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training metrics . fix cnn model

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This commit is contained in:
Dobromir Popov
2025-09-09 03:43:20 +03:00
parent a3029d09c2
commit 55fb865e7f
2 changed files with 59 additions and 21 deletions
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36
core/orchestrator.py
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@@ -293,14 +293,34 @@ class TradingOrchestrator:
result = load_best_checkpoint("cnn") result = load_best_checkpoint("cnn")
if result: if result:
file_path, metadata = result file_path, metadata = result
self.model_states['cnn']['initial_loss'] = 0.412 # Actually load the model weights from the checkpoint
self.model_states['cnn']['current_loss'] = metadata.loss or 0.0187 try:
self.model_states['cnn']['best_loss'] = metadata.loss or 0.0134 checkpoint_data = torch.load(file_path, map_location=self.device)
self.model_states['cnn']['checkpoint_loaded'] = True if 'model_state_dict' in checkpoint_data:
self.model_states['cnn']['checkpoint_filename'] = metadata.checkpoint_id self.cnn_model.load_state_dict(checkpoint_data['model_state_dict'])
checkpoint_loaded = True logger.info(f"CNN model weights loaded from: {file_path}")
loss_str = f"{metadata.loss:.4f}" if metadata.loss is not None else "N/A" elif 'state_dict' in checkpoint_data:
logger.info(f"CNN checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})") self.cnn_model.load_state_dict(checkpoint_data['state_dict'])
logger.info(f"CNN model weights loaded from: {file_path}")
else:
# Try loading directly as state dict
self.cnn_model.load_state_dict(checkpoint_data)
logger.info(f"CNN model weights loaded directly from: {file_path}")
# Update model states
self.model_states['cnn']['initial_loss'] = checkpoint_data.get('initial_loss', 0.412)
self.model_states['cnn']['current_loss'] = metadata.loss or checkpoint_data.get('loss', 0.0187)
self.model_states['cnn']['best_loss'] = metadata.loss or checkpoint_data.get('best_loss', 0.0134)
self.model_states['cnn']['checkpoint_loaded'] = True
self.model_states['cnn']['checkpoint_filename'] = metadata.checkpoint_id
checkpoint_loaded = True
loss_str = f"{metadata.loss:.4f}" if metadata.loss is not None else "N/A"
logger.info(f"CNN checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})")
except Exception as load_error:
logger.warning(f"Failed to load CNN model weights: {load_error}")
# Continue with fresh model but mark as loaded for metadata purposes
self.model_states['cnn']['checkpoint_loaded'] = True
checkpoint_loaded = True
except Exception as e: except Exception as e:
logger.warning(f"Error loading CNN checkpoint: {e}") logger.warning(f"Error loading CNN checkpoint: {e}")

44
web/clean_dashboard.py
View File

@@ -7346,7 +7346,7 @@ class CleanTradingDashboard:
} }
metadata = save_checkpoint( metadata = save_checkpoint(
model=checkpoint_data, model=model, # Pass the actual model, not checkpoint_data
model_name="enhanced_cnn", model_name="enhanced_cnn",
model_type="cnn", model_type="cnn",
performance_metrics=performance_metrics, performance_metrics=performance_metrics,
@@ -8016,21 +8016,32 @@ class CleanTradingDashboard:
def get_model_performance_metrics(self) -> Dict[str, Any]: def get_model_performance_metrics(self) -> Dict[str, Any]:
"""Get detailed performance metrics for all models""" """Get detailed performance metrics for all models"""
try: try:
if not hasattr(self, 'training_performance'): # Check both possible structures
training_metrics = None
if hasattr(self, 'training_performance_metrics'):
training_metrics = self.training_performance_metrics
elif hasattr(self, 'training_performance'):
training_metrics = self.training_performance
if not training_metrics:
return {} return {}
performance_metrics = {} performance_metrics = {}
for model_name, metrics in self.training_performance.items(): for model_name, metrics in training_metrics.items():
if metrics['training_times']: # Safely check for training_times key
avg_training = sum(metrics['training_times']) / len(metrics['training_times']) training_times = metrics.get('training_times', [])
max_training = max(metrics['training_times']) total_calls = metrics.get('total_calls', 0)
min_training = min(metrics['training_times'])
if training_times and len(training_times) > 0:
avg_training = sum(training_times) / len(training_times)
max_training = max(training_times)
min_training = min(training_times)
performance_metrics[model_name] = { performance_metrics[model_name] = {
'avg_training_time_ms': round(avg_training * 1000, 2), 'avg_training_time_ms': round(avg_training * 1000, 2),
'max_training_time_ms': round(max_training * 1000, 2), 'max_training_time_ms': round(max_training * 1000, 2),
'min_training_time_ms': round(min_training * 1000, 2), 'min_training_time_ms': round(min_training * 1000, 2),
'total_calls': metrics['total_calls'], 'total_calls': total_calls,
'training_frequency_hz': round(1.0 / avg_training if avg_training > 0 else 0, 1) 'training_frequency_hz': round(1.0 / avg_training if avg_training > 0 else 0, 1)
} }
else: else:
@@ -8038,14 +8049,21 @@ class CleanTradingDashboard:
'avg_training_time_ms': 0, 'avg_training_time_ms': 0,
'max_training_time_ms': 0, 'max_training_time_ms': 0,
'min_training_time_ms': 0, 'min_training_time_ms': 0,
'total_calls': 0, 'total_calls': total_calls,
'training_frequency_hz': 0 'training_frequency_hz': 0
} }
return performance_metrics return performance_metrics
except Exception as e: except Exception as e:
logger.error(f"Error getting performance metrics: {e}") logger.error(f"Error getting performance metrics: {e}")
return {} # Return empty dict for each expected model to prevent further errors
return {
'decision': {'avg_training_time_ms': 0, 'max_training_time_ms': 0, 'min_training_time_ms': 0, 'total_calls': 0, 'training_frequency_hz': 0},
'cob_rl': {'avg_training_time_ms': 0, 'max_training_time_ms': 0, 'min_training_time_ms': 0, 'total_calls': 0, 'training_frequency_hz': 0},
'dqn': {'avg_training_time_ms': 0, 'max_training_time_ms': 0, 'min_training_time_ms': 0, 'total_calls': 0, 'training_frequency_hz': 0},
'cnn': {'avg_training_time_ms': 0, 'max_training_time_ms': 0, 'min_training_time_ms': 0, 'total_calls': 0, 'training_frequency_hz': 0},
'transformer': {'avg_training_time_ms': 0, 'max_training_time_ms': 0, 'min_training_time_ms': 0, 'total_calls': 0, 'training_frequency_hz': 0}
}
def create_clean_dashboard(data_provider: Optional[DataProvider] = None, orchestrator: Optional[TradingOrchestrator] = None, trading_executor: Optional[TradingExecutor] = None): def create_clean_dashboard(data_provider: Optional[DataProvider] = None, orchestrator: Optional[TradingOrchestrator] = None, trading_executor: Optional[TradingExecutor] = None):