test passing save_test_result in test_trainer

tests/test_trainer.py

@@ -49,14 +49,17 @@ def test_trainer(tmp_path: Path) -> None:
         learning_rate=1e-2,
         epochs=5,
     )
-    dir_name = "test_tmp_dir"
-    test_dir = tmp_path / dir_name
-    trainer.train(train_loader, val_loader, save_dir=test_dir)
+    trainer.train(
+        train_loader,
+        val_loader,
+        save_dir=tmp_path,
+        save_test_result=tmp_path / "test-preds.json",
+    )
     for param in chgnet.composition_model.parameters():
         assert param.requires_grad is False
-    assert test_dir.is_dir(), "Training dir was not created"
+    assert tmp_path.is_dir(), "Training dir was not created"
 
-    output_files = [file.name for file in test_dir.iterdir()]
+    output_files = [file.name for file in tmp_path.iterdir()]
     for prefix in ("epoch", "bestE_", "bestF_"):
         n_matches = sum(file.startswith(prefix) for file in output_files)
         assert (
@@ -79,16 +82,14 @@ def test_trainer_composition_model(tmp_path: Path) -> None:
         learning_rate=1e-2,
         epochs=5,
     )
-    dir_name = "test_tmp_dir2"
-    test_dir = tmp_path / dir_name
     initial_weights = chgnet.composition_model.state_dict()["fc.weight"].clone()
     trainer.train(
-        train_loader, val_loader, save_dir=test_dir, train_composition_model=True
+        train_loader, val_loader, save_dir=tmp_path, train_composition_model=True
     )
     for param in chgnet.composition_model.parameters():
         assert param.requires_grad is True
 
-    output_files = list(test_dir.iterdir())
+    output_files = list(tmp_path.iterdir())
     weights_path = next(file for file in output_files if file.name.startswith("epoch"))
     new_chgnet = CHGNet.from_file(weights_path)
     for param in new_chgnet.composition_model.parameters():