Partly revert docs for CI.

recipes/001_first.py

@@ -43,15 +43,13 @@
 # `force_reload=True` argument forces downloading the sampler from the registry.
 # If we set `force_reload` to `False`, we use the cached data in our local storage if available.
 
-SimpleBaseSampler = optunahub.load_module("samplers/simple").SimpleBaseSampler
+SimpleSampler = optunahub.load_module("samplers/simple").SimpleSampler
 
 
-class MySampler(SimpleBaseSampler):  # type: ignore
+class MySampler(SimpleSampler):  # type: ignore
     # By default, search space will be estimated automatically like Optuna's built-in samplers.
-    # You can fix the search spacd by `search_space` argument of `SimpleBaseSampler` class.
-    def __init__(
-        self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None
-    ) -> None:
+    # You can fix the search spacd by `search_space` argument of `SimpleSampler` class.
+    def __init__(self, search_space: dict[str, optuna.distributions.BaseDistribution]) -> None:
         super().__init__(search_space)
         self._rng = np.random.RandomState()
 
@@ -69,10 +67,6 @@ def sample_relative(
         # `search_space` argument must be identical to `search_space` argument input to `__init__` method.
         # This method is automatically invoked by Optuna and `SimpleBaseSampler`.
 
-        # If search space is empty, all parameter values are sampled randomly by SimpleBaseSampler.
-        if search_space == {}:
-            return {}
-
         params = {}  # type: dict[str, Any]
         for n, d in search_space.items():
             if isinstance(d, optuna.distributions.FloatDistribution):
@@ -98,7 +92,7 @@ def objective(trial: optuna.trial.Trial) -> float:
 ###################################################################################################
 # This sampler can be used in the same way as other Optuna samplers.
 # In the following example, we create a study and optimize it using `MySampler` class.
-sampler = MySampler()
+sampler = MySampler({"x": optuna.distributions.FloatDistribution(-10, 10)})
 study = optuna.create_study(sampler=sampler)
 study.optimize(objective, n_trials=100)