target encoding feature: 默认使用统计信息进行特征筛选

autox/feature_engineer/fe_target_encoding.py

@@ -44,17 +44,13 @@ def fit(self, df, target, df_feature_type = None, silence_cols = [], select_all
                     self.ops.append([feature])
 
         if not self.select_all:
-            if self.target is not None:
-                # 训练模型，对group_col进行筛选
-                pass
-            else:
-                # 通过统计信息进行筛选
-                del_targetencoding_cols = []
-                for targetencoding_col in self.ops:
-                    if df.drop_duplicates(targetencoding_col).shape[0] > df.shape[0] * 0.05:
-                        del_targetencoding_cols.append(targetencoding_col)
-                for targetencoding_col in del_targetencoding_cols:
-                    self.ops.remove(targetencoding_col)
+            # 通过统计信息进行筛选
+            del_targetencoding_cols = []
+            for targetencoding_col in self.ops:
+                if df.drop_duplicates(targetencoding_col).shape[0] > df.shape[0] * 0.05:
+                    del_targetencoding_cols.append(targetencoding_col)
+            for targetencoding_col in del_targetencoding_cols:
+                self.ops.remove(targetencoding_col)
 
     def get_ops(self):
         return self.ops