修改--修改fastapi中的方法
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@ -6,127 +6,127 @@ manager = ModelManager()
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# 获取所有预处理方法
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# # 获取所有预处理方法
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print("--------------------------------------------获取预处理方法---------------------------------------------------")
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# print("--------------------------------------------获取预处理方法---------------------------------------------------")
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methods = manager.get_models()
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# methods = manager.get_models()
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print("模型列表:")
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# print("模型列表:")
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print(methods)
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# print(methods)
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print("--------------------------------------------获取预处理方法 end ---------------------------------------------------")
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# print("--------------------------------------------获取预处理方法 end ---------------------------------------------------")
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print("--------------------------------------------获取方法详细信息---------------------------------------------------")
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print("--------------------------------------------获取方法详细信息---------------------------------------------------")
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# 获取特定方法的详细信息
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# 获取特定方法的详细信息
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method_details = manager.get_model_details('SVC')
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method_details = manager.get_model_details('LinearRegression')
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print("\nSVC方法详情:")
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print("\nLinearRegression方法详情:")
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print(method_details)
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print(method_details)
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print("--------------------------------------------获取方法详细信息 end ---------------------------------------------------")
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print("--------------------------------------------获取方法详细信息 end ---------------------------------------------------")
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print("--------------------------------------------评价指标 ---------------------------------------------------")
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# print("--------------------------------------------评价指标 ---------------------------------------------------")
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# 获取评价指标
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# # 获取评价指标
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print(manager.get_metrics())
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# print(manager.get_metrics())
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print("--------------------------------------------评价指标 end ---------------------------------------------------")
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# print("--------------------------------------------评价指标 end ---------------------------------------------------")
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print("--------------------------------------------获取所有已训练模型 ---------------------------------------------------")
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# print("--------------------------------------------获取所有已训练模型 ---------------------------------------------------")
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# 获取所有已训练模型
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# # 获取所有已训练模型
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result = manager.get_finished_models(
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# result = manager.get_finished_models(
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page=1,
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# page=1,
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page_size=10,
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# page_size=10,
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experiment_name='breast_cancer_classification_3'
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# experiment_name='breast_cancer_classification_3'
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)
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# )
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# 打印结果
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# # 打印结果
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print("\n已训练模型列表:")
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# print("\n已训练模型列表:")
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print(f"状态: {result['status']}")
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# print(f"状态: {result['status']}")
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if result['status'] == 'success':
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# if result['status'] == 'success':
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print(f"\n总数: {result['total_count']}")
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# print(f"\n总数: {result['total_count']}")
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print(f"当前页: {result['page']}")
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# print(f"当前页: {result['page']}")
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print(f"每页数量: {result['page_size']}")
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# print(f"每页数量: {result['page_size']}")
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print("\n模型列表:")
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# print("\n模型列表:")
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for model in result['models']:
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# for model in result['models']:
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'''
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# '''
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'run_id': run['run_id'],
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# 'run_id': run['run_id'],
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'experiment_id': run['experiment_id'],
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# 'experiment_id': run['experiment_id'],
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'''
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# '''
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print(f"run_id", model['run_id'])
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# print(f"run_id", model['run_id'])
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print(f"experiment_id", model['experiment_id'])
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# print(f"experiment_id", model['experiment_id'])
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print(f"算法: {model['algorithm']}")
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# print(f"算法: {model['algorithm']}")
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print(f"任务类型: {model['task_type']}")
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# print(f"任务类型: {model['task_type']}")
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print(f"数据集: {model['dataset']}")
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# print(f"数据集: {model['dataset']}")
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print(f"训练开始时间: {model['training_start_time']}")
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# print(f"训练开始时间: {model['training_start_time']}")
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print(f"训练结束时间: {model['training_end_time']}")
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# print(f"训练结束时间: {model['training_end_time']}")
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print("模型参数:")
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# print("模型参数:")
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for k, v in model['parameters'].items():
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# for k, v in model['parameters'].items():
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print(f" {k}: {v}")
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# print(f" {k}: {v}")
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print("评估指标:")
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# print("评估指标:")
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for metric_name, metric_value in model['metrics'].items():
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# for metric_name, metric_value in model['metrics'].items():
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print(f" {metric_name}: {metric_value:.4f}")
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# print(f" {metric_name}: {metric_value:.4f}")
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else:
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# else:
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print(f"错误信息: {result['message']}")
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# print(f"错误信息: {result['message']}")
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print("--------------------------------------------获取所有已训练模型 end ---------------------------------------------------")
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# print("--------------------------------------------获取所有已训练模型 end ---------------------------------------------------")
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print("--------------------------------------------模型训练---------------------------------------------------")
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# print("--------------------------------------------模型训练---------------------------------------------------")
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# 加载数据
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# # 加载数据
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train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv')
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# train_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/train_breast_cancer_20250219_144629.csv')
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val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv')
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# val_data = pd.read_csv('/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629/val_breast_cancer_20250219_144629.csv')
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# 准备特征和标签
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# # 准备特征和标签
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X_train = train_data.drop('target', axis=1)
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# X_train = train_data.drop('target', axis=1)
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y_train = train_data['target']
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# y_train = train_data['target']
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X_val = val_data.drop('target', axis=1)
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# X_val = val_data.drop('target', axis=1)
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y_val = val_data['target']
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# y_val = val_data['target']
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# 模型配置
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# # 模型配置
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model_config = {
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# model_config = {
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'algorithm': 'XGBClassifier',
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# 'algorithm': 'XGBClassifier',
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'task_type': 'classification',
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# 'task_type': 'classification',
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'dataset' : '/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629',
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# 'dataset' : '/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629',
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'params': {
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# 'params': {
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'n_estimators': 100,
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# 'n_estimators': 100,
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'learning_rate': 0.1,
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# 'learning_rate': 0.1,
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'max_depth': 6,
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# 'max_depth': 6,
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'random_state': 42
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# 'random_state': 42
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}
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# }
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}
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# }
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# 训练模型, 删除训练实验时要删除 mlruns/.trash/ 回收站里的文件
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# # 训练模型, 删除训练实验时要删除 mlruns/.trash/ 回收站里的文件
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# 模型文件 直接在 mlruns/文件夹下
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# # 模型文件 直接在 mlruns/文件夹下
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for i in range(3, 4):
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# for i in range(3, 4):
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result = manager.train_model(
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# result = manager.train_model(
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{
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# {
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'features': X_train,
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# 'features': X_train,
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'labels': y_train
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# 'labels': y_train
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},
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# },
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{
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# {
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'features': X_val,
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# 'features': X_val,
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'labels': y_val
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# 'labels': y_val
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},
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# },
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model_config,
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# model_config,
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f'breast_cancer_classification_{i}'
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# f'breast_cancer_classification_{i}'
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)
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# )
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# 打印结果
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# # 打印结果
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print("\n训练结果:")
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# print("\n训练结果:")
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print(f"状态: {result['status']}")
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# print(f"状态: {result['status']}")
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if result['status'] == 'success':
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# if result['status'] == 'success':
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print(f"\nMLflow运行ID: {result['run_id']}")
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# print(f"\nMLflow运行ID: {result['run_id']}")
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print("\n评估指标:")
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# print("\n评估指标:")
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for metric_name, metric_value in result['metrics'].items():
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# for metric_name, metric_value in result['metrics'].items():
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print(f"{metric_name}: {metric_value:.4f}")
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# print(f"{metric_name}: {metric_value:.4f}")
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else:
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# else:
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print(f"错误信息: {result['message']}")
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# print(f"错误信息: {result['message']}")
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print("-------------------------------------------模型训练 end ---------------------------------------------------")
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# print("-------------------------------------------模型训练 end ---------------------------------------------------")
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print("--------------------------------------------模型预测 ---------------------------------------------------")
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# print("--------------------------------------------模型预测 ---------------------------------------------------")
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print(manager.predict(run_id = "33939ea6d8ce4d43a268f23f7361651e",\
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# print(manager.predict(run_id = "33939ea6d8ce4d43a268f23f7361651e",\
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data_path="/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_145614/test_breast_cancer_20250219_145614.csv",\
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# data_path="/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_145614/test_breast_cancer_20250219_145614.csv",\
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output_path="predictions/pred_breast_cancer_20250219_145614.csv" ,\
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# output_path="predictions/pred_breast_cancer_20250219_145614.csv" ,\
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metrics= ["accuracy", "f1", "precision", "recall"] ))
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# metrics= ["accuracy", "f1", "precision", "recall"] ))
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print("-------------------------------------------模型预测 end ---------------------------------------------------")
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# print("-------------------------------------------模型预测 end ---------------------------------------------------")
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44
main.py
44
main.py
@ -4,6 +4,7 @@ from fastapi.security import OAuth2PasswordBearer
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from fastapi.responses import JSONResponse
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from fastapi.responses import JSONResponse
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from fastapi.exceptions import RequestValidationError
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from fastapi.exceptions import RequestValidationError
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from typing import Optional, Dict, List
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from typing import Optional, Dict, List
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from contextlib import asynccontextmanager
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import uvicorn
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import uvicorn
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from pathlib import Path
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from pathlib import Path
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import logging
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import logging
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@ -16,13 +17,35 @@ from api.data_api import router as data_router
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from api.model_api import router as model_router
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from api.model_api import router as model_router
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from api.system_api import router as system_router
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from api.system_api import router as system_router
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# 设置watchfiles 日志级别为warning
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logging.getLogger("watchfiles").setLevel(logging.WARNING)
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# 生命周期管理
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""生命周期管理（替代原来的startup/shutdown事件）"""
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# 启动时的初始化操作
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logger.info("Server starting up...")
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Path("dataset/dataset_raw").mkdir(parents=True, exist_ok=True)
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Path("dataset/dataset_processed").mkdir(parents=True, exist_ok=True)
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Path(".log").mkdir(exist_ok=True)
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logger.info("Server started successfully")
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yield # 应用运行期间
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# 关闭时的清理操作
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logger.info("Server shutting down...")
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# 创建FastAPI应用
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# 创建FastAPI应用
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app = FastAPI(
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app = FastAPI(
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title="机器学习平台API",
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title="机器学习平台API",
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description="提供数据处理、模型训练和系统监控功能的API服务",
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description="提供数据处理、模型训练和系统监控功能的API服务",
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version="1.0.0",
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version="1.0.0",
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docs_url="/docs",
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docs_url="/docs",
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redoc_url="/redoc"
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redoc_url="/redoc",
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lifespan=lifespan # 使用新的生命周期管理方式
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)
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)
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# 加载配置
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# 加载配置
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@ -141,30 +164,17 @@ async def health_check():
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"environment": config.get('environment', 'production')
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"environment": config.get('environment', 'production')
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}
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}
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# 启动事件
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@app.on_event("startup")
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async def startup_event():
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"""服务启动时的初始化操作"""
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logger.info("Server starting up...")
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# 创建必要的目录
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Path("dataset/dataset_raw").mkdir(parents=True, exist_ok=True)
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Path("dataset/dataset_processed").mkdir(parents=True, exist_ok=True)
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Path(".log").mkdir(exist_ok=True)
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logger.info("Server started successfully")
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# 关闭事件
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@app.on_event("shutdown")
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async def shutdown_event():
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"""服务关闭时的清理操作"""
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logger.info("Server shutting down...")
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if __name__ == "__main__":
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if __name__ == "__main__":
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uvicorn.run(
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uvicorn.run(
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"main:app",
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"main:app",
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host=config.get('host', '0.0.0.0'),
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host=config.get('host', '0.0.0.0'),
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port=config.get('port', 8992),
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port=config.get('port', 8992),
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# reload=True 支持热重载
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reload=config.get('debug', True),
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reload=config.get('debug', True),
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workers=config.get('workers', 4),
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workers=config.get('workers', 4),
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log_level=config.get('log_level', 'info'),
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log_level=config.get('log_level', 'warning'),
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access_log=True
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access_log=True
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)
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)
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@ -159,6 +159,7 @@ classification_algorithms:
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- "自然语言处理。"
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- "自然语言处理。"
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regression_algorithms:
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regression_algorithms:
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LinearRegression:
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LinearRegression:
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principle: "线性回归通过最小化数据点与回归线之间的误差平方和，来拟合一条最佳的直线。"
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principle: "线性回归通过最小化数据点与回归线之间的误差平方和，来拟合一条最佳的直线。"
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advantages:
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advantages:
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@ -310,15 +311,16 @@ clustering_algorithms:
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- "初始化仍然可能影响最终结果"
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- "初始化仍然可能影响最终结果"
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applicable_scenarios: "适用于K均值聚类方法，并且希望改进初始中心选择的场景。"
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applicable_scenarios: "适用于K均值聚类方法，并且希望改进初始中心选择的场景。"
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HierarchicalKMeans:
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# 没实现这个方法
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principle: "层次化K均值结合了层次聚类和K均值聚类的方法，逐步将样本合并到已有簇中，形成层次化结构。"
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# HierarchicalKMeans:
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advantages:
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# principle: "层次化K均值结合了层次聚类和K均值聚类的方法，逐步将样本合并到已有簇中，形成层次化结构。"
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- "能够自动确定簇的数量"
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# advantages:
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- "生成的树状图有助于理解数据结构"
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# - "能够自动确定簇的数量"
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disadvantages:
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# - "生成的树状图有助于理解数据结构"
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- "计算量大，尤其是在数据量较大时"
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# disadvantages:
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- "对噪声和离群点敏感"
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# - "计算量大，尤其是在数据量较大时"
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applicable_scenarios: "适用于不确定簇的数量且数据结构较复杂的情况。"
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# - "对噪声和离群点敏感"
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# applicable_scenarios: "适用于不确定簇的数量且数据结构较复杂的情况。"
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FCM:
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FCM:
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principle: "模糊C均值（FCM）允许每个数据点属于多个簇，基于隶属度来进行聚类。"
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principle: "模糊C均值（FCM）允许每个数据点属于多个簇，基于隶属度来进行聚类。"
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@ -202,23 +202,23 @@ classification_algorithms:
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regression_algorithms:
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regression_algorithms:
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LinearRegression:
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LinearRegression:
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parameters:
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parameters:
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- name: "fit_intercept"
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- name: "fit_intercept"
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type: "bool"
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type: "bool"
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default: "True"
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default: "True"
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description: "是否计算截距。"
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description: "是否计算截距。"
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- name: "normalize"
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- name: "normalize"
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type: "bool"
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type: "bool"
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default: "False"
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default: "False"
|
||||||
description: "是否对数据进行归一化处理。（已弃用）"
|
description: "是否对数据进行归一化处理。（已弃用）"
|
||||||
- name: "copy_X"
|
- name: "copy_X"
|
||||||
type: "bool"
|
type: "bool"
|
||||||
default: "True"
|
default: "True"
|
||||||
description: "是否复制输入数据。"
|
description: "是否复制输入数据。"
|
||||||
- name: "n_jobs"
|
- name: "n_jobs"
|
||||||
type: "int"
|
type: "int"
|
||||||
default: "None"
|
default: "None"
|
||||||
description: "用于计算的并行作业数。"
|
description: "用于计算的并行作业数。"
|
||||||
|
|
||||||
PolynomialRegression:
|
PolynomialRegression:
|
||||||
parameters:
|
parameters:
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user