Update code comments

sml/metrics/classification/classification.py

@@ -29,21 +29,21 @@ def confusion_matrix(y_true, y_pred, labels, sample_weight=None, normalize=None)
     y_true = jnp.array(y_true)
     y_pred = jnp.array(y_pred)
 
-    # 获取标签的数量
+    # Get the number of tags
     num_labels = len(labels)
 
-    # 初始化混淆矩阵
+    # Initialize the confusion matrix
     cm = jnp.zeros((num_labels, num_labels), dtype=jnp.int32)
 
-    # 计算混淆矩阵
+    # Calculate the confusion matrix
     for i, label in enumerate(labels):
-        # 获取真实标签和预测标签为当前标签的布尔值
+        # Get the true label and predicted label as the Boolean value of the current label
         true_mask = (y_true == label)
         pred_mask = (y_pred == label)
 
-        # 更新混淆矩阵
+        # Update the confusion matrix
         for j, _ in enumerate(labels):
-            # 计算 TP, FP, FN, TN
+            # Calculate TP, FP, FN, TN
             cm = cm.at[i, j].set(jnp.sum(true_mask & (y_pred == j)))
 
     return cm
@@ -72,22 +72,21 @@ def top_k_accuracy_score(
     the top `k` labels predicted (ranked by predicted scores).
     """
 
-    # 转换 y_true 和 y_score 为 JAX 数组
     y_true = jnp.asarray(y_true)
     y_score = jnp.asarray(y_score)
 
     if labels is not None:
-        # 如果提供了标签，确保 y_true 和 y_score 包含在 labels 中
+        # If labels are provided, make sure y_true and y_score are included in labels
         labels = jnp.asarray(labels)
         y_true = jnp.searchsorted(labels, y_true, sorter=jnp.argsort(labels))
 
-    # 计算每个样本的前 k 个预测的索引
+    # Compute the indices of the top k predictions for each sample
     top_k_indices = jnp.argsort(y_score, axis=1)[:, -k:]
 
-    # 检查 y_true 是否在前 k 个预测中
+    # Check if y_true is among the top k predictions
     y_true_in_top_k = jnp.any(jnp.isin(y_true[:, None], top_k_indices), axis=1)
 
-    # 计算准确率
+    # Calculate accuracy
     correct_predictions = jnp.sum(y_true_in_top_k)
 
     if sample_weight is not None: