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CMA-ES with User Prior

You can provide the initial parameters, i.e. mean vector and covariance matrix, for CMA-ES with this sampler.

Abstract

As the Optuna CMA-ES sampler does not support any flexible ways to initialize the parameters of the Gaussian distribution, so I created a workaround to do so.

Class or Function Names

  • UserPriorCmaEsSampler

In principle, most arguments follow optuna.samplers.CmaEsSampler, but some parts are modified.

For example, UserPriorCmaEsSampler does not support source_trials and use_separable_cma due to their incompatibility. Instead, we replaced x0 and sigma0 in CmaEsSampler with mu0 and cov0. In CmaEsSampler, we needed to provide x0 as dict and sigma0 only as float. By adding param_names to the requirement, we can now give mu0 (previously x0) and cov0 (previously sigma0) as np.ndarray. Note that the order of each dimension in mu0 and cov0 must be consistent with that in param_names.

Installation

$ pip install optunahub cmaes

Example

The simplest code example is as follows:

import numpy as np
import optuna
import optunahub


def objective(trial: optuna.Trial) -> float:
    x = trial.suggest_float("x", -50, -40)
    y = trial.suggest_int("y", -5, 5)
    return (x + 43)**2 + (y - 2)**2


if __name__ == "__main__":
    module = optunahub.load_module(package="samplers/user_prior_cmaes")
    # ``with_margin=True`` because the search space has an integer parameter.
    sampler = module.UserPriorCmaEsSampler(
        param_names=["x", "y"], mu0=np.array([-48., 3.]), cov0=np.diag([2., 0.2]), with_margin=True
    )
    study = optuna.create_study(sampler=sampler)
    study.optimize(objective, n_trials=20)
    print(study.best_trial.value, study.best_trial.params)

Although UserPriorCmaEsSampler CANNOT support log scale from the sampler side, we have a workaround to do so:

import math

import numpy as np
import optuna
import optunahub


def objective(trial: optuna.Trial) -> float:
    # For example, trial.suggest_float("x", 1e-5, 1.0, log=True) can be encoded as:
    x = 10 ** trial.suggest_float("log10_x", -5, 0)
    # trial.suggest_float("y", 2, 1024, log=True) can be encoded as:
    y = 2 ** trial.suggest_float("log2_y", 1, 10)
    # In general, trial.suggest_float("z", low, high, log=True) can be encoded as:
    low, high = 3, 81
    b = 3  # The base of log can be any positive number.
    z = b ** trial.suggest_float("logb_z", math.log(low, b), math.log(high, b))
    return x**2 + y**2 + z**2


if __name__ == "__main__":
    module = optunahub.load_module(package="samplers/user_prior_cmaes")
    sampler = module.UserPriorCmaEsSampler(
        param_names=["log10_x", "log2_y", "logb_z"],
        mu0=np.array([-4, 8, 3]),
        cov0=np.diag([0.2, 1., 0.1]),
    )
    study = optuna.create_study(sampler=sampler)
    study.optimize(objective, n_trials=20)
    print(study.best_trial.value, study.best_trial.params)
Package
samplers/user_prior_cmaes
Author
Shuhei Watanabe
License
MIT License
Verified Optuna version
  • 4.0.0
Last update
2025-01-08