import DE_tuning_setup
from optTune import tMOPSO, linearFunction
from matplotlib import pyplot
tuningOpt = tMOPSO(optAlg=DE_tuning_setup.run_DE_on_Ros_ND,
CPV_lb=DE_tuning_setup.CPV_lb,
CPV_ub=DE_tuning_setup.CPV_ub,
CPV_validity_checks=DE_tuning_setup.CPV_validity_checks,
OFE_budgets=DE_tuning_setup.OFE_budgets_to_tune_under,
sampleSizes=DE_tuning_setup.sampleSizes,
resampling_interruption_confidence=0.6,
gammaBudget=DE_tuning_setup.tuningBudget,
OFE_assessment_overshoot_function=linearFunction(2, 100),
N=10,
saveTo='tMOPSO_tuning_DE.data')
print(tuningOpt)
#extracting data from the Pareto-optimal front Approximation
OFE_budgets = [d.fv[0] for d in tuningOpt.PFA.designs]
Fmin_values = [d.fv[1] for d in tuningOpt.PFA.designs]
log_OFE_budgets = [d.xv[0] for d in tuningOpt.PFA.designs]
N_values = [int(d.xv[1]) for d in tuningOpt.PFA.designs]
Cr_values = [d.xv[2] for d in tuningOpt.PFA.designs]
F_values = [d.xv[3] for d in tuningOpt.PFA.designs]
line_Cr = pyplot.semilogx(OFE_budgets, Cr_values, 'b^')[0]
line_F = pyplot.semilogx(OFE_budgets, F_values, 'rx')[0]
pyplot.ylabel('Cr, F')
pyplot.twinx()
line_N = pyplot.semilogx(OFE_budgets, N_values, 'go')[0]
Tf = 0.0)
return get_F_vals_at_specified_OFE_budgets(F=func.f_hist, E=func.OFE_hist, E_desired=OFE_budgets)
def CPV_valid(CPVs, OFE_budget):
if CPVs[0] < 5:
return False,'dwell,CPVs[0] < 5'
if CPVs[1] < 0.0001:
return False,'CPVs[1] < 0.0001'
return True,''
tuningOpt = tMOPSO(
optAlg = run_simulated_annealing,
CPV_lb = numpy.array([10, 0.0]),
CPV_ub = numpy.array([50, 5.0]),
CPV_validity_checks = CPV_valid,
OFE_budgets=numpy.logspace(1,3,30).astype(int),
sampleSizes = [2,8,20], #resampling size of 30
resampling_interruption_confidence = 0.6,
gammaBudget = 30*1000*50, #increase to get a smoother result ...
OFE_assessment_overshoot_function = linearFunction(2, 100 ),
N = 10,
)
print(tuningOpt)
Fmin_values = [ d.fv[1] for d in tuningOpt.PFA.designs ]
OFE_budgets = [ d.fv[0] for d in tuningOpt.PFA.designs ]
dwell_values = [ int(d.xv[1]) for d in tuningOpt.PFA.designs ]
m_values = [ d.xv[2] for d in tuningOpt.PFA.designs ]
print('OFE budget Fmin dwell m ')
for a,b,c,d in zip(OFE_budgets, Fmin_values, dwell_values, m_values):
#! /usr/bin/env python
import numpy, tuning_setups, batchOpenMPI, plotlib
from optTune import tMOPSO
from matplotlib import pyplot
batchOpenMPI.begin_MPI_loop()
#adjust which CEC problem to tune under, etcetera in tuning_setups.py
runAlg = tuning_setups.batchOpenMPI_wrapper(tuning_setups.PSO_batch,
tuning_setups.prob_ID)
tuningOpt = tMOPSO(
optAlg=runAlg,
CPV_lb=tuning_setups.PSO_CPV_lb,
CPV_ub=tuning_setups.PSO_CPV_ub,
CPV_validity_checks=tuning_setups.PSO_CPV_validity_checks,
OFE_budgets=tuning_setups.PSO_OFE_budgets,
sampleSizes=tuning_setups.PSO_sampleSizes, #resampling size of 25
resampling_interruption_confidence=tuning_setups.PSO_alpha,
gammaBudget=tuning_setups.PSO_gammaBudget, #tuning budget
addtoBatch=runAlg.addtoBatch,
processBatch=batchOpenMPI.processBatch)
batchOpenMPI.end_MPI_loop(print_stats=True) #release workers, and print stats
print(tuningOpt)
OFE_budgets = [d.fv[0] for d in tuningOpt.PFA.designs]
Fmin_values = [d.fv[1] for d in tuningOpt.PFA.designs]
log_OFE_budgets = [d.xv[0] for d in tuningOpt.PFA.designs]
N_values = [int(d.xv[1]) for d in tuningOpt.PFA.designs]
import DE_tuning_setup
from optTune import tMOPSO, linearFunction
from matplotlib import pyplot
tuningOpt = tMOPSO(
optAlg = DE_tuning_setup.run_DE_on_Ros_ND,
CPV_lb = DE_tuning_setup.CPV_lb,
CPV_ub = DE_tuning_setup.CPV_ub,
CPV_validity_checks = DE_tuning_setup.CPV_validity_checks,
OFE_budgets= DE_tuning_setup.OFE_budgets_to_tune_under,
sampleSizes = DE_tuning_setup.sampleSizes,
resampling_interruption_confidence = 0.6,
gammaBudget = DE_tuning_setup.tuningBudget,
OFE_assessment_overshoot_function = linearFunction(2, 100 ),
N = 10,
saveTo = 'tMOPSO_tuning_DE.data'
)
print(tuningOpt)
#extracting data from the Pareto-optimal front Approximation
OFE_budgets = [ d.fv[0] for d in tuningOpt.PFA.designs ]
Fmin_values = [ d.fv[1] for d in tuningOpt.PFA.designs ]
log_OFE_budgets = [ d.xv[0] for d in tuningOpt.PFA.designs ]
N_values = [ int(d.xv[1]) for d in tuningOpt.PFA.designs ]
Cr_values = [ d.xv[2] for d in tuningOpt.PFA.designs ]
F_values = [ d.xv[3] for d in tuningOpt.PFA.designs ]
line_Cr = pyplot.semilogx(OFE_budgets, Cr_values, 'b^')[0]
line_F = pyplot.semilogx(OFE_budgets, F_values, 'rx')[0]
pyplot.ylabel('Cr, F')
#! /usr/bin/env python
import numpy, tuning_setups, batchOpenMPI, plotlib
from optTune import tMOPSO
from matplotlib import pyplot
batchOpenMPI.begin_MPI_loop()
# adjust which CEC problem to tune under, etcetera in tuning_setups.py
runAlg = tuning_setups.batchOpenMPI_wrapper(tuning_setups.DE_batch, tuning_setups.prob_ID)
tuningOpt = tMOPSO(
optAlg=runAlg,
CPV_lb=tuning_setups.DE_CPV_lb,
CPV_ub=tuning_setups.DE_CPV_ub,
CPV_validity_checks=tuning_setups.DE_CPV_validity_checks,
OFE_budgets=tuning_setups.DE_OFE_budgets,
sampleSizes=tuning_setups.DE_sampleSizes, # resampling size of 25
resampling_interruption_confidence=tuning_setups.DE_alpha,
gammaBudget=tuning_setups.DE_gammaBudget, # tuning budget
addtoBatch=runAlg.addtoBatch,
processBatch=batchOpenMPI.processBatch,
)
batchOpenMPI.end_MPI_loop(print_stats=True) # release workers, and print stats
print(tuningOpt)
OFE_budgets = [d.fv[0] for d in tuningOpt.PFA.designs]
Fmin_values = [d.fv[1] for d in tuningOpt.PFA.designs]
log_OFE_budgets = [d.xv[0] for d in tuningOpt.PFA.designs]
N_values = [int(d.xv[1]) for d in tuningOpt.PFA.designs]
def CPV_valid(CPVs, OFE_budget):
if CPVs[0] < 5:
return False, 'dwell,CPVs[0] < 5'
if CPVs[1] < 0.0001:
return False, 'CPVs[1] < 0.0001'
return True, ''
tuningOpt = tMOPSO(
optAlg=anneal,
CPV_lb=numpy.array([10, 0.0]),
CPV_ub=numpy.array([50, 5.0]),
CPV_validity_checks=CPV_valid,
OFE_budgets=numpy.logspace(1, 3, 30).astype(int),
sampleSizes=[2, 8, 20], #resampling size of 30
resampling_interruption_confidence=0.6,
gammaBudget=30 * 1000 * 50, #increase to get a smoother result ...
OFE_assessment_overshoot_function=linearFunction(2, 100),
N=10,
printLevel=1,
)
print(tuningOpt)
Fmin_values = [d.fv[1] for d in tuningOpt.PFA.designs]
OFE_budgets = [d.fv[0] for d in tuningOpt.PFA.designs]
dwell_values = [int(d.xv[1]) for d in tuningOpt.PFA.designs]
m_values = [d.xv[2] for d in tuningOpt.PFA.designs]
print('OFE budget Fmin dwell m ')
for a, b, c, d in zip(OFE_budgets, Fmin_values, dwell_values, m_values):