feat: 🎸 update metric_forward and fix bugs in l1 and l2 loss

basicts/losses/losses.py

@@ -1,15 +1,17 @@
+from typing import Optional
+
 import numpy as np
 import torch
 import torch.nn.functional as F
 
 
-def l1_loss(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+def l1_loss(prediction: torch.Tensor, target: torch._tensor, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = "mean") -> torch.Tensor:
     """unmasked mae."""
 
     return F.l1_loss(prediction, target)
 
 
-def l2_loss(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+def l2_loss(prediction: torch.Tensor, target: torch.Tensor, size_average: Optional[bool] = None, reduce: Optional[bool] = None, reduction: str = "mean") -> torch.Tensor:
     """unmasked mse"""
 
     return F.mse_loss(prediction, target)

basicts/runners/base_tsf_runner.py

@@ -308,13 +308,18 @@ def metric_forward(self, metric_func, args) -> torch.Tensor:
             # support partial function
             # users can define their partial function in the config file
             # e.g., functools.partial(masked_mase, freq="4", null_val=np.nan)
-            if "null_val" in covariate_names and "null_val" not in metric_func.keywords: # if null_val is required but not provided
+            if "null_val" in metric_func.keywords: # null_val is provided
+                # assert self.null_val is None, "Null_val is provided in metric function. The CFG.NULL_VAL should not be set."
+                pass # error when using multiple metrics, some of which require null_val and some do not
+            elif "null_val" in covariate_names: # null_val is required but not provided
                 args["null_val"] = self.null_val
             metric_item = metric_func(**args)
         elif callable(metric_func):
             # is a function
             # filter out keys that are not in function arguments
-            metric_item = metric_func(**args, null_val=self.null_val)
+            if "null_val" in covariate_names: # null_val is required
+                args["null_val"] = self.null_val
+            metric_item = metric_func(**args)
         else:
             raise TypeError("Unknown metric type: {0}".format(type(metric_func)))
         return metric_item