src/qinfer/tests/test_smc.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# test_smc.py: Checks that properties and methods of
# SMCUpdater work as intended.
##
# © 2017, Chris Ferrie (csferrie@gmail.com) and
# Christopher Granade (cgranade@cgranade.com).
#
# 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.
#
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##
## FEATURES ###################################################################
from __future__ import absolute_import
from __future__ import division # Ensures that a/b is always a float.
## IMPORTS ####################################################################
import numpy as np
from numpy.testing import assert_equal, assert_almost_equal
from qinfer.tests.base_test import DerandomizedTestCase, MockModel, assert_warns
from qinfer.abstract_model import FiniteOutcomeModel
from qinfer.distributions import UniformDistribution
from qinfer.smc import SMCUpdater
from qinfer._exceptions import ApproximationWarning
## CLASSES ####################################################################
class DecimationModel(MockModel):
r"""
Two-outcome model whose likelihood upon a "0" outcome returns 1 for the
first :math:`\alpha` of its model parameters and
0 for the rest, where :math:`\alpha` is an experiment parameter.
As with most other mock models, this is not a valid statistical
model, but is
useful in decimating particle clouds in tests,
as this reduces the ESS of an SMC updater using this
model by a factor of :math:`\alpha` after each "datum."
"""
@property
def expparams_dtype(self):
return [('alpha', float)]
def likelihood(self, outcomes, modelparams, expparams):
super(DecimationModel, self).likelihood(outcomes, modelparams, expparams)
assert expparams.shape == (1,) # Only defined for single experiments.
pr0 = np.ones((modelparams.shape[0], expparams.shape[0])) / 2
idx_dec_at = np.ceil(expparams['alpha'][0] * modelparams.shape[0]).astype(np.int)
pr0[idx_dec_at:, :] = 0
return FiniteOutcomeModel.pr0_to_likelihood_array(outcomes, pr0)
## TEST CASES ################################################################
class TestSMCEffectiveSampleSize(DerandomizedTestCase):
"""
Tests that the SMCUpdater class correctly implements the effective
sample size criterion as a resampling threshold, postselection
herald, etc.
"""
def setUp(self):
super(TestSMCEffectiveSampleSize, self).setUp()
self.model = DecimationModel()
def _mk_updater(self, n_particles, **kwargs):
return SMCUpdater(self.model, n_particles, UniformDistribution([0, 1]), **kwargs)
def test_low_n_ess_warning(self):
n_particles = 1000
updater = self._mk_updater(n_particles, resample_thresh=0.0)
outcomes = np.array([0], dtype=int)
expparams = np.ones((1,), dtype=self.model.expparams_dtype)
expparams['alpha'][0] = 2 / 1000 # Force the particle number to be 2.
with assert_warns(ApproximationWarning):
updater.update(outcomes, expparams)
def test_resample_thresh(self):
n_updates = 10
n_particles = 1000
updater = self._mk_updater(n_particles, resample_thresh=0.5)
outcomes = np.array([0], dtype=int)
expparams = np.ones((1,), dtype=self.model.expparams_dtype)
expparams['alpha'][0] = 0.3 # Something less than the threshold, force resampling.
for idx_update in range(n_updates):
updater.update(outcomes, expparams)
assert_equal(updater.resample_count, 1 + idx_update)
def test_min_n_ess(self):
n_updates = 6
n_particles = 4 ** n_updates # Pick factor of 4 to avoid discretization errors.
updater = self._mk_updater(n_particles, resample_thresh=0.0)
outcomes = np.array([0], dtype=int)
expparams = np.empty((1,), dtype=self.model.expparams_dtype)
for idx_update in range(n_updates):
expparams['alpha'][0] = 4 ** -(idx_update + 1)
updater.update(outcomes, expparams)
assert_equal(updater.min_n_ess, 4 ** (n_updates - idx_update - 1))
# Force a resample and ensure that the min_n_ess remains the same.
updater.resample()
assert_equal(updater.min_n_ess, 4 ** (n_updates - idx_update - 1))