checkpoint manager

test_enhanced_cnn_adapter.py

@@ -0,0 +1,87 @@
+"""
+Test Enhanced CNN Adapter
+
+This script tests the EnhancedCNNAdapter with standardized input format.
+"""
+
+import logging
+import time
+from datetime import datetime
+
+from core.standardized_data_provider import StandardizedDataProvider
+from core.enhanced_cnn_adapter import EnhancedCNNAdapter
+from core.data_models import create_model_output
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def test_cnn_adapter():
+    """Test the EnhancedCNNAdapter with standardized input format"""
+    try:
+        # Initialize data provider
+        symbols = ['ETH/USDT', 'BTC/USDT']
+        timeframes = ['1s', '1m', '1h', '1d']
+        data_provider = StandardizedDataProvider(symbols=symbols, timeframes=timeframes)
+        
+        # Initialize CNN adapter
+        cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
+        
+        # Load best checkpoint if available
+        cnn_adapter.load_best_checkpoint()
+        
+        # Get standardized input data
+        logger.info("Getting standardized input data...")
+        base_data = data_provider.get_base_data_input('ETH/USDT')
+        
+        if base_data is None:
+            logger.error("Failed to get base data input")
+            return
+        
+        # Make prediction
+        logger.info("Making prediction...")
+        model_output = cnn_adapter.predict(base_data)
+        
+        # Log prediction
+        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
+        
+        # Store model output
+        data_provider.store_model_output(model_output)
+        
+        # Add training sample (simulated)
+        logger.info("Adding training sample...")
+        cnn_adapter.add_training_sample(base_data, 'BUY', 0.05)
+        
+        # Train model
+        logger.info("Training model...")
+        metrics = cnn_adapter.train(epochs=1)
+        
+        # Log training metrics
+        logger.info(f"Training metrics: {metrics}")
+        
+        # Make another prediction
+        logger.info("Making another prediction...")
+        model_output = cnn_adapter.predict(base_data)
+        
+        # Log prediction
+        logger.info(f"Prediction: {model_output.predictions['action']} with confidence {model_output.confidence:.4f}")
+        
+        # Test model output manager
+        logger.info("Testing model output manager...")
+        output_manager = data_provider.get_model_output_manager()
+        
+        # Get current outputs
+        current_outputs = output_manager.get_all_current_outputs('ETH/USDT')
+        logger.info(f"Current outputs: {len(current_outputs)} models")
+        
+        # Evaluate model performance
+        metrics = output_manager.evaluate_model_performance('ETH/USDT', 'enhanced_cnn_v1')
+        logger.info(f"Performance metrics: {metrics}")
+        
+        logger.info("Test completed successfully")
+        
+    except Exception as e:
+        logger.error(f"Error in test: {e}", exc_info=True)
+
+if __name__ == "__main__":
+    test_cnn_adapter()