inrefence predictions fix
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Dobromir Popov
193
test_enhanced_inference_logging.py
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193
test_enhanced_inference_logging.py
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#!/usr/bin/env python3
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"""
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Test Enhanced Inference Logging
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This script tests the enhanced inference logging system that stores
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full input features for training feedback.
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"""
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import sys
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import os
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import logging
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import numpy as np
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from datetime import datetime
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# Add project root to path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from core.enhanced_cnn_adapter import EnhancedCNNAdapter
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from core.data_models import BaseDataInput, OHLCVBar
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from utils.database_manager import get_database_manager
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from utils.inference_logger import get_inference_logger
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def create_test_base_data():
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"""Create test BaseDataInput with realistic data"""
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# Create OHLCV bars for different timeframes
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def create_ohlcv_bars(symbol, timeframe, count=300):
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bars = []
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base_price = 3000.0 if 'ETH' in symbol else 50000.0
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for i in range(count):
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price = base_price + np.random.normal(0, base_price * 0.01)
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bars.append(OHLCVBar(
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symbol=symbol,
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timestamp=datetime.now(),
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open=price,
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high=price * 1.002,
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low=price * 0.998,
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close=price + np.random.normal(0, price * 0.005),
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volume=np.random.uniform(100, 1000),
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timeframe=timeframe
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))
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return bars
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base_data = BaseDataInput(
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symbol="ETH/USDT",
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timestamp=datetime.now(),
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ohlcv_1s=create_ohlcv_bars("ETH/USDT", "1s", 300),
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ohlcv_1m=create_ohlcv_bars("ETH/USDT", "1m", 300),
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ohlcv_1h=create_ohlcv_bars("ETH/USDT", "1h", 300),
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ohlcv_1d=create_ohlcv_bars("ETH/USDT", "1d", 300),
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btc_ohlcv_1s=create_ohlcv_bars("BTC/USDT", "1s", 300),
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technical_indicators={
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'rsi': 45.5,
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'macd': 0.12,
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'bb_upper': 3100.0,
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'bb_lower': 2900.0,
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'volume_ma': 500.0
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}
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)
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return base_data
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def test_enhanced_inference_logging():
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"""Test the enhanced inference logging system"""
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logger.info("=== Testing Enhanced Inference Logging ===")
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try:
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# Initialize CNN adapter
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cnn_adapter = EnhancedCNNAdapter(checkpoint_dir="models/enhanced_cnn")
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logger.info("✅ CNN adapter initialized")
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# Create test data
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base_data = create_test_base_data()
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logger.info("✅ Test data created")
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# Make a prediction (this should log inference data)
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logger.info("Making prediction...")
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model_output = cnn_adapter.predict(base_data)
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logger.info(f"✅ Prediction made: {model_output.predictions['action']} (confidence: {model_output.confidence:.3f})")
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# Verify inference was logged to database
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db_manager = get_database_manager()
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recent_inferences = db_manager.get_recent_inferences(cnn_adapter.model_name, limit=1)
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if recent_inferences:
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latest_inference = recent_inferences[0]
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logger.info(f"✅ Inference logged to database:")
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logger.info(f" Model: {latest_inference.model_name}")
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logger.info(f" Action: {latest_inference.action}")
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logger.info(f" Confidence: {latest_inference.confidence:.3f}")
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logger.info(f" Processing time: {latest_inference.processing_time_ms:.1f}ms")
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logger.info(f" Has input features: {latest_inference.input_features is not None}")
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if latest_inference.input_features is not None:
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logger.info(f" Input features shape: {latest_inference.input_features.shape}")
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logger.info(f" Input features sample: {latest_inference.input_features[:5]}")
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else:
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logger.error("❌ No inference records found in database")
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return False
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# Test training data loading from inference history
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logger.info("Testing training data loading from inference history...")
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original_training_count = len(cnn_adapter.training_data)
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cnn_adapter._load_training_data_from_inference_history()
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new_training_count = len(cnn_adapter.training_data)
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logger.info(f"✅ Training data loaded: {original_training_count} -> {new_training_count} samples")
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# Test prediction evaluation
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logger.info("Testing prediction evaluation...")
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evaluation_metrics = cnn_adapter.evaluate_predictions_against_outcomes(hours_back=1)
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logger.info(f"✅ Evaluation metrics: {evaluation_metrics}")
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# Test training with inference data
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if new_training_count >= cnn_adapter.batch_size:
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logger.info("Testing training with inference data...")
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training_metrics = cnn_adapter.train(epochs=1)
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logger.info(f"✅ Training completed: {training_metrics}")
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else:
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logger.info("⚠️ Not enough training data for training test")
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return True
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except Exception as e:
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logger.error(f"❌ Test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_database_query_methods():
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"""Test the new database query methods"""
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logger.info("=== Testing Database Query Methods ===")
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try:
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db_manager = get_database_manager()
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# Test getting inference records for training
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training_records = db_manager.get_inference_records_for_training(
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model_name="enhanced_cnn",
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hours_back=24,
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limit=10
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)
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logger.info(f"✅ Found {len(training_records)} training records")
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for i, record in enumerate(training_records[:3]): # Show first 3
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logger.info(f" Record {i+1}:")
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logger.info(f" Action: {record.action}")
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logger.info(f" Confidence: {record.confidence:.3f}")
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logger.info(f" Has features: {record.input_features is not None}")
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if record.input_features is not None:
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logger.info(f" Features shape: {record.input_features.shape}")
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return True
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except Exception as e:
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logger.error(f"❌ Database query test failed: {e}")
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return False
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def main():
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"""Run all tests"""
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logger.info("Starting Enhanced Inference Logging Tests")
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# Test 1: Enhanced inference logging
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test1_passed = test_enhanced_inference_logging()
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# Test 2: Database query methods
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test2_passed = test_database_query_methods()
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# Summary
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logger.info("=== Test Summary ===")
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logger.info(f"Enhanced Inference Logging: {'✅ PASSED' if test1_passed else '❌ FAILED'}")
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logger.info(f"Database Query Methods: {'✅ PASSED' if test2_passed else '❌ FAILED'}")
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if test1_passed and test2_passed:
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logger.info("🎉 All tests passed! Enhanced inference logging is working correctly.")
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logger.info("The system now:")
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logger.info(" - Stores full input features with each inference")
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logger.info(" - Can retrieve inference data for training feedback")
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logger.info(" - Supports continuous learning from inference history")
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logger.info(" - Evaluates prediction accuracy over time")
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else:
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logger.error("❌ Some tests failed. Please check the implementation.")
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if __name__ == "__main__":
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main()
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