==========
An OpenMDAO driver allowing access to DAKOTA.
This can be installed using plugin install in an openmdao environment after
the succesful installation of pyDAKOTA
import driver using:
from dakota_driver.driver import pydakdriver
driver = self.add('driver',pydakdriver())
driver.UQ()
driver.samples = 2000
driver.stdout = 'dakota.out'
driver.stderr = 'dakota.err'
driver.add_special_distribution('lcoe_se.A', 'normal', mean = 10, std_dev = 1)
driver.add_parameter('lcoe_se.k',low = 0.3, high = 3)
python tests/ouu_test.py
python tests/pydaktest.py
==================================================================================================
There is no default setting of the pydakdriver. One of these configuration functions must be called.
While some parameters can only be set through configuration function calls (listed in arguments), all paramteters can be set using objects as listed in options. Because of this, arguments are not listed as options. However, these paramters still can be set using objects in the same fasion as the listed options, unless otherwise noted. Additionally, configuration functions are listed. The functions are either broad convinience functions, or the only way available to set a specific paramter.
pydakdriver.UQ( UQ_type = 'sampling', use_seed = True)
description: uncertainty quantification driver configuration
arguments:
UQ_type = dakota uncertainty quantification procedure
options:
'sampling'
description: monte carlo sampling
use_seed = use seed if True, do not specify DAKOTA seed if false
Option Descriptions
------------------
sample_type = random sampling approach
options: 'lhs', 'random', incremental_lhs, incremental_random
samples = number of samples to be taken
usage: pydakdrive.Parameter_Study( study_type = 'vector')
description:
explores the effect of parametric changes within simulation models by
computing re- sponse data sets at a selection of points in the parameter space
study_type = type of parameter study
options:
'vector':
description:
performs a parameter study along a line between any two points
in an n-dimensional parameter space, where the user specifies the number of steps used in the study.
configured with 'final_point' and 'num_steps'
'multi-dim':
description:
Forms a regular lattice or hypergrid in an n-dimensional parameter space, where the user
specifies the number of intervals used for each parameter
configured with: 'partitions'
'list':
description:
The user supplies a list of points in an n-dimensional space where Dakota
will evaluate response data from the simulation code.
configured with: 'list_of_points'
'centered'
description:
Given a point in an n-dimensional parameter space, this method evaluates nearby points
along the coordinate axes of the parameter space. The user selects the number of steps
and the step size.
configured with: 'step_vector', 'steps_per_variable'
Option Descriptions
------------------
final_point
description:
list which defines the final values for each variable on the vector to be used in the vector parameter study
(the initial points are defined values variables are set to using openmdao API)
num_steps:
description: number of steps to be taken from initial point to final point
partitions: list describin number of intervals for each parameter in multi-dim study
list_of_points: List of variable values to evaluate in a list parameter study
Note: this is a "list of lists". ex: driver.list_of_points = [[2,3,4,5],[4,3,2,1]]
step_vector: Number of sampling steps along the vector in parameter study
steps_per_variable: Number of steps to take in each dimension of a centered parameter study
usage: pydakdrive.Optimization( opt_type='optpp_newton', interval_type = 'forward')
description: optimize one or multiple objectives
opt_type = type of optimization
defaults:
convergence_tolerance = 1.e-8, max_iterations = 200, max_function_evaluations = 2000
options:
'optpp_newton'
description: Newton method based optimization
Notes:
Expects analytical gradients in provideJ. Numerical gradiants can be set using
driver.numerical_gradients().
Computes Numerical Hessians.
'efficient_global'
description: DAKOTA Global Optimization
configured with: seed
'conmin'
description:ONstrained function MINimization
configured with:
constraint_tolerance = 1.e-8
interval_type:
Specifies how to compute gradients and hessians
options are 'forward', 'central'
Option Descriptions
-------------------
convergence_tolerance: Stopping criterion based on convergence of the objective function
seed = random number generator seed
max_iterations = Stopping criteria based on number of iterations (different than max_function_evaluations)
constraint_tolerance: maximum allowable value of constraint violation still considered to be feasible
================================================================================================== Notes for Future Development
- DAKOTA's Design of Experiment analysis could easily be incorporated into this driver
- There is currently no support for analytical hessian evaluations. as fns is the keyword for function evaluations and fnGrads is the keyword for gradient evaluations, fnHessians is the keyword for hessian evaluations. Gradient evaluations have only been tested for single gradient evaluations, and may potentially fail under multiple gradient functions. These functionalities are implemented in the "dakota_callback" function in driver.py