def GIA(self, desired_number_of_models):
metrics_objective_direction = []
metrics_variables = []
for i in self.objectives:
(pol, var) = i
metrics_objective_direction.append(pol)
metrics_variables.append(var)
# Non-Parallel
GIAOptionsNP = GuidedImprovementAlgorithmOptions(verbosity=0, \
incrementallyWriteLog=False, \
writeTotalTimeFilename="timefile.csv", \
writeRandomSeedsFilename="randomseed.csv", useCallLogs=False, num_models=desired_number_of_models, magnifying_glass=Options.MAGNIFYING_GLASS)
if Options.CORES == 1:
GIAAlgorithmNP = GuidedImprovementAlgorithm(self, self.solver, metrics_variables, \
metrics_objective_direction, [], options=GIAOptionsNP)
'''featurevars instead of []'''
outfilename = str("giaoutput").strip()
ParetoFront = GIAAlgorithmNP.ExecuteGuidedImprovementAlgorithm(outfilename)
if not Options.SUPPRESS_MODELS:
if not ParetoFront:
standard_print("UNSAT")
for i in ParetoFront:
self.printVars(i)
return ParetoFront
else:
parSolver = ParSolver.ParSolver(self, self.module, self.solver, metrics_variables, metrics_objective_direction)
ParetoFront = parSolver.run()
for i in ParetoFront:
self.printStartDelimeter()
standard_print(i)
standard_print("")
self.printEndDelimeter()
return ParetoFront
