Update loss.py

torchts/nn/loss.py

@@ -1,15 +1,15 @@
 import torch
 
 
-def masked_mae_loss(y_pred, y_true):
+def masked_mae_loss(y_pred: torch.tensor, y_true: torch.tensor) -> torch.tensor:
     """Calculate masked mean absolute error loss
 
     Args:
         y_pred (torch.Tensor): Predicted values
         y_true (torch.Tensor): True values
 
     Returns:
-        torch.Tensor: Loss
+        torch.Tensor: output loss
     """
     mask = (y_true != 0).float()
     mask /= mask.mean()
@@ -21,9 +21,7 @@ def masked_mae_loss(y_pred, y_true):
     return loss.mean()
 
 
-def mis_loss(
-    y_pred: torch.tensor, y_true: torch.tensor, interval: float
-) -> torch.tensor:
+def mis_loss(y_pred: torch.tensor, y_true: torch.tensor, interval: float) -> torch.tensor:
     """Calculate MIS loss
 
     Args:
@@ -32,7 +30,7 @@ def mis_loss(
         interval (float): confidence interval (e.g. 0.95 for 95% confidence interval)
 
     Returns:
-        torch.tensor: output losses
+        torch.tensor: output loss
     """
     alpha = 1 - interval
     lower = y_pred[:, 0::2]
@@ -46,7 +44,7 @@ def mis_loss(
     return loss
 
 
-def quantile_loss(y_pred: torch.tensor, y_true: torch.tensor, quantile: float) -> float:
+def quantile_loss(y_pred: torch.tensor, y_true: torch.tensor, quantile: float) -> torch.tensor:
     """Calculate quantile loss
 
     Args:
@@ -55,9 +53,48 @@ def quantile_loss(y_pred: torch.tensor, y_true: torch.tensor, quantile: float) -
         quantile (float): quantile (e.g. 0.5 for median)
 
     Returns:
-        float: output losses
+        torch.tensor: output loss
     """
+    assert 0 < quantile < 1, "Quantile must be in (0, 1)"
     errors = y_true - y_pred
     loss = torch.max((quantile - 1) * errors, quantile * errors)
     loss = torch.mean(loss)
     return loss
+
+
+def log_loss(y_pred: torch.tensor, y_true: torch.tensor) -> torch.tensor:
+    """Ensure the predictions are in the range (0, 1)
+    Args:
+        y_pred (torch.tensor): Predicted values
+        y_true (torch.tensor): True values
+
+    Returns:
+        torch.tensor: output loss
+    """
+    y_pred = torch.clamp(y_pred, 1e-7, 1 - 1e-7)
+    return -torch.mean(y_true * torch.log(y_pred) + (1 - y_true) * torch.log(1 - y_pred))
+
+
+def mape_loss(y_pred: torch.tensor, y_true: torch.tensor) -> torch.tensor:
+    """Calculate mean absolute percentage loss
+    Args:
+        y_pred (torch.tensor): Predicted values
+        y_true (torch.tensor): True values
+
+    Returns:
+        torch.tensor: output loss
+    """
+    return torch.mean(torch.abs((y_true - y_pred) / y_true)) * 100
+
+
+def smape_loss(y_pred: torch.tensor, y_true: torch.tensor) -> torch.tensor:
+    """Calculate symmetric mean absolute percentage loss
+    Args:
+        y_pred (torch.tensor): Predicted values
+        y_true (torch.tensor): True values
+
+    Returns:
+        torch.tensor: output loss
+    """
+    denominator = (torch.abs(y_true) + torch.abs(y_pred)) / 2.0
+    return torch.mean(torch.abs(y_true - y_pred) / denominator) * 100