Add log and step attributes to integer and float hyperparameter

discrete_optimization/generic_tools/hyperparameters/hyperparameter.py

@@ -94,9 +94,13 @@ class IntegerHyperparameter(Hyperparameter):
     If None, the hyperparameter value has no upper bound.
 
     """
+
     step: int = 1
     """step to discretize."""
 
+    log: bool = False
+    """Whether to sample the value in a logarithmic scale."""
+
     default: Optional[int] = None
     """Default value for the hyperparameter.
 
@@ -129,6 +133,7 @@ def suggest_with_optuna(
         low: Optional[int] = None,
         high: Optional[int] = None,
         step: Optional[int] = None,
+        log: Optional[bool] = None,
         prefix: str = "",
         **kwargs: Any,
     ) -> Any:
@@ -139,6 +144,7 @@ def suggest_with_optuna(
             low: can be used to restrict lower bound
             high: can be used to restrict upper bound
             step: can be used to discretize by a given step
+            log: whether to sample the value in a logarithmic scale
             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_int()`
@@ -152,7 +158,9 @@ def suggest_with_optuna(
             high = self.high
         if step is None:
             step = self.step
-        return trial.suggest_int(name=prefix + self.name, low=low, high=high, step=step, **kwargs)  # type: ignore
+        if log is None:
+            log = self.log
+        return trial.suggest_int(name=prefix + self.name, low=low, high=high, step=step, log=log, **kwargs)  # type: ignore
 
 
 @dataclass
@@ -173,6 +181,12 @@ class FloatHyperparameter(Hyperparameter):
 
     """
 
+    step: Optional[float] = None
+    """step to discretize if not None."""
+
+    log: bool = False
+    """Whether to sample the value in a logarithmic scale."""
+
     default: Optional[float] = None
     """Default value for the hyperparameter.
 
@@ -204,6 +218,7 @@ def suggest_with_optuna(
         trial: optuna.trial.Trial,
         low: Optional[float] = None,
         high: Optional[float] = None,
+        log: Optional[bool] = None,
         prefix: str = "",
         **kwargs: Any,
     ) -> Any:
@@ -213,6 +228,10 @@ def suggest_with_optuna(
             trial: optuna Trial used for choosing the hyperparameter value
             low: can be used to restrict lower bound
             high: can be used to restrict upper bound
+            log: whether to sample the value in a logarithmic scale
+            step: step of discretization if specified.
+                If explicitely set to None, no discretization performed.
+                By default, use self.step (and thus default discretization only if self.step not None)
             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_float()`
@@ -224,8 +243,14 @@ def suggest_with_optuna(
             low = self.low
         if high is None:
             high = self.high
+        if log is None:
+            log = self.log
+        if "step" in kwargs:
+            step = kwargs.pop("step")
+        else:
+            step = self.step
         return trial.suggest_float(
-            name=prefix + self.name, low=low, high=high, **kwargs
+            name=prefix + self.name, low=low, high=high, log=log, step=step, **kwargs  # type: ignore
         )
 
 

tests/generic_tools/hyperparameters/test_hyperparameter.py

@@ -33,7 +33,7 @@ class BigMethod(Enum):
 class DummySolver(SolverDO):
     hyperparameters = [
         IntegerHyperparameter("nb", low=0, high=2, default=1),
-        FloatHyperparameter("coeff", low=-1.0, high=1.0, default=1.0),
+        FloatHyperparameter("coeff", low=-1.0, high=1.0, default=1.0, step=0.25),
         CategoricalHyperparameter("use_it", choices=[True, False], default=True),
         EnumHyperparameter("method", enum=Method, default=Method.GREEDY),
     ]
@@ -255,6 +255,36 @@ def objective(trial: optuna.Trial) -> float:
     assert len(study.trials) == 2 * 2 * 5 * 1
 
 
+def test_suggest_with_optuna_default_float_step():
+    def objective(trial: optuna.Trial) -> float:
+        # hyperparameters for the chosen solver
+        suggested_hyperparameters_kwargs = (
+            DummySolver.suggest_hyperparameters_with_optuna(
+                trial=trial,
+                kwargs_by_name={
+                    "nb": dict(high=1),
+                    "use_it": dict(choices=[True]),
+                },
+            )
+        )
+        assert len(suggested_hyperparameters_kwargs) == 4
+        assert isinstance(suggested_hyperparameters_kwargs["method"], Method)
+        assert 0 <= suggested_hyperparameters_kwargs["nb"]
+        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"] is True
+
+        return 0.0
+
+    study = optuna.create_study(
+        sampler=optuna.samplers.BruteForceSampler(),
+    )
+    study.optimize(objective)
+
+    assert len(study.trials) == 2 * 2 * 9 * 1
+
+
 def test_suggest_with_optuna_with_choices_dict():
     def objective(trial: optuna.Trial) -> float:
         # hyperparameters for the chosen solver