def evolution_search(f, para_b):
begin_time = datetime.now()
Timestamps_list = []
Target_list = []
Parameters_list = []
keys = list(para_b.keys())
bounds = np.array(list(para_b.values()), dtype=np.float)
dim = len(keys)
plog = PrintLog(keys)
para_value = np.empty((1, dim))
plog.print_header(initialization=True)
for col, (lower, upper) in enumerate(bounds):
para_value.T[col] = np.random.RandomState().uniform(lower, upper)
para_value = para_value.ravel().tolist()
plog.print_header(initialization=False)
es = cma.CMAEvolutionStrategy(para_value, 0.2, {'maxiter': 60, 'popsize': 50})
# es = cma.CMAEvolutionStrategy(para_value, 0.5)
while not es.stop():
solutions = es.ask()
es.tell(solutions, [f(x) for x in solutions])
# es.tell(*es.ask_and_eval(f))
# es.disp()
res = es.result
# metric = f(**params_dic)
Parameters_list.append(res[0].tolist())
Target_list.append(1-res[1])
elapse_time = (datetime.now() - begin_time).total_seconds()
Timestamps_list.append(elapse_time)
# print("The best candidate: ", res[0])
# print("The best result: ", res[1])
plog.print_step(res[0], 1-res[1])
return Timestamps_list, Target_list, Parameters_list
def random_search(f, para_b, num):
begin_time = datetime.now()
Timestamps_list = []
Target_list = []
keys = list(para_b.keys())
bounds = np.array(list(para_b.values()), dtype=np.float)
dim = len(keys)
plog = PrintLog(keys)
parameter_list = np.empty((num, dim))
plog.print_header(initialization=True)
for col, (lower, upper) in enumerate(bounds):
parameter_list.T[col] = np.random.RandomState().uniform(lower,
upper,
size=num)
plog.print_header(initialization=False)
for i in range(num):
params_dic = dict(zip(keys, parameter_list[i]))
metric = f(**params_dic)
Target_list.append(metric)
elapse_time = (datetime.now() - begin_time).total_seconds()
Timestamps_list.append(elapse_time)
plog.print_step(parameter_list[i], metric)
return Timestamps_list, Target_list, parameter_list.tolist()
def evolution_search(f, para_b):
begin_time = datetime.now()
Timestamps_list = []
Target_list = []
Parameters_list = []
keys = list(para_b.keys())
dim = len(keys)
plog = PrintLog(keys)
min = np.ones(dim)
max = np.ones(dim)
value_list = list(parameters.values())
for i_v in range(dim):
min[i_v] = value_list[i_v][0]
max[i_v] = value_list[i_v][1]
bounds = (min, max)
plog.print_header(initialization=True)
my_topology = Star()
my_options ={'c1': 0.6, 'c2': 0.3, 'w': 0.4}
my_swarm = P.create_swarm(n_particles=20, dimensions=dim, options=my_options, bounds=bounds) # The Swarm Class
iterations = 30 # Set 100 iterations
for i in range(iterations):
# Part 1: Update personal best
# for evaluated_result in map(evaluate, my_swarm.position):
# my_swarm.current_cost = np.append(evaluated_result)
# for best_personal_result in map(evaluate, my_swarm.pbest_pos): # Compute personal best pos
# my_swarm.pbest_cost = np.append(my_swarm.pbest_cost, best_personal_result)
my_swarm.current_cost = np.array(list(map(evaluate, my_swarm.position)))
#print(my_swarm.current_cost)
my_swarm.pbest_cost = np.array(list(map(evaluate, my_swarm.pbest_pos)))
my_swarm.pbest_pos, my_swarm.pbest_cost = P.compute_pbest(my_swarm) # Update and store
# Part 2: Update global best
# Note that gbest computation is dependent on your topology
if np.min(my_swarm.pbest_cost) < my_swarm.best_cost:
my_swarm.best_pos, my_swarm.best_cost = my_topology.compute_gbest(my_swarm)
# Let's print our output
#if i % 2 == 0:
# print('Iteration: {} | my_swarm.best_cost: {:.4f}'.format(i + 1, my_swarm.best_cost))
# Part 3: Update position and velocity matrices
# Note that position and velocity updates are dependent on your topology
my_swarm.velocity = my_topology.compute_velocity(my_swarm)
my_swarm.position = my_topology.compute_position(my_swarm)
Parameters_list.append(my_swarm.best_pos.tolist())
Target_list.append(1-my_swarm.best_cost)
elapse_time = (datetime.now() - begin_time).total_seconds()
Timestamps_list.append(elapse_time)
# print("The best candidate: ", my_swarm.best_pos)
# print("The best result: ", res[1])
plog.print_step(my_swarm.best_pos, 1 - my_swarm.best_cost)
if i == 0:
plog.print_header(initialization=False)
return Timestamps_list, Target_list, Parameters_list
class CmaEs:
def __init__(self, func, para):
# "para" is the diction of parameters which needs to be optimized.
# "para" --> {'x_1': (0,10), 'x_2': (-1, 1),..., 'x_n':(Lower_bound, Upper_bound)}
self.f = func
self.begin_time = datetime.now()
self.timestamps_list = []
self.target_list = []
self.parameters_list = []
self.pop_list = []
self.keys = list(para.keys())
self.bounds = np.array(list(para.values()), dtype=np.float)
self.dim = len(self.keys)
self.plog = PrintLog(self.keys)
self.para_value = np.empty((1, self.dim))
self.plog.print_header(initialization=True)
for col, (lower, upper) in enumerate(self.bounds):
self.para_value.T[col] = np.random.RandomState().uniform(
lower, upper)
self.para_value = self.para_value.ravel().tolist()
def evaluate(self, input):
result = self.f(input[0], input[1])
return -result
def run(self, max_iter=20, pop_size=10, sigma=0.5):
# "sigma0" is the initial standard deviation.
# The problem variables should have been scaled, such that a single standard deviation
# on all variables is useful and the optimum is expected to lie within about `x0` +- ``3*sigma0``.
# See also options 'scaling_of_variables'. Often one wants to check for solutions close to the initial point.
# This allows, for example, for an easier check of consistency of the
# objective function and its interfacing with the optimizer.
# In this case, a much "smaller" 'sigma0' is advisable.
sigma_0 = sigma
# "conf_para" is used to configure the parameters in CMA-ES algorithm
# "conf_para" --> {'maxiter': 20, 'popsize': 20}
conf_para = {'maxiter': max_iter, 'popsize': pop_size}
es = cma.CMAEvolutionStrategy(self.para_value, sigma_0, conf_para)
self.plog.print_header(initialization=False)
while not es.stop():
solutions = es.ask()
self.pop_list.append(solutions)
es.tell(solutions, [self.evaluate(x) for x in solutions])
# es.tell(*es.ask_and_eval(f))
# es.disp()
res = es.result
# metric = f(**params_dic)
self.parameters_list.append(res[0].tolist())
self.target_list.append(-res[1])
elapse_time = (datetime.now() - self.begin_time).total_seconds()
self.timestamps_list.append(elapse_time)
# print("The best candidate: ", res[0])
# print("The best result: ", res[1])
self.plog.print_step(res[0], -res[1])
return self.timestamps_list, self.target_list, self.parameters_list, self.pop_list
class CNPs_Optimization:
def __init__(self, func, para):
# "para" is the diction of parameters which needs to be optimized.
# "para" --> {'x_1': (0,10), 'x_2': (-1, 1),..., 'x_n':(Lower_bound, Upper_bound)}
self.f = func
self.begin_time = datetime.now()
self.timestamps_list = []
self.target_list = []
self.parameters_list = []
self.pop_list = []
self.keys = list(para.keys())
self.bounds = np.array(list(para.values()), dtype=np.float)
self.dim = len(self.keys)
self.plog = PrintLog(self.keys)
def initialization(self, pop_size):
para_value = np.empty((pop_size, self.dim))
self.plog.print_header(initialization=True)
for col, (lower, upper) in enumerate(self.bounds):
para_value[:, col] = np.random.RandomState().uniform(
lower, upper, pop_size)
target_value = np.empty((pop_size, 1))
for i in range(pop_size):
target_value[i] = self.f(para_value[i])
# print(target_value[i])
# print(para_value[i])
self.plog.print_step(para_value[i], target_value[i][0])
return para_value, target_value
def model_build(self):
dim_r = 4
dim_h_hidden = 128
dim_g_hidden = 128
x_context = tf.placeholder(tf.float32, shape=(None, self.dim))
y_context = tf.placeholder(tf.float32, shape=(None, 1))
x_target = tf.placeholder(tf.float32, shape=(None, self.dim))
y_target = tf.placeholder(tf.float32, shape=(None, 1))
neural_process = NeuralProcess(x_context, y_context, x_target,
y_target, dim_r, dim_h_hidden,
dim_g_hidden)
return neural_process
def model_run(self,
neural_process,
train_op,
sess,
x_init,
y_init,
n_iter_cnp=5001):
num_init = len(y_init)
plot_freq = 1000
for iter in range(n_iter_cnp):
N_context = np.random.randint(1, num_init, 1)
# create feed_dict containing context and target sets
feed_dict = neural_process.helper_context_and_target(
x_init, y_init, N_context)
# optimisation step
a = sess.run(train_op, feed_dict=feed_dict)
if iter % plot_freq == 0:
print(a[1])
def maximize(self, num_iter=3, pop_size=10, uncertain_rate=0.2):
pop_para, pop_target = self.initialization(pop_size)
self.pop_list.append(pop_para)
pop_max = np.max(pop_target)
self.target_list.append(pop_max)
idx_max = np.where(pop_target == pop_max)[0]
max_para = np.squeeze(pop_para[idx_max])
self.parameters_list.append(max_para.tolist())
elapse_time = (datetime.now() - self.begin_time).total_seconds()
self.timestamps_list.append(elapse_time)
self.plog.print_header(initialization=False)
tf.reset_default_graph()
cnp_model = self.model_build()
sess = tf.Session()
train_op_and_loss = cnp_model.init_NP(learning_rate=0.001)
init = tf.global_variables_initializer()
sess.run(init)
n_op_iter = num_iter
for iter_op in range(n_op_iter):
self.model_run(cnp_model,
train_op_and_loss,
sess,
x_init=pop_para,
y_init=pop_target)
num_candidate = 1000
x_candidate = np.empty((num_candidate, self.dim))
for col, (lower, upper) in enumerate(self.bounds):
x_candidate[:, col] = np.random.RandomState().uniform(
lower, upper, num_candidate)
predict_candidate = cnp_model.posterior_predict(
pop_para, pop_target, x_candidate)
_, y_candidate_mu, y_candidate_sigma = sess.run(predict_candidate)
num_uncertain = int(pop_size * uncertain_rate)
if num_uncertain < 1:
num_uncertain = 1
num_select = pop_size - num_uncertain
y_candidate_mu = np.squeeze(y_candidate_mu)
y_candidate_sigma = np.squeeze(y_candidate_sigma)
ind_mu = np.argpartition(y_candidate_mu, -num_select)[-num_select:]
x_mu_select = x_candidate[ind_mu]
ind_sigma = np.argpartition(y_candidate_sigma,
-num_uncertain)[-num_uncertain:]
x_sigma_select = x_candidate[ind_sigma]
x_select = np.unique(np.concatenate((x_mu_select, x_sigma_select),
axis=0),
axis=0)
_, idx_d = np.unique(np.concatenate((pop_para, x_select), axis=0),
axis=0,
return_index=True)
# remove the same item
idx_d = idx_d - pop_para.shape[0]
idx_d = np.delete(idx_d, np.where(idx_d < 0))
x_select = x_select[idx_d]
n_selected = np.shape(x_select)[
0] # final number of candidate which are selected.
y_select = np.empty((n_selected, 1))
for i in range(n_selected):
y_select[i] = self.f(x_select[i])
self.plog.print_step(x_select[i], y_select[i][0])
self.pop_list.append(x_select)
pop_max = np.max(y_select)
self.target_list.append(pop_max)
idx_max = np.where(y_select == pop_max)[0]
max_para = np.squeeze(x_select[idx_max])
self.parameters_list.append(max_para.tolist())
elapse_time = (datetime.now() - self.begin_time).total_seconds()
self.timestamps_list.append(elapse_time)
pop_para = np.concatenate((pop_para, x_select), axis=0)
pop_target = np.concatenate((pop_target, y_select), axis=0)
print("The %d-th iteration is completed!" % iter_op)