Add HEBO sampler

package/samplers/hebo/LICENSE

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+MIT License
+
+Copyright (c) 2024 Hideaki Imamura
+
+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/samplers/hebo/README.md

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+---
+author: HideakiImamura
+title: HEBO (Heteroscedastic and Evolutionary Bayesian Optimisation)
+description: HEBO addresses the problem of noisy and heterogeneous objective functions by using a heteroscedastic Gaussian process and an evolutionary algorithm.
+tags: ["sampler", "Bayesian optimization", "Heteroscedastic Gaussian process", "Evolutionary algorithm"]
+optuna_versions: ["3.6.1"]
+license: "MIT License" 
+---
+
+## Class or Function Names
+- HEBOSampler
+
+## Installation
+```bash
+pip install -r requirements.txt
+git clone [email protected]:huawei-noah/HEBO.git
+cd HEBO/HEBO
+pip install -e .
+```
+
+## Example
+```python
+search_space = {
+    "x": FloatDistribution(-10, 10),
+    "y": IntDistribution(0, 10),
+
+}
+sampler = HEBOSampler(search_space)
+study = optuna.create_study(sampler=sampler)
+```
+See [`example.py`](https://github.com/optuna/optunahub-registry/blob/main/package/samplers/hebo/example.py) for a full example.
+![History Plot](images/hebo_optimization_history.png "History Plot")
+
+
+## Others
+
+HEBO is the winning submission to the [NeurIPS 2020 Black-Box Optimisation Challenge](https://bbochallenge.com/leaderboard). 
+Please refer to [the official repository of HEBO](https://github.com/huawei-noah/HEBO/tree/master/HEBO) for more details.
+
+### Reference
+
+Cowen-Rivers, Alexander I., et al. "An Empirical Study of Assumptions in Bayesian Optimisation." arXiv preprint arXiv:2012.03826 (2021).

package/samplers/hebo/__init__.py

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+from .sampler import HEBOSampler
+
+
+__all__ = ["HEBOSampler"]

package/samplers/hebo/example.py

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+import optuna
+import optunahub
+
+
+module = optunahub.load_module("samplers/hebo")
+HEBOSampler = module.HEBOSampler
+
+
+def objective(trial: optuna.trial.Trial) -> float:
+    x = trial.suggest_float("x", -10, 10)
+    y = trial.suggest_int("y", -10, 10)
+    return x**2 + y**2
+
+
+if __name__ == "__main__":
+    sampler = HEBOSampler(
+        {
+            "x": optuna.distributions.FloatDistribution(-10, 10),
+            "y": optuna.distributions.IntDistribution(-10, 10),
+        }
+    )
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=100)
+    print(study.best_trial.params)
+
+    fig = optuna.visualization.plot_optimization_history(study)
+    fig.write_image("hebo_optimization_history.png")

package/samplers/hebo/requirements.txt

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+optuna
+optunahub
+hebo@git+https://github.com/huawei-noah/HEBO.git#subdirectory=HEBO

package/samplers/hebo/sampler.py

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+from __future__ import annotations
+
+from optuna.distributions import BaseDistribution
+from optuna.distributions import CategoricalDistribution
+from optuna.distributions import FloatDistribution
+from optuna.distributions import IntDistribution
+from optuna.study import Study
+from optuna.trial import FrozenTrial
+import optunahub
+
+from hebo.design_space.design_space import DesignSpace
+from hebo.optimizers.hebo import HEBO
+
+
+SimpleSampler = optunahub.load_module("samplers/simple").SimpleSampler
+
+
+class HEBOSampler(SimpleSampler):  # type: ignore
+    def __init__(self, search_space: dict[str, BaseDistribution]) -> None:
+        super().__init__(search_space)
+        self._hebo = HEBO(self._convert_to_hebo_design_space(search_space))
+
+    def sample_relative(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> dict[str, float]:
+        params_pd = self._hebo.suggest()
+
+        params = {}
+        for name in search_space.keys():
+            params[name] = params_pd[name].to_numpy()[0]
+        return params
+
+    def _convert_to_hebo_design_space(
+        self, search_space: dict[str, BaseDistribution]
+    ) -> DesignSpace:
+        design_space = []
+        for name, distribution in search_space.items():
+            if isinstance(distribution, FloatDistribution) and not distribution.log:
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "num",
+                        "lb": distribution.low,
+                        "ub": distribution.high,
+                    }
+                )
+            elif isinstance(distribution, FloatDistribution) and distribution.log:
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "pow",
+                        "lb": distribution.low,
+                        "ub": distribution.high,
+                    }
+                )
+            elif isinstance(distribution, IntDistribution) and distribution.log:
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "pow_int",
+                        "lb": distribution.low,
+                        "ub": distribution.high,
+                    }
+                )
+            elif isinstance(distribution, IntDistribution) and distribution.step:
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "step_int",
+                        "lb": distribution.low,
+                        "ub": distribution.high,
+                        "step": distribution.step,
+                    }
+                )
+            elif isinstance(distribution, IntDistribution):
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "int",
+                        "lb": distribution.low,
+                        "ub": distribution.high,
+                    }
+                )
+            elif isinstance(distribution, CategoricalDistribution):
+                design_space.append(
+                    {
+                        "name": name,
+                        "type": "cat",
+                        "categories": distribution.choices,
+                    }
+                )
+            else:
+                raise NotImplementedError(f"Unsupported distribution: {distribution}")
+        return DesignSpace().parse(design_space)