# Copyright 2020- The Blackjax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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"""Public API for the Stochastic gradient Nosé-Hoover Thermostat kernel."""
from typing import Callable, NamedTuple, Union
import blackjax.sgmcmc.diffusions as diffusions
from blackjax.base import SamplingAlgorithm
from blackjax.types import ArrayLikeTree, ArrayTree, PRNGKey
from blackjax.util import generate_gaussian_noise
__all__ = ["SGNHTState", "init", "build_kernel", "as_top_level_api"]
[docs]
class SGNHTState(NamedTuple):
r"""State of the SGNHT algorithm.
Parameters
----------
position
Current position in the sample space.
momentum
Current momentum in the sample space.
xi
Scalar thermostat controlling kinetic energy.
"""
[docs]
def init(position: ArrayLikeTree, rng_key: PRNGKey, xi: float) -> SGNHTState:
momentum = generate_gaussian_noise(rng_key, position)
return SGNHTState(position, momentum, xi)
[docs]
def build_kernel(alpha: float = 0.01, beta: float = 0) -> Callable:
"""Stochastic gradient Nosé-Hoover Thermostat (SGNHT) algorithm."""
integrator = diffusions.sgnht(alpha, beta)
def kernel(
rng_key: PRNGKey,
state: SGNHTState,
grad_estimator: Callable,
minibatch: ArrayLikeTree,
step_size: float,
temperature: float = 1.0,
) -> ArrayTree:
position, momentum, xi = state
logdensity_grad = grad_estimator(position, minibatch)
position, momentum, xi = integrator(
rng_key, position, momentum, xi, logdensity_grad, step_size, temperature
)
return SGNHTState(position, momentum, xi)
return kernel
[docs]
def as_top_level_api(
grad_estimator: Callable,
alpha: float = 0.01,
beta: float = 0.0,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the SGNHT kernel.
The general sgnht kernel (:meth:`blackjax.sgmcmc.sgnht.build_kernel`, alias
`blackjax.sgnht.build_kernel`) can be cumbersome to manipulate. Since most users
only need to specify the kernel parameters at initialization time, we
provide a helper function that specializes the general kernel.
Example
-------
To initialize a SGNHT kernel one needs to specify a schedule function, which
returns a step size at each sampling step, and a gradient estimator
function. Here for a constant step size, and `data_size` data samples:
.. code::
grad_estimator = blackjax.sgmcmc.gradients.grad_estimator(logprior_fn, loglikelihood_fn, data_size)
We can now initialize the sgnht kernel and the state.
.. code::
sgnht = blackjax.sgnht(grad_estimator)
state = sgnht.init(rng_key, position)
Assuming we have an iterator `batches` that yields batches of data we can
perform one step:
.. code::
step_size = 1e-3
minibatch = next(batches)
new_state = sgnht.step(rng_key, state, minibatch, step_size)
Kernels are not jit-compiled by default so you will need to do it manually:
.. code::
step = jax.jit(sgnht.step)
new_state = step(rng_key, state, minibatch, step_size)
Parameters
----------
grad_estimator
A function that takes a position, a batch of data and returns an estimation
of the gradient of the log-density at this position.
Returns
-------
A ``SamplingAlgorithm``.
"""
kernel = build_kernel(alpha, beta)
def init_fn(
position: ArrayLikeTree,
rng_key: PRNGKey,
init_xi: Union[None, float] = None,
):
return init(position, rng_key, init_xi or alpha)
def step_fn(
rng_key: PRNGKey,
state: SGNHTState,
minibatch: ArrayLikeTree,
step_size: float,
temperature: float = 1,
) -> SGNHTState:
return kernel(rng_key, state, grad_estimator, minibatch, step_size, temperature)
return SamplingAlgorithm(init_fn, step_fn) # type: ignore[arg-type]
