Better training logging for GAP (#247)

src/metatrain/experimental/gap/trainer.py

@@ -38,17 +38,16 @@ def train(
             raise ValueError("GAP only supports a single training dataset")
         if len(validation_datasets) != 1:
             raise ValueError("GAP only supports a single validation dataset")
+        outputs_dict = model.dataset_info.targets
+        if len(outputs_dict.keys()) > 1:
+            raise NotImplementedError("More than one output is not supported yet.")
+        output_name = next(iter(outputs_dict.keys()))
 
         # Perform checks on the datasets:
         logger.info("Checking datasets for consistency")
         check_datasets(train_datasets, validation_datasets)
 
-        logger.info("Training on device cpu")
-
-        outputs_dict = model.dataset_info.targets
-        if len(outputs_dict.keys()) > 1:
-            raise NotImplementedError("More than one output is not supported yet.")
-        output_name = next(iter(outputs_dict.keys()))
+        logger.info(f"Training on device cpu with dtype {dtype}")
 
         # Calculate and set the composition weights:
         logger.info("Calculating composition weights")
@@ -58,7 +57,6 @@ def train(
         model.set_composition_weights(target_name, composition_weights, species)
 
         logger.info("Setting up data loaders")
-
         if len(train_datasets[0][0][output_name].keys) > 1:
             raise NotImplementedError(
                 "Found more than 1 key in targets. Assuming "
@@ -72,6 +70,8 @@ def train(
         )
         model._keys = train_y.keys
         train_structures = [sample["system"] for sample in train_dataset]
+
+        logger.info("Fitting composition energies")
         composition_energies = torch.zeros(len(train_y.block().values), dtype=dtype)
         for i, structure in enumerate(train_structures):
             for j, s in enumerate(species):
@@ -88,12 +88,12 @@ def train(
         )
         if len(train_y[0].gradients_list()) > 0:
             train_block.add_gradient("positions", train_y[0].gradient("positions"))
-
         train_y = metatensor.torch.TensorMap(
             train_y.keys,
             [train_block],
         )
 
+        logger.info("Calculating SOAP features")
         if len(train_y[0].gradients_list()) > 0:
             train_tensor = model._soap_torch_calculator.compute(
                 train_structures, gradients=["positions"]
@@ -113,11 +113,12 @@ def train(
         train_tensor = torch_tensor_map_to_core(train_tensor)
         train_y = torch_tensor_map_to_core(train_y)
 
+        logger.info("Selecting sparse points")
         lens = len(train_tensor[0].values)
         if model._sampler._n_to_select > lens:
             raise ValueError(
-                f"""number of sparse points ({model._sampler._n_to_select})
- should be smaller than the number of environments ({lens})"""
+                f"Number of sparse points ({model._sampler._n_to_select}) "
+                f"should be smaller than the number of environments ({lens})"
             )
         sparse_points = model._sampler.fit_transform(train_tensor)
         sparse_points = metatensor.operations.remove_gradients(sparse_points)
@@ -127,9 +128,7 @@ def train(
         else:
             alpha_forces = self.hypers["regularizer_forces"]
 
-        logger.info(f"Training on device cpu with dtype {dtype}")
-        logger.info("Fitting GAP")
-
+        logger.info("Fitting GAP model")
         model._subset_of_regressors.fit(
             train_tensor,
             sparse_points,