from function.model_manager import ModelManager
import pandas as pd

# 创建模型管理器实例
manager = ModelManager()



# # 获取所有预处理方法
# print("--------------------------------------------获取预处理方法---------------------------------------------------")
# methods = manager.get_models()
# print("模型列表:")
# print(methods)
# print("--------------------------------------------获取预处理方法 end ---------------------------------------------------")


print("--------------------------------------------获取方法详细信息---------------------------------------------------")
# 获取特定方法的详细信息
method_details = manager.get_model_details('LinearRegression')
print("\nLinearRegression方法详情:")
print(method_details) 
print("--------------------------------------------获取方法详细信息 end ---------------------------------------------------")



# print("--------------------------------------------评价指标 ---------------------------------------------------")
# # 获取评价指标
# print(manager.get_metrics())
# print("--------------------------------------------评价指标 end ---------------------------------------------------")


# print("--------------------------------------------获取所有已训练模型 ---------------------------------------------------")
# # 获取所有已训练模型
# result = manager.get_finished_models(
#     page=1,
#     page_size=10,
#     experiment_name='breast_cancer_classification_3'
# )

# # 打印结果
# print("\n已训练模型列表:")
# print(f"状态: {result['status']}")
# if result['status'] == 'success':
#     print(f"\n总数: {result['total_count']}")
#     print(f"当前页: {result['page']}")
#     print(f"每页数量: {result['page_size']}")
#     print("\n模型列表:")
#     for model in result['models']:
#         '''
#             'run_id': run['run_id'],
#             'experiment_id': run['experiment_id'],
#         '''
#         print(f"run_id", model['run_id'])
#         print(f"experiment_id", model['experiment_id'])
#         print(f"算法: {model['algorithm']}")
#         print(f"任务类型: {model['task_type']}")
#         print(f"数据集: {model['dataset']}")

#         print(f"训练开始时间: {model['training_start_time']}")
#         print(f"训练结束时间: {model['training_end_time']}")
#         print("模型参数:")
#         for k, v in model['parameters'].items():
#             print(f" {k}: {v}")
#         print("评估指标:")
#         for metric_name, metric_value in model['metrics'].items():
#             print(f"  {metric_name}: {metric_value:.4f}")
# else:
#     print(f"错误信息: {result['message']}") 

# print("--------------------------------------------获取所有已训练模型 end ---------------------------------------------------")


# print("--------------------------------------------模型训练---------------------------------------------------")
# # 加载数据
# train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv')
# val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv')

# # 准备特征和标签
# X_train = train_data.drop('target', axis=1)
# y_train = train_data['target']
# X_val = val_data.drop('target', axis=1)
# y_val = val_data['target']

# # 模型配置
# model_config = {
#     'algorithm': 'XGBClassifier',
#     'task_type': 'classification',
#     'dataset' : '/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629',
#     'params': {
#         'n_estimators': 100,
#         'learning_rate': 0.1,
#         'max_depth': 6,
#         'random_state': 42
#     }
# }

# # 训练模型,  删除训练实验时要删除 mlruns/.trash/  回收站里的文件
# # 模型文件 直接在 mlruns/文件夹下
# for i in range(3, 4):
#     result = manager.train_model(
#         {
#             'features': X_train,
#             'labels': y_train
#         },
#         {
#             'features': X_val,
#             'labels': y_val
#         },
#         model_config,
#         f'breast_cancer_classification_{i}'
#     )

# # 打印结果
# print("\n训练结果:")
# print(f"状态: {result['status']}")
# if result['status'] == 'success':
#     print(f"\nMLflow运行ID: {result['run_id']}")
#     print("\n评估指标:")
#     for metric_name, metric_value in result['metrics'].items():
#         print(f"{metric_name}: {metric_value:.4f}")
# else:
#     print(f"错误信息: {result['message']}") 

# print("-------------------------------------------模型训练 end ---------------------------------------------------")

# print("--------------------------------------------模型预测 ---------------------------------------------------")
# print(manager.predict(run_id = "33939ea6d8ce4d43a268f23f7361651e",\
#                             data_path="/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_145614/test_breast_cancer_20250219_145614.csv",\
#                             output_path="predictions/pred_breast_cancer_20250219_145614.csv" ,\
#                             metrics= ["accuracy", "f1", "precision", "recall"] ))

# print("-------------------------------------------模型预测 end ---------------------------------------------------")