Add metrics for time series classification

Author: yisongfu

basicts/metrics/cls_metrics.py

@@ -0,0 +1,60 @@
+import torch
+
+
+def accuracy(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate the accuracy of predictions.
+
+    Args:
+        pred (torch.Tensor): The predicted values as a tensor.
+        target (torch.Tensor): The ground truth values as a tensor with the same shape as `pred`.
+
+    Returns:
+        torch.Tensor: A scalar tensor representing the accuracy.
+    """
+    return (pred == target).float().mean()
+
+def precision(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate the precision of predictions.
+
+    Args:
+        pred (torch.Tensor): The predicted values as a tensor.
+        target (torch.Tensor): The ground truth values as a tensor with the same shape as `pred`.
+
+    Returns:
+        torch.Tensor: A scalar tensor representing the precision.
+    """
+    true_positives = (pred == target).float().sum()
+    false_positives = (pred != target).float().sum()
+    return true_positives / (true_positives + false_positives)
+
+def recall(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate the recall of predictions.
+
+    Args:
+        pred (torch.Tensor): The predicted values as a tensor.
+        target (torch.Tensor): The ground truth values as a tensor with the same shape as `pred`.
+
+    Returns:
+        torch.Tensor: A scalar tensor representing the recall.
+    """
+    true_positives = (pred == target).float().sum()
+    false_negatives = (pred != target).float().sum()
+    return true_positives / (true_positives + false_negatives)
+
+def f1_score(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+    """
+    Calculate the F1 score of predictions.
+
+    Args:
+        pred (torch.Tensor): The predicted values as a tensor.
+        target (torch.Tensor): The ground truth values as a tensor with the same shape as `pred`.
+
+    Returns:
+        torch.Tensor: A scalar tensor representing the F1 score.
+    """
+    precision_item = precision(pred, target)
+    recall_item = recall(pred, target)
+    return 2 * (precision_item * recall_item) / (precision_item + recall_item)