Allow user-defined objective names in hyperparameter importance plots

optuna/visualization/_param_importances.py

@@ -68,6 +68,48 @@ def _get_importances_info(
     )
 
 
+def _get_importances_infos(
+    study: Study,
+    evaluator: BaseImportanceEvaluator | None,
+    params: list[str] | None,
+    target: Callable[[FrozenTrial], float] | None,
+    target_name: str,
+) -> tuple[_ImportancesInfo, ...]:
+    metric_names = study.metric_names
+    if target or not study._is_multi_objective():
+        target_name = metric_names[0] if metric_names is not None and not target else target_name
+        importances_infos: tuple[_ImportancesInfo, ...] = (
+            _get_importances_info(
+                study,
+                evaluator,
+                params,
+                target=target,
+                target_name=target_name,
+            ),
+        )
+
+    else:
+        n_objectives = len(study.directions)
+        target_names = (
+            metric_names
+            if metric_names is not None
+            else (f"{target_name} {objective_id}" for objective_id in range(n_objectives))
+        )
+
+        importances_infos = tuple(
+            _get_importances_info(
+                study,
+                evaluator,
+                params,
+                target=lambda t: t.values[objective_id],
+                target_name=target_name,
+            )
+            for objective_id, target_name in enumerate(target_names)
+        )
+
+    return importances_infos
+
+
 def plot_param_importances(
     study: Study,
     evaluator: BaseImportanceEvaluator | None = None,
@@ -137,34 +179,16 @@ def objective(trial):
                 importance of the first objective, use ``target=lambda t: t.values[0]`` for the
                 target parameter.
         target_name:
-            Target's name to display on the legend.
+            Target's name to display on the legend. Names set via
+            :meth:`~optuna.study.Study.set_metric_names` will be used if ``target`` is :obj:`None`,
+            overriding this argument.
 
     Returns:
         A :class:`plotly.graph_objs.Figure` object.
     """
 
     _imports.check()
-
-    if target or not study._is_multi_objective():
-        importances_infos: tuple[_ImportancesInfo, ...] = (
-            _get_importances_info(
-                study, evaluator, params, target=target, target_name=target_name
-            ),
-        )
-
-    else:
-        n_objectives = len(study.directions)
-        importances_infos = tuple(
-            _get_importances_info(
-                study,
-                evaluator,
-                params,
-                target=lambda t: t.values[objective_id],
-                target_name=f"{target_name} {objective_id}",
-            )
-            for objective_id in range(n_objectives)
-        )
-
+    importances_infos = _get_importances_infos(study, evaluator, params, target, target_name)
     return _get_importances_plot(importances_infos, study)
 
 

optuna/visualization/matplotlib/_param_importances.py

@@ -9,7 +9,7 @@
 from optuna.logging import get_logger
 from optuna.study import Study
 from optuna.trial import FrozenTrial
-from optuna.visualization._param_importances import _get_importances_info
+from optuna.visualization._param_importances import _get_importances_infos
 from optuna.visualization._param_importances import _ImportancesInfo
 from optuna.visualization.matplotlib._matplotlib_imports import _imports
 
@@ -86,32 +86,16 @@ def objective(trial):
                 importance of the first objective, use ``target=lambda t: t.values[0]`` for the
                 target parameter.
         target_name:
-            Target's name to display on the axis label.
+            Target's name to display on the axis label. Names set via
+            :meth:`~optuna.study.Study.set_metric_names` will be used if ``target`` is :obj:`None`,
+            overriding this argument.
 
     Returns:
         A :class:`matplotlib.axes.Axes` object.
     """
 
     _imports.check()
-
-    if target or not study._is_multi_objective():
-        importances_infos: tuple[_ImportancesInfo, ...] = (
-            _get_importances_info(study, evaluator, params, target, target_name),
-        )
-
-    else:
-        n_objectives = len(study.directions)
-        importances_infos = tuple(
-            _get_importances_info(
-                study,
-                evaluator,
-                params,
-                target=lambda t: t.values[objective_id],
-                target_name=f"{target_name} {objective_id}",
-            )
-            for objective_id in range(n_objectives)
-        )
-
+    importances_infos = _get_importances_infos(study, evaluator, params, target, target_name)
     return _get_importances_plot(importances_infos)
 
 

tests/visualization_tests/test_param_importances.py

@@ -19,6 +19,7 @@
 from optuna.trial import Trial
 from optuna.visualization import plot_param_importances as plotly_plot_param_importances
 from optuna.visualization._param_importances import _get_importances_info
+from optuna.visualization._param_importances import _get_importances_infos
 from optuna.visualization._param_importances import _ImportancesInfo
 from optuna.visualization._plotly_imports import go
 from optuna.visualization.matplotlib import plot_param_importances as plt_plot_param_importances
@@ -139,6 +140,24 @@ def test_get_param_importances_info_empty(
     )
 
 
+@pytest.mark.parametrize(
+    "specific_create_study,objective_names",
+    [(create_study, ["Foo"]), (_create_multiobjective_study, ["Foo", "Bar"])],
+)
+def test_get_param_importances_infos_custom_objective_names(
+    specific_create_study: Callable[[], Study], objective_names: list[str]
+) -> None:
+    study = specific_create_study()
+    study.set_metric_names(objective_names)
+    n_objectives = len(study.directions)
+
+    infos = _get_importances_infos(
+        study, evaluator=None, params=["param_a"], target=None, target_name="Objective Value"
+    )
+    assert len(infos) == n_objectives
+    assert all(info.target_name == expected for info, expected in zip(infos, objective_names))
+
+
 def test_switch_label_when_param_insignificant() -> None:
     def _objective(trial: Trial) -> int:
         x = trial.suggest_int("x", 0, 2)