def run_DE_on_Ros_ND(CPV_tuple, OFE_budgets, randomSeed):
X_min, f_best_hist, X_hist, F_hist = DE_opt(objfun=Ros_ND,
x_lb=-5.0 * numpy.ones(prob_d),
x_ub=5.0 * numpy.ones(prob_d),
Np=int(CPV_tuple[0]),
Cr=CPV_tuple[1],
F=CPV_tuple[2],
evals=max(OFE_budgets),
printLevel=0)
F = numpy.array(f_best_hist)
OFEs_made = int(CPV_tuple[0]) * numpy.arange(1, len(X_hist) + 1)
return get_F_vals_at_specified_OFE_budgets(F, OFEs_made, OFE_budgets)
def run_DE_on_Ros_ND(CPV_tuple, OFE_budgets, randomSeed):
X_min, f_best_hist, X_hist, F_hist = DE_opt(
objfun = Ros_ND,
x_lb = -5.0 * numpy.ones(prob_d),
x_ub = 5.0 * numpy.ones(prob_d),
Np = int(CPV_tuple[0]),
Cr = CPV_tuple[1],
F = CPV_tuple[2],
evals = max(OFE_budgets),
printLevel=0
)
F = numpy.array(f_best_hist)
OFEs_made = int(CPV_tuple[0])*numpy.arange(1,len(X_hist)+1)
return get_F_vals_at_specified_OFE_budgets(F, OFEs_made, OFE_budgets)
evals=max(OFE_budgets),
printLevel=0)
F = numpy.array(f_best_hist)
OFEs_made = int(CPV_tuple[0]) * numpy.arange(1, len(X_hist) + 1)
return get_F_vals_at_specified_OFE_budgets(F, OFEs_made, OFE_budgets)
def CPV_validity_checks(CPV_array, OFE_budget):
'check tuning constraints'
N, Cr, F = CPV_array
if OFE_budget < N:
return False, 'OFE_budget < N'
if N < 5:
return False, 'N < 5'
if Cr < 0 or Cr > 1:
return False, 'Cr not in [0,1]'
if F < 0:
return False, 'F < 0'
return True, ""
#initilization bounds
CPV_lb = numpy.array([5, 0.0, 0.0])
CPV_ub = numpy.array([50, 1.0, 1.0])
OFE_budgets_to_tune_under = numpy.logspace(1, 3, 30).astype(int)
sampleSizes = [2, 8, 20]
tuningBudget = 50 * 1000 * 30 # tuning budget is equvialent of assessing 50 CPV tuples upto 1000 OFEs using 30 resampling runs each
F = CPV_tuple[2],
evals = max(OFE_budgets),
printLevel=0
)
F = numpy.array(f_best_hist)
OFEs_made = int(CPV_tuple[0])*numpy.arange(1,len(X_hist)+1)
return get_F_vals_at_specified_OFE_budgets(F, OFEs_made, OFE_budgets)
def CPV_validity_checks(CPV_array, OFE_budget):
'check tuning constraints'
N, Cr, F = CPV_array
if OFE_budget < N :
return False, 'OFE_budget < N'
if N < 5:
return False, 'N < 5'
if Cr < 0 or Cr > 1 :
return False, 'Cr not in [0,1]'
if F < 0:
return False, 'F < 0'
return True, ""
#initilization bounds
CPV_lb = numpy.array([ 5, 0.0, 0.0 ])
CPV_ub = numpy.array([ 50, 1.0, 1.0 ])
OFE_budgets_to_tune_under = numpy.logspace(1,3,30).astype(int)
sampleSizes = [2,8,20]
tuningBudget = 50*1000*30 # tuning budget is equvialent of assessing 50 CPV tuples upto 1000 OFEs using 30 resampling runs each