unify model names

NN/models/cnn_model.py

@@ -6,8 +6,6 @@ Much larger and more sophisticated architecture for better learning
 
 import os
 import logging
-import numpy as np
-import matplotlib.pyplot as plt
 from datetime import datetime
 import math
 
@@ -15,10 +13,30 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader, TensorDataset
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 import torch.nn.functional as F
 from typing import Dict, Any, Optional, Tuple
 
+# Try to import optional dependencies
+try:
+    import numpy as np
+    HAS_NUMPY = True
+except ImportError:
+    np = None
+    HAS_NUMPY = False
+
+try:
+    import matplotlib.pyplot as plt
+    HAS_MATPLOTLIB = True
+except ImportError:
+    plt = None
+    HAS_MATPLOTLIB = False
+
+try:
+    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+    HAS_SKLEARN = True
+except ImportError:
+    HAS_SKLEARN = False
+
 # Import checkpoint management
 from NN.training.model_manager import save_checkpoint, load_best_checkpoint
 from NN.training.model_manager import create_model_manager
@@ -423,38 +441,46 @@ class EnhancedCNNModel(nn.Module):
             'features': self._memory_barrier(processed_features)
         }
     
-    def predict(self, feature_matrix: np.ndarray) -> Dict[str, Any]:
+    def predict(self, feature_matrix) -> Dict[str, Any]:
         """
         Make predictions on feature matrix
         Args:
-            feature_matrix: numpy array of shape [sequence_length, features]
+            feature_matrix: tensor or numpy array of shape [sequence_length, features]
         Returns:
             Dictionary with prediction results
         """
         self.eval()
-        
+
         with torch.no_grad():
             # Convert to tensor and add batch dimension
-            if isinstance(feature_matrix, np.ndarray):
+            if HAS_NUMPY and isinstance(feature_matrix, np.ndarray):
                 x = torch.FloatTensor(feature_matrix).unsqueeze(0)  # Add batch dim
-            else:
+            elif isinstance(feature_matrix, torch.Tensor):
                 x = feature_matrix.unsqueeze(0)
-            
+            else:
+                x = torch.FloatTensor(feature_matrix).unsqueeze(0)
+
             # Move to device
             device = next(self.parameters()).device
             x = x.to(device)
-            
+
             # Forward pass
             outputs = self.forward(x)
-            
+
             # Extract results with proper shape handling
-            probs = outputs['probabilities'].cpu().numpy()[0]
-            confidence_tensor = outputs['confidence'].cpu().numpy()
-            regime = outputs['regime'].cpu().numpy()[0]
-            volatility = outputs['volatility'].cpu().numpy()
-            
+            if HAS_NUMPY:
+                probs = outputs['probabilities'].cpu().numpy()[0]
+                confidence_tensor = outputs['confidence'].cpu().numpy()
+                regime = outputs['regime'].cpu().numpy()[0]
+                volatility = outputs['volatility'].cpu().numpy()
+            else:
+                probs = outputs['probabilities'].cpu().tolist()[0]
+                confidence_tensor = outputs['confidence'].cpu().tolist()
+                regime = outputs['regime'].cpu().tolist()[0]
+                volatility = outputs['volatility'].cpu().tolist()
+
             # Handle confidence shape properly
-            if isinstance(confidence_tensor, np.ndarray):
+            if HAS_NUMPY and isinstance(confidence_tensor, np.ndarray):
                 if confidence_tensor.ndim == 0:
                     confidence = float(confidence_tensor.item())
                 elif confidence_tensor.size == 1:
@@ -465,7 +491,7 @@ class EnhancedCNNModel(nn.Module):
                 confidence = float(confidence_tensor)
             
             # Handle volatility shape properly
-            if isinstance(volatility, np.ndarray):
+            if HAS_NUMPY and isinstance(volatility, np.ndarray):
                 if volatility.ndim == 0:
                     volatility = float(volatility.item())
                 elif volatility.size == 1:
@@ -474,20 +500,29 @@ class EnhancedCNNModel(nn.Module):
                     volatility = float(volatility[0] if len(volatility) > 0 else 0.0)
             else:
                 volatility = float(volatility)
-            
+
             # Determine action (0=BUY, 1=SELL for 2-action system)
-            action = int(np.argmax(probs))
+            if HAS_NUMPY:
+                action = int(np.argmax(probs))
+            else:
+                action = int(torch.argmax(torch.tensor(probs)).item())
             action_confidence = float(probs[action])
-            
+
+            # Convert logits to list
+            if HAS_NUMPY:
+                raw_logits = outputs['logits'].cpu().numpy()[0].tolist()
+            else:
+                raw_logits = outputs['logits'].cpu().tolist()[0]
+
             return {
                 'action': action,
                 'action_name': 'BUY' if action == 0 else 'SELL',
                 'confidence': float(confidence),
                 'action_confidence': action_confidence,
-                'probabilities': probs.tolist(),
-                'regime_probabilities': regime.tolist(),
+                'probabilities': probs if isinstance(probs, list) else probs.tolist(),
+                'regime_probabilities': regime if isinstance(regime, list) else regime.tolist(),
                 'volatility_prediction': float(volatility),
-                'raw_logits': outputs['logits'].cpu().numpy()[0].tolist()
+                'raw_logits': raw_logits
             }
     
     def get_memory_usage(self) -> Dict[str, Any]: