def sa(model,
p=sa_default_prob,
threshold=0.001,
max_tries=1000,
optimal='low'):
best_can = model.gen_candidate()
while best_can is None:
best_can = model.gen_candidate()
normalize = prerun(model)
best_score = normalize(model.aggregate(best_can))
curr_can = best_can
curr_score = best_score
out = []
for i in range(0, max_tries):
if i % 50 == 0:
out += ["\n" + str(best_score) + " "]
new_can = model.gen_candidate()
if new_can is None:
out += ["."]
continue
new_score = normalize(model.aggregate(new_can))
if optimal == 'low':
flag = True
if new_score < best_score:
best_score = new_score
best_can = new_can
out += ["!"]
flag = False
if new_score < curr_score:
curr_score = new_score
curr_can = new_can
out += ["+"]
flag = False
elif p(curr_score, new_score, i / max_tries) < random.random():
curr_score = new_score
curr_can = new_can
out += ["?"]
flag = False
if best_score < threshold:
break
if flag is True:
out += "."
print ''.join(out)
print "\niterations:" + str(i + 1)
print "Score:" + str(best_score)
return best_can, best_score
def sa(model, p=sa_default_prob, threshold=0.001, max_tries=1000, optimal='low'):
best_can = model.gen_candidate()
while best_can is None:
best_can = model.gen_candidate()
normalize = prerun(model)
best_score = normalize(model.aggregate(best_can))
curr_can = best_can
curr_score = best_score
out = []
for i in range(0, max_tries):
if i % 50 == 0:
out += ["\n" + str(best_score) + " "]
new_can = model.gen_candidate()
if new_can is None:
out += ["."]
continue
new_score = normalize(model.aggregate(new_can))
if optimal == 'low':
flag = True
if new_score < best_score:
best_score = new_score
best_can = new_can
out += ["!"]
flag = False
if new_score < curr_score:
curr_score = new_score
curr_can = new_can
out += ["+"]
flag = False
elif p(curr_score, new_score, i / max_tries) < random.random():
curr_score = new_score
curr_can = new_can
out += ["?"]
flag = False
if best_score < threshold:
break
if flag is True:
out += "."
print ''.join(out)
print "\niterations:" + str(i + 1)
print "Score:" + str(best_score)
return best_can, best_score
def sa(model, p=sa_default_prob, threshold=0.001, max_tries=1000, lives=5, era_size=50, era0=None):
normalize = prerun(model)
aggregate = model.aggregate
def n_score(can):
return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
return (n_score(can1) < n_score(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)
# This stores a list of era entries, i.e a list of [list of objective scores for every candidate in the era]
# Assume one era is 5 candidates
# One Era entry for a model with 2 objectives: [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]
# All era entries will be stored in eras (Assume 2 eras): [[[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]],
# [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]]
if not era0:
best_can = model.gen_candidate()
while best_can is None:
best_can = model.gen_candidate()
best_score = n_score(best_can)
curr_can = best_can
curr_score = best_score
curr_era = []
else:
# List of List. Need to deepcopy internal list too
curr_era = []
era0_copy = list(era0)
for can in era0_copy:
curr_era += []
model.eval(can)
obj_scores = [x for x in can.scores]
curr_era += [obj_scores]
best_can = era0_copy[0]
for can in era0_copy:
if type1(best_can, can):
best_can = can
curr_can = era0_copy[len(era0_copy) - 1]
curr_score = n_score(curr_can)
best_score = n_score(best_can)
out = []
eras = []
curr_lives = lives
i = -1
# If in `lives` eras there is no improvement, EXIT
# If iterations > max_tries, EXIT
# If n_score <threshold, EXIT
while True:
i += 1
if i == max_tries:
out += ["\nReached max tries"]
if curr_era:
eras += [curr_era]
curr_era = []
break
# Beginning of a new ERA
if i % era_size == 0:
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
curr_era = []
if len(eras) > 1:
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2(eras[last_index - 1], eras[last_index])):
curr_lives = lives
else:
curr_lives -= 1
if curr_lives == 0:
out += ["\nNo more Lives"]
break
new_can = model.gen_candidate()
if new_can is None:
out += ["."]
model.eval(curr_can)
obj_scores = [x for x in curr_can.scores]
curr_era += [obj_scores]
continue
model.eval(new_can)
obj_scores = [x for x in new_can.scores]
curr_era += [obj_scores]
new_score = n_score(new_can)
flag = True
if new_score < best_score:
best_score = new_score
best_can = new_can
out += ["!"]
flag = False
if new_score < curr_score:
curr_score = new_score
curr_can = new_can
out += ["+"]
flag = False
elif p(curr_score, new_score, i / max_tries) < random.random():
curr_score = new_score
curr_can = new_can
out += ["?"]
flag = False
if best_score < threshold:
out += ["\nScore satisfies Threshold"]
break
if flag is True:
out += "."
if curr_era:
eras += [curr_era]
print ''.join(out)
print "\niterations:" + str(i)
print "Score:" + str(best_score)
return best_can, best_score
def mws(model, p=0.5, threshold=0.001, max_tries=100, max_changes=10, era_size=10, era0=None, lives=5):
best_can = None
normalize = prerun(model)
aggregate = model.aggregate
def n_score(can):
return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
return (n_score(can1) < n_score(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 new one is worse
total += a12(obj_scores1, obj_scores2)
n += 1
return (total / n >= 0.5)
best_score = 1.0
if not era0:
curr_era = []
curr_lives = lives
else:
# List of List. Need to deepcopy internal list too
curr_era = []
era0_copy = list(era0)
for can in era0_copy:
curr_era += []
model.eval(can)
obj_scores = [x for x in can.scores]
curr_era += [obj_scores]
best_can = era0_copy[0]
for can in era0_copy:
can_score = n_score(can)
if can_score < n_score(best_can):
best_can = can
best_score = can_score
# This stores a list of era entries, i.e a list of [list of objective scores for every candidate in the era]
# Assume one era is 5 candidates
# One Era entry for a model with 2 objectives: [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]
# All era entries will be stored in eras (Assume 2 eras): [[[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]],
# [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]]
eras = []
out = []
candidate = None
i = -1
thresh_flag = False
while not thresh_flag:
i += 1
j = 0
if i == max_tries:
if curr_era:
eras += [curr_era]
curr_era = []
out += ["\nReached Max Tries"]
break
if i % era_size == 0:
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
curr_era = []
if len(eras) > 1:
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2( eras[last_index - 1], eras[last_index])):
curr_lives = lives
else:
curr_lives -= 1
if curr_lives == 0:
out += ["\nNo more Lives"]
break
if candidate is not None:
prev_candidate = candidate
if i == 0:
while candidate is None:
candidate = model.gen_candidate()
else:
candidate = model.gen_candidate()
# could not generate a valid candidate after patience tries
if candidate is None:
out += ["."]
model.eval(prev_candidate)
obj_scores = [x for x in prev_candidate.scores]
curr_era += [obj_scores]
continue
if best_can is None:
best_can = candidate
best_score = n_score(candidate)
for j in range(0, max_changes):
model.eval(candidate)
score = n_score(candidate)
model.eval(candidate)
obj_scores = [x for x in candidate.scores]
curr_era += [obj_scores]
if score < best_score:
out += ["!"]
best_can = candidate
best_score = score
if best_score < threshold:
if curr_era:
eras += [curr_era]
curr_era = []
out += ["\nScore satisfies threshold"]
thresh_flag = True
break
# choose a random decision
c = random.randrange(0, len(model.decs))
if p < random.random():
# change the decision randomly
# ensure it is valid
patience = model.patience
while(patience > 0):
new_can = Candidate(dec_vals=list(candidate.dec_vals))
new_can.dec_vals[c] = model.decs[c].generate_valid_val()
if model.ok(new_can):
candidate = new_can
out += ["?"]
break
patience -= 1
if patience == 0:
out += ["."]
else:
orig_score = n_score(candidate)
candidate = mws_optimize(model, candidate, c)
new_score = normalize(model.aggregate(candidate))
if orig_score != new_score:
out += ["+"]
else:
out += ["."]
print ''.join(out)
print "\niterations:" + str(max_changes * i + j)
print "Best Score:" + str(normalize(model.aggregate(best_can)))
return best_can, normalize(model.aggregate(best_can))
def mws(model, p=0.5, threshold=0.001, max_tries=100, max_changes=10, optimal='low'):
best_can = None
normalize = prerun(model)
out = []
if optimal == 'low':
best_score = 1.0
else:
best_score = 0.0
for i in range(0, max_tries):
candidate = model.gen_candidate()
if i % 10 == 0:
out += ["\n" + str(best_score) + " "]
# could not generate a valid candidate after patience tries
if candidate is None:
out += ["."]
continue
if best_can is None:
best_can = candidate
best_score = normalize(model.aggregate(candidate))
for j in range(0, max_changes):
model.eval(candidate)
score = normalize(model.aggregate(candidate))
if optimal == 'low':
if score < threshold:
out += ["\niterations:" + str(i * max_changes + j)]
out += ["Score:" + str(score)]
print ''.join(out)
return candidate, score
if score < best_score:
out += ["!"]
best_can = candidate
best_score = score
else:
if score > threshold:
out += ["iterations:" + str(i * max_changes + j)]
out += ["Score:" + str(score)]
print ''.join(out)
return candidate, score
if score > best_score:
out += ["!"]
best_can = candidate
best_score = score
# choose a random decision
c = random.randrange(0, len(model.decs))
if p < random.random():
# change the decision randomly
# ensure it is valid
patience = model.patience
while(patience > 0):
new_can = Candidate(dec_vals=list(candidate.dec_vals))
new_can.dec_vals[c] = model.decs[c].generate_valid_val()
if model.ok(new_can):
candidate = new_can
out += ["?"]
break
patience -= 1
if patience == 0:
out += ["."]
else:
orig_score = normalize(model.aggregate(candidate))
candidate = mws_optimize(model, candidate, c, optimal)
new_score = normalize(model.aggregate(candidate))
if orig_score != new_score:
out += ["+"]
else:
out += ["."]
print ''.join(out)
print "\niterations:" + str(max_changes * max_tries)
print "Best Score:" + str(normalize(model.aggregate(best_can)))
return best_can, normalize(model.aggregate(best_can))
def mws(model, p=0.5, threshold=0.001, max_tries=500, max_changes=10, era_size=100, era0=None, lives=5):
best_can = None
max_tries = max_tries / max_changes
normalize = prerun(model)
aggregate = model.aggregate
def n_score(can):
return aggregate(can)
# return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
return (n_score(can1) < n_score(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 new one is worse
# total += a12(obj_scores1, obj_scores2)
# n += 1
# return (total / n >= 0.5)
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)
# Currently returns true if even one of the objectives have improved
# print "here:" + str(len(era2))
# print "*****#############*************"
for index, objective in enumerate(era2):
# print "comparing:\n" + str(era1[index])
# print "and\n"
# print str(objective)
# print "******"
a12_score = a12(era1[index], objective)
# print "######"
# print a12_score
# print "######"
if (a12_score >= 0.56):
# print "######"
# print objective
# print era1[index]
# print a12_score
# print "######"
return True
# print "######"
# print a12_score
# print "######"
return False
# best_score = 1.0
if not era0:
print "ERRRRRRRRRRRRRRRRRRRRRRRRRRRROR"
curr_era = [[] for _ in model.objectives()]
curr_lives = lives
else:
# List of List. Need to deepcopy internal list too
curr_lives = lives
era0_copy = []
for can in era0:
new_can = Candidate(dec_vals=can.dec_vals, scores=can.scores)
era0_copy += [new_can]
curr_era = [[] for _ in model.objectives()]
for can in era0_copy:
model.eval(can)
obj_scores = [x for x in can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
best_can = era0_copy[0]
best_score = n_score(best_can)
for can in era0_copy:
if type1(can, best_can):
best_can = can
best_score = n_score(best_can)
# curr_can = era0_copy[len(era0_copy) - 1]
# curr_score = n_score(curr_can)
# This stores a list of era entries, i.e a list of [list of objective scores for every candidate in the era]
# Assume one era is 5 candidates
# One Era entry for a model with 2 objectives: [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]
# All era entries will be stored in eras (Assume 2 eras): [[[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]],
# [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]]
eras = []
out = []
candidate = None
i = -1
thresh_flag = False
while not thresh_flag:
# print "New Try"
i += 1
j = 0
if i == max_tries:
if curr_era:
eras += [curr_era]
# curr_era = [[] for _ in model.objectives()]
# print "Reached max tries"
out += ["\nReached Max Tries"]
break
if (i * max_changes) % era_size == 0:
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
# print len(curr_era[0])
curr_era = [[] for _ in model.objectives()]
if len(eras) > 1:
# print str(i)+":"+str(i*max_changes)+":lives", curr_lives
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2(eras[last_index - 1], eras[last_index])):
curr_lives += lives
else:
curr_lives -= 1
if curr_lives == 0:
# print "No more"
out += ["\nNo more Lives"]
break
if candidate is not None:
prev_candidate = candidate
if i == 0:
while candidate is None:
candidate = model.gen_candidate()
else:
candidate = model.gen_candidate()
# could not generate a valid candidate after patience tries
if candidate is None:
out += ["."]
model.eval(prev_candidate)
obj_scores = [x for x in prev_candidate.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
continue
if best_can is None:
best_can = candidate
best_score = n_score(candidate)
for j in range(0, max_changes):
model.eval(candidate)
score = n_score(candidate)
# print score
model.eval(candidate)
obj_scores = [x for x in candidate.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
if type1(candidate, best_can): # score < best_score:
out += ["!"]
best_can = candidate
best_score = score
# if best_score < threshold:
# if curr_era:
# eras += [curr_era]
# curr_era = [[] for _ in model.objectives()]
# out += ["\nScore satisfies threshold"]
# thresh_flag = True
# break
# choose a random decision
c = random.randrange(0, len(model.decs))
if p < random.random():
# change the decision randomly
# ensure it is valid
patience = model.patience
while(patience > 0):
new_can = Candidate(dec_vals=list(candidate.dec_vals))
new_can.dec_vals[c] = model.decs[c].generate_valid_val()
if model.ok(new_can):
candidate = new_can
out += ["?"]
break
patience -= 1
if patience == 0:
out += ["."]
else:
orig_score = n_score(candidate)
candidate = mws_optimize(model, candidate, c, type1)
new_score = normalize(model.aggregate(candidate))
if orig_score != new_score:
out += ["+"]
else:
out += ["."]
# print ''.join(out)
# print "\niterations:" + str(max_changes * i + j)
# print "Best Score:" + str(normalize(model.aggregate(best_can)))
# print eras[len(eras)-1]
return best_can, model.aggregate(best_can), eras[len(eras) - 1]
def sa(model, p=sa_default_prob, threshold=0.001, max_tries=100000, lives=10, era_size=2000, era0=None):
normalize = prerun(model)
aggregate = model.aggregate
def actual_n_score(can):
return normalize(aggregate(can))
def n_score(can):
return aggregate(can)
# return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
# res = cdom(can1.dec_vals, can2.dec_vals)
# # print "*************************************"
# # print res
# # print "*************************************"
# if res == can1.dec_vals:
# # print "TrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrue"
# return True
# else:
# return False
return (n_score(can1) < n_score(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)
# Currently returns true if even one of the objectives have improved
# print "here:" + str(len(era2))
# print "*****#############*************"
for index, objective in enumerate(era2):
# print "comparing:\n" + str(era1[index])
# print "and\n"
# print str(objective)
# print "******"
a12_score = a12(era1[index], objective)
# print "######"
# print a12_score
# print "######"
if (a12_score >= 0.56):
# print "######"
# print objective
# print era1[index]
# print a12_score
# print "######"
return True
# print "######"
# print a12_score
# print "######"
return False
# One era is a list of size era_size
# Each element is a list with all the values of an objective in that era
# So basically: era = [[can1.obj1_score, can2.obj1_score],
# [can1.obj2.score, can2.obj2.score]]
if not era0:
best_can = model.gen_candidate()
while best_can is None:
best_can = model.gen_candidate()
best_score = n_score(best_can)
curr_can = best_can
curr_score = best_score
curr_era = [[] for _ in model.objectives()]
else:
# List of List. Need to deepcopy internal list too
era0_copy = []
for can in era0:
new_can = Candidate(dec_vals=can.dec_vals, scores=can.scores)
era0_copy += [new_can]
curr_era = [[] for _ in model.objectives()]
for can in era0_copy:
model.eval(can)
obj_scores = [x for x in can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
best_can = era0_copy[0]
for can in era0_copy:
if type1(can, best_can):
best_can = can
curr_can = era0_copy[len(era0_copy) - 1]
curr_score = n_score(curr_can)
best_score = n_score(best_can)
out = []
eras = []
curr_lives = lives
i = -1
# If in `lives` eras there is no improvement, EXIT
# If iterations > max_tries, EXIT
# If n_score <threshold, EXIT
gen_count = 0
none_count = 0
early_termination = False
random_jumps = 0
gt_50 = 0
while True:
i += 1
if i == max_tries:
out += ["\nReached max tries"]
if curr_era:
eras += [curr_era]
curr_era = [[] for _ in model.objectives()]
break
# Beginning of a new ERA
if i % era_size == 0:
# print "Random Jumps this era:" + str(random_jumps)
# print "gt_50 this era:" + str(gt_50)
gt_50 = 0
random_jumps = 0
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
curr_era = [[] for _ in model.objectives()]
if len(eras) > 1:
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2(eras[last_index - 1], eras[last_index])):
curr_lives += lives
else:
curr_lives -= 1
if curr_lives == 0:
print "No more lives"
out += ["\nNo more Lives"]
early_termination = True
break
new_can = model.gen_candidate()
# new_can = model.gen_can_from_prev(curr_can)
gen_count += 1
if new_can is None:
none_count += 1
out += ["."]
model.eval(curr_can)
obj_scores = [x for x in curr_can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
continue
model.eval(new_can)
obj_scores = [x for x in new_can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
new_score = n_score(new_can)
flag = True
# if new_score < best_score:
if type1(new_can, best_can):
best_score = new_score
best_can = new_can
# print "!"
out += ["!"]
flag = False
# if new_score < curr_score:
norm_curr_score = actual_n_score(curr_can)
norm_new_score = actual_n_score(new_can)
prob = p(norm_curr_score, norm_new_score, ((i / max_tries)))
# print "****************************"
# print (norm_curr_score - norm_new_score)
# print (i/max_tries)
if(prob>0.5):
gt_50 += 1
# print str(prob)
if type1(new_can, curr_can):
# print "new can won"
curr_score = new_score
curr_can = new_can
out += ["+"]
flag = False
elif prob < random.random():
# print "random jump"
curr_score = new_score
curr_can = new_can
out += ["?"]
random_jumps += 1
flag = False
# else:
# print "_!_"
if best_score < threshold:
early_termination = True
out += ["\nScore satisfies Threshold"]
break
if flag is True:
out += "."
if curr_era:
eras += [curr_era]
# for era in eras:
# print era
# print "******"
# break
# print eras[len(eras) - 2]
# print len(eras[0])
# print len(eras[len(eras) - 2])
# print ''.join(out)
# print "gen_count:" + str(gen_count)
# print "none_count" + str(none_count)
# print "\nLen (eras):" + str(len(eras))
# print "\niterations:" + str(i)
# print "Score:" + str(best_score)
return best_can, best_score, eras[len(eras) - 2]
def sa(model,
p=sa_default_prob,
threshold=0.001,
max_tries=100000,
lives=10,
era_size=2000,
era0=None):
normalize = prerun(model)
aggregate = model.aggregate
def actual_n_score(can):
return normalize(aggregate(can))
def n_score(can):
return aggregate(can)
# return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
# res = cdom(can1.dec_vals, can2.dec_vals)
# # print "*************************************"
# # print res
# # print "*************************************"
# if res == can1.dec_vals:
# # print "TrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrueTrue"
# return True
# else:
# return False
return (n_score(can1) < n_score(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)
# Currently returns true if even one of the objectives have improved
# print "here:" + str(len(era2))
# print "*****#############*************"
for index, objective in enumerate(era2):
# print "comparing:\n" + str(era1[index])
# print "and\n"
# print str(objective)
# print "******"
a12_score = a12(era1[index], objective)
# print "######"
# print a12_score
# print "######"
if (a12_score >= 0.56):
# print "######"
# print objective
# print era1[index]
# print a12_score
# print "######"
return True
# print "######"
# print a12_score
# print "######"
return False
# One era is a list of size era_size
# Each element is a list with all the values of an objective in that era
# So basically: era = [[can1.obj1_score, can2.obj1_score],
# [can1.obj2.score, can2.obj2.score]]
if not era0:
best_can = model.gen_candidate()
while best_can is None:
best_can = model.gen_candidate()
best_score = n_score(best_can)
curr_can = best_can
curr_score = best_score
curr_era = [[] for _ in model.objectives()]
else:
# List of List. Need to deepcopy internal list too
era0_copy = []
for can in era0:
new_can = Candidate(dec_vals=can.dec_vals, scores=can.scores)
era0_copy += [new_can]
curr_era = [[] for _ in model.objectives()]
for can in era0_copy:
model.eval(can)
obj_scores = [x for x in can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
best_can = era0_copy[0]
for can in era0_copy:
if type1(can, best_can):
best_can = can
curr_can = era0_copy[len(era0_copy) - 1]
curr_score = n_score(curr_can)
best_score = n_score(best_can)
out = []
eras = []
curr_lives = lives
i = -1
# If in `lives` eras there is no improvement, EXIT
# If iterations > max_tries, EXIT
# If n_score <threshold, EXIT
gen_count = 0
none_count = 0
early_termination = False
random_jumps = 0
gt_50 = 0
while True:
i += 1
if i == max_tries:
out += ["\nReached max tries"]
if curr_era:
eras += [curr_era]
curr_era = [[] for _ in model.objectives()]
break
# Beginning of a new ERA
if i % era_size == 0:
# print "Random Jumps this era:" + str(random_jumps)
# print "gt_50 this era:" + str(gt_50)
gt_50 = 0
random_jumps = 0
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
curr_era = [[] for _ in model.objectives()]
if len(eras) > 1:
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2(eras[last_index - 1], eras[last_index])):
curr_lives += lives
else:
curr_lives -= 1
if curr_lives == 0:
print "No more lives"
out += ["\nNo more Lives"]
early_termination = True
break
new_can = model.gen_candidate()
# new_can = model.gen_can_from_prev(curr_can)
gen_count += 1
if new_can is None:
none_count += 1
out += ["."]
model.eval(curr_can)
obj_scores = [x for x in curr_can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
continue
model.eval(new_can)
obj_scores = [x for x in new_can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
new_score = n_score(new_can)
flag = True
# if new_score < best_score:
if type1(new_can, best_can):
best_score = new_score
best_can = new_can
# print "!"
out += ["!"]
flag = False
# if new_score < curr_score:
norm_curr_score = actual_n_score(curr_can)
norm_new_score = actual_n_score(new_can)
prob = p(norm_curr_score, norm_new_score, ((i / max_tries)))
# print "****************************"
# print (norm_curr_score - norm_new_score)
# print (i/max_tries)
if (prob > 0.5):
gt_50 += 1
# print str(prob)
if type1(new_can, curr_can):
# print "new can won"
curr_score = new_score
curr_can = new_can
out += ["+"]
flag = False
elif prob < random.random():
# print "random jump"
curr_score = new_score
curr_can = new_can
out += ["?"]
random_jumps += 1
flag = False
# else:
# print "_!_"
if best_score < threshold:
early_termination = True
out += ["\nScore satisfies Threshold"]
break
if flag is True:
out += "."
if curr_era:
eras += [curr_era]
# for era in eras:
# print era
# print "******"
# break
# print eras[len(eras) - 2]
# print len(eras[0])
# print len(eras[len(eras) - 2])
# print ''.join(out)
# print "gen_count:" + str(gen_count)
# print "none_count" + str(none_count)
# print "\nLen (eras):" + str(len(eras))
# print "\niterations:" + str(i)
# print "Score:" + str(best_score)
return best_can, best_score, eras[len(eras) - 2]
def mws(model,
p=0.5,
threshold=0.001,
max_tries=500,
max_changes=10,
era_size=100,
era0=None,
lives=5):
best_can = None
max_tries = max_tries / max_changes
normalize = prerun(model)
aggregate = model.aggregate
def n_score(can):
return aggregate(can)
# return normalize(aggregate(can))
# if energy of can1 is less than that of can2
# can1 is better and this returns true
def type1(can1, can2):
return (n_score(can1) < n_score(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 new one is worse
# total += a12(obj_scores1, obj_scores2)
# n += 1
# return (total / n >= 0.5)
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)
# Currently returns true if even one of the objectives have improved
# print "here:" + str(len(era2))
# print "*****#############*************"
for index, objective in enumerate(era2):
# print "comparing:\n" + str(era1[index])
# print "and\n"
# print str(objective)
# print "******"
a12_score = a12(era1[index], objective)
# print "######"
# print a12_score
# print "######"
if (a12_score >= 0.56):
# print "######"
# print objective
# print era1[index]
# print a12_score
# print "######"
return True
# print "######"
# print a12_score
# print "######"
return False
# best_score = 1.0
if not era0:
print "ERRRRRRRRRRRRRRRRRRRRRRRRRRRROR"
curr_era = [[] for _ in model.objectives()]
curr_lives = lives
else:
# List of List. Need to deepcopy internal list too
curr_lives = lives
era0_copy = []
for can in era0:
new_can = Candidate(dec_vals=can.dec_vals, scores=can.scores)
era0_copy += [new_can]
curr_era = [[] for _ in model.objectives()]
for can in era0_copy:
model.eval(can)
obj_scores = [x for x in can.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
best_can = era0_copy[0]
best_score = n_score(best_can)
for can in era0_copy:
if type1(can, best_can):
best_can = can
best_score = n_score(best_can)
# curr_can = era0_copy[len(era0_copy) - 1]
# curr_score = n_score(curr_can)
# This stores a list of era entries, i.e a list of [list of objective scores for every candidate in the era]
# Assume one era is 5 candidates
# One Era entry for a model with 2 objectives: [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]
# All era entries will be stored in eras (Assume 2 eras): [[[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]],
# [[0.5,0.5], [0.2,0.3], [0.5,0.5], [0.2,0.3], [0.5,0.5]]]
eras = []
out = []
candidate = None
i = -1
thresh_flag = False
while not thresh_flag:
# print "New Try"
i += 1
j = 0
if i == max_tries:
if curr_era:
eras += [curr_era]
# curr_era = [[] for _ in model.objectives()]
# print "Reached max tries"
out += ["\nReached Max Tries"]
break
if (i * max_changes) % era_size == 0:
out += ["\n" + str(best_score) + " "]
if curr_era:
eras += [curr_era]
# print len(curr_era[0])
curr_era = [[] for _ in model.objectives()]
if len(eras) > 1:
# print str(i)+":"+str(i*max_changes)+":lives", curr_lives
last_index = len(eras) - 1
# If there is improvement reset lives, else decrement
if (type2(eras[last_index - 1], eras[last_index])):
curr_lives += lives
else:
curr_lives -= 1
if curr_lives == 0:
# print "No more"
out += ["\nNo more Lives"]
break
if candidate is not None:
prev_candidate = candidate
if i == 0:
while candidate is None:
candidate = model.gen_candidate()
else:
candidate = model.gen_candidate()
# could not generate a valid candidate after patience tries
if candidate is None:
out += ["."]
model.eval(prev_candidate)
obj_scores = [x for x in prev_candidate.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
continue
if best_can is None:
best_can = candidate
best_score = n_score(candidate)
for j in range(0, max_changes):
model.eval(candidate)
score = n_score(candidate)
# print score
model.eval(candidate)
obj_scores = [x for x in candidate.scores]
for index, score in enumerate(obj_scores):
curr_era[index] += [score]
if type1(candidate, best_can): # score < best_score:
out += ["!"]
best_can = candidate
best_score = score
# if best_score < threshold:
# if curr_era:
# eras += [curr_era]
# curr_era = [[] for _ in model.objectives()]
# out += ["\nScore satisfies threshold"]
# thresh_flag = True
# break
# choose a random decision
c = random.randrange(0, len(model.decs))
if p < random.random():
# change the decision randomly
# ensure it is valid
patience = model.patience
while (patience > 0):
new_can = Candidate(dec_vals=list(candidate.dec_vals))
new_can.dec_vals[c] = model.decs[c].generate_valid_val()
if model.ok(new_can):
candidate = new_can
out += ["?"]
break
patience -= 1
if patience == 0:
out += ["."]
else:
orig_score = n_score(candidate)
candidate = mws_optimize(model, candidate, c, type1)
new_score = normalize(model.aggregate(candidate))
if orig_score != new_score:
out += ["+"]
else:
out += ["."]
# print ''.join(out)
# print "\niterations:" + str(max_changes * i + j)
# print "Best Score:" + str(normalize(model.aggregate(best_can)))
# print eras[len(eras)-1]
return best_can, model.aggregate(best_can), eras[len(eras) - 1]
