Add PLMBOSampler

package/samplers/plmbo/LICENSE

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+MIT License
+
+Copyright (c) 2024 Ryota Ozaki
+
+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/plmbo/README.md

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+---
+author: "Ryota Ozaki"
+title: "PLMBO (Preference Learning Multi-Objective Bayesian Optimization)"
+description: "Interatctive multi-objective Bayesian optimization based on user preference"
+tags: ["sampler", "interactive optimization", "Bayesian optimization", "multi-objective optimization", "preference learning", "active learning"]
+optuna_versions: ["3.6.1"]
+license: "MIT License"
+---
+
+## Class or Function Names
+- PLMBOSampler
+
+## Installation
+```sh
+pip install -r https://raw.githubusercontent.com/optuna/optunahub-registry/main/package/samplers/plmbo/requirements.txt
+```
+
+## Example
+```python
+from __future__ import annotations
+
+import matplotlib.pyplot as plt
+import optuna
+import optunahub
+from optuna.distributions import FloatDistribution
+import numpy as np
+
+
+PLMBOSampler = optunahub.load_module(  # type: ignore
+    "samplers/plmbo",
+).PLMBOSampler
+
+if __name__ == "__main__":
+    f_sigma = 0.01
+
+    def obj_func1(x):
+        return np.sin(x[0]) + x[1]
+
+    def obj_func2(x):
+        return -np.sin(x[0]) - x[1] + 0.1
+
+    def obs_obj_func(x):
+        return np.array(
+            [
+                obj_func1(x) + np.random.normal(0, f_sigma),
+                obj_func2(x) + np.random.normal(0, f_sigma),
+            ]
+        )
+
+    def objective(trial: optuna.Trial):
+        x1 = trial.suggest_float("x1", 0, 1)
+        x2 = trial.suggest_float("x2", 0, 1)
+        values = obs_obj_func(np.array([x1, x2]))
+        return float(values[0]), float(values[1])
+
+    sampler = PLMBOSampler(
+        {
+            "x1": FloatDistribution(0, 1),
+            "x2": FloatDistribution(0, 1),
+        }
+    )
+    study = optuna.create_study(sampler=sampler, directions=["minimize", "minimize"])
+    study.optimize(objective, n_trials=20)
+
+    optuna.visualization.matplotlib.plot_pareto_front(study)
+    plt.show()
+```
+
+## Others
+### Reference
+R Ozaki, K Ishikawa, Y Kanzaki, S Takeno, I Takeuchi, and M Karasuyama. (2024). Multi-Objective Bayesian Optimization with Active Preference Learning. Proceedings of the AAAI Conference on Artificial Intelligence.
+
+### Bibtex
+```
+@inproceedings{ozaki2024multi,
+  title={Multi-Objective Bayesian Optimization with Active Preference Learning},
+  author={Ozaki, Ryota and Ishikawa, Kazuki and Kanzaki, Youhei and Takeno, Shion and Takeuchi, Ichiro and Karasuyama, Masayuki},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={38},
+  number={13},
+  pages={14490--14498},
+  year={2024}
+}
+```

package/samplers/plmbo/__init__.py

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+from .plmbo import PLMBOSampler
+
+
+__all__ = ["PLMBOSampler"]

package/samplers/plmbo/example.py

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+# mypy: ignore-errors
+from __future__ import annotations
+
+import matplotlib.pyplot as plt
+import numpy as np
+import optuna
+from optuna.distributions import FloatDistribution
+import optunahub
+
+
+PLMBOSampler = optunahub.load_module(  # type: ignore
+    "samplers/plmbo",
+).PLMBOSampler
+
+if __name__ == "__main__":
+    f_sigma = 0.01
+
+    def obj_func1(x):
+        return np.sin(x[0]) + x[1]
+
+    def obj_func2(x):
+        return -np.sin(x[0]) - x[1] + 0.1
+
+    def obs_obj_func(x):
+        return np.array(
+            [
+                obj_func1(x) + np.random.normal(0, f_sigma),
+                obj_func2(x) + np.random.normal(0, f_sigma),
+            ]
+        )
+
+    def objective(trial: optuna.Trial):
+        x1 = trial.suggest_float("x1", 0, 1)
+        x2 = trial.suggest_float("x2", 0, 1)
+        values = obs_obj_func(np.array([x1, x2]))
+        return float(values[0]), float(values[1])
+
+    sampler = PLMBOSampler(
+        {
+            "x1": FloatDistribution(0, 1),
+            "x2": FloatDistribution(0, 1),
+        }
+    )
+    study = optuna.create_study(sampler=sampler, directions=["minimize", "minimize"])
+    study.optimize(objective, n_trials=20)
+
+    optuna.visualization.matplotlib.plot_pareto_front(study)
+    plt.show()