test: stabilize experimental and observability tests

Author: mkumar73

tests/test_models.py

@@ -14,6 +14,7 @@
 import pytest
 from sklearn.model_selection import train_test_split
 
+from deeptab import set_seed
 from deeptab.models import (
     ENODELSS,
     MLPLSS,
@@ -434,6 +435,8 @@ def test_tabtransformer_fit_predict(cls, task, classification_data_with_cat, reg
 
 @pytest.mark.parametrize("cls", EXPERIMENTAL_CLASSIFIERS)
 def test_experimental_classifier_fit_predict_evaluate(cls, classification_data):
+    # Experimental models are numerically less stable; seed for deterministic CI.
+    set_seed(RANDOM_STATE)
     X_train, X_test, y_train, y_test = classification_data
     model = cls()
     model.fit(X_train, y_train, **FIT_KWARGS)
@@ -460,6 +463,8 @@ def test_experimental_classifier_fit_predict_evaluate(cls, classification_data):
 
 @pytest.mark.parametrize("cls", EXPERIMENTAL_REGRESSORS)
 def test_experimental_regressor_fit_predict_evaluate(cls, regression_data):
+    # Experimental models are numerically less stable; seed for deterministic CI.
+    set_seed(RANDOM_STATE)
     X_train, X_test, y_train, y_test = regression_data
     model = cls()
     model.fit(X_train, y_train, **FIT_KWARGS)
@@ -476,6 +481,8 @@ def test_experimental_regressor_fit_predict_evaluate(cls, regression_data):
 
 @pytest.mark.parametrize("cls", EXPERIMENTAL_LSS_MODELS)
 def test_experimental_lss_fit_predict_evaluate(cls, regression_data):
+    # Experimental models are numerically less stable; seed for deterministic CI.
+    set_seed(RANDOM_STATE)
     X_train, X_test, y_train, y_test = regression_data
     model = cls()
     model.fit(X_train, y_train, family="normal", **FIT_KWARGS)

tests/test_observability.py

@@ -177,6 +177,11 @@ def test_build_lightning_loggers_tensorboard_absent(monkeypatch):
 def test_build_lightning_loggers_user_logger_does_not_replace(monkeypatch):
     """User-provided logger is appended alongside built-in trackers."""
     user_logger = _FakeLogger()
+    # Stub the optional tensorboard import guard so the test does not depend on
+    # the 'tensorboard' package being installed in the environment.
+    fake_tb_mod = ModuleType("torch.utils.tensorboard")
+    fake_tb_mod.SummaryWriter = MagicMock()  # type: ignore[attr-defined]
+    monkeypatch.setitem(sys.modules, "torch.utils.tensorboard", fake_tb_mod)
     # Mock TensorBoardLogger
     fake_tb = MagicMock()
     fake_lpl = MagicMock()