def test_set_transformed_inputs(self):
for dtype in (torch.float, torch.double):
train_x = torch.rand(5, 1, dtype=dtype, device=self.device)
train_y = torch.rand(5, 1, dtype=dtype, device=self.device)
tf = Normalize(
d=1,
bounds=torch.tensor([[0.0], [2.0]], dtype=dtype, device=self.device),
transform_on_preprocess=False,
)
model = SingleTaskGP(train_x, train_y, input_transform=tf)
self.assertTrue(torch.equal(model.train_inputs[0], train_x))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
# check that input transform is only applied when the transform
# is a transform_on_preprocess is True
self.assertTrue(torch.equal(model.train_inputs[0], train_x))
tf.transform_on_preprocess = True
_set_transformed_inputs(mll)
self.assertTrue(torch.equal(model.train_inputs[0], tf(train_x)))
model.eval()
# test no set_train_data method
mock_model = MockGP(MockPosterior())
mock_model.train_inputs = (train_x,)
mock_model.likelihood = model.likelihood
mock_model.input_transform = tf
mll = ExactMarginalLogLikelihood(mock_model.likelihood, mock_model)
with self.assertRaises(BotorchError):
_set_transformed_inputs(mll)
class GaussianProcess(object):
def __init__(self, dx, param_normalizer, *args, **kwargs):
print(dx)
self.param_normalizer = param_normalizer
self.data_normalizer = normalization_tools.Standardizer()
self.gp = None
def fit(self, x_train, y_train):
# normalize parameter (=input) data
x_train_norm = self.param_normalizer.project_to(x_train)
# normalize the data
y_train_norm = self.data_normalizer.standardize(y_train)
self.gp = SingleTaskGP(x_train_norm, y_train_norm)
self.gp.likelihood.noise_covar.register_constraint(
"raw_noise", GreaterThan(1e-5))
mll = ExactMarginalLogLikelihood(self.gp.likelihood, self.gp)
fit_gpytorch_model(mll)
return self.gp
def predict(self, x):
x_norm = self.param_normalizer.project_to(x)
self.gp.eval()
self.gp.likelihood.eval()
with torch.set_grad_enabled(False):
pred = self.gp(x_norm)
return self.data_normalizer.unstandardize(pred.mean.view(
-1, 1)), self.data_normalizer.unstandardize_wo_mean(pred.variance)
def bo_loop(gp_model: SingleTaskGP, acq_func_id: str,
acq_func_kwargs: Dict[str, Any], acq_func_opt_kwargs: Dict[str,
Any],
bounds: Tensor, tkwargs: Dict[str, Any], q: int, num_restarts: int,
raw_initial_samples, seed: int,
num_MC_sample_acq: int) -> Iterable[Any]:
# seed everything
np.random.seed(seed)
torch.manual_seed(seed)
# put on proper device
# we want to maximize
fmax = torch.quantile(gp_model.train_targets, .9).item()
print(f"Using good point cutoff {fmax:.2f}")
device = gp_model.train_inputs[0].device
bounds = bounds.to(**tkwargs)
gp_model.eval()
acq_func_kwargs['best_f'] = fmax
acq_func = query_acq_func(acq_func_id=acq_func_id,
acq_func_kwargs=acq_func_kwargs,
gp_model=gp_model,
q=q,
num_MC_samples_acq=num_MC_sample_acq
) # if q is 1 use analytic acquisitions
acq_func.to(**tkwargs)
options = {
'batch_limit': 100
} if acq_func_opt_kwargs == {} else acq_func_opt_kwargs
print("Start acquisition function optimization...")
if q == 1:
# use optimize_acq (with LBFGS)
candidate, acq_value = optimize_acqf(acq_function=acq_func,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_initial_samples,
return_best_only=True,
options=options)
else:
candidate, acq_value = optimize_acqf_torch(
acq_function=acq_func,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_initial_samples,
return_best_only=True,
options=options,
)
print(f"Acquired {candidate} with acquisition value {acq_value}")
return candidate.to(device=device)
def update(self, inputs, targets, *args, **kwargs):
inputs = inputs.view(-1, self.input_dim)
targets = targets.view(-1, self.target_dim)
self._raw_inputs = [torch.cat([*self._raw_inputs, inputs], dim=-2)]
self._raw_targets = torch.cat([self._raw_targets, targets], dim=-2)
for i in range(inputs.shape[-2]):
new_x = self.stem(inputs[i].unsqueeze(0))
new_y = targets[i].unsqueeze(0)
_, ranked_models = torch.sort(self._construct_weights(new_x),
dim=0,
descending=True)
num_candidates = math.ceil(len(self.models) / 2)
assignment = None
for model_idx in ranked_models[:num_candidates]:
num_data = self.models[model_idx].train_targets.size(-1)
if num_data >= self.max_data_per_model:
continue
else:
assignment = model_idx.squeeze(-1)
######################
# dummy to init caches
self.models[assignment](new_x)
######################
new_model = self.models[
assignment].condition_on_observations(new_x, new_y)
self.models[assignment] = new_model
self.update_model_caches()
break
if assignment is None:
print("Adding new model")
assignment = torch.tensor((len(self.models), ),
device=new_x.device)
new_model = SingleTaskGP(new_x,
new_y,
covar_module=self.covar_module)
new_model.likelihood.initialize(noise=self.noise)
new_model.eval()
self.models.append(new_model)
self.update_model_caches()
self._assignments = torch.cat([self._assignments, assignment])
self.train()
features = self._refresh_features()
train_dist = self(features)
loss = -self.mll(train_dist, [m.train_targets for m in self.models])
loss.backward()
self.optimizer.step()
gp_loss = stem_loss = loss.item()
return gp_loss, stem_loss
def gp_fit_test(x_train: Tensor,
y_train: Tensor,
error_train: Tensor,
x_test: Tensor,
y_test: Tensor,
error_test: Tensor,
gp_obj_model: SingleTaskGP,
gp_error_model: SingleTaskGP,
tkwargs: Dict[str, Any],
gp_test_folder: str,
obj_out_wp: bool = False,
err_out_wp: bool = False) -> None:
"""
1) Estimates mean test error between predicted and the true objective function values.
2) Estimates mean test error between predicted recon. error by the gp_model and the true recon. error of the vae_model.
:param x_train: normalised points at which the gps were trained
:param y_train: objective value function corresponding to x_train that were used as targets of `gp_obj_model`
:param error_train: reconstruction error value at points x_train that were used as targets of `gp_error_model`
:param x_test: normalised test points
:param y_test: objective value function corresponding to x_test
:param error_test: reconstruction error at test points
:param gp_obj_model: the gp model trained to predict the black box objective function values
:param gp_error_model: the gp model trained to predict reconstruction error
:param tkwargs: dict of type and device
:param gp_test_folder: folder to save test results
:param obj_out_wp: if the `gp_obj_model` was trained with output warping then need to apply the same transform
:param err_out_wp: if the `gp_error_model` was trained with output warping then need to apply the same transform
:return: (Sum_i||true_y_i - pred_y_i||^2 / n_points, Sum_i||true_recon_i - pred_recon_i||^2 / n_points)
"""
do_robust = True if gp_error_model is not None else False
if not os.path.exists(gp_test_folder):
os.mkdir(gp_test_folder)
gp_obj_model.eval()
gp_obj_model.to(tkwargs['device'])
y_train = y_train.view(-1)
if do_robust:
gp_error_model.eval()
gp_error_model.to(tkwargs['device'])
error_train = error_train.view(-1)
with torch.no_grad():
if obj_out_wp:
Y_numpy = y_train.cpu().numpy()
if Y_numpy.min() <= 0:
y_train = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(),
method='yeo-johnson'))
else:
y_train = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(), method='box-cox'))
if y_train.std() < 0.5:
Y_numpy = y_train.numpy()
y_train = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(),
method='yeo-johnson')).to(x_train)
Y_numpy = y_test.cpu().numpy()
if Y_numpy.min() <= 0:
y_test = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(),
method='yeo-johnson'))
else:
y_test = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(), method='box-cox'))
if y_test.std() < 0.5:
Y_numpy = y_test.numpy()
y_test = torch.FloatTensor(
power_transform(Y_numpy / Y_numpy.std(),
method='yeo-johnson')).to(x_test)
y_train = y_train.view(-1).to(**tkwargs)
y_test = y_test.view(-1).to(**tkwargs)
gp_obj_val_model_mse_train = (
gp_obj_model.posterior(x_train).mean.view(-1) -
y_train).pow(2).div(len(y_train))
gp_obj_val_model_mse_test = (
gp_obj_model.posterior(x_test).mean.view(-1) - y_test).pow(2).div(
len(y_test))
torch.save(
gp_obj_val_model_mse_train,
os.path.join(gp_test_folder, 'gp_obj_val_model_mse_train.npz'))
torch.save(gp_obj_val_model_mse_test,
os.path.join(gp_test_folder, 'gp_obj_val_model_test.npz'))
print(
f'GP training fit on objective value: MSE={gp_obj_val_model_mse_train.sum().item():.5f}'
)
print(
f'GP testing fit on objective value: MSE={gp_obj_val_model_mse_test.sum().item():.5f}'
)
if do_robust:
if err_out_wp:
error_train = error_train.view(-1, 1)
R_numpy = error_train.cpu().numpy()
if R_numpy.min() <= 0:
error_train = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='yeo-johnson'))
else:
error_train = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='box-cox'))
if error_train.std() < 0.5:
R_numpy = error_train.numpy()
error_train = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='yeo-johnson')).to(x_train)
R_numpy = error_test.cpu().numpy()
if R_numpy.min() <= 0:
error_test = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='yeo-johnson'))
else:
error_test = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='box-cox'))
if error_test.std() < 0.5:
R_numpy = error_test.numpy()
error_test = torch.FloatTensor(
power_transform(R_numpy / R_numpy.std(),
method='yeo-johnson')).to(x_test)
error_train = error_train.view(-1).to(**tkwargs)
error_test = error_test.view(-1).to(**tkwargs)
pred_recon_train = gp_error_model.posterior(x_train).mean.view(-1)
pred_recon_test = gp_error_model.posterior(x_test).mean.view(-1)
gp_error_model_mse_train = (error_train -
pred_recon_train).pow(2).div(
len(error_train))
gp_error_model_mse_test = (error_test -
pred_recon_test).pow(2).div(
len(error_test))
torch.save(
gp_error_model_mse_train,
os.path.join(gp_test_folder, 'gp_error_model_mse_train.npz'))
torch.save(
gp_error_model_mse_test,
os.path.join(gp_test_folder, 'gp_error_model_mse_test.npz'))
print(
f'GP training fit on reconstruction errors: MSE={gp_error_model_mse_train.sum().item():.5f}'
)
print(
f'GP testing fit on reconstruction errors: MSE={gp_error_model_mse_test.sum().item():.5f}'
)
torch.save(error_test,
os.path.join(gp_test_folder, f"true_rec_err_z.pt"))
torch.save(error_train,
os.path.join(gp_test_folder, f"error_train.pt"))
torch.save(x_train, os.path.join(gp_test_folder, f"train_x.pt"))
torch.save(x_test, os.path.join(gp_test_folder, f"test_x.pt"))
torch.save(y_train, os.path.join(gp_test_folder, f"y_train.pt"))
torch.save(x_test, os.path.join(gp_test_folder, f"X_test.pt"))
torch.save(y_test, os.path.join(gp_test_folder, f"y_test.pt"))
# y plots
plt.hist(y_train.cpu().numpy(),
bins=100,
label='y train',
alpha=0.5,
density=True)
plt.hist(gp_obj_model.posterior(x_train).mean.view(
-1).detach().cpu().numpy(),
bins=100,
label='y pred',
alpha=0.5,
density=True)
plt.legend()
plt.title('Training set')
plt.savefig(os.path.join(gp_test_folder, 'gp_obj_train.pdf'))
plt.close()
plt.hist(gp_obj_val_model_mse_train.detach().cpu().numpy(),
bins=100,
alpha=0.5,
density=True)
plt.title('MSE of gp_obj_val model on training set')
plt.savefig(os.path.join(gp_test_folder, 'gp_obj_train_mse.pdf'))
plt.close()
plt.hist(y_test.cpu().numpy(),
bins=100,
label='y true',
alpha=0.5,
density=True)
plt.hist(gp_obj_model.posterior(x_test).mean.detach().cpu().numpy(),
bins=100,
alpha=0.5,
label='y pred',
density=True)
plt.legend()
plt.title('Validation set')
plt.savefig(os.path.join(gp_test_folder, 'gp_obj_test.pdf'))
plt.close()
plt.hist(gp_obj_val_model_mse_test.detach().cpu().numpy(),
bins=100,
alpha=0.5,
density=True)
plt.title('MSE of gp_obj_val model on validation set')
plt.savefig(os.path.join(gp_test_folder, 'gp_obj_test_mse.pdf'))
plt.close()
if do_robust:
# error plots
plt.hist(error_train.cpu().numpy(),
bins=100,
label='error train',
alpha=0.5,
density=True)
plt.hist(
gp_error_model.posterior(x_train).mean.detach().cpu().numpy(),
bins=100,
label='error pred',
alpha=0.5,
density=True)
plt.legend()
plt.title('Training set')
plt.savefig(os.path.join(gp_test_folder, 'gp_error_train.pdf'))
plt.close()
plt.hist(gp_error_model_mse_train.detach().cpu().numpy(),
bins=100,
alpha=0.5,
density=True)
plt.title('MSE of gp_error model on training set')
plt.savefig(os.path.join(gp_test_folder, 'gp_error_train_mse.pdf'))
plt.close()
plt.hist(error_test.cpu().numpy(),
bins=100,
label='error true',
alpha=0.5,
density=True)
plt.hist(
gp_error_model.posterior(x_test).mean.detach().cpu().numpy(),
bins=100,
alpha=0.5,
label='error pred',
density=True)
plt.legend()
plt.title('Validation set')
plt.savefig(os.path.join(gp_test_folder, 'gp_error_test.pdf'))
plt.close()
plt.hist(gp_error_model_mse_test.detach().cpu().numpy(),
bins=100,
alpha=0.5,
density=True)
plt.title('MSE of gp_error model on validation set')
plt.savefig(os.path.join(gp_test_folder, 'gp_error_test_mse.pdf'))
plt.close()
# y-error plots
y_train_sorted, indices_train = torch.sort(y_train)
error_train_sorted = error_train[indices_train]
gp_y_train_pred_sorted, indices_train_pred = torch.sort(
gp_obj_model.posterior(x_train).mean.view(-1))
gp_r_train_pred_sorted = (gp_error_model.posterior(
x_train).mean.view(-1))[indices_train_pred]
plt.scatter(y_train_sorted.cpu().numpy(),
error_train_sorted.cpu().numpy(),
label='true',
marker='+')
plt.scatter(gp_y_train_pred_sorted.detach().cpu().numpy(),
gp_r_train_pred_sorted.detach().cpu().numpy(),
label='pred',
marker='*')
plt.xlabel('y train targets')
plt.ylabel('recon. error train targets')
plt.title('y_train vs. error_train')
plt.legend()
plt.savefig(
os.path.join(gp_test_folder, 'scatter_obj_error_train.pdf'))
plt.close()
y_test_std_sorted, indices_test = torch.sort(y_test)
error_test_sorted = error_test[indices_test]
gp_y_test_pred_sorted, indices_test_pred = torch.sort(
gp_obj_model.posterior(x_test).mean.view(-1))
gp_r_test_pred_sorted = (gp_error_model.posterior(
x_test).mean.view(-1))[indices_test_pred]
plt.scatter(y_test_std_sorted.cpu().numpy(),
error_test_sorted.cpu().numpy(),
label='true',
marker='+')
plt.scatter(gp_y_test_pred_sorted.detach().cpu().numpy(),
gp_r_test_pred_sorted.detach().cpu().numpy(),
label='pred',
marker='*')
plt.xlabel('y test targets')
plt.ylabel('recon. error test targets')
plt.title('y_test vs. error_test')
plt.legend()
plt.savefig(
os.path.join(gp_test_folder, 'scatter_obj_error_test.pdf'))
plt.close()
# error var plots
error_train_sorted, indices_train_pred = torch.sort(error_train)
# error_train_sorted = error_train
# indices_train_pred = np.arange(len(error_train))
gp_r_train_pred_sorted = gp_error_model.posterior(
x_train).mean[indices_train_pred].view(-1)
gp_r_train_pred_std_sorted = gp_error_model.posterior(
x_train).variance.view(-1).sqrt()[indices_train_pred]
plt.scatter(np.arange(len(indices_train_pred)),
error_train_sorted.cpu().numpy(),
label='err true',
marker='+',
color='C1',
s=15)
plt.errorbar(
np.arange(len(indices_train_pred)),
gp_r_train_pred_sorted.detach().cpu().numpy().flatten(),
yerr=gp_r_train_pred_std_sorted.detach().cpu().numpy().flatten(
),
fmt='*',
alpha=0.05,
label='err pred',
color='C0',
ecolor='C0')
plt.scatter(np.arange(len(indices_train_pred)),
gp_r_train_pred_sorted.detach().cpu().numpy(),
marker='*',
alpha=0.2,
s=10,
color='C0')
# plt.scatter(np.arange(len(indices_train_pred)),
# (gp_r_train_pred_sorted + gp_r_train_pred_std_sorted).detach().cpu().numpy(),
# label='err pred mean+std', marker='.')
# plt.scatter(np.arange(len(indices_train_pred)),
# (gp_r_train_pred_sorted - gp_r_train_pred_std_sorted).detach().cpu().numpy(),
# label='err pred mean-std', marker='.')
plt.legend()
plt.title('error predictions and uncertainty on train set')
plt.savefig(
os.path.join(gp_test_folder, 'gp_error_train_uncertainty.pdf'))
plt.close()
error_test_sorted, indices_test_pred = torch.sort(error_test)
# error_test_sorted = error_test
# indices_test_pred = np.arange(len(error_test_sorted))
gp_r_test_pred_sorted = gp_error_model.posterior(x_test).mean.view(
-1)[indices_test_pred]
gp_r_test_pred_std_sorted = gp_error_model.posterior(
x_test).variance.view(-1).sqrt()[indices_test_pred]
plt.scatter(np.arange(len(indices_test_pred)),
error_test_sorted.cpu().numpy(),
label='err true',
marker='+',
color='C1',
s=15)
plt.errorbar(
np.arange(len(indices_test_pred)),
gp_r_test_pred_sorted.detach().cpu().numpy().flatten(),
yerr=gp_r_test_pred_std_sorted.detach().cpu().numpy().flatten(
),
marker='*',
alpha=0.05,
label='err pred',
color='C0',
ecolor='C0')
plt.scatter(np.arange(len(indices_test_pred)),
gp_r_test_pred_sorted.detach().cpu().numpy().flatten(),
marker='*',
color='C0',
alpha=0.2,
s=10)
# plt.scatter(np.arange(len(indices_test_pred)),
# (gp_r_test_pred_sorted + gp_r_test_pred_std_sorted).detach().cpu().numpy(),
# label='err pred mean+std', marker='.')
# plt.scatter(np.arange(len(indices_test_pred)),
# (gp_r_test_pred_sorted - gp_r_test_pred_std_sorted).detach().cpu().numpy(),
# label='err pred mean-std', marker='.')
plt.legend()
plt.title('error predictions and uncertainty on test set')
plt.savefig(
os.path.join(gp_test_folder, 'gp_error_test_uncertainty.pdf'))
plt.close()
# y var plots
y_train_std_sorted, indices_train = torch.sort(y_train)
gp_y_train_pred_sorted = gp_obj_model.posterior(
x_train).mean[indices_train].view(-1)
gp_y_train_pred_std_sorted = gp_obj_model.posterior(
x_train).variance.sqrt()[indices_train].view(-1)
plt.scatter(np.arange(len(indices_train)),
y_train_std_sorted.cpu().numpy(),
label='y true',
marker='+',
color='C1',
s=15)
plt.scatter(np.arange(len(indices_train)),
gp_y_train_pred_sorted.detach().cpu().numpy(),
marker='*',
alpha=0.2,
s=10,
color='C0')
plt.errorbar(
np.arange(len(indices_train)),
gp_y_train_pred_sorted.detach().cpu().numpy().flatten(),
yerr=gp_y_train_pred_std_sorted.detach().cpu().numpy().flatten(),
fmt='*',
alpha=0.05,
label='y pred',
color='C0',
ecolor='C0')
# plt.scatter(np.arange(len(indices_train_pred)),
# (gp_y_train_pred_sorted+gp_y_train_pred_std_sorted).detach().cpu().numpy(),
# label='y pred mean+std', marker='.')
# plt.scatter(np.arange(len(indices_train_pred)),
# (gp_y_train_pred_sorted-gp_y_train_pred_std_sorted).detach().cpu().numpy(),
# label='y pred mean-std', marker='.')
plt.legend()
plt.title('y predictions and uncertainty on train set')
plt.savefig(
os.path.join(gp_test_folder, 'gp_obj_val_train_uncertainty.pdf'))
plt.close()
y_test_std_sorted, indices_test = torch.sort(y_test)
gp_y_test_pred_sorted = gp_obj_model.posterior(x_test).mean.view(
-1)[indices_test]
gp_y_test_pred_std_sorted = gp_obj_model.posterior(
x_test).variance.view(-1).sqrt()[indices_test]
plt.scatter(np.arange(len(indices_test)),
y_test_std_sorted.cpu().numpy(),
label='y true',
marker='+',
color='C1',
s=15)
plt.errorbar(
np.arange(len(indices_test)),
gp_y_test_pred_sorted.detach().cpu().numpy().flatten(),
yerr=gp_y_test_pred_std_sorted.detach().cpu().numpy().flatten(),
fmt='*',
alpha=0.05,
label='y pred',
color='C0',
ecolor='C0')
plt.scatter(np.arange(len(indices_test)),
gp_y_test_pred_sorted.detach().cpu().numpy(),
marker='*',
alpha=0.2,
s=10,
color='C0')
# plt.scatter(np.arange(len(indices_test_pred)),
# (gp_y_test_pred_sorted + gp_y_test_pred_std_sorted).detach().cpu().numpy(),
# label='y pred mean+std', marker='.')
# plt.scatter(np.arange(len(indices_test_pred)),
# (gp_y_test_pred_sorted - gp_y_test_pred_std_sorted).detach().cpu().numpy(),
# label='y pred mean-std', marker='.')
plt.legend()
plt.title('y predictions and uncertainty on test set')
plt.savefig(
os.path.join(gp_test_folder, 'gp_obj_val_test_uncertainty.pdf'))
plt.close()
def gp_torch_train(train_x: Tensor,
train_y: Tensor,
n_inducing_points: int,
tkwargs: Dict[str, Any],
init,
scale: bool,
covar_name: str,
gp_file: Optional[str],
save_file: str,
input_wp: bool,
outcome_transform: Optional[OutcomeTransform] = None,
options: Dict[str, Any] = None) -> SingleTaskGP:
assert train_y.ndim > 1, train_y.shape
assert gp_file or init, (gp_file, init)
likelihood = gpytorch.likelihoods.GaussianLikelihood()
if init:
# build hyp
print("Initialize GP hparams...")
print("Doing Kmeans init...")
assert n_inducing_points > 0, n_inducing_points
kmeans = MiniBatchKMeans(n_clusters=n_inducing_points,
batch_size=min(10000, train_x.shape[0]),
n_init=25)
start_time = time.time()
kmeans.fit(train_x.cpu().numpy())
end_time = time.time()
print(f"K means took {end_time - start_time:.1f}s to finish...")
inducing_points = torch.from_numpy(kmeans.cluster_centers_.copy())
output_scale = None
if scale:
output_scale = train_y.var().item()
lscales = torch.empty(1, train_x.shape[1])
for i in range(train_x.shape[1]):
lscales[0, i] = torch.pdist(train_x[:, i].view(
-1, 1)).median().clamp(min=0.01)
base_covar_module = query_covar(covar_name=covar_name,
scale=scale,
outputscale=output_scale,
lscales=lscales)
covar_module = InducingPointKernel(base_covar_module,
inducing_points=inducing_points,
likelihood=likelihood)
input_warp_tf = None
if input_wp:
# Apply input warping
# initialize input_warping transformation
input_warp_tf = CustomWarp(
indices=list(range(train_x.shape[-1])),
# use a prior with median at 1.
# when a=1 and b=1, the Kumaraswamy CDF is the identity function
concentration1_prior=LogNormalPrior(0.0, 0.75**0.5),
concentration0_prior=LogNormalPrior(0.0, 0.75**0.5),
)
model = SingleTaskGP(train_x,
train_y,
covar_module=covar_module,
likelihood=likelihood,
input_transform=input_warp_tf,
outcome_transform=outcome_transform)
else:
# load model
output_scale = 1 # will be overwritten when loading model
lscales = torch.ones(
train_x.shape[1]) # will be overwritten when loading model
base_covar_module = query_covar(covar_name=covar_name,
scale=scale,
outputscale=output_scale,
lscales=lscales)
covar_module = InducingPointKernel(base_covar_module,
inducing_points=torch.empty(
n_inducing_points,
train_x.shape[1]),
likelihood=likelihood)
input_warp_tf = None
if input_wp:
# Apply input warping
# initialize input_warping transformation
input_warp_tf = Warp(
indices=list(range(train_x.shape[-1])),
# use a prior with median at 1.
# when a=1 and b=1, the Kumaraswamy CDF is the identity function
concentration1_prior=LogNormalPrior(0.0, 0.75**0.5),
concentration0_prior=LogNormalPrior(0.0, 0.75**0.5),
)
model = SingleTaskGP(train_x,
train_y,
covar_module=covar_module,
likelihood=likelihood,
input_transform=input_warp_tf,
outcome_transform=outcome_transform)
print("Loading GP from file")
state_dict = torch.load(gp_file)
model.load_state_dict(state_dict)
print("GP regression")
start_time = time.time()
model.to(**tkwargs)
model.train()
mll = ExactMarginalLogLikelihood(model.likelihood, model)
# set approx_mll to False since we are using an exact marginal log likelihood
# fit_gpytorch_model(mll, optimizer=fit_gpytorch_torch, approx_mll=False, options=options)
fit_gpytorch_torch(mll,
options=options,
approx_mll=False,
clip_by_value=True if input_wp else False,
clip_value=10.0)
end_time = time.time()
print(f"Regression took {end_time - start_time:.1f}s to finish...")
print("Save GP model...")
torch.save(model.state_dict(), save_file)
print("Done training of GP.")
model.eval()
return model
def render_singletask_gp(
ax: [plt.Axes, Axes3D, Sequence[plt.Axes]],
data_x: to.Tensor,
data_y: to.Tensor,
idcs_sel: list,
data_x_min: to.Tensor = None,
data_x_max: to.Tensor = None,
x_label: str = '',
y_label: str = '',
z_label: str = '',
min_gp_obsnoise: float = None,
resolution: int = 201,
num_stds: int = 2,
alpha: float = 0.3,
color: chr = None,
curve_label: str = 'mean',
heatmap_cmap: colors.Colormap = None,
show_legend_posterior: bool = True,
show_legend_std: bool = False,
show_legend_data: bool = True,
legend_data_cmap: colors.Colormap = None,
colorbar_label: str = None,
title: str = None,
render3D: bool = True,
) -> plt.Figure:
"""
Fit the GP posterior to the input data and plot the mean and std as well as the data points.
There are 3 options: 1D plot (infered by data dimensions), 2D plot
.. note::
If you want to have a tight layout, it is best to pass axes of a figure with `tight_layout=True` or
`constrained_layout=True`.
:param ax: axis of the figure to plot on, only in case of a 2-dim heat map plot provide 2 axis
:param data_x: data to plot on the x-axis
:param data_y: data to process and plot on the y-axis
:param idcs_sel: selected indices of the input data
:param data_x_min: explicit minimum value for the evaluation grid, by default this value is extracted from `data_x`
:param data_x_max: explicit maximum value for the evaluation grid, by default this value is extracted from `data_x`
:param x_label: label for x-axis
:param y_label: label for y-axis
:param z_label: label for z-axis (3D plot only)
:param min_gp_obsnoise: set a minimal noise value (normalized) for the GP, if `None` the GP has no measurement noise
:param resolution: number of samples for the input (corresponds to x-axis resolution of the plot)
:param num_stds: number of standard deviations to plot around the mean
:param alpha: transparency (alpha-value) for the std area
:param color: color (e.g. 'k' for black), `None` invokes the default behavior
:param curve_label: label for the mean curve (1D plot only)
:param heatmap_cmap: color map forwarded to `render_heatmap()` (2D plot only), `None` to use Pyrado's default
:param show_legend_posterior: flag if the legend entry for the posterior should be printed (affects mean and std)
:param show_legend_std: flag if a legend entry for the std area should be printed
:param show_legend_data: flag if a legend entry for the individual data points should be printed
:param legend_data_cmap: color map for the sampled points, default is 'binary'
:param colorbar_label: label for the color bar (2D plot only)
:param title: title displayed above the figure, set to `None` to suppress the title
:param render3D: use 3D rendering if possible
:return: handle to the resulting figure
"""
if data_x.ndim != 2:
raise pyrado.ShapeErr(
msg=
"The GP's input data needs to be of shape num_samples x dim_input!"
)
data_x = data_x[:, idcs_sel] # forget the rest
dim_x = data_x.shape[1] # samples are along axis 0
if data_y.ndim != 2:
raise pyrado.ShapeErr(given=data_y,
expected_match=to.Size([data_x.shape[0], 1]))
if legend_data_cmap is None:
legend_data_cmap = plt.get_cmap('binary')
# Project to normalized input and standardized output
if data_x_min is None or data_x_max is None:
data_x_min, data_x_max = to.min(data_x, dim=0)[0], to.max(data_x,
dim=0)[0]
data_y_mean, data_y_std = to.mean(data_y, dim=0), to.std(data_y, dim=0)
data_x = (data_x - data_x_min) / (data_x_max - data_x_min)
data_y = (data_y - data_y_mean) / data_y_std
# Create and fit the GP model
gp = SingleTaskGP(data_x, data_y)
if min_gp_obsnoise is not None:
gp.likelihood.noise_covar.register_constraint(
'raw_noise', GreaterThan(min_gp_obsnoise))
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.train()
fit_gpytorch_model(mll)
print_cbt('Fitted the SingleTaskGP.', 'g')
argmax_pmean_norm, argmax_pmean_val_stdzed = optimize_acqf(
acq_function=PosteriorMean(gp),
bounds=to.stack([to.zeros(dim_x), to.ones(dim_x)]),
q=1,
num_restarts=500,
raw_samples=1000)
# Project back
argmax_posterior = argmax_pmean_norm * (data_x_max -
data_x_min) + data_x_min
argmax_pmean_val = argmax_pmean_val_stdzed * data_y_std + data_y_mean
print_cbt(
f'Converged to argmax of the posterior mean: {argmax_posterior.numpy()}',
'g')
mll.eval()
gp.eval()
if dim_x == 1:
# Evaluation grid
x_grid = np.linspace(min(data_x),
max(data_x),
resolution,
endpoint=True).flatten()
x_grid = to.from_numpy(x_grid)
# Mean and standard deviation of the surrogate model
posterior = gp.posterior(x_grid)
mean = posterior.mean.detach().flatten()
std = to.sqrt(posterior.variance.detach()).flatten()
# Project back from normalized input and standardized output
x_grid = x_grid * (data_x_max - data_x_min) + data_x_min
data_x = data_x * (data_x_max - data_x_min) + data_x_min
data_y = data_y * data_y_std + data_y_mean
mean = mean * data_y_std + data_y_mean
std *= data_y_std # double-checked with posterior.mvn.confidence_region()
# Plot the curve
plt.fill_between(x_grid.numpy(),
mean.numpy() - num_stds * std.numpy(),
mean.numpy() + num_stds * std.numpy(),
alpha=alpha,
color=color)
ax.plot(x_grid.numpy(), mean.numpy(), color=color)
# Plot the queried data points
scat_plot = ax.scatter(data_x.numpy().flatten(),
data_y.numpy().flatten(),
marker='o',
c=np.arange(data_x.shape[0], dtype=np.int),
cmap=legend_data_cmap)
if show_legend_data:
scat_legend = ax.legend(
*scat_plot.legend_elements(fmt='{x:.0f}'), # integer formatter
bbox_to_anchor=(0., 1.1, 1., -0.1),
title='query points',
ncol=data_x.shape[0],
loc='upper center',
mode='expand',
borderaxespad=0.,
handletextpad=-0.5)
ax.add_artist(scat_legend)
# Increase vertical space between subplots when printing the data labels
# plt.tight_layout(pad=2.) # ignore argument
# plt.subplots_adjust(hspace=0.6)
# Plot the argmax of the posterior mean
# ax.scatter(argmax_posterior.item(), argmax_pmean_val, c='darkorange', marker='o', s=60, label='argmax')
ax.axvline(argmax_posterior.item(),
c='darkorange',
lw=1.5,
label='argmax')
if show_legend_posterior:
ax.add_artist(ax.legend(loc='lower right'))
elif dim_x == 2:
# Create mesh grid matrices from x and y vectors
# x0_grid = to.linspace(min(data_x[:, 0]), max(data_x[:, 0]), resolution)
# x1_grid = to.linspace(min(data_x[:, 1]), max(data_x[:, 1]), resolution)
x0_grid = to.linspace(0, 1, resolution)
x1_grid = to.linspace(0, 1, resolution)
x0_mesh, x1_mesh = to.meshgrid([x0_grid, x1_grid])
x0_mesh, x1_mesh = x0_mesh.t(), x1_mesh.t(
) # transpose not necessary but makes identical mesh as np.meshgrid
# Mean and standard deviation of the surrogate model
x_test = to.stack([
x0_mesh.reshape(resolution**2, 1),
x1_mesh.reshape(resolution**2, 1)
], -1).squeeze(1)
posterior = gp.posterior(
x_test) # identical to gp.likelihood(gp(x_test))
mean = posterior.mean.detach().reshape(resolution, resolution)
std = to.sqrt(posterior.variance.detach()).reshape(
resolution, resolution)
# Project back from normalized input and standardized output
data_x = data_x * (data_x_max - data_x_min) + data_x_min
data_y = data_y * data_y_std + data_y_mean
mean_raw = mean * data_y_std + data_y_mean
std_raw = std * data_y_std
if render3D:
# Project back from normalized input and standardized output (custom for 3D)
x0_mesh = x0_mesh * (data_x_max[0] - data_x_min[0]) + data_x_min[0]
x1_mesh = x1_mesh * (data_x_max[1] - data_x_min[1]) + data_x_min[1]
lower = mean_raw - num_stds * std_raw
upper = mean_raw + num_stds * std_raw
# Plot a 2D surface in 3D
ax.plot_surface(x0_mesh.numpy(), x1_mesh.numpy(), mean_raw.numpy())
ax.plot_surface(x0_mesh.numpy(),
x1_mesh.numpy(),
lower.numpy(),
color='r',
alpha=alpha)
ax.plot_surface(x0_mesh.numpy(),
x1_mesh.numpy(),
upper.numpy(),
color='r',
alpha=alpha)
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.set_zlabel(z_label)
# Plot the queried data points
scat_plot = ax.scatter(data_x[:, 0].numpy(),
data_x[:, 1].numpy(),
data_y.numpy(),
marker='o',
c=np.arange(data_x.shape[0], dtype=np.int),
cmap=legend_data_cmap)
if show_legend_data:
scat_legend = ax.legend(
*scat_plot.legend_elements(
fmt='{x:.0f}'), # integer formatter
bbox_to_anchor=(0.05, 1.1, 0.95, -0.1),
loc='upper center',
ncol=data_x.shape[0],
mode='expand',
borderaxespad=0.,
handletextpad=-0.5)
ax.add_artist(scat_legend)
# Plot the argmax of the posterior mean
x, y = argmax_posterior[0, 0], argmax_posterior[0, 1]
ax.scatter(x,
y,
argmax_pmean_val,
c='darkorange',
marker='*',
s=60)
# ax.plot((x, x), (y, y), (data_y.min(), data_y.max()), c='k', ls='--', lw=1.5)
else:
if not len(ax) == 4:
raise pyrado.ShapeErr(
msg='Provide 4 axes! 2 heat maps and 2 color bars.')
# Project back normalized input and standardized output (custom for 2D)
x0_grid_raw = x0_grid * (data_x_max[0] -
data_x_min[0]) + data_x_min[0]
x1_grid_raw = x1_grid * (data_x_max[1] -
data_x_min[1]) + data_x_min[1]
# Plot a 2D image
df_mean = pd.DataFrame(mean_raw.numpy(),
columns=x0_grid_raw.numpy(),
index=x1_grid_raw.numpy())
render_heatmap(df_mean,
ax_hm=ax[0],
ax_cb=ax[1],
x_label=x_label,
y_label=y_label,
annotate=False,
fig_canvas_title='Returns',
tick_label_prec=2,
add_sep_colorbar=True,
cmap=heatmap_cmap,
colorbar_label=colorbar_label,
num_major_ticks_hm=3,
num_major_ticks_cb=2,
colorbar_orientation='horizontal')
df_std = pd.DataFrame(std_raw.numpy(),
columns=x0_grid_raw.numpy(),
index=x1_grid_raw.numpy())
render_heatmap(
df_std,
ax_hm=ax[2],
ax_cb=ax[3],
x_label=x_label,
y_label=y_label,
annotate=False,
fig_canvas_title='Standard Deviations',
tick_label_prec=2,
add_sep_colorbar=True,
cmap=heatmap_cmap,
colorbar_label=colorbar_label,
num_major_ticks_hm=3,
num_major_ticks_cb=2,
colorbar_orientation='horizontal',
norm=colors.Normalize()) # explicitly instantiate a new norm
# Plot the queried data points
for i in [0, 2]:
scat_plot = ax[i].scatter(data_x[:, 0].numpy(),
data_x[:, 1].numpy(),
marker='o',
s=15,
c=np.arange(data_x.shape[0],
dtype=np.int),
cmap=legend_data_cmap)
if show_legend_data:
scat_legend = ax[i].legend(
*scat_plot.legend_elements(
fmt='{x:.0f}'), # integer formatter
bbox_to_anchor=(0., 1.1, 1., 0.05),
loc='upper center',
ncol=data_x.shape[0],
mode='expand',
borderaxespad=0.,
handletextpad=-0.5)
ax[i].add_artist(scat_legend)
# Plot the argmax of the posterior mean
ax[0].scatter(argmax_posterior[0, 0],
argmax_posterior[0, 1],
c='darkorange',
marker='*',
s=60) # steelblue
ax[2].scatter(argmax_posterior[0, 0],
argmax_posterior[0, 1],
c='darkorange',
marker='*',
s=60) # steelblue
# ax[0].axvline(argmax_posterior[0, 0], c='w', ls='--', lw=1.5)
# ax[0].axhline(argmax_posterior[0, 1], c='w', ls='--', lw=1.5)
# ax[2].axvline(argmax_posterior[0, 0], c='w', ls='--', lw=1.5)
# ax[2].axhline(argmax_posterior[0, 1], c='w', ls='--', lw=1.5)
else:
raise pyrado.ValueErr(msg='Can only plot 1-dim or 2-dim data!')
return plt.gcf()
bounds=torch.Tensor([[-1], [1]]),
q=1,
num_restarts=5,
raw_samples=20,
)
candidate_y = obj_noisy(candidate_x)
train_x = torch.cat([train_x, candidate_x])
train_y = torch.cat([train_y, candidate_y])
model = model.condition_on_observations(X=candidate_x, Y=candidate_y)
# Train GP...
mll = ExactMarginalLogLikelihood(model.likelihood, model)
fit_gpytorch_model(mll)
# Plotting...
model.eval()
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
plt.title(f"Bayesian Opt. without derivatives, Iteration {it}")
test_x = torch.linspace(-1, 1, steps=100)
with torch.no_grad():
posterior = model.posterior(test_x)
# these are 2 std devs from mean
lower, upper = posterior.mvn.confidence_region()
ax.plot(test_x.cpu().numpy(),
obj(test_x).cpu().numpy(),
'r--',
label="true, noiseless objective")
ax.plot(train_x.cpu().numpy(), train_y.cpu().numpy(), 'k*', alpha=0.1, label="observations")
