Support for suggest_int in neldermead sampler. (#373)

* Support for suggest_int.

* Fix for ruff.

aiaccel/hpo/samplers/nelder_mead_sampler.py

@@ -95,6 +95,9 @@ def sample_independent(
         param_index = list(self._search_space.keys()).index(param_name)
         param_value = trial.user_attrs["params"][param_index]
 
+        if isinstance(param_distribution, optuna.distributions.IntDistribution):
+            param_value = int(param_value)
+
         contains = param_distribution._contains(param_distribution.to_internal_repr(param_value))
         if not contains:
             warnings.warn(

tests/hpo/samplers/results_ackley_int.csv

@@ -0,0 +1,31 @@
+x,y,objective
+-2,9.014286128198322,14.586422044008508
+4,1.973169683940732,9.375537945818003
+-6,-6.880109593275947,14.844107008867795
+-2,-0.6931908436032099,6.507841622369518
+3,-7.7343072878608,14.961217967282048
+0,4.827137774183542,10.531632891221612
+2,-3.5471589338460197,10.452142477225951
+1,-1.4535847568386293,6.1133866373150045
+-5,-4.119945284382571,12.343741882384256
+1,0.4498909418599062,4.566368541284509
+5,-0.3105029713755132,11.5007990848864
+0,-0.5975188755462857,3.243659824018007
+0,1.3059568231522496,4.7031260630541
+0,0.61607142815453,3.251423326258305
+-1,-0.4313383892516619,4.526245031477097
+-1,-0.21103105647376988,3.5486389761771218
+0,0.22958360908201417,1.599587459483838
+1,0.4498909418599062,4.566368541284509
+0,-0.9840066946188015,2.605064206336659
+1,-0.1569042099905017,3.212903780630767
+0,-0.26705787637944767,1.8966954751653244
+1,0.946532427321368,3.6137337954984194
+0,-0.5013719141337591,3.0872498857179833
+0,-0.018737133648716736,0.06231823476971021
+0,-0.37721154276839375,2.6051598902371538
+0,0.5880580182016912,3.2368252189682347
+0,-0.13589415252587253,0.8089974198411198
+0,-0.38421489525660346,2.6410686067975107
+0,0.07613398299735977,0.3625465075510812
+0,0.19329100187451556,1.2947108387400936

tests/hpo/samplers/test_nelder_mead_sampler.py

@@ -201,7 +201,7 @@ def test_after_trial_sub_sampler(
         mock_iter.method.assert_not_called()
 
 
-def ackley(x: list[float]) -> float:
+def ackley(x: list[int | float]) -> float:
     # Ackley function
     y = (
         -20 * np.exp(-0.2 * np.sqrt(0.5 * (x[0] ** 2 + x[1] ** 2)))
@@ -234,7 +234,7 @@ def sphere_sleep(x: list[float]) -> float:
 class BaseTestNelderMead:
     def common_setup(
         self,
-        search_space: dict[str, tuple[float, float]],
+        search_space: dict[str, tuple[int | float, int | float]],
         objective: Callable[[list[float]], float],
         result_file_name: str,
         study: optuna.Study,
@@ -435,3 +435,29 @@ def validation(self, results: list[dict[str | Any, str | Any]]) -> None:
             assert math.isclose(trial.params["x"], float(result["x"]), rel_tol=0.000001)
             assert math.isclose(trial.params["y"], float(result["y"]), rel_tol=0.000001)
             assert math.isclose(trial.values[0], float(result["objective"]), rel_tol=0.000001)
+
+
+class TestNelderMeadAckleyInteger(BaseTestNelderMead):
+    def setup_method(self) -> None:
+        search_space = {"x": (-10, 10), "y": (-10.0, 10.0)}
+        sampler = NelderMeadSampler(search_space=search_space, seed=42)
+
+        self.common_setup(
+            search_space=search_space,
+            objective=ackley,
+            result_file_name="results_ackley_int.csv",
+            study=optuna.create_study(sampler=sampler),
+        )
+
+    def validation(self, results: list[dict[str | Any, str | Any]]) -> None:
+        for trial, result in zip(self.study.trials, results, strict=False):
+            assert math.isclose(trial.params["x"], float(result["x"]), rel_tol=0.000001)
+            assert math.isclose(trial.params["y"], float(result["y"]), rel_tol=0.000001)
+            assert math.isclose(trial.values[0], float(result["objective"]), rel_tol=0.000001)
+
+    def func(self, trial: optuna.trial.Trial) -> float:
+        params: list[int | float] = []
+        params.append(trial.suggest_int("x", *[int(item) for item in self.search_space["x"]]))
+        params.append(trial.suggest_float("y", *self.search_space["y"]))
+
+        return self.objective(params)