def gradient(self, inputs):
# unpack inputs
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
# prep outputs
grads = obunch()
grads.f = obunch()
grads.c = obunch()
grads.c2 = obunch()
# the math
grads.f.x1 = 2 * x1
grads.f.x2 = 2 * x2
grads.f.x3 = 2 * x3
grads.c.x1 = 1.
grads.c.x2 = 1.
grads.c.x3 = np.array([1., 0, 0])
grads.c2.x1 = np.zeros([3, 1])
grads.c2.x2 = np.zeros([3, 1])
grads.c2.x3 = np.eye(3)
return grads
def gradient(self,inputs):
# unpack inputs
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
# prep outputs
grads = obunch()
grads.f = obunch()
grads.c = obunch()
grads.c2 = obunch()
# the math
grads.f.x1 = 2*x1
grads.f.x2 = 2*x2
grads.f.x3 = 2*x3
grads.c.x1 = 1.
grads.c.x2 = 1.
grads.c.x3 = np.array([1.,0,0])
grads.c2.x1 = np.zeros([3,1])
grads.c2.x2 = np.zeros([3,1])
grads.c2.x3 = np.eye(3)
return grads
def __call__(self, variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables, obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step, (array_type, list, tuple)):
step = [step] * nx
step = atleast_2d_row(step)
if not step.shape[0] == 1:
step = step.T
values_run = np.vstack(
[values_init,
np.tile(values_init, [nx, 1]) + np.diag(step[0])])
variables_run = [
deepcopy(variables).unpack_array(val) for val in values_run
]
# run the function
#for i in variables_run: print i
results = self.function(variables_run)
#for i in results: print i
results_values = np.vstack(
[atleast_2d_row(res.pack_array('vector')) for res in results])
# pack results
gradients_values = (results_values[1:, :] -
results_values[None, 0, :]) / step.T
gradients_values = np.ravel(gradients_values)
variables = variables.unpack_array(values_init[0] * 0.0)
gradients = deepcopy(results[0])
if not isinstance(gradients, obunch):
gradients = obunch(gradients)
for i, g in gradients.items():
if isinstance(g, array_type):
g = atleast_2d_col(g)
gradients[i] = deepcopy(variables)
for j, v in gradients[i].items():
if isinstance(v, array_type):
v = atleast_2d_row(v)
gradients[i][j] = g * v
gradients.unpack_array(gradients_values)
return gradients
def __call__(self,variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables,obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step,(array_type,list,tuple)):
step = [step] * nx
step = atleast_2d_row(step)
if not step.shape[0] == 1:
step = step.T
values_run = np.vstack([ values_init ,
np.tile(values_init,[nx,1]) + np.diag(step[0]) ])
variables_run = [ deepcopy(variables).unpack_array(val) for val in values_run ]
# run the function
#for i in variables_run: print i
results = self.function(variables_run)
#for i in results: print i
results_values = np.vstack([ atleast_2d_row( res.pack_array('vector') ) for res in results ])
# pack results
gradients_values = ( results_values[1:,:] - results_values[None,0,:] ) / step.T
gradients_values = np.ravel( gradients_values )
variables = variables.unpack_array( values_init[0] * 0.0 )
gradients = deepcopy(results[0])
if not isinstance(gradients,obunch):
gradients = obunch(gradients)
for i,g in gradients.items():
if isinstance(g,array_type):
g = atleast_2d_col(g)
gradients[i] = deepcopy(variables)
for j,v in gradients[i].items():
if isinstance(v,array_type):
v = atleast_2d_row(v)
gradients[i][j] = g * v
gradients.unpack_array(gradients_values)
return gradients
def build_surrogate(X,F,DF,XB,dLim=-2,nAS=None,**hypers):
from VyPy.regression import active_subspace, gpr
# learn the active subpace by gradients
print 'Learn Active Subspaces ...'
W,d = as_learn.gradient(DF)
# pick number of dimensions
nLim = np.sum( d > 10**dLim )
if nAS is None:
nAS = nLim
else:
if nAS > nLim:
nAS = nLim
print ' Number of dimensions = %i' % nAS
# down select the number of dimensions
U = W[:,0:nAS]
# forward map training data to active space
Y = as_project.simple(X,U)
DFDY = as_project.simple(DF,U)
YB = np.vstack([ np.min(Y,axis=0) ,
np.max(Y,axis=0) ]).T * 1.3
# build the surrogate
M_Y = gpr.library.Gaussian(YB,Y,F,DFDY, **hypers )
#M_Y = gpr.library.Gaussian(YB,Y,F,None, **hypers )
# convenience function handles
g_y = M_Y.predict_YI
g_x = Active_Subspace_Surrogate(g_y,U)
# pack results
results = obunch()
results.tag = 'active subspace model'
results.XB = XB
results.X = X
results.F = F
results.DF = DF
results.W = W
results.d = d
results.U = U
results.Y = Y
results.YB = YB
results.M_Y = M_Y
results.g_x = g_x
results.g_y = g_y
return results
def test_func(inputs):
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
#print inputs
f = x1**2 + x2**2 + np.sum(x3)**2
c = x1 + x2 + x3[0]
c2 = x3 - 1.
outputs = obunch()
outputs.f = f
outputs.c = c
outputs.c2 = c2
#print outputs
return outputs
def function(self,inputs):
# unpack inputs
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
# the math
f = x1**2 + x2**2 + np.sum(x3)**2
c = x1 + x2 + x3[0]
c2 = x3 - 1.
# pack outputs
outputs = obunch()
outputs.f = f
outputs.c = c
outputs.c2 = c2
return outputs
def test_func(inputs):
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
#print inputs
f = x1**2 + x2**2 + np.sum(x3)**2
c = x1 + x2 + x3[0]
c2 = x3 - 1.
outputs = obunch()
outputs.f = f
outputs.c = c
outputs.c2 = c2
#print outputs
return outputs
def function(self, inputs):
# unpack inputs
x1 = inputs.x1
x2 = inputs['x2']
x3 = inputs.x3
# the math
f = x1**2 + x2**2 + np.sum(x3)**2
c = x1 + x2 + x3[0]
c2 = x3 - 1.
# pack outputs
outputs = obunch()
outputs.f = f
outputs.c = c
outputs.c2 = c2
return outputs
def __call__(self,variables):
step = self.step
variables = deepcopy(variables)
if not isinstance(variables,obunch):
variables = obunch(variables)
# arrayify variables
values_init = variables.pack_array('vector')
values_init = atleast_2d_row(values_init)
nx = values_init.shape[1]
# prepare step
if not isinstance(step,(array_type,list,tuple)):
step = [step] * nx
step = atleast_2d_col(step)
if not step.shape[1] == 1:
step = step.T
values_run = np.hstack([ values_init ,
np.tile(values_init,[nx,1]) + np.diag(step) ])
# run the function
results = self.function(values_run)
# pack results
gradients_values = ( results[1:,:] - results[None,0,:] ) / step
gradients_values = np.ravel(gradients_values)
variables.unpack_array( values_init * 0.0 )
gradients = deepcopy(results[0])
for k in gradients.keys():
gradients[k] = deepcopy(variables)
gradients.unpack_array(gradients_values)
return gradients
def __init__(self):
self.verbose = True
self.other_options = obunch()
def main():
""" test the SLSQP Optimizer, with a test problem function """
# ------------------------------------------------------------------
# Get the problem
# ------------------------------------------------------------------
#from problem_function import setup_problem
from problem_evaluator import setup_problem
problem = setup_problem()
# Gradients
evaluator = problem.objectives['f'].evaluator
variables = obunch()
variables.x1 = 0.
variables.x2 = 0.
variables.x3 = np.array([0.,0.,0.])
grad_1 = evaluator.gradient(variables)
print grad_1
evaluator.gradient = opt.gradients.FiniteDifference(evaluator.function)
grad_2 = evaluator.gradient(variables)
print grad_2
return
# ------------------------------------------------------------------
# Setup Driver
# ------------------------------------------------------------------
driver = opt.drivers.SLSQP()
driver.max_iterations = 1000
driver.verbose = True
# ------------------------------------------------------------------
# Run the Problem
# ------------------------------------------------------------------
results = driver.run(problem)
print 'Results:'
print results
# ------------------------------------------------------------------
# Check Results
# ------------------------------------------------------------------
# the expected results
truth = problem.truth
# the checking function
def check(a,b):
return np.abs(a-b)
delta = truth.do_recursive(check,results)
print 'Error to Expected:'
print delta
assert np.all( delta.pack_array() < 1e-6 )
assert len( delta.pack_array() ) == 10
# done!
return
def setup_problem():
# initialize the problem
problem = opt.Problem()
# variables, list style
problem.variables = [
# [ 'tag' , x0, (lb ,ub) , scl ],
['x1', 0., (-2., 2.), 1.0],
['x2', 0., (-2., 2.), 1.0],
]
# variables, array style
var = opt.Variable()
var.tag = 'x3'
var.initial = np.array([10., 10., 10.])
ub = np.array([30.] * 3)
lb = np.array([-1.] * 3)
var.bounds = (lb, ub)
var.scale = opt.scaling.Linear(scale=4.0, center=10.0)
problem.variables.append(var)
# objectives
problem.objectives = [
# [ func , 'tag', scl ],
[test_func, 'f', 1.0],
]
# constraints, list style
problem.constraints = [
# [ func , ('tag' ,'><=', val), scl] ,
[test_func, ('c', '=', 1.), 1.0],
]
# constraints, array style
con = opt.Equality()
con.evaluator = test_func
con.tag = 'c2'
con.sense = '='
con.edge = np.array([3., 3., 3.])
problem.constraints.append(con)
# print
print problem
# expected answer
truth = obunch()
truth.variables = obunch()
truth.objectives = obunch()
truth.equalities = obunch()
truth.variables.x1 = -1.5
truth.variables.x2 = -1.5
truth.variables.x3 = np.array([4., 4., 4.])
truth.objectives.f = 148.5
truth.equalities.c = 1.0
truth.equalities.c2 = np.array([3., 3., 3.])
problem.truth = truth
# done!
return problem
def __init__(self):
self.verbose = True
self.other_options = obunch()
def setup_problem():
# initialize the problem
problem = opt.Problem()
# setup variables, list style
problem.variables = [
# [ 'tag' , x0, (lb ,ub) , scl ],
[ 'x1' , 0., (-2.,2.) , 1.0 ],
[ 'x2' , 0., (-2.,2.) , 1.0 ],
]
# setup variables, arrays
var = opt.Variable()
var.tag = 'x3'
var.initial = np.array([10., 10., 10.])
ub = np.array([30.]*3)
lb = np.array([-1.]*3)
var.bounds = (lb,ub)
var.scale = opt.scaling.Linear(scale=4.0,center=10.0)
problem.variables.append(var)
# initialize evaluator
test_eval = Test_Evaluator()
# setup objective
problem.objectives = [
# [ func , 'tag', scl ],
[ test_eval, 'f', 1.0 ],
]
# setup constraint, list style
problem.constraints = [
# [ func , ('tag' ,'><=', val), scl] ,
[ test_eval, ('c','=',1.), 1.0 ],
]
# setup constraint, array style
con = opt.Equality()
con.evaluator = test_eval
con.tag = 'c2'
con.sense = '='
con.edge = np.array([3.,3.,3.])
problem.constraints.append(con)
# print
print problem
# expected answer
truth = obunch()
truth.variables = obunch()
truth.objectives = obunch()
truth.equalities = obunch()
truth.variables.x1 = -1.5
truth.variables.x2 = -1.5
truth.variables.x3 = np.array([ 4., 4., 4.])
truth.objectives.f = 148.5
truth.equalities.c = 1.0
truth.equalities.c2 = np.array([ 3., 3., 3.])
problem.truth = truth
# done!
return problem
from VyPy.data import obunch, odict
import matplotlib
from matplotlib.colors import hex2color
colors = obunch()
colors.sky_blue = odict()
colors.sky_blue[-2] = hex2color('#cadee5')
colors.sky_blue[-1] = hex2color('#84cfeb')
colors.sky_blue[0] = hex2color('#18aae0')
colors.sky_blue[1] = hex2color('#197192')
colors.sky_blue[2] = hex2color('#112128')
colors.hot_purple = odict()
colors.hot_purple[-2] = hex2color('#e5cadd')
colors.hot_purple[-1] = hex2color('#e77ac7')
colors.hot_purple[0] = hex2color('#e018a5')
colors.hot_purple[1] = hex2color('#841665')
colors.hot_purple[2] = hex2color('#281121')
colors.yellow_green = odict()
colors.yellow_green[-2] = hex2color('#dfe5ca')
colors.yellow_green[-1] = hex2color('#c7e77a')
colors.yellow_green[0] = hex2color('#a9e018')
colors.yellow_green[1] = hex2color('#678416')
colors.yellow_green[2] = hex2color('#222811')
def main():
""" test the SLSQP Optimizer, with a test problem function """
# ------------------------------------------------------------------
# Get the problem
# ------------------------------------------------------------------
#from problem_function import setup_problem
from problem_evaluator import setup_problem
problem = setup_problem()
# Gradients
evaluator = problem.objectives['f'].evaluator
variables = obunch()
variables.x1 = 0.
variables.x2 = 0.
variables.x3 = np.array([0., 0., 0.])
grad_1 = evaluator.gradient(variables)
print grad_1
evaluator.gradient = opt.gradients.FiniteDifference(evaluator.function)
grad_2 = evaluator.gradient(variables)
print grad_2
return
# ------------------------------------------------------------------
# Setup Driver
# ------------------------------------------------------------------
driver = opt.drivers.SLSQP()
driver.max_iterations = 1000
driver.verbose = True
# ------------------------------------------------------------------
# Run the Problem
# ------------------------------------------------------------------
results = driver.run(problem)
print 'Results:'
print results
# ------------------------------------------------------------------
# Check Results
# ------------------------------------------------------------------
# the expected results
truth = problem.truth
# the checking function
def check(a, b):
return np.abs(a - b)
delta = truth.do_recursive(check, results)
print 'Error to Expected:'
print delta
assert np.all(delta.pack_array() < 1e-6)
assert len(delta.pack_array()) == 10
# done!
return