Fix CategoricalHyperparameter.suggest_with_optuna()

Was not taking into account choices arg

Also add kwargs to all suggest_with_optuna signature

discrete_optimization/generic_tools/hyperparameters/hyperparameter.py

@@ -189,9 +189,10 @@ def suggest_with_optuna(
         Returns:
 
         """
-        if self.choices is not None and "choices" not in kwargs:
-            kwargs["choices"] = self.choices
-        return trial.suggest_categorical(name=prefix + self.name, **kwargs)
+        if choices is None:
+            choices = self.choices
+
+        return trial.suggest_categorical(name=prefix + self.name, choices=choices, **kwargs)  # type: ignore
 
 
 class EnumHyperparameter(CategoricalHyperparameter):
@@ -280,7 +281,7 @@ def suggest_with_optuna(
             choices = self.choices
         choices_str = [self.choices_cls2str[c] for c in choices]
         choice_str = trial.suggest_categorical(
-            name=prefix + self.name, choices=choices_str
+            name=prefix + self.name, choices=choices_str, **kwargs  # type: ignore
         )
         return self.choices_str2cls[choice_str]
 
@@ -312,6 +313,7 @@ def suggest_with_optuna(
         kwargs_by_name: Optional[Dict[str, Dict[str, Any]]] = None,
         fixed_hyperparameters: Optional[Dict[str, Any]] = None,
         prefix: str = "",
+        **kwargs,
     ) -> Dict[str, Any]:
         """Suggest hyperparameter value for an Optuna trial.
 
@@ -331,6 +333,7 @@ def suggest_with_optuna(
                 if the subsolver class is not suggested by this method, but already fixed.
             prefix: prefix to add to optuna corresponding parameter name
               (useful for disambiguating hyperparameters from subsolvers in case of meta-solvers)
+            **kwargs: passed to `trial.suggest_categorical()`
 
         Returns:
 
@@ -344,4 +347,5 @@ def suggest_with_optuna(
             kwargs_by_name=kwargs_by_name,
             fixed_hyperparameters=fixed_hyperparameters,
             prefix=prefix,
+            **kwargs,  # type: ignore
         )

tests/generic_tools/hyperparameters/test_hyperparameter.py

@@ -133,7 +133,11 @@ def objective(trial: optuna.Trial) -> float:
         suggested_hyperparameters_kwargs = (
             DummySolver.suggest_hyperparameters_with_optuna(
                 trial=trial,
-                kwargs_by_name={"coeff": dict(step=0.5), "nb": dict(high=1)},
+                kwargs_by_name={
+                    "coeff": dict(step=0.5),
+                    "nb": dict(high=1),
+                    "use_it": dict(choices=[True]),
+                },
             )
         )
         assert len(suggested_hyperparameters_kwargs) == 4
@@ -142,7 +146,7 @@ def objective(trial: optuna.Trial) -> float:
         assert 1 >= suggested_hyperparameters_kwargs["nb"]
         assert -1.0 <= suggested_hyperparameters_kwargs["coeff"]
         assert 1.0 >= suggested_hyperparameters_kwargs["coeff"]
-        assert suggested_hyperparameters_kwargs["use_it"] in (True, False)
+        assert suggested_hyperparameters_kwargs["use_it"] is True
 
         return 0.0
 
@@ -151,7 +155,7 @@ def objective(trial: optuna.Trial) -> float:
     )
     study.optimize(objective)
 
-    assert len(study.trials) == 2 * 2 * 5 * 2
+    assert len(study.trials) == 2 * 2 * 5 * 1
 
 
 def test_suggest_with_optuna_meta_solver():