def test_lcm_icm_equivalence(self):
# Training points are every 0.1 in [0,1] (note that they're the same for both tasks)
train_x = torch.linspace(0, 1, 100)
# y1 function is sin(2*pi*x) with noise N(0, 0.04)
train_y1 = torch.sin(train_x.data * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2
# y2 function is cos(2*pi*x) with noise N(0, 0.04)
train_y2 = torch.cos(train_x.data * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2
# Create a train_y which interleaves the two
train_y = torch.stack([train_y1, train_y2], -1)
likelihood = MultitaskGaussianLikelihood(num_tasks=2)
model = MultitaskGPModel(train_x, train_y, likelihood)
# Use the adam optimizer
optimizer = torch.optim.Adam([{"params": model.parameters()}], lr=0.1) # Includes GaussianLikelihood parameters
model.train()
likelihood.train()
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
n_iter = 50
for _ in range(n_iter):
optimizer.zero_grad()
output = model(train_x)
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
model.eval()
likelihood.eval()
# Make predictions for LCM
with torch.no_grad():
test_x = torch.linspace(0, 1, 51)
observed_pred = likelihood(model(test_x))
mean = observed_pred.mean
model_icm = MultitaskGPModel_ICM(train_x, train_y, likelihood)
likelihood = MultitaskGaussianLikelihood(num_tasks=2)
model_icm.train()
likelihood.train()
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model_icm)
optimizer = torch.optim.Adam(
[{"params": model_icm.parameters()}], lr=0.1 # Includes GaussianLikelihood parameters
)
for _ in range(n_iter):
optimizer.zero_grad()
output = model_icm(train_x)
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
model_icm.eval()
likelihood.eval()
# Make predictions for ICM
with torch.no_grad():
test_x = torch.linspace(0, 1, 51)
observed_pred_icm = likelihood(model_icm(test_x))
mean_icm = observed_pred_icm.mean
# Make sure predictions from LCM with one base kernel and ICM are the same.
self.assertLess((mean - mean_icm).pow(2).mean(), 1e-2)
def test_train_and_eval(self):
# We're manually going to set the hyperparameters to something they shouldn't be
likelihood = MultitaskGaussianLikelihood(num_tasks=4)
model = LMCModel()
# Find optimal model hyperparameters
model.train()
likelihood.train()
optimizer = torch.optim.Adam([
{'params': model.parameters()},
{'params': likelihood.parameters()},
], lr=0.01)
# Our loss object. We're using the VariationalELBO, which essentially just computes the ELBO
mll = gpytorch.mlls.VariationalELBO(likelihood, model, num_data=train_y.size(0))
# We use more CG iterations here because the preconditioner introduced in the NeurIPS paper seems to be less
# effective for VI.
for i in range(400):
# Within each iteration, we will go over each minibatch of data
optimizer.zero_grad()
output = model(train_x)
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
for param in model.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
for param in likelihood.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
# Test the model
model.eval()
likelihood.eval()
# Make predictions for both sets of test points, and check MAEs.
with torch.no_grad(), gpytorch.settings.max_eager_kernel_size(1):
batch_predictions = likelihood(model(train_x))
preds1 = batch_predictions.mean[:, 0]
preds2 = batch_predictions.mean[:, 1]
preds3 = batch_predictions.mean[:, 2]
preds4 = batch_predictions.mean[:, 3]
mean_abs_error1 = torch.mean(torch.abs(train_y[..., 0] - preds1))
mean_abs_error2 = torch.mean(torch.abs(train_y[..., 1] - preds2))
mean_abs_error3 = torch.mean(torch.abs(train_y[..., 2] - preds3))
mean_abs_error4 = torch.mean(torch.abs(train_y[..., 3] - preds4))
self.assertLess(mean_abs_error1.squeeze().item(), 0.15)
self.assertLess(mean_abs_error2.squeeze().item(), 0.15)
self.assertLess(mean_abs_error3.squeeze().item(), 0.15)
self.assertLess(mean_abs_error4.squeeze().item(), 0.15)
# Smoke test for getting predictive uncertainties
lower, upper = batch_predictions.confidence_region()
self.assertEqual(lower.shape, train_y.shape)
self.assertEqual(upper.shape, train_y.shape)
def test_multitask_gp_mean_abs_error(self, cuda=False):
train_x, train_y = self._get_data(cuda=cuda)
likelihood = MultitaskGaussianLikelihood(num_tasks=2)
model = MultitaskGPModel(train_x, train_y, likelihood)
if cuda:
model.cuda()
# Find optimal model hyperparameters
model.train()
likelihood.train()
# Use the adam optimizer
optimizer = torch.optim.Adam(
model.parameters(),
lr=0.1) # Includes GaussianLikelihood parameters
# "Loss" for GPs - the marginal log likelihood
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
n_iter = 50
for _ in range(n_iter):
# Zero prev backpropped gradients
optimizer.zero_grad()
# Make predictions from training data
# Again, note feeding duplicated x_data and indices indicating which task
output = model(train_x)
# TODO: Fix this view call!!
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
# Test the model
model.eval()
likelihood.eval()
test_x = torch.linspace(
0,
1,
51,
device=torch.device("cuda") if cuda else torch.device("cpu"))
test_y1 = torch.sin(test_x * (2 * pi))
test_y2 = torch.cos(test_x * (2 * pi))
test_preds = likelihood(model(test_x)).mean
mean_abs_error_task_1 = torch.mean(
torch.abs(test_y1 - test_preds[:, 0]))
mean_abs_error_task_2 = torch.mean(
torch.abs(test_y2 - test_preds[:, 1]))
self.assertLess(mean_abs_error_task_1.squeeze().item(), 0.05)
self.assertLess(mean_abs_error_task_2.squeeze().item(), 0.05)
def test_train_on_single_set_test_on_batch(self):
# We're manually going to set the hyperparameters to something they shouldn't be
likelihood = MultitaskGaussianLikelihood(
log_noise_prior=gpytorch.priors.NormalPrior(loc=torch.zeros(1),
scale=torch.ones(1),
log_transform=True),
num_tasks=2,
)
gp_model = ExactGPModel(train_x1, train_y1, likelihood)
mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
# Find optimal model hyperparameters
gp_model.train()
likelihood.train()
optimizer = optim.Adam(list(gp_model.parameters()) +
list(likelihood.parameters()),
lr=0.1)
optimizer.n_iter = 0
gp_model.train()
likelihood.train()
optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
for _ in range(50):
optimizer.zero_grad()
output = gp_model(train_x1)
loss = -mll(output, train_y1).sum()
loss.backward()
optimizer.step()
for param in gp_model.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
for param in likelihood.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
# Test the model
gp_model.eval()
likelihood.eval()
# Make predictions for both sets of test points, and check MAEs.
batch_predictions = likelihood(gp_model(test_x12))
preds1 = batch_predictions.mean[0]
preds2 = batch_predictions.mean[1]
mean_abs_error1 = torch.mean(torch.abs(test_y1 - preds1))
mean_abs_error2 = torch.mean(torch.abs(test_y2 - preds2))
self.assertLess(mean_abs_error1.squeeze().item(), 0.05)
self.assertLess(mean_abs_error2.squeeze().item(), 0.05)
def test_train_and_eval(self):
# We're manually going to set the hyperparameters to something they shouldn't be
likelihood = MultitaskGaussianLikelihood(num_tasks=2)
gp_model = ExactGPModel(train_x, train_y12, likelihood)
mll = gpytorch.ExactMarginalLogLikelihood(likelihood, gp_model)
# Find optimal model hyperparameters
gp_model.train()
likelihood.train()
optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
optimizer.n_iter = 0
for _ in range(75):
optimizer.zero_grad()
output = gp_model(train_x)
loss = -mll(output, train_y12).sum()
loss.backward()
optimizer.step()
for param in gp_model.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
for param in likelihood.parameters():
self.assertTrue(param.grad is not None)
self.assertGreater(param.grad.norm().item(), 0)
# Test the model
gp_model.eval()
likelihood.eval()
# Make predictions for both sets of test points, and check MAEs.
with torch.no_grad(), gpytorch.settings.max_eager_kernel_size(1):
batch_predictions = likelihood(gp_model(test_x))
preds1 = batch_predictions.mean[:, 0]
preds2 = batch_predictions.mean[:, 1]
mean_abs_error1 = torch.mean(torch.abs(test_y1 - preds1))
mean_abs_error2 = torch.mean(torch.abs(test_y2 - preds2))
self.assertLess(mean_abs_error1.squeeze().item(), 0.01)
self.assertLess(mean_abs_error2.squeeze().item(), 0.01)
# Smoke test for getting predictive uncertainties
lower, upper = batch_predictions.confidence_region()
self.assertEqual(lower.shape, test_y12.shape)
self.assertEqual(upper.shape, test_y12.shape)
def test_multitask_low_rank_noise_covar(self):
likelihood = MultitaskGaussianLikelihood(n_tasks=2, rank=1)
model = MultitaskGPModel(train_x, train_y, likelihood)
# Find optimal model hyperparameters
model.train()
likelihood.train()
# Use the adam optimizer
optimizer = torch.optim.Adam(
[{
"params": model.parameters()
}], # Includes GaussianLikelihood parameters
lr=0.1,
)
# "Loss" for GPs - the marginal log likelihood
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
n_iter = 50
for _ in range(n_iter):
# Zero prev backpropped gradients
optimizer.zero_grad()
# Make predictions from training data
# Again, note feeding duplicated x_data and indices indicating which task
output = model(train_x)
# TODO: Fix this view call!!
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
# Test the model
model.eval()
likelihood.eval()
n_tasks = 2
task_noise_covar_factor = likelihood.task_noise_covar_factor
log_noise = likelihood.log_noise
task_noise_covar = task_noise_covar_factor.matmul(
task_noise_covar_factor.transpose(
-1, -2)) + log_noise.exp() * torch.eye(n_tasks)
self.assertGreater(task_noise_covar[0, 1].data.squeeze().item(), 0.05)
class GP(BaseModel):
support_grad = True
support_multi_output = True
def __init__(self, num_cont, num_enum, num_out, **conf):
super().__init__(num_cont, num_enum, num_out, **conf)
self.lr = conf.get('lr', 3e-2)
self.num_epochs = conf.get('num_epochs', 100)
self.verbose = conf.get('verbose', False)
self.print_every = conf.get('print_every', 10)
self.noise_free = conf.get('noise_free', False)
self.pred_likeli = conf.get('pred_likeli', True)
self.noise_lb = conf.get('noise_lb', 1e-5)
self.xscaler = TorchMinMaxScaler((-1, 1))
self.yscaler = TorchStandardScaler()
def fit_scaler(self, Xc: Tensor, Xe: Tensor, y: Tensor):
if Xc is not None and Xc.shape[1] > 0:
self.xscaler.fit(Xc)
self.yscaler.fit(y)
def xtrans(self, Xc: Tensor, Xe: Tensor, y: Tensor = None):
if Xc is not None and Xc.shape[1] > 0:
Xc_t = self.xscaler.transform(Xc)
else:
Xc_t = torch.zeros(Xe.shape[0], 0)
if Xe is None:
Xe_t = torch.zeros(Xc.shape[0], 0).long()
else:
Xe_t = Xe.long()
if y is not None:
y_t = self.yscaler.transform(y)
return Xc_t, Xe_t, y_t
else:
return Xc_t, Xe_t
def fit(self, Xc: Tensor, Xe: Tensor, y: Tensor):
Xc, Xe, y = filter_nan(Xc, Xe, y, 'all')
self.fit_scaler(Xc, Xe, y)
Xc, Xe, y = self.xtrans(Xc, Xe, y)
assert (Xc.shape[1] == self.num_cont)
assert (Xe.shape[1] == self.num_enum)
assert (y.shape[1] == self.num_out)
self.Xc = Xc
self.Xe = Xe
self.y = y
n_constr = GreaterThan(self.noise_lb)
n_prior = LogNormalPrior(-4.63, 0.5)
if self.num_out == 1:
self.lik = GaussianLikelihood(noise_constraint=n_constr,
noise_prior=n_prior)
else:
self.lik = MultitaskGaussianLikelihood(num_tasks=self.num_out,
noise_constraint=n_constr,
noise_prior=n_prior)
self.gp = GPyTorchModel(self.Xc, self.Xe, self.y, self.lik,
**self.conf)
if self.num_out == 1: # XXX: only tuned for single-output BO
if self.num_cont > 0:
self.gp.kern.outputscale = self.y.var()
lscales = self.gp.kern.base_kernel.lengthscale.detach().clone(
).view(1, -1)
for i in range(self.num_cont):
lscales[0, i] = torch.pdist(self.Xc[:, i].view(
-1, 1)).median().clamp(min=0.02)
self.gp.kern.base_kernel.lengthscale = lscales
if self.noise_free:
self.gp.likelihood.noise = self.noise_lb * 1.1
self.gp.likelihood.raw_noise.requires_grad = False
else:
self.gp.likelihood.noise = max(1e-2, self.noise_lb)
self.gp.train()
self.lik.train()
opt = torch.optim.LBFGS(self.gp.parameters(),
lr=self.lr,
max_iter=5,
line_search_fn='strong_wolfe')
mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.lik, self.gp)
for epoch in range(self.num_epochs):
def closure():
dist = self.gp(self.Xc, self.Xe)
loss = -1 * mll(dist, self.y.squeeze())
opt.zero_grad()
loss.backward()
return loss
opt.step(closure)
if self.verbose and ((epoch + 1) % self.print_every == 0
or epoch == 0):
print('After %d epochs, loss = %g' %
(epoch + 1, closure().item()),
flush=True)
self.gp.eval()
self.lik.eval()
def predict(self, Xc, Xe):
Xc, Xe = self.xtrans(Xc, Xe)
with gpytorch.settings.fast_pred_var(), gpytorch.settings.debug(False):
pred = self.gp(Xc, Xe)
if self.pred_likeli:
pred = self.lik(pred)
mu_ = pred.mean.reshape(-1, self.num_out)
var_ = pred.variance.reshape(-1, self.num_out)
mu = self.yscaler.inverse_transform(mu_)
var = var_ * self.yscaler.std**2
return mu, var
def sample_y(self, Xc, Xe, n_samples=1) -> FloatTensor:
"""
Should return (n_samples, Xc.shape[0], self.num_out)
"""
Xc, Xe = self.xtrans(Xc, Xe)
with gpytorch.settings.debug(False):
if self.pred_likeli:
pred = self.lik(self.gp(Xc, Xe))
else:
pred = self.gp(Xc, Xe)
samp = pred.rsample(torch.Size(
(n_samples, ))).view(n_samples, Xc.shape[0], self.num_out)
return self.yscaler.inverse_transform(samp)
def sample_f(self):
raise NotImplementedError(
'Thompson sampling is not supported for GP, use `sample_y` instead'
)
@property
def noise(self):
if self.num_out == 1:
return (self.gp.likelihood.noise * self.yscaler.std**2).view(
self.num_out).detach()
else:
return (self.gp.likelihood.noise_covar.noise *
self.yscaler.std**2).view(self.num_out).detach()
def train_gp(train_x, train_y, use_ard, num_steps, hypers={}):
"""Fit a GP model where train_x is in [0, 1]^d and train_y is standardized."""
assert train_x.ndim == 2
assert train_y.ndim == 2
assert train_x.shape[0] == train_y.shape[0]
# Create hyper parameter bounds
noise_constraint = Interval(5e-4, 0.2)
if use_ard:
lengthscale_constraint = Interval(0.005, 2.0)
else:
lengthscale_constraint = Interval(0.005, math.sqrt(
train_x.shape[1])) # [0.005, sqrt(dim)]
outputscale_constraint = Interval(0.05, 20.0)
# Create models
likelihood = MultitaskGaussianLikelihood(
num_tasks=train_y.size(-1),
noise_constraint=noise_constraint,
).to(device=train_x.device, dtype=train_y.dtype)
ard_dims = train_x.shape[1] if use_ard else None
model = GP(
train_x=train_x,
train_y=train_y,
likelihood=likelihood,
lengthscale_constraint=lengthscale_constraint,
outputscale_constraint=outputscale_constraint,
ard_dims=ard_dims,
).to(device=train_x.device, dtype=train_x.dtype)
# Find optimal model hyperparameters
model.train()
likelihood.train()
# "Loss" for GPs - the marginal log likelihood
mll = ExactMarginalLogLikelihood(likelihood, model)
# Initialize model hypers
if hypers:
model.load_state_dict(hypers)
else:
hypers = {}
hypers["covar_module.outputscale"] = 1.0
hypers["covar_module.base_kernel.lengthscale"] = 0.5
hypers["likelihood.noise"] = 0.005
model.initialize(**hypers)
# Use the adam optimizer
optimizer = torch.optim.Adam([{"params": model.parameters()}], lr=0.1)
for _ in range(num_steps):
optimizer.zero_grad()
output = model(train_x)
loss = -mll(output, train_y)
loss.backward()
optimizer.step()
# Switch to eval mode
model.eval()
likelihood.eval()
return model
