#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
r"""
Tools for model fitting.
"""
import time
import warnings
from collections import OrderedDict
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
import numpy as np
from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
from scipy.optimize import Bounds, minimize
from torch import Tensor
from torch.optim.adam import Adam
from torch.optim.optimizer import Optimizer
from ..exceptions.warnings import OptimizationWarning
from .numpy_converter import TorchAttr, module_to_array, set_params_with_array
from .utils import _filter_kwargs, _get_extra_mll_args, check_convergence
ParameterBounds = Dict[str, Tuple[Optional[float], Optional[float]]]
[docs]class OptimizationIteration(NamedTuple):
itr: int
fun: float
time: float
[docs]def fit_gpytorch_torch(
mll: MarginalLogLikelihood,
bounds: Optional[ParameterBounds] = None,
optimizer_cls: Optimizer = Adam,
options: Optional[Dict[str, Any]] = None,
track_iterations: bool = True,
) -> Tuple[MarginalLogLikelihood, 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.
Returns:
2-element tuple containing
- mll with parameters optimized in-place.
- List of OptimizationIteration objects with information on each
iteration. If track_iterations is False, this will be an empty list.
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 {})
optimizer = optimizer_cls(
params=[{"params": mll.parameters()}],
**_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
converged = False
train_inputs, train_targets = mll.model.train_inputs, mll.model.train_targets
while not converged:
optimizer.zero_grad()
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
converged = check_convergence(
loss_trajectory=loss_trajectory,
param_trajectory=param_trajectory,
options={"maxiter": optim_options["maxiter"]},
)
return mll, iterations
[docs]def fit_gpytorch_scipy(
mll: MarginalLogLikelihood,
bounds: Optional[ParameterBounds] = None,
method: str = "L-BFGS-B",
options: Optional[Dict[str, Any]] = None,
track_iterations: bool = True,
) -> Tuple[MarginalLogLikelihood, List[OptimizationIteration]]:
r"""Fit a gpytorch model by maximizing MLL with a scipy 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 dictionary mapping parameter names to tuples of lower and upper
bounds.
method: Solver type, passed along to scipy.minimize.
options: Dictionary of solver options, passed along to scipy.minimize.
track_iterations: Track the function values and wall time for each
iteration.
Returns:
2-element tuple containing
- MarginalLogLikelihood with parameters optimized in-place.
- List of OptimizationIteration objects with information on each
iteration. If track_iterations is False, this will be an empty list.
Example:
>>> gp = SingleTaskGP(train_X, train_Y)
>>> mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
>>> mll.train()
>>> fit_gpytorch_scipy(mll)
>>> mll.eval()
"""
x0, property_dict, bounds = module_to_array(module=mll, bounds=bounds)
x0 = x0.astype(np.float64)
if bounds is not None:
bounds = Bounds(lb=bounds[0], ub=bounds[1], keep_feasible=True)
xs = []
ts = []
t1 = time.time()
def store_iteration(xk):
xs.append(xk.copy())
ts.append(time.time() - t1)
cb = store_iteration if track_iterations else None
res = minimize(
_scipy_objective_and_grad,
x0,
args=(mll, property_dict),
bounds=bounds,
method=method,
jac=True,
options=options,
callback=cb,
)
iterations = []
if track_iterations:
for i, xk in enumerate(xs):
obj, _ = _scipy_objective_and_grad(
x=xk, mll=mll, property_dict=property_dict
)
iterations.append(OptimizationIteration(i, obj, ts[i]))
if not res.success:
msg = res.message.decode("ascii")
warnings.warn(
f"Fitting failed with the optimizer reporting '{msg}'", OptimizationWarning
)
# Set to optimum
mll = set_params_with_array(mll, res.x, property_dict)
return mll, iterations
def _scipy_objective_and_grad(
x: np.ndarray, mll: MarginalLogLikelihood, property_dict: Dict[str, TorchAttr]
) -> Tuple[float, np.ndarray]:
r"""Get objective and gradient in format that scipy expects.
Args:
x: The (flattened) input parameters.
mll: The MarginalLogLikelihood module to evaluate.
property_dict: The property dictionary required to "unflatten" the input
parameter vector, as generated by `module_to_array`.
Returns:
2-element tuple containing
- The objective value.
- The gradient of the objective.
"""
mll = set_params_with_array(mll, x, property_dict)
train_inputs, train_targets = mll.model.train_inputs, mll.model.train_targets
mll.zero_grad()
try: # catch linear algebra errors in gpytorch
output = mll.model(*train_inputs)
args = [output, train_targets] + _get_extra_mll_args(mll)
loss = -mll(*args).sum()
except RuntimeError as e:
if "singular" in e.args[0]:
return float("nan"), np.full_like(x, "nan")
else:
raise e # pragma: nocover
loss.backward()
param_dict = OrderedDict(mll.named_parameters())
grad = []
for p_name in property_dict:
t = param_dict[p_name].grad
if t is None:
# this deals with parameters that do not affect the loss
grad.append(np.zeros(property_dict[p_name].shape.numel()))
else:
grad.append(t.detach().view(-1).cpu().double().clone().numpy())
mll.zero_grad()
return loss.item(), np.concatenate(grad)
