def test_roundtrip(self):
for dtype in (torch.float, torch.double):
train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
train_Y1 = train_X.sum(dim=-1)
train_Y2 = train_X[:, 0] - train_X[:, 1]
train_Y = torch.stack([train_Y1, train_Y2], dim=-1)
# SingleTaskGP
batch_gp = SingleTaskGP(train_X, train_Y)
list_gp = batched_to_model_list(batch_gp)
batch_gp_recov = model_list_to_batched(list_gp)
sd_orig = batch_gp.state_dict()
sd_recov = batch_gp_recov.state_dict()
self.assertTrue(set(sd_orig) == set(sd_recov))
self.assertTrue(all(torch.equal(sd_orig[k], sd_recov[k]) for k in sd_orig))
# FixedNoiseGP
batch_gp = FixedNoiseGP(train_X, train_Y, torch.rand_like(train_Y))
list_gp = batched_to_model_list(batch_gp)
batch_gp_recov = model_list_to_batched(list_gp)
sd_orig = batch_gp.state_dict()
sd_recov = batch_gp_recov.state_dict()
self.assertTrue(set(sd_orig) == set(sd_recov))
self.assertTrue(all(torch.equal(sd_orig[k], sd_recov[k]) for k in sd_orig))
# SingleTaskMultiFidelityGP
for lin_trunc in (False, True):
batch_gp = SingleTaskMultiFidelityGP(
train_X, train_Y, iteration_fidelity=1, linear_truncated=lin_trunc
)
list_gp = batched_to_model_list(batch_gp)
batch_gp_recov = model_list_to_batched(list_gp)
sd_orig = batch_gp.state_dict()
sd_recov = batch_gp_recov.state_dict()
self.assertTrue(set(sd_orig) == set(sd_recov))
self.assertTrue(
all(torch.equal(sd_orig[k], sd_recov[k]) for k in sd_orig)
)
def test_roundtrip(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
train_X = torch.rand(10, 2, device=device, dtype=dtype)
train_Y1 = train_X.sum(dim=-1)
train_Y2 = train_X[:, 0] - train_X[:, 1]
train_Y = torch.stack([train_Y1, train_Y2], dim=-1)
# SingleTaskGP
batch_gp = SingleTaskGP(train_X, train_Y)
list_gp = batched_to_model_list(batch_gp)
batch_gp_recov = model_list_to_batched(list_gp)
sd_orig = batch_gp.state_dict()
sd_recov = batch_gp_recov.state_dict()
self.assertTrue(set(sd_orig) == set(sd_recov))
self.assertTrue(
all(torch.equal(sd_orig[k], sd_recov[k]) for k in sd_orig))
# FixedNoiseGP
batch_gp = FixedNoiseGP(train_X, train_Y, torch.rand_like(train_Y))
list_gp = batched_to_model_list(batch_gp)
batch_gp_recov = model_list_to_batched(list_gp)
sd_orig = batch_gp.state_dict()
sd_recov = batch_gp_recov.state_dict()
self.assertTrue(set(sd_orig) == set(sd_recov))
self.assertTrue(
all(torch.equal(sd_orig[k], sd_recov[k]) for k in sd_orig))
def test_sample_all_priors(self, cuda=False):
device = torch.device("cuda" if cuda else "cpu")
for dtype in (torch.float, torch.double):
train_X = torch.rand(3, 5, device=device, dtype=dtype)
train_Y = torch.rand(3, 1, device=device, dtype=dtype)
model = SingleTaskGP(train_X=train_X, train_Y=train_Y)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
mll.to(device=device, dtype=dtype)
original_state_dict = dict(deepcopy(mll.model.state_dict()))
sample_all_priors(model)
# make sure one of the hyperparameters changed
self.assertTrue(
dict(model.state_dict())["likelihood.noise_covar.raw_noise"] !=
original_state_dict["likelihood.noise_covar.raw_noise"])
# check that lengthscales are all different
ls = model.covar_module.base_kernel.raw_lengthscale.view(
-1).tolist()
self.assertTrue(all(ls[0] != ls[i]) for i in range(1, len(ls)))
# change one of the priors to SmoothedBoxPrior
model.covar_module = ScaleKernel(
MaternKernel(
nu=2.5,
ard_num_dims=model.train_inputs[0].shape[-1],
batch_shape=model._aug_batch_shape,
lengthscale_prior=SmoothedBoxPrior(3.0, 6.0),
),
batch_shape=model._aug_batch_shape,
outputscale_prior=GammaPrior(2.0, 0.15),
)
original_state_dict = dict(deepcopy(mll.model.state_dict()))
with warnings.catch_warnings(
record=True) as ws, settings.debug(True):
sample_all_priors(model)
self.assertEqual(len(ws), 1)
self.assertTrue("rsample" in str(ws[0].message))
# the lengthscale should not have changed because sampling is
# not implemented for SmoothedBoxPrior
self.assertTrue(
torch.equal(
dict(model.state_dict())
["covar_module.base_kernel.raw_lengthscale"],
original_state_dict[
"covar_module.base_kernel.raw_lengthscale"],
))
# set setting_closure to None and make sure RuntimeError is raised
prior_tuple = model.likelihood.noise_covar._priors["noise_prior"]
model.likelihood.noise_covar._priors["noise_prior"] = (
prior_tuple[0],
prior_tuple[1],
None,
)
with self.assertRaises(RuntimeError):
sample_all_priors(model)
def one_step_acquisition_gp(oracle,
full_train_X,
full_train_Y,
acq,
q,
bounds,
dim,
domain,
domain_image,
state_dict=None,
plot_stuff=False):
model = SingleTaskGP(full_train_X, full_train_Y)
mll = ExactMarginalLogLikelihood(model.likelihood, model)
if state_dict is not None:
model.load_state_dict(state_dict)
fit_gpytorch_model(mll)
candidate, EI = get_candidate(model, acq, full_train_Y, q, bounds, dim)
if acq == 'EI' and dim == 1 and plot_stuff:
plot_util(oracle, model, EI, domain, domain_image, None, full_train_X,
full_train_Y, candidate)
candidate_image = oracle(candidate)
full_train_X = torch.cat([full_train_X, candidate])
full_train_Y = torch.cat([full_train_Y, candidate_image])
state_dict = model.state_dict()
return full_train_X, full_train_Y, model, candidate, candidate_image, state_dict
def fit_model(self):
"""
If no state_dict exists, fits the model and saves the state_dict.
Otherwise, constructs the model but uses the fit given by the state_dict.
"""
# read the data
data_list = list()
for i in range(1, 31):
data_file = os.path.join(script_dir, "port_evals",
"port_n=100_seed=%d" % i)
data_list.append(torch.load(data_file))
# join the data together
X = torch.cat([data_list[i]["X"] for i in range(len(data_list))],
dim=0).squeeze(-2)
Y = torch.cat([data_list[i]["Y"] for i in range(len(data_list))],
dim=0).squeeze(-2)
# fit GP
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
transform=None,
initial_value=noise_prior_mode,
),
)
# We save the state dict to avoid fitting the GP every time which takes ~3 mins
try:
state_dict = torch.load(
os.path.join(script_dir, "portfolio_surrogate_state_dict.pt"))
model = SingleTaskGP(X,
Y,
likelihood,
outcome_transform=Standardize(m=1))
model.load_state_dict(state_dict)
except FileNotFoundError:
model = SingleTaskGP(X,
Y,
likelihood,
outcome_transform=Standardize(m=1))
mll = ExactMarginalLogLikelihood(model.likelihood, model)
from time import time
start = time()
fit_gpytorch_model(mll)
print("fitting took %s seconds" % (time() - start))
torch.save(
model.state_dict(),
os.path.join(script_dir, "portfolio_surrogate_state_dict.pt"),
)
self.model = model
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
