def run_test(training_size,
scoring_function,
parameter_bounds,
corr_kernel,
n_cluster,
prior='GCP',
log=True):
x_training = []
y_training = []
for i in range(training_size):
x = [
np.random.uniform(parameter_bounds[j][0], parameter_bounds[j][1])
for j in range(parameter_bounds.shape[0])
]
x_training.append(x)
y_training.append(scoring_function(x)[0])
if (prior == 'GP'):
gp = GaussianProcess(theta0=.1 * np.ones(parameter_bounds.shape[0]),
thetaL=0.001 * np.ones(parameter_bounds.shape[0]),
thetaU=10. * np.ones(parameter_bounds.shape[0]),
random_start=5,
nugget=nugget)
gp.fit(x_training, y_training)
likelihood = gp.reduced_likelihood_function_value_
else:
gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
normalize=True,
coef_latent_mapping=0.4,
n_clusters=n_clusters)
gcp.fit(x_training, y_training)
likelihood = gcp.reduced_likelihood_function_value_
if not log:
likelihood = np.exp(likelihood)
return likelihood
def run_test(training_size,
prediction_size,
function_name,
corr_kernel,
n_cluster,
prior='GCP'):
scoring_function = functions[function_name]
parameter_bounds = all_parameter_bounds[function_name]
x_training = []
y_training = []
for i in range(training_size):
x = [
np.random.uniform(parameter_bounds[j][0], parameter_bounds[j][1])
for j in range(parameter_bounds.shape[0])
]
x_training.append(x)
y_training.append(scoring_function(x)[0])
if (isInt):
x_training, y_training = compute_unique2(
np.asarray(x_training, dtype=np.int32), np.asarray(y_training))
candidates = []
real_y = []
for i in range(prediction_size):
x = [
np.random.uniform(parameter_bounds[j][0], parameter_bounds[j][1])
for j in range(parameter_bounds.shape[0])
]
candidates.append(x)
real_y.append(scoring_function(x)[0])
real_y = np.asarray(real_y)
if (isInt):
candidates = np.asarray(candidates, dtype=np.int32)
if (prior == 'GP'):
gp = GaussianProcess(theta0=.1 * np.ones(parameter_bounds.shape[0]),
thetaL=0.001 * np.ones(parameter_bounds.shape[0]),
thetaU=10. * np.ones(parameter_bounds.shape[0]),
random_start=5,
nugget=nugget)
gp.fit(x_training, y_training)
pred = gp.predict(candidates)
likelihood = gp.reduced_likelihood_function_value_
else:
gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
normalize=True,
coef_latent_mapping=coef_latent_mapping,
n_clusters=n_clusters)
gcp.fit(x_training, y_training)
likelihood = gcp.reduced_likelihood_function_value_
if not (integratedPrediction):
pred = gcp.predict(candidates)
else:
pred, _, _, _ = gcp.predict(candidates,
eval_MSE=True,
eval_confidence_bounds=True,
integratedPrediction=True)
mse = np.mean((pred - real_y)**2.)
# Normalize
mse = mse / (np.std(real_y)**2.)
likelihood = np.exp(likelihood)
return [mse, likelihood]
k += 1
idx_val = idx_val[idxk]
idxk = list(idx_val)
else:
return False,0
return True,idx_val[0]
params,output,m_o,std_o = get_exp_data(4001,495)
rand_candidates = utils.sample_random_candidates(1000,parameter_bounds,None,isInt=np.ones(5))
gcp = GaussianCopulaProcess(nugget = 1e-10,
corr= 'squared_exponential',
random_start=5,
n_clusters=1,
coef_latent_mapping = 0.1,
try_optimize=True)
gcp.fit(params,m_o,output,obs_noise=std_o)
gp = GaussianProcess(theta0= 0.1 ,
thetaL = 0.001,
random_start=1,
thetaU = 10.,
nugget=1e-10)
gp.fit(params,m_o)
gp_pred,sigma = gp.predict(rand_candidates,eval_MSE=True)
gp_bu = np.asarray(gp_pred) + 1.96*np.sqrt(sigma)
print gp.theta_
predictions,mse,bl,bu = gcp.predict(rand_candidates,eval_MSE=True,eval_confidence_bounds=True,integratedPrediction= False,coef_bound=.5)
def find_best_candidate_with_GCP(X,
raw_Y,
mean_Y,
std_Y,
args,
rand_candidates,
verbose,
acquisition_function='Simple'):
corr_kernel = args[0]
n_clusters = args[1]
GCP_mapWithNoise = args[2]
GCP_useAllNoisyY = args[3]
GCP_model_noise = args[4]
nugget = args[5]
GCP_upperBound_coef = args[6]
mean_gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
n_clusters=n_clusters,
mapWithNoise=GCP_mapWithNoise,
useAllNoisyY=GCP_useAllNoisyY,
model_noise=GCP_model_noise,
try_optimize=True)
mean_gcp.fit(X, mean_Y, raw_Y, obs_noise=std_Y)
if (verbose == 2):
print('GCP theta :' + str(mean_gcp.theta))
if (acquisition_function == 'Simple'):
predictions = mean_gcp.predict(rand_candidates,
eval_MSE=False,
eval_confidence_bounds=False)
best_candidate_idx = np.argmax(predictions)
best_candidate = rand_candidates[best_candidate_idx]
if (verbose == 2):
print 'Hopefully :', best_candidate, predictions[
best_candidate_idx]
elif (acquisition_function == 'UCB'):
predictions,MSE,boundL,boundU = \
mean_gcp.predict(rand_candidates,eval_MSE=True,eval_confidence_bounds=True,coef_bound = GCP_upperBound_coef)
best_candidate_idx = np.argmax(boundU)
best_candidate = rand_candidates[best_candidate_idx]
if (verbose == 2):
print 'Hopefully :', best_candidate, predictions[
best_candidate_idx], boundU[best_candidate_idx]
elif (acquisition_function == 'MaxLowerBound'):
predictions,MSE,boundL,boundU = \
mean_gcp.predict(rand_candidates,eval_MSE=True,eval_confidence_bounds=True,coef_bound = GCP_upperBound_coef)
best_candidate_idx = np.argmax(boundL)
best_candidate = rand_candidates[best_candidate_idx]
if (verbose == 2):
print 'Hopefully :', best_candidate, predictions[
best_candidate_idx], boundL[best_candidate_idx], boundU[
best_candidate_idx]
elif (acquisition_function == 'EI'):
predictions,MSE = \
mean_gcp.predict(rand_candidates,eval_MSE=True,transformY=False) # we want the predictions in the GP space
y_best = np.max(mean_Y)
sigma = np.sqrt(MSE)
ei = [ gcp_compute_ei((rand_candidates[i]-mean_gcp.X_mean)/mean_gcp.X_std,predictions[i],sigma[i],y_best, \
mean_gcp.mapping,mean_gcp.mapping_derivative) \
for i in range(rand_candidates.shape[0]) ]
best_candidate_idx = np.argmax(ei)
best_candidate = rand_candidates[best_candidate_idx]
if (verbose == 2):
print 'Hopefully :', best_candidate, predictions[
best_candidate_idx], ei[best_candidate_idx]
else:
print('Acquisition function not handled...')
return best_candidate
candidates.append(x)
real_y.append(branin_f(x)[0])
real_y = np.asarray(real_y)
candidates = np.asarray(candidates)
for n_clusters in all_n_clusters:
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1, projection='3d')
ax.set_title("GCP prediction")
gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
n_clusters=n_clusters,
coef_latent_mapping=coef_latent_mapping,
mapWithNoise=GCP_mapWithNoise,
useAllNoisyY=False,
model_noise=None,
try_optimize=True)
gcp.fit(x_training, y_training)
print '\nGCP fitted'
print 'Theta', gcp.theta
print 'Likelihood', np.exp(gcp.reduced_likelihood_function_value_)
predictions,MSE,boundL,boundU = \
gcp.predict(candidates,eval_MSE=True,eval_confidence_bounds=True,coef_bound = 1.96,integratedPrediction=integratedPrediction)
pred_error = np.mean((predictions - np.asarray(real_y))**2.)
print 'MSE', pred_error
plt.title('Density estimation')
fig2 = plt.figure()
plt.title("GCP prediction")
n_rows = (n_clusters_max + 1) / 2
if not ((n_clusters_max + 1) % 2 == 0):
n_rows += 1
mf_plt_axis = np.asarray(range(100)) / 100.
mapping_functions_plot = []
for n_clusters in range(1, n_clusters_max + 1):
gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
normalize=True,
coef_latent_mapping=0.4,
n_clusters=n_clusters)
gcp.fit(x_training, y_training)
likelihood = gcp.reduced_likelihood_function_value_
print 'LGCP-' + str(n_clusters) + ' fitted'
print 'Theta', gcp.theta
print 'Likelihood', likelihood
if (n_clusters > 1):
centers = np.asarray([
gcp.centroids[i][0] * gcp.X_std + gcp.X_mean
for i in range(gcp.n_clusters)
],
dtype=np.int32)
X_init = parameters
Y_init = list(mean_outputs)
if (save_plots):
save_data = np.asarray([X_init[:, 0], np.asarray(Y_init)]).T
np.savetxt('data_plots/train_data_plot.csv', save_data, delimiter=',')
for i in range(nb_GCP_steps):
rand_candidates = utils.sample_candidates(n_candidates, parameter_bounds,
isInt)
if (sampling_model == 'GCP'):
mean_gcp = GaussianCopulaProcess(nugget=nugget,
corr=corr_kernel,
random_start=5,
n_clusters=n_clusters,
mapWithNoise=GCP_mapWithNoise,
useAllNoisyY=GCP_useAllNoisyY,
model_noise=GCP_model_noise,
try_optimize=True)
mean_gcp.fit(parameters,
mean_outputs,
raw_outputs,
obs_noise=std_outputs)
if (acquisition_function == 'UCB'):
predictions,MSE,boundL,boundU = \
mean_gcp.predict(rand_candidates,eval_MSE=True,eval_confidence_bounds=True,coef_bound = GCP_upperBound_coef)
best_candidate_idx = np.argmax(boundU)
best_candidate = rand_candidates[best_candidate_idx]
idx = np.argsort(rand_candidates[:, 0])
