def printBestFromRankedBF():
for dim in dimensions:
for fid in functions:
print("Results for F{} in {}dim:".format(fid, dim))
# print(findGivenInRankedBF(dim, 1, given))
results = getBestFromRankedBF(dim, fid, num=10)
for ES, rank, fit in results:
print("Rank: {0:>4}\t{1:>33}\t{2}".format(
rank + 1, intToRepr(ES), fit))
print()
def get_default_hyperparameter_values(params, dim, rep, budget=1000):
"""
Reads the inital hyperparameter values from a single-split CMA-ES
:param params: list of the required parameters
:param dim: dimensionality of the problem
:param rep: the algorithm variant
:param budget: optional budget (should not change parameter values?)
:return: the inital hyperparameter values for all keys in params
"""
alg = SingleSplitCMAES(dim, None, representation=intToRepr(rep),
representation2=None, split=None, budget=budget, seed=0)
return alg.get_default_values(params)
def printComparisonGivenInBF(given=None):
# Add/remove choices as you wish
if given is None:
given = default_ESs
for dim in dimensions:
for fid in functions:
print("Results for F{} in {}dim:".format(fid, dim))
results = findGivenInRankedBF(dim, fid, given)
for ES in results:
print("Rank: {0:>4}\t{1:>33}\t{2}".format(
ES.rank + 1, intToRepr(ES.ES), ES.fitness))
print()
def printComparisonTable(given=None):
# Add/remove choices as you wish
if given is None:
given = default_ESs
os.chdir(ga_location)
with open('ga_results.dat') as f:
ga_results = cPickle.load(f)
print('\\hline')
print(
'F-ID & N & Brute Force & ERT & FCE & GA & ERT & FCE & Relative \\\\')
print('\\hline')
print('\\hline')
for fid in functions:
for dim in dimensions:
results = findGivenInRankedBF(dim, fid, given)
results.sort(key=lambda a: a.rank)
bf_string = reprToString(intToRepr(results[0].ES))
bf_ERT = "{:>9.6g}".format(
results[0].fitness.ERT
) if results[0].fitness.ERT is not None else " N/A"
bf_FCE = results[0].fitness.FCE
ga_string = reprToString(ga_results[dim][fid].ES[:11])
ga_ERT = "{:>9.6g}".format(
ga_results[dim][fid].fitness.ERT
) if ga_results[dim][fid].fitness.ERT is not None else " N/A"
ga_FCE = ga_results[dim][fid].fitness.FCE
if results[0].fitness.ERT is not None:
relative_fitness = results[0].fitness.ERT / ga_results[dim][
fid].fitness.ERT
else:
relative_fitness = results[0].fitness.FCE / ga_results[dim][
fid].fitness.FCE
print(
'F{0:<2} & {1:>2} & {2} & {3} & {4:>9.4g} & {5} & {6} & {7:>9.4g} & {8:>8.3g} \\\\'
.format(fid, dim, bf_string, bf_ERT, bf_FCE, ga_string, ga_ERT,
ga_FCE, relative_fitness))
print('\\hline')
def printRelativeComparisonBestGAAndGivenBF(given=None):
# Add/remove choices as you wish
if given is None:
given = default_ESs
os.chdir(ga_location)
with open('ga_results.dat') as f:
ga_results = cPickle.load(f)
all_relatives = []
relatives = {i: [] for i in range(len(default_ESs))}
counts = [0] * len(default_ESs)
for dim in dimensions:
for fid in functions:
print("Results for F{} in {}dim:".format(fid, dim))
results = findGivenInRankedBF(dim, fid, given)
ranks = []
for i, ES in enumerate(results):
if ES.fitness.ERT is not None:
relative_fitness = ES.fitness.ERT / ga_results[dim][
fid].fitness.ERT
relatives[i].append(relative_fitness)
elif ga_results[dim][
fid].fitness.ERT is not None and ga_results[dim][
fid].fitness.FCE > ES.fitness.FCE:
relative_fitness = "Unclear???"
else:
relative_fitness = ES.fitness.FCE / ga_results[dim][
fid].fitness.FCE
all_relatives.append(relative_fitness)
ranks.append(ES.rank)
print("Rank: {0:>4}\t{1:>33}\t{2}".format(
ES.rank + 1, intToRepr(ES.ES), relative_fitness))
for i, rank in enumerate(ranks):
if rank == min(ranks):
counts[i] += 1
print()
print(all_relatives)
print(relatives)
print(counts)
def test_int_to_repr(self):
for rep in self.reps:
self.assertEqual(intToRepr(rep.integer), rep.representation)