v0.11.0

Highlights

CatCMA [Hamano+, GECCO2024]

arXiv: https://arxiv.org/pdf/2405.09962

CatCMA is a method for mixed-category optimization problems, which is the problem of simultaneously optimizing continuous and categorical variables. CatCMA employs the joint probability distribution of multivariate Gaussian and categorical distributions as the search distribution.

Usage is like below:

import numpy as np
from cmaes import CatCMA


def sphere_com(x, c):
    dim_co = len(x)
    dim_ca = len(c)
    if dim_co < 2:
        raise ValueError("dimension must be greater one")
    sphere = sum(x * x)
    com = dim_ca - sum(c[:, 0])
    return sphere + com


def rosenbrock_clo(x, c):
    dim_co = len(x)
    dim_ca = len(c)
    if dim_co < 2:
        raise ValueError("dimension must be greater one")
    rosenbrock = sum(100 * (x[:-1] ** 2 - x[1:]) ** 2 + (x[:-1] - 1) ** 2)
    clo = dim_ca - (c[:, 0].argmin() + c[:, 0].prod() * dim_ca)
    return rosenbrock + clo


def mc_proximity(x, c, cat_num):
    dim_co = len(x)
    dim_ca = len(c)
    if dim_co < 2:
        raise ValueError("dimension must be greater one")
    if dim_co != dim_ca:
        raise ValueError(
            "number of dimensions of continuous and categorical variables "
            "must be equal in mc_proximity"
        )

    c_index = np.argmax(c, axis=1) / cat_num
    return sum((x - c_index) ** 2) + sum(c_index)


if __name__ == "__main__":
    cont_dim = 5
    cat_dim = 5
    cat_num = np.array([3, 4, 5, 5, 5])
    # cat_num = 3 * np.ones(cat_dim, dtype=np.int64)
    optimizer = CatCMA(mean=3.0 * np.ones(cont_dim), sigma=1.0, cat_num=cat_num)

    for generation in range(200):
        solutions = []
        for _ in range(optimizer.population_size):
            x, c = optimizer.ask()
            value = mc_proximity(x, c, cat_num)
            if generation % 10 == 0:
                print(f"#{generation} {value}")
            solutions.append(((x, c), value))
        optimizer.tell(solutions)

        if optimizer.should_stop():
            break

What's Changed

• Add support for Python 3.12 by @c-bata in #153
• Remove setup.py and use build module by @c-bata in #154
• Fix CI failures by @c-bata in #158
• get mean by @nomuramasahir0 in #159
• add question template by @nomuramasahir0 in #162
• fix sigma setting by @nomuramasahir0 in #160
• Add GitHub action setting for continuous benchmark by @c-bata in #168
• fix typo by @nomuramasahir0 in #169
• fix BIPOP-CMA in visualization by @nomuramasahir0 in #170
• update readme by @nomuramasahir0 in #171
• update by @nomuramasahir0 in #172
• fix the old_sigma assertion when lr_adapt=True by @Kreyparion in #174
• remove kurobako dependency by @nomuramasahir0 in #175
• Support for numpy v2.0 by @porink0424 in #177
• catcma (GECCO2024) by @ha-mano in #178
• fix CatCMA by @ha-mano in #179
• Update README.md by @ha-mano in #181
• Bump the version up to v0.11.0 by @c-bata in #183

New Contributors

• @Kreyparion made their first contribution in #174
• @porink0424 made their first contribution in #177
• @ha-mano made their first contribution in #178

Full Changelog: v0.10.0...v0.11.0