Follow review comments

optuna/visualization/_terminator_improvement.py

@@ -53,7 +53,7 @@ def _get_improvement_info(
     error_evaluator: BaseErrorEvaluator | None = None,
 ) -> _ImprovementInfo:
     if study._is_multi_objective():
-        raise ValueError("This function does not support multi-objective optimization `study`.")
+        raise ValueError("This function does not support multi-objective optimization study.")
 
     if improvement_evaluator is None:
         improvement_evaluator = RegretBoundEvaluator()

tests/visualization_tests/test_terminator_improvement.py

@@ -17,6 +17,8 @@
 from optuna.terminator import StaticErrorEvaluator
 from optuna.testing.objectives import fail_objective
 from optuna.testing.visualization import prepare_study_with_trials
+from optuna.trial import create_trial
+from optuna.trial import TrialState
 from optuna.visualization import plot_terminator_improvement as plotly_plot_terminator_improvement
 from optuna.visualization._terminator_improvement import _get_improvement_info
 from optuna.visualization._terminator_improvement import _get_y_range
@@ -34,29 +36,39 @@ def _create_study_with_failed_trial() -> Study:
     return study
 
 
-def test_study_is_multi_obj() -> None:
+def _prepare_study_with_cross_validation_scores() -> Study:
+    study = create_study()
+    for _ in range(3):
+        trial = study.ask({"x": FloatDistribution(0, 1)})
+        report_cross_validation_scores(trial, [1.0, 2.0])
+        study.tell(trial, 0)
+    return study
+
+
+def test_study_is_multi_objective() -> None:
     study = create_study(directions=["minimize", "minimize"])
     with pytest.raises(ValueError):
         _get_improvement_info(study=study)
 
 
 @parametrize_plot_terminator_improvement
 @pytest.mark.parametrize(
-    "specific_create_study",
+    "specific_create_study, plot_error",
     [
-        create_study,
-        _create_study_with_failed_trial,
-        prepare_study_with_trials,
+        (create_study, False),
+        (_create_study_with_failed_trial, False),
+        (prepare_study_with_trials, False),
+        (_prepare_study_with_cross_validation_scores, False),
+        (_prepare_study_with_cross_validation_scores, True),
     ],
 )
-@pytest.mark.parametrize("plot_error", [False, True])
 def test_plot_terminator_improvement(
     plot_terminator_improvement: Callable[..., Any],
     specific_create_study: Callable[[], Study],
     plot_error: bool,
 ) -> None:
     study = specific_create_study()
-    figure = plot_terminator_improvement(study)
+    figure = plot_terminator_improvement(study, plot_error)
     figure.write_image(BytesIO())
 
 
@@ -74,31 +86,27 @@ def test_get_terminator_improvement_info_empty(
 
 
 @pytest.mark.parametrize("get_error", [False, True])
-@pytest.mark.parametrize("improvement_evaluator_class", [lambda: RegretBoundEvaluator(), None])
+@pytest.mark.parametrize(
+    "improvement_evaluator_class", [lambda: RegretBoundEvaluator(), lambda: None]
+)
 @pytest.mark.parametrize(
     "error_evaluator_class",
     [
         lambda: CrossValidationErrorEvaluator(),
         lambda: StaticErrorEvaluator(0),
-        None,
+        lambda: None,
     ],
 )
 def test_get_improvement_info(
     get_error: bool,
-    improvement_evaluator_class: Callable[[], BaseImprovementEvaluator] | None,
-    error_evaluator_class: Callable[[], BaseErrorEvaluator] | None,
+    improvement_evaluator_class: Callable[[], BaseImprovementEvaluator | None],
+    error_evaluator_class: Callable[[], BaseErrorEvaluator | None],
 ) -> None:
-    study = create_study()
-    for _ in range(3):
-        trial = study.ask({"x": FloatDistribution(0, 1)})
-        report_cross_validation_scores(trial, [1.0, 2.0])
-        study.tell(trial, 0)
+    study = _prepare_study_with_cross_validation_scores()
 
-    improvement_evaluator = (
-        None if improvement_evaluator_class is None else improvement_evaluator_class()
+    info = _get_improvement_info(
+        study, get_error, improvement_evaluator_class(), error_evaluator_class()
     )
-    error_evaluator = None if error_evaluator_class is None else error_evaluator_class()
-    info = _get_improvement_info(study, get_error, improvement_evaluator, error_evaluator)
     assert info.trial_numbers == [0, 1, 2]
     assert len(info.improvements) == 3
     if get_error:
@@ -109,6 +117,19 @@ def test_get_improvement_info(
         assert info.errors is None
 
 
+def test_get_improvement_info_started_with_failed_trials() -> None:
+    study = create_study()
+    for _ in range(3):
+        study.add_trial(create_trial(state=TrialState.FAIL))
+    trial = study.ask({"x": FloatDistribution(0, 1)})
+    study.tell(trial, 0)
+
+    info = _get_improvement_info(study)
+    assert info.trial_numbers == [3]
+    assert len(info.improvements) == 1
+    assert info.errors is None
+
+
 @pytest.mark.parametrize(
     "info",
     [