贝叶斯调参优化, 多线程优化

main.py

@@ -143,15 +143,16 @@ class LightGBM(object):
         params = {
             "objective": "binary",  # 指定二分类任务
             "metric": "binary_logloss",  # 评估指标为二分类的log损失
-            "num_leaves": 31,  # 叶子节点数
-            "learning_rate": 0.005,  # 学习率
-            "bagging_fraction": 0.9,  # 建树的样本采样比例
-            "feature_fraction": 0.8,  # 建树的特征选择比例
-            "bagging_freq": 5,  # k 意味着每 k 次迭代执行bagging
-            "num_threads": 4,  # 线程数量
+            "num_leaves": 36,  # 叶子节点数
+            "learning_rate":  0.08479152931388902,  # 学习率
+            "bagging_fraction": 0.6588121592044218,  # 建树的样本采样比例
+            "feature_fraction": 0.4572757903437793,  # 建树的特征选择比例
+            "bagging_freq": 2,  # k 意味着每 k 次迭代执行bagging
+            "num_threads": 16,  # 线程数量
+            "mini_child_samples": 71
         }
         # 训练模型
-        num_round = 2000
+        num_round = 100
         print("开始训练......")
         bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])
         bst.save_model(self.model)
@@ -215,10 +216,10 @@ class LightGBM(object):
 
 if __name__ == "__main__":
     L = LightGBM()
-    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)
-    # L.train_model()
-    # L.evaluate_model()
-    # L.feature_importance()
+    # 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)
+    L.train_model()
+    L.evaluate_model()
+    L.feature_importance()