AutoSampler

Abstract

This package automatically selects an appropriate sampler for the provided search space based on the developers’ recommendation. The following article provides detailed information about AutoSampler.

• 📰 AutoSampler: Automatic Selection of Optimization Algorithms in Optuna

Class or Function Names

• AutoSampler

This sampler currently accepts only seed and constraints_func. constraints_func enables users to handle constraints along with the objective function. These arguments follow the same convention as the other samplers, so please take a look at the reference.

Installation

This sampler requires optional dependencies of Optuna.

$ pip install optunahub cmaes torch scipy

Note that since we may update the implementation of AutoSampler, it is highly encouraged to use the latest version of Optuna.

Example

import optuna
import optunahub


def objective(trial):
  x = trial.suggest_float("x", -5, 5)
  y = trial.suggest_float("y", -5, 5)
  return x**2 + y**2


module = optunahub.load_module(package="samplers/auto_sampler")
study = optuna.create_study(sampler=module.AutoSampler())
study.optimize(objective, n_trials=300)

Test

To execute the tests for AutoSampler, please run the following commands. The test file is provided in the package.

pip install pytest

pytest package/samplers/auto_sampler/tests/