Introduce the optunahub.benchmarks module

docs/source/_templates/custom_summary.rst

@@ -0,0 +1,7 @@
+{{ fullname | escape | underline }}
+
+.. autoclass:: {{ fullname }}
+   :members:
+   :special-members: __init__, __call__
+   :inherited-members:
+   :undoc-members:

docs/source/benchmarks.rst

@@ -0,0 +1,11 @@
+.. module:: optunahub.benchmarks
+
+optunahub.benchmarks
+====================
+
+.. autosummary::
+    :toctree: generated/
+    :nosignatures:
+    :template: custom_summary.rst
+
+    optunahub.benchmarks.BaseProblem

docs/source/conf.py

@@ -19,6 +19,7 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinx.ext.napoleon",
+    "sphinx.ext.viewcode",
 ]
 
 templates_path = ["_templates"]

docs/source/reference.rst

@@ -5,4 +5,5 @@ Reference
     :maxdepth: 1
 
     optunahub
-    samplers
+    samplers
+    benchmarks

docs/source/samplers.rst

@@ -6,5 +6,6 @@ optunahub.samplers
 .. autosummary::
    :toctree: generated/
    :nosignatures:
+   :template: custom_summary.rst
 
    optunahub.samplers.SimpleBaseSampler

optunahub/__init__.py

@@ -1,7 +1,8 @@
+from optunahub import benchmarks
 from optunahub import samplers
 from optunahub.hub import load_local_module
 from optunahub.hub import load_module
 from optunahub.version import __version__
 
 
-__all__ = ["load_module", "load_local_module", "__version__", "samplers"]
+__all__ = ["__version__", "benchmarks", "load_local_module", "load_module", "samplers"]

optunahub/benchmarks/__init__.py

@@ -0,0 +1,6 @@
+from ._base_problem import BaseProblem
+
+
+__all__ = [
+    "BaseProblem",
+]

optunahub/benchmarks/_base_problem.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from abc import ABCMeta
+from abc import abstractmethod
+from typing import Any
+from typing import Sequence
+
+import optuna
+
+
+class BaseProblem(metaclass=ABCMeta):
+    """Base class for optimization problems."""
+
+    def __call__(self, trial: optuna.Trial) -> float | Sequence[float]:
+        """Objective function for Optuna. By default, this method calls :meth:`evaluate` with the parameters defined in :attr:`search_space`.
+
+        Args:
+            trial: Optuna trial object.
+        Returns:
+            The objective value or a sequence of the objective values for multi-objective optimization.
+        """
+        params = {}
+        for name, dist in self.search_space.items():
+            params[name] = trial._suggest(name, dist)
+            trial._check_distribution(name, dist)
+        return self.evaluate(params)
+
+    def evaluate(self, params: dict[str, Any]) -> float | Sequence[float]:
+        """Evaluate the objective function.
+
+        Args:
+            params: Dictionary of input parameters.
+
+        Returns:
+            The objective value or a sequence of the objective values for multi-objective optimization.
+
+        Example:
+            ::
+
+                def evaluate(self, params: dict[str, Any]) -> float:
+                    x = params["x"]
+                    y = params["y"]
+                    return x ** 2 + y
+        """
+        raise NotImplementedError
+
+    @property
+    def search_space(self) -> dict[str, optuna.distributions.BaseDistribution]:
+        """Return the search space.
+
+        Returns:
+            Dictionary of search space. Each dictionary element consists of the parameter name and distribution (see `optuna.distributions <https://optuna.readthedocs.io/en/stable/reference/distributions.html>`__).
+
+        Example:
+            ::
+
+                @property
+                def search_space(self) -> dict[str, optuna.distributions.BaseDistribution]:
+                    return {
+                        "x": optuna.distributions.FloatDistribution(low=0, high=1),
+                        "y": optuna.distributions.CategoricalDistribution(choices=[0, 1, 2]),
+                    }
+        """
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def directions(self) -> list[optuna.study.StudyDirection]:
+        """Return the optimization directions.
+
+        Returns:
+            List of `optuna.study.direction <https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.StudyDirection.html>`__.
+
+        Example:
+            ::
+
+                @property
+                def directions(self) -> list[optuna.study.StudyDirection]:
+                    return [optuna.study.StudyDirection.MINIMIZE]
+        """
+        ...

tests/test_benchmarks.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+import optuna
+
+import optunahub
+
+
+def test_base_problem() -> None:
+    class TestProblem(optunahub.benchmarks.BaseProblem):
+        def evaluate(self, params: dict[str, float]) -> float:
+            x = params["x"]
+            return x**2
+
+        @property
+        def search_space(self) -> dict[str, optuna.distributions.BaseDistribution]:
+            return {"x": optuna.distributions.FloatDistribution(low=-1, high=1)}
+
+        @property
+        def directions(self) -> list[optuna.study.StudyDirection]:
+            return [optuna.study.StudyDirection.MINIMIZE]
+
+    problem = TestProblem()
+    study = optuna.create_study(directions=problem.directions)
+    study.optimize(problem, n_trials=20)  # verify no error occurs