Update visual regression test

optuna_dashboard/_serializer.py

@@ -5,20 +5,20 @@ from typing import List
 from typing import Tuple
 from typing import Union
 
+import numpy as np
 from optuna.distributions import BaseDistribution
 from optuna.study import StudySummary
 from optuna.trial import FrozenTrial
-import numpy as np
 
 from . import _note as note
 
 
 try:
-    from typing import TypedDict
     from typing import Literal
+    from typing import TypedDict
 except ImportError:
-    from typing_extensions import TypedDict
     from typing_extensions import Literal
+    from typing_extensions import TypedDict
 
 
 MAX_ATTR_LENGTH = 1024
@@ -125,6 +125,7 @@ def serialize_frozen_trial(study_id: int, trial: FrozenTrial) -> Dict[str, Any]:
         elif np.isneginf(value):
             serialized_value = "-inf"
         else:
+            assert np.isfinite(value)
             serialized_value = value
         serialized_intermediate_values.append({"step": step, "value": serialized_value})
     serialized["intermediate_values"] = serialized_intermediate_values

visual_regression_test.py

@@ -87,13 +87,15 @@ def create_dummy_storage() -> optuna.storages.InMemoryStorage:
 
     study.optimize(objective_single_dynamic, n_trials=50)
 
-    # Single objective study with 'inf' value
+    # Single objective study with 'inf', '-inf', or 'nan' value
     study = optuna.create_study(study_name="single-inf", storage=storage)
 
     def objective_single_inf(trial: optuna.Trial) -> float:
         x = trial.suggest_float("x", -10, 10)
-        if x > 0:
-            return math.inf
+        if trial.number % 3 == 0:
+            return float("inf")
+        elif trial.number % 3 == 1:
+            return float("-inf")
         else:
             return x**2
 
@@ -152,13 +154,19 @@ def create_dummy_storage() -> optuna.storages.InMemoryStorage:
 
     study.optimize(objective_prune_without_report, n_trials=100)
 
-    # Single objective pruned after reported 'inf' value
+    # Single objective pruned after reported 'inf', '-inf', or 'nan'
     study = optuna.create_study(study_name="single-inf-report", storage=storage)
 
     def objective_single_inf_report(trial: optuna.Trial) -> float:
         x = trial.suggest_float("x", -10, 10)
+        if trial.number % 3 == 0:
+            trial.report(float("inf"), 1)
+        elif trial.number % 3 == 1:
+            trial.report(float("-inf"), 1)
+        else:
+            trial.report(float("nan"), 1)
+
         if x > 0:
-            trial.report(math.inf, 1)
             raise optuna.TrialPruned()
         else:
             return x**2