贝叶斯调参优化, 多线程优化

main.py

@@ -56,7 +56,7 @@ class LightGBM(object):
         ]
         self.split_c = 0.99
         self.yc = 0.8
-        self.model = "lightgbm_tag_train_02.bin"
+        self.model = "lightgbm_tag_train_04.bin"
 
     def bays_params(self, trial):
         """
@@ -163,7 +163,7 @@ class LightGBM(object):
         评估模型性能
         :return:
         """
-        fw = open("summary_tag_03.txt", "a+", encoding="utf-8")
+        fw = open("summary_tag_04.txt", "a+", encoding="utf-8")
         # 测试数据
         with open("produce_data/x_data_total_return_predict.json") as f1:
             x_list = json.loads(f1.read())
@@ -180,7 +180,7 @@ class LightGBM(object):
             X_test[key] = pd.to_numeric(X_test[key], errors="coerce")
         bst = lgb.Booster(model_file=self.model)
         y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)
-        y_pred_binary = [0 if i <= 0.164189 else 1 for i in list(y_pred)]
+        y_pred_binary = [0 if i <= 0.147637 else 1 for i in list(y_pred)]
         # 转换为二进制输出
         score_list = []
         for index, item in enumerate(list(y_pred)):