# get posterior predictive
full_pred_mean, full_pred_covar = model.joint_posterior_predictive(
x_train_normalised, y_train_normalised, x_test_normalised, noise=True)
# unnormalise the predictive outputs
full_pred_mean = full_pred_mean * train_sd[-1] + train_mean[-1]
full_pred_covar = full_pred_covar * (train_sd[-1])**2
full_logsigmaf2 = deepcopy(model.logsigmaf2)
full_logl2 = deepcopy(model.logl2)
full_logsigman2 = deepcopy(model.logsigman2)
### VARIATIONAL GP ###
grid = [1, 15] + list(range(30, 451, 30)) + [455]
no_replicates = 10
LL_results = np.zeros([len(grid), no_replicates])
for grid_index, no_inducing in enumerate(grid):
for replicate_index in range(no_replicates):
varGP = variational_GP(x_train_normalised.data.numpy(),
np.expand_dims(
y_train_normalised.data.numpy(), 1),
no_inducing=no_inducing,
freeze_hyperparam=False)
varGP.optimize_parameters(500, 'Adam', learning_rate=0.1)
var_LL_lower_bound = varGP.Fv()
LL_results[grid_index, replicate_index] = var_LL_lower_bound
np.savetxt('LL_results_not_frozen.tsv', LL_results, delimiter='\t')
full_logl2 = deepcopy(model.logl2)
full_logsigman2 = deepcopy(model.logsigman2)
### VARIATIONAL GP ###
grid = [1, 15] + list(range(30, 451, 30)) + [455]
no_replicates = 10
KL_results = np.zeros([len(grid), no_replicates])
for grid_index, no_inducing in enumerate(grid):
for replicate_index in range(no_replicates):
varGP = variational_GP(x_train_normalised.data.numpy(),
np.expand_dims(
y_train_normalised.data.numpy(), 1),
no_inducing=no_inducing,
freeze_hyperparam=True,
logsigmaf2=full_logsigmaf2,
logl2=full_logl2,
logsigman2=full_logsigman2)
varGP.optimize_parameters(500, 'Adam', learning_rate=0.01)
var_pred_mean, var_pred_covar = varGP.joint_posterior_predictive(
x_test_normalised.data.numpy(), noise=True)
var_pred_mean = var_pred_mean * train_sd[-1] + train_mean[-1]
#import pdb; pdb.set_trace()
var_pred_covar = var_pred_covar * (train_sd[-1]**2)
#var_pred_var = torch.diag(var_pred_covar)
# Compute KL divergence
#import pdb; pdb.set_trace()
train_inputs, train_outputs, test_inputs = pickle.load(f)
no_train = train_outputs.size
no_test = test_inputs.shape[0]
# convert to torch tensors
train_inputs = torch.Tensor(train_inputs)
train_inputs = torch.unsqueeze(train_inputs, 1) # 1 dimensional data
train_outputs = torch.Tensor(train_outputs)
test_inputs = torch.Tensor(test_inputs)
test_inputs = torch.unsqueeze(test_inputs, 1) # 1 dimensional data
### VARIATIONAL GP ###
no_inducing = 15
varGP = variational_GP(train_inputs.data.numpy(),
np.expand_dims(train_outputs.data.numpy(), 1),
no_inducing=no_inducing,
kernel='matern')
var_pred_mean, var_pred_covar = varGP.joint_posterior_predictive(
test_inputs.data.numpy())
# record initial inducing point locations
initial_inducing = deepcopy(torch.squeeze(varGP.Xm).data.numpy())
varGP.optimize_parameters(10000, 'Adam', learning_rate=0.02)
var_pred_mean, var_pred_covar = varGP.joint_posterior_predictive(
test_inputs.data.numpy(),
noise=True) # plot error bars with observation noise
# final inducing points
final_inducing = torch.squeeze(varGP.Xm).data.numpy()
### VARIATIONAL GP ###
#Set random seed for reproducibility
random.seed(0)
grid = [1, 15] + list(range(30, 451, 30)) + [455]
no_replicates = 10
SMSE_results = np.zeros([len(grid), no_replicates])
SNLP_results = np.zeros([len(grid), no_replicates])
for grid_index, no_inducing in enumerate(grid):
for replicate_index in range(no_replicates):
varGP = variational_GP(x_train_normalised.data.numpy(),
np.expand_dims(
y_train_normalised.data.numpy(), 1),
no_inducing=no_inducing)
varGP.optimize_parameters(1000, 'Adam', learning_rate=0.01)
pred_mean, pred_covar = varGP.joint_posterior_predictive(
x_test_normalised.data.numpy(), noise=True)
pred_mean = torch.squeeze(pred_mean * train_sd[-1] +
train_mean[-1])
pred_var = torch.diag(pred_covar)
pred_var = pred_var * (train_sd[-1])**2
SNLP_varGP = SNLP(pred_mean, pred_var, y_test, train_mean[-1],
train_sd[-1])
SMSE_varGP = SMSE(torch.Tensor(pred_mean), y_test)
SMSE_results[grid_index, replicate_index] = SMSE_varGP
SNLP_results[grid_index, replicate_index] = SNLP_varGP