# Source code for qinfer.resamplers

```
#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# resamplers.py: Implementations of various resampling algorithms.
##
# © 2017, Chris Ferrie ([email protected]) and
# Christopher Granade ([email protected]).
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
##
## FEATURES ###################################################################
from __future__ import absolute_import
from __future__ import division
## ALL ########################################################################
# We use __all__ to restrict what globals are visible to external modules.
__all__ = [
'Resampler',
'LiuWestResampler'
]
## IMPORTS ####################################################################
import numpy as np
import scipy.linalg as la
import warnings
from ._due import due, BibTeX
from .utils import outer_product, particle_meanfn, particle_covariance_mtx, sqrtm_psd
from abc import ABCMeta, abstractmethod, abstractproperty
from future.utils import with_metaclass
import qinfer.clustering
from qinfer._exceptions import ResamplerWarning, ResamplerError
from qinfer.distributions import ParticleDistribution
## LOGGING ####################################################################
import logging
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
## CLASSES ####################################################################
[docs]class Resampler(with_metaclass(ABCMeta, object)):
[docs] @abstractmethod
def __call__(self, model, particle_dist,
n_particles=None,
precomputed_mean=None, precomputed_cov=None
):
"""
Resample the particles given by ``particle_weights`` and
``particle_locations``, drawing ``n_particles`` new particles.
:param Model model: Model from which the particles are drawn,
used to define the valid region for resampling.
:param ParticleDistribution paricle_dist: The particle distribution to
be resampled.
:param int n_particles: Number of new particles to draw, or
`None` to draw the same number as the original distribution.
:param np.ndarray precomputed_mean: Mean of the original
distribution, or `None` if this should be computed by the resampler.
:param np.ndarray precomputed_cov: Covariance of the original
distribution, or `None` if this should be computed by the resampler.
:return ParticleDistribution: Resampled particle distribution
"""
class ClusteringResampler(object):
r"""
Creates a resampler that breaks the particles into clusters, then applies
a secondary resampling algorithm to each cluster independently.
:param secondary_resampler: Resampling algorithm to be applied to each
cluster. If ``None``, defaults to ``LiuWestResampler()``.
"""
def __init__(self, eps=0.5, secondary_resampler=None, min_particles=5, metric='euclidean', weighted=False, w_pow=0.5, quiet=True):
warnings.warn("This class is deprecated, and will be removed in a future version.", DeprecationWarning)
self.secondary_resampler = (
secondary_resampler
if secondary_resampler is not None
else LiuWestResampler()
)
self.eps = eps
self.quiet = quiet
self.min_particles = min_particles
self.metric = metric
self.weighted = weighted
self.w_pow = w_pow
## METHODS ##
def __call__(self, model, particle_weights, particle_locations):
## TODO: docstring.
# Allocate new arrays to hold the weights and locations.
new_weights = np.empty(particle_weights.shape)
new_locs = np.empty(particle_locations.shape)
# Loop over clusters, calling the secondary resampler for each.
# The loop should include -1 if noise was found.
for cluster_label, cluster_particles in clustering.particle_clusters(
particle_locations, particle_weights,
eps=self.eps, min_particles=self.min_particles, metric=self.metric,
weighted=self.weighted, w_pow=self.w_pow,
quiet=self.quiet
):
# If we are resampling the NOISE label, we must use the global moments.
if cluster_label == clustering.NOISE:
extra_args = {
"precomputed_mean": particle_meanfn(particle_weights, particle_locations, lambda x: x),
"precomputed_cov": particle_covariance_mtx(particle_weights, particle_locations)
}
else:
extra_args = {}
# Pass the particles in that cluster to the secondary resampler
# and record the new weights and locations.
cluster_ws, cluster_locs = self.secondary_resampler(model,
particle_weights[cluster_particles],
particle_locations[cluster_particles],
**extra_args
)
# Renormalize the weights of each resampled particle by the total
# weight of the cluster to which it belongs.
cluster_ws /= np.sum(particle_weights[cluster_particles])
# Store the updated cluster.
new_weights[cluster_particles] = cluster_ws
new_locs[cluster_particles] = cluster_locs
# Assert that we have not introduced any NaNs or Infs by resampling.
assert np.all(np.logical_not(np.logical_or(
np.isnan(new_locs), np.isinf(new_locs)
)))
return new_weights, new_locs
[docs]class LiuWestResampler(Resampler):
r"""
Creates a resampler instance that applies the algorithm of
[LW01]_ to redistribute the particles.
:param float a: Value of the parameter :math:`a` of the [LW01]_ algorithm
to use in resampling.
:param float h: Value of the parameter :math:`h` to use, or `None` to
use that corresponding to :math:`a`.
:param int maxiter: Maximum number of times to attempt to resample within
the space of valid models before giving up.
:param bool debug: Because the resampler can generate large amounts of
debug information, nothing is output to the logger, even at DEBUG level,
unless this flag is True.
:param bool postselect: If `True`, ensures that models are valid by
postselecting.
:param float zero_cov_comp: Amount of covariance to be added to every
parameter during resampling in the case that the estimated covariance
has zero norm.
:param callable kernel: Callable function ``kernel(*shape)`` that returns samples
from a resampling distribution with mean 0 and variance 1.
:param int default_n_particles: The default number of particles to draw during
a resampling action. If ``None``, the number of redrawn particles
redrawn will be equal to the number of particles given.
The value of ``default_n_particles`` can be overridden by any integer
value of ``n_particles`` given to ``__call__``.
.. warning::
The [LW01]_ algorithm preserves the first two moments of the
distribution (in expectation over the random choices made by the
resampler) if and only if :math:`a^2 + h^2 = 1`, as is set by the
``h=None`` keyword argument.
"""
@due.dcite(
BibTeX("""
@incollection{liu_combined_2001,
title = {Combined Parameter and State Estimation in Simulation-Based Filtering},
timestamp = {2013-01-28T21:57:35Z},
urldate = {2013-01-28},
booktitle = {Sequential {Monte Carlo} Methods in Practice},
publisher = {{Springer-Verlag, New York}},
author = {Liu, Jane and West, Mike},
editor = {De Freitas and Gordon, NJ},
year = {2001}
}
"""),
description="Liu-West resampler",
tags=['implementation']
)
def __init__(self,
a=0.98, h=None, maxiter=1000, debug=False, postselect=True,
zero_cov_comp=1e-10,
default_n_particles=None,
kernel=np.random.randn
):
self._default_n_particles = default_n_particles
self.a = a # Implicitly calls the property setter below to set _h.
if h is not None:
self._override_h = True
self._h = h
self._maxiter = maxiter
self._debug = debug
self._postselect = postselect
self._zero_cov_comp = zero_cov_comp
self._kernel = kernel
_override_h = False
## PROPERTIES ##
@property
def a(self):
return self._a
@a.setter
def a(self, new_a):
self._a = new_a
if not self._override_h:
self._h = np.sqrt(1 - new_a**2)
## METHODS ##
[docs] def __call__(self, model, particle_dist,
n_particles=None,
precomputed_mean=None, precomputed_cov=None
):
"""
Resample the particles according to algorithm given in
[LW01]_.
"""
# Possibly recompute moments, if not provided.
if precomputed_mean is None:
mean = particle_dist.est_mean()
else:
mean = precomputed_mean
if precomputed_cov is None:
cov = particle_dist.est_covariance_mtx()
else:
cov = precomputed_cov
if n_particles is None:
if self._default_n_particles is None:
n_particles = particle_dist.n_particles
else:
n_particles = self._default_n_particles
# parameters in the Liu and West algorithm
a, h = self._a, self._h
if la.norm(cov, 'fro') == 0:
# The norm of the square root of S is literally zero, such that
# the error estimated in the next step will not make sense.
# We fix that by adding to the covariance a tiny bit of the
# identity.
warnings.warn(
"Covariance has zero norm; adding in small covariance in "
"resampler. Consider increasing n_particles to improve covariance "
"estimates.",
ResamplerWarning
)
cov = self._zero_cov_comp * np.eye(cov.shape[0])
S, S_err = sqrtm_psd(cov)
if not np.isfinite(S_err):
raise ResamplerError(
"Infinite error in computing the square root of the "
"covariance matrix. Check that n_ess is not too small.")
S = np.real(h * S)
# Give shorter names to weights, locations, and nr. of random variables
w = particle_dist.particle_weights
l = particle_dist.particle_locations
n_rvs = particle_dist.n_rvs
new_locs = np.empty((n_particles, n_rvs))
cumsum_weights = np.cumsum(w)
idxs_to_resample = np.arange(n_particles, dtype=int)
# Loop as long as there are any particles left to resample.
n_iters = 0
# Draw j with probability self.particle_weights[j].
# We do this by drawing random variates uniformly on the interval
# [0, 1], then see where they belong in the CDF.
js = cumsum_weights.searchsorted(
np.random.random((idxs_to_resample.size,)),
side='right'
)
# Set mu_i to a x_j + (1 - a) mu.
# FIXME This should use particle_dist.particle_mean
mus = a * l[js,:] + (1 - a) * mean
while idxs_to_resample.size and n_iters < self._maxiter:
# Keep track of how many iterations we used.
n_iters += 1
# Draw x_i from N(mu_i, S).
new_locs[idxs_to_resample, :] = mus + np.dot(S, self._kernel(n_rvs, mus.shape[0])).T
# Now we remove from the list any valid models.
# We write it out in a longer form than is strictly necessary so
# that we can validate assertions as we go. This is helpful for
# catching models that may not hold to the expected postconditions.
resample_locs = new_locs[idxs_to_resample, :]
if self._postselect:
valid_mask = model.are_models_valid(resample_locs)
else:
valid_mask = np.ones((resample_locs.shape[0],), dtype=bool)
assert valid_mask.ndim == 1, "are_models_valid returned tensor, expected vector."
n_invalid = np.sum(np.logical_not(valid_mask))
if self._debug and n_invalid > 0:
logger.debug(
"LW resampler found {} invalid particles; repeating.".format(
n_invalid
)
)
assert (
(
len(valid_mask.shape) == 1
or len(valid_mask.shape) == 2 and valid_mask.shape[-1] == 1
) and valid_mask.shape[0] == resample_locs.shape[0]
), (
"are_models_valid returned wrong shape {} "
"for input of shape {}."
).format(valid_mask.shape, resample_locs.shape)
idxs_to_resample = idxs_to_resample[np.nonzero(np.logical_not(
valid_mask
))[0]]
# This may look a little weird, but it should delete the unused
# elements of js, so that we don't need to reallocate.
js = js[np.logical_not(valid_mask)]
mus = mus[:idxs_to_resample.size, :]
if idxs_to_resample.size:
# We failed to force all models to be valid within maxiter attempts.
# This means that we could be propagating out invalid models, and
# so we should warn about that.
warnings.warn((
"Liu-West resampling failed to find valid models for {} "
"particles within {} iterations."
).format(idxs_to_resample.size, self._maxiter), ResamplerWarning)
if self._debug:
logger.debug("LW resampling completed in {} iterations.".format(n_iters))
# Now we reset the weights to be uniform, letting the density of
# particles represent the information that used to be stored in the
# weights. This is done by SMCUpdater, and so we simply need to return
# the new locations here.
new_weights = np.ones((n_particles,)) / n_particles
return ParticleDistribution(particle_locations=new_locs,
particle_weights=new_weights)
```