完成--完成获取数据预处理方法列表,获取方法详情

data_process/method_reader_date_process.py

@@ -11,6 +11,7 @@ class MethodReader:
         """初始化方法读取器"""
         self.logger = logging.getLogger(__name__)
         self.method_config = self._load_method_config()
+        self.parameter_config = self._load_parameter_config()
         
     def _load_method_config(self) -> Dict:
         """加载方法配置文件"""
@@ -29,6 +30,22 @@ class MethodReader:
             self.logger.error(f"Error loading method config: {str(e)}")
             raise
             
+    def _load_parameter_config(self) -> Dict:
+        """加载参数配置文件"""
+        try:
+            config_path = Path('date_preprocessing/parameter.yaml')
+            if not config_path.exists():
+                raise FileNotFoundError(f"Parameter 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 parameter config")
+            return config
+        except Exception as e:
+            self.logger.error(f"Error loading parameter config: {str(e)}")
+            raise
+            
     def get_preprocessing_methods(self) -> Dict:
         """获取预处理方法列表"""
         try:
@@ -76,23 +93,40 @@ class MethodReader:
     def get_method_details(self, method_name: str) -> Dict:
         """获取指定方法的详细信息"""
         try:
-            # 在各个方法类别中查找
+            # 在各个方法类别中查找方法原理和优缺点
+            method_info = None
             for category in ['data_scaler_methods', 'missing_value_handling_methods', 'outlier_detection_methods']:
                 if method_name in self.method_config.get(category, {}):
                     method_info = self.method_config[category][method_name]
+                    break
+                    
+            if method_info is None:
+                raise ValueError(f"Method {method_name} not found in method config")
+                
+            # 查找方法参数信息
+            parameter_info = None
+            for category in ['data_scaler_methods', 'missing_value_handling_methods', 'outlier_detection_methods']:
+                if method_name in self.parameter_config.get(category, {}):
+                    parameter_info = self.parameter_config[category][method_name]
+                    break
+                    
+            if parameter_info is None:
+                raise ValueError(f"Method {method_name} not found in parameter config")
+                
+            # 组合返回信息
             return {
                 "status": "success",
                 "method": {
                     "name": method_name,
+                    "description": parameter_info.get('description', ''),
                     "principle": method_info.get('principle', ''),
                     "advantages": method_info.get('advantages', []),
                     "disadvantages": method_info.get('disadvantages', []),
-                            "applicable_scenarios": method_info.get('applicable_scenarios', [])
+                    "applicable_scenarios": method_info.get('applicable_scenarios', []),
+                    "parameters": parameter_info.get('parameters', [])
                 }
             }
             
-            raise ValueError(f"Method {method_name} not found")
-            
         except Exception as e:
             self.logger.error(f"Error getting method details: {str(e)}")
             return {

date_feature/parameter.yaml

@@ -1,186 +1,255 @@
 feature_engineering_methods_parameters:
 
   LabelEncoder:
-    parameters: {}
+    description: "将分类标签编码为整数。"
+    parameters: []
 
   KBinsDiscretizer:
+    description: "将连续数据分箱为离散数据。"
     parameters:
-      n_bins:
+      - name: "n_bins"
-        description: "指定每个特征要分成的箱数。可以是单个整数，表示所有特征使用相同的箱数；也可以是形状为 (n_features,) 的数组，为每个特征指定不同的箱数。"
+        type: "int or array-like"
         default: 5
-      encode:
+        description: "指定每个特征要分成的箱数。可以是单个整数，表示所有特征使用相同的箱数；也可以是形状为 (n_features,) 的数组，为每个特征指定不同的箱数。"
+      - name: "encode"
+        type: "str"
+        default: "onehot"
         description: "指定离散化后输出的编码方式。可选值包括 'onehot'（独热编码）、'onehot-dense'（密集独热编码）、'ordinal'（序数编码）。"
-        default: 'onehot'
+      - name: "strategy"
-      strategy:
+        type: "str"
+        default: "quantile"
         description: "定义分箱策略。可选值包括 'uniform'（均匀分箱）、'quantile'（分位数分箱）、'kmeans'（K-Means 聚类分箱）。"
-        default: 'quantile'
 
   FunctionTransformer:
+    description: "对数据应用自定义函数进行转换。"
     parameters:
-      func:
+      - name: "func"
+        type: "callable"
+        default: null
         description: "要应用于输入数据的函数。"
+      - name: "inverse_func"
+        type: "callable"
         default: null
-      inverse_func:
         description: "func 的逆函数，如果存在。"
-        default: null
+      - name: "validate"
-      validate:
+        type: "bool"
-        description: "布尔值，指示是否在转换前验证输入数据。"
         default: false
-      accept_sparse:
+        description: "指示是否在转换前验证输入数据。"
-        description: "布尔值，指示是否接受稀疏矩阵作为输入。"
+      - name: "accept_sparse"
+        type: "bool"
         default: false
-      check_inverse:
+        description: "指示是否接受稀疏矩阵作为输入。"
-        description: "布尔值，指示在适合期间是否检查 func 和 inverse_func 是否互为逆函数。"
+      - name: "check_inverse"
+        type: "bool"
         default: true
-      kw_args:
+        description: "指示在适合期间是否检查 func 和 inverse_func 是否互为逆函数。"
+      - name: "kw_args"
+        type: "dict"
+        default: null
         description: "传递给 func 的其他关键字参数。"
+      - name: "inv_kw_args"
+        type: "dict"
         default: null
-      inv_kw_args:
         description: "传递给 inverse_func 的其他关键字参数。"
-        default: null
 
   PowerTransformer:
+    description: "对数据进行幂变换以使其更符合正态分布。"
     parameters:
-      method:
+      - name: "method"
+        type: "str"
+        default: "yeo-johnson"
         description: "指定变换方法。可选值包括 'yeo-johnson' 和 'box-cox'。"
-        default: 'yeo-johnson'
+      - name: "standardize"
-      standardize:
+        type: "bool"
-        description: "布尔值，指示是否在变换后将数据标准化为零均值和单位方差。"
         default: true
-      copy:
+        description: "指示是否在变换后将数据标准化为零均值和单位方差。"
-        description: "布尔值，指示是否复制输入数据，或在原地进行变换。"
+      - name: "copy"
+        type: "bool"
         default: true
+        description: "指示是否复制输入数据，或在原地进行变换。"
 
   QuantileTransformer:
+    description: "将数据转换为均匀分布或正态分布。"
     parameters:
-      n_quantiles:
+      - name: "n_quantiles"
-        description: "用于分位数变换的分位数数量。"
+        type: "int"
         default: 1000
-      output_distribution:
+        description: "用于分位数变换的分位数数量。"
+      - name: "output_distribution"
+        type: "str"
+        default: "uniform"
         description: "指定输出分布。可选值包括 'uniform' 和 'normal'。"
-        default: 'uniform'
+      - name: "ignore_implicit_zeros"
-      ignore_implicit_zeros:
+        type: "bool"
-        description: "布尔值，指示是否忽略隐式零。"
         default: false
-      subsample:
+        description: "指示是否忽略隐式零。"
+      - name: "subsample"
+        type: "int"
+        default: 100000
         description: "用于计算分位数的子样本大小。"
-        default: 1e5
+      - name: "random_state"
-      random_state:
+        type: "int or None"
-        description: "用于随机数生成的种子。"
         default: null
-      copy:
+        description: "用于随机数生成的种子。"
-        description: "布尔值，指示是否复制输入数据，或在原地进行变换。"
+      - name: "copy"
+        type: "bool"
         default: true
+        description: "指示是否复制输入数据，或在原地进行变换。"
 
   FeatureHasher:
+    description: "使用哈希技巧将特征映射到向量。"
     parameters:
-      n_features:
+      - name: "n_features"
-        description: "哈希空间的维度。"
+        type: "int"
         default: 1048576
-      input_type:
+        description: "哈希空间的维度。"
+      - name: "input_type"
+        type: "str"
+        default: "dict"
         description: "输入数据的类型。可选值包括 'dict' 和 'pair'。"
-        default: 'dict'
+      - name: "dtype"
-      dtype:
+        type: "type"
+        default: "float64"
         description: "输出数据的类型。"
-        default: 'float64'
+      - name: "alternate_sign"
-      alternate_sign:
+        type: "bool"
-        description: "布尔值，指示是否在哈希时使用交替符号。"
         default: true
+        description: "指示是否在哈希时使用交替符号。"
 
   DictVectorizer:
+    description: "将符号表示的特征（如字典）转换为稀疏矩阵。"
     parameters:
-      dtype:
+      - name: "dtype"
+        type: "type"
+        default: "float64"
         description: "输出数据的类型。"
-        default: 'float64'
+      - name: "separator"
-      separator:
+        type: "str"
+        default: "="
         description: "用于分隔特征名称的分隔符。"
-        default: '='
+      - name: "sparse"
-      sparse:
+        type: "bool"
-        description: "布尔值，指示是否返回稀疏矩阵。"
         default: true
-      sort:
+        description: "指示是否返回稀疏矩阵。"
-        description: "布尔值，指示是否对特征名称排序。"
+      - name: "sort"
+        type: "bool"
         default: true
+        description: "指示是否对特征名称排序。"
+
 
   PCA:
+  description: "主成分分析，用于降维。"
   parameters:
-      n_components:
+    - name: "n_components"
+      type: "int, float, None or str"
+      default: null
       description: "要保留的主成分数量。可以是整数、浮点数或 'mle'。"
-        default: null
+    - name: "copy"
-      copy:
+      type: "bool"
-        description: "布尔值，指示是否复制输入数据，或在原地进行变换。"
       default: true
-      whiten:
+      description: "指示是否复制输入数据，或在原地进行变换。"
-        description: "布尔值，指示是否对主成分进行白化。"
+    - name: "whiten"
+      type: "bool"
       default: false
-      svd_solver:
+      description: "指示是否对主成分进行白化。"
+    - name: "svd_solver"
+      type: "str"
+      default: "auto"
       description: "用于计算 SVD 的方法。可选值包括 'auto'、'full'、'arpack' 和 'randomized'。"
-        default: 'auto'
+    - name: "tol"
-      tol:
+      type: "float"
-        description: "奇异值分解的容差。"
       default: 0.0
-      iterated_power:
+      description: "奇异值分解的容差。"
-        description: "用于随机化 SVD 的迭代次数。"
+    - name: "iterated_power"
+      type: "int or 'auto'"
       default: 'auto'
-      random_state:
+      description: "用于随机化 SVD 的迭代次数。"
-        description: "用于随机数生成的种子。"
+    - name: "random_state"
+      type: "int or None"
       default: null
+      description: "用于随机数生成的种子。"
 
   SelectKBest:
+    description: "选择最重要的 K 个特征。"
     parameters:
-      score_func:
+      - name: "score_func"
+        type: "callable"
+        default: "f_classif"
         description: "用于计算特征得分的函数。"
-        default: 'f_classif'
+      - name: "k"
-      k:
+        type: "int"
-        description: "要选择的特征数量。"
         default: 10
+        description: "要选择的特征数量。"
 
   RFE:
+    description: "递归特征消除，用于选择最重要的特征。"
     parameters:
-      estimator:
+      - name: "estimator"
+        type: "object"
+        default: null
         description: "用于特征选择的基模型。"
+      - name: "n_features_to_select"
+        type: "int"
         default: null
-      n_features_to_select:
         description: "要选择的特征数量。"
-        default: null
+      - name: "step"
-      step:
+        type: "int"
-        description: "每次迭代要移除的特征数量。"
         default: 1
-      verbose:
+        description: "每次迭代要移除的特征数量。"
-        description: "控制冗长模式的整数。"
+      - name: "verbose"
+        type: "int"
         default: 0
+        description: "控制冗长模式的整数。"
 
   PolynomialFeatures:
+    description: "生成多项式特征，增加模型的非线性能力。"
     parameters:
-      degree:
+      - name: "degree"
-        description: "生成多项式特征的最高次数。"
+        type: "int"
         default: 2
-      interaction_only:
+        description: "生成多项式特征的最高次数。"
-        description: "布尔值，指示是否仅包含交互项。"
+      - name: "interaction_only"
+        type: "bool"
         default: false
-      include_bias:
+        description: "指示是否仅包含交互项。"
-        description: "布尔值，指示是否包含偏置列。"
+      - name: "include_bias"
+        type: "bool"
         default: true
-      order:
+        description: "指示是否包含偏置列。"
+      - name: "order"
+        type: "str"
+        default: "C"
         description: "输出特征的顺序。可选值包括 'C' 和 'F'。"
-        default: 'C'
 
   OneHotEncoder:
+    description: "将分类特征转换为独热编码。"
     parameters:
-      categories:
+      - name: "categories"
+        type: "str or list or 'auto'"
+        default: "auto"
         description: "指定每个特征的类别。"
-        default: 'auto'
+      - name: "drop"
-      drop:
+        type: "str or array-like"
-        description: "指定要从每个特征中删除的类别。"
         default: null
-      sparse:
+        description: "指定要从每个特征中删除的类别。"
-        description: "布尔值，指示是否返回稀疏矩阵。"
+      - name: "sparse"
+        type: "bool"
         default: true
-      dtype:
+        description: "指示是否返回稀疏矩阵。"
+      - name: "dtype"
+        type: "type"
+        default: "float64"
         description: "输出数据的类型。"
-        default: 'float64'
+      - name: "handle_unknown"
-      handle_unknown:
+        type: "str"
-        description: "指定如何处理未知类别。可选值"
+        default: "error"
+        description: "指定如何处理未知类别。可选值包括 'error'（抛出异常）、'ignore'（忽略）。"
+      - name: "max_categories"
+        type: "int or None"
+        default: null
+        description: "在类别过多时，将类别限制为最大类别数量。"
+
+  
 
 
   

date_feature/parameter_new.yaml

@@ -1,255 +0,0 @@
-feature_engineering_methods_parameters:
-
-  LabelEncoder:
-    description: "将分类标签编码为整数。"
-    parameters: []
-
-  KBinsDiscretizer:
-    description: "将连续数据分箱为离散数据。"
-    parameters:
-      - name: "n_bins"
-        type: "int or array-like"
-        default: 5
-        description: "指定每个特征要分成的箱数。可以是单个整数，表示所有特征使用相同的箱数；也可以是形状为 (n_features,) 的数组，为每个特征指定不同的箱数。"
-      - name: "encode"
-        type: "str"
-        default: "onehot"
-        description: "指定离散化后输出的编码方式。可选值包括 'onehot'（独热编码）、'onehot-dense'（密集独热编码）、'ordinal'（序数编码）。"
-      - name: "strategy"
-        type: "str"
-        default: "quantile"
-        description: "定义分箱策略。可选值包括 'uniform'（均匀分箱）、'quantile'（分位数分箱）、'kmeans'（K-Means 聚类分箱）。"
-
-  FunctionTransformer:
-    description: "对数据应用自定义函数进行转换。"
-    parameters:
-      - name: "func"
-        type: "callable"
-        default: null
-        description: "要应用于输入数据的函数。"
-      - name: "inverse_func"
-        type: "callable"
-        default: null
-        description: "func 的逆函数，如果存在。"
-      - name: "validate"
-        type: "bool"
-        default: false
-        description: "指示是否在转换前验证输入数据。"
-      - name: "accept_sparse"
-        type: "bool"
-        default: false
-        description: "指示是否接受稀疏矩阵作为输入。"
-      - name: "check_inverse"
-        type: "bool"
-        default: true
-        description: "指示在适合期间是否检查 func 和 inverse_func 是否互为逆函数。"
-      - name: "kw_args"
-        type: "dict"
-        default: null
-        description: "传递给 func 的其他关键字参数。"
-      - name: "inv_kw_args"
-        type: "dict"
-        default: null
-        description: "传递给 inverse_func 的其他关键字参数。"
-
-  PowerTransformer:
-    description: "对数据进行幂变换以使其更符合正态分布。"
-    parameters:
-      - name: "method"
-        type: "str"
-        default: "yeo-johnson"
-        description: "指定变换方法。可选值包括 'yeo-johnson' 和 'box-cox'。"
-      - name: "standardize"
-        type: "bool"
-        default: true
-        description: "指示是否在变换后将数据标准化为零均值和单位方差。"
-      - name: "copy"
-        type: "bool"
-        default: true
-        description: "指示是否复制输入数据，或在原地进行变换。"
-
-  QuantileTransformer:
-    description: "将数据转换为均匀分布或正态分布。"
-    parameters:
-      - name: "n_quantiles"
-        type: "int"
-        default: 1000
-        description: "用于分位数变换的分位数数量。"
-      - name: "output_distribution"
-        type: "str"
-        default: "uniform"
-        description: "指定输出分布。可选值包括 'uniform' 和 'normal'。"
-      - name: "ignore_implicit_zeros"
-        type: "bool"
-        default: false
-        description: "指示是否忽略隐式零。"
-      - name: "subsample"
-        type: "int"
-        default: 100000
-        description: "用于计算分位数的子样本大小。"
-      - name: "random_state"
-        type: "int or None"
-        default: null
-        description: "用于随机数生成的种子。"
-      - name: "copy"
-        type: "bool"
-        default: true
-        description: "指示是否复制输入数据，或在原地进行变换。"
-
-  FeatureHasher:
-    description: "使用哈希技巧将特征映射到向量。"
-    parameters:
-      - name: "n_features"
-        type: "int"
-        default: 1048576
-        description: "哈希空间的维度。"
-      - name: "input_type"
-        type: "str"
-        default: "dict"
-        description: "输入数据的类型。可选值包括 'dict' 和 'pair'。"
-      - name: "dtype"
-        type: "type"
-        default: "float64"
-        description: "输出数据的类型。"
-      - name: "alternate_sign"
-        type: "bool"
-        default: true
-        description: "指示是否在哈希时使用交替符号。"
-
-  DictVectorizer:
-    description: "将符号表示的特征（如字典）转换为稀疏矩阵。"
-    parameters:
-      - name: "dtype"
-        type: "type"
-        default: "float64"
-        description: "输出数据的类型。"
-      - name: "separator"
-        type: "str"
-        default: "="
-        description: "用于分隔特征名称的分隔符。"
-      - name: "sparse"
-        type: "bool"
-        default: true
-        description: "指示是否返回稀疏矩阵。"
-      - name: "sort"
-        type: "bool"
-        default: true
-        description: "指示是否对特征名称排序。"
-
-
-  PCA:
-  description: "主成分分析，用于降维。"
-  parameters:
-    - name: "n_components"
-      type: "int, float, None or str"
-      default: null
-      description: "要保留的主成分数量。可以是整数、浮点数或 'mle'。"
-    - name: "copy"
-      type: "bool"
-      default: true
-      description: "指示是否复制输入数据，或在原地进行变换。"
-    - name: "whiten"
-      type: "bool"
-      default: false
-      description: "指示是否对主成分进行白化。"
-    - name: "svd_solver"
-      type: "str"
-      default: "auto"
-      description: "用于计算 SVD 的方法。可选值包括 'auto'、'full'、'arpack' 和 'randomized'。"
-    - name: "tol"
-      type: "float"
-      default: 0.0
-      description: "奇异值分解的容差。"
-    - name: "iterated_power"
-      type: "int or 'auto'"
-      default: 'auto'
-      description: "用于随机化 SVD 的迭代次数。"
-    - name: "random_state"
-      type: "int or None"
-      default: null
-      description: "用于随机数生成的种子。"
-
-  SelectKBest:
-    description: "选择最重要的 K 个特征。"
-    parameters:
-      - name: "score_func"
-        type: "callable"
-        default: "f_classif"
-        description: "用于计算特征得分的函数。"
-      - name: "k"
-        type: "int"
-        default: 10
-        description: "要选择的特征数量。"
-
-  RFE:
-    description: "递归特征消除，用于选择最重要的特征。"
-    parameters:
-      - name: "estimator"
-        type: "object"
-        default: null
-        description: "用于特征选择的基模型。"
-      - name: "n_features_to_select"
-        type: "int"
-        default: null
-        description: "要选择的特征数量。"
-      - name: "step"
-        type: "int"
-        default: 1
-        description: "每次迭代要移除的特征数量。"
-      - name: "verbose"
-        type: "int"
-        default: 0
-        description: "控制冗长模式的整数。"
-
-  PolynomialFeatures:
-    description: "生成多项式特征，增加模型的非线性能力。"
-    parameters:
-      - name: "degree"
-        type: "int"
-        default: 2
-        description: "生成多项式特征的最高次数。"
-      - name: "interaction_only"
-        type: "bool"
-        default: false
-        description: "指示是否仅包含交互项。"
-      - name: "include_bias"
-        type: "bool"
-        default: true
-        description: "指示是否包含偏置列。"
-      - name: "order"
-        type: "str"
-        default: "C"
-        description: "输出特征的顺序。可选值包括 'C' 和 'F'。"
-
-  OneHotEncoder:
-    description: "将分类特征转换为独热编码。"
-    parameters:
-      - name: "categories"
-        type: "str or list or 'auto'"
-        default: "auto"
-        description: "指定每个特征的类别。"
-      - name: "drop"
-        type: "str or array-like"
-        default: null
-        description: "指定要从每个特征中删除的类别。"
-      - name: "sparse"
-        type: "bool"
-        default: true
-        description: "指示是否返回稀疏矩阵。"
-      - name: "dtype"
-        type: "type"
-        default: "float64"
-        description: "输出数据的类型。"
-      - name: "handle_unknown"
-        type: "str"
-        default: "error"
-        description: "指定如何处理未知类别。可选值包括 'error'（抛出异常）、'ignore'（忽略）。"
-      - name: "max_categories"
-        type: "int or None"
-        default: null
-        description: "在类别过多时，将类别限制为最大类别数量。"
-
-  
-
-
-  

example_method_reader.py

@@ -0,0 +1,14 @@
+from data_process.method_reader_date_process import MethodReader
+
+# 创建方法读取器实例
+reader = MethodReader()
+
+# 获取所有预处理方法
+methods = reader.get_preprocessing_methods()
+print("预处理方法列表:")
+print(methods)
+
+# 获取特定方法的详细信息
+method_details = reader.get_method_details('StandardScaler')
+print("\nStandardScaler方法详情:")
+print(method_details) 

mlruns/0/meta.yaml

@@ -0,0 +1,6 @@
+artifact_location: mlflow-artifacts:/0
+creation_time: 1739520200398
+experiment_id: '0'
+last_update_time: 1739520200398
+lifecycle_stage: active
+name: Default

tests/test_method_reader.py

@@ -0,0 +1,49 @@
+import unittest
+from data_process.method_reader_date_process import MethodReader
+
+class TestMethodReader(unittest.TestCase):
+    def setUp(self):
+        self.reader = MethodReader()
+        
+    def test_get_preprocessing_methods(self):
+        result = self.reader.get_preprocessing_methods()
+        self.assertEqual(result['status'], 'success')
+        self.assertIsInstance(result['methods'], list)
+        
+        # 检查返回的方法列表
+        methods = result['methods']
+        self.assertTrue(any(m['name'] == 'data_scaler' for m in methods))
+        self.assertTrue(any(m['name'] == 'missing_value_handler' for m in methods))
+        self.assertTrue(any(m['name'] == 'outlier_detector' for m in methods))
+        
+    def test_get_method_details(self):
+        # 测试获取StandardScaler的详细信息
+        result = self.reader.get_method_details('StandardScaler')
+        self.assertEqual(result['status'], 'success')
+        self.assertEqual(result['method']['name'], 'StandardScaler')
+        
+        # 检查返回的详细信息字段
+        method = result['method']
+        self.assertIn('description', method)
+        self.assertIn('principle', method)
+        self.assertIn('advantages', method)
+        self.assertIn('disadvantages', method)
+        self.assertIn('applicable_scenarios', method)
+        self.assertIn('parameters', method)
+        
+        # 检查参数信息
+        parameters = method['parameters']
+        self.assertIsInstance(parameters, list)
+        if parameters:
+            param = parameters[0]
+            self.assertIn('name', param)
+            self.assertIn('type', param)
+            self.assertIn('default', param)
+            self.assertIn('description', param)
+        
+        # 测试获取不存在的方法
+        result = self.reader.get_method_details('NonExistentMethod')
+        self.assertEqual(result['status'], 'error')
+        
+if __name__ == '__main__':
+    unittest.main()