def _setUp(self, double=False):
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=self.device,
dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi))
train_yvar = torch.tensor(0.1**2, device=self.device)
noise = torch.tensor(NOISE, device=self.device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]],
device=self.device,
dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=self.device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(self.model_st.likelihood,
self.model_st)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
self.mll_st = fit_gpytorch_model(self.mll_st,
options={"maxiter": 5},
max_retries=1)
model_fn = FixedNoiseGP(self.train_x, self.train_y,
self.train_yvar.expand_as(self.train_y))
self.model_fn = model_fn.to(device=self.device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(self.model_fn.likelihood,
self.model_fn)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
self.mll_fn = fit_gpytorch_model(self.mll_fn,
options={"maxiter": 5},
max_retries=1)
def _setUp(self, double=False, cuda=False, expand=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device,
dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi))
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
if expand:
self.train_x = self.train_x.expand(-1, 2)
ics = torch.tensor([[0.5, 1.0]], device=device, dtype=dtype)
else:
ics = torch.tensor([[0.5]], device=device, dtype=dtype)
self.initial_conditions = ics
self.f_best = self.train_y.max().item()
model = SingleTaskGP(self.train_x, self.train_y)
self.model = model.to(device=device, dtype=dtype)
self.mll = ExactMarginalLogLikelihood(self.model.likelihood,
self.model)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=OptimizationWarning)
self.mll = fit_gpytorch_model(self.mll,
options={"maxiter": 1},
max_retries=1)
def _setUp(self, double=False, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi)).squeeze(-1)
train_yvar = torch.tensor(0.1 ** 2, device=device)
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]], device=device, dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(
self.model_st.likelihood, self.model_st
)
self.mll_st = fit_gpytorch_model(self.mll_st, options={"maxiter": 5})
model_fn = FixedNoiseGP(
self.train_x, self.train_y, self.train_yvar.expand_as(self.train_y)
)
self.model_fn = model_fn.to(device=device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(
self.model_fn.likelihood, self.model_fn
)
self.mll_fn = fit_gpytorch_model(self.mll_fn, options={"maxiter": 5})
def _setUp(self, double=False, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi)).squeeze(-1)
train_yvar = torch.tensor(0.1 ** 2, device=device)
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
self.train_yvar = train_yvar
self.bounds = torch.tensor([[0.0], [1.0]], device=device, dtype=dtype)
model_st = SingleTaskGP(self.train_x, self.train_y)
self.model_st = model_st.to(device=device, dtype=dtype)
self.mll_st = ExactMarginalLogLikelihood(
self.model_st.likelihood, self.model_st
)
self.mll_st = fit_gpytorch_model(self.mll_st, options={"maxiter": 5})
model_fn = FixedNoiseGP(
self.train_x, self.train_y, self.train_yvar.expand_as(self.train_y)
)
self.model_fn = model_fn.to(device=device, dtype=dtype)
self.mll_fn = ExactMarginalLogLikelihood(
self.model_fn.likelihood, self.model_fn
)
self.mll_fn = fit_gpytorch_model(self.mll_fn, options={"maxiter": 5})
def initialize_model(x0, y0, n=5):
# initialize botorch GP model
# generate prior xs and ys for GP
train_x = 2 * torch.rand(n, latent_dim, device=device).float() - 1
if not args.inf_norm:
train_x = latent_proj(train_x, args.eps)
train_obj = obj_func(train_x, x0, y0)
mean, std = train_obj.mean(), train_obj.std()
if args.standardize:
train_obj = (train_obj - train_obj.mean()) / train_obj.std()
best_observed_value = train_obj.max().item()
# define models for objective and constraint
model = SingleTaskGP(train_X=train_x, train_Y=train_obj[:, None])
model = model.to(train_x)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
mll = mll.to(train_x)
return train_x, train_obj, mll, model, best_observed_value, mean, std
def _setUp(self, double=False, cuda=False, expand=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
dtype = torch.double if double else torch.float
train_x = torch.linspace(0, 1, 10, device=device, dtype=dtype).unsqueeze(-1)
train_y = torch.sin(train_x * (2 * math.pi)).squeeze(-1)
noise = torch.tensor(NOISE, device=device, dtype=dtype)
self.train_x = train_x
self.train_y = train_y + noise
if expand:
self.train_x = self.train_x.expand(-1, 2)
ics = torch.tensor([[0.5, 1.0]], device=device, dtype=dtype)
else:
ics = torch.tensor([[0.5]], device=device, dtype=dtype)
self.initial_conditions = ics
self.f_best = self.train_y.max().item()
model = SingleTaskGP(self.train_x, self.train_y)
self.model = model.to(device=device, dtype=dtype)
self.mll = ExactMarginalLogLikelihood(self.model.likelihood, self.model)
self.mll = fit_gpytorch_model(self.mll, options={"maxiter": 1})
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
