formatting

tests/test_utils/test_training_utils.py

@@ -13,7 +13,7 @@
 import os
 import tempfile
 import lightning.pytorch as pl
-from lightning.pytorch.callbacks import (EarlyStopping, ModelCheckpoint)
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
 from lightning.pytorch import Trainer
 from torchmetrics import Accuracy, R2Score
 import numpy as np
@@ -50,13 +50,11 @@ class SomeFusionModelClass:
     fold = 1
     extra_log_string_dict = {"param1": "value1", "param2": 42}
 
-    checkpoint_name = set_checkpoint_name(
-        fusion_model, fold, extra_log_string_dict
-    )
+    checkpoint_name = set_checkpoint_name(fusion_model, fold, extra_log_string_dict)
 
     assert (
-            checkpoint_name
-            == "SomeFusionModelClass_fold_1_param1_value1_param2_42_{epoch:02d}"
+        checkpoint_name
+        == "SomeFusionModelClass_fold_1_param1_value1_param2_42_{epoch:02d}"
     )
 
 
@@ -68,9 +66,7 @@ class SomeFusionModelClass:
     fold = None
     extra_log_string_dict = {"param1": "value1", "param2": 42}
 
-    checkpoint_name = set_checkpoint_name(
-        fusion_model, fold, extra_log_string_dict
-    )
+    checkpoint_name = set_checkpoint_name(fusion_model, fold, extra_log_string_dict)
 
     assert checkpoint_name == "SomeFusionModelClass_param1_value1_param2_42_{epoch:02d}"
 
@@ -83,9 +79,7 @@ class SomeFusionModelClass:
     fold = 2
     extra_log_string_dict = None
 
-    checkpoint_name = set_checkpoint_name(
-        fusion_model, fold, extra_log_string_dict
-    )
+    checkpoint_name = set_checkpoint_name(fusion_model, fold, extra_log_string_dict)
 
     assert checkpoint_name == "SomeFusionModelClass_fold_2_{epoch:02d}"
 
@@ -149,7 +143,8 @@ class SomeFusionModelClass:
     k = None
 
     checkpoint_filenames = get_checkpoint_filenames_for_subspace_models(
-        subspace_method, k)
+        subspace_method, k
+    )
 
     expected_filenames = [
         "subspace_SomeFusionModelClass_SubspaceModel1_key_value",
@@ -228,10 +223,10 @@ def test_get_checkpoint_filename_for_trained_fusion_model_not_found(params, mode
 
     # Attempt to get a checkpoint filename when no matching file exists
     with pytest.raises(
-            ValueError, match=r"Could not find checkpoint file with name .*"
+        ValueError, match=r"Could not find checkpoint file with name .*"
     ):
         get_checkpoint_filename_for_trained_fusion_model(
-            params['checkpoint_dir'], model, checkpoint_file_suffix
+            params["checkpoint_dir"], model, checkpoint_file_suffix
         )
 
 
@@ -253,7 +248,7 @@ def test_get_checkpoint_filename_for_trained_fusion_model_multiple_files(params,
 
     # Attempt to get a checkpoint filename when multiple matching files exist
     with pytest.raises(
-            ValueError, match=r"Found multiple checkpoint files with name .*"
+        ValueError, match=r"Found multiple checkpoint files with name .*"
     ):
         get_checkpoint_filename_for_trained_fusion_model(
             params["checkpoint_dir"], model, checkpoint_file_suffix
@@ -279,7 +274,10 @@ def mock_logger():
 
 def test_init_trainer_default(mock_logger):
     # Test initializing trainer with default parameters
-    trainer = init_trainer(mock_logger, output_paths={}, )
+    trainer = init_trainer(
+        mock_logger,
+        output_paths={},
+    )
     assert trainer is not None
     assert isinstance(trainer, Trainer)
     assert trainer.max_epochs == 1000
@@ -289,14 +287,18 @@ def test_init_trainer_default(mock_logger):
     assert trainer.checkpoint_callback is not None
 
 
-@pytest.mark.filterwarnings("ignore:.*GPU available but not used*.", )
+@pytest.mark.filterwarnings(
+    "ignore:.*GPU available but not used*.",
+)
 def test_init_trainer_custom_early_stopping(mock_logger):
     # Test initializing trainer with a custom early stopping callback
     # custom_early_stopping = Mock()
-    custom_early_stopping = EarlyStopping(monitor="val_loss",
-                                          patience=3,
-                                          verbose=True,
-                                          mode="max", )
+    custom_early_stopping = EarlyStopping(
+        monitor="val_loss",
+        patience=3,
+        verbose=True,
+        mode="max",
+    )
     trainer = init_trainer(
         mock_logger, output_paths={}, own_early_stopping_callback=custom_early_stopping
     )
@@ -311,7 +313,10 @@ def test_init_trainer_custom_early_stopping(mock_logger):
     assert isinstance(trainer.callbacks[0], EarlyStopping)
     assert trainer.callbacks[0] == custom_early_stopping
     for key in custom_early_stopping.__dict__:
-        assert custom_early_stopping.__dict__[key] == trainer.early_stopping_callback.__dict__[key]
+        assert (
+            custom_early_stopping.__dict__[key]
+            == trainer.early_stopping_callback.__dict__[key]
+        )
 
     assert trainer.checkpoint_callback is not None
 
@@ -339,7 +344,11 @@ def test_init_trainer_with_accelerator_and_devices(mock_logger):
     # Test initializing trainer with custom accelerator and devices
 
     params = {"accelerator": "cpu", "devices": 3}
-    trainer = init_trainer(mock_logger, output_paths={}, training_modifications={"accelerator": "cpu", "devices": 3})
+    trainer = init_trainer(
+        mock_logger,
+        output_paths={},
+        training_modifications={"accelerator": "cpu", "devices": 3},
+    )
 
     assert trainer is not None
     assert isinstance(trainer, Trainer)
@@ -441,7 +450,7 @@ def __init__(self, model):
 
     # Get the final validation metrics
     with pytest.raises(
-            ValueError,
-            match=r"not in trainer.callback_metrics.keys()",
+        ValueError,
+        match=r"not in trainer.callback_metrics.keys()",
     ):
         get_final_val_metrics(trainer)