图片打印加上中文

utils/common.py

@@ -92,11 +92,11 @@ class MLModel:
 
         fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
         ax1.bar(np.arange(len(precision)), precision)
-        ax1.set_title('Precision')
+        ax1.set_title('Precision(精确率)')
         ax2.bar(np.arange(len(recall)), recall)
-        ax2.set_title('Recall')
+        ax2.set_title('Recall(召回率)')
         ax3.bar(np.arange(len(f1)), f1)
-        ax3.set_title('F1 Score')
+        ax3.set_title('F1 Score(F1得分)')
         # 保存图片
         parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
         evaluate_result_path = os.path.join(parent_dir, self.config['evaluate_result_path'])
@@ -179,18 +179,18 @@ class MLModel:
             # 打印训练和验证过程的可视化图片
             plt.close('all')
             fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
-            ax1.plot(train_loss_history, label='Train Loss')
-            ax1.plot(val_loss_history, label='Validation Loss')
+            ax1.plot(train_loss_history, label='Train Loss(训练损失)')
+            ax1.plot(val_loss_history, label='Validation Loss(验证损失)')
             ax1.set_title('Loss')
             ax1.legend()
-            ax2.plot(train_acc_history, label='Train Accuracy')
-            ax2.plot(val_acc_history, label='Validation Accuracy')
+            ax2.plot(train_acc_history, label='Train Accuracy(训练正确率)')
+            ax2.plot(val_acc_history, label='Validation Accuracy(验证正确率)')
             ax2.set_title('Accuracy')
             ax2.legend()
-            ax3.plot(val_f1_history, label='Validation F1')
-            ax3.plot(val_precision_history, label='Validation Precision')
-            ax3.plot(val_recall_history, label='Validation Recall')
-            ax3.set_title('Precision Recall F1-Score (Macro Mean)')
+            ax3.plot(val_f1_history, label='Validation F1(验证F1得分)')
+            ax3.plot(val_precision_history, label='Validation Precision(验证精确率)')
+            ax3.plot(val_recall_history, label='Validation Recall(验证召回率)')
+            ax3.set_title('Precision Recall F1-Score (Macro Mean)(宏平均)')
             ax3.legend()
             # 保存图片
             parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))