Support numpy scalars in Trial.user_attrs

optuna_dashboard/_cached_extra_study_property.py

@@ -2,12 +2,14 @@ from __future__ import annotations
 
 import copy
 import threading
+from typing import Any
 from typing import List
 from typing import Optional
 from typing import Set
 from typing import Tuple
 from typing import TYPE_CHECKING
 
+import numpy as np
 from optuna.distributions import BaseDistribution
 from optuna.trial import FrozenTrial
 from optuna.trial import TrialState
@@ -84,12 +86,11 @@ class _CachedExtraStudyProperty:
 
         self._cursor = next_cursor
 
+    def _is_sortable_value(self, v: Any) -> bool:
+        return not isinstance(v, bool) and isinstance(v, (int, float, np.integer, np.floating))
+
     def _update_user_attrs(self, trial: FrozenTrial) -> None:
-        # TODO(c-bata): Support numpy-specific number types.
-        current_user_attrs = {
-            k: not isinstance(v, bool) and isinstance(v, (int, float))
-            for k, v in trial.user_attrs.items()
-        }
+        current_user_attrs = {k: self._is_sortable_value(v) for k, v in trial.user_attrs.items()}
         for attr_name, current_is_sortable in current_user_attrs.items():
             is_sortable = self._union_user_attrs.get(attr_name)
             if is_sortable is None:

optuna_dashboard/_serializer.py

@@ -104,6 +104,8 @@ def serialize_attrs(attrs: dict[str, Any]) -> list[Attribute]:
             value = "<binary object>"
         elif isinstance(v, str):
             value = v
+        elif isinstance(v, (np.floating, np.integer)):
+            value = v.item()
         else:
             value = json.dumps(v)
             value = value[:MAX_ATTR_LENGTH] if len(value) > MAX_ATTR_LENGTH else value

python_tests/test_cached_extra_study_property.py

@@ -4,6 +4,7 @@ from typing import Any
 from unittest import TestCase
 import warnings
 
+import numpy as np
 import optuna
 from optuna import create_trial
 from optuna.distributions import BaseDistribution
@@ -254,11 +255,29 @@ class _CachedExtraStudyPropertyUserAttrs(TestCase):
 
     def test_infer_sortable(self) -> None:
         user_attrs_list: list[dict[str, Any]] = [
-            {"a": 1, "b": 1, "c": 1, "d": "a", "e": 1, "f": True},
+            {
+                "a": 1,
+                "b": 1,
+                "c": 1,
+                "d": "a",
+                "e": 1,
+                "f": True,
+                "g": np.float128(1.1),
+                "h": np.int64(2),
+            },
             {"a": 2, "b": "a", "c": "a", "d": "a"},
             {"a": 3, "b": None, "c": 3, "d": "a", "e": 3},
         ]
-        expected = {"a": True, "b": False, "c": False, "d": False, "e": True, "f": False}
+        expected = {
+            "a": True,
+            "b": False,
+            "c": False,
+            "d": False,
+            "e": True,
+            "f": False,
+            "g": True,
+            "h": True,
+        }
 
         trials = []
         for user_attrs in user_attrs_list:

python_tests/test_serializers.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import sys
 
+import numpy as np
 import optuna
 from optuna_dashboard._serializer import serialize_attrs
 from optuna_dashboard._serializer import serialize_study_detail
@@ -25,6 +26,32 @@ def test_serialize_dict() -> None:
     assert len(serialized) <= 1
 
 
+def test_serialize_numpy_integer() -> None:
+    serialized = serialize_attrs(
+        {
+            "int8": np.int8(1),
+            "int16": np.int16(1),
+            "int32": np.int32(1),
+            "int64": np.int64(1),
+        }
+    )
+    assert len(serialized) == 4
+    assert all([v["value"] == 1 for v in serialized])
+
+
+def test_serialize_numpy_floating() -> None:
+    serialized = serialize_attrs(
+        {
+            "float16": np.float16(1.0),
+            "float32": np.float32(1.0),
+            "float64": np.float64(1.0),
+            "float128": np.float128(1.0),
+        }
+    )
+    assert len(serialized) == 4
+    assert all([v["value"] == 1.0 for v in serialized])
+
+
 @pytest.mark.skipif(sys.version_info < (3, 8), reason="BoTorch dropped Python3.7 support")
 def test_get_study_detail_is_preferential() -> None:
     storage = optuna.storages.InMemoryStorage()