模型优化部分初步完成

api/optimize_api.py

@@ -0,0 +1,160 @@
+from fastapi import APIRouter, Query, Path, Body, HTTPException, BackgroundTasks
+from typing import Dict, List, Optional, Any, Union
+from pydantic import BaseModel, Field
+from function.optimize_manager import OptimizeManager
+import logging
+from datetime import datetime
+
+# 创建路由
+router = APIRouter()
+
+# 初始化优化管理器
+optimize_manager = OptimizeManager()
+
+# 初始化日志
+logger = logging.getLogger(__name__)
+
+# 请求体模型定义
+class OptimizationRequest(BaseModel):
+    run_id: str = Field(..., description="已训练模型的运行ID")
+    method: str = Field(..., description="优化方法名称，如 'GridSearchCV'")
+    parameters: Dict = Field(..., description="优化方法参数")
+    data_path: Optional[str] = Field(None, description="数据集路径")
+    output_dir: Optional[str] = Field(None, description="输出目录路径")
+    experiment_name: str = Field(..., description="实验名称")
+    
+    class Config:
+        json_schema_extra = {
+            "example": {
+                "run_id": "bd3697dc238c4d1587e0f4f319d04448",
+                "method": "GridSearchCV",
+                "parameters": {
+                    "max_depth": [3, 5, 7],
+                    "n_estimators": [50, 100, 200],
+                    "min_samples_split": [2, 5, 10]
+                },
+                "data_path": "dataset/dataset_processed/test_optimize/train.csv",
+                "output_dir": None,
+                "experiment_name": "测试模型优化方法"
+            }
+        }
+
+# API接口实现
+@router.get("/methods", summary="获取优化方法列表")
+async def get_optimize_methods():
+    """获取所有可用的模型优化方法列表"""
+    try:
+        methods = optimize_manager.get_optimize_methods()
+        return methods
+    except Exception as e:
+        logger.error(f"Error getting optimization methods: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取优化方法失败: {str(e)}")
+
+@router.get("/method/{method_name}", summary="获取优化方法详情")
+async def get_optimize_method_details(
+    method_name: str = Path(..., description="优化方法名称")
+):
+    """获取特定优化方法的详细信息"""
+    try:
+        method_details = optimize_manager.get_optimize_method_details(method_name)
+        return method_details
+    except Exception as e:
+        logger.error(f"Error getting method details for {method_name}: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取方法详情失败: {str(e)}")
+
+@router.post("/model", summary="执行模型优化")
+async def optimize_model(
+    request: OptimizationRequest = Body(..., description="优化请求参数")
+):
+    """启动模型优化任务"""
+    try:
+        # 执行优化任务
+        result = optimize_manager.run_optimization(
+            run_id=request.run_id,
+            method=request.method,
+            parameters=request.parameters,
+            data_path=request.data_path,
+            output_dir=request.output_dir,
+            experiment_name=request.experiment_name
+        )
+        
+        return {
+            "status": "success",
+            "message": "优化任务已执行",
+            "run_id": result['optimization'].get("run_id"),
+            "optimized_model_id": result['optimization'].get("optimized_model_id")
+            # "best_params": result.get("best_params"),
+            # "best_score": result.get("best_score"),
+            # "optimized_model_id": result.get("optimized_model_id")
+        }
+    except Exception as e:
+        logger.error(f"Error starting optimization task: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"启动优化任务失败: {str(e)}")
+
+@router.get("/tasks", summary="获取优化任务列表")
+async def get_optimization_tasks(
+    experiment_name: Optional[str] = None,
+    page: int = Query(1, description="页码", ge=1),
+    page_size: int = Query(10, description="每页任务数量", ge=1, le=100),
+    status: Optional[str] = Query(None, description="任务状态过滤")
+):
+    """获取优化任务列表"""
+    try:
+        result = optimize_manager.get_optimization_tasks(experiment_name,page, page_size, status)
+        return result
+    except Exception as e:
+        logger.error(f"Error getting optimization tasks: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取优化任务列表失败: {str(e)}")
+
+@router.get("/task/{task_id}", summary="获取优化任务详情")
+async def get_optimization_task(
+    task_id: str = Path(..., description="任务ID")
+):
+    """获取特定优化任务的详细信息"""
+    try:
+        result = optimize_manager.get_optimization_task(task_id)
+        if result["status"] == "error":
+            raise HTTPException(status_code=404, detail=result["message"])
+        return result
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error getting optimization task {task_id}: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取优化任务详情失败: {str(e)}")
+
+@router.post("/task/{task_id}/cancel", summary="取消优化任务")
+async def cancel_optimization_task(
+    task_id: str = Path(..., description="任务ID")
+):
+    """取消正在运行的优化任务"""
+    try:
+        result = optimize_manager.cancel_optimization_task(task_id)
+        if result["status"] == "error":
+            raise HTTPException(status_code=400, detail=result["message"])
+        return result
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error cancelling optimization task {task_id}: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"取消优化任务失败: {str(e)}")
+
+@router.delete("/task/{task_id}", summary="删除优化任务")
+async def delete_optimization_task(
+    task_id: str = Path(..., description="任务ID")
+):
+    """删除优化任务及其相关资源"""
+    try:
+        result = optimize_manager.delete_optimization_task(task_id)
+        if result["status"] == "error":
+            if "任务不存在" in result["message"]:
+                raise HTTPException(status_code=404, detail=result["message"])
+            elif "任务正在运行中" in result.get("details", {}).get("reason", ""):
+                raise HTTPException(status_code=400, detail="无法删除正在运行的任务")
+            else:
+                raise HTTPException(status_code=400, detail=result["message"])
+        return result
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error deleting optimization task {task_id}: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"删除优化任务失败: {str(e)}") 

dataset/dataset_processed/test_optimize/test.csv

@@ -0,0 +1,115 @@
+mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target
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example_optimize_manager.py

@@ -0,0 +1,72 @@
+from function.optimize_manager import OptimizeManager
+import pandas as pd
+import numpy as np
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import os
+import time
+import json
+from datetime import datetime
+
+# 创建优化管理器实例
+manager = OptimizeManager()
+
+# 准备测试数据
+print("--------------------------------------------准备测试数据---------------------------------------------------")
+# 加载乳腺癌数据集
+data = load_breast_cancer()
+X = pd.DataFrame(data.data, columns=data.feature_names)
+y = pd.Series(data.target, name='target')
+
+# 数据预处理
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+X_scaled = pd.DataFrame(X_scaled, columns=X.columns)
+
+# 分割数据集
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# 保存数据集
+os.makedirs('dataset/dataset_processed/test_optimize', exist_ok=True)
+train_data = pd.concat([X_train, y_train], axis=1)
+train_data.to_csv('dataset/dataset_processed/test_optimize/train.csv', index=False)
+test_data = pd.concat([X_test, y_test], axis=1)
+test_data.to_csv('dataset/dataset_processed/test_optimize/test.csv', index=False)
+
+print("测试数据准备完成")
+print("--------------------------------------------准备测试数据 end---------------------------------------------------")
+
+print("--------------------------------------------获取优化方法---------------------------------------------------")
+# 获取所有优化方法
+methods = manager.get_optimize_methods()
+print("优化方法列表:")
+print(methods)
+print("--------------------------------------------获取优化方法 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_optimize_method_details('GridSearchCV')
+print("\nGridSearchCV方法详情:")
+print(method_details)
+print("--------------------------------------------获取方法详细信息 end---------------------------------------------------")
+
+print("---------------------------------------------测试优化模型-----------------------------------------------------------")
+run_id = "bd3697dc238c4d1587e0f4f319d04448" # 运行id
+method = "GridSearchCV"
+parameters = {
+        "max_depth": [3, 5, 7, 10, None],
+        "n_estimators": [50, 100, 200],
+        "min_samples_split": [2, 5, 10]
+}
+data_path = "dataset/dataset_processed/test_optimize/train.csv"
+output_dir = None
+experiment_name = "测试模型优化方法"
+back = manager.run_optimization(run_id=run_id, method=method, parameters=parameters,
+                         data_path=data_path,
+                         output_dir=output_dir,
+                         experiment_name=experiment_name)
+
+print(back)
+
+print("---------------------------------------------测试优化模型end--------------------------------------------------------")

function/optimize_manager.py

@@ -1,12 +1,25 @@
-
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Any, Union
 from pathlib import Path
 import logging
 from datetime import datetime
 import yaml
 import mlflow
 from mlflow.tracking import MlflowClient
-
+import pandas as pd
+import numpy as np
+import os
+import json
+import time
+import uuid
+from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
+from sklearn.metrics import (
+    accuracy_score, precision_score, recall_score, f1_score, roc_auc_score,
+    mean_absolute_error, mean_squared_error, r2_score, explained_variance_score
+)
+import importlib
+from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
+from optuna import create_study
+import optuna
 
 
 class OptimizeManager:
@@ -16,17 +29,20 @@ class OptimizeManager:
         self.logger = logging.getLogger(__name__)
 
         self._setup_logging()
-        self._load_method_config()
-        self._load_parameter_config()
+        self.method_config = self._load_method_config()
+        self.parameter_config = self._load_parameter_config()
 
         # 初始化MLflow客户端
-        self.mlflow_uri = self.config.get('mlflow_uri', 'http://127.0.1.202:5000')
+        self.mlflow_uri = self.config.get('mlflow_uri', 'http://127.0.0.1:5000')
         mlflow.set_tracking_uri(self.mlflow_uri)
         self.client = MlflowClient()
-        pass
+        
+        # 优化任务存储目录
+        self.optimize_dir = Path('optimized_models')
+        self.optimize_dir.mkdir(exist_ok=True)
 
     def _setup_logging(self):
-
+        """设置日志"""
         # 相对于程序运行目录
         log_dir = Path('.log')
         log_dir.mkdir(exist_ok=True)
@@ -42,32 +58,522 @@ class OptimizeManager:
         self.logger.addHandler(file_handler)
         self.logger.setLevel(logging.INFO)
 
-    def _load_method_config(self):
+    def _load_method_config(self) -> Dict:
         """加载模型优化方法配置文件"""
         config_path = Path('optimize/method.yaml')
         if not config_path.exists():
             raise FileNotFoundError(f"Method config file not found at {config_path}")
         
         with open(config_path, 'r', encoding='utf-8') as f:
-            config_optimize = yaml.safe_load(f)
-
-        self.logger.info("Successfully loader optimize method config")
-
-        config = config_optimize
+            config = yaml.safe_load(f)
 
+        self.logger.info("Successfully loaded optimize method config")
         return config
     
-    def _load_parameter_config(self):
+    def _load_parameter_config(self) -> Dict:
+        """加载优化方法参数配置文件"""
         config_path = Path('optimize/parameter.yaml')
         if not config_path.exists():
-            self.logger.error("error load optimize parameter config")
+            self.logger.error("Error loading optimize parameter config")
             raise FileNotFoundError(f"Parameter config file not found at {config_path}")
         
         with open(config_path, 'r', encoding='utf-8') as f:
-            config_parameter = yaml.safe_load(f)
+            config = yaml.safe_load(f)
         
-        self.logger.info("Successfullu load optimize parameter config")
+        self.logger.info("Successfully loaded optimize parameter config")
+        return config
+    
+    def get_optimize_methods(self) -> Dict:
+        """获取所有优化方法列表"""
+        try:
+            methods = []
+            for name, details in self.method_config['optimization_methods'].items():
+                methods.append({
+                    'name': name,
+                    'description': details.get('description', ''),
+                    'advantages': details.get('advantages', []),
+                    'disadvantages': details.get('disadvantages', []),
+                    'applicable_scenarios': details.get('applicable_scenarios', [])
+                })
+            
+            return {
+                'status': 'success',
+                'methods': methods
+            }
+        except Exception as e:
+            error_msg = f"Error getting optimization methods: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+    
+    def get_optimize_method_details(self, method_name: str) -> Dict:
+        """获取指定优化方法的详细信息"""
+        try:
+            if method_name not in self.method_config['optimization_methods']:
+                return {
+                    'status': 'error',
+                    'message': f"Method {method_name} not found"
+                }
+            
+            method_details = self.method_config['optimization_methods'][method_name]
+            parameters = []
+            
+            if method_name in self.parameter_config['optimization_methods']:
+                parameters = self.parameter_config['optimization_methods'][method_name].get('parameters', [])
+            
+            return {
+                'status': 'success',
+                'method': {
+                    'name': method_name,
+                    'description': method_details.get('description', ''),
+                    'advantages': method_details.get('advantages', []),
+                    'disadvantages': method_details.get('disadvantages', []),
+                    'applicable_scenarios': method_details.get('applicable_scenarios', []),
+                    'parameters': parameters
+                }
+            }
+        except Exception as e:
+            error_msg = f"Error getting optimization method details: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+    
+    def _get_model_from_run(self, run_id: str):
+        """从MLflow运行中获取模型"""
+        try:
+            run = self.client.get_run(run_id)
+            model_uri = f"runs:/{run_id}/model"
+            model = mlflow.sklearn.load_model(model_uri)
+            dataset = run.data.params.get('dataset', None)
+            
+            # 获取模型类型
+            model_type = run.data.params.get('algorithm', 'Unknown')
+            
+            return model, model_type, dataset
+        except Exception as e:
+            error_msg = f"Error loading model from run {run_id}: {str(e)}"
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+    
+    def _load_data(self, dataset: str):
+        """加载数据集"""
+        try:
+            if not os.path.exists(dataset):
+                raise FileNotFoundError(f"Data file not found at {dataset}")
+            dataset_train_name = 'train_'+dataset.split('/')[-1]+'.csv'
+            dataset_val_name = 'val_'+dataset.split('/')[-1]+'.csv'
+            data_train = pd.read_csv(os.path.join(dataset, dataset_train_name))
+            data_val = pd.read_csv(os.path.join(dataset, dataset_val_name))
+            X_train = data_train.drop('target', axis=1)
+            y_train = data_train['target']
+            
+            X_val = data_val.drop('target', axis=1)
+            y_val = data_val['target']
+            return X_train, y_train, X_val, y_val
+        except Exception as e:
+            error_msg = f"Error loading data from {dataset}: {str(e)}"
+            self.logger.error(error_msg)
+            raise ValueError(error_msg)
+    
+    def _calculate_metrics(self, y_true, y_pred, task_type: str) -> Dict:
+        """计算评估指标"""
+        metrics = {}
+        
+        if task_type == 'classification':
+            # 分类指标
+            metrics['accuracy'] = float(accuracy_score(y_true, y_pred))
+            metrics['precision'] = float(precision_score(y_true, y_pred, average='weighted'))
+            metrics['recall'] = float(recall_score(y_true, y_pred, average='weighted'))
+            metrics['f1'] = float(f1_score(y_true, y_pred, average='weighted'))
+            
+            # 如果是二分类问题，计算ROC AUC
+            if len(np.unique(y_true)) == 2:
+                metrics['roc_auc'] = float(roc_auc_score(y_true, y_pred))
+        
+        elif task_type == 'regression':
+            # 回归指标
+            metrics['mae'] = float(mean_absolute_error(y_true, y_pred))
+            metrics['mse'] = float(mean_squared_error(y_true, y_pred))
+            metrics['rmse'] = float(np.sqrt(metrics['mse']))
+            metrics['r2'] = float(r2_score(y_true, y_pred))
+            metrics['explained_variance'] = float(explained_variance_score(y_true, y_pred))
+        
+        return metrics
+    
+    def _get_optimizer(self, method: str, model, parameters: Dict, scoring: str, cv: int, n_jobs: int, verbose: int):
+        """获取优化器实例"""
+        if method == 'GridSearchCV':
+            return GridSearchCV(
+                estimator=model,
+                param_grid=parameters.get('param_grid', {}),
+                scoring=scoring,
+                cv=cv,
+                n_jobs=n_jobs,
+                verbose=verbose,
+                return_train_score=True
+            )
+        elif method == 'RandomizedSearchCV':
+            return RandomizedSearchCV(
+                estimator=model,
+                param_distributions=parameters.get('param_distributions', {}),
+                n_iter=parameters.get('n_iter', 10),
+                scoring=scoring,
+                cv=cv,
+                n_jobs=n_jobs,
+                verbose=verbose,
+                random_state=parameters.get('random_state', 42),
+                return_train_score=True
+            )
+        else:
+            raise ValueError(f"Unsupported optimization method: {method}")
+    
+    def run_optimization(self, run_id: str, method: str, parameters: Dict, data_path: str, 
+                         output_dir: str, experiment_name: str) -> Dict:
+        """执行模型优化"""
+        try:
+            # 生成优化任务ID
+            task_id = f"opt_{datetime.now():%Y%m%d}_{uuid.uuid4().hex[:6]}"
+            task_dir = self.optimize_dir / task_id
+            task_dir.mkdir(exist_ok=True)
+            
+            # 记录开始时间
+            start_time = datetime.now()
+            
+            # 加载原始模型
+            model, model_type, dataset = self._get_model_from_run(run_id)
+            
+            # 加载数据
+            X_train, y_train, X_val, y_val = self._load_data(dataset)
+            
+            # 获取任务类型
+            run = self.client.get_run(run_id)
+            task_type = run.data.params.get('task_type', 'classification')
+            
+            # 设置MLflow实验
+            mlflow.set_experiment(experiment_name)
+            
+            with mlflow.start_run(run_name=f"optimize_{method}_{task_id}") as opt_run:
+                # 记录原始模型信息
+                mlflow.log_param('original_run_id', run_id)
+                mlflow.log_param('original_model', model_type)
+                mlflow.log_param('optimization_method', method)
+                mlflow.log_param('data_path', dataset)
+                
+                # 记录优化参数
+                for param_name, param_value in parameters.items():
+                    if isinstance(param_value, dict):
+                        mlflow.log_param(param_name, json.dumps(param_value))
+                    else:
+                        mlflow.log_param(param_name, param_value)
+                
+                # 获取优化器
+                optimizer = self._get_optimizer(
+                    method=method,
+                    model=model,
+                    parameters=parameters,
+                    scoring=parameters.get('scoring', 'accuracy'),
+                    cv=parameters.get('cv', 5),
+                    n_jobs=parameters.get('n_jobs', -1),
+                    verbose=parameters.get('verbose', 1)
+                )
+                
+                # 执行优化
+                self.logger.info(f"Starting optimization with {method} for model {model_type}")
+                optimizer.fit(X_train, y_train)
+                
+                # 记录最佳参数和得分
+                mlflow.log_param('best_params', json.dumps(optimizer.best_params_))
+                mlflow.log_metric('best_score', optimizer.best_score_)
+                
+                # 保存CV结果
+                cv_results = pd.DataFrame(optimizer.cv_results_)
+                cv_results_path = task_dir / 'cv_results.csv'
+                cv_results.to_csv(cv_results_path, index=False)
+                mlflow.log_artifact(str(cv_results_path))
+                
+                # 计算CV结果摘要
+                cv_results_summary = {
+                    'mean_fit_time': float(cv_results['mean_fit_time'].mean()),
+                    'mean_score_time': float(cv_results['mean_score_time'].mean()),
+                    'param_combinations': len(cv_results),
+                    'score_range': [float(cv_results['mean_test_score'].min()), 
+                                   float(cv_results['mean_test_score'].max())]
+                }
+                
+                # 使用最佳参数重新训练模型
+                best_model = optimizer.best_estimator_
+                
+                # 保存优化后的模型
+                mlflow.sklearn.log_model(best_model, "optimized_model")
+                
+                # 记录结束时间和执行时间
+                end_time = datetime.now()
+                execution_time = end_time - start_time
+                mlflow.log_param('execution_time', str(execution_time))
+                mlflow.log_param('start_time', start_time.isoformat())
+                mlflow.log_param('end_time', end_time.isoformat())
+                
+                # 保存优化任务信息
+                print("run_id: ", opt_run.info.run_id)
+                task_info = {
+                    'id': task_id,
+                    'run_id': opt_run.info.run_id,
+                    'experiment_id': opt_run.info.experiment_id,
+                    'task_id': task_id,
+                    'method': method,
+                    'original_model': model_type,
+                    'parameters': parameters,
+                    'best_params': optimizer.best_params_,
+                    'best_score': float(optimizer.best_score_),
+                    'cv_results_summary': cv_results_summary,
+                    'cv_results_file': str(cv_results_path),
+                    'optimized_model_id': run_id,
+                    'execution_time': str(execution_time),
+                    'experiment_name': experiment_name,
+                    'status': 'completed',
+                    'start_time': start_time.isoformat(),
+                    'end_time': end_time.isoformat()
+                }
+                
+                # 保存任务信息到文件
+                with open(task_dir / 'task_info.json', 'w') as f:
+                    json.dump(task_info, f, indent=2)
+                
+                self.logger.info(f"Optimization completed. Task ID: {task_id}, Run ID: {opt_run.info.run_id}")
+                
+                return {
+                    'status': 'success',
+                    'optimization': {
+                        'id': task_id,
+                        'run_id': opt_run.info.run_id,
+                        'experiment_id': opt_run.info.experiment_id,
+                        'task_id': task_id,
+                        'method': method,
+                        'original_model': model_type,
+                        'best_params': optimizer.best_params_,
+                        'best_score': float(optimizer.best_score_),
+                        'cv_results_file': str(cv_results_path),
+                        'optimized_model_id': run_id, 
+                        'execution_time': str(execution_time),
+                        'status': 'completed',
+                        'timestamp': end_time.isoformat()
+                    }
+                }
+                
+        except Exception as e:
+            error_msg = f"Error running optimization: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg,
+                'details': {
+                    'error_type': type(e).__name__,
+                    'error_message': str(e)
+                }
+            }
+    
+    def get_optimization_tasks(self, experiment_name: Optional[str] = None, 
+                              page: int = 1, page_size: int = 10, status:str = 0) -> Dict:
+        """获取优化任务列表"""
+        try:
+            tasks = []
+            
+            # 遍历优化任务目录
+            for task_dir in sorted(self.optimize_dir.glob('*'), reverse=True):
+                if not task_dir.is_dir():
+                    continue
+                
+                task_info_path = task_dir / 'task_info.json'
+                if not task_info_path.exists():
+                    continue
+                
+                with open(task_info_path, 'r') as f:
+                    task_info = json.load(f)
+                
+                # 如果指定了实验名称，则过滤
+                if experiment_name and task_info.get('experiment_name', None) != experiment_name:
+                    continue
+                
+                # 添加简化的任务信息
+                tasks.append({
+                    'id': task_info['id'],
+                    'run_id': task_info['run_id'],
+                    'task_id': task_info['task_id'],
+                    'method': task_info['method'],
+                    'original_model': task_info['original_model'],
+                    'best_score': task_info['best_score'],
+                    'status': task_info['status'],
+                    'start_time': task_info['start_time'],
+                    'end_time': task_info['end_time'],
+                    'experiment_name': task_info.get('experiment_name', None),
+                    'optimized_model_id': task_info['optimized_model_id']
+                })
+            
+            # 分页
+            total_count = len(tasks)
+            start_idx = (page - 1) * page_size
+            end_idx = start_idx + page_size
+            paginated_tasks = tasks[start_idx:end_idx]
+            
+            return {
+                'status': 'success',
+                'tasks': paginated_tasks,
+                'total_count': total_count,
+                'page': page,
+                'page_size': page_size
+            }
+            
+        except Exception as e:
+            error_msg = f"Error getting optimization tasks: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+    
+    def get_optimization_task(self, task_id: str) -> Dict:
+        """获取优化任务详情"""
+        try:
+            task_dir = self.optimize_dir / task_id
+            task_info_path = task_dir / 'task_info.json'
+            
+            if not task_info_path.exists():
+                return {
+                    'status': 'error',
+                    'message': f"Task {task_id} not found"
+                }
+            
+            with open(task_info_path, 'r') as f:
+                task_info = json.load(f)
+            
+            return {
+                'status': 'success',
+                'task': task_info
+            }
+            
+        except Exception as e:
+            error_msg = f"Error getting optimization task {task_id}: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+    
+    def cancel_optimization_task(self, task_id: str) -> Dict:
+        """取消优化任务"""
+        try:
+            task_dir = self.optimize_dir / task_id
+            task_info_path = task_dir / 'task_info.json'
+            
+            if not task_info_path.exists():
+                return {
+                    'status': 'error',
+                    'message': "任务不存在",
+                    'details': {
+                        'reason': "任务不存在"
+                    }
+                }
+            
+            with open(task_info_path, 'r') as f:
+                task_info = json.load(f)
+            
+            if task_info['status'] == 'completed':
+                return {
+                    'status': 'error',
+                    'message': "无法取消任务",
+                    'details': {
+                        'reason': "任务已完成"
+                    }
+                }
+            
+            # 更新任务状态
+            task_info['status'] = 'cancelled'
+            task_info['cancel_time'] = datetime.now().isoformat()
+            
+            with open(task_info_path, 'w') as f:
+                json.dump(task_info, f, indent=2)
+            
+            # 如果有MLflow运行ID，尝试终止运行
+            if 'optimized_model_id' in task_info:
+                try:
+                    self.client.set_terminated(task_info['optimized_model_id'])
+                except:
+                    pass
+            
+            return {
+                'status': 'success',
+                'message': "优化任务已取消",
+                'task_id': task_id,
+                'cancel_time': task_info['cancel_time']
+            }
+            
+        except Exception as e:
+            error_msg = f"Error cancelling optimization task {task_id}: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+    
+    def delete_optimization_task(self, task_id: str) -> Dict:
+        """删除优化任务"""
+        try:
+            task_dir = self.optimize_dir / task_id
+            task_info_path = task_dir / 'task_info.json'
+            
+            if not task_info_path.exists():
+                return {
+                    'status': 'error',
+                    'message': "删除任务失败",
+                    'details': {
+                        'reason': "任务不存在"
+                    }
+                }
+            
+            with open(task_info_path, 'r') as f:
+                task_info = json.load(f)
+            
+            if task_info['status'] == 'running':
+                return {
+                    'status': 'error',
+                    'message': "删除任务失败",
+                    'details': {
+                        'reason': "任务正在运行中"
+                    }
+                }
+            
+            # mlflow 删除实验
+            self.client.delete_run(task_info['run_id'])
+            
+            # 收集要删除的文件
+            deleted_files = []
+            for file_path in task_dir.glob('*'):
+                deleted_files.append(str(file_path))
+            
+            # 删除任务目录
+            import shutil
+            shutil.rmtree(task_dir)
 
-        config  = config_parameter
 
-        return config
+            
+            return {
+                'status': 'success',
+                'message': "优化任务已删除",
+                'details': {
+                    'task_id': task_id,
+                    'deleted_files': deleted_files
+                }
+            }
+            
+        except Exception as e:
+            error_msg = f"Error deleting optimization task {task_id}: {str(e)}"
+            self.logger.error(error_msg)
+            return {
+                'status': 'error',
+                'message': error_msg
+            }
+        

main.py

@@ -16,6 +16,7 @@ import time
 from api.data_api import router as data_router
 from api.model_api import router as model_router
 from api.system_api import router as system_router
+from api.optimize_api import router as optimize_router
 
 
 # 设置watchfiles 日志级别为warning
@@ -153,6 +154,13 @@ app.include_router(
     responses={404: {"description": "Not found"}},
 )
 
+app.include_router(
+    optimize_router,
+    prefix="/optimize",
+    tags=["模型优化"],
+    responses={404: {"description": "Not found"}},
+)
+
 # 健康检查
 @app.get("/health")
 async def health_check():
@@ -174,7 +182,7 @@ if __name__ == "__main__":
         port=config.get('port', 8992),
         # reload=True 支持热重载 
         # reload=config.get('debug', False),
-        workers=config.get('workers', 4),
+        workers=config.get('workers', 1),
         log_level=config.get('log_level', 'warning'),
         access_log=True
     ) 

mlartifacts/268435227580594043/1d283117a78943d6a7cd3297b189ec8b/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.13159947395324706,0.014510434802840732,0.006738471984863281,0.0020981337714673955,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/1d283117a78943d6a7cd3297b189ec8b/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: 5fe0768e25244e3d9a2ecc2b8385fe47
+run_id: 1d283117a78943d6a7cd3297b189ec8b
+utc_time_created: '2025-03-18 02:53:24.438851'

mlartifacts/268435227580594043/1d283117a78943d6a7cd3297b189ec8b/artifacts/optimized_model/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.9.19
+- pip<=24.0
+- pip:
+  - mlflow==2.20.1
+  - cloudpickle==3.1.0
+  - numpy==1.26.4
+  - pandas==2.2.2
+  - psutil==6.0.0
+  - scikit-learn==1.5.2
+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/268435227580594043/1d283117a78943d6a7cd3297b189ec8b/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/268435227580594043/1d283117a78943d6a7cd3297b189ec8b/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlartifacts/268435227580594043/1eb67ea443284056bcf263b060183752/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.09886403083801269,0.009114179383532501,0.005813694000244141,0.0016351179571628893,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/1eb67ea443284056bcf263b060183752/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: 61e622d8e8284809b090595b3eb8249b
+run_id: 1eb67ea443284056bcf263b060183752
+utc_time_created: '2025-03-18 02:33:11.079709'

mlartifacts/268435227580594043/1eb67ea443284056bcf263b060183752/artifacts/optimized_model/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.9.19
+- pip<=24.0
+- pip:
+  - mlflow==2.20.1
+  - cloudpickle==3.1.0
+  - numpy==1.26.4
+  - pandas==2.2.2
+  - psutil==6.0.0
+  - scikit-learn==1.5.2
+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/268435227580594043/1eb67ea443284056bcf263b060183752/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/268435227580594043/1eb67ea443284056bcf263b060183752/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlartifacts/268435227580594043/263dffa53a4741e68b9f7dd598a92775/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.11665167808532714,0.01410450350966376,0.004927492141723633,7.809081647548583e-05,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/263dffa53a4741e68b9f7dd598a92775/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: 4b1dc876bcf34df3b69bcecbdcbb2433
+run_id: 263dffa53a4741e68b9f7dd598a92775
+utc_time_created: '2025-03-18 02:58:27.086121'

mlartifacts/268435227580594043/263dffa53a4741e68b9f7dd598a92775/artifacts/optimized_model/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.9.19
+- pip<=24.0
+- pip:
+  - mlflow==2.20.1
+  - cloudpickle==3.1.0
+  - numpy==1.26.4
+  - pandas==2.2.2
+  - psutil==6.0.0
+  - scikit-learn==1.5.2
+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/268435227580594043/263dffa53a4741e68b9f7dd598a92775/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/268435227580594043/263dffa53a4741e68b9f7dd598a92775/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlartifacts/268435227580594043/27d04cc4ecf34c33b9dbdce350a40b92/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.12402424812316895,0.021315426297943304,0.005871343612670899,0.0015934169405603148,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/27d04cc4ecf34c33b9dbdce350a40b92/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: 409e32e09454429ca8f50063356671a9
+run_id: 27d04cc4ecf34c33b9dbdce350a40b92
+utc_time_created: '2025-03-18 02:44:12.094128'

mlartifacts/268435227580594043/27d04cc4ecf34c33b9dbdce350a40b92/artifacts/optimized_model/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.9.19
+- pip<=24.0
+- pip:
+  - mlflow==2.20.1
+  - cloudpickle==3.1.0
+  - numpy==1.26.4
+  - pandas==2.2.2
+  - psutil==6.0.0
+  - scikit-learn==1.5.2
+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/268435227580594043/27d04cc4ecf34c33b9dbdce350a40b92/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/268435227580594043/27d04cc4ecf34c33b9dbdce350a40b92/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlartifacts/268435227580594043/396eb46d2da14409b023601374c854d9/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.1234919548034668,0.02111802375015912,0.007743406295776367,0.002167678316605068,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/396eb46d2da14409b023601374c854d9/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: e8db502a00864fb5842b96744f765fb4
+run_id: 396eb46d2da14409b023601374c854d9
+utc_time_created: '2025-03-18 03:07:44.056414'

mlartifacts/268435227580594043/396eb46d2da14409b023601374c854d9/artifacts/optimized_model/conda.yaml

@@ -0,0 +1,15 @@
+channels:
+- conda-forge
+dependencies:
+- python=3.9.19
+- pip<=24.0
+- pip:
+  - mlflow==2.20.1
+  - cloudpickle==3.1.0
+  - numpy==1.26.4
+  - pandas==2.2.2
+  - psutil==6.0.0
+  - scikit-learn==1.5.2
+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/268435227580594043/396eb46d2da14409b023601374c854d9/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/268435227580594043/396eb46d2da14409b023601374c854d9/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlartifacts/268435227580594043/47be246200ad47c196c8be7a1a89470b/artifacts/cv_results.csv

@@ -0,0 +1,2 @@
+mean_fit_time,std_fit_time,mean_score_time,std_score_time,params,split0_test_score,split1_test_score,split2_test_score,split3_test_score,split4_test_score,mean_test_score,std_test_score,rank_test_score,split0_train_score,split1_train_score,split2_train_score,split3_train_score,split4_train_score,mean_train_score,std_train_score
+0.17565064430236815,0.04894339206468253,0.023302555084228516,0.0008791832567045095,{},0.9607843137254902,0.9607843137254902,0.9607843137254902,0.9215686274509803,0.9411764705882353,0.9490196078431372,0.015686274509803956,1,1.0,1.0,1.0,1.0,1.0,1.0,0.0

mlartifacts/268435227580594043/47be246200ad47c196c8be7a1a89470b/artifacts/optimized_model/MLmodel

@@ -0,0 +1,20 @@
+artifact_path: optimized_model
+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.9.19
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.5.2
+mlflow_version: 2.20.1
+model_size_bytes: 96534
+model_uuid: efbad4618fe64fe8941ec1085a2adbf3
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+  - scipy==1.13.1
+  - xgboost==2.1.4
+name: mlflow-env

mlartifacts/780687096365534715/903ab190690e496aa74e6aaefac8a7cb/artifacts/optimized_model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.9.19
+build_dependencies:
+- pip==24.0
+- setuptools==60.2.0
+- wheel==0.43.0
+dependencies:
+- -r requirements.txt

mlartifacts/780687096365534715/903ab190690e496aa74e6aaefac8a7cb/artifacts/optimized_model/requirements.txt

@@ -0,0 +1,8 @@
+mlflow==2.20.1
+cloudpickle==3.1.0
+numpy==1.26.4
+pandas==2.2.2
+psutil==6.0.0
+scikit-learn==1.5.2
+scipy==1.13.1
+xgboost==2.1.4

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/meta.yaml

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+artifact_uri: mlflow-artifacts:/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/artifacts
+end_time: 1742268656503
+entry_point_name: ''
+experiment_id: '780687096365534715'
+lifecycle_stage: active
+run_id: 0e7f80468ba3451d98e2ed54dafe71a6
+run_name: optimize_GridSearchCV_opt_20250318_b50499
+run_uuid: 0e7f80468ba3451d98e2ed54dafe71a6
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1742268652828
+status: 3
+tags: []
+user_id: admin-root

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/metrics/best_score

@@ -0,0 +1 @@
+1742268654037 0.9490196078431372 0

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/best_params

@@ -0,0 +1 @@
+{}

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/data_path

@@ -0,0 +1 @@
+/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250219_144629

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/end_time

@@ -0,0 +1 @@
+2025-03-18T11:30:56.488706

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/execution_time

@@ -0,0 +1 @@
+0:00:03.902550

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/max_depth

@@ -0,0 +1 @@
+[3, 5, 7]

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/min_samples_split

@@ -0,0 +1 @@
+[2, 5, 10]

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/n_estimators

@@ -0,0 +1 @@
+[50, 100, 200]

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/optimization_method

@@ -0,0 +1 @@
+GridSearchCV

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/original_model

@@ -0,0 +1 @@
+XGBClassifier

mlruns/780687096365534715/0e7f80468ba3451d98e2ed54dafe71a6/params/original_run_id

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+bd3697dc238c4d1587e0f4f319d04448