code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def minimize_lbfgs(
fun: Callable,
x0: ArrayLikeTree,
maxiter: int = 30,
maxcor: float = 10,
gtol: float = 1e-08,
ftol: float = 1e-05,
maxls: int = 1000,
**lbfgs_kwargs,
) -> tuple[OptStep, LBFGSHistory]:
"""
Minimize a function using L-BFGS
Parameters
----------
fun... |
Minimize a function using L-BFGS
Parameters
----------
fun:
function of the form f(x) where x is a pytree and returns a real scalar.
The function should be composed of operations with vjp defined.
x0:
initial guess
maxiter:
maximum number of iterations
maxco... | minimize_lbfgs | python | blackjax-devs/blackjax | blackjax/optimizers/lbfgs.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/lbfgs.py | Apache-2.0 |
def lbfgs_recover_alpha(alpha_lm1, s_l, z_l, epsilon=1e-12):
"""
Compute diagonal elements of the inverse Hessian approximation from optimation path.
It implements the inner loop body of Algorithm 3 in :cite:p:`zhang2022pathfinder`.
Parameters
----------
alpha_lm1
The diagonal element o... |
Compute diagonal elements of the inverse Hessian approximation from optimation path.
It implements the inner loop body of Algorithm 3 in :cite:p:`zhang2022pathfinder`.
Parameters
----------
alpha_lm1
The diagonal element of the inverse Hessian approximation of the previous iteration
s_... | lbfgs_recover_alpha | python | blackjax-devs/blackjax | blackjax/optimizers/lbfgs.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/lbfgs.py | Apache-2.0 |
def lbfgs_inverse_hessian_factors(S, Z, alpha):
"""
Calculates factors for inverse hessian factored representation.
It implements formula II.2 of:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
"""
param_dims = S.shape[-1]
StZ = S.T @ Z
R = j... |
Calculates factors for inverse hessian factored representation.
It implements formula II.2 of:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
| lbfgs_inverse_hessian_factors | python | blackjax-devs/blackjax | blackjax/optimizers/lbfgs.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/lbfgs.py | Apache-2.0 |
def lbfgs_inverse_hessian_formula_2(alpha, beta, gamma):
"""
Calculates inverse hessian from factors as in formula II.3 of:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
"""
param_dims = alpha.shape[0]
dsqrt_alpha = jnp.diag(jnp.sqrt(alpha))
ids... |
Calculates inverse hessian from factors as in formula II.3 of:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
| lbfgs_inverse_hessian_formula_2 | python | blackjax-devs/blackjax | blackjax/optimizers/lbfgs.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/lbfgs.py | Apache-2.0 |
def bfgs_sample(rng_key, num_samples, position, grad_position, alpha, beta, gamma):
"""
Draws approximate samples of target distribution.
It implements Algorithm 4 in:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
"""
if not isinstance(num_samples, ... |
Draws approximate samples of target distribution.
It implements Algorithm 4 in:
Pathfinder: Parallel quasi-newton variational inference, Lu Zhang et al., arXiv:2108.03782
| bfgs_sample | python | blackjax-devs/blackjax | blackjax/optimizers/lbfgs.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/optimizers/lbfgs.py | Apache-2.0 |
def as_top_level_api(
logdensity_estimator: Callable,
gradient_estimator: Callable,
zeta: float = 1,
num_partitions: int = 512,
energy_gap: float = 100,
min_energy: float = 0,
) -> SamplingAlgorithm:
r"""Implements the (basic) user interface for the Contour SGLD kernel.
Parameters
-... | Implements the (basic) user interface for the Contour SGLD kernel.
Parameters
----------
logdensity_estimator
A function that returns an estimation of the model's logdensity given
a position and a batch of data.
gradient_estimator
A function that takes a position, a batch of dat... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/sgmcmc/csgld.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/csgld.py | Apache-2.0 |
def overdamped_langevin():
"""Euler solver for overdamped Langevin diffusion.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
"""
def one_step(
rng_key: PRNGKey,
position: ArrayLikeTree,
logdensity_grad: ArrayLikeTree,
step_size: float,
temperatu... | Euler solver for overdamped Langevin diffusion.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
| overdamped_langevin | python | blackjax-devs/blackjax | blackjax/sgmcmc/diffusions.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/diffusions.py | Apache-2.0 |
def sghmc(alpha: float = 0.01, beta: float = 0):
"""Euler solver for the diffusion equation of the SGHMC algorithm :cite:p:`chen2014stochastic`,
with parameters alpha and beta scaled according to :cite:p:`ma2015complete`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
"""
def one_... | Euler solver for the diffusion equation of the SGHMC algorithm :cite:p:`chen2014stochastic`,
with parameters alpha and beta scaled according to :cite:p:`ma2015complete`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
| sghmc | python | blackjax-devs/blackjax | blackjax/sgmcmc/diffusions.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/diffusions.py | Apache-2.0 |
def sgnht(alpha: float = 0.01, beta: float = 0):
"""Euler solver for the diffusion equation of the SGNHT algorithm :cite:p:`ding2014bayesian`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
"""
def one_step(
rng_key: PRNGKey,
position: ArrayLikeTree,
momentum: ... | Euler solver for the diffusion equation of the SGNHT algorithm :cite:p:`ding2014bayesian`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
| sgnht | python | blackjax-devs/blackjax | blackjax/sgmcmc/diffusions.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/diffusions.py | Apache-2.0 |
def logdensity_estimator(
logprior_fn: Callable, loglikelihood_fn: Callable, data_size: int
) -> Callable:
"""Builds a simple estimator for the log-density.
This estimator first appeared in :cite:p:`robbins1951stochastic`. The `logprior_fn` function has a
single argument: the current position (value o... | Builds a simple estimator for the log-density.
This estimator first appeared in :cite:p:`robbins1951stochastic`. The `logprior_fn` function has a
single argument: the current position (value of parameters). The
`loglikelihood_fn` takes two arguments: the current position and a batch of
data; if there ... | logdensity_estimator | python | blackjax-devs/blackjax | blackjax/sgmcmc/gradients.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/gradients.py | Apache-2.0 |
def logdensity_estimator_fn(
position: ArrayLikeTree, minibatch: ArrayLikeTree
) -> ArrayTree:
"""Return an approximation of the log-posterior density.
Parameters
----------
position
The current value of the random variables.
batch
The current... | Return an approximation of the log-posterior density.
Parameters
----------
position
The current value of the random variables.
batch
The current batch of data
Returns
-------
An approximation of the value of the log-posterior density fun... | logdensity_estimator_fn | python | blackjax-devs/blackjax | blackjax/sgmcmc/gradients.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/gradients.py | Apache-2.0 |
def grad_estimator(
logprior_fn: Callable, loglikelihood_fn: Callable, data_size: int
) -> Callable:
"""Build a simple estimator for the gradient of the log-density."""
logdensity_estimator_fn = logdensity_estimator(
logprior_fn, loglikelihood_fn, data_size
)
return jax.grad(logdensity_esti... | Build a simple estimator for the gradient of the log-density. | grad_estimator | python | blackjax-devs/blackjax | blackjax/sgmcmc/gradients.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/gradients.py | Apache-2.0 |
def control_variates(
logdensity_grad_estimator: Callable,
centering_position: ArrayLikeTree,
data: ArrayLikeTree,
) -> Callable:
"""Builds a control variate gradient estimator :cite:p:`baker2019control`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
Parameters
----------
... | Builds a control variate gradient estimator :cite:p:`baker2019control`.
This algorithm was ported from :cite:p:`coullon2022sgmcmcjax`.
Parameters
----------
logdensity_grad_estimator
A function that approximates the target's gradient function.
data
The full dataset.
centering_p... | control_variates | python | blackjax-devs/blackjax | blackjax/sgmcmc/gradients.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/gradients.py | Apache-2.0 |
def cv_grad_estimator_fn(
position: ArrayLikeTree, minibatch: ArrayLikeTree
) -> ArrayTree:
"""Return an approximation of the log-posterior density.
Parameters
----------
position
The current value of the random variables.
batch
The current ba... | Return an approximation of the log-posterior density.
Parameters
----------
position
The current value of the random variables.
batch
The current batch of data. The first dimension is assumed to be the
batch dimension.
Returns
-------... | cv_grad_estimator_fn | python | blackjax-devs/blackjax | blackjax/sgmcmc/gradients.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/gradients.py | Apache-2.0 |
def build_kernel(alpha: float = 0.01, beta: float = 0) -> Callable:
"""Stochastic gradient Hamiltonian Monte Carlo (SgHMC) algorithm."""
integrator = diffusions.sghmc(alpha, beta)
def kernel(
rng_key: PRNGKey,
position: ArrayLikeTree,
grad_estimator: Callable,
minibatch: Arr... | Stochastic gradient Hamiltonian Monte Carlo (SgHMC) algorithm. | build_kernel | python | blackjax-devs/blackjax | blackjax/sgmcmc/sghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/sghmc.py | Apache-2.0 |
def as_top_level_api(
grad_estimator: Callable,
num_integration_steps: int = 10,
alpha: float = 0.01,
beta: float = 0,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the SGHMC kernel.
The general sghmc kernel builder (:meth:`blackjax.sgmcmc.sghmc.build_kernel`, alias
`... | Implements the (basic) user interface for the SGHMC kernel.
The general sghmc kernel builder (:meth:`blackjax.sgmcmc.sghmc.build_kernel`, alias
`blackjax.sghmc.build_kernel`) can be cumbersome to manipulate. Since most users
only need to specify the kernel parameters at initialization time, we
provide ... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/sgmcmc/sghmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/sghmc.py | Apache-2.0 |
def build_kernel() -> Callable:
"""Stochastic gradient Langevin Dynamics (SgLD) algorithm."""
integrator = diffusions.overdamped_langevin()
def kernel(
rng_key: PRNGKey,
position: ArrayLikeTree,
grad_estimator: Callable,
minibatch: ArrayLikeTree,
step_size: float,
... | Stochastic gradient Langevin Dynamics (SgLD) algorithm. | build_kernel | python | blackjax-devs/blackjax | blackjax/sgmcmc/sgld.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/sgld.py | Apache-2.0 |
def as_top_level_api(
grad_estimator: Callable,
) -> SamplingAlgorithm:
"""Implements the (basic) user interface for the SGLD kernel.
The general sgld kernel builder (:meth:`blackjax.sgmcmc.sgld.build_kernel`, alias
`blackjax.sgld.build_kernel`) can be cumbersome to manipulate. Since most users
onl... | Implements the (basic) user interface for the SGLD kernel.
The general sgld kernel builder (:meth:`blackjax.sgmcmc.sgld.build_kernel`, alias
`blackjax.sgld.build_kernel`) can be cumbersome to manipulate. Since most users
only need to specify the kernel parameters at initialization time, we
provide a he... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/sgmcmc/sgld.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/sgld.py | Apache-2.0 |
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 cumber... | 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... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/sgmcmc/sgnht.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/sgmcmc/sgnht.py | Apache-2.0 |
def build_kernel(
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
target_ess: float,
root_solver: Callable = solver.dichotomy,
**extra_parameters,
) -> Callable:
r"""Build a Tempered SMC step using an adaptiv... | Build a Tempered SMC step using an adaptive schedule.
Parameters
----------
logprior_fn: Callable
A function that computes the log-prior density.
loglikelihood_fn: Callable
A function that returns the log-likelihood density.
mcmc_kernel_factory: Callable
A callable function ... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/adaptive_tempered.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/adaptive_tempered.py | Apache-2.0 |
def as_top_level_api(
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
mcmc_parameters: dict,
resampling_fn: Callable,
target_ess: float,
root_solver: Callable = solver.dichotomy,
num_mcmc_steps: int = 10,
**extra_parameters,
) ->... | Implements the (basic) user interface for the Adaptive Tempered SMC kernel.
Parameters
----------
logprior_fn
The log-prior function of the model we wish to draw samples from.
loglikelihood_fn
The log-likelihood function of the model we wish to draw samples from.
mcmc_step_fn
... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/smc/adaptive_tempered.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/adaptive_tempered.py | Apache-2.0 |
def step(
rng_key: PRNGKey,
state: SMCState,
update_fn: Callable,
weight_fn: Callable,
resample_fn: Callable,
num_resampled: Optional[int] = None,
) -> tuple[SMCState, SMCInfo]:
"""General SMC sampling step.
`update_fn` here corresponds to the Markov kernel $M_{t+1}$, and `weight_fn`
... | General SMC sampling step.
`update_fn` here corresponds to the Markov kernel $M_{t+1}$, and `weight_fn`
corresponds to the potential function $G_t$. We first use `update_fn` to
generate new particles from the current ones, weigh these particles using
`weight_fn` and resample them with `resample_fn`.
... | step | python | blackjax-devs/blackjax | blackjax/smc/base.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/base.py | Apache-2.0 |
def update_and_take_last(
mcmc_init_fn,
tempered_logposterior_fn,
shared_mcmc_step_fn,
num_mcmc_steps,
n_particles,
):
"""Given N particles, runs num_mcmc_steps of a kernel starting at each particle, and
returns the last values, waisting the previous num_mcmc_steps-1
samples per chain.
... | Given N particles, runs num_mcmc_steps of a kernel starting at each particle, and
returns the last values, waisting the previous num_mcmc_steps-1
samples per chain.
| update_and_take_last | python | blackjax-devs/blackjax | blackjax/smc/base.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/base.py | Apache-2.0 |
def log_ess(log_weights: Array) -> float:
"""Compute the effective sample size.
Parameters
----------
log_weights: 1D Array
log-weights of the sample
Returns
-------
log_ess: float
The logarithm of the effective sample size
"""
return 2 * jsp.special.logsumexp(log_... | Compute the effective sample size.
Parameters
----------
log_weights: 1D Array
log-weights of the sample
Returns
-------
log_ess: float
The logarithm of the effective sample size
| log_ess | python | blackjax-devs/blackjax | blackjax/smc/ess.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/ess.py | Apache-2.0 |
def ess_solver(
logdensity_fn: Callable,
particles: ArrayLikeTree,
target_ess: float,
max_delta: float,
root_solver: Callable,
):
"""ESS solver for computing the next increment of SMC tempering.
Parameters
----------
logdensity_fn: Callable
The log probability function we wi... | ESS solver for computing the next increment of SMC tempering.
Parameters
----------
logdensity_fn: Callable
The log probability function we wish to sample from.
particles: SMCState
Current state of the tempered SMC algorithm
target_ess: float
The relative ESS targeted for th... | ess_solver | python | blackjax-devs/blackjax | blackjax/smc/ess.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/ess.py | Apache-2.0 |
def unshared_parameters_and_step_fn(mcmc_parameters, mcmc_step_fn):
"""Splits MCMC parameters into two dictionaries. The shared dictionary
represents the parameters common to all chains, and the unshared are
different per chain.
Binds the step fn using the shared parameters.
"""
shared_mcmc_para... | Splits MCMC parameters into two dictionaries. The shared dictionary
represents the parameters common to all chains, and the unshared are
different per chain.
Binds the step fn using the shared parameters.
| unshared_parameters_and_step_fn | python | blackjax-devs/blackjax | blackjax/smc/from_mcmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/from_mcmc.py | Apache-2.0 |
def build_kernel(
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
update_strategy: Callable = update_and_take_last,
):
"""SMC step from MCMC kernels.
Builds MCMC kernels from the input parameters, which may change across iterations.
Moreover, it defines the way such ... | SMC step from MCMC kernels.
Builds MCMC kernels from the input parameters, which may change across iterations.
Moreover, it defines the way such kernels are used to update the particles. This layer
adapts an API defined in terms of kernels (mcmc_step_fn and mcmc_init_fn) into an API
that depends on an u... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/from_mcmc.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/from_mcmc.py | Apache-2.0 |
def build_kernel(
smc_algorithm,
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
mcmc_parameter_update_fn: Callable[
[PRNGKey, SMCState, SMCInfo], Dict[str, ArrayTree]
],
num_mcmc_steps: int = 10,
... | In the context of an SMC sampler (whose step_fn returning state has a .particles attribute), there's an inner
MCMC that is used to perturbate/update each of the particles. This adaptation tunes some parameter of that MCMC,
based on particles. The parameter type must be a valid JAX type.
Parameters
----... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/inner_kernel_tuning.py | Apache-2.0 |
def as_top_level_api(
smc_algorithm,
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
mcmc_parameter_update_fn: Callable[
[PRNGKey, SMCState, SMCInfo], Dict[str, ArrayTree]
],
initial_parameter_value,
... | In the context of an SMC sampler (whose step_fn returning state
has a .particles attribute), there's an inner MCMC that is used
to perturbate/update each of the particles. This adaptation tunes some
parameter of that MCMC, based on particles.
The parameter type must be a valid JAX type.
Parameters
... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/smc/inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/inner_kernel_tuning.py | Apache-2.0 |
def init(particles: ArrayLikeTree, num_datapoints: int) -> PartialPosteriorsSMCState:
"""num_datapoints are the number of observations that could potentially be
used in a partial posterior. Since the initial data_mask is all 0s, it
means that no likelihood term will be added (only prior).
"""
num_pa... | num_datapoints are the number of observations that could potentially be
used in a partial posterior. Since the initial data_mask is all 0s, it
means that no likelihood term will be added (only prior).
| init | python | blackjax-devs/blackjax | blackjax/smc/partial_posteriors_path.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/partial_posteriors_path.py | Apache-2.0 |
def build_kernel(
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
num_mcmc_steps: Optional[int],
mcmc_parameters: ArrayTree,
partial_logposterior_factory: Callable[[Array], Callable],
update_strategy=update_and_take_last,
) -> Callable:
"""Build the Partial Poste... | Build the Partial Posteriors (data tempering) SMC kernel.
The distribution's trajectory includes increasingly adding more
datapoints to the likelihood. See Section 2.2 of https://arxiv.org/pdf/2007.11936
Parameters
----------
mcmc_step_fn
A function that computes the log density of the prior... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/partial_posteriors_path.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/partial_posteriors_path.py | Apache-2.0 |
def as_top_level_api(
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
mcmc_parameters: dict,
resampling_fn: Callable,
num_mcmc_steps,
partial_logposterior_factory: Callable,
update_strategy=update_and_take_last,
) -> SamplingAlgorithm:
"""A factory that wraps the kernel into a SamplingAl... | A factory that wraps the kernel into a SamplingAlgorithm object.
See build_kernel for full documentation on the parameters.
| as_top_level_api | python | blackjax-devs/blackjax | blackjax/smc/partial_posteriors_path.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/partial_posteriors_path.py | Apache-2.0 |
def esjd(m):
"""Implements ESJD (expected squared jumping distance). Inner Mahalanobis distance
is computed using the Cholesky decomposition of M=LLt, and then inverting L.
Whenever M is symmetrical definite positive then it must exist a Cholesky Decomposition.
For example, if M is the Covariance Matrix... | Implements ESJD (expected squared jumping distance). Inner Mahalanobis distance
is computed using the Cholesky decomposition of M=LLt, and then inverting L.
Whenever M is symmetrical definite positive then it must exist a Cholesky Decomposition.
For example, if M is the Covariance Matrix of Metropolis-Hasti... | esjd | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def update_parameter_distribution(
key: PRNGKey,
previous_param_samples: ArrayLikeTree,
previous_particles: ArrayLikeTree,
latest_particles: ArrayLikeTree,
measure_of_chain_mixing: Callable,
alpha: float,
sigma_parameters: ArrayLikeTree,
acceptance_probability: Array,
):
"""Given an ... | Given an existing parameter distribution that was used to mutate previous_particles
into latest_particles, updates that parameter distribution by resampling from previous_param_samples after adding
noise to those samples. The weights used are a linear function of the measure of chain mixing.
Only works with... | update_parameter_distribution | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def build_pretune(
mcmc_init_fn: Callable,
mcmc_step_fn: Callable,
alpha: float,
sigma_parameters: ArrayLikeTree,
n_particles: int,
performance_of_chain_measure_factory: Callable = default_measure_factory,
natural_parameters: Optional[List[str]] = None,
positive_parameters: Optional[List... | Implements Buchholz et al https://arxiv.org/pdf/1808.07730 pretuning procedure.
The goal is to maintain a probability distribution of parameters, in order
to assign different values to each inner MCMC chain.
To have performant parameters for the distribution at step t, it takes a single step, measures
t... | build_pretune | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def pretune_and_update(key, state: StateWithParameterOverride, logposterior):
"""
Updates the parameters that need to be pretuned and returns the rest.
"""
new_parameter_distribution, chain_mixing_measurement = pretune(
key, state, logposterior
)
old_parameter... |
Updates the parameters that need to be pretuned and returns the rest.
| pretune_and_update | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def build_kernel(
smc_algorithm,
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
pretune_fn: Callable,
num_mcmc_steps: int = 10,
update_strategy=update_and_take_last,
**extra_parameters,
) -> Callable:
... | In the context of an SMC sampler (whose step_fn returning state has a .particles attribute), there's an inner
MCMC that is used to perturbate/update each of the particles. This adaptation tunes some parameter of that MCMC,
based on particles. The parameter type must be a valid JAX type.
Parameters
----... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def pretuned_step(
rng_key: PRNGKey,
state,
num_mcmc_steps: int,
mcmc_parameters: dict,
logposterior_fn: Callable,
log_weights_fn: Callable,
) -> tuple[smc.base.SMCState, SMCInfoWithParameterDistribution]:
"""Wraps the output of smc.from_mcmc.build_kernel into... | Wraps the output of smc.from_mcmc.build_kernel into a pretuning + step method.
This one should be a subtype of the former, in the sense that a usage of the former
can be replaced with an instance of this one.
| pretuned_step | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def as_top_level_api(
smc_algorithm,
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
num_mcmc_steps: int,
initial_parameter_value: ArrayLikeTree,
pretune_fn: Callable,
**extra_parameters,
):
"""In the... | In the context of an SMC sampler (whose step_fn returning state has a .particles attribute), there's an inner
MCMC that is used to perturbate/update each of the particles. This adaptation tunes some parameter of that MCMC,
based on particles. The parameter type must be a valid JAX type.
Parameters
----... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/smc/pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/pretuning.py | Apache-2.0 |
def dichotomy(fun, min_delta, max_delta, eps=1e-4, max_iter=100):
"""Solves for delta by dichotomy.
If max_delta is such that fun(max_delta) > 0, then we assume that max_delta
can be used as an increment in the tempering.
Parameters
----------
fun: Callable
The decreasing function to s... | Solves for delta by dichotomy.
If max_delta is such that fun(max_delta) > 0, then we assume that max_delta
can be used as an increment in the tempering.
Parameters
----------
fun: Callable
The decreasing function to solve, we must have fun(min_delta) > 0, fun(max_delta) < 0
min_delta: ... | dichotomy | python | blackjax-devs/blackjax | blackjax/smc/solver.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/solver.py | Apache-2.0 |
def build_kernel(
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
resampling_fn: Callable,
update_strategy: Callable = update_and_take_last,
update_particles_fn: Optional[Callable] = None,
) -> Callable:
"""Build the base Tempered SMC ke... | Build the base Tempered SMC kernel.
Tempered SMC uses tempering to sample from a distribution given by
.. math::
p(x) \propto p_0(x) \exp(-V(x)) \mathrm{d}x
where :math:`p_0` is the prior distribution, typically easy to sample from
and for which the density is easy to compute, and :math:`\exp... | build_kernel | python | blackjax-devs/blackjax | blackjax/smc/tempered.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/tempered.py | Apache-2.0 |
def kernel(
rng_key: PRNGKey,
state: TemperedSMCState,
num_mcmc_steps: int,
lmbda: float,
mcmc_parameters: dict,
) -> tuple[TemperedSMCState, smc.base.SMCInfo]:
"""Move the particles one step using the Tempered SMC algorithm.
Parameters
----------
... | Move the particles one step using the Tempered SMC algorithm.
Parameters
----------
rng_key
JAX PRNGKey for randomness
state
Current state of the tempered SMC algorithm
lmbda
Current value of the tempering parameter
mcmc_parameters
... | kernel | python | blackjax-devs/blackjax | blackjax/smc/tempered.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/tempered.py | Apache-2.0 |
def as_top_level_api(
logprior_fn: Callable,
loglikelihood_fn: Callable,
mcmc_step_fn: Callable,
mcmc_init_fn: Callable,
mcmc_parameters: dict,
resampling_fn: Callable,
num_mcmc_steps: Optional[int] = 10,
update_strategy=update_and_take_last,
update_particles_fn=None,
) -> SamplingAl... | Implements the (basic) user interface for the Adaptive Tempered SMC kernel.
Parameters
----------
logprior_fn
The log-prior function of the model we wish to draw samples from.
loglikelihood_fn
The log-likelihood function of the model we wish to draw samples from.
mcmc_step_fn
... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/smc/tempered.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/tempered.py | Apache-2.0 |
def update_waste_free(
mcmc_init_fn,
logposterior_fn,
mcmc_step_fn,
n_particles: int,
p: int,
num_resampled,
num_mcmc_steps=None,
):
"""
Given M particles, mutates them using p-1 steps. Returns M*P-1 particles,
consistent of the initial plus all the intermediate steps, thus imple... |
Given M particles, mutates them using p-1 steps. Returns M*P-1 particles,
consistent of the initial plus all the intermediate steps, thus implementing a
waste-free update function
See Algorithm 2: https://arxiv.org/abs/2011.02328
| update_waste_free | python | blackjax-devs/blackjax | blackjax/smc/waste_free.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/waste_free.py | Apache-2.0 |
def update(rng_key, position, step_parameters):
"""
Given the initial particles, runs a chain starting at each.
The combines the initial particles with all the particles generated
at each step of each chain.
"""
states, infos = jax.vmap(mcmc_kernel)(rng_key, position, ste... |
Given the initial particles, runs a chain starting at each.
The combines the initial particles with all the particles generated
at each step of each chain.
| update | python | blackjax-devs/blackjax | blackjax/smc/waste_free.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/waste_free.py | Apache-2.0 |
def update_scale_from_acceptance_rate(
scales: jax.Array,
acceptance_rates: jax.Array,
target_acceptance_rate: float = 0.234,
) -> jax.Array:
"""
Given N chains from some MCMC algorithm like Random Walk Metropolis
and N scale factors, each associated to a different chain.
Updates the scale f... |
Given N chains from some MCMC algorithm like Random Walk Metropolis
and N scale factors, each associated to a different chain.
Updates the scale factors taking into account acceptance rates and
the average acceptance rate.
Under certain assumptions it is known that the optimal acceptance rate
... | update_scale_from_acceptance_rate | python | blackjax-devs/blackjax | blackjax/smc/tuning/from_kernel_info.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/smc/tuning/from_kernel_info.py | Apache-2.0 |
def init(
position: ArrayLikeTree,
optimizer: GradientTransformation,
*optimizer_args,
**optimizer_kwargs,
) -> MFVIState:
"""Initialize the mean-field VI state."""
mu = jax.tree.map(jnp.zeros_like, position)
rho = jax.tree.map(lambda x: -2.0 * jnp.ones_like(x), position)
opt_state = opt... | Initialize the mean-field VI state. | init | python | blackjax-devs/blackjax | blackjax/vi/meanfield_vi.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/meanfield_vi.py | Apache-2.0 |
def step(
rng_key: PRNGKey,
state: MFVIState,
logdensity_fn: Callable,
optimizer: GradientTransformation,
num_samples: int = 5,
stl_estimator: bool = True,
) -> tuple[MFVIState, MFVIInfo]:
"""Approximate the target density using the mean-field approximation.
Parameters
----------
... | Approximate the target density using the mean-field approximation.
Parameters
----------
rng_key
Key for JAX's pseudo-random number generator.
init_state
Initial state of the mean-field approximation.
logdensity_fn
Function that represents the target log-density to approxima... | step | python | blackjax-devs/blackjax | blackjax/vi/meanfield_vi.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/meanfield_vi.py | Apache-2.0 |
def as_top_level_api(
logdensity_fn: Callable,
optimizer: GradientTransformation,
num_samples: int = 100,
):
"""High-level implementation of Mean-Field Variational Inference.
Parameters
----------
logdensity_fn
A function that represents the log-density function associated with
... | High-level implementation of Mean-Field Variational Inference.
Parameters
----------
logdensity_fn
A function that represents the log-density function associated with
the distribution we want to sample from.
optimizer
Optax optimizer to use to optimize the ELBO.
num_samples
... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/vi/meanfield_vi.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/meanfield_vi.py | Apache-2.0 |
def approximate(
rng_key: PRNGKey,
logdensity_fn: Callable,
initial_position: ArrayLikeTree,
num_samples: int = 200,
*, # lgbfs parameters
maxiter=30,
maxcor=10,
maxls=1000,
gtol=1e-08,
ftol=1e-05,
**lbfgs_kwargs,
) -> tuple[PathfinderState, PathfinderInfo]:
"""Pathfinde... | Pathfinder variational inference algorithm.
Pathfinder locates normal approximations to the target density along a
quasi-Newton optimization path, with local covariance estimated using
the inverse Hessian estimates produced by the L-BFGS optimizer.
Function implements the algorithm 3 in :cite:p:`zhang... | approximate | python | blackjax-devs/blackjax | blackjax/vi/pathfinder.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/pathfinder.py | Apache-2.0 |
def path_finder_body_fn(rng_key, S, Z, alpha_l, theta, theta_grad):
"""The for loop body in Algorithm 1 of the Pathfinder paper."""
beta, gamma = lbfgs_inverse_hessian_factors(S.T, Z.T, alpha_l)
phi, logq = bfgs_sample(
rng_key=rng_key,
num_samples=num_samples,
... | The for loop body in Algorithm 1 of the Pathfinder paper. | path_finder_body_fn | python | blackjax-devs/blackjax | blackjax/vi/pathfinder.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/pathfinder.py | Apache-2.0 |
def sample(
rng_key: PRNGKey,
state: PathfinderState,
num_samples: Union[int, tuple[()], tuple[int]] = (),
) -> ArrayTree:
"""Draw from the Pathfinder approximation of the target distribution.
Parameters
----------
rng_key
PRNG key
state
PathfinderState containing inform... | Draw from the Pathfinder approximation of the target distribution.
Parameters
----------
rng_key
PRNG key
state
PathfinderState containing information for sampling
num_samples
Number of samples to draw
Returns
-------
Samples drawn from the approximate Pathfinde... | sample | python | blackjax-devs/blackjax | blackjax/vi/pathfinder.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/pathfinder.py | Apache-2.0 |
def as_top_level_api(logdensity_fn: Callable) -> PathFinderAlgorithm:
"""Implements the (basic) user interface for the pathfinder kernel.
Pathfinder locates normal approximations to the target density along a
quasi-Newton optimization path, with local covariance estimated using
the inverse Hessian esti... | Implements the (basic) user interface for the pathfinder kernel.
Pathfinder locates normal approximations to the target density along a
quasi-Newton optimization path, with local covariance estimated using
the inverse Hessian estimates produced by the L-BFGS optimizer.
Pathfinder returns draws from the... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/vi/pathfinder.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/pathfinder.py | Apache-2.0 |
def init(
initial_particles: ArrayLikeTree,
kernel_parameters: dict[str, Any],
optimizer: optax.GradientTransformation,
) -> SVGDState:
"""
Initializes Stein Variational Gradient Descent Algorithm.
Parameters
----------
initial_particles
Initial set of particles to start the opt... |
Initializes Stein Variational Gradient Descent Algorithm.
Parameters
----------
initial_particles
Initial set of particles to start the optimization
kernel_paremeters
Arguments to the kernel function
optimizer
Optax compatible optimizer, which conforms to the `optax.Gra... | init | python | blackjax-devs/blackjax | blackjax/vi/svgd.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/svgd.py | Apache-2.0 |
def kernel(
state: SVGDState,
grad_logdensity_fn: Callable,
kernel: Callable,
**grad_params,
) -> SVGDState:
"""
Performs one step of Stein Variational Gradient Descent.
See Algorithm 1 of :cite:p:`liu2016stein`.
Parameters
----------
... |
Performs one step of Stein Variational Gradient Descent.
See Algorithm 1 of :cite:p:`liu2016stein`.
Parameters
----------
state
SVGDState object containing information about previous iteration
grad_logdensity_fn
gradient, or an estimate, of the ... | kernel | python | blackjax-devs/blackjax | blackjax/vi/svgd.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/svgd.py | Apache-2.0 |
def update_median_heuristic(state: SVGDState) -> SVGDState:
"""Median heuristic for setting the bandwidth of RBF kernels.
A reasonable middle-ground for choosing the `length_scale` of the RBF kernel
is to pick the empirical median of the squared distance between particles.
This strategy is called the m... | Median heuristic for setting the bandwidth of RBF kernels.
A reasonable middle-ground for choosing the `length_scale` of the RBF kernel
is to pick the empirical median of the squared distance between particles.
This strategy is called the median heuristic.
| update_median_heuristic | python | blackjax-devs/blackjax | blackjax/vi/svgd.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/svgd.py | Apache-2.0 |
def as_top_level_api(
grad_logdensity_fn: Callable,
optimizer,
kernel: Callable = rbf_kernel,
update_kernel_parameters: Callable = update_median_heuristic,
):
"""Implements the (basic) user interface for the svgd algorithm :cite:p:`liu2016stein`.
Parameters
----------
grad_logdensity_fn... | Implements the (basic) user interface for the svgd algorithm :cite:p:`liu2016stein`.
Parameters
----------
grad_logdensity_fn
gradient, or an estimate, of the target log density function to samples approximately from
optimizer
Optax compatible optimizer, which conforms to the `optax.Gra... | as_top_level_api | python | blackjax-devs/blackjax | blackjax/vi/svgd.py | https://github.com/blackjax-devs/blackjax/blob/master/blackjax/vi/svgd.py | Apache-2.0 |
def test_hmc(self):
"""Count the number of times the logdensity is compiled when using HMC.
The logdensity is compiled twice: when initializing the state and when
compiling the kernel.
"""
@chex.assert_max_traces(n=2)
def logdensity_fn(x):
return jscipy.sta... | Count the number of times the logdensity is compiled when using HMC.
The logdensity is compiled twice: when initializing the state and when
compiling the kernel.
| test_hmc | python | blackjax-devs/blackjax | tests/test_compilation.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/test_compilation.py | Apache-2.0 |
def test_nuts(self):
"""Count the number of times the logdensity is compiled when using NUTS.
The logdensity is compiled twice: when initializing the state and when
compiling the kernel.
"""
@chex.assert_max_traces(n=2)
def logdensity_fn(x):
return jscipy.s... | Count the number of times the logdensity is compiled when using NUTS.
The logdensity is compiled twice: when initializing the state and when
compiling the kernel.
| test_nuts | python | blackjax-devs/blackjax | tests/test_compilation.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/test_compilation.py | Apache-2.0 |
def test_hmc_warmup(self):
"""Count the number of times the logdensity is compiled when using window
adaptation to adapt the value of the step size and the inverse mass
matrix for the HMC algorithm.
"""
@chex.assert_max_traces(n=3)
def logdensity_fn(x):
retu... | Count the number of times the logdensity is compiled when using window
adaptation to adapt the value of the step size and the inverse mass
matrix for the HMC algorithm.
| test_hmc_warmup | python | blackjax-devs/blackjax | tests/test_compilation.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/test_compilation.py | Apache-2.0 |
def test_nuts_warmup(self):
"""Count the number of times the logdensity is compiled when using window
adaptation to adapt the value of the step size and the inverse mass
matrix for the NUTS algorithm.
"""
@chex.assert_max_traces(n=3)
def logdensity_fn(x):
re... | Count the number of times the logdensity is compiled when using window
adaptation to adapt the value of the step size and the inverse mass
matrix for the NUTS algorithm.
| test_nuts_warmup | python | blackjax-devs/blackjax | tests/test_compilation.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/test_compilation.py | Apache-2.0 |
def check_compatible(self, initial_state, progress_bar):
"""
Runs 10 steps with `run_inference_algorithm` starting with
`initial_state` and potentially a progress bar.
"""
_ = run_inference_algorithm(
rng_key=self.key,
initial_state=initial_state,
... |
Runs 10 steps with `run_inference_algorithm` starting with
`initial_state` and potentially a progress bar.
| check_compatible | python | blackjax-devs/blackjax | tests/test_util.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/test_util.py | Apache-2.0 |
def test_preconditioning_matrix(self, seed):
"""Test two different ways of using pre-conditioning matrix has exactly same effect.
We follow the discussion in Appendix G of the Barker 2020 paper.
"""
key = jax.random.key(seed)
init_key, inference_key = jax.random.split(key, 2)
... | Test two different ways of using pre-conditioning matrix has exactly same effect.
We follow the discussion in Appendix G of the Barker 2020 paper.
| test_preconditioning_matrix | python | blackjax-devs/blackjax | tests/mcmc/test_barker.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_barker.py | Apache-2.0 |
def HarmonicOscillator(inv_mass_matrix, k=1.0, m=1.0):
"""Potential and Kinetic energy of an harmonic oscillator."""
def neg_potential_energy(q):
return -jnp.sum(0.5 * k * jnp.square(q["x"]))
def kinetic_energy(p, position=None):
del position
v = jnp.multiply(inv_mass_matrix, p["x"... | Potential and Kinetic energy of an harmonic oscillator. | HarmonicOscillator | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def FreeFall(inv_mass_matrix, g=1.0):
"""Potential and kinetic energy of a free-falling object."""
def neg_potential_energy(q):
return -jnp.sum(g * q["x"])
def kinetic_energy(p, position=None):
del position
v = jnp.multiply(inv_mass_matrix, p["x"])
return jnp.sum(0.5 * jnp.... | Potential and kinetic energy of a free-falling object. | FreeFall | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def PlanetaryMotion(inv_mass_matrix):
"""Potential and kinetic energy for planar planetary motion."""
def neg_potential_energy(q):
return 1.0 / jnp.power(q["x"] ** 2 + q["y"] ** 2, 0.5)
def kinetic_energy(p, position=None):
del position
z = jnp.stack([p["x"], p["y"]], axis=-1)
... | Potential and kinetic energy for planar planetary motion. | PlanetaryMotion | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def MultivariateNormal(inv_mass_matrix):
"""Potential and kinetic energy for a multivariate normal distribution."""
def log_density(q):
q, _ = ravel_pytree(q)
return stats.multivariate_normal.logpdf(q, jnp.zeros_like(q), inv_mass_matrix)
def kinetic_energy(p, position=None):
del po... | Potential and kinetic energy for a multivariate normal distribution. | MultivariateNormal | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def test_esh_momentum_update(self, dims):
"""
Test the numerically efficient version of the momentum update currently
implemented match the naive implementation according to the Equation 16 in
:cite:p:`robnik2023microcanonical`
"""
step_size = 1e-3
key0, key1 = ja... |
Test the numerically efficient version of the momentum update currently
implemented match the naive implementation according to the Equation 16 in
:cite:p:`robnik2023microcanonical`
| test_esh_momentum_update | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def test_non_separable(self):
"""Test the integration of a non-separable Hamiltonian with a known
closed-form solution, as defined in https://arxiv.org/abs/1609.02212.
"""
def neg_potential(q):
return -0.5 * (q**2 + 1)
def kinetic_energy(p, position=None):
... | Test the integration of a non-separable Hamiltonian with a known
closed-form solution, as defined in https://arxiv.org/abs/1609.02212.
| test_non_separable | python | blackjax-devs/blackjax | tests/mcmc/test_integrators.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_integrators.py | Apache-2.0 |
def test_invalid(self, shape, is_inv):
"""Test formatting raises error for invalid shapes"""
mass_matrix = jnp.zeros(shape=shape)
with self.assertRaisesRegex(
ValueError, "The mass matrix has the wrong number of dimensions"
):
metrics._format_covariance(mass_matri... | Test formatting raises error for invalid shapes | test_invalid | python | blackjax-devs/blackjax | tests/mcmc/test_metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_metrics.py | Apache-2.0 |
def test_gaussian_euclidean_ndim_invalid(self, shape):
"""Test Gaussian Euclidean Function returns correct function invalid ndim"""
x = jnp.ones(shape=shape)
with self.assertRaisesRegex(
ValueError, "The mass matrix has the wrong number of dimensions"
):
_ = metri... | Test Gaussian Euclidean Function returns correct function invalid ndim | test_gaussian_euclidean_ndim_invalid | python | blackjax-devs/blackjax | tests/mcmc/test_metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_metrics.py | Apache-2.0 |
def test_gaussian_euclidean_dim_1(self):
"""Test Gaussian Euclidean Function with ndim 1"""
inverse_mass_matrix = jnp.asarray([1 / 4], dtype=self.dtype)
momentum, kinetic_energy, _, scale = metrics.gaussian_euclidean(
inverse_mass_matrix
)
arbitrary_position = jnp.as... | Test Gaussian Euclidean Function with ndim 1 | test_gaussian_euclidean_dim_1 | python | blackjax-devs/blackjax | tests/mcmc/test_metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_metrics.py | Apache-2.0 |
def test_gaussian_euclidean_dim_2(self):
"""Test Gaussian Euclidean Function with ndim 2"""
inverse_mass_matrix = jnp.asarray(
[[2 / 3, 0.5], [0.5, 3 / 4]], dtype=self.dtype
)
momentum, kinetic_energy, _, scale = metrics.gaussian_euclidean(
inverse_mass_matrix
... | Test Gaussian Euclidean Function with ndim 2 | test_gaussian_euclidean_dim_2 | python | blackjax-devs/blackjax | tests/mcmc/test_metrics.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_metrics.py | Apache-2.0 |
def test_normal_univariate(self, initial_position):
"""
Move samples are generated in the univariate case,
with std following sigma, and independently of the position.
"""
keys = jax.random.split(self.key, 200)
proposal = normal(sigma=jnp.array([1.0]))
samples = [... |
Move samples are generated in the univariate case,
with std following sigma, and independently of the position.
| test_normal_univariate | python | blackjax-devs/blackjax | tests/mcmc/test_proposal.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_proposal.py | Apache-2.0 |
def test_one_step_addition(self):
"""New position is an addition to previous position.
Since the density == 1, the proposal is accepted.
The random step may depend on the previous position
"""
rng_key = jax.random.key(0)
initial_position = jnp.array([50.0])
def r... | New position is an addition to previous position.
Since the density == 1, the proposal is accepted.
The random step may depend on the previous position
| test_one_step_addition | python | blackjax-devs/blackjax | tests/mcmc/test_random_walk_without_chex.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_random_walk_without_chex.py | Apache-2.0 |
def test_proposal_is_independent_of_position(self):
"""New position does not depend on previous position"""
rng_key = jax.random.key(0)
initial_position = jnp.array([50.0])
other_position = jnp.array([15000.0])
step = build_irmh()
for previous_position in [initial_posit... | New position does not depend on previous position | test_proposal_is_independent_of_position | python | blackjax-devs/blackjax | tests/mcmc/test_random_walk_without_chex.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_random_walk_without_chex.py | Apache-2.0 |
def test_non_symmetric_proposal(self):
"""
Given that proposal_logdensity_fn is included,
thus the proposal is non-symmetric.
When computing the acceptance of the proposed state
Then proposal_logdensity_fn value is taken into account
"""
rng_key = jax.random.key(0... |
Given that proposal_logdensity_fn is included,
thus the proposal is non-symmetric.
When computing the acceptance of the proposed state
Then proposal_logdensity_fn value is taken into account
| test_non_symmetric_proposal | python | blackjax-devs/blackjax | tests/mcmc/test_random_walk_without_chex.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_random_walk_without_chex.py | Apache-2.0 |
def test_generate_reject(self):
"""
Steps from previous state,
Builds a proposal from the new state
and given that the sampling rule rejects,
the prev_state is proposed again
"""
rng_key = jax.random.key(0)
prev_state = RWState(jnp.array([30.0]), 15.0)
... |
Steps from previous state,
Builds a proposal from the new state
and given that the sampling rule rejects,
the prev_state is proposed again
| test_generate_reject | python | blackjax-devs/blackjax | tests/mcmc/test_random_walk_without_chex.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_random_walk_without_chex.py | Apache-2.0 |
def test_window_adaptation(
self, case, is_mass_matrix_diagonal, window_adapt_config
):
"""Test the HMC kernel and the Stan warmup."""
rng_key, init_key0, init_key1 = jax.random.split(self.key, 3)
x_data = jax.random.normal(init_key0, shape=(1000, 1))
y_data = 3 * x_data + ja... | Test the HMC kernel and the Stan warmup. | test_window_adaptation | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def __init__(self, d, condition_number):
"""numpy_seed is used to generate a random rotation for the covariance matrix.
If None, the covariance matrix is diagonal."""
self.ndims = d
self.name = "IllConditionedGaussian"
self.condition_numbe... | numpy_seed is used to generate a random rotation for the covariance matrix.
If None, the covariance matrix is diagonal. | __init__ | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def test_pathfinder_adaptation(
self,
algorithm,
num_warmup_steps,
initial_position,
num_sampling_steps,
parameters,
):
"""Test the HMC kernel and the Stan warmup."""
rng_key, init_key0, init_key1 = jax.random.split(self.key, 3)
x_data = jax.ra... | Test the HMC kernel and the Stan warmup. | test_pathfinder_adaptation | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def test_meads(self):
"""Test the MEADS adaptation w/ GHMC kernel."""
rng_key, init_key0, init_key1 = jax.random.split(self.key, 3)
x_data = jax.random.normal(init_key0, shape=(1000, 1))
y_data = 3 * x_data + jax.random.normal(init_key1, shape=x_data.shape)
logposterior_fn_ = fu... | Test the MEADS adaptation w/ GHMC kernel. | test_meads | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def test_chees(self, jitter_generator):
"""Test the ChEES adaptation w/ HMC kernel."""
rng_key, init_key0, init_key1 = jax.random.split(self.key, 3)
x_data = jax.random.normal(init_key0, shape=(1000, 1))
y_data = 3 * x_data + jax.random.normal(init_key1, shape=x_data.shape)
logp... | Test the ChEES adaptation w/ HMC kernel. | test_chees | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def generate_multivariate_target(self, rng=None):
"""Genrate a Multivariate Normal distribution as target."""
if rng is None:
loc = jnp.array([0.0, 3])
scale = jnp.array([1.0, 2.0])
rho = jnp.array(0.75)
else:
loc_rng, scale_rng, rho_rng = jax.rand... | Genrate a Multivariate Normal distribution as target. | generate_multivariate_target | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def test_mcse(self, algorithm, parameters, is_mass_matrix_diagonal):
"""Test convergence using Monte Carlo CLT across multiple chains."""
pos_init_key, sample_key = jax.random.split(self.key)
(
logdensity_fn,
true_loc,
true_scale,
true_rho,
... | Test convergence using Monte Carlo CLT across multiple chains. | test_mcse | python | blackjax-devs/blackjax | tests/mcmc/test_sampling.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/mcmc/test_sampling.py | Apache-2.0 |
def test_dual_averaging(self):
"""We test the dual averaging algorithm by searching for the point that
minimizes the gradient of a simple function.
"""
# we need to wrap the gradient in a namedtuple as we optimize for a target
# acceptance probability in the context of HMC.
... | We test the dual averaging algorithm by searching for the point that
minimizes the gradient of a simple function.
| test_dual_averaging | python | blackjax-devs/blackjax | tests/optimizers/test_optimizers.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/optimizers/test_optimizers.py | Apache-2.0 |
def test_minimize_lbfgs(self, maxiter, maxcor):
"""Test if dot product between approximate inverse hessian and gradient is
the same between two loop recursion algorthm of LBFGS and formulas of the
pathfinder paper"""
def regression_logprob(log_scale, coefs, preds, x):
"""Lin... | Test if dot product between approximate inverse hessian and gradient is
the same between two loop recursion algorthm of LBFGS and formulas of the
pathfinder paper | test_minimize_lbfgs | python | blackjax-devs/blackjax | tests/optimizers/test_optimizers.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/optimizers/test_optimizers.py | Apache-2.0 |
def test_recover_diag_inv_hess(self):
"Compare inverse Hessian estimation from LBFGS with known groundtruth."
nd = 5
mean = np.linspace(3.0, 50.0, nd)
cov = np.diag(np.linspace(1.0, 10.0, nd))
def loss_fn(x):
return -stats.multivariate_normal.logpdf(x, mean, cov)
... | Compare inverse Hessian estimation from LBFGS with known groundtruth. | test_recover_diag_inv_hess | python | blackjax-devs/blackjax | tests/optimizers/test_optimizers.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/optimizers/test_optimizers.py | Apache-2.0 |
def test_recover_posterior(self, ndim):
"""Test if pathfinder is able to estimate well enough the posterior of a
normal-normal conjugate model"""
def logp_posterior_conjugate_normal_model(
x, observed, prior_mu, prior_prec, true_prec
):
n = observed.shape[0]
... | Test if pathfinder is able to estimate well enough the posterior of a
normal-normal conjugate model | test_recover_posterior | python | blackjax-devs/blackjax | tests/optimizers/test_pathfinder.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/optimizers/test_pathfinder.py | Apache-2.0 |
def test_scale_when_aceptance_below_optimal(self):
"""
Given that the acceptance rate is below optimal,
the scale gets reduced.
"""
np.testing.assert_allclose(
update_scale_from_acceptance_rate(
scales=jnp.array([0.5]), acceptance_rates=jnp.array([0.2]... |
Given that the acceptance rate is below optimal,
the scale gets reduced.
| test_scale_when_aceptance_below_optimal | python | blackjax-devs/blackjax | tests/smc/test_inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_inner_kernel_tuning.py | Apache-2.0 |
def test_scale_when_aceptance_above_optimal(self):
"""
Given that the acceptance rate is above optimal
the scale increases
-------
"""
np.testing.assert_allclose(
update_scale_from_acceptance_rate(
scales=jnp.array([0.5]), acceptance_rates=jnp.... |
Given that the acceptance rate is above optimal
the scale increases
-------
| test_scale_when_aceptance_above_optimal | python | blackjax-devs/blackjax | tests/smc/test_inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_inner_kernel_tuning.py | Apache-2.0 |
def test_scale_mean_smoothes(self):
"""
The end result depends on the mean acceptance rate,
smoothing the results
"""
np.testing.assert_allclose(
update_scale_from_acceptance_rate(
scales=jnp.array([0.5, 0.5]), acceptance_rates=jnp.array([0.3, 0.2])
... |
The end result depends on the mean acceptance rate,
smoothing the results
| test_scale_mean_smoothes | python | blackjax-devs/blackjax | tests/smc/test_inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_inner_kernel_tuning.py | Apache-2.0 |
def test_tuning_pretuning(self):
"""
Tests that we can apply tuning on some parameters
and pretuning in some others at the same time.
"""
(
init_particles,
logprior_fn,
loglikelihood_fn,
) = self.particles_prior_loglikelihood()
... |
Tests that we can apply tuning on some parameters
and pretuning in some others at the same time.
| test_tuning_pretuning | python | blackjax-devs/blackjax | tests/smc/test_inner_kernel_tuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_inner_kernel_tuning.py | Apache-2.0 |
def test_measure_of_chain_mixing_identity(self):
"""
Given identity matrix and 1. acceptance probability
then the mixing is the square of norm 2.
"""
m = np.eye(2)
acceptance_probabilities = np.array([1.0, 1.0])
chain_mixing = esjd(m)(
self.previous_p... |
Given identity matrix and 1. acceptance probability
then the mixing is the square of norm 2.
| test_measure_of_chain_mixing_identity | python | blackjax-devs/blackjax | tests/smc/test_pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_pretuning.py | Apache-2.0 |
def test_measure_of_chain_mixing_with_non_1_acceptance_rate(self):
"""
Given identity matrix
then the mixing is the square of norm 2. multiplied by the acceptance rate
"""
m = np.eye(2)
acceptance_probabilities = np.array([0.5, 0.2])
chain_mixing = esjd(m)(
... |
Given identity matrix
then the mixing is the square of norm 2. multiplied by the acceptance rate
| test_measure_of_chain_mixing_with_non_1_acceptance_rate | python | blackjax-devs/blackjax | tests/smc/test_pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_pretuning.py | Apache-2.0 |
def test_update_param_distribution(self):
"""
Given an extremely good mixing on one chain,
and that the alpha parameter is 0, then the parameters
of that chain with a slight mutation due to noise are reused.
"""
(
new_parameter_distribution,
chain... |
Given an extremely good mixing on one chain,
and that the alpha parameter is 0, then the parameters
of that chain with a slight mutation due to noise are reused.
| test_update_param_distribution | python | blackjax-devs/blackjax | tests/smc/test_pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_pretuning.py | Apache-2.0 |
def test_update_multi_sigmas(self):
"""
When we have multiple parameters, the performance is attached to its combination
so sampling must work accordingly.
"""
(
new_parameter_distribution,
chain_mixing_measurement,
) = update_parameter_distributio... |
When we have multiple parameters, the performance is attached to its combination
so sampling must work accordingly.
| test_update_multi_sigmas | python | blackjax-devs/blackjax | tests/smc/test_pretuning.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_pretuning.py | Apache-2.0 |
def test_ess_solver_multivariate(self, target_ess):
"""
Posterior with more than one variable. Let's assume we want to
sample from P(x) x ~ N(mean, cov) x in R^{2}
"""
num_particles = 1000
mean = jnp.zeros((1, 2))
cov = jnp.diag(jnp.array([1, 1]))
_logdens... |
Posterior with more than one variable. Let's assume we want to
sample from P(x) x ~ N(mean, cov) x in R^{2}
| test_ess_solver_multivariate | python | blackjax-devs/blackjax | tests/smc/test_smc_ess.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_smc_ess.py | Apache-2.0 |
def test_ess_solver_posterior_signature(self, target_ess):
"""
Posterior with more than one variable. Let's assume we want to
sample from P(x,y) x ~ N(mean, cov) y ~ N(mean, cov)
"""
num_particles = 1000
mean = jnp.zeros((1, 2))
cov = jnp.diag(jnp.array([1, 1]))
... |
Posterior with more than one variable. Let's assume we want to
sample from P(x,y) x ~ N(mean, cov) y ~ N(mean, cov)
| test_ess_solver_posterior_signature | python | blackjax-devs/blackjax | tests/smc/test_smc_ess.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_smc_ess.py | Apache-2.0 |
def normal_logdensity_fn(x, chol_cov):
"""minus log-density of a centered multivariate normal distribution"""
dim = chol_cov.shape[0]
y = jax.scipy.linalg.solve_triangular(chol_cov, x, lower=True)
normalizing_constant = (
np.sum(np.log(np.abs(np.diag(chol_cov)))) + dim * np.log(2 * np.pi) / 2.0
... | minus log-density of a centered multivariate normal distribution | normal_logdensity_fn | python | blackjax-devs/blackjax | tests/smc/test_tempered_smc.py | https://github.com/blackjax-devs/blackjax/blob/master/tests/smc/test_tempered_smc.py | Apache-2.0 |
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