def main_neighbor(args, mp_n):
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree
]
config = parse_configuration(args.config[0])
with open(args.output[0], 'w') as fout:
fout.write('')
call = "python3 SynthPrefGen.py -o " + args.output[0] + " "
prob = list(map(lambda x: str(x), args.problem))
call += '-p ' + ' '.join(prob) + ' '
if len(args.learn_conf) == 1:
call += '-i ' + args.learn_conf[0] + ' '
call += args.config[0] + " >> timing.dat"
runs = 25
label = ''
for holder in config[1]:
label += pill_label(agent_types, holder, config[0])
label += ';' + str(config[1][0].size)
with open(args.output[0], 'a') as fout:
fout.write('(' + label + ')')
start = time.time()
pool = mp.Pool(mp_n)
pool.map(sys_call_wait, [call for i in range(runs)])
with open(args.output[0], 'a') as fout:
fout.write("\n")
print("Full Time:", time.time() - start)
return 0
def main(args):
print(args)
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
agents = []
# Build agents.
for agent in config[1]:
agents.append(make_agent(agent, agent_types, config[0]))
# Write agents, if applicable
if args.agent_folder is not None:
if not os.path.isdir(args.agent_folder[0]):
os.mkdir(args.agent_folder[0])
for agent in agents:
a_file = args.agent_folder[0] + "/agent" + str(
agent[0].id) + ".pref"
with open(a_file, 'w') as fout:
fout.write(str(agent[0].model))
# Build example set.
ex_set = ExampleSet()
for agent in agents:
temp_set = build_example_set(agent[0], agent[1], config[0])
ex_set.add_example_list(temp_set.example_list())
# Write example set to file.
with open(args.output[0], 'w') as fout:
fout.write(str(config[0]) + "\n")
for example in ex_set.example_list():
fout.write(str(example) + "\n")
return
def main(args, mp_n):
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree
]
config = parse_configuration(args.config[0])
with open(args.output[0], 'w') as fout:
fout.write('')
for holder in config[1]:
with open('temp_agent.config', 'w') as fout:
fout.write(str(config[0]) + "\n")
fout.write(str(holder))
call = "python3 SynthPrefGen.py -l " + str(args.layers[0]) + ' '
prob = list(map(lambda x: str(x), args.problem))
call += '-p ' + ' '.join(prob) + ' '
if len(args.learn_conf) == 1:
call += '-i ' + args.learn_conf[0] + ' '
call += "-o " + args.output[0] + " "
call += "temp_agent.config >> timing.dat"
runs = 25
print(call)
with open(args.output[0], 'a') as fout:
fout.write('(' + pill_label(agent_types, holder, config[0]) + ';' +
str(holder.size) + ')')
pool = mp.Pool(mp_n)
pool.map(sys_call_wait, [call for i in range(runs)])
with open(args.output[0], 'a') as fout:
fout.write("\n")
return 0
def main_hillclimb_rr(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
info = {}
l_class = None
if len(args.learn_conf) == 1:
l_config = parse_configuration(args.learn_conf[0])
info = l_config[1][0].info
for type in agent_types:
if l_config[1][0].type.lower() == type.string_id().lower():
l_class = type
else:
info['clauses'] = 1
info['literals'] = 1
info['ranks'] = 5
l_class = RankingPrefFormula
runs = 400
max_eval = 0
start = time()
stats = [100]
agents = []
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
for i in range(runs):
learner = l_class.random(config[0], info)
learner = SA.hillclimb(learner, ex_set, SA.evaluate_util)
# learner = SA.hillclimb(learner, ex_set, SA.evaluate_maximin)
eval = SA.evaluate_util(learner, ex_set)
# eval = SA.evaluate_maximin(learner, ex_set)
if eval > max_eval:
max_eval = eval
if (i + 1) % (stats[0]) == 0:
stats.append(max_eval)
stats = list(map(lambda x: str(x), stats))
with open(args.output[0], 'a') as fout:
fout.write(',(' + ';'.join(stats) + ')')
print("Time:", time() - start)
def main_build_neighbor_monte_carlo(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
info = {}
l_class = None
if len(args.learn_conf) == 1:
l_config = parse_configuration(args.learn_conf[0])
info = l_config[1][0].info
for type in agent_types:
if l_config[1][0].type.lower() == type.string_id().lower():
l_class = type
else:
info['clauses'] = 1
info['literals'] = 1
info['ranks'] = 5
l_class = RankingPrefFormula
results = []
runs = 250
start = time()
agents = []
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
for i in range(runs):
learner = l_class.random(config[0], info)
# learner = SA.hillclimb(learner, ex_set, SA.evaluate_util)
learner = SA.hillclimb(learner, ex_set, SA.evaluate_maximin)
# results.append(SA.evaluate_util(learner, ex_set))
results.append(SA.evaluate_maximin(learner, ex_set))
average_maxima = 0.0
for i in results:
average_maxima += i
average_maxima = average_maxima / (len(results))
stats = [runs, min(results), average_maxima, max(results)]
stats = list(map(lambda x: str(x), stats))
with open(args.output[0], 'a') as fout:
fout.write(',(' + ';'.join(stats) + ')')
print("Time:", time() - start)
def main_learn_joint_SA_mm(args):
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
config = parse_configuration(args.config[0])
info = {}
l_class = None
if len(args.learn_conf) == 1:
l_config = parse_configuration(args.learn_conf[0])
info = l_config[1][0].info
for type in agent_types:
if l_config[1][0].type.lower() == type.string_id().lower():
l_class = type
else:
info['clauses'] = 1
info['literals'] = 1
info['ranks'] = 5
l_class = RankingPrefFormula
agents = []
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
for train, valid in ex_set.crossvalidation(5):
start = time()
learner = l_class.random(config[0], info)
# learner = LPM.random(config[0], info)
learner = learn_SA_mm(learner, train)
print(time() - start)
training = evaluate_multi(train, learner)
validation = evaluate_multi(valid, learner)
training = ';'.join(list(map(lambda x: str(x), training)))
validation = ';'.join(list(map(lambda x: str(x), validation)))
temp = ';'.join([training, validation])
with open(args.output[0], 'a') as fout:
fout.write(',(' + temp + ')')
del temp
del learner
del train
del valid
del ex_set
def main_learn_SA(args):
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
config = parse_configuration(args.config[0])
info = {}
l_class = None
if len(args.learn_conf) == 1:
l_config = parse_configuration(args.learn_conf[0])
info = l_config[1][0].info
for type in agent_types:
if l_config[1][0].type.lower() == type.string_id().lower():
l_class = type
else:
info['clauses'] = 1
info['literals'] = 1
info['ranks'] = 5
l_class = RankingPrefFormula
for holder in config[1]:
agent = make_agent(holder, agent_types, config[0])
ex_set = build_example_set(agent[0], agent[1], config[0])
proportion = ex_proport(ex_set)
proportion = list(map(lambda x: str(x), proportion))
proportion = ';'.join(proportion)
for train, valid in ex_set.crossvalidation(5):
start = time()
learner = l_class.random(config[0], info)
# learner = RankingPrefFormula.random(config[0],info)
# learner = LPM.random(config[0], info)
learner = learn_SA(learner, train)
print(time() - start)
training = evaluate_rep(train, learner)
validation = evaluate_rep(valid, learner)
temp = ';'.join([str(training), str(validation), proportion])
with open(args.output[0], 'a') as fout:
fout.write(',(' + temp + ')')
def main_build_neighbor(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
info = {}
info['clauses'] = 1
info['literals'] = 1
info['ranks'] = 3
agents = []
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
n_graph = NeighborGraph()
agents = list(map(lambda x: x[0], agents))
start = time()
for rpf in RankingPrefFormula.each(config[0], info):
# eval = evaluate_rep_full_multi(agents, rpf, config[0])
# eval = evaluate_rep_full_maximin(agents, rpf, config[0])
# eval = evaluate_rep(ex_set, rpf)
eval = SA.evaluate_maximin(rpf, ex_set)
n_graph.add_node(rpf.node_str(), eval)
for neighbor in rpf.neighbors():
n_graph.add_arc(rpf.node_str(), neighbor.node_str())
maxima = n_graph.local_maxima()
average_maxima = 0.0
min_maxima = None
for node in maxima:
average_maxima += n_graph.get(node)
if min_maxima is None or n_graph.get(node) < min_maxima:
min_maxima = n_graph.get(node)
average_maxima = average_maxima / (len(maxima))
stats = [len(maxima), min_maxima, average_maxima, n_graph.global_maxima()]
stats = list(map(lambda x: str(x), stats))
with open(args.output[0], 'a') as fout:
fout.write(',(' + ';'.join(stats) + ')')
print("Time:", time() - start)
print("Local Minima Count:", len(n_graph.local_minima()))
print("Local Maxima Count:", len(maxima))
print("Minimum Local Maxima:", min_maxima)
print("Average Local Maxima Value:", average_maxima)
print("Maximum:", n_graph.global_maxima())
print("Minimum:", n_graph.global_minima())
print("Average:", n_graph.average())
print("Number of Nodes:", len(n_graph))
def main_learn_joint_nn(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
info = {}
agents = []
learn_device = None
# with open(args.output[0],'w') as fout:
# fout.write('')
if torch.cuda.is_available():
learn_device = torch.device('cuda')
else:
learn_device = torch.device('cpu')
layers = [256, 256, 256]
layer_cut = max(0, args.layers[0])
layers = layers[:layer_cut]
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
for train, valid in ex_set.crossvalidation(5):
# train.to_tensors(learn_device)
# valid.to_tensors(learn_device)
start = time()
learner = train_neural_preferences(train, layers, 1000, config[0],
learn_device)
# learner.to(eval_device)
learner.eval()
training = evaluate_cuda_multi(train, learner, learn_device)
validation = evaluate_cuda_multi(valid, learner, learn_device)
print(time() - start)
# pills.append('(' + str(training) + ';' + str(validation) + ')')
training = ';'.join(list(map(lambda x: str(x), training)))
validation = ';'.join(list(map(lambda x: str(x), validation)))
temp = ';'.join([training, validation])
with open(args.output[0], 'a') as fout:
fout.write(',(' + temp + ')')
torch.cuda.empty_cache()
del temp
del learner
del train
del valid
del ex_set
def main_learn_lpm_full(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
for holder in config[1]:
agent = make_agent(holder, agent_types, config[0])
ex_set = build_example_set(agent[0], agent[1], config[0])
for train, valid in ex_set.crossvalidation(5):
start = time()
learner = LPM.learn_greedy(train, config[0])
print(time() - start)
training = evaluate_rep(train, learner)
validation = evaluate_rep(valid, learner)
full_eval = evaluate_rep_full(agent[0], learner, config[0])
temp = ';'.join([str(training), str(validation), str(full_eval)])
with open(args.output[0], 'a') as fout:
fout.write(',(' + temp + ')')
def main_learn_nn(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
agents = []
learn_device = None
# with open(args.output[0],'w') as fout:
# fout.write('')
if torch.cuda.is_available():
learn_device = torch.device('cuda')
else:
learn_device = torch.device('cpu')
layers = [256, 256, 256]
layer_cut = max(0, args.layers[0])
layers = layers[:layer_cut]
for holder in config[1]:
single_run(args, holder, agent_types, config, layers, learn_device)
def main_learn_joint_lpm_mm(args):
config = parse_configuration(args.config[0])
agent_types = [
LPM, RankingPrefFormula, PenaltyLogic, WeightedAverage, CPnet, CLPM,
LPTree, ASO
]
agents = []
for holder in config[1]:
agents.append(make_agent(holder, agent_types, config[0]))
ex_set = build_example_set_multi(agents, config[0])
for train, valid in ex_set.crossvalidation(5):
start = time()
learner = LPM.learn_greedy_maximin(train, config[0])
print(time() - start)
training = evaluate_multi(train, learner)
validation = evaluate_multi(valid, learner)
training = ';'.join(list(map(lambda x: str(x), training)))
validation = ';'.join(list(map(lambda x: str(x), validation)))
temp = ';'.join([training, validation])
with open(args.output[0], 'a') as fout:
fout.write(',(' + temp + ')')