A new strategy is added haha

ChanLun_Classifier.py

@@ -62,15 +62,15 @@ class ChanLunClassifier:
         # 默认XGBoost参数
         default_params = {
             'objective': 'binary:logistic',
-            'max_depth': 4,
-            'eta': 0.03,
+            'max_depth': 8,
+            'eta': 0.01,
             'subsample': 0.8,
             'colsample_bytree': 0.8,
             'eval_metric': 'auc',
-            'gamma': 0.1,
-            'min_child_weight': 3,
-            'alpha': 1, # L1正则化
-            'lambda': 3, # L2正则化
+            'gamma': 0.0,
+            'min_child_weight': 1,
+            'alpha': 0, # L1正则化
+            'lambda': 0.5, # L2正则化
             'scale_pos_weight': 1
         }
         
@@ -208,7 +208,7 @@ class ChanLunClassifier:
         else:
             self.model = xgb.Booster()
             self.model.load_model(model_file_path)
-    def find_best_params(self, dataframe=None, save_csv=False, csv_path_prefix='param_'):
+    def find_best_params(self, dataframe=None, save_csv=False, csv_path_prefix='param_', model_name=None):
         """
         寻找最佳参数组合
         :param dataframe: 输入的DataFrame，如果为None则使用初始化时的dataframe
@@ -240,7 +240,7 @@ class ChanLunClassifier:
             param_info = f"eta{params['eta']}_depth{params['max_depth']}"
             train_csv_path = f"{csv_path_prefix}train_{param_info}.csv" if save_csv else None
             
-            model = self.train_model(dataframe=dataframe, data_file_path=train_csv_path, custom_params=params)
+            model = self.train_model(dataframe=dataframe, data_file_path=train_csv_path, custom_params=params, model_name=model_name)
             
             # 分割数据集，后20%用于测试
             if dataframe is None:
@@ -298,6 +298,8 @@ class ChanLunClassifier:
         for klc in klc_list:
             if klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
                 sample_list.append(klc)
+        klc_count = 0
+        print('Processing data...')
         for klc in sample_list:
             if bi_index >= len(bi_list):
                 bi_index = len(bi_list) - 1
@@ -333,6 +335,10 @@ class ChanLunClassifier:
             
             feature_data.append(feature_vec)
             labels.append(label)
+            klc_count += 1
+            percent = klc_count/len(sample_list)*100
+            if percent % 10 == 0:
+                print('Data processed:', percent, '%')
         for index, key in enumerate(feature_keys):
             print(index, key, feature_data[0][index])
         # 如果需要保存到CSV