cave.reader.configurator_run module¶
-
class
cave.reader.configurator_run.
ConfiguratorRun
(scenario, original_runhistory, validated_runhistory, trajectory, options, path_to_folder=None, ta_exec_dir=None, file_format=None, validation_format=None, reduced_to_budgets=None, output_dir=None)[source]¶ Bases:
object
ConfiguratorRuns load and maintain information about individual configurator runs. There are different supported formats, like: BOHB, SMAC3, SMAC2 and CSV This class is responsible for providing a scenario, a runhistory and a trajectory and handling original/validated data appropriately. To create a ConfiguratorRun from a folder, use Configurator.from_folder()
- Parameters
scenario (Scenario) – scenario
validated_runhistory (original_runhistory,) – runhistores containing only the original evaluated data (during optimization process) or the validated data where points of interest are reevaluated after the optimization process
trajectory (List[dict]) – a trajectory of the best performing configurations at each point in time
options (dict) – options can define a number of custom settings
path_to_folder (str) – path to the physical folder containing the data
ta_exec_dir (str) – path to the target-algorithm-execution-directory. This is only important for SMAC-optimized data
validation_format (file_format,) – will be autodetected some point soon, until then, specify the file-format (SMAC2, SMAC3, BOHB, etc…)
reduced_to_budgets (List str int or float) – budgets, with which this cr is associated
output_dir (str) – where to save analysis-data for this cr
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_check_rh_for_inc_and_def
(rh, name='')[source]¶ Check if default and incumbent are evaluated on all instances in this rh
- Parameters
rh (RunHistory) – runhistory to be checked
name (str) – name for logging-purposes
- Returns
return_value – False if either inc or def was not evaluated on all train/test-instances
- Return type
bool
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_init_pimp_and_validator
(alternative_output_dir=None)[source]¶ Create ParameterImportance-object and use it’s trained model for validation and further predictions. We pass a combined (original + validated) runhistory, so that the returned model will be based on as much information as possible
- Parameters
alternative_output_dir (str) – e.g. for budgets we want pimp to use an alternative output-dir (subfolders per budget)
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_validate_default_and_incumbents
(method, ta_exec_dir)[source]¶ Validate default and incumbent configurations on all instances possible. Either use validation (physically execute the target algorithm) or EPM-estimate and update according runhistory (validation -> self.global_validated_rh; epm -> self.global_epm_rh).
- Parameters
method (str) – epm or validation
ta_exec_dir (str) – path from where the target algorithm can be executed as found in scenario (only used for actual validation)
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classmethod
from_folder
(folder: str, ta_exec_dir: str, options, file_format: str = 'SMAC3', validation_format: str = 'NONE', output_dir=None)[source]¶ Initialize scenario, runhistory and incumbent from folder
- Parameters
folder (string) – output-dir of this configurator-run -> this is also the ‘id’ for a single run in parallel optimization
ta_exec_dir (string) – if the execution directory for the SMAC-run differs from the cwd, there might be problems loading instance-, feature- or PCS-files in the scenario-object. since instance- and PCS-files are necessary, specify the path to the execution-dir of SMAC here
file_format (string) – from [SMAC2, SMAC3, BOHB, APT, CSV]
validation_format (string) – from [SMAC2, SMAC3, APT, CSV, NONE], in which format to look for validated data
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cave.reader.configurator_run.
_changedir
(newdir)[source]¶ Helper function to change directory, for example to create a scenario from file, where paths to the instance- and feature-files are relative to the original SMAC-execution-directory. Same with target algorithms that need be executed for validation.