def de(model, frontier_size=10, cop=0.5, ea=0.5, repeat=100, threshold=0.01):
normalizers = prerun_each_obj(model, runs=10000)
out = []
def energy(candidate, eval_func=model.eval, normalizers=normalizers):
# This evaluates the objs and stores them candidate.scores
eval_func(candidate)
# Just for fun
normalized_scores = [
normalize(x) for normalize, x in zip(normalizers, candidate.scores)
]
# The distance of score of each objective from hell
hell_dist = [(1 - x) for x in normalized_scores]
sum_of_squares = sum([x**2 for x in hell_dist])
energy = 1 - (math.sqrt(sum_of_squares) / math.sqrt(len(hell_dist)))
return energy
frontier = []
for i in range(frontier_size):
can = model.gen_candidate()
while can is None:
can = model.gen_candidate()
frontier += [can]
total = 0
n = 0
best_score = 1.0
for j in range(repeat):
if j % 10 == 0:
out += ["\n" + str(best_score) + " "]
total, n = de_update(frontier, cop, ea, energy, out)
if total / n < threshold:
best_score = total / n
out += ["!"]
break
elif total / n < best_score:
best_score = total / n
out += ["!"]
print ''.join(out)
print "\nNumber of repeat:" + str(j + 1)
print "Best Score:" + str(best_score)
return frontier, best_score
def de(model, frontier_size=10, cop=0.5, ea=0.5, repeat=100, threshold=0.01):
normalizers = prerun_each_obj(model, runs=10000)
out = []
def energy(candidate, eval_func=model.eval, normalizers=normalizers):
# This evaluates the objs and stores them candidate.scores
eval_func(candidate)
# Just for fun
normalized_scores = [normalize(x) for normalize, x in zip(normalizers, candidate.scores)]
# The distance of score of each objective from hell
hell_dist = [(1 - x) for x in normalized_scores]
sum_of_squares = sum([x ** 2 for x in hell_dist])
energy = 1 - (math.sqrt(sum_of_squares) / math.sqrt(len(hell_dist)))
return energy
frontier = []
for i in range(frontier_size):
can = model.gen_candidate()
while can is None:
can = model.gen_candidate()
frontier += [can]
total = 0
n = 0
best_score = 1.0
for j in range(repeat):
if j % 10 == 0:
out += ["\n" + str(best_score) + " "]
total, n = de_update(frontier, cop, ea, energy, out)
if total / n < threshold:
best_score = total / n
out += ["!"]
break
elif total / n < best_score:
best_score = total / n
out += ["!"]
print ''.join(out)
print "\nNumber of repeat:" + str(j + 1)
print "Best Score:" + str(best_score)
return frontier, best_score
def de(model,
frontier_size=10,
cop=0.5,
ea=0.5,
repeat=100,
threshold=0.01,
era_size=10,
era0=None,
lives=5):
normalizers = prerun_each_obj(model, runs=10000)
out = []
def energy(candidate, eval_func=model.eval, normalizers=normalizers):
# This evaluates the objs and stores them candidate.scores
eval_func(candidate)
# Just for fun
normalized_scores = [
normalize(x) for normalize, x in zip(normalizers, candidate.scores)
]
# The distance of score of each objective from hell
hell_dist = [(1 - x) for x in normalized_scores]
sum_of_squares = sum([x**2 for x in hell_dist])
energy = 1 - (math.sqrt(sum_of_squares) / math.sqrt(len(hell_dist)))
return energy
def type1(can1, can2):
return (energy(can1) < energy(can2))
def type2(era1, era2):
# a12 returns times that lst1 is greater than lst2
total = 0
n = 0
for obj_scores1, obj_scores2 in zip(era1, era2):
# If this is 1, that means era1 is greater more often
# If minimizing, this means era1 is worse
total += a12(obj_scores1, obj_scores2)
n += 1
return (total / n >= 0.5)
frontier = []
total = 0
n = 0
if not era0:
for i in range(frontier_size):
can = model.gen_candidate()
while can is None:
can = model.gen_candidate()
frontier += [can]
total += energy(can)
n += 1
else:
frontier = list(era0)
total = sum([energy(can) for can in frontier])
n = len(frontier)
curr_era = []
for can in frontier:
curr_era += [[energy(can)]]
# Currently treating candidates as having only one objective i.e. energy
# which we're minimizing
eras = [curr_era]
curr_era = []
best_score = total / n
curr_lives = lives
early_end = False
for j in range(repeat):
if j % 10 == 0:
out += ["\n" + str(best_score) + " "]
total, n = de_update(frontier, cop, ea, energy, out)
if total / n < threshold:
best_score = total / n
out += ["!"]
out += ["\nScore satisfies Threshold"]
break
elif total / n < best_score:
best_score = total / n
out += ["!"]
for can in frontier:
curr_era += [[energy(can)]]
eras += [curr_era]
curr_era = []
if len(eras) > 1:
if type2(eras[len(eras) - 2], eras[len(eras) - 1]):
curr_lives = lives
else:
curr_lives -= 1
if curr_lives == 0:
out += ["\nNo more Lives"]
break
print ''.join(out)
print "\nNumber of repeats:" + str(j + 1)
print "Best Score:" + str(best_score)
return frontier, best_score
def de(model, frontier_size=10, cop=0.5, ea=0.5, repeat=100, threshold=0.01, era_size=10, era0=None, lives=5):
normalizers = prerun_each_obj(model, runs=10000)
out = []
def energy(candidate, eval_func=model.eval, normalizers=normalizers):
# This evaluates the objs and stores them candidate.scores
eval_func(candidate)
# Just for fun
normalized_scores = [normalize(x) for normalize, x in zip(normalizers, candidate.scores)]
# The distance of score of each objective from hell
hell_dist = [(1 - x) for x in normalized_scores]
sum_of_squares = sum([x ** 2 for x in hell_dist])
energy = 1 - (math.sqrt(sum_of_squares) / math.sqrt(len(hell_dist)))
return energy
def type1(can1, can2):
return (energy(can1) < energy(can2))
def type2(era1, era2):
# a12 returns times that lst1 is greater than lst2
total = 0
n = 0
for obj_scores1, obj_scores2 in zip(era1, era2):
# If this is 1, that means era1 is greater more often
# If minimizing, this means era1 is worse
total += a12(obj_scores1, obj_scores2)
n += 1
return (total / n >= 0.5)
frontier = []
total = 0
n = 0
if not era0:
for i in range(frontier_size):
can = model.gen_candidate()
while can is None:
can = model.gen_candidate()
frontier += [can]
total += energy(can)
n += 1
else:
frontier = list(era0)
total = sum([energy(can) for can in frontier])
n = len(frontier)
curr_era = []
for can in frontier:
curr_era += [[energy(can)]]
# Currently treating candidates as having only one objective i.e. energy
# which we're minimizing
eras = [curr_era]
curr_era = []
best_score = total / n
curr_lives = lives
early_end = False
for j in range(repeat):
if j % 10 == 0:
out += ["\n" + str(best_score) + " "]
total, n = de_update(frontier, cop, ea, energy, out)
if total / n < threshold:
best_score = total / n
out += ["!"]
out += ["\nScore satisfies Threshold"]
break
elif total / n < best_score:
best_score = total / n
out += ["!"]
for can in frontier:
curr_era += [[energy(can)]]
eras += [curr_era]
curr_era = []
if len(eras) > 1:
if type2(eras[len(eras) - 2], eras[len(eras) - 1]):
curr_lives = lives
else:
curr_lives -= 1
if curr_lives == 0:
out += ["\nNo more Lives"]
break
print ''.join(out)
print "\nNumber of repeats:" + str(j + 1)
print "Best Score:" + str(best_score)
return frontier, best_score
