处理用户模型

main_spider.py

@@ -130,14 +130,15 @@ class LightGBM(object):
         )
         test_data = lgb.Dataset(X_test, label=Y_test, reference=train_data)
         params = {
-            'num_leaves': 25,
-            'learning_rate': 0.00435469653451866,
-            'feature_fraction': 0.8659696885542688,
-            'bagging_fraction': 0.4671847911224712,
+            'num_leaves': 20,
+            'learning_rate': 0.03372815687364156,
+            'feature_fraction': 0.48848665517495693,
+            'bagging_fraction': 0.679118348482125,
             'bagging_freq': 1,
-            # 'min_child_samples': 65,
-            "num_threads": 16,  # 线程数量
+            "num_threads": 16,
+            'min_child_samples': 38
         }
+
         # 训练模型
         num_round = 100
         print("开始训练......")
@@ -204,20 +205,20 @@ class LightGBM(object):
 
 
 if __name__ == "__main__":
-    # i = int(input("输入 1 训练， 输入 2 预测:\n"))
-    # if i == 1:
-    #     f = "train"
-    #     dt = "whole"
-    #     L = LightGBM(flag=f, dt=dt)
-    #     L.train_model()
-    # elif i == 2:
-    #     f = "predict"
-    #     dt = int(input("输入日期， 16-21:\n"))
-    #     L = LightGBM(flag=f, dt=dt)
-    #     # L.evaluate_model()
-    #     L.feature_importance()
-    L = LightGBM("train", "whole")
-    study = optuna.create_study(direction='maximize')
-    study.optimize(L.bays_params, n_trials=100)
-    print('Number of finished trials:', len(study.trials))
-    print('Best trial:', study.best_trial.params)
+    i = int(input("输入 1 训练， 输入 2 预测:\n"))
+    if i == 1:
+        f = "train"
+        dt = "whole"
+        L = LightGBM(flag=f, dt=dt)
+        L.train_model()
+    elif i == 2:
+        f = "predict"
+        dt = int(input("输入日期， 16-21:\n"))
+        L = LightGBM(flag=f, dt=dt)
+        L.evaluate_model()
+        L.feature_importance()
+    # L = LightGBM("train", "whole")
+    # study = optuna.create_study(direction='maximize')
+    # study.optimize(L.bays_params, n_trials=100)
+    # print('Number of finished trials:', len(study.trials))
+    # print('Best trial:', study.best_trial.params)