169 lines
5.8 KiB
Python
169 lines
5.8 KiB
Python
#!/usr/bin/env python
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"""
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简单集成测试 - 验证训练函数调用是否正确
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"""
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import sys
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import os
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sys.path.append('server')
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def test_import_structure():
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"""测试导入结构"""
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print("=== 测试导入结构 ===")
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try:
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# 测试训练函数导入
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from trainers.tcn_trainer import train_product_model_with_tcn
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from trainers.kan_trainer import train_product_model_with_kan
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from trainers.transformer_trainer import train_product_model_with_transformer
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from trainers.mlstm_trainer import train_product_model_with_mlstm
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print("✅ 所有训练函数导入成功")
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# 测试多店铺数据工具导入
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from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
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print("✅ 多店铺数据工具导入成功")
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return True
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except Exception as e:
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print(f"❌ 导入失败: {e}")
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return False
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def test_function_signatures():
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"""测试函数签名"""
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print("\n=== 测试函数签名 ===")
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try:
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from trainers.tcn_trainer import train_product_model_with_tcn
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from trainers.kan_trainer import train_product_model_with_kan
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from trainers.transformer_trainer import train_product_model_with_transformer
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from trainers.mlstm_trainer import train_product_model_with_mlstm
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import inspect
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# 检查TCN函数签名
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tcn_sig = inspect.signature(train_product_model_with_tcn)
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tcn_params = list(tcn_sig.parameters.keys())
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expected_params = ['product_id', 'store_id', 'training_mode', 'aggregation_method']
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missing_params = [p for p in expected_params if p not in tcn_params]
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if missing_params:
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print(f"❌ TCN函数缺少参数: {missing_params}")
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else:
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print("✅ TCN函数签名正确")
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# 检查KAN函数签名
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kan_sig = inspect.signature(train_product_model_with_kan)
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kan_params = list(kan_sig.parameters.keys())
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missing_params = [p for p in expected_params if p not in kan_params]
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if missing_params:
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print(f"❌ KAN函数缺少参数: {missing_params}")
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else:
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print("✅ KAN函数签名正确")
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# 检查Transformer函数签名
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transformer_sig = inspect.signature(train_product_model_with_transformer)
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transformer_params = list(transformer_sig.parameters.keys())
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missing_params = [p for p in expected_params if p not in transformer_params]
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if missing_params:
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print(f"❌ Transformer函数缺少参数: {missing_params}")
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else:
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print("✅ Transformer函数签名正确")
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# 检查mLSTM函数签名
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mlstm_sig = inspect.signature(train_product_model_with_mlstm)
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mlstm_params = list(mlstm_sig.parameters.keys())
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missing_params = [p for p in expected_params if p not in mlstm_params]
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if missing_params:
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print(f"❌ mLSTM函数缺少参数: {missing_params}")
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else:
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print("✅ mLSTM函数签名正确")
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return True
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except Exception as e:
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print(f"❌ 签名检查失败: {e}")
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return False
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def test_data_file_existence():
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"""测试数据文件是否存在"""
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print("\n=== 测试数据文件 ===")
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data_file = 'pharmacy_sales_multi_store.csv'
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if os.path.exists(data_file):
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print(f"✅ 多店铺数据文件存在: {data_file}")
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# 检查文件大小
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file_size = os.path.getsize(data_file)
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print(f"📊 文件大小: {file_size} bytes")
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return True
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else:
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print(f"❌ 多店铺数据文件不存在: {data_file}")
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return False
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def test_predictor_import():
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"""测试PharmacyPredictor导入（不初始化）"""
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print("\n=== 测试PharmacyPredictor导入 ===")
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try:
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# 只导入类，不初始化（避免matplotlib依赖）
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from core.predictor import PharmacyPredictor
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print("✅ PharmacyPredictor类导入成功")
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# 检查train_model方法是否存在
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if hasattr(PharmacyPredictor, 'train_model'):
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print("✅ train_model方法存在")
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# 检查方法签名
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import inspect
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sig = inspect.signature(PharmacyPredictor.train_model)
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params = list(sig.parameters.keys())
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expected_params = ['store_id', 'training_mode', 'aggregation_method']
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missing_params = [p for p in expected_params if p not in params]
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if missing_params:
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print(f"❌ train_model方法缺少参数: {missing_params}")
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else:
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print("✅ train_model方法签名正确")
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else:
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print("❌ train_model方法不存在")
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return True
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except Exception as e:
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print(f"❌ PharmacyPredictor导入失败: {e}")
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return False
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def main():
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"""主测试函数"""
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print("🚀 开始简单集成测试")
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tests_passed = 0
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total_tests = 4
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# 测试导入结构
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if test_import_structure():
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tests_passed += 1
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# 测试函数签名
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if test_function_signatures():
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tests_passed += 1
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# 测试数据文件
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if test_data_file_existence():
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tests_passed += 1
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# 测试PharmacyPredictor导入
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if test_predictor_import():
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tests_passed += 1
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print(f"\n📊 测试结果: {tests_passed}/{total_tests} 项测试通过")
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if tests_passed == total_tests:
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print("🎉 所有测试通过！训练集成应该正常工作")
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else:
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print("⚠️ 部分测试失败，需要进一步检查")
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if __name__ == "__main__":
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main() |