import_model

Import surrogate model from archive file

Specification

• Alias: None

• Arguments: None

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Optional		filename_prefix	User-customizable portion of exported/imported surrogate model filenames
Required (Choose One)	Surrogate Import Format	text_archive	Surrogate model plain-text archive file format
		binary_archive	Surrogate model binary archive file format

Description

Imports a surrogate model from a file in binary_archive or text_archive format, typically instead of constructing it from generated or imported data. Importing can result in significant time savings with some models, such as Gaussian processes. The file from which to import is further specified with the child keywords filename_prefix and binary_archive or text_archive.

Default Behavior

When used in the context of an iteratively adapted or rebuilt surrogate, the imported model will only replace the initial surrogate model. Subsequent surrogate model builds require imported build points or a design of experiments method specified through dace_method_pointer.

Usage Tips

When importing a surrogate model, it is crucial that the global surrogate model part of the Dakota input file be identical for export and import, except for changing export_model and its child keywords to those needed for import_model. Any other keywords such as specifying a dace_iterator or imported points must remain intact to satisfy internal surrogate constructor requirements.

All variables over which the surrogate model was built (typically the active variables) when exported must be present in the Dakota study that imports it. The variables in the importing study will be matched by descriptor to those in the surrogate model. This allows the order and type of variables to change between export and import, for example to fix unimportant parameters as state variables even if they were previously active design variables.

For example, suppose the original Dakota study for the surrogate build had active uniform uncertain variables: a, b, c, d, and inactive state variables s, t, and the surrogate gets built over a, b, c, d. The follow-on study could perform design optimization over active design variables b and d, with inactive state variables a, c, s, t, held at fixed initial_state values. In this case the values of a, b, c, d will get mapped to the surrogate and s, t dropped when evaluating it. So it is important the state values for s and t haven’t changed in some way that invalidates the surrogate.

While it might be possible to import surrogates externally-generated using the surfpack binary or the experimental dakota.surrogates Python module, it is untested and not the primary use case for this capability.

Examples

The following model block exports a Gaussian process after it’s built:

model
  id_model = 'SURR_M'
  surrogate global
    dace_method_pointer = 'DACE'
    experimental_gaussian_process
      metrics 'root_mean_squared'
        cross_validation folds 5
      export_model
        filename_prefix = 'gp_export'
        formats = binary_archive
      export_approx_points_file 'gp_values.dat'
      export_approx_variance_file 'gp_variance.dat'

And this model block imports it using a nearly identical model specification:

model
  id_model = 'SURR_M'
  surrogate global
    dace_method_pointer = 'DACE'
    experimental_gaussian_process
      metrics 'root_mean_squared'
        cross_validation folds 5
      import_model
        filename_prefix = 'gp_export'
    binary_archive
      export_approx_points_file 'gp_values.dat'
      export_approx_variance_file 'gp_variance.dat'