Arguments¶
If you are using Python, have a look here for a detailed API reference. When using the commandline, view the basic command options via
python scripts/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 ‘False’ by default. (Warning: This only works on Linux. Use with caution!)
- 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_2022-07-14_08:13:31_599946.
- 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).