cbbo-array.toml

# This configuration uses an array for the benchmarks

title = "CBBO Benchmark"

benchmark = [
    "Ackley",
    "Branin",
    "Hartmann6D",
    "Levy",
    "Michal",
    "Rosen",
    "Schwefel",
    "Shekel",
]

[evaluator]
method = "thread"
num_workers = 1

[search]
max_evals = 10
num_replications = 2

[search.method.random]
package = "deephyper.hpo"
name = "RandomSearch"

[search.method.cbo_093]
package = "deephyper.hpo"
name = "CBO"
kwargs.acq_optimizer = "ga"

[search.method.cbo]
package = "deephyper.hpo"
name = "CBO"
kwargs.acq_optimizer = "ga"

# [search.method.cbo_no_outlier]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"

# [search.method.cbo_mixedga]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "mixedga"

# [search.method.cbo_ga_init_random]
# # test with random initialization for ga
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"

# [search.method.cbo_lhs]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"
# kwargs.initial_point_generator = "lhs"

# [search.method.cbo_n_estimators]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"
# kwargs.surrogate_model_kwargs = { n_estimators = 50, min_samples_split = 2, min_impurity_decrease = 0.005, scheduler = { patience = 10, params = { min_impurity_decrease = { factor = 0.2 } } } }

# [search.method.cbo_n_initial_points]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"
# kwargs.n_initial_points = 21

# [search.method.cbo_kappa_5]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_func_kwargs = { kappa = 5.0 }
# kwargs.acq_optimizer = "ga"

# [search.method.cbo_kappa_period_25]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_func_kwargs = { kappa = 1.96, scheduler = { type = "periodic-exp-decay", patience = 25, kappa_final = 0.01 } }
# kwargs.acq_optimizer = "ga"

# [search.method.cbo_min_samples_split]
# package = "deephyper.hpo"
# name = "CBO"
# kwargs.acq_optimizer = "ga"
# kwargs.surrogate_model_kwargs = { n_estimators = 10, min_samples_split = 8, min_impurity_decrease = 0.0 }

[search.method.tpe]
package = "cbbo_benchmarks.optuna"
name = "OptunaSearch"
kwargs.sampler = "TPE"

# [search.method.cmaes]
# package = "deephyper_benchmark.hpo.optuna"
# name = "OptunaSearch"
# kwargs.sampler = "CMAES"

# [search.method.smac]
# package = "deephyper_benchmark.hpo.optuna"
# name = "OptunaSearch"
# kwargs.sampler = "SMAC"