# Copyright 2019-2020 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Optional, Sequence, Tuple
import tensorflow as tf
from gpflow.base import Parameter
__all__ = ["SamplingHelper"]
[docs]class SamplingHelper:
"""
This helper makes it easy to read from variables being set with a prior and
writes values back to the same variables.
Example:
model = ... # Create a GPflow model
hmc_helper = SamplingHelper(model.log_posterior_density, model.trainable_parameters)
target_log_prob_fn = hmc_helper.target_log_prob_fn
current_state = hmc_helper.current_state
hmc = tfp.mcmc.HamiltonianMonteCarlo(target_log_prob_fn=target_log_prob_fn, ...)
adaptive_hmc = tfp.mcmc.SimpleStepSizeAdaptation(hmc, ...)
@tf.function
def run_chain_fn():
return mcmc.sample_chain(
num_samples, num_burnin_steps, current_state, kernel=adaptive_hmc)
hmc_samples = run_chain_fn()
parameter_samples = hmc_helper.convert_to_constrained_values(hmc_samples)
"""
def __init__(
self, target_log_prob_fn: Callable[[], tf.Tensor], parameters: Sequence[Parameter]
) -> None:
"""
:param target_log_prob_fn: a callable which returns the log-density of the model
under the target distribution; needs to implicitly depend on the `parameters`.
E.g. `model.log_posterior_density`.
:param parameters: List of :class:`gpflow.Parameter` used as a state of the Markov chain.
E.g. `model.trainable_parameters`
Note that each parameter must have been given a prior.
"""
if not all(isinstance(p, Parameter) and p.prior is not None for p in parameters):
raise ValueError(
"`parameters` should only contain gpflow.Parameter objects with priors"
)
self._parameters = parameters
self._target_log_prob_fn = target_log_prob_fn
self._variables = [p.unconstrained_variable for p in parameters]
@property
def current_state(self) -> Sequence[tf.Variable]:
"""Return the current state of the unconstrained variables, used in HMC."""
return self._variables
@property
def target_log_prob_fn(
self,
) -> Callable[..., Tuple[tf.Tensor, Callable[..., Tuple[tf.Tensor, Sequence[None]]]]]:
"""
The target log probability, adjusted to allow for optimisation to occur on the tracked
unconstrained underlying variables.
"""
variables_list = self.current_state
@tf.custom_gradient
def _target_log_prob_fn_closure(
*variables: tf.Variable,
) -> Tuple[tf.Tensor, Callable[..., Tuple[tf.Tensor, Sequence[None]]]]:
for v_old, v_new in zip(variables_list, variables):
v_old.assign(v_new)
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(variables_list)
log_prob = self._target_log_prob_fn()
# Now need to correct for the fact that the prob fn is evaluated on the
# constrained space while we wish to evaluate it in the unconstrained space
for param in self._parameters:
if param.transform is not None:
x = param.unconstrained_variable
log_det_jacobian = param.transform.forward_log_det_jacobian(
x, x.shape.ndims
)
log_prob += tf.reduce_sum(log_det_jacobian)
@tf.function
def grad_fn(
dy: tf.Tensor, variables: Optional[tf.Tensor] = None
) -> Tuple[tf.Tensor, Sequence[None]]:
grad = tape.gradient(log_prob, variables_list)
return grad, [None] * len(variables_list)
return log_prob, grad_fn
return _target_log_prob_fn_closure
[docs] def convert_to_constrained_values(
self, hmc_samples: Sequence[tf.Tensor]
) -> Sequence[tf.Tensor]:
"""
Converts list of unconstrained values in `hmc_samples` to constrained
versions. Each value in the list corresponds to an entry in parameters
passed to the constructor; for parameters that have a transform, the
constrained representation is returned.
"""
values = []
for hmc_value, param in zip(hmc_samples, self._parameters):
if param.transform is not None:
value = param.transform.forward(hmc_value)
else:
value = hmc_value
values.append(value)
return values