test wandb_path and wandb_kwargs in test_trainer

tests/test_trainer.py

@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING
 
 import numpy as np
+import pytest
 import torch
 from pymatgen.core import Lattice, Structure
 
@@ -36,7 +37,7 @@
 )
 
 
-def test_trainer(tmp_path: Path) -> None:
+def test_trainer(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
     chgnet = CHGNet.load()
     train_loader, val_loader, _test_loader = get_train_val_test_loader(
         data, batch_size=16, train_ratio=0.9, val_ratio=0.05
@@ -47,11 +48,13 @@ def test_trainer(tmp_path: Path) -> None:
         optimizer="Adam",
         criterion="MSE",
         learning_rate=1e-2,
-        epochs=5,
+        epochs=500,
+        wandb_path="/",
+        wandb_kwargs=dict(anonymous="allow"),
     )
     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)
     for param in chgnet.composition_model.parameters():
         assert param.requires_grad is False
     assert test_dir.is_dir(), "Training dir was not created"
@@ -63,6 +66,12 @@ def test_trainer(tmp_path: Path) -> None:
             n_matches == 1
         ), f"Expected 1 {prefix} file, found {n_matches} in {output_files}"
 
+    # expect ImportError when passing wandb_path without wandb installed
+    err_msg = "Weights and Biases not installed. pip install wandb to use wandb logging"
+    with monkeypatch.context() as ctx, pytest.raises(ImportError, match=err_msg):  # noqa: PT012
+        ctx.setattr("chgnet.trainer.trainer.wandb", None)
+        _ = Trainer(model=chgnet, wandb_path="radicalai/chgnet-test-finetune")
+
 
 def test_trainer_composition_model(tmp_path: Path) -> None:
     chgnet = CHGNet.load()