feature_importance

main.py

@@ -53,9 +53,9 @@ class LightGBM(object):
             "out_share_cnt",
             "out_collection_cnt",
         ]
-        self.split_c = 0.98
+        self.split_c = 0.99
         self.yc = 0.8
-        self.model = "lightgbm_tag_train_01.bin"
+        self.model = "lightgbm_tag_train_02.bin"
 
     def generate_x_data(self):
         """
@@ -117,7 +117,7 @@ class LightGBM(object):
             "num_threads": 4,  # 线程数量
         }
         # 训练模型
-        num_round = 500
+        num_round = 1000
         print("开始训练......")
         bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])
         bst.save_model(self.model)
@@ -128,7 +128,7 @@ class LightGBM(object):
         评估模型性能
         :return:
         """
-        fw = open("summary_tag_01.txt", "a+", encoding="utf-8")
+        fw = open("summary_tag_02.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())
@@ -181,6 +181,6 @@ class LightGBM(object):
 
 if __name__ == "__main__":
     L = LightGBM()
-    # L.train_model()
+    L.train_model()
     L.evaluate_model()
-    # L.feature_importance()
+    L.feature_importance()