182 lines
6.5 KiB
Python
182 lines
6.5 KiB
Python
"""
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Test script for ModelOutputManager
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This script tests the extensible model output storage functionality
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"""
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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import logging
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from datetime import datetime
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from core.model_output_manager import ModelOutputManager
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from core.data_models import create_model_output, ModelOutput
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def test_model_output_manager():
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"""Test the ModelOutputManager functionality"""
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print("Testing ModelOutputManager...")
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# Initialize the manager
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manager = ModelOutputManager(cache_dir="test_cache/model_outputs", max_history=100)
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print(f"✅ ModelOutputManager initialized")
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print(f" Supported model types: {manager.get_supported_model_types()}")
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# Test 1: Store outputs from different model types
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print("\n1. Testing model output storage...")
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# Create outputs from different model types
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models_to_test = [
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('cnn', 'enhanced_cnn_v1', 'BUY', 0.85),
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('rl', 'dqn_agent_v2', 'SELL', 0.72),
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('lstm', 'lstm_predictor', 'HOLD', 0.65),
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('transformer', 'transformer_v1', 'BUY', 0.91),
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('orchestrator', 'main_orchestrator', 'BUY', 0.78)
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]
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symbol = 'ETH/USDT'
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stored_outputs = []
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for model_type, model_name, action, confidence in models_to_test:
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# Create model output with hidden states for cross-model feeding
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hidden_states = {
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'layer_1': [0.1, 0.2, 0.3],
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'layer_2': [0.4, 0.5, 0.6],
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'attention_weights': [0.7, 0.8, 0.9]
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} if model_type in ['cnn', 'transformer'] else None
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metadata = {
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'model_version': '1.0',
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'training_iterations': 1000,
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'last_updated': datetime.now().isoformat()
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}
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model_output = create_model_output(
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model_type=model_type,
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model_name=model_name,
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symbol=symbol,
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action=action,
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confidence=confidence,
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hidden_states=hidden_states,
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metadata=metadata
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)
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# Store the output
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success = manager.store_output(model_output)
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if success:
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print(f"✅ Stored {model_type} output: {action} ({confidence})")
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stored_outputs.append(model_output)
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else:
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print(f"❌ Failed to store {model_type} output")
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# Test 2: Retrieve current outputs
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print("\n2. Testing output retrieval...")
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all_current = manager.get_all_current_outputs(symbol)
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print(f"✅ Retrieved {len(all_current)} current outputs for {symbol}")
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for model_name, output in all_current.items():
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print(f" {model_name} ({output.model_type}): {output.predictions['action']} - {output.confidence}")
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# Test 3: Cross-model feeding
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print("\n3. Testing cross-model feeding...")
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cross_model_states = manager.get_cross_model_states(symbol, 'dqn_agent_v2')
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print(f"✅ Retrieved cross-model states for RL model: {len(cross_model_states)} models")
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for model_name, states in cross_model_states.items():
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if states:
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print(f" {model_name}: {len(states)} hidden state layers")
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# Test 4: Consensus prediction
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print("\n4. Testing consensus prediction...")
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consensus = manager.get_consensus_prediction(symbol, confidence_threshold=0.7)
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if consensus:
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print(f"✅ Consensus prediction: {consensus['action']} (confidence: {consensus['confidence']:.3f})")
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print(f" Votes: {consensus['votes']}")
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print(f" Models: {consensus['model_types']}")
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else:
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print("⚠️ No consensus reached (insufficient high-confidence predictions)")
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# Test 5: Performance summary
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print("\n5. Testing performance tracking...")
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performance = manager.get_performance_summary(symbol)
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print(f"✅ Performance summary for {symbol}:")
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print(f" Active models: {performance['active_models']}")
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for model_name, stats in performance['model_stats'].items():
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print(f" {model_name} ({stats['model_type']}): {stats['predictions']} predictions, "
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f"avg confidence: {stats['avg_confidence']}")
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# Test 6: Custom model type support
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print("\n6. Testing custom model type support...")
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# Add a custom model type
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manager.add_custom_model_type('hybrid_ensemble')
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# Create output with custom model type
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custom_output = create_model_output(
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model_type='hybrid_ensemble',
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model_name='custom_ensemble_v1',
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symbol=symbol,
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action='BUY',
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confidence=0.88,
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metadata={'ensemble_size': 5, 'voting_method': 'weighted'}
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)
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success = manager.store_output(custom_output)
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if success:
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print("✅ Custom model type 'hybrid_ensemble' stored successfully")
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else:
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print("❌ Failed to store custom model type")
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print(f" Updated supported types: {len(manager.get_supported_model_types())} types")
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# Test 7: Historical outputs
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print("\n7. Testing historical output tracking...")
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# Store a few more outputs to build history
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for i in range(3):
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historical_output = create_model_output(
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model_type='cnn',
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model_name='enhanced_cnn_v1',
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symbol=symbol,
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action='HOLD',
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confidence=0.6 + i * 0.1
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)
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manager.store_output(historical_output)
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history = manager.get_output_history(symbol, 'enhanced_cnn_v1', count=5)
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print(f"✅ Retrieved {len(history)} historical outputs for enhanced_cnn_v1")
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for i, output in enumerate(history):
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print(f" {i+1}. {output.predictions['action']} ({output.confidence}) at {output.timestamp}")
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# Test 8: Active model types
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print("\n8. Testing active model type detection...")
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active_types = manager.get_model_types_active(symbol)
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print(f"✅ Active model types for {symbol}: {active_types}")
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print("\n✅ ModelOutputManager test completed successfully!")
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print("\nKey features verified:")
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print("✓ Extensible model type support (CNN, RL, LSTM, Transformer, Custom)")
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print("✓ Cross-model feeding with hidden states")
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print("✓ Historical output tracking")
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print("✓ Performance analytics")
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print("✓ Consensus prediction calculation")
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print("✓ Metadata management")
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print("✓ Thread-safe storage operations")
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return manager
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if __name__ == "__main__":
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test_model_output_manager() |