Add bbob package

package/benchmarks/bbob/LICENSE

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+MIT License
+
+Copyright (c) 2024 Preferred Networks, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

package/benchmarks/bbob/README.md

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+---
+author: Optuna team
+title: The blackbox optimization benchmarking (bbob) test suite
+description: The blackbox optimization benchmarking (bbob) test suite consists of 24 noiseless single-objective test functions including Sphere, Ellipsoidal, Rastrigin, Rosenbrock, etc. This package is a wrapper of the COCO (COmparing Continuous Optimizers) experiments library.
+tags: [benchmark, continuous optimization, BBOB, COCO]
+optuna_versions: [4.1.0]
+license: MIT License
+---
+
+## Abstract
+
+The blackbox optimization benchmarking (bbob) test suite comprises 24 noiseless single-objective test functions. BBOB is one of the most widely used test suites to evaluate and compare the performance of blackbox optimization algorithms. Each benchmark function is provided in dimensions \[2, 3, 5, 10, 20, 40\] with 110 instances.
+
+## APIs
+
+### class `Problem(function_id: int, dimension: int, instance_id: int = 1)`
+
+- `function_id`: [ID of the bbob benchmark function](https://numbbo.github.io/coco/testsuites/bbob) to use. It must be in the range of `[1, 24]`.
+- `dimension`: Dimension of the benchmark function. It must be in `[2, 3, 5, 10, 20, 40]`.
+- `instance_id`: ID of the instance of the benchmark function. It must be in the range of `[1, 110]`.
+
+#### Methods and Properties
+
+- `search_space`: Return the search space.
+  - Return type: `dict[str, optuna.distributions.BaseDistribution]`
+- `directions`: Return the optimization directions.
+  - Return type: `list[optuna.study.StudyDirection]`
+- `__call__(trial: optuna.Trial)`: Evaluate the objective function and return the objective value.
+  - Parameters:
+    - `trial`: Optuna trial object.
+  - Return type: `float`
+- `evaluate(params: dict[str, float])`: Evaluate the objective function.
+  - Parameters:
+    - `params`: Decision variable like `{"x0": x1_value, "x1": x1_value, ..., "xn": xn_value}`. The number of parameters must be equal to `dimension`.
+  - Return type: `float`
+
+It is also available that properties defiend in [cocoex.Problem](https://numbbo.github.io/coco-doc/apidocs/cocoex/cocoex.Problem.html) such as `number_of_objectives`.
+
+## Installation
+
+Please install the [coco-experiment](https://github.com/numbbo/coco-experiment/tree/main/build/python) package.
+
+```shell
+pip install -U coco-experiment
+```
+
+## Example
+
+```python
+import optuna
+import optunahub
+
+
+bbob = optunahub.load_module("benchmarks/bbob")
+sphere2d = bbob.Problem(function_id=1, dimension=2, instance_id=1)
+
+study = optuna.create_study(directions=sphere2d.directions)
+study.optimize(sphere2d, n_trials=20)
+
+print(study.best_trial.params, study.best_trial.value)
+```
+
+## List of Benchmark Functions
+
+Please refer to [the paper](https://numbbo.github.io/gforge/downloads/download16.00/bbobdocfunctions.pdf) for details about each benchmark function.
+
+**Category**
+
+1. Separable Functions
+1. Functions with low or moderate conditioning
+1. Functions with high conditioning and unimodal
+1. Multi-modal functions with adequate global structure
+1. Multi-modal functions with weak global structure
+
+| Category  | Function ID | Function Name                                                                                         |
+|-----------|-------------|-------------------------------------------------------------------------------------------------------|
+| 1         | 1           | [Sphere Function](https://numbbo.it/bbob/functions/f01.html)                                          |
+| 1         | 2           | [Separable Ellipsoidal Function](https://numbbo.it/bbob/functions/f02.html)                           |
+| 1         | 3           | [Rastrigin Function](https://numbbo.it/bbob/functions/f03.html)                                       |
+| 1         | 4           | [Büche-Rastrigin Function](https://numbbo.it/bbob/functions/f04.html)                                 |
+| 1         | 5           | [Linear Slope](https://numbbo.it/bbob/functions/f05.html)                                             |
+| 2         | 6           | [Attractive Sector Function](https://numbbo.it/bbob/functions/f06.html)                               |
+| 2         | 7           | [Step Ellipsoidal Function](https://numbbo.it/bbob/functions/f07.html)                                |
+| 2         | 8           | [Rosenbrock Function, original](https://numbbo.it/bbob/functions/f08.html)                            |
+| 2         | 9           | [Rosenbrock Function, rotated](https://numbbo.it/bbob/functions/f09.html)                             |
+| 3         | 10          | [Ellipsoidal Function](https://numbbo.it/bbob/functions/f10.html)                                     |
+| 3         | 11          | [Discus Function](https://numbbo.it/bbob/functions/f11.html)                                          |
+| 3         | 12          | [Bent Cigar Function](https://numbbo.it/bbob/functions/f12.html)                                      |
+| 3         | 13          | [Sharp Ridge Function](https://numbbo.it/bbob/functions/f13.html)                                     |
+| 3         | 14          | [Different Powers Function](https://numbbo.it/bbob/functions/f14.html)                                |
+| 4         | 15          | [Rastrigin Function](https://numbbo.it/bbob/functions/f15.html)                                       |
+| 4         | 16          | [Weierstrass Function](https://numbbo.it/bbob/functions/f16.html)                                     |
+| 4         | 17          | [Schaffer's F7 Function](https://numbbo.it/bbob/functions/f17.html)                                   |
+| 4         | 18          | [Schaffer's F7 Function, moderately ill-conditioned](https://numbbo.it/bbob/functions/f18.html)       |
+| 4         | 19          | [Composite Griewank-Rosenbrock Function F8F2](https://numbbo.it/bbob/functions/f19.html)              |
+| 5         | 20          | [Schwefel Function](https://numbbo.it/bbob/functions/f20.html)                                        |
+| 5         | 21          | [Gallagher's Gaussian 101-me Peaks Function](https://numbbo.it/bbob/functions/f21.html)               |
+| 5         | 22          | [Gallagher's Gaussian 21-hi Peaks Function](https://numbbo.it/bbob/functions/f22.html)                |
+| 5         | 23          | [Katsuura Function](https://numbbo.it/bbob/functions/f23.html)                                        |
+| 5         | 24          | [Lunacek bi-Rastrigin Function](https://numbbo.it/bbob/functions/f24.html)                            |
+
+![BBOB Plots](images/bbob.png)
+
+## Reference
+
+Finck, S., Hansen, N., Ros, R., & Auger, A. [Real-Parameter Black-Box Optimization Benchmarking 2009: Presentation of the Noiseless Functions](https://numbbo.github.io/gforge/downloads/download16.00/bbobdocfunctions.pdf).

package/benchmarks/bbob/__init__.py

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+from ._bbob import Problem
+
+
+__all__ = ["Problem"]

package/benchmarks/bbob/_bbob.py

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+from __future__ import annotations
+
+from typing import Any
+from typing import Sequence
+
+import cocoex as ex
+import optuna
+import optunahub
+
+
+class Problem(optunahub.benchmarks.BaseProblem):
+    """Wrapper class for COCO bbob test suite.
+    https://numbbo.it/bbob/overview.html
+
+    1 Separable Functions
+        f1: Sphere Function
+        f2: Separable Ellipsoidal Function
+        f3: Rastrigin Function
+        f4: Büche-Rastrigin Function
+        f5: Linear Slope
+    2 Functions with low or moderate conditioning
+        f6: Attractive Sector Function
+        f7: Step Ellipsoidal Function
+        f8: Rosenbrock Function, original
+        f9: Rosenbrock Function, rotated
+    3 Functions with high conditioning and unimodal
+        f10: Ellipsoidal Function
+        f11: Discus Function
+        f12: Bent Cigar Function
+        f13: Sharp Ridge Function
+        f14: Different Powers Function
+    4 Multi-modal functions with adequate global structure
+        f15: Rastrigin Function
+        f16: Weierstrass Function
+        f17: Schaffer's F7 Function
+        f18: Schaffer's F7 Function, moderately ill-conditioned
+        f19: Composite Griewank-Rosenbrock Function F8F2
+    5 Multi-modal functions with weak global structure
+        f20: Schwefel Function
+        f21: Gallagher's Gaussian 101-me Peaks Function
+        f22: Gallagher's Gaussian 21-hi Peaks Function
+        f23: Katsuura Function
+        f24: Lunacek bi-Rastrigin Function
+    """
+
+    def __init__(self, function_id: int, dimension: int, instance_id: int = 1):
+        """Initialize the problem.
+        Args:
+            function_id: Function index in [1, 24].
+            dimension: Dimension of the problem in [2, 3, 5, 10, 20, 40].
+            instance_id: Instance index in [1, 110].
+
+        Please refer to the COCO documentation for the details of the available properties.
+        https://numbbo.github.io/coco-doc/apidocs/cocoex/cocoex.Problem.html
+        """
+
+        assert 1 <= function_id <= 24, "function_id must be in [1, 24]"
+        assert dimension in [2, 3, 5, 10, 20, 40], "dimension must be in [2, 3, 5, 10, 20, 40]"
+        assert 1 <= instance_id <= 110, "instance_id must be in [1, 110]"
+
+        self._problem = ex.Suite("bbob", "", "").get_problem_by_function_dimension_instance(
+            function=function_id, dimension=dimension, instance=instance_id
+        )
+        self._search_space = {
+            f"x{i}": optuna.distributions.FloatDistribution(
+                low=self._problem.lower_bounds[i],
+                high=self._problem.upper_bounds[i],
+            )
+            for i in range(self._problem.dimension)
+        }
+
+    @property
+    def search_space(self) -> dict[str, optuna.distributions.BaseDistribution]:
+        """Return the search space."""
+        return self._search_space
+
+    @property
+    def directions(self) -> Sequence[optuna.study.StudyDirection]:
+        """Return the optimization directions."""
+        return [optuna.study.StudyDirection.MINIMIZE]
+
+    def evaluate(self, params: dict[str, float]) -> float:
+        """Evaluate the objective function.
+        Args:
+            params:
+                Decision variable, e.g., evaluate({"x0": 1.0, "x1": 2.0}).
+                The number of parameters must be equal to the dimension of the problem.
+        Returns:
+            The objective value.
+
+        """
+        return self._problem(list(params.values()))
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._problem, name)
+
+    def __del__(self) -> None:
+        self._problem.free()

package/benchmarks/bbob/requirements.txt

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+coco-experiment