = outputs['value'].item()
-            
+
+            # Convert probabilities to list (works with or without numpy)
+            if HAS_NUMPY:
+                probabilities = price_probs.cpu().numpy()[0].tolist()
+            else:
+                probabilities = price_probs.cpu().tolist()[0]
+
             return {
                 'predicted_direction': predicted_direction,  # 0=DOWN, 1=SIDEWAYS, 2=UP
                 'confidence': confidence,
                 'value': value,
-                'probabilities': price_probs.cpu().numpy()[0],
+                'probabilities': probabilities,
                 'direction_text': ['DOWN', 'SIDEWAYS', 'UP'][predicted_direction]
             }