add catcma

package/samplers/catcma/LICENSE

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+MIT License
+
+Copyright (c) 2024 Masahiro Nomura
+
+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/catcma/README.md

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+---
+author: "Masahiro Nomura"
+title: "CatCMA Sampler"
+description: "Categorical and Continuous Optimization with CMA-ES."
+tags: ["sampler", "Mixed-variable optimization", "Evolutionary algorithms", "CMA-ES"]
+optuna_versions: ["3.6.1"]
+license: "MIT License"
+---
+
+## Class or Function Names
+- CatCMASampler
+
+## Installation
+```bash
+pip install -r requirements.txt
+```
+
+## Example
+```python
+import numpy as np
+import optuna
+from optuna.distributions import CategoricalDistribution
+from optuna.distributions import FloatDistribution
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> float:
+    x1 = trial.suggest_float("x1", -1, 1)
+    x2 = trial.suggest_float("x2", -1, 1)
+    x3 = trial.suggest_float("x3", -1, 1)
+    X = np.array([x1, x2, x3])
+
+    c1 = trial.suggest_categorical("c1", [0, 1, 2])
+    c2 = trial.suggest_categorical("c2", [0, 1, 2])
+    c3 = trial.suggest_categorical("c3", [0, 1, 2])
+    C = np.array([c1, c2, c3])
+
+    return sum(X**2) + len(C) - sum(C == 0)
+
+
+if __name__ == "__main__":
+    mod = optunahub.load_module(
+        package="samplers/catcma",
+    )
+    CatCmaSampler = mod.CatCmaSampler
+
+    study = optuna.create_study(
+        sampler=CatCmaSampler(
+            search_space={
+                "x1": FloatDistribution(-1, 1),
+                "x2": FloatDistribution(-1, 1),
+                "x3": FloatDistribution(-1, 1),
+                "c1": CategoricalDistribution([0, 1, 2]),
+                "c2": CategoricalDistribution([0, 1, 2]),
+                "c3": CategoricalDistribution([0, 1, 2]),
+            }
+        )
+    )
+    study.optimize(objective, n_trials=20)
+    print(study.best_params)
+
+    # You can omit the search space definition before optimization.
+    # Then, the search space will be estimated during the first trial.
+    # In this case, independent_sampler (default: RandomSampler) will be used instead of the CatCma algorithm for the first trial.
+    study = optuna.create_study(sampler=CatCmaSampler())
+    study.optimize(objective, n_trials=20)
+    print(study.best_params)
+```
+
+## Others
+
+### Reference
+
+Ryoki Hamano, Shota Saito, Masahiro Nomura, Kento Uchida, Shinichi Shirakawa , CatCMA : Stochastic Optimization for Mixed-Category Problems, GECCO'24
+
+See the [paper](https://arxiv.org/abs/2405.09962) for more details.
+
+
+### BibTeX
+```bibtex
+@article{hamano2024catcma,
+  title={CatCMA: Stochastic Optimization for Mixed-Category Problems},
+  author={Hamano, Ryoki and Saito, Shota and Nomura, Masahiro and Uchida, Kento and Shirakawa, Shinichi},
+  journal={arXiv preprint arXiv:2405.09962},
+  year={2024}
+}
+```
+

package/samplers/catcma/__init__.py

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+from .catcma import CatCmaSampler
+
+
+__all__ = ["CatCmaSampler"]