feat: 训练支持自动划分验证集，无需手工选择两个数据集

frontend/src/views/TrainingPage.vue

@@ -16,7 +16,7 @@
         </el-form-item>
         
         <el-form-item label="训练数据集">
-          <el-select v-model="trainingConfig.train_dataset_id" placeholder="选择训练数据集">
+          <el-select v-model="trainingConfig.train_dataset_id" placeholder="选择数据集">
             <el-option 
               v-for="dataset in trainingDatasets" 
               :key="dataset.id"
@@ -26,7 +26,15 @@
           </el-select>
         </el-form-item>
         
-        <el-form-item label="验证数据集">
+        <el-form-item label="验证集划分">
+          <el-switch v-model="trainingConfig.auto_split" active-text="自动" inactive-text="手动" />
+        </el-form-item>
+        
+        <el-form-item v-if="trainingConfig.auto_split" label="验证集比例">
+          <el-slider v-model="trainingConfig.split_ratio" :min="0.1" :max="0.4" :step="0.05" show-input style="width: 300px" />
+        </el-form-item>
+        
+        <el-form-item v-else label="验证数据集">
           <el-select v-model="trainingConfig.validation_dataset_id" placeholder="选择验证数据集">
             <el-option 
               v-for="dataset in validationDatasets" 
@@ -299,6 +307,8 @@ const trainingConfig = ref({
   type: '',
   train_dataset_id: null,
   validation_dataset_id: null,
+  auto_split: true,
+  split_ratio: 0.2,
   models: ['pytorch', 'xgboost', 'lightgbm', 'gbm', 'rf']
 })
 
@@ -352,8 +362,8 @@ const startTraining = async () => {
       ElMessage.warning('请选择训练数据集')
       return
     }
-    if (!trainingConfig.value.validation_dataset_id) {
+    if (!trainingConfig.value.auto_split && !trainingConfig.value.validation_dataset_id) {
-      ElMessage.warning('请选择验证数据集')
+      ElMessage.warning('请选择验证数据集或开启自动划分')
       return
     }
     if (trainingConfig.value.models.length === 0) {

src/data_preparation.py

@@ -40,8 +40,8 @@ class DataPreparation:
         self.feature_scaler = StandardScaler()
         self.target_scaler = StandardScaler()
         
-    def prepare_training_data(self, equipment_data, equipment_type, batch_size=32):
+    def prepare_training_data(self, equipment_data, equipment_type, batch_size=32, validation_split=None):
-        """准备训练数据"""
+        """准备训练数据，可选自动划分验证集"""
         try:
             logger.info(f"Preparing training data for {equipment_type}")
             
@@ -91,12 +91,7 @@ class DataPreparation:
             X_scaled = self.feature_scaler.fit_transform(X)
             y_scaled = self.target_scaler.fit_transform(y.reshape(-1, 1)).ravel()
             
-            # 创建数据集和数据加载器
+            result = {
-            dataset = EquipmentDataset(X_scaled, y_scaled)
-            dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
-            
-            return {
-                'dataloader': dataloader,
                 'feature_names': feature_names,
                 'feature_scaler': self.feature_scaler,
                 'target_scaler': self.target_scaler,
@@ -105,6 +100,25 @@ class DataPreparation:
                 'y': y_scaled
             }
             
+            # 如果指定了验证集比例，自动划分
+            if validation_split is not None and 0 < validation_split < 1:
+                from sklearn.model_selection import train_test_split
+                X_train, X_val, y_train, y_val = train_test_split(
+                    X_scaled, y_scaled, test_size=validation_split, random_state=42
+                )
+                result['X'] = X_train
+                result['y'] = y_train
+                result['X_val'] = X_val
+                result['y_val'] = y_val
+                logger.info(f"Auto-split: train={X_train.shape[0]}, val={X_val.shape[0]}")
+            
+            # 创建数据集和数据加载器（仅用于训练集）
+            dataset = EquipmentDataset(result['X'], result['y'])
+            dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+            result['dataloader'] = dataloader
+            
+            return result
+            
         except Exception as e:
             logger.error(f"Error in data preparation: {str(e)}")
             raise Exception(f"Training error: {str(e)}")

src/routes.py

@@ -303,6 +303,8 @@ def train_model():
         train_dataset_id = data.get('train_dataset_id')
         validation_dataset_id = data.get('validation_dataset_id')
         models = data.get('models', [])
+        auto_split = data.get('auto_split', False)  # 自动划分训练/验证集
+        split_ratio = data.get('split_ratio', 0.2)   # 验证集比例，默认 20%
         
         logger.info(f"Training dataset: {train_dataset_id}")
         logger.info(f"Validation dataset: {validation_dataset_id}")
@@ -383,9 +385,19 @@ def train_model():
         data_processor = DataPreparation()
         
         # 准备训练数据
+        if auto_split and not validation_dataset_id:
+            # 自动划分模式：从训练数据中随机拆分验证集
+            logger.info(f"Auto-splitting training data with validation ratio={split_ratio}")
+            train_prepared = data_processor.prepare_training_data(
+                train_data, equipment_type, validation_split=split_ratio
+            )
+            validation_prepared = {
+                'X': train_prepared.pop('X_val'),
+                'y': train_prepared.pop('y_val'),
+            }
+        else:
+            # 传统模式：单独查询验证数据集
             train_prepared = data_processor.prepare_training_data(train_data, equipment_type)
-        
-        # 准备验证数据（如果有）
             validation_prepared = None
             if validation_data:
                 validation_prepared = data_processor.prepare_validation_data(