Merge branch 'zsj_devp' of Ding/AcidificationModel into master

api/app/routes.py

@@ -1,5 +1,6 @@
 import sqlite3
 from io import BytesIO
+import pickle
 
 from flask import Blueprint, request, jsonify, current_app, send_file
 from werkzeug.security import check_password_hash, generate_password_hash
@@ -19,6 +20,7 @@ import logging
 from sqlalchemy import text, select, MetaData, Table, func
 from .tasks import train_model_task
 from datetime import datetime
+from sklearn.metrics import r2_score
 
 # 配置日志
 logging.basicConfig(level=logging.DEBUG)
@@ -897,16 +899,19 @@ def delete_model(model_id, delete_dataset=False):
         session.commit()
         
         # 2. 删除模型文件
-        model_file = f"rf_model_{model_id}.pkl"
-        model_path = os.path.join(current_app.config['MODEL_SAVE_PATH'], model_file)
-        if os.path.exists(model_path):
-            try:
+        model_path = model.ModelFilePath
+        try:
+            if os.path.exists(model_path):
                 os.remove(model_path)
-            except OSError as e:
+            else:
                 # 如果删除文件失败,回滚数据库操作
-                session.rollback()
-                logger.error(f'删除模型文件失败: {str(e)}')
-                return jsonify({'error': f'删除模型文件失败: {str(e)}'}), 500
+                session.rollback() 
+                logger.warning(f'模型文件不存在: {model_path}')
+        except OSError as e:
+            # 如果删除文件失败,回滚数据库操作
+            session.rollback() 
+            logger.error(f'删除模型文件失败: {str(e)}')
+            return jsonify({'error': f'删除模型文件失败: {str(e)}'}), 500
 
         # 3. 如果需要删除关联的数据集
         if delete_dataset and dataset_id:
@@ -926,7 +931,7 @@ def delete_model(model_id, delete_dataset=False):
 
         response_data = {
             'message': '模型删除成功',
-            'deleted_files': [model_file]
+            'deleted_files': [model_path]
         }
         
         if delete_dataset:
@@ -1660,4 +1665,66 @@ def kriging_interpolation():
         )
         return jsonify(result)
     except Exception as e:
-        return jsonify({"error": str(e)}), 500
+        return jsonify({"error": str(e)}), 500
+
+@bp.route('/model-scatter-data/<int:model_id>', methods=['GET'])
+def get_model_scatter_data(model_id):
+    """
+    获取指定模型的散点图数据(真实值vs预测值)
+    
+    @param model_id: 模型ID
+    @return: JSON响应，包含散点图数据
+    """
+    Session = sessionmaker(bind=db.engine)
+    session = Session()
+    
+    try:
+        # 查询模型信息
+        model = session.query(Models).filter_by(ModelID=model_id).first()
+        if not model:
+            return jsonify({'error': '未找到指定模型'}), 404
+            
+        # 加载模型
+        with open(model.ModelFilePath, 'rb') as f:
+            ML_model = pickle.load(f)
+            
+        # 根据数据类型加载测试数据
+        if model.Data_type == 'reflux':
+            X_test = pd.read_csv('uploads/data/X_test_reflux.csv')
+            Y_test = pd.read_csv('uploads/data/Y_test_reflux.csv')
+        elif model.Data_type == 'reduce':
+            X_test = pd.read_csv('uploads/data/X_test_reduce.csv')
+            Y_test = pd.read_csv('uploads/data/Y_test_reduce.csv')
+        else:
+            return jsonify({'error': '不支持的数据类型'}), 400
+            
+        # 获取预测值
+        y_pred = ML_model.predict(X_test)
+        
+        # 生成散点图数据
+        scatter_data = [
+            [float(true), float(pred)] 
+            for true, pred in zip(Y_test.iloc[:, 0], y_pred)
+        ]
+        
+        # 计算R²分数
+        r2 = r2_score(Y_test, y_pred)
+        
+        # 获取数据范围，用于绘制对角线
+        y_min = min(min(Y_test.iloc[:, 0]), min(y_pred))
+        y_max = max(max(Y_test.iloc[:, 0]), max(y_pred))
+        
+        return jsonify({
+            'scatter_data': scatter_data,
+            'r2_score': float(r2),
+            'y_range': [float(y_min), float(y_max)],
+            'model_name': model.Model_name,
+            'model_type': model.Model_type
+        }), 200
+        
+    except Exception as e:
+        logger.error(f'获取模型散点图数据失败: {str(e)}', exc_info=True)
+        return jsonify({'error': f'获取数据失败: {str(e)}'}), 500
+        
+    finally:
+        session.close()