docs: ✏️ support partial function and update metric_forward

basicts/data/__init__.py

@@ -3,7 +3,7 @@
 from easytorch.utils.registry import scan_modules
 
 from .registry import SCALER_REGISTRY
-from .dataset import TimeSeriesForecastingDataset
+from .dataset_zoo.simple_tsf_dataset import TimeSeriesForecastingDataset
 
 __all__ = ["SCALER_REGISTRY", "TimeSeriesForecastingDataset"]
 

basicts/data/dataset.py → basicts/data/dataset_zoo/simple_tsf_dataset.py

@@ -3,7 +3,7 @@
 import torch
 from torch.utils.data import Dataset
 
-from ..utils import load_pkl
+from ...utils import load_pkl
 
 
 class TimeSeriesForecastingDataset(Dataset):

basicts/runners/base_tsf_runner.py

@@ -1,4 +1,5 @@
 import math
+import inspect
 import functools
 from typing import Tuple, Union, Optional, Dict
 
@@ -300,16 +301,19 @@ def metric_forward(self, metric_func, args) -> torch.Tensor:
         Returns:
             torch.Tensor: metric value.
         """
-        # filter out keys that are not in function arguments
-        args = {k: v for k, v in args.items() if k in metric_func.__code__.co_varnames}
+        covariate_names = inspect.signature(metric_func).parameters.keys()
+        args = {k: v for k, v in args.items() if k in covariate_names}
 
         if isinstance(metric_func, functools.partial):
             # 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
+                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)
         else:
             raise TypeError("Unknown metric type: {0}".format(type(metric_func)))

basicts/utils/__init__.py

@@ -1,5 +1,9 @@
 from .serialization import load_adj, load_pkl, dump_pkl, load_node2vec_emb
 from .misc import clock, check_nan_inf, remove_nan_inf
+from .misc import partial_func as partial
 from .xformer import data_transformation_4_xformer
 
-__all__ = ["load_adj", "load_pkl", "dump_pkl", "load_node2vec_emb", "clock", "check_nan_inf", "remove_nan_inf", "data_transformation_4_xformer"]
+__all__ = ["load_adj", "load_pkl", "dump_pkl",
+           "load_node2vec_emb", "clock", "check_nan_inf",
+           "remove_nan_inf", "data_transformation_4_xformer",
+           "partial"]

basicts/utils/misc.py

@@ -1,8 +1,17 @@
 import time
+from functools import partial
 
 import torch
 
 
+class partial_func(partial):
+    """partial class.
+        __str__ in functools.partial contains the address of the function, which changes randomly and will disrupt easytorch's md5 calculation.
+    """
+
+    def __str__(self):
+        return "partial({}, {})".format(self.func.__name__, self.keywords)
+
 def clock(func):
     """clock decorator"""
     def clocked(*args, **kw):