201 lines
6.8 KiB
Python
201 lines
6.8 KiB
Python
#!/usr/bin/env python
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"""
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测试统一模型管理功能
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"""
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import sys
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import os
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sys.path.append('server')
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def test_model_manager():
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"""测试模型管理器基本功能"""
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print("=== 测试模型管理器 ===")
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try:
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from utils.model_manager import model_manager
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# 测试列出模型
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models = model_manager.list_models()
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print(f"OK: 模型管理器初始化成功")
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print(f"INFO: 找到 {len(models)} 个模型")
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if models:
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print("\n模型列表:")
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for i, model in enumerate(models[:3]): # 只显示前3个
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print(f"{i+1}. {model.get('filename', 'N/A')}")
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print(f" 产品: {model.get('product_id', 'N/A')}")
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print(f" 类型: {model.get('model_type', 'N/A')}")
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print(f" 模式: {model.get('training_mode', 'N/A')}")
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print(f" 版本: {model.get('version', 'N/A')}")
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if model.get('metrics'):
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print(f" RMSE: {model.get('metrics', {}).get('rmse', 'N/A')}")
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return True
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except Exception as e:
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print(f"FAIL: 模型管理器测试失败: {e}")
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return False
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def test_api_models_endpoint():
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"""测试API模型端点"""
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print("\n=== 测试API模型端点 ===")
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try:
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import requests
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# 测试获取模型列表
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response = requests.get("http://localhost:5000/api/models", timeout=5)
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if response.status_code == 200:
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data = response.json()
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models = data.get('data', [])
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print(f"OK: API返回 {len(models)} 个模型")
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if models:
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print("\nAPI返回的模型示例:")
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model = models[0]
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print(f" 模型ID: {model.get('model_id', 'N/A')}")
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print(f" 产品: {model.get('product_name', 'N/A')}")
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print(f" 类型: {model.get('model_type', 'N/A')}")
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print(f" 训练模式: {model.get('training_mode', 'N/A')}")
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return True
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else:
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print(f"FAIL: API返回错误状态码: {response.status_code}")
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return False
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except ImportError:
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print("WARN: requests模块未安装，跳过API测试")
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return True
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except Exception as e:
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# 检查是否是连接错误
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if "ConnectionError" in str(type(e)) or "连接" in str(e):
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print("WARN: 无法连接到API服务器 (可能未启动)")
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return True # 不算失败，因为服务器可能未启动
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else:
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print(f"FAIL: API测试失败: {e}")
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return False
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def test_training_and_model_save():
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"""测试训练并验证模型保存"""
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print("\n=== 测试训练和模型保存 ===")
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try:
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from core.predictor import PharmacyPredictor
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from utils.model_manager import model_manager
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# 记录训练前的模型数量
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models_before = len(model_manager.list_models())
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print(f"INFO: 训练前模型数量: {models_before}")
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# 创建预测器并训练
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predictor = PharmacyPredictor()
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print("INFO: 开始训练测试（TCN模型，2轮次）...")
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metrics = predictor.train_model(
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product_id='P001',
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model_type='tcn',
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epochs=2,
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training_mode='product'
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)
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if metrics:
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print("OK: 训练成功完成")
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# 检查模型是否正确保存
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models_after = model_manager.list_models()
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new_models = len(models_after) - models_before
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if new_models > 0:
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print(f"OK: 新增 {new_models} 个模型")
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# 查找新训练的模型
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latest_models = model_manager.list_models(product_id='P001', model_type='tcn')
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if latest_models:
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latest_model = latest_models[0]
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print(f"INFO: 最新模型文件名: {latest_model.get('filename', 'N/A')}")
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print(f"INFO: 模型包含管理信息: {'model_manager_info' in str(latest_model)}")
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return True
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else:
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print("WARN: 训练成功但未检测到新模型文件")
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return True
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else:
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print("WARN: 训练未返回指标（可能是预期的数据不足错误）")
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return True
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except Exception as e:
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print(f"INFO: 训练测试异常: {e}")
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# 检查是否是预期的数据不足错误
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if "数据不足" in str(e):
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print("INFO: 这是预期的数据不足错误，错误处理正常")
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return True
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else:
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return False
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def test_filename_parsing():
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"""测试文件名解析功能"""
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print("\n=== 测试文件名解析 ===")
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try:
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from utils.model_manager import model_manager
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# 测试各种文件名格式
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test_filenames = [
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"tcn_product_P001_v1.pth",
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"mlstm_store_S001_P001_v2.pth",
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"kan_global_P001_sum_v1.pth",
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"transformer_model_product_P001_v1.pth", # 旧格式
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]
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for filename in test_filenames:
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info = model_manager.parse_model_filename(filename)
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if info:
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print(f"OK: {filename}")
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print(f" 类型: {info.get('model_type')}, 产品: {info.get('product_id')}")
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print(f" 模式: {info.get('training_mode')}, 版本: {info.get('version')}")
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else:
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print(f"FAIL: 无法解析 {filename}")
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return True
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except Exception as e:
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print(f"FAIL: 文件名解析测试失败: {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("开始模型管理功能测试")
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tests_passed = 0
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total_tests = 4
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# 测试模型管理器
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if test_model_manager():
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tests_passed += 1
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# 测试文件名解析
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if test_filename_parsing():
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tests_passed += 1
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# 测试API端点
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if test_api_models_endpoint():
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tests_passed += 1
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# 测试训练和模型保存
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if test_training_and_model_save():
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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("SUCCESS: 模型管理功能测试通过！")
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print("\n建议:")
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print("1. 启动API服务器: uv run ./server/api.py")
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print("2. 在前端查看模型管理页面")
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print("3. 进行一次完整训练测试模型命名")
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else:
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print("WARNING: 部分测试失败，需要检查问题")
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if __name__ == "__main__":
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main() |