完成--完成获取模型评价指标方法

doc/接口文档code.md

@@ -334,29 +334,33 @@ GET /api/metrics
 Response:
 {
     "status": "success",
-    "metrics": {
+    "metric": {
         "classification": [
             {
                 "name": "accuracy",
                 "description": "准确率",
-                "range": [0, 1]
+                "range": [0, 1],
+                "interpretation": "值越大越好"
             },
             {
                 "name": "f1",
                 "description": "F1分数",
-                "range": [0, 1]
+                "range": [0, 1],
+                "interpretation": "值越大越好"
             }
         ],
         "regression": [
             {
                 "name": "mse",
                 "description": "均方误差",
-                "range": [0, null]
+                "range": [0, null],
+                "interpretation": "值越小越好"
             },
             {
                 "name": "mae",
                 "description": "平均绝对误差",
-                "range": [0, null]
+                "range": [0, null],
+                "interpretation": "值越小越好"
             }
         ]
     }

example_method_reader_metric.py

@@ -0,0 +1,7 @@
+
+from function.method_reader_metric import MethodReader
+
+
+
+method_reader = MethodReader()
+print(method_reader.get_metrics())

function/method_reader_metric.py

@@ -0,0 +1,79 @@
+import yaml
+from typing import Dict, List
+import os
+import logging
+from pathlib import Path
+
+class MethodReader:
+    """方法配置读取器"""
+    
+    def __init__(self):
+        """初始化方法读取器"""
+        self.logger = logging.getLogger(__name__)
+        self.method_config = self._load_metrics()
+        
+        
+    def _load_metrics(self) -> Dict:
+        """加载方法配置文件"""
+        try:
+            config_path = Path('model/metrics.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 = yaml.safe_load(f)
+                
+            self.logger.info("Successfully loaded method config")
+            return config
+            
+        except Exception as e:
+            self.logger.error(f"Error loading method config: {str(e)}")
+            raise
+            
+    
+            
+    def get_metrics(self) -> Dict:
+        """获取预处理方法列表"""
+        try:
+            metrics = []
+            
+            # 分类方法
+            classification_metrics = self.method_config.get('classification', {})
+            if classification_metrics:
+                metrics.append({
+                    "name": "classification_metrics",
+                    "description": "分类方法评价指标",
+                    "metric": classification_metrics
+                })
+                
+            # 回归方法
+            regression_metrics = self.method_config.get('regression', {})
+            if regression_metrics:
+                metrics.append({
+                    "name": "regression_metrics",
+                    "description": "回归方法评价指标",
+                    "metric": regression_metrics
+                })
+                
+            # 聚类方法
+            clustering_metrics = self.method_config.get('clustering', {})
+            if clustering_metrics:
+                metrics.append({
+                    "name": "clustering_metrics",
+                    "description": "聚类方法评价指标",
+                    "metric": clustering_metrics
+                })
+                
+            return {
+                "status": "success",
+                "metric": metrics
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Error getting preprocessing methods: {str(e)}")
+            return {
+                "status": "error",
+                "error": str(e)
+            }
+            
+    

model/metrics.yaml

@@ -0,0 +1,57 @@
+classification:
+  - name: "accuracy"
+    description: "分类正确的样本占总样本数的比例。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "precision"
+    description: "预测为正类的样本中，真正类的比例。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "recall"
+    description: "真正类样本中被正确预测的比例。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "f1-score"
+    description: "精确率和召回率的调和平均值。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "roc_auc"
+    description: "ROC 曲线下的面积，衡量模型区分正负样本的能力。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+
+regression:
+  - name: "mean_absolute_error"
+    description: "平均绝对误差，表示预测值与真实值之差的绝对值的均值。"
+    range: [0, +∞]
+    interpretation: "值越小效果越好"
+  - name: "mean_squared_error"
+    description: "均方误差，表示预测值与真实值之差的平方的均值。"
+    range: [0, +∞]
+    interpretation: "值越小效果越好"
+  - name: "r2_score"
+    description: "决定系数，表示模型解释数据方差的能力，1 表示完美拟合。"
+    range: [-∞, 1]
+    interpretation: "值越大效果越好"
+  - name: "explained_variance_score"
+    description: "解释方差，衡量预测数据与真实数据的方差相似程度。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+
+clustering:
+  - name: "adjusted_rand_score"
+    description: "调整兰德指数，衡量聚类结果与真实标签的相似度。"
+    range: [-1, 1]
+    interpretation: "值越大效果越好"
+  - name: "homogeneity_score"
+    description: "同质性得分，衡量聚类的纯度，即每个聚类是否只包含单一类别的样本。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "completeness_score"
+    description: "完整性得分，衡量所有同类别样本是否被正确聚类到同一组。"
+    range: [0, 1]
+    interpretation: "值越大效果越好"
+  - name: "silhouette_score"
+    description: "轮廓系数，衡量样本在其簇内的紧密度和与其他簇的分离度。"
+    range: [-1, 1]
+    interpretation: "值越大效果越好"