def test_fit_gpytorch_model_singular(self):
options = {"disp": False, "maxiter": 5}
for dtype in (torch.float, torch.double):
X_train = torch.ones(2, 2, device=self.device, dtype=dtype)
Y_train = torch.zeros(2, 1, device=self.device, dtype=dtype)
test_likelihood = GaussianLikelihood(
noise_constraint=GreaterThan(-1e-7, transform=None, initial_value=0.0)
)
gp = SingleTaskGP(X_train, Y_train, likelihood=test_likelihood)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
# this will do multiple retries (and emit warnings, which is desired)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
fit_gpytorch_model(mll, options=options, max_retries=2)
self.assertTrue(
any(issubclass(w.category, NumericalWarning) for w in ws)
)
# ensure that we fail if noise ensures that jitter does not help
gp.likelihood = GaussianLikelihood(
noise_constraint=Interval(-2, -1, transform=None, initial_value=-1.5)
)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
with self.assertRaises(NotPSDError):
fit_gpytorch_model(mll, options=options, max_retries=2)
# ensure we can handle NaNErrors in the optimizer
with mock.patch.object(SingleTaskGP, "__call__", side_effect=NanError):
gp = SingleTaskGP(X_train, Y_train, likelihood=test_likelihood)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
fit_gpytorch_model(
mll, options={"disp": False, "maxiter": 1}, max_retries=1
)
def test_fit_gpytorch_model_singular(self):
options = {"disp": False, "maxiter": 5}
for dtype in (torch.float, torch.double):
X_train = torch.ones(2, 2, device=self.device, dtype=dtype)
Y_train = torch.zeros(2, 1, device=self.device, dtype=dtype)
test_likelihood = GaussianLikelihood(
noise_constraint=GreaterThan(-1e-7, transform=None, initial_value=0.0)
)
gp = SingleTaskGP(X_train, Y_train, likelihood=test_likelihood)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
# this will do multiple retries (and emit warnings, which is desired)
with warnings.catch_warnings(record=True) as ws, settings.debug(True):
fit_gpytorch_model(mll, options=options, max_retries=2)
self.assertTrue(
any(issubclass(w.category, NumericalWarning) for w in ws)
)
# ensure that we fail if noise ensures that jitter does not help
gp.likelihood = GaussianLikelihood(
noise_constraint=Interval(-2, -1, transform=None, initial_value=-1.5)
)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
with self.assertLogs(level="DEBUG") as logs:
fit_gpytorch_model(mll, options=options, max_retries=2)
self.assertTrue(any("NotPSDError" in log for log in logs.output))
# ensure we can handle NaNErrors in the optimizer
with mock.patch.object(SingleTaskGP, "__call__", side_effect=NanError):
gp = SingleTaskGP(X_train, Y_train, likelihood=test_likelihood)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
mll.to(device=self.device, dtype=dtype)
fit_gpytorch_model(
mll, options={"disp": False, "maxiter": 1}, max_retries=1
)
# ensure we catch NotPSDErrors
with mock.patch.object(SingleTaskGP, "__call__", side_effect=NotPSDError):
mll = self._getModel()
with self.assertLogs(level="DEBUG") as logs:
fit_gpytorch_model(mll, max_retries=2)
for retry in [1, 2]:
self.assertTrue(
any(
f"Fitting failed on try {retry} due to a NotPSDError."
in log
for log in logs.output
)
)
# Failure due to optimization warning
def optimize_w_warning(mll, **kwargs):
warnings.warn("Dummy warning.", OptimizationWarning)
return mll, None
mll = self._getModel()
with self.assertLogs(level="DEBUG") as logs, settings.debug(True):
fit_gpytorch_model(mll, optimizer=optimize_w_warning, max_retries=2)
self.assertTrue(
any("Fitting failed on try 1." in log for log in logs.output)
)