106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
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#!/usr/bin/env python3
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"""
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快速测试GA_optimize模块的核心功能
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"""
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import sys
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import os
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sys.path.append('.')
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import GA_optimize
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import numpy as np
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import time
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def quick_test():
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print("=== GA_optimize Quick Test ===")
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try:
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print("1. Testing module import... ✓")
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print("2. Creating optimizer instance...")
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optimizer = GA_optimize.TTAEGeneticOptimizer()
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print(" Optimizer created successfully ✓")
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print("3. Testing cache system...")
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cache = GA_optimize.TTAEnsembleCache('./test_cache')
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cache.add_prediction('gru', 'test_session', 0, 'original', np.random.rand(1, 10, 40))
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cached = cache.get_prediction('gru', 'test_session', 0, 'original')
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assert cached is not None, "Cache retrieval failed"
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print(" Cache system working ✓")
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print("4. Testing parameter bounds...")
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# 测试参数范围
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gru_weight = 0.6
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tta_weights = [2.0, 1.0, 0.5, 0.0, 0.0]
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print(f" Test params: gru_weight={gru_weight}, tta_weights={tta_weights} ✓")
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print("5. Testing fitness function with dummy data...")
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# 使用少量数据测试适应度函数
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limited_test_data = {}
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sessions = list(optimizer.test_data.keys())[:2] # 只取前2个session
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for session in sessions:
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data = optimizer.test_data[session]
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limited_data = {}
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for key, value in data.items():
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if isinstance(value, list):
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limited_data[key] = value[:2] # 只取前2个试验
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else:
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limited_data[key] = value
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limited_test_data[session] = limited_data
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# 暂时替换测试数据
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original_data = optimizer.test_data
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optimizer.test_data = limited_test_data
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optimizer.trials_per_session = {k: len(v['neural_features']) for k, v in limited_test_data.items()}
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print(" Limited test data prepared ✓")
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print(f" Testing with {sum(optimizer.trials_per_session.values())} trials")
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# 生成少量预测进行测试
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print("6. Generating small cache for testing...")
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start_time = time.time()
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optimizer.generate_all_predictions()
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cache_time = time.time() - start_time
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print(f" Cache generation completed in {cache_time:.2f}s ✓")
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print("7. Testing parameter evaluation...")
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test_params = [0.5, 1.0, 1.0, 0.0, 0.0, 0.0]
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start_time = time.time()
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per = optimizer.evaluate_parameters(test_params[0], test_params[1:6])
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eval_time = time.time() - start_time
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print(f" Parameter evaluation completed in {eval_time:.2f}s ✓")
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print(f" Test PER: {per:.2f}%")
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print("8. Testing fitness function...")
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fitness = optimizer.fitness_function(None, test_params, 0)
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print(f" Fitness value: {fitness:.2f} ✓")
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# 恢复原始数据
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optimizer.test_data = original_data
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print("\n=== All Tests Passed! ===")
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print("✓ Module import and initialization")
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print("✓ Cache system functionality")
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print("✓ Model loading and setup")
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print("✓ Data preprocessing")
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print("✓ Prediction generation and caching")
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print("✓ Parameter evaluation")
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print("✓ Fitness function")
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print("\nThe GA_optimize module is ready for full optimization!")
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return True
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except Exception as e:
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print(f"\n❌ Test failed with error: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = quick_test()
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if success:
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print("\n🎉 Ready to run full genetic algorithm optimization!")
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print("To run full optimization, use: python GA_optimize.py")
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else:
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print("\n⚠️ Please fix the issues before running full optimization.")
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