https://github.com/GPflow/GPflow
Tip revision: f6338da0861599fa91ffe273dbefe47c8db7f23e authored by Artem Artemev on 19 February 2020, 13:33:39 UTC
Update
Update
Tip revision: f6338da
mcmc.py
# Copyright 2019 Artem Artemev @awav, Eric Hambro @condnsdmatters
#
# 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, Sequence, Optional, TypeVar
import tensorflow as tf
from gpflow.base import Parameter
__all__ = ["SamplingHelper"]
ModelParameters = Sequence[TypeVar("ModelParameter", tf.Variable, Parameter)]
LogProbabilityFunction = Callable[[ModelParameters], tf.Tensor]
class SamplingHelper:
"""
Helper reads from variables being set with a prior and writes values back to the same variables.
Example:
model = <Create GPflow model>
hmc_helper = SamplingHelper(model.log_marginal_likelihood, m.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_samples_to_parameter_values(hmc_samples)
Args:
target_log_prob_fn: Python callable which represents log-density under the target distribution.
model_parameters: List of `tensorflow.Variable`s or `gpflow.Parameter`s used as a state of the Markov chain.
"""
def __init__(self, target_log_prob_fn: LogProbabilityFunction, model_parameters: ModelParameters):
assert all([isinstance(p, (Parameter, tf.Variable)) for p in model_parameters])
self._model_parameters = model_parameters
self._target_log_prob_fn = target_log_prob_fn
self._parameters = []
self._unconstrained_variables = []
for p in self._model_parameters:
self._unconstrained_variables.append(p.unconstrained_variable if isinstance(p, Parameter) else p)
self._parameters.append(p)
@property
def current_state(self):
"""Return the current state of the unconstrained variables, used in HMC."""
return self._unconstrained_variables
@property
def target_log_prob_fn(self):
""" 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):
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._model_parameters:
if isinstance(param, Parameter) and 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, variables: Optional[tf.Tensor] = None):
grad = tape.gradient(log_prob, variables_list)
return grad, [None] * len(variables)
return log_prob, grad_fn
return _target_log_prob_fn_closure
def convert_constrained_values(self, hmc_samples):
"""
Converts list of `unconstrained_values` to constrained versions. Each value in the list correspond to an entry in
`self.parameters`; in case that object is a `gpflow.Parameter`, the `forward` method of its transform
will be applied first.
"""
values = []
for hmc_variable, param in zip(hmc_samples, self._parameters):
if isinstance(param, Parameter) and param.transform is not None:
value = param.transform.forward(hmc_variable)
else:
value = hmc_variable
values.append(value.numpy())
return values