def test_exponential_moving_average(self):
for dtype in (torch.float, torch.double):
tkwargs = {"device": self.device, "dtype": dtype}
# test max iter
sc = ExpMAStoppingCriterion(maxiter=2)
self.assertEqual(sc.maxiter, 2)
self.assertEqual(sc.n_window, 10)
self.assertEqual(sc.rel_tol, 1e-5)
self.assertFalse(sc.evaluate(fvals=torch.ones(1, **tkwargs)))
self.assertTrue(sc.evaluate(fvals=torch.zeros(1, **tkwargs)))
# test convergence
n_window = 4
for minimize in (True, False):
# test basic
sc = ExpMAStoppingCriterion(minimize=minimize,
n_window=n_window,
rel_tol=0.0375)
self.assertEqual(sc.rel_tol, 0.0375)
self.assertIsNone(sc._prev_fvals)
weights_exp = torch.tensor([0.1416, 0.1976, 0.2758, 0.3849])
self.assertTrue(
torch.allclose(sc.weights, weights_exp, atol=1e-4))
f_vals = 1 + torch.linspace(1, 0, 25, **tkwargs)**2
if not minimize:
f_vals = -f_vals
for i, fval in enumerate(f_vals):
if sc.evaluate(fval):
self.assertEqual(i, 10)
break
# test multiple components
sc = ExpMAStoppingCriterion(minimize=minimize,
n_window=n_window,
rel_tol=0.0375)
df = torch.linspace(0, 0.1, 25, **tkwargs)
if not minimize:
df = -df
f_vals = torch.stack([f_vals, f_vals + df], dim=-1)
for i, fval in enumerate(f_vals):
if sc.evaluate(fval):
self.assertEqual(i, 10)
break
def optimize_acqf_cyclic(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
cyclic_options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
) -> float:
r"""Generate a set of `q` candidates via cyclic optimization.
Args:
acq_function: An AcquisitionFunction
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization
options: Options for candidate generation.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`
equality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) = rhs`
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
batch_initial_conditions: A tensor to specify the initial conditions.
If no initial conditions are provided, the default initialization will
be used.
cyclic_options: Options for stopping criterion for outer cyclic optimization.
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- a `q`-dim tensor of expected acquisition
values, where the `i`th value is the acquistion value conditional on
having observed all candidates except candidate `i`.
Example:
>>> # generate `q=3` candidates cyclically using 15 random restarts
>>> # 256 raw samples, and 4 cycles
>>>
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> candidates, acq_value_list = optimize_acqf_cyclic(
>>> qEI, bounds, 3, 15, 256, cyclic_options={"maxiter": 4}
>>> )
"""
# for the first cycle, optimize the q candidates sequentially
candidates, acq_vals = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=True,
sequential=True,
)
if q > 1:
cyclic_options = cyclic_options or {}
stopping_criterion = ExpMAStoppingCriterion(**cyclic_options)
stop = stopping_criterion.evaluate(fvals=acq_vals)
base_X_pending = acq_function.X_pending
idxr = torch.ones(q, dtype=torch.bool, device=bounds.device)
while not stop:
for i in range(q):
# optimize only candidate i
idxr[i] = 0
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates[idxr]], dim=-2)
if base_X_pending is not None
else candidates[idxr]
)
candidate_i, acq_val_i = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=candidates[i].unsqueeze(0),
return_best_only=True,
sequential=True,
)
candidates[i] = candidate_i
acq_vals[i] = acq_val_i
idxr[i] = 1
stop = stopping_criterion.evaluate(fvals=acq_vals)
acq_function.set_X_pending(base_X_pending)
return candidates, acq_vals
def fit_gpytorch_torch(
mll: MarginalLogLikelihood,
bounds: Optional[ParameterBounds] = None,
optimizer_cls: Optimizer = Adam,
options: Optional[Dict[str, Any]] = None,
track_iterations: bool = True,
approx_mll: bool = True,
) -> Tuple[MarginalLogLikelihood, Dict[str, Union[float, List[OptimizationIteration]]]]:
r"""Fit a gpytorch model by maximizing MLL with a torch optimizer.
The model and likelihood in mll must already be in train mode.
Note: this method requires that the model has `train_inputs` and `train_targets`.
Args:
mll: MarginalLogLikelihood to be maximized.
bounds: A ParameterBounds dictionary mapping parameter names to tuples
of lower and upper bounds. Bounds specified here take precedence
over bounds on the same parameters specified in the constraints
registered with the module.
optimizer_cls: Torch optimizer to use. Must not require a closure.
options: options for model fitting. Relevant options will be passed to
the `optimizer_cls`. Additionally, options can include: "disp"
to specify whether to display model fitting diagnostics and "maxiter"
to specify the maximum number of iterations.
track_iterations: Track the function values and wall time for each
iteration.
approx_mll: If True, use gpytorch's approximate MLL computation (
according to the gpytorch defaults based on the training at size).
Unlike for the deterministic algorithms used in fit_gpytorch_scipy,
this is not an issue for stochastic optimizers.
Returns:
2-element tuple containing
- mll with parameters optimized in-place.
- Dictionary with the following key/values:
"fopt": Best mll value.
"wall_time": Wall time of fitting.
"iterations": List of OptimizationIteration objects with information on each
iteration. If track_iterations is False, will be empty.
Example:
>>> gp = SingleTaskGP(train_X, train_Y)
>>> mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
>>> mll.train()
>>> fit_gpytorch_torch(mll)
>>> mll.eval()
"""
optim_options = {"maxiter": 100, "disp": True, "lr": 0.05}
optim_options.update(options or {})
exclude = optim_options.pop("exclude", None)
if exclude is not None:
mll_params = [
t for p_name, t in mll.named_parameters() if p_name not in exclude
]
else:
mll_params = list(mll.parameters())
optimizer = optimizer_cls(
params=[{"params": mll_params}],
**_filter_kwargs(optimizer_cls, **optim_options),
)
# get bounds specified in model (if any)
bounds_: ParameterBounds = {}
if hasattr(mll, "named_parameters_and_constraints"):
for param_name, _, constraint in mll.named_parameters_and_constraints():
if constraint is not None and not constraint.enforced:
bounds_[param_name] = constraint.lower_bound, constraint.upper_bound
# update with user-supplied bounds (overwrites if already exists)
if bounds is not None:
bounds_.update(bounds)
iterations = []
t1 = time.time()
param_trajectory: Dict[str, List[Tensor]] = {
name: [] for name, param in mll.named_parameters()
}
loss_trajectory: List[float] = []
i = 0
stop = False
stopping_criterion = ExpMAStoppingCriterion(
**_filter_kwargs(ExpMAStoppingCriterion, **optim_options)
)
train_inputs, train_targets = mll.model.train_inputs, mll.model.train_targets
while not stop:
optimizer.zero_grad()
with gpt_settings.fast_computations(log_prob=approx_mll):
output = mll.model(*train_inputs)
# we sum here to support batch mode
args = [output, train_targets] + _get_extra_mll_args(mll)
loss = -mll(*args).sum()
loss.backward()
loss_trajectory.append(loss.item())
for name, param in mll.named_parameters():
param_trajectory[name].append(param.detach().clone())
if optim_options["disp"] and (
(i + 1) % 10 == 0 or i == (optim_options["maxiter"] - 1)
):
print(f"Iter {i + 1}/{optim_options['maxiter']}: {loss.item()}")
if track_iterations:
iterations.append(OptimizationIteration(i, loss.item(), time.time() - t1))
optimizer.step()
# project onto bounds:
if bounds_:
for pname, param in mll.named_parameters():
if pname in bounds_:
param.data = param.data.clamp(*bounds_[pname])
i += 1
stop = stopping_criterion.evaluate(fvals=loss.detach())
info_dict = {
"fopt": loss_trajectory[-1],
"wall_time": time.time() - t1,
"iterations": iterations,
}
return mll, info_dict
def gen_candidates_torch(
initial_conditions: Tensor,
acquisition_function: Callable,
lower_bounds: Optional[Union[float, Tensor]] = None,
upper_bounds: Optional[Union[float, Tensor]] = None,
optimizer: Type[Optimizer] = torch.optim.Adam,
options: Optional[Dict[str, Union[float, str]]] = None,
verbose: bool = True,
fixed_features: Optional[Dict[int, Optional[float]]] = None,
) -> Iterable[Any]: # -> Tuple[Tensor, Any, Optional[Tensor]]:
r"""Generate a set of candidates using a `torch.optim` optimizer.
Optimizes an acquisition function starting from a set of initial candidates
using an optimizer from `torch.optim`.
Args:
initial_conditions: Starting points for optimization.
acquisition_function: Acquisition function to be used.
lower_bounds: Minimum values for each column of initial_conditions.
upper_bounds: Maximum values for each column of initial_conditions.
optimizer (Optimizer): The pytorch optimizer to use to perform
candidate search.
options: Options used to control the optimization. Includes
maxiter: Maximum number of iterations
verbose: If True, provide verbose output.
fixed_features: This is a dictionary of feature indices to values, where
all generated candidates will have features fixed to these values.
If the dictionary value is None, then that feature will just be
fixed to the clamped value and not optimized. Assumes values to be
compatible with lower_bounds and upper_bounds!
Returns:
2-element tuple containing
- The set of generated candidates.
- The acquisition value for each t-batch.
"""
options = options or {}
_jitter = options.get('jitter', 0.)
clamped_candidates = columnwise_clamp(
X=initial_conditions, lower=lower_bounds, upper=upper_bounds
).requires_grad_(True)
candidates = fix_features(clamped_candidates, fixed_features)
bayes_optimizer = optimizer(
params=[clamped_candidates], lr=options.get("lr", 0.025)
)
i = 0
stop = False
stopping_criterion = ExpMAStoppingCriterion(
**_filter_kwargs(ExpMAStoppingCriterion, **options)
)
while not stop:
i += 1
batch_loss = acquisition_function(candidates)
loss = -batch_loss.sum()
if verbose:
print("Iter: {} - Value: {:.3f}".format(i, -(loss.item())))
if torch.isnan(loss):
print('loss is nan, exiting optimization of the acquisition function.')
break
bayes_optimizer.zero_grad()
loss.backward()
if options.get('clip_gradient', False):
torch.nn.utils.clip_grad_value_(clamped_candidates, clip_value=options.get('clip_value', 10.))
bayes_optimizer.step()
clamped_candidates.data = columnwise_clamp(
clamped_candidates, lower_bounds + _jitter, upper_bounds - _jitter
)
candidates = fix_features(clamped_candidates, fixed_features)
stop = stopping_criterion.evaluate(fvals=loss.detach())
# clamped_candidates = columnwise_clamp(
# X=candidates, lower=lower_bounds, upper=upper_bounds, raise_on_violation=True
# )
with torch.no_grad():
batch_acquisition = acquisition_function(candidates)
return candidates, batch_acquisition
def gen_candidates_torch(
initial_conditions: Tensor,
acquisition_function: AcquisitionFunction,
lower_bounds: Optional[Union[float, Tensor]] = None,
upper_bounds: Optional[Union[float, Tensor]] = None,
optimizer: Type[Optimizer] = torch.optim.Adam,
options: Optional[Dict[str, Union[float, str]]] = None,
callback: Optional[Callable[[int, Tensor, Tensor], NoReturn]] = None,
fixed_features: Optional[Dict[int, Optional[float]]] = None,
) -> Tuple[Tensor, Tensor]:
r"""Generate a set of candidates using a `torch.optim` optimizer.
Optimizes an acquisition function starting from a set of initial candidates
using an optimizer from `torch.optim`.
Args:
initial_conditions: Starting points for optimization.
acquisition_function: Acquisition function to be used.
lower_bounds: Minimum values for each column of initial_conditions.
upper_bounds: Maximum values for each column of initial_conditions.
optimizer (Optimizer): The pytorch optimizer to use to perform
candidate search.
options: Options used to control the optimization. Includes
maxiter: Maximum number of iterations
callback: A callback function accepting the current iteration, loss,
and gradients as arguments. This function is executed after computing
the loss and gradients, but before calling the optimizer.
fixed_features: This is a dictionary of feature indices to values, where
all generated candidates will have features fixed to these values.
If the dictionary value is None, then that feature will just be
fixed to the clamped value and not optimized. Assumes values to be
compatible with lower_bounds and upper_bounds!
Returns:
2-element tuple containing
- The set of generated candidates.
- The acquisition value for each t-batch.
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0., 0.], [1., 2.]])
>>> Xinit = gen_batch_initial_conditions(
>>> qEI, bounds, q=3, num_restarts=25, raw_samples=500
>>> )
>>> batch_candidates, batch_acq_values = gen_candidates_torch(
initial_conditions=Xinit,
acquisition_function=qEI,
lower_bounds=bounds[0],
upper_bounds=bounds[1],
)
"""
options = options or {}
# if there are fixed features we may optimize over a domain of lower dimension
if fixed_features:
subproblem = _remove_fixed_features_from_optimization(
fixed_features=fixed_features,
acquisition_function=acquisition_function,
initial_conditions=initial_conditions,
lower_bounds=lower_bounds,
upper_bounds=upper_bounds,
inequality_constraints=None,
equality_constraints=None,
)
# call the routine with no fixed_features
clamped_candidates, batch_acquisition = gen_candidates_torch(
initial_conditions=subproblem.initial_conditions,
acquisition_function=subproblem.acquisition_function,
lower_bounds=subproblem.lower_bounds,
upper_bounds=subproblem.upper_bounds,
optimizer=optimizer,
options=options,
callback=callback,
fixed_features=None,
)
clamped_candidates = subproblem.acquisition_function._construct_X_full(
clamped_candidates
)
return clamped_candidates, batch_acquisition
_clamp = partial(columnwise_clamp, lower=lower_bounds, upper=upper_bounds)
clamped_candidates = _clamp(initial_conditions).requires_grad_(True)
_optimizer = optimizer(params=[clamped_candidates], lr=options.get("lr", 0.025))
i = 0
stop = False
stopping_criterion = ExpMAStoppingCriterion(
**_filter_kwargs(ExpMAStoppingCriterion, **options)
)
while not stop:
i += 1
with torch.no_grad():
X = _clamp(clamped_candidates).requires_grad_(True)
loss = -acquisition_function(X).sum()
grad = torch.autograd.grad(loss, X)[0]
if callback:
callback(i, loss, grad)
def assign_grad():
_optimizer.zero_grad()
clamped_candidates.grad = grad
return loss
_optimizer.step(assign_grad)
stop = stopping_criterion.evaluate(fvals=loss.detach())
clamped_candidates = _clamp(clamped_candidates)
with torch.no_grad():
batch_acquisition = acquisition_function(clamped_candidates)
return clamped_candidates, batch_acquisition
def gen_candidates_torch(
initial_conditions: Tensor,
acquisition_function: Callable,
lower_bounds: Optional[Union[float, Tensor]] = None,
upper_bounds: Optional[Union[float, Tensor]] = None,
optimizer: Type[Optimizer] = torch.optim.Adam,
options: Optional[Dict[str, Union[float, str]]] = None,
verbose: bool = True,
fixed_features: Optional[Dict[int, Optional[float]]] = None,
) -> Tuple[Tensor, Tensor]:
r"""Generate a set of candidates using a `torch.optim` optimizer.
Optimizes an acquisition function starting from a set of initial candidates
using an optimizer from `torch.optim`.
Args:
initial_conditions: Starting points for optimization.
acquisition_function: Acquisition function to be used.
lower_bounds: Minimum values for each column of initial_conditions.
upper_bounds: Maximum values for each column of initial_conditions.
optimizer (Optimizer): The pytorch optimizer to use to perform
candidate search.
options: Options used to control the optimization. Includes
maxiter: Maximum number of iterations
verbose: If True, provide verbose output.
fixed_features: This is a dictionary of feature indices to values, where
all generated candidates will have features fixed to these values.
If the dictionary value is None, then that feature will just be
fixed to the clamped value and not optimized. Assumes values to be
compatible with lower_bounds and upper_bounds!
Returns:
2-element tuple containing
- The set of generated candidates.
- The acquisition value for each t-batch.
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0., 0.], [1., 2.]])
>>> Xinit = gen_batch_initial_conditions(
>>> qEI, bounds, q=3, num_restarts=25, raw_samples=500
>>> )
>>> batch_candidates, batch_acq_values = gen_candidates_torch(
initial_conditions=Xinit,
acquisition_function=qEI,
lower_bounds=bounds[0],
upper_bounds=bounds[1],
)
"""
options = options or {}
clamped_candidates = columnwise_clamp(
X=initial_conditions, lower=lower_bounds, upper=upper_bounds
).requires_grad_(True)
candidates = fix_features(clamped_candidates, fixed_features)
bayes_optimizer = optimizer(
params=[clamped_candidates], lr=options.get("lr", 0.025)
)
param_trajectory: Dict[str, List[Tensor]] = {"candidates": []}
loss_trajectory: List[float] = []
i = 0
stop = False
stopping_criterion = ExpMAStoppingCriterion(
**_filter_kwargs(ExpMAStoppingCriterion, **options)
)
while not stop:
i += 1
loss = -acquisition_function(candidates).sum()
if verbose:
print("Iter: {} - Value: {:.3f}".format(i, -(loss.item())))
loss_trajectory.append(loss.item())
param_trajectory["candidates"].append(candidates.clone())
def closure():
bayes_optimizer.zero_grad()
loss.backward()
return loss
bayes_optimizer.step(closure)
clamped_candidates.data = columnwise_clamp(
clamped_candidates, lower_bounds, upper_bounds
)
candidates = fix_features(clamped_candidates, fixed_features)
stop = stopping_criterion.evaluate(fvals=loss.detach())
clamped_candidates = columnwise_clamp(
X=candidates, lower=lower_bounds, upper=upper_bounds, raise_on_violation=True
)
with torch.no_grad():
batch_acquisition = acquisition_function(candidates)
return candidates, batch_acquisition