Arguments

If you are using Python, have a look here for a detailed API reference. When using the comamndline, view the basic command options via

python smac.py --help

or view all other options in the following:

Main Options

hydra_iterations

number of hydra iterations. Only active if mode is set to Hydra Default: 3.

hydra_validation

set to validate incumbents on. valX => validation set of size training_set * 0.X Default: train.

incumbents_per_round

number of configurations to keep per psmac/hydra iteration. Default: 1.

mode

Configuration mode. Default: SMAC4AC.

n_optimizers

number of optimizers to run in parallel per psmac/hydra iteration. Default: 1.

psmac_validate

Validate all psmac configurations.

random_configuration_chooser

path to a python module containing a class RandomConfigurationChooserImpl`implementing the interface of `RandomConfigurationChooser

restore_state

Path to directory with SMAC-files.

scenario_file

Scenario file in AClib format.

seed

Random Seed. Default: 1.

verbose_level

Verbosity level. Default: 20.

SMAC Options

abort_on_first_run_crash

If true, SMAC will abort if the first run of the target algorithm crashes. Default: True.

acq_opt_challengers

Number of challengers returned by acquisition function optimization. Also influences the number of randomly sampled configurations to optimized the acquisition function Default: 5000.

always_race_default

Race new incumbents always against default configuration.

hydra_iterations

number of hydra iterations. Only active if mode is set to Hydra Default: 3.

input_psmac_dirs

For parallel SMAC, multiple output-directories are used.

intens_adaptive_capping_slackfactork

Slack factor of adpative capping (factor * adpative cutoff). Only active if obj is runtime. If set to very large number it practically deactivates adaptive capping. Default: 1.2.

intens_min_chall

Minimal number of challengers to be considered in each intensification run (> 1). Set to 1 and in combination with very small intensification-percentage. it will deactivate randomly sampled configurations (and hence, extrapolation of random forest will be an issue.) Default: 2.

intensification_percentage

The fraction of time to be used on intensification (versus choice of next Configurations). Default: 0.5.

limit_resources

If true, SMAC will use pynisher to limit time and memory for the target algorithm. Allows SMAC to use all resources available. Applicable only to func TAEs. Set to ‘True’ by default. (Use with caution!) Default: True.

maxR

Maximum number of calls per configuration. Default: 2000.

minR

Minimum number of calls per configuration. Default: 1.

output_dir

Specifies the output-directory for all emerging files, such as logging and results. Default: smac3-output_2021-11-05_09:44:55_835573.

rand_prob

probablity to run a random configuration instead of configuration optimized on the acquisition function Default: 0.5.

random_configuration_chooser

path to a python module containing a class`RandomConfigurationChooserImpl` implementingthe interface of RandomConfigurationChooser

rf_do_bootstrapping

Use bootstraping in random forest. Default: True.

rf_max_depth

Maximum depth of each tree in the random forest. Default: 20.

rf_min_samples_leaf

Minimum required number of samples in each leaf of a tree in the random forest. Default: 3.

rf_min_samples_split

Minimum number of samples to split for building a tree in the random forest. Default: 3.

rf_num_trees

Number of trees in the random forest (> 1). Default: 10.

rf_ratio_features

Ratio of sampled features in each split ([0.,1.]). Default: 0.8333333333333334.

shared_model

Whether to run SMAC in parallel mode.

sls_max_steps

Maximum number of local search steps in one iteration during the optimization of the acquisition function.

sls_n_steps_plateau_walk

Maximum number of steps on plateaus during the optimization of the acquisition function. Default: 10.

transform_y

Transform all observed cost values via log-transformations or inverse scaling. The subfix “s” indicates that SMAC scales the y-values accordingly to apply the transformation. Default: NONE.

use_ta_time

Instead of measuring SMAC’s wallclock time, only consider time reported by the target algorithm (ta).