Changes from review

src/metatrain/utils/scaler.py

@@ -16,6 +16,10 @@ class Scaler(torch.nn.Module):
     A class that scales the targets of regression problems to unit standard
     deviation.
 
+    In most cases, this should be used in conjunction with a composition model
+    (that removes the multi-dimensional "mean" across the composition space) and/or
+    other additive models. See the `train_model` method for more details.
+
     :param model_hypers: A dictionary of model hyperparameters. The paramater is ignored
         and is only present to be consistent with the general model API.
     :param dataset_info: An object containing information about the dataset, including
@@ -38,6 +42,8 @@ def __init__(self, model_hypers: Dict, dataset_info: DatasetInfo):
 
         self.new_targets: Dict[str, TargetInfo] = dataset_info.targets
         self.outputs: Dict[str, ModelOutput] = {}
+        # Initially, the scales are empty. They will be expanded as new outputs
+        # are registered with `_add_output`.
         self.register_buffer("scales", torch.ones((0,), dtype=torch.float64))
         self.output_name_to_output_index: Dict[str, int] = {}
         for target_name, target_info in self.dataset_info.targets.items():
@@ -155,26 +161,16 @@ def forward(
         :raises ValueError: If an output does not have a corresponding
             scale in the scaler model.
         """
-
-        for output_name in outputs:
-            if output_name.startswith("mtt::aux::") or output_name == "features":
-                continue
-            if output_name not in self.outputs.keys():
-                raise ValueError(
-                    f"output key {output_name} is not supported by this scaler "
-                    "model."
-                )
-
         scaled_outputs: Dict[str, TensorMap] = {}
         for target_key, target in outputs.items():
-            if target_key.startswith("mtt::aux::") or target_key == "features":
-                scaled_outputs[target_key] = target
-            else:
+            if target_key in self.outputs:
                 scale = float(
                     self.scales[self.output_name_to_output_index[target_key]].item()
                 )
                 scaled_target = metatensor.torch.multiply(target, scale)
                 scaled_outputs[target_key] = scaled_target
+            else:
+                scaled_outputs[target_key] = target
 
         return scaled_outputs