def test_cache_root_decomposition(self):
tkwargs = {"device": self.device}
for dtype in (torch.float, torch.double):
tkwargs["dtype"] = dtype
# test mt-mvn
train_x = torch.rand(2, 1, **tkwargs)
train_y = torch.rand(2, 2, **tkwargs)
test_x = torch.rand(2, 1, **tkwargs)
model = SingleTaskGP(train_x, train_y)
sampler = IIDNormalSampler(1)
with torch.no_grad():
posterior = model.posterior(test_x)
acqf = DummyCachedCholeskyAcqf(
model=model,
sampler=sampler,
objective=GenericMCObjective(lambda Y: Y[..., 0]),
)
baseline_L = torch.eye(2, **tkwargs)
with mock.patch(
EXTRACT_BATCH_COVAR_PATH,
wraps=extract_batch_covar) as mock_extract_batch_covar:
with mock.patch(CHOLESKY_PATH,
return_value=baseline_L) as mock_cholesky:
acqf._cache_root_decomposition(posterior=posterior)
mock_extract_batch_covar.assert_called_once_with(
posterior.mvn)
mock_cholesky.assert_called_once()
# test mvn
model = SingleTaskGP(train_x, train_y[:, :1])
with torch.no_grad():
posterior = model.posterior(test_x)
with mock.patch(
EXTRACT_BATCH_COVAR_PATH) as mock_extract_batch_covar:
with mock.patch(CHOLESKY_PATH,
return_value=baseline_L) as mock_cholesky:
acqf._cache_root_decomposition(posterior=posterior)
mock_extract_batch_covar.assert_not_called()
mock_cholesky.assert_called_once()
self.assertTrue(torch.equal(acqf._baseline_L, baseline_L))
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()
import torch from botorch.test_functions import Branin from botorch.models import SingleTaskGP from botorch.fit import fit_gpytorch_model from botorch.models.transforms import Standardize from gpytorch.mlls import ExactMarginalLogLikelihood from parametric_bandit.discrete_KG import DiscreteKGAlg torch.manual_seed(0) # generate input n = 10 noise_std = 0.1 function = Branin(noise_std=0.1) dim = function.dim train_X = torch.rand((n, dim)) train_Y = function(train_X).unsqueeze(-1) # fit model gp = SingleTaskGP(train_X, train_Y, outcome_transform=Standardize(m=1)) mll = ExactMarginalLogLikelihood(gp.likelihood, gp) fit_gpytorch_model(mll) # get mu and Sigma mu = gp.posterior(train_X).mean Sigma = gp.posterior(train_X).mvn.covariance_matrix # initiate the algorithm for testing dkg = DiscreteKGAlg(M=n, error=noise_std**2, mu_0=mu, Sigma_0=Sigma) print(dkg.find_maximizer())
def test_cache_root(self):
sample_cached_path = (
"botorch.acquisition.cached_cholesky.sample_cached_cholesky")
raw_state_dict = {
"likelihood.noise_covar.raw_noise":
torch.tensor([[0.0895], [0.2594]], dtype=torch.float64),
"mean_module.constant":
torch.tensor([[-0.4545], [-0.1285]], dtype=torch.float64),
"covar_module.raw_outputscale":
torch.tensor([1.4876, 1.4897], dtype=torch.float64),
"covar_module.base_kernel.raw_lengthscale":
torch.tensor([[[-0.7202, -0.2868]], [[-0.8794, -1.2877]]],
dtype=torch.float64),
}
# test batched models (e.g. for MCMC)
for train_batch_shape, m, dtype in product(
(torch.Size([]), torch.Size([3])), (1, 2),
(torch.float, torch.double)):
state_dict = deepcopy(raw_state_dict)
for k, v in state_dict.items():
if m == 1:
v = v[0]
if len(train_batch_shape) > 0:
v = v.unsqueeze(0).expand(*train_batch_shape, *v.shape)
state_dict[k] = v
tkwargs = {"device": self.device, "dtype": dtype}
if m == 2:
objective = GenericMCObjective(lambda Y, X: Y.sum(dim=-1))
else:
objective = None
for k, v in state_dict.items():
state_dict[k] = v.to(**tkwargs)
all_close_kwargs = ({
"atol": 1e-1,
"rtol": 0.0,
} if dtype == torch.float else {
"atol": 1e-4,
"rtol": 0.0
})
torch.manual_seed(1234)
train_X = torch.rand(*train_batch_shape, 3, 2, **tkwargs)
train_Y = (
torch.sin(train_X * 2 * pi) +
torch.randn(*train_batch_shape, 3, 2, **tkwargs))[..., :m]
train_Y = standardize(train_Y)
model = SingleTaskGP(
train_X,
train_Y,
)
if len(train_batch_shape) > 0:
X_baseline = train_X[0]
else:
X_baseline = train_X
model.load_state_dict(state_dict, strict=False)
# test sampler with collapse_batch_dims=False
sampler = IIDNormalSampler(5, seed=0, collapse_batch_dims=False)
with self.assertRaises(UnsupportedError):
qNoisyExpectedImprovement(
model=model,
X_baseline=X_baseline,
sampler=sampler,
objective=objective,
prune_baseline=False,
cache_root=True,
)
sampler = IIDNormalSampler(5, seed=0)
torch.manual_seed(0)
acqf = qNoisyExpectedImprovement(
model=model,
X_baseline=X_baseline,
sampler=sampler,
objective=objective,
prune_baseline=False,
cache_root=True,
)
orig_base_samples = acqf.base_sampler.base_samples.detach().clone()
sampler2 = IIDNormalSampler(5, seed=0)
sampler2.base_samples = orig_base_samples
torch.manual_seed(0)
acqf_no_cache = qNoisyExpectedImprovement(
model=model,
X_baseline=X_baseline,
sampler=sampler2,
objective=objective,
prune_baseline=False,
cache_root=False,
)
for q, batch_shape in product(
(1, 3), (torch.Size([]), torch.Size([3]), torch.Size([4, 3]))):
test_X = (0.3 +
0.05 * torch.randn(*batch_shape, q, 2, **tkwargs)
).requires_grad_(True)
with mock.patch(
sample_cached_path,
wraps=sample_cached_cholesky) as mock_sample_cached:
torch.manual_seed(0)
val = acqf(test_X)
mock_sample_cached.assert_called_once()
val.sum().backward()
base_samples = acqf.sampler.base_samples.detach().clone()
X_grad = test_X.grad.clone()
test_X2 = test_X.detach().clone().requires_grad_(True)
acqf_no_cache.sampler.base_samples = base_samples
with mock.patch(
sample_cached_path,
wraps=sample_cached_cholesky) as mock_sample_cached:
torch.manual_seed(0)
val2 = acqf_no_cache(test_X2)
mock_sample_cached.assert_not_called()
self.assertTrue(torch.allclose(val, val2, **all_close_kwargs))
val2.sum().backward()
self.assertTrue(
torch.allclose(X_grad, test_X2.grad, **all_close_kwargs))
# test we fall back to standard sampling for
# ill-conditioned covariances
acqf._baseline_L = torch.zeros_like(acqf._baseline_L)
with warnings.catch_warnings(
record=True) as ws, settings.debug(True):
with torch.no_grad():
acqf(test_X)
self.assertEqual(len(ws), 1)
self.assertTrue(issubclass(ws[-1].category, BotorchWarning))
# test w/ posterior transform
X_baseline = torch.rand(2, 1)
model = SingleTaskGP(X_baseline, torch.randn(2, 1))
pt = ScalarizedPosteriorTransform(weights=torch.tensor([-1]))
with mock.patch.object(
qNoisyExpectedImprovement,
"_cache_root_decomposition",
) as mock_cache_root:
acqf = qNoisyExpectedImprovement(
model=model,
X_baseline=X_baseline,
sampler=IIDNormalSampler(1),
posterior_transform=pt,
prune_baseline=False,
cache_root=True,
)
tf_post = model.posterior(X_baseline, posterior_transform=pt)
self.assertTrue(
torch.allclose(
tf_post.mean,
mock_cache_root.call_args[-1]["posterior"].mean))
y_new_raw = noisy_nonlin_fcn(x_new_raw, noise_std=noise_std)
# Plot the model
fig = plt.figure(figsize=(8, 6))
gs = fig.add_gridspec(2, 1, height_ratios=[2, 1])
ax_gp = fig.add_subplot(gs[0])
ax_acq = fig.add_subplot(gs[1])
X_test = to.linspace(x_min, x_max, 501)
X_test_raw = to.linspace(x_min_raw, x_max_raw, 501)
with to.no_grad():
# Get the observations
y_test_raw = noisy_nonlin_fcn(X_test_raw, noise_std=noise_std)
# Get the posterior
posterior = gp.posterior(X_test)
mean = posterior.mean
mean_raw = mean * y_test_raw.std() + y_test_raw.mean()
lower, upper = posterior.mvn.confidence_region()
lower = lower * y_test_raw.std() + y_test_raw.mean()
upper = upper * y_test_raw.std() + y_test_raw.mean()
ax_gp.plot(X_test_raw.numpy(),
y_test_raw.numpy(),
"k--",
label="f(x)")
ax_gp.plot(X_test_raw.numpy(),
mean_raw.numpy(),
"b-",
lw=2,
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()
class Experiment:
"""
The class for running experiments
"""
# dict of expected attributes and default values
attr_list = {
"dim_w": 1,
"num_fantasies": 10,
"num_restarts": 20,
"raw_multiplier": 25,
"alpha": 0.7,
"q": 1,
"num_repetitions": 10,
"verbose": False,
"maxiter": 1000,
"CVaR": False,
"random_sampling": False,
"expectation": False,
"dtype": torch.float32,
"device": torch.device("cpu"),
"apx": True,
"apx_cvar": False,
"tts_apx_cvar": False,
"disc": True,
"tts_frequency": 10,
"num_inner_restarts": 10,
"inner_raw_multiplier": 5,
"weights": None,
"fix_samples": True,
"one_shot": False,
"low_fantasies": 4,
"random_w": False,
}
def __init__(self, function: str, **kwargs) -> None:
"""
The experiment settings:
:param function: The problem function to be used.
:param noise_std: standard deviation of the function evaluation noise.
:param dim_w: Dimension of the w component.
:param num_samples: Number of samples of w to be used to evaluate C/VaR.
:param w_samples: option to explicitly specify the samples. If given,
num_samples is ignored. One of these is necessary!
:param num_fantasies: Number of fantasy models to construct in evaluating rhoKG.
:param num_restarts: Number of random restarts for optimization of rhoKG.
:param raw_multiplier: Raw_samples = num_restarts * raw_multiplier
:param alpha: The risk level of C/VaR.
:param q: Number of parallel solutions to evaluate. Think qKG.
:param num_repetitions: Number of posterior samples used for E[rho[F]]
:param verbose: Print more stuff, such as current best value.
:param maxiter: (Maximum) number of iterations allowed for L-BFGS-B algorithm.
:param CVaR: If true, use CVaR instead of VaR, i.e. CVaRKG. The default is VaR.
:param random_sampling: If true, we will use random sampling - no KG.
:param expectation: If true, we are running BQO optimization.
:param dtype: The tensor dtype for the experiment
:param device: The device to use. Defaults to CPU.
:param apx: If True, the rhoKGapx algorithm is used.
:param apx_cvar: If True, we use ApxCVaRKG. Overwrites other options!
:param tts_apx_cvar: If True, we use TTSApxCVaRKG. Overwrites other options!
:param disc: If True, the optimization of acqf is done with w restricted to
the set w_samples
:param tts_frequency: The frequency of two-time-scale optimization.
If 1, we do normal nested optimization. Default is 1.
:param num_inner_restarts: Inner restarts for nested optimization
:param inner_raw_multiplier: raw multipler for nested optimization
:param weights: If w_samples are not uniformly distributed, these are the sample
weights, summing up to 1, i.e. probability mass function.
A 1-dim tensor of size num_samples
:param fix_samples: When W is continuous, this determines whether the samples
are redrawn at each call to rhoKG or fixed to a random realization.
If w_samples are specified, this gets overwritten.
:param one_shot: Uses one-shot optimization.
DO NOT USE unless you know what you're doing.
:param low_fantasies: see AbsKG.change_num_fantasies for details. This reduces
the number of fantasies used during raw sample evaluation to reduce the
computational cost.
:param random_w: If this is True, the w component of the candidate is fixed to
a random realization instead of being optimized. This is only for
presenting a comparison in the paper, and should not be used.
"""
if "seed" in kwargs.keys():
warnings.warn("Seed should be set outside. It will be ignored!")
self.function = function_picker(
function,
noise_std=kwargs.get("noise_std"),
negate=getattr(kwargs, "negate", False),
)
self.dim = self.function.dim
# read the attributes with default values
# set the defaults first, then overwrite.
# this lets us store everything passed with kwargs
for key in self.attr_list.keys():
setattr(self, key, self.attr_list[key])
for key in kwargs.keys():
setattr(self, key, kwargs[key])
self.dim_x = self.dim - self.dim_w
if kwargs.get("w_samples") is not None:
self.w_samples = (
kwargs["w_samples"]
.reshape(-1, self.dim_w)
.to(dtype=self.dtype, device=self.device)
)
self.num_samples = self.w_samples.size(0)
self.fixed_samples = True
elif "num_samples" in kwargs.keys():
self.num_samples = kwargs["num_samples"]
self.w_samples = None
warnings.warn("w_samples is None and will be randomized at each iteration")
else:
raise ValueError("Either num_samples or w_samples must be specified!")
if self.expectation:
self.num_repetitions = 0
if self.weights is not None:
self.weights = self.weights.to(self.w_samples)
self.X = torch.empty(0, self.dim).to(dtype=self.dtype, device=self.device)
self.Y = torch.empty(0, 1).to(dtype=self.dtype, device=self.device)
self.model = None
self.low_fantasies = kwargs.get("low_fantasies", None)
if self.apx_cvar and self.tts_apx_cvar:
raise ValueError(
"apx_cvar and tts_apx_cvar cannot be true at the same time!"
)
if self.tts_apx_cvar:
inner_optimizer = InnerApxCVaROptimizer
else:
inner_optimizer = InnerOptimizer
self.inner_optimizer = inner_optimizer(
num_restarts=self.num_inner_restarts,
raw_multiplier=self.inner_raw_multiplier,
dim_x=self.dim_x,
maxiter=self.maxiter,
inequality_constraints=self.function.inequality_constraints,
dtype=self.dtype,
device=self.device,
)
if self.apx_cvar:
optimizer = ApxCVaROptimizer
elif self.one_shot:
optimizer = OneShotOptimizer
else:
optimizer = Optimizer
self.optimizer = optimizer(
num_restarts=self.num_restarts,
raw_multiplier=self.raw_multiplier,
num_fantasies=self.num_fantasies,
dim=self.dim,
dim_x=self.dim_x,
q=self.q,
maxiter=self.maxiter,
inequality_constraints=self.function.inequality_constraints,
low_fantasies=self.low_fantasies,
dtype=self.dtype,
device=self.device,
)
if self.fix_samples:
self.fixed_samples = self.w_samples
else:
self.fixed_samples = None
self.passed = False # error handling
self.fit_count = 0
def change_dtype_device(
self, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None
) -> None:
r"""
This changes the dtype and device of all experiment tensors, and refits the GP
model.
:param dtype: The torch.dtype to use
:param device: The device to use
"""
if dtype is None and device is None:
return None
dtype = dtype or self.dtype
device = device or self.device
for key, value in vars(self).items():
if isinstance(value, Tensor):
setattr(self, key, value.to(dtype=dtype, device=device))
self.dtype = dtype
self.device = torch.device(device)
if self.X.numel():
self.fit_gp()
def initialize_gp(self, init_samples: Tensor = None, n: int = None) -> None:
"""
Initialize the gp with the given set of samples or number of samples.
If none given, then defaults to n = 2 dim + 2 random samples.
:param init_samples: Tensor of samples to initialize with. Overrides n.
:param n: number of samples to initialize with
"""
if init_samples is not None:
self.X = init_samples.reshape(-1, self.dim).to(
dtype=self.dtype, device=self.device
)
else:
self.X = constrained_rand(
(n or 2 * self.dim + 2, self.dim),
self.function.inequality_constraints,
dtype=self.dtype,
device=self.device,
)
self.Y = self.function(self.X)
self.fit_gp()
def fit_gp(self) -> None:
"""
Re-fits the GP using the most up to date data.
"""
noise_prior = GammaPrior(1.1, 0.5)
noise_prior_mode = (noise_prior.concentration - 1) / noise_prior.rate
likelihood = GaussianLikelihood(
noise_prior=noise_prior,
batch_shape=[],
noise_constraint=GreaterThan(
# 0.000005, # minimum observation noise assumed in the GP model
0.0001,
transform=None,
initial_value=noise_prior_mode,
),
)
self.model = SingleTaskGP(
self.X, self.Y, likelihood, outcome_transform=Standardize(m=1)
)
mll = ExactMarginalLogLikelihood(self.model.likelihood, self.model)
fit_gpytorch_model(mll)
# dummy computation to be safe with gp fit
try:
dummy = torch.rand(
(1, self.q, self.dim), dtype=self.dtype, device=self.device
)
_ = self.model.posterior(dummy).mean
except RuntimeError as err:
if self.fit_count < 5:
self.fit_count += 1
self.Y = self.Y + torch.randn_like(self.Y) * 0.001
self.fit_gp()
else:
raise err
self.fit_count = 0
self.passed = False
def current_best(
self, past_only: bool = False, inner_seed: int = None
) -> Tuple[Tensor, Tensor]:
"""
Solve the inner optimization problem to return the current optimum
:param past_only: If true, maximize over previously evaluated x only.
:param inner_seed: Used for sampling randomness in InnerRho
:return: Current best solution and value
"""
if self.w_samples is None:
w_samples = torch.rand(
self.num_samples, self.dim_w, dtype=self.dtype, device=self.device
)
else:
w_samples = self.w_samples
inner_rho = InnerRho(
inner_seed=inner_seed,
**{_: vars(self)[_] for _ in vars(self) if _ != "w_samples"},
w_samples=w_samples
)
if past_only:
past_x = self.X[:, : self.dim_x]
with torch.no_grad():
values = inner_rho(past_x)
best = torch.argmax(values)
current_best_sol = past_x[best]
current_best_value = -values[best]
else:
current_best_sol, current_best_value = self.optimizer.optimize_inner(
inner_rho
)
if self.verbose:
print(
"Current best solution, value: ", current_best_sol, current_best_value
)
return current_best_sol, current_best_value
def one_iteration(self, **kwargs) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""
Do a single iteration of the algorithm
:param kwargs: ignored
:return: current best solution & value, kg value and candidate (next sample)
"""
iteration_start = time()
inner_seed = int(torch.randint(100000, (1,)))
self.optimizer.new_iteration()
self.inner_optimizer.new_iteration()
current_best_sol, current_best_value = self.current_best(
past_only=self.apx, inner_seed=inner_seed
)
if self.random_sampling:
candidate = constrained_rand(
(self.q, self.dim),
self.function.inequality_constraints,
dtype=self.dtype,
device=self.device,
)
value = torch.tensor([0]).to(candidate)
else:
if self.apx_cvar:
acqf = ApxCVaRKG(current_best_rho=current_best_value, **vars(self))
elif self.tts_apx_cvar:
acqf = TTSApxCVaRKG(
current_best_rho=current_best_value,
inner_optimizer=self.inner_optimizer.optimize,
**{_: vars(self)[_] for _ in vars(self) if _ != "inner_optimizer"}
)
elif self.one_shot:
acqf = OneShotrhoKG(
current_best_rho=current_best_value,
inner_seed=inner_seed,
**vars(self)
)
elif self.apx:
acqf = rhoKGapx(
current_best_rho=current_best_value,
past_x=self.X[:, : self.dim_x],
inner_seed=inner_seed,
**vars(self)
)
else:
acqf = rhoKG(
inner_optimizer=self.inner_optimizer.optimize,
current_best_rho=current_best_value,
inner_seed=inner_seed,
**{_: vars(self)[_] for _ in vars(self) if _ != "inner_optimizer"}
)
if self.disc:
candidate, value = self.optimizer.optimize_outer(
acqf, self.w_samples, random_w=self.random_w
)
else:
candidate, value = self.optimizer.optimize_outer(
acqf, random_w=self.random_w
)
candidate = candidate.detach()
value = value.detach()
if self.verbose:
print("Candidate: ", candidate, " KG value: ", value)
iteration_end = time()
print("Iteration completed in %s" % (iteration_end - iteration_start))
if self.one_shot or self.apx_cvar:
candidate_point = candidate[..., : self.q * self.dim].reshape(
self.q, self.dim
)
else:
candidate_point = candidate.reshape(self.q, self.dim)
observation = self.function(candidate_point)
# update the model input data for refitting
self.X = torch.cat((self.X, candidate_point), dim=0)
self.Y = torch.cat((self.Y, observation), dim=0)
# noting that X and Y are updated
self.passed = True
# construct and fit the GP
self.fit_gp()
# noting that gp fit successfully updated
self.passed = False
return current_best_sol, current_best_value, value, candidate_point
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")
ax.plot(candidate_x.cpu().numpy(), candidate_y.cpu().numpy(), 'r*', label="candidate point")
ax.plot(test_x.cpu().numpy(), posterior.mean.cpu().numpy(), 'b', label="GP posterior")
ax.fill_between(test_x.cpu().numpy(), lower.cpu().numpy(), upper.cpu().numpy(), alpha=0.5)
plt.legend(loc="lower left")
plt.tight_layout()
plt.savefig(f"/tmp/vanilla_bo_{it}.jpg")