From c11a4d6bdd85ccceca18fbe8370054742ca43960 Mon Sep 17 00:00:00 2001
From: haotian <2421912570@qq.com>
Date: Wed, 9 Apr 2025 16:03:17 +0800
Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9--=E4=BF=AE=E6=94=B9bug,=20?=
 =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 api/__pycache__/optimize_api.cpython-39.pyc   | Bin 5995 -> 5947 bytes
 api/optimize_api.py                           |   5 +++--
 doc/各领域应用(附带数据集).txt                |  20 +++++++++++++++---
 .../optimize_manager.cpython-39.pyc           | Bin 13846 -> 13904 bytes
 function/optimize_manager.py                  |  15 +++++++------
 5 files changed, 28 insertions(+), 12 deletions(-)

diff --git a/api/__pycache__/optimize_api.cpython-39.pyc b/api/__pycache__/optimize_api.cpython-39.pyc
index 285d8479e4f5d3127b7d638b360320879ca18943..9a211951a9df09146d9b631a346fbc7314ccb3b0 100644
GIT binary patch
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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 e6d8a0f3205cde241a2cec06cfc980610fb2b6a6..baf72e7951289979f0b587c54cc980964a3465ad 100644
GIT binary patch
delta 853
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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)