diff --git a/api/__pycache__/optimize_api.cpython-39.pyc b/api/__pycache__/optimize_api.cpython-39.pyc index 285d847..9a21195 100644 Binary files a/api/__pycache__/optimize_api.cpython-39.pyc and b/api/__pycache__/optimize_api.cpython-39.pyc differ diff --git a/api/optimize_api.py b/api/optimize_api.py index 73b5cb3..d6987d1 100644 --- a/api/optimize_api.py +++ b/api/optimize_api.py @@ -81,8 +81,9 @@ async def optimize_model( 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") + "optimization": result.get("optimization") + # "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") diff --git a/doc/各领域应用(附带数据集).txt b/doc/各领域应用(附带数据集).txt index dbecdb9..54661a8 100644 --- a/doc/各领域应用(附带数据集).txt +++ b/doc/各领域应用(附带数据集).txt @@ -3,6 +3,7 @@ (2)LSTM,随机森林,生存分析模型 (3)NASA Turbofan Engine Degradation Simulation ①https://www.kaggle.com/datasets/behrad3d/nasa-cmaps + 数据集介绍: 该数据集旨在模拟航空发动机在运行过程中的性能退化，可用机器学习的方法来预测发动机的剩余寿命.该数据集收集了21个传感器通道的数据,以及3个影响发动机工作状态的变量（例如飞行高度、马赫数）. 数据集解析: 数据集路径: dataset/dataset_raw/CMaps 训练集: @@ -45,6 +46,8 @@ (2)XGBoost,LSTM,Prophet时间序列模型 (3)Wind Turbine Scada Dataset https://www.kaggle.com/datasets/berkerisen/wind-turbine-scada-dataset/discussion/520518 + 数据集介绍:Wind Turbine SCADA Dataset 是风力发电机组监控与数据采集系统（Supervisory Control and Data Acquisition, SCADA）记录的实际运行数据集，通常包含风力涡轮机在运行过程中生成的多维时间序列数据。这些数据通过传感器实时采集，记录了涡轮机的运行状态、环境条件、电气参数等信息，是研究风力发电机组性能、故障诊断和能效优化的关键资源。 + 数据集解析: 数据集路径:dataset/dataset_raw/WindTurbine/wind_turbine_scada.csv @@ -60,7 +63,7 @@ 标准缩放--结果分析: RandomForestRegressor:mae 123. - XGBRegressor:mae 116 + XGBRegressor:mae 116 LGBMRegressor: mae 113 AdaBoostRegressor mae:277 不进行缩放--结果分析 @@ -94,7 +97,8 @@ https://github.com/camaramm/tennessee-eastman-profBraatz 数据集路径: dataset/dataset_raw/tennessee-eastman-profBraatz-master - 数据集解析: 这是是一个用于分类任务的数据集d00代表正常.d01代表第一类错误.d02代表第二类错误.d02_te.dat 表示 测试数据, d02.dat代表训练数据. 每个文件有52列. + 数据集解析: 这是是一个用于分类任务的数据集d00代表正常.d01代表第一类错误.d02代表第二类错误.d02_te.dat 表示 测试数据, d02.dat代表训练数据. 每个文件有52列,即52个特征. + 4.物流与供应链-需求预测 @@ -127,4 +131,14 @@ 10.制造业-产品质量缺陷分类 (1)利用图像分类检测产品表面缺陷 (2)CNN,迁移学习 - (3)NEU-DET：包含6类钢材表面缺陷图像（滚痕、裂纹等），来自东北大学。 \ No newline at end of file + (3)NEU-DET：包含6类钢材表面缺陷图像（滚痕、裂纹等），来自东北大学。 + +11.工业-混凝土强度预测 + (1)通过混凝土的材料配比来预测混凝土的强度 + (2)随机森林,岭回归等 + (3)Revisiting a Concrete Strength regression + https://www.kaggle.com/datasets/maajdl/yeh-concret-data + dataset_raw/Concrete_data_yeh.csv + + 数据集解析: + 1030行,9列. 前8列为混凝土特征,最后一列为混凝土强度. diff --git a/function/__pycache__/optimize_manager.cpython-39.pyc b/function/__pycache__/optimize_manager.cpython-39.pyc index e6d8a0f..baf72e7 100644 Binary files a/function/__pycache__/optimize_manager.cpython-39.pyc and b/function/__pycache__/optimize_manager.cpython-39.pyc differ diff --git a/function/optimize_manager.py b/function/optimize_manager.py index 0ec1426..46e0dc4 100644 --- a/function/optimize_manager.py +++ b/function/optimize_manager.py @@ -151,7 +151,7 @@ class OptimizeManager: dataset = run.data.params.get('dataset', None) # 获取模型类型 - model_type = run.data.params.get('algorithm', 'Unknown') + model_type = run.data.params.get('task_type', 'Unknown') return model, model_type, dataset except Exception as e: @@ -168,11 +168,12 @@ class OptimizeManager: 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'] + target_col_train = data_train.columns[-1] + target_col_val = data_val.columns[-1] + X_train = data_train.iloc[:, :-1] + y_train = data_train[target_col_train] + X_val = data_val.iloc[:, :-1] + y_val = data_val[target_col_val] return X_train, y_train, X_val, y_val except Exception as e: error_msg = f"Error loading data from {dataset}: {str(e)}" @@ -247,7 +248,7 @@ class OptimizeManager: 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) + X_train, y_train, X_val, y_val = self._load_data(data_path) # 获取任务类型 run = self.client.get_run(run_id)