def test_rosen_integers():
X = [2, 4, 6, 8]
y = 100*((4 - 2**2)**2) + (2 - 1)**2 \
+ 100*((6 - 4**2)**2) + (4 - 1)**2 \
+ 100*((8 - 6**2)**2) + (6 - 1)**2
assert functions.Rosenbrock(X) == y
def initiate_rosenbrock_data(initial_n=20,
effective_dim=2,
high_dim=25,
replications=100):
fileObject = open('high_dim_rosenbrock_initial_data', 'wb')
all_A = np.random.normal(0, 1, [replications, effective_dim, high_dim])
all_s = np.empty((replications, initial_n, effective_dim))
all_f_s = np.empty((replications, initial_n, 1))
test_func = functions.Rosenbrock()
for i in range(replications):
cnv_prj = projections.ConvexProjection(all_A[i])
all_s[i] = lhs(effective_dim, initial_n) * 2 * np.sqrt(
effective_dim) - np.sqrt(effective_dim)
all_f_s[i] = test_func.evaluate(cnv_prj.evaluate(all_s[i]))
pickle.dump(all_A, fileObject)
pickle.dump(all_s, fileObject)
pickle.dump(all_f_s, fileObject)
fileObject.close()
def Run_Main(low_dim=2,
high_dim=20,
initial_n=20,
total_itr=100,
func_type='Branin',
matrix_type='simple',
kern_inp_type='Y',
A_input=None,
s=None,
xl=None,
xu=None,
active_var=None,
hyper_opt_interval=20,
ARD=False,
variance=1.,
length_scale=None,
box_size=None,
noise_var=0,
slice_number=None):
if slice_number is None:
slice_number = low_dim + 1
if active_var is None:
active_var = np.arange(high_dim)
if box_size is None:
box_size = math.sqrt(low_dim)
if hyper_opt_interval is None:
hyper_opt_interval = 10
# Specifying the type of objective function
if func_type == 'Branin':
test_func = functions.Branin(active_var, noise_var=noise_var)
elif func_type == 'Rosenbrock':
test_func = functions.Rosenbrock(active_var, noise_var=noise_var)
elif func_type == 'Hartmann6':
test_func = functions.Hartmann6(active_var, noise_var=noise_var)
elif func_type == 'Col':
test_func = functions.colville(active_var, noise_var=noise_var)
elif func_type == 'CAMEL':
test_func = functions.camel3(active_var, noise_var=noise_var)
elif func_type == 'MNIST':
test_func = functions.MNIST(active_var)
else:
TypeError('The input for func_type variable is invalid, which is',
func_type)
return
best_results = np.zeros([1, total_itr + initial_n])
elapsed = np.zeros([1, total_itr + initial_n])
# generate embedding matrix via samples
#f_s = test_func.evaluate(np.array(xl))
f_s_true = test_func.evaluate_true(xl)
# get project matrix B using Semi-LSIR
B = SSIR(low_dim, xl, f_s_true, xu, slice_number, k=3)
embedding_sample = np.matmul(xl, B)
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
# Specifying the input type of kernel
if kern_inp_type == 'Y':
kern_inp = kernel_inputs.InputY(B)
input_dim = low_dim
elif kern_inp_type == 'X':
kern_inp = kernel_inputs.InputX(B)
input_dim = high_dim
elif kern_inp_type == 'psi':
kern_inp = kernel_inputs.InputPsi(B)
input_dim = high_dim
else:
TypeError('The input for kern_inp_type variable is invalid, which is',
kern_inp_type)
return
# Generating GP model
k = GPy.kern.Matern52(input_dim=input_dim,
ARD=ARD,
variance=variance,
lengthscale=length_scale)
m = GPy.models.GPRegression(kern_inp.evaluate(embedding_sample),
f_s_true,
kernel=k)
m.likelihood.variance = 1e-6
bounds = np.zeros((high_dim, 2))
bounds[:, 0] = -1
bounds[:, 1] = 1
ac = acquisition.ACfunction(B,
m,
initial_size=initial_n,
low_dimension=low_dim)
for i in range(total_itr):
start = timeit.default_timer()
#Updating GP model
m.set_XY(kern_inp.evaluate(embedding_sample), f_s_true)
m.optimize()
#find X to max UCB(BX)
es = cma.CMAEvolutionStrategy(high_dim * [0], 0.5, {'bounds': [-1, 1]})
iter = 0
u = []
ac.set_fs_true(max(f_s_true))
#_, maxD = ac.acfunctionEI(max(f_s), low_dim)
if i != 0 and (i) % 20 == 0:
print("update")
while not es.stop() and iter != 2:
iter += 1
X = es.ask()
es.tell(X,
[ac.newfunction(x)
for x in X]) #set UCB or EI in newfunction() manually
# if i != 0 and (i) % 10 == 0:
u.append(es.result[0])
#es.disp() # doctest: +ELLIPSIS
#return candidate X
maxx = es.result[0].reshape((1, high_dim))
else:
while not es.stop() and iter != 2:
iter += 1
X = es.ask()
es.tell(X,
[ac.newfunction(x)
for x in X]) #set UCB or EI in newfunction() manually
#es.disp() # doctest: +ELLIPSIS
#return candidate X
maxx = es.result[0].reshape((1, high_dim))
#_,maxD = ac.acfunctionEI(max(f_s),low_dim)
#initial qp
#ac.updateQP(maxD)
#solve qp
#res = minimize(ac.qp,np.zeros((high_dim,1)),method='SLSQP',bounds=bounds,options={'maxiter': 5, 'disp': True})
#maxx = res.x
#print("qp fun = ",res.fun)
es = np.matmul(maxx, B) #maxx:1000*1 B:1000*6
embedding_sample = np.append(embedding_sample, es, axis=0)
xl = np.append(xl, maxx, axis=0)
#f_s = np.append(f_s, test_func.evaluate(maxx), axis=0)
f_s_true = np.append(f_s_true, test_func.evaluate_true(maxx), axis=0)
#update project matrix B
if i != 0 and (i) % 20 == 0:
print("update")
#get top "inital_n" from xl
xlidex = np.argsort(-f_s_true, axis=0).reshape(-1)[:initial_n]
f_s_special = f_s_true[xlidex]
xl_special = xl[xlidex]
#get top unlabeled data from xu
xu = np.array(u)
B = SSIR(low_dim, xl_special, f_s_special, xu, slice_number, k=3)
embedding_sample = np.matmul(xl, B)
ac.resetflag(B)
# Collecting data
stop = timeit.default_timer()
print("iter = ", i, "maxobj = ", np.max(f_s_true))
best_results[0, i + initial_n] = np.max(f_s_true)
elapsed[0, i + initial_n] = stop - start
return best_results, elapsed, embedding_sample, f_s_true
def RunMain(low_dim=2,
high_dim=25,
initial_n=20,
total_itr=100,
func_type='Branin',
s=None,
active_var=None,
ARD=False,
variance=1.,
length_scale=None,
box_size=None,
high_to_low=None,
sign=None,
hyper_opt_interval=20,
noise_var=0):
"""
:param high_dim: the dimension of high dimensional search space
:param low_dim: The effective dimension of the algorithm.
:param initial_n: the number of initial points
:param total_itr: the number of iterations of algorithm. The total
number of test function evaluations is initial_n + total_itr
:param func_type: the name of test function
:param s: initial points
:param active_var: a vector with the size of greater or equal to
the number of active variables of test function. The values of
vector are integers less than high_dim value.
:param ARD: if TRUE, kernel is isomorphic
:param variance: signal variance of the kernel
:param length_scale: length scale values of the kernel
:param box_size: this variable indicates the search space [-box_size, box_size]^d
:param high_to_low: a vector with D elements. each element can have a value from {0,..,d-1}
:param sign: a vector with D elements. each element is either +1 or -1.
:param hyper_opt_interval: the number of iterations between two consecutive
hyper parameters optimizations
:param noise_var: noise variance of the test functions
:return: a tuple of best values of each iteration, all observed points, and
corresponding test function values of observed points
"""
if active_var is None:
active_var = np.arange(high_dim)
if box_size is None:
box_size = 1
if high_to_low is None:
high_to_low = np.random.choice(range(low_dim), high_dim)
if sign is None:
sign = np.random.choice([-1, 1], high_dim)
#Specifying the type of objective function
if func_type == 'Branin':
test_func = functions.Branin(active_var, noise_var=noise_var)
elif func_type == 'Rosenbrock':
test_func = functions.Rosenbrock(active_var, noise_var=noise_var)
elif func_type == 'Hartmann6':
test_func = functions.Hartmann6(active_var, noise_var=noise_var)
elif func_type == 'StybTang':
test_func = functions.StybTang(active_var, noise_var=noise_var)
else:
TypeError('The input for func_type variable is invalid, which is',
func_type)
return
best_results = np.zeros([1, total_itr + initial_n])
elapsed = np.zeros([1, total_itr + initial_n])
# Creating the initial points. The shape of s is nxD
if s is None:
s = lhs(low_dim, initial_n) * 2 * box_size - box_size
f_s = test_func.evaluate(back_projection(s, high_to_low, sign, box_size))
f_s_true = test_func.evaluate_true(
back_projection(s, high_to_low, sign, box_size))
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
# Building and fitting a new GP model
kern = GPy.kern.Matern52(input_dim=low_dim,
ARD=ARD,
variance=variance,
lengthscale=length_scale)
m = GPy.models.GPRegression(s, f_s, kernel=kern)
m.likelihood.variance = 1e-3
# Main loop
for i in range(total_itr):
start = timeit.default_timer()
# Updating GP model
m.set_XY(s, f_s)
if (i + initial_n <= 25
and i % 5 == 0) or (i + initial_n > 25
and i % hyper_opt_interval == 0):
m.optimize()
# Maximizing acquisition function
D = lhs(low_dim, 2000) * 2 * box_size - box_size
mu, var = m.predict(D)
ei_d = EI(len(D), max(f_s), mu, var)
index = np.argmax(ei_d)
# Adding the new point to our sample
s = np.append(s, [D[index]], axis=0)
new_high_point = back_projection(D[index], high_to_low, sign, box_size)
f_s = np.append(f_s, test_func.evaluate(new_high_point), axis=0)
f_s_true = np.append(f_s_true,
test_func.evaluate_true(new_high_point),
axis=0)
stop = timeit.default_timer()
best_results[0, i + initial_n] = np.max(f_s_true)
elapsed[0, i + initial_n] = stop - start
# if func_type == 'WalkerSpeed':
# eng.quit()
high_s = back_projection(s, high_to_low, sign, box_size)
return best_results, elapsed, s, f_s, f_s_true, high_s
def Run_Main(low_dim=2,
high_dim=20,
initial_n=20,
total_itr=100,
func_type='Branin',
matrix_type='simple',
kern_inp_type='Y',
A_input=None,
s=None,
xl=None,
xu=None,
active_var=None,
hyper_opt_interval=10,
ARD=False,
variance=1.,
length_scale=None,
box_size=None,
noise_var=0,
slice_number=None):
if slice_number is None:
slice_number = low_dim + 1
if active_var is None:
active_var = np.arange(high_dim)
if box_size is None:
box_size = math.sqrt(low_dim)
if hyper_opt_interval is None:
hyper_opt_interval = 10
# Specifying the type of objective function
if func_type == 'Branin':
test_func = functions.Branin(active_var, noise_var=noise_var)
elif func_type == 'Rosenbrock':
test_func = functions.Rosenbrock(active_var, noise_var=noise_var)
elif func_type == 'Hartmann6':
test_func = functions.Hartmann6(active_var, noise_var=noise_var)
elif func_type == 'StybTang':
test_func = functions.StybTang(active_var, noise_var=noise_var)
elif func_type == 'Col':
test_func = functions.colville(active_var, noise_var=noise_var)
elif func_type == 'MNIST':
test_func = functions.MNIST(active_var)
else:
TypeError('The input for func_type variable is invalid, which is',
func_type)
return
best_results = np.zeros([1, total_itr + initial_n])
elapsed = np.zeros([1, total_itr + initial_n])
total_best_results = np.zeros([1, total_itr + initial_n])
# generate embedding matrix via samples
#f_s = test_func.evaluate(xl)
f_s_true = test_func.evaluate_true(xl)
B = SSIR(low_dim, xl, f_s_true, xu, slice_number, k=3)
Bplus = pinv(B).T #6*100 with T ; otherwise, 100*6
cnv_prj = projections.ConvexProjection(Bplus)
#embedding_sample = np.matmul(xl,B.T)
box = np.sum(B, axis=1)
print(box)
#box_bound = np.empty((2, low_dim))
# for i in range(low_dim):
# for j in range(2):
# if j == 0:
# box_bound[j][i] = -np.abs(box[i])
# else:
# box_bound[j][i] = np.abs(box[i])
# Initiating first sample
if s is None:
#s = lhs(low_dim, initial_n) * 2 * box_size - box_size
# D = []
# for i in range(low_dim):
# D.append(lhs(1, initial_n) * 2 * np.abs(box[i]) - np.abs(box[i]))
s = lhs(low_dim, 2000) * 2 * np.max(np.abs(box)) - np.max(np.abs(box))
#s = np.array(D).reshape((initial_n,low_dim))
# get low-dimensional representations
s = np.matmul(xl, B.T)
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
# Specifying the input type of kernel
kern_inp, input_dim = specifyKernel("Y",
Bplus=Bplus,
low_dim=low_dim,
high_dim=high_dim)
# Generating GP model
k = GPy.kern.Matern52(input_dim=input_dim,
ARD=ARD,
variance=variance,
lengthscale=length_scale)
m = GPy.models.GPRegression(kern_inp.evaluate(s), f_s_true, kernel=k)
m.likelihood.variance = 1e-6
ac = acquisition.ACfunction(B,
m,
initial_size=initial_n,
low_dimension=low_dim)
# Main loop of the algorithm
ei_d = 0
D = 0
for i in range(total_itr):
print("i = ", i)
start = timeit.default_timer()
#update project matrix every 20 iterations
if i != 0 and (i) % 20 == 0:
print("update")
idx = np.argsort(
np.array(-ei_d),
axis=0).reshape(-1)[:100] #get 100 unlabeled data index
xu = cnv_prj.evaluate(
D[idx]) #project the unlabeled data to high-dimensional space
xlidex = np.argsort(-f_s_true, axis=0).reshape(
-1)[:initial_n] # get 'inital_n' labeled data index
xl_special = cnv_prj.evaluate(
s[xlidex]) #project the labeled data to high-dimensional space
f_s_special = f_s_true[
xlidex] # evaluate the labeled data to get response value
B = SSIR(low_dim, xl_special, f_s_special, xu, slice_number,
k=3) # perform SEMI-LSIR to update B
Bplus = pinv(B).T
specifyKernel("Y", B, low_dim, high_dim)
cnv_prj = projections.ConvexProjection(Bplus)
box = np.sum(B, axis=1) # update low-dimensional search space
#f_s = test_func.evaluate(cnv_prj.evaluate(s))
f_s_true = test_func.evaluate_true(cnv_prj.evaluate(s))
print(box)
# Updating GP model
m.set_XY(kern_inp.evaluate(s), f_s_true)
#if (i + initial_n <= 25 and i % 5 == 0) or (i + initial_n > 25 and i % hyper_opt_interval == 0):
m.optimize()
# finding the next point for sampling
# D = []
# for a in range(low_dim):
# D.append(lhs(1, 2000) * 2 * np.abs(box[a]) - np.abs(box[a]))
# D = np.array(D).reshape((2000, low_dim))
D = lhs(low_dim, 2000) * 2 * np.max(np.abs(box)) - np.max(np.abs(box))
#D = lhs(low_dim, 2000) * 2 * box_size - box_size
#test = kern_inp.evaluate(D)
mu, var = m.predict(kern_inp.evaluate(D))
#UCB
ei_d = ac.originalUCB(mu, var)
#EI
#ei_d = EI(len(D), max(f_s_true), mu, var)
index = np.argmax(ei_d)
#xl = np.append(xl,cnv_prj.evaluate([D[index]]),axis=0)
s = np.append(s, [D[index]], axis=0)
#f_s = np.append(f_s, test_func.evaluate(cnv_prj.evaluate([D[index]])), axis=0)
f_s_true = np.append(f_s_true,
test_func.evaluate_true(
cnv_prj.evaluate([D[index]])),
axis=0)
# Collecting data
stop = timeit.default_timer()
print("iter = ", i, "maxobj = ", np.max(f_s_true))
best_results[0, i + initial_n] = np.max(f_s_true)
elapsed[0, i + initial_n] = stop - start
for i in range(initial_n + total_itr):
total_best_results[0, i] = np.max(best_results[0, :i + 1])
# if func_type == 'WalkerSpeed':
# eng.quit()
return total_best_results, elapsed, s, f_s_true #cnv_prj.evaluate(s)
def Run_Main(low_dim=2, high_dim=20, initial_n=20, total_itr=100, func_type='Branin',
matrix_type='simple', kern_inp_type='Y', A_input=None, s=None, active_var=None,
hyper_opt_interval=20, ARD=False, variance=1., length_scale=None, box_size=None,
noise_var=0,slice_number=None):
if slice_number is None:
slice_number = low_dim+1
if active_var is None:
active_var= np.arange(high_dim)
if box_size is None:
box_size = math.sqrt(low_dim)
if hyper_opt_interval is None:
hyper_opt_interval = 10
# Specifying the type of objective function
if func_type == 'Branin':
test_func = functions.Branin(active_var, noise_var=noise_var)
elif func_type == 'Rosenbrock':
test_func = functions.Rosenbrock(active_var, noise_var=noise_var)
elif func_type == 'Hartmann6':
test_func = functions.Hartmann6(active_var, noise_var=noise_var)
elif func_type == 'Col':
test_func = functions.colville(active_var, noise_var=noise_var)
elif func_type == 'MNIST':
test_func = functions.MNIST(active_var)
elif func_type == 'CAMEL':
test_func = functions.camel3(active_var, noise_var=noise_var)
else:
TypeError('The input for func_type variable is invalid, which is', func_type)
return
best_results = np.zeros([1, total_itr + initial_n])
elapsed = np.zeros([1, total_itr + initial_n])
# generate embedding matrix via samples
#f_s = test_func.evaluate(np.array(s))
f_s_true = test_func.evaluate_true(s)
B = SIR(low_dim,s,f_s_true,slice_number)
embedding_sample = np.matmul(s,B)
for i in range(initial_n):
best_results[0, i] = np.max(f_s_true[0:i + 1])
for i in range(initial_n):
best_results[0,i]=np.max(f_s_true[0:i+1])
# Specifying the input type of kernel
if kern_inp_type == 'Y':
kern_inp = kernel_inputs.InputY(B)
input_dim = low_dim
elif kern_inp_type == 'X':
kern_inp = kernel_inputs.InputX(B)
input_dim = high_dim
elif kern_inp_type == 'psi':
kern_inp = kernel_inputs.InputPsi(B)
input_dim = high_dim
else:
TypeError('The input for kern_inp_type variable is invalid, which is', kern_inp_type)
return
# Generating GP model
k = GPy.kern.Matern52(input_dim=input_dim, ARD=ARD, variance=variance, lengthscale=length_scale)
m = GPy.models.GPRegression(kern_inp.evaluate(embedding_sample), f_s_true, kernel=k)
m.likelihood.variance = 1e-6
bounds = np.zeros((high_dim,2))
bounds[:,0]=-1
bounds[:,1]=1
ac = acquisition.ACfunction(B,m,initial_size=initial_n,low_dimension=low_dim)
for i in range(total_itr):
start = timeit.default_timer()
#Updating GP model
m.set_XY(kern_inp.evaluate(embedding_sample),f_s_true)
m.optimize()
#CMA_ES
# D = lhs(high_dim, 2000) * 2 * box_size - box_size
# ac_value = AC_function(m,B,D)
# solution = np.concatenate((D,ac_value),axis=1)
#
# for item in solution:
# solutions.append((item[:-1],item[-1]))
# cma_iteration = 5
# res = np.zeros((cma_iteration,high_dim))
# keep = 0
# for generation in range(cma_iteration):
# solutions = []
# for _ in range(cma_es.population_size):
# x = cma_es.ask()
# value = -AC_function(m, B, x).reshape(1)
# solutions.append((x, float(value)))
# #print("generation = ", generation,"value = ",value,"\n")
# cma_es.tell(solutions)
# a = 0
# for sol in solutions:
# if sol[1]<keep:
# keep = sol[1]
# a =np.array(sol[0]).reshape((1,high_dim))
# res[generation] = a[:]
# maxx = res[cma_iteration-1]
#*****
#D = lhs(high_dim, 2000) * 2 * box_size - box_size
#test = ac.acfunctionUCB(D)
#*****
es = cma.CMAEvolutionStrategy(high_dim * [0], 0.5, {'bounds': [-1, 1]})
iter = 0
while not es.stop() and iter !=2:
iter+=1
X = es.ask()
es.tell(X, [ac.acfunctionUCB(x) for x in X])
#es.disp() # doctest: +ELLIPSIS
#es.optimize(cma.ff.rosen)
#es.optimize(acfunction.acfunction)
maxx = es.result[0]
s = np.matmul(maxx.T,B).reshape((1,low_dim)) #maxx:1000*1 B:1000*6
embedding_sample = np.append(embedding_sample, s, axis=0)
#f_s = np.append(f_s, test_func.evaluate(maxx), axis=0 )
f_s_true = np.append(f_s_true, test_func.evaluate_true(maxx),axis=0)
# Collecting data
stop = timeit.default_timer()
print("iter = ", i, "maxobj = ", np.max(f_s_true))
best_results[0, i + initial_n] = np.max(f_s_true)
elapsed[0, i + initial_n] = stop - start
return best_results, elapsed, embedding_sample, f_s_true
def RunRembo(low_dim=2, high_dim=20, initial_n=20, total_itr=100, func_type='Branin',
matrix_type='simple', kern_inp_type='Y', A_input=None, s=None, active_var=None,
hyper_opt_interval=20, ARD=False, variance=1., length_scale=None, box_size=None,
noise_var=0):
""""
:param low_dim: the dimension of low dimensional search space
:param high_dim: the dimension of high dimensional search space
:param initial_n: the number of initial points
:param total_itr: the number of iterations of algorithm. The total
number of test function evaluations is initial_n + total_itr
:param func_type: the name of test function
:param matrix_type: the type of projection matrix
:param kern_inp_type: the type of projection. Projected points
are used as the input of kernel
:param A_input: a projection matrix with iid gaussian elements.
The size of matrix is low_dim * high_dim
:param s: initial points
:param active_var: a vector with the size of greater or equal to
the number of active variables of test function. The values of
vector are integers less than high_dim value.
:param hyper_opt_interval: the number of iterations between two consecutive
hyper parameters optimizations
:param ARD: if TRUE, kernel is isomorphic
:param variance: signal variance of the kernel
:param length_scale: length scale values of the kernel
:param box_size: this variable indicates the search space [-box_size, box_size]^d
:param noise_var: noise variance of the test functions
:return: a tuple of best values of each iteration, all observed points, and
corresponding test function values of observed points
"""
if active_var is None:
active_var= np.arange(high_dim)
if box_size is None:
box_size=math.sqrt(low_dim)
if hyper_opt_interval is None:
hyper_opt_interval = 10
#Specifying the type of objective function
if func_type=='Branin':
test_func = functions.Branin(active_var, noise_var=noise_var)
elif func_type=='Rosenbrock':
test_func = functions.Rosenbrock(active_var, noise_var=noise_var)
elif func_type=='Hartmann6':
test_func = functions.Hartmann6(active_var, noise_var=noise_var)
elif func_type == 'StybTang':
test_func = functions.StybTang(active_var, noise_var=noise_var)
else:
TypeError('The input for func_type variable is invalid, which is', func_type)
return
#Specifying the type of embedding matrix
if matrix_type=='simple':
matrix=projection_matrix.SimpleGaussian(low_dim, high_dim)
elif matrix_type=='normal':
matrix= projection_matrix.Normalized(low_dim, high_dim)
elif matrix_type=='orthogonal':
matrix = projection_matrix.Orthogonalized(low_dim, high_dim)
else:
TypeError('The input for matrix_type variable is invalid, which is', matrix_type)
return
# Generating matrix A
if A_input is not None:
matrix.A = A_input
A = matrix.evaluate()
#Specifying the input type of kernel
if kern_inp_type=='Y':
kern_inp = kernel_inputs.InputY(A)
input_dim=low_dim
elif kern_inp_type=='X':
kern_inp = kernel_inputs.InputX(A)
input_dim = high_dim
elif kern_inp_type == 'psi':
kern_inp = kernel_inputs.InputPsi(A)
input_dim = high_dim
else:
TypeError('The input for kern_inp_type variable is invalid, which is', kern_inp_type)
return
#Specifying the convex projection
cnv_prj=projections.ConvexProjection(A)
best_results=np.zeros([1,total_itr + initial_n])
elapsed = np.zeros([1, total_itr + initial_n])
# Initiating first sample # Sample points are in [-d^1/2, d^1/2]
if s is None:
s = lhs(low_dim, initial_n) * 2 * box_size - box_size
f_s = test_func.evaluate(cnv_prj.evaluate(s))
f_s_true = test_func.evaluate_true(cnv_prj.evaluate(s))
for i in range(initial_n):
best_results[0,i]=np.max(f_s_true[0:i+1])
# Generating GP model
k = GPy.kern.Matern52(input_dim=input_dim, ARD=ARD, variance=variance, lengthscale=length_scale)
m = GPy.models.GPRegression(kern_inp.evaluate(s), f_s, kernel=k)
m.likelihood.variance = 1e-6
# Main loop of the algorithm
for i in range(total_itr):
start = timeit.default_timer()
# Updating GP model
m.set_XY(kern_inp.evaluate(s),f_s)
if (i+initial_n<=25 and i % 5 == 0) or (i+initial_n>25 and i % hyper_opt_interval == 0):
m.optimize()
# finding the next point for sampling
D = lhs(low_dim, 2000) * 2 * box_size - box_size
mu, var = m.predict(kern_inp.evaluate(D))
ei_d = EI(len(D), max(f_s), mu, var)
index = np.argmax(ei_d)
s = np.append(s, [D[index]], axis=0)
f_s = np.append(f_s, test_func.evaluate(cnv_prj.evaluate([D[index]])), axis=0)
f_s_true = np.append(f_s_true, test_func.evaluate_true(cnv_prj.evaluate([D[index]])), axis=0)
#Collecting data
stop = timeit.default_timer()
best_results[0,i + initial_n]=np.max(f_s_true)
elapsed[0, i + initial_n] = stop - start
# if func_type == 'WalkerSpeed':
# eng.quit()
return best_results, elapsed, s, f_s, f_s_true, cnv_prj.evaluate(s)
def RunRembo(low_dim=2,
high_dim=20,
initial_n=20,
total_itr=100,
func_type='Branin',
matrix_type='simple',
kern_inp_type='Y',
A_input=None,
s=None,
f_s=None):
#Specifying the type of objective function
if func_type == 'Branin':
test_func = functions.Branin()
elif func_type == 'Rosenbrock':
test_func = functions.Rosenbrock()
elif func_type == 'Hartmann6':
test_func = functions.Hartmann6()
else:
TypeError('The input for func_type variable is invalid, which is',
func_type)
return
#Specifying the type of embedding matrix
if matrix_type == 'simple':
matrix = projection_matrix.SimpleGaussian(low_dim, high_dim)
elif matrix_type == 'normal':
matrix = projection_matrix.Normalized(low_dim, high_dim)
elif matrix_type == 'orthogonal':
matrix = projection_matrix.Orthogonalized(low_dim, high_dim)
else:
TypeError('The input for matrix_type variable is invalid, which is',
matrix_type)
return
# Generating matrix A
if A_input is not None:
matrix.A = A_input
A = matrix.evaluate()
#Specifying the input type of kernel
if kern_inp_type == 'Y':
kern_inp = kernel_inputs.InputY(A)
elif kern_inp_type == 'X':
kern_inp = kernel_inputs.InputX(A)
elif kern_inp_type == 'psi':
kern_inp = kernel_inputs.InputPsi(A)
else:
TypeError('The input for kern_inp_type variable is invalid, which is',
kern_inp_type)
return
#Specifying the convex projection
cnv_prj = projections.ConvexProjection(A)
best_results = np.zeros([1, total_itr])
# Initiating first sample # Sample points are in [-d^1/2, d^1/2]
if s is None:
s = lhs(low_dim,
initial_n) * 2 * math.sqrt(low_dim) - math.sqrt(low_dim)
f_s = test_func.evaluate(cnv_prj.evaluate(s))
# Generating GP model
k = CustomMatern52(input_dim=low_dim, input_type=kern_inp)
m = GPy.models.GPRegression(s, f_s, kernel=k)
m.likelihood.variance = 1e-6
m.optimize()
# Main loop of the algorithm
for i in range(total_itr):
D = lhs(low_dim, 1000) * 2 * math.sqrt(low_dim) - math.sqrt(low_dim)
# Updating GP model
m.set_XY(s, f_s)
if (i + 1) % 5 == 0:
m.optimize()
mu, var = m.predict(D)
# finding the next point for sampling
ei_d = EI(len(D), max(f_s), mu, var)
index = np.argmax(ei_d)
s = np.append(s, [D[index]], axis=0)
f_s = np.append(f_s,
test_func.evaluate(cnv_prj.evaluate([D[index]])),
axis=0)
#Collecting data
best_results[0, i] = np.max(f_s)
# max_index = np.argmax(f_s)
# max_point = s[max_index]
# max_value = f_s[max_index]
#
# print('The best value is: ', max_value,
# '\n \n at the point: ', max_point,
# '\n \n with Ay value: ', test_func.scale_domain(cnv_prj.evaluate([max_point])),
# '\n\nin the iteration: ', max_index)
return best_results, s, f_s
def test_rosen_decimals():
X = [0.5, 0.1]
y = 100 * ((0.1 - 0.5**2)**2) + (0.5 - 1)**2
assert functions.Rosenbrock(X) == y