# Return the error measure.
def total_time(parameters, measures):
with open("measures", "a") as f:
f.write("parameters = %r, measures = %r\n" % (parameters, measures))
# Now with numerical integration, we want to minimize total time, not the time/rel
# return sum(map(float, measures["TIME"])) / sum(map(float, measures["RELATIONS"]))
return sum(map(float, measures["SIEVETIME"]))
# Define parameter optimization problem.
data = ModelData(LAS)
struct = ModelStructure(objective=total_time) # Unconstrained
model = Model(modelData=data, modelStructure=struct)
# Solve parameter optimization problem.
NOMAD.set_parameter(
name='DISPLAY_STATS',
value='%3dBBE %7.1eSOL %8.3eOBJ %5.2fTIME %5.2SIEVETIME')
# to limit the number of evaluations, define the environment variable
# NOMAD_MAX_BB_EVAL, for example:
# NOMAD_MAX_BB_EVAL=100 ./optimize.sh ...
import os
max_bb_eval = os.getenv("NOMAD_MAX_BB_EVAL")
if max_bb_eval != None:
NOMAD.set_parameter(name='MAX_BB_EVAL', value=int(max_bb_eval))
if MPI > 1:
NOMAD.set_mpi_config("np", MPI)
NOMAD.solve(blackbox=model)
'EIGENALS',
'FMINSRF2',
'FMINSURF',
'GENROSE',
'HIELOW',
'MANCINO',
'NCB20',
'NCB20B',
'NONDQUAR',
'POWER',
'SENSORS',
'SINQUAD',
'TESTQUAD',
'TRIDIA',
'WOODS']]
# Define parameter optimization problem.
data = ModelData(algorithm=trunk,
problems=problems,
parameters=params)
struct = ModelStructure(objective=get_error,
constraints=[]) # Unconstrained
blackbox = Model(modelData=data, modelStructure=struct)
# Solve parameter optimization problem.
NOMADMPI.set_mpi_config(name='np', value=8)
NOMADMPI.set_mpi_config(name='-host', value='lin01,lin02,lin03,lin04')
NOMADMPI.set_parameter(name='MAX_BB_EVAL', value=50)
NOMADMPI.set_parameter(name='DISPLAY_DEGREE', value=2)
NOMADMPI.solve(model=blackbox)
def get_error(parameters, measures):
val = sum(measures["FEVAL"])
return val
# Parameters being tuned and problem list.
par_names = ['eta1', 'eta2', 'gamma1', 'gamma2', 'gamma3']
params = [param for param in trunk.parameters if param.name in par_names]
problems = [
problem for problem in CUTEr if problem.name in [
'BDQRTIC', 'BROYDN7D', 'BRYBND', 'CURLY10', 'CURLY20', 'CURLY30',
'CRAGGLVY', 'DIXON3DQ', 'EIGENALS', 'FMINSRF2', 'FMINSURF', 'GENROSE',
'HIELOW', 'MANCINO', 'NCB20', 'NCB20B', 'NONDQUAR', 'POWER', 'SENSORS',
'SINQUAD', 'TESTQUAD', 'TRIDIA', 'WOODS'
]
]
# Define parameter optimization problem.
data = ModelData(algorithm=trunk, problems=problems, parameters=params)
struct = ModelStructure(objective=get_error, constraints=[]) # Unconstrained
model = Model(modelData=data, modelStructure=struct, platform=SMP)
# Solve parameter optimization problem.
NOMADMPI.set_mpi_config(name='np', value=5)
NOMADMPI.set_parameter(name='MAX_BB_EVAL', value=5)
NOMADMPI.set_parameter(name='DISPLAY_DEGREE', value=2)
NOMADMPI.solve(blackbox=model)
def get_error(parameters, measures):
val = sum(measures["FEVAL"])
return val
# Parameters being tuned and problem list.
par_names = ['eta1', 'eta2', 'gamma1', 'gamma2', 'gamma3']
params = [param for param in trunk.parameters if param.name in par_names]
problems = [
problem for problem in CUTEr if problem.name in [
'BDQRTIC', 'BROYDN7D', 'BRYBND', 'CURLY10', 'CURLY20', 'CURLY30',
'CRAGGLVY', 'DIXON3DQ', 'EIGENALS', 'FMINSRF2', 'FMINSURF', 'GENROSE',
'HIELOW', 'MANCINO', 'NCB20', 'NCB20B', 'NONDQUAR', 'POWER', 'SENSORS',
'SINQUAD', 'TESTQUAD', 'TRIDIA', 'WOODS'
]
]
# Define parameter optimization problem.
data = ModelData(algorithm=trunk, problems=problems, parameters=params)
struct = ModelStructure(objective=get_error, constraints=[]) # Unconstrained
blackbox = Model(modelData=data, modelStructure=struct)
# Solve parameter optimization problem.
NOMADMPI.set_mpi_config(name='np', value=8)
NOMADMPI.set_mpi_config(name='-host', value='lin01,lin02,lin03,lin04')
NOMADMPI.set_parameter(name='MAX_BB_EVAL', value=50)
NOMADMPI.set_parameter(name='DISPLAY_DEGREE', value=2)
NOMADMPI.solve(model=blackbox)
"EIGENALS",
"FMINSRF2",
"FMINSURF",
"GENROSE",
"HIELOW",
"MANCINO",
"NCB20",
"NCB20B",
"NONDQUAR",
"POWER",
"SENSORS",
"SINQUAD",
"TESTQUAD",
"TRIDIA",
"WOODS",
]
]
# Define parameter optimization problem.
data = ModelData(algorithm=trunk, problems=problems, parameters=params)
struct = ModelStructure(objective=get_error, constraints=[]) # Unconstrained
model = Model(modelData=data, modelStructure=struct, platform=SMP)
# Solve parameter optimization problem.
NOMADMPI.set_mpi_config(name="np", value=5)
NOMADMPI.set_parameter(name="MAX_BB_EVAL", value=5)
NOMADMPI.set_parameter(name="DISPLAY_DEGREE", value=2)
NOMADMPI.solve(blackbox=model)