"""
sensitivity.py -- Driver to calculate the gradient of a workflow and return
it as a driver output.
SensitivityDriver includes a differentiator slot where the differentiation
method can be plugged. Fake finite difference is supported.
"""
# pylint: disable-msg=C0103
#public symbols
__all__ = ['SensitivityDriver']
import logging
from openmdao.main.numpy_fallback import zeros
from openmdao.main.datatypes.api import Array, List
from openmdao.main.driver_uses_derivatives import DriverUsesDerivatives
from openmdao.main.hasconstraints import HasConstraints
from openmdao.main.hasparameters import HasParameters
from openmdao.main.hasobjective import HasObjectives
from openmdao.util.decorators import add_delegate
from openmdao.main.interfaces import IHasParameters, IHasObjectives, \
IHasConstraints, implements
@add_delegate(HasParameters, HasObjectives, HasConstraints)
[docs]class SensitivityDriver(DriverUsesDerivatives):
"""Driver to calculate the gradient of a workflow and return
it as a driver output. The gradient is calculated from all
inputs (Parameters) to all outputs (Objectives and Constraints).
SensitivityDriver includes a differentiator slot where the differentiation
method can be plugged. Fake finite difference is supported.
"""
implements(IHasParameters, IHasObjectives, IHasConstraints)
dF = Array(zeros((0, 0),'d'), iotype='out', desc='Sensitivity of the '
'objectives with respect to the parameters. Index 1 is the '
'objective output, while index 2 is the parameter input.')
dG = Array(zeros((0, 0),'d'), iotype='out', desc='Sensitivity of the '
'constraints with respect to the parameters. Index 1 is the '
'constraint output, while index 2 is the parameter input.')
F = Array(zeros((0, 0),'d'), iotype='out', desc='Values of the objectives '
'which sensitivities are taken around.')
G = Array(zeros((0, 0),'d'), iotype='out', desc='Values of the constraints '
'which sensitivities are taken around.')
dF_names = List([], iotype='out', desc='Objective names that '
'correspond to our array indices.')
dG_names = List([], iotype='out', desc='Constraint names that '
'correspond to our array indices.')
dx_names = List([], iotype='out', desc='Parameter names that '
'correspond to our array indices.')
F = Array(zeros(0,'d'), iotype='out', desc='Objective baseline values '
'where sensitivity is evaluated.')
G = Array(zeros(0,'d'), iotype='out', desc='Constraint baseline values '
'where sensitivity is evaluated.')
x = Array(zeros(0,'d'), iotype='out', desc='Parameter baseline values '
'where sensitivity is evaluated.')
[docs] def execute(self):
"""Calculate the gradient of the workflow."""
self._check()
# Calculate gradient of the workflow
self.ffd_order = 1
self.differentiator.calc_gradient()
self.ffd_order = 0
inputs = self.get_parameters().keys()
objs = self.get_objectives().keys()
constraints = list(self.get_eq_constraints().keys() + \
self.get_ineq_constraints().keys())
self.dF = zeros((len(objs), len(inputs)), 'd')
self.dG = zeros((len(constraints), len(inputs)), 'd')
self.F = zeros(len(objs), 'd')
self.G = zeros(len(constraints), 'd')
self.x = zeros(len(inputs), 'd')
self.dF_names = []
self.dG_names = []
self.dx_names = []
for i, input_name in enumerate(inputs):
self.dx_names.append(input_name)
self.x[i] = self.differentiator.base_param[input_name]
for j, output_name in enumerate(objs):
self.dF[j][i] = self.differentiator.get_derivative(output_name,
wrt=input_name)
self.dF_names.append(output_name)
self.F[j] = self.differentiator.base_data[output_name]
for j, output_name in enumerate(constraints):
self.dG[j][i] = self.differentiator.get_derivative(output_name,
wrt=input_name)
self.dG_names.append(output_name)
self.G[j] = self.differentiator.base_data[output_name]
# Sensitivity is sometimes run sequentially using different submodels,
# so we need to return the state to the baseline value.
self.differentiator.reset_state()
self.record_case()
def _check(self):
"""Make sure we aren't missing inputs or outputs."""
if len(self.get_parameters().values()) < 1:
msg = "Missing inputs for gradient calculation"
self.raise_exception(msg, ValueError)
if len(self.get_objectives().values()) + \
len(self.get_eq_constraints().values()) + \
len(self.get_ineq_constraints().values()) < 1:
msg = "Missing outputs for gradient calculation"
self.raise_exception(msg, ValueError)
