完成--完成获取系统训练历史方法

doc/接口文档code.md

@@ -644,25 +644,64 @@ Error Response:
 
 ### 3.2 获取训练历史
 ```http
-GET /api/system/history
+GET /api/system/history?page=1&page_size=10&start_time=2025-02-01&end_time=2025-02-19&status=completed&experiment_name=breast_cancer_classification
+
+Parameters:
+- page: 页码 (默认: 1)
+- page_size: 每页数量 (默认: 10)
+- start_time: 开始时间 (可选, 格式: YYYY-MM-DD)
+- end_time: 结束时间 (可选, 格式: YYYY-MM-DD)
+- status: 运行状态过滤 (可选: completed, failed, running)
+- experiment_name: 实验名称过滤 (可选)
+
 
 Response:
 {
     "status": "success",
     "history": [
         {
-            "task_id": "train_20230820_001",
+            "run_id": "7970364d490f4e0aa0375c2db26215f3",
-            "model": "xgboost",
+            "experiment_id": "656341556838275234",
+            "experiment_name": "breast_cancer_classification",
+            "model_name": "XGBClassifier",
             "dataset": "breast_cancer",
-            "start_time": "2023-08-20T10:00:00",
+            "start_time": "2025-02-19T08:43:02",
-            "end_time": "2023-08-20T10:30:00",
+            "end_time": "2025-02-19T08:43:05",
+            "duration": "3s",
             "status": "completed",
+            "parameters": {
+                "max_depth": 6,
+                "learning_rate": 0.1,
+                "n_estimators": 100
+            },
             "metrics": {
-                "accuracy": 0.95,
+                "accuracy": 0.956,
-                "f1": 0.94
+                "precision": 0.962,
+                "recall": 0.935,
+                "f1": 0.948
+            },
+            "tags": {
+                "version": "v1.0",
+                "author": "admin"
             }
         }
-    ]
+    ],
+    "pagination": {
+        "current_page": 1,
+        "page_size": 10,
+        "total_pages": 5,
+        "total_items": 42
+    }
+}
+
+Error Response:
+{
+    "status": "error",
+    "message": "获取训练历史失败",
+    "details": {
+        "error_type": "MLflowError",
+        "error_message": "Failed to connect to MLflow server"
+    }
 }
 ```
 
@@ -1018,3 +1057,4 @@ Authorization: Bearer <token>
    - 使用多个评估指标
    - 考虑模型的稳定性
    - 关注模型在特定场景的表现
+   - 关注模型在特定场景的表现

example_system_moniter_train_history.py

@@ -0,0 +1,4 @@
+from function.system_monitor import SystemMonitor
+
+system_monitor = SystemMonitor()
+print(system_monitor.get_training_history())

function/system_monitor.py

@@ -6,6 +6,9 @@ from typing import Dict, List, Optional
 import logging
 from pathlib import Path
 import time
+import mlflow
+from mlflow.tracking import MlflowClient
+import pandas as pd
 
 class SystemMonitor:
     """系统资源监控类"""
@@ -14,6 +17,7 @@ class SystemMonitor:
         """初始化系统监控器"""
         self.logger = logging.getLogger(__name__)
         self._setup_logging()
+        self.mlflow_client = MlflowClient()
         
     def _setup_logging(self):
         """设置日志"""
@@ -206,3 +210,138 @@ class SystemMonitor:
                     'error_message': str(e)
                 }
             }
+
+    def get_training_history(
+        self,
+        page: int = 1,
+        page_size: int = 10,
+        start_time: Optional[str] = None,
+        end_time: Optional[str] = None,
+        status: Optional[str] = None,
+        experiment_name: Optional[str] = None
+    ) -> Dict:
+        """
+        获取训练历史记录
+        
+        Args:
+            page: 页码
+            page_size: 每页数量
+            start_time: 开始时间 (YYYY-MM-DD)
+            end_time: 结束时间 (YYYY-MM-DD)
+            status: 运行状态过滤
+            experiment_name: 实验名称过滤
+            
+        Returns:
+            训练历史记录
+        """
+        try:
+            # 构建过滤条件
+            filter_string = []
+            
+            if status:
+                filter_string.append(f"status = '{status.upper()}'")
+                
+            if start_time:
+                start_timestamp = int(pd.Timestamp(start_time).timestamp() * 1000)
+                filter_string.append(f"start_time >= {start_timestamp}")
+                
+            if end_time:
+                end_timestamp = int(pd.Timestamp(end_time).timestamp() * 1000)
+                filter_string.append(f"start_time <= {end_timestamp}")
+                
+            filter_string = " and ".join(filter_string) if filter_string else None
+            
+            # 获取实验ID
+            if experiment_name:
+                experiment = mlflow.get_experiment_by_name(experiment_name)
+                if experiment is None:
+                    return {
+                        'status': 'error',
+                        'message': f'实验 {experiment_name} 不存在'
+                    }
+                experiment_ids = [experiment.experiment_id]
+            else:
+                experiments = mlflow.search_experiments()
+                experiment_ids = [exp.experiment_id for exp in experiments]
+            
+            # 获取所有运行记录
+            all_runs = []
+            for exp_id in experiment_ids:
+                runs = mlflow.search_runs(
+                    experiment_ids=[exp_id],
+                    filter_string=filter_string,
+                    order_by=['start_time DESC']
+                )
+                if not runs.empty:
+                    all_runs.extend(runs.to_dict('records'))
+            
+            # 计算分页
+            total_items = len(all_runs)
+            total_pages = (total_items + page_size - 1) // page_size
+            start_idx = (page - 1) * page_size
+            end_idx = min(start_idx + page_size, total_items)
+            
+            # 格式化运行记录
+            history = []
+            for run in all_runs[start_idx:end_idx]:
+                # 获取实验信息
+                experiment = mlflow.get_experiment(run['experiment_id'])
+                
+                # 计算持续时间
+                start_time = pd.to_datetime(run['start_time'])
+                end_time = pd.to_datetime(run['end_time']) if run['end_time'] else pd.Timestamp.now()
+                duration = end_time - start_time
+                
+                # 收集参数和指标
+                params = {}
+                metrics = {}
+                tags = {}
+                for key, value in run.items():
+                    if key.startswith('params.'):
+                        params[key.replace('params.', '')] = value
+                    elif key.startswith('metrics.'):
+                        metrics[key.replace('metrics.', '')] = value
+                    elif key.startswith('tags.'):
+                        tags[key.replace('tags.', '')] = value
+                
+                # 格式化记录
+                history_item = {
+                    'run_id': run['run_id'],
+                    'experiment_id': run['experiment_id'],
+                    'experiment_name': experiment.name,
+                    'model_name': params.get('algorithm', 'Unknown'),
+                    'dataset': params.get('dataset', 'Unknown'),
+                    'start_time': start_time.strftime('%Y-%m-%dT%H:%M:%S'),
+                    'end_time': end_time.strftime('%Y-%m-%dT%H:%M:%S'),
+                    'duration': str(duration).split('.')[0],  # 去除微秒部分
+                    'status': run['status'],
+                    'parameters': params,
+                    'metrics': metrics,
+                    'tags': tags
+                }
+                history.append(history_item)
+            
+            self.logger.info(f"成功获取训练历史记录, 共{total_items}条记录")
+            
+            return {
+                'status': 'success',
+                'history': history,
+                'pagination': {
+                    'current_page': page,
+                    'page_size': page_size,
+                    'total_pages': total_pages,
+                    'total_items': total_items
+                }
+            }
+            
+        except Exception as e:
+            error_msg = f"获取训练历史失败: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': '获取训练历史失败',
+                'details': {
+                    'error_type': type(e).__name__,
+                    'error_message': str(e)
+                }
+            }