修改--修改接口文档code中的接口返回参数

doc/接口文档.md

@@ -189,11 +189,23 @@
 # 开发计划
 ## 1.整理数据预处理和特征选择方法, 模型算法及其参数.
     - 让AI整理,保存成yaml文件
-    - 2天
+    - 1天
+## 2. 实现数据预处理部分
+    - 顺带测试
+    - 1天
 
-## 2.开发系统接口及测试
+## 3.实现模型训练部分
+
+    - 试着进行模型优化
     - AI 辅助开发
-    - 5天
+    - 2天
+## 4.实现系统监控部分
+    - 1天
+## 5.实现系统接口
+    - 1天
+## 6.整体测试及完善
+    - 1天
+
 
 
 

doc/接口文档code.md

@@ -12,17 +12,20 @@ Response:
         {
             "name": "missing_value_handler",
             "description": "缺失值处理",
-            "category": "data_cleaning"
+            "method": ["SimpleImputer", "IterativeImputer", "KNNImputer", "MissingIndicator"]
+            
         },
         {
             "name": "outlier_detector",
             "description": "异常值检测",
-            "category": "data_cleaning"
+            "method":["IsolationForest",...]
+           
         },
         {
             "name": "standardizer",
             "description": "标准化处理",
-            "category": "feature_scaling"
+            "method" : ["StandardScaler", ...]
+            
         }
     ]
 }
@@ -44,7 +47,7 @@ Response:
         "parameters": [
             {
                 "name": "missing_values",
-                "type": ["int", "float", "str", "np.nan" , "None"],
+                "type":"int, float, str, np.nan, None",
                 "default": "np.nan",
                 "description": "指定需要填充的缺失值。进行修改。"
             },
@@ -71,19 +74,22 @@ Response:
     "status": "success",
     "methods": [
         {
-            "name": "standard_scaler",
-            "description": "标准化缩放",
-            "category": "scaling"
+            "name": "KBinsDiscretizer",
+            "description": "将连续特征离散化，即将数值特征分桶，转换为离散的整数值或独热编码。",
+
         },
         {
-            "name": "polynomial_features",
-            "description": "多项式特征",
-            "category": "feature_creation"
+            "name": "SelectKBest",
+            "description": "根据统计检验方法选择得分最高的 K 个特征，进行特征选择。",
+
         },
         {
             "name": "pca",
-            "description": "主成分分析",
-            "category": "dimension_reduction"
+            "description": "主成分分析，通过线性变换将高维数据投影到低维空间，保留数据的主要信息。",
+
+        },
+        {
+          ...,
         }
     ]
 }
@@ -97,20 +103,23 @@ Response:
 {
     "status": "success",
     "method": {
-        "name": "standard_scaler",
-        "description": "标准化缩放",
+        "name": "SelectKBest",
+        "description": "选择最重要的 K 个特征。",
         "parameters": [
             {
-                "name": "with_mean",
-                "type": "boolean",
-                "default": true,
-                "description": "是否减去均值"
+                "name": "score_func",
+                "type": "callable",
+                "default": f_classif,
+                "description": "用于计算特征得分的函数。"
             },
             {
-                "name": "with_std",
-                "type": "boolean",
-                "default": true,
-                "description": "是否除以标准差"
+                "name": "k",
+                "type": "int",
+                "default": 10,
+                "description": "要选择的特征数量。"
+            },
+            {
+              ...,
             }
         ]
     }