Fix E721 errors

optuna · Aug 17, 2024 · e765fc8 · e765fc8
1 parent f08ee22
commit e765fc8
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Showing 2 changed files with 7 additions and 7 deletions.
diff --git a/tests/lightgbm/test_optimize.py b/tests/lightgbm/test_optimize.py
@@ -286,7 +286,7 @@ def test_no_eval_set_args(self) -> None:
                 callbacks=[early_stopping(stopping_rounds=2)],
             )
 
-        assert excinfo.type == ValueError
+        assert excinfo.type is ValueError
         assert str(excinfo.value) == "`valid_sets` is required."
 
     @pytest.mark.parametrize(
@@ -314,7 +314,7 @@ def test_inconsistent_study_direction(self, metric: str, study_direction: str) -
                 study=study,
             )
 
-        assert excinfo.type == ValueError
+        assert excinfo.type is ValueError
         assert str(excinfo.value).startswith("Study direction is inconsistent with the metric")
 
     def test_with_minimum_required_args(self) -> None:
@@ -769,7 +769,7 @@ def test_inconsistent_study_direction(self, metric: str, study_direction: str) -
                 study=study,
             )
 
-        assert excinfo.type == ValueError
+        assert excinfo.type is ValueError
         assert str(excinfo.value).startswith("Study direction is inconsistent with the metric")
 
     def test_with_minimum_required_args(self) -> None:

diff --git a/tests/sklearn/test_sklearn.py b/tests/sklearn/test_sklearn.py
@@ -112,8 +112,8 @@ def test_optuna_search_properties() -> None:
     assert np.allclose(optuna_search.classes_, np.array([0, 1, 2]))
     assert optuna_search.n_trials_ == 10
     assert optuna_search.user_attrs_ == {"dataset": "blobs"}
-    assert type(optuna_search.predict_log_proba(X)) == np.ndarray
-    assert type(optuna_search.predict_proba(X)) == np.ndarray
+    assert isinstance(optuna_search.predict_log_proba(X), np.ndarray)
+    assert isinstance(optuna_search.predict_proba(X), np.ndarray)
 
 
 @pytest.mark.filterwarnings("ignore::UserWarning")
@@ -139,8 +139,8 @@ def test_optuna_search_transforms() -> None:
             est, {}, cv=3, error_score="raise", random_state=0, return_train_score=True
         )
     optuna_search.fit(X)
-    assert type(optuna_search.transform(X)) == np.ndarray
-    assert type(optuna_search.inverse_transform(X)) == np.ndarray
+    assert isinstance(optuna_search.transform(X), np.ndarray)
+    assert isinstance(optuna_search.inverse_transform(X), np.ndarray)
 
 
 def test_optuna_search_invalid_estimator() -> None: