Update target version of black from Python 3.8 to 3.9.

pyproject.toml

@@ -169,7 +169,7 @@ version = {attr = "optuna_integration.version.__version__"}
 
 [tool.black]
 line-length = 99
-target-version = ['py38']
+target-version = ['py39']
 exclude = '''
 /(
     \.eggs

tests/botorch/test_botorch.py

@@ -476,11 +476,12 @@ def test_botorch_n_startup_trials() -> None:
         sampler = BoTorchSampler(n_startup_trials=2, independent_sampler=independent_sampler)
     study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler)
 
-    with patch.object(
-        independent_sampler, "sample_independent", wraps=independent_sampler.sample_independent
-    ) as mock_independent, patch.object(
-        sampler, "sample_relative", wraps=sampler.sample_relative
-    ) as mock_relative:
+    with (
+        patch.object(
+            independent_sampler, "sample_independent", wraps=independent_sampler.sample_independent
+        ) as mock_independent,
+        patch.object(sampler, "sample_relative", wraps=sampler.sample_relative) as mock_relative,
+    ):
         study.optimize(
             lambda t: [t.suggest_float("x0", 0, 1), t.suggest_float("x1", 0, 1)], n_trials=3
         )

tests/cma/test_cma.py

@@ -122,13 +122,16 @@ def test_sample_relative_n_startup_trials() -> None:
 
         # The independent sampler is used for Trial#0 and Trial#1.
         # The CMA-ES is used for Trial#2.
-        with patch.object(
-            independent_sampler,
-            "sample_independent",
-            wraps=independent_sampler.sample_independent,
-        ) as mock_independent, patch.object(
-            sampler, "sample_relative", wraps=sampler.sample_relative
-        ) as mock_relative:
+        with (
+            patch.object(
+                independent_sampler,
+                "sample_independent",
+                wraps=independent_sampler.sample_independent,
+            ) as mock_independent,
+            patch.object(
+                sampler, "sample_relative", wraps=sampler.sample_relative
+            ) as mock_relative,
+        ):
             study.optimize(
                 lambda t: t.suggest_int("x", -1, 1) + t.suggest_int("y", -1, 1),
                 n_trials=3,

tests/lightgbm/test_optimize.py

@@ -512,17 +512,19 @@ def test_when_a_step_does_not_improve_best_score(self) -> None:
         tuner = LightGBMTuner(params, dataset, valid_sets=valid_sets)
         assert not tuner.higher_is_better()
 
-        with mock.patch("lightgbm.train"), mock.patch.object(
-            _BaseTuner, "_get_booster_best_score", return_value=0.9
+        with (
+            mock.patch("lightgbm.train"),
+            mock.patch.object(_BaseTuner, "_get_booster_best_score", return_value=0.9),
         ):
             tuner.tune_feature_fraction()
 
         assert "feature_fraction" in tuner.best_params
         assert tuner.best_score == 0.9
 
         # Assume that tuning `num_leaves` doesn't improve the `best_score`.
-        with mock.patch("lightgbm.train"), mock.patch.object(
-            _BaseTuner, "_get_booster_best_score", return_value=1.1
+        with (
+            mock.patch("lightgbm.train"),
+            mock.patch.object(_BaseTuner, "_get_booster_best_score", return_value=1.1),
         ):
             tuner.tune_num_leaves()
 
@@ -562,9 +564,10 @@ def test_run_show_progress_bar(self, show_progress_bar: bool, expected: int) ->
             show_progress_bar=show_progress_bar,
         )
 
-        with mock.patch.object(
-            _BaseTuner, "_get_booster_best_score", return_value=1.0
-        ), mock.patch("tqdm.tqdm") as mock_tqdm:
+        with (
+            mock.patch.object(_BaseTuner, "_get_booster_best_score", return_value=1.0),
+            mock.patch("tqdm.tqdm") as mock_tqdm,
+        ):
             tuner.run()
 
         assert mock_tqdm.call_count == expected
@@ -918,9 +921,10 @@ def test_run_show_progress_bar(self, show_progress_bar: bool, expected: int) ->
             params, dataset, study=study, time_budget=1, show_progress_bar=show_progress_bar
         )
 
-        with mock.patch.object(
-            _OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]
-        ), mock.patch("tqdm.tqdm") as mock_tqdm:
+        with (
+            mock.patch.object(_OptunaObjectiveCV, "_get_cv_scores", return_value=[1.0]),
+            mock.patch("tqdm.tqdm") as mock_tqdm,
+        ):
             tuner.run()
 
         assert mock_tqdm.call_count == expected