src/qinfer/tests/test_precession_model.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# test_abstract_model.py: Checks that Model works properly.
##
# © 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.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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##
## FEATURES ###################################################################
from __future__ import division # Ensures that a/b is always a float.
from __future__ import absolute_import
## IMPORTS ####################################################################
import numpy as np
from numpy.testing import assert_equal, assert_almost_equal, assert_array_less
from qinfer.tests.base_test import DerandomizedTestCase
from qinfer.abstract_model import (
Model)
from qinfer import ScoreMixin, SimplePrecessionModel, UniformDistribution
from qinfer.smc import SMCUpdater,SMCUpdaterBCRB
# replace analytical score with numerical
class NumericalSimplePrecessionModel(ScoreMixin, SimplePrecessionModel):
pass
class TestSMCUpdater(DerandomizedTestCase):
# True model parameter for test
MODELPARAMS = np.array([1,])
TEST_EXPPARAMS = np.linspace(1.,10.,100,dtype=np.float)
PRIOR = UniformDistribution([[0,2]])
N_PARTICLES = 10000
TEST_TARGET_COV = np.array([[0.01]])
def setUp(self):
super(TestSMCUpdater,self).setUp()
self.precession_model = SimplePrecessionModel()
self.num_precession_model = NumericalSimplePrecessionModel()
self.expparams = TestSMCUpdater.TEST_EXPPARAMS.reshape(-1,1)
self.outcomes = self.precession_model.simulate_experiment(TestSMCUpdater.MODELPARAMS,
TestSMCUpdater.TEST_EXPPARAMS,repeat=1 ).reshape(-1,1)
self.updater = SMCUpdater(self.precession_model,
TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR)
self.updater_bayes = SMCUpdaterBCRB(self.precession_model,
TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR,adaptive=True)
self.num_updater = SMCUpdater(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR)
self.num_updater_bayes = SMCUpdaterBCRB(self.num_precession_model,
TestSMCUpdater.N_PARTICLES,TestSMCUpdater.PRIOR,adaptive=True)
def test_smc_fitting(self):
"""
Checks that the fitters converge on true value on simple precession_model. Is a stochastic
test but I ran 100 times and there were no fails, with these parameters.
"""
self.updater.batch_update(self.outcomes,self.expparams)
self.updater_bayes.batch_update(self.outcomes,self.expparams)
self.num_updater.batch_update(self.outcomes,self.expparams)
self.num_updater_bayes.batch_update(self.outcomes,self.expparams)
#Assert that models have learned true model parameters from data
#test means
assert_almost_equal(self.updater.est_mean(),TestSMCUpdater.MODELPARAMS,2)
assert_almost_equal(self.updater_bayes.est_mean(),TestSMCUpdater.MODELPARAMS,2)
assert_almost_equal(self.num_updater.est_mean(),TestSMCUpdater.MODELPARAMS,2)
assert_almost_equal(self.num_updater_bayes.est_mean(),TestSMCUpdater.MODELPARAMS,2)
#Assert that covariances have been reduced below thresholds
#test covs
assert_array_less(self.updater.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.updater_bayes.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.num_updater.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV)
assert_array_less(self.num_updater_bayes.est_covariance_mtx(),TestSMCUpdater.TEST_TARGET_COV)
def test_bim(self):
"""
Checks that the fitters converge on true value on simple precession_model. Is a stochastic
test but I ran 100 times and there were no fails, with these parameters.
"""
bim_currents = []
num_bim_currents = []
bim_adaptives = []
num_bim_adaptives = []
#track bims throughout experiments
for i in range(self.outcomes.shape[0]):
self.updater_bayes.update(self.outcomes[i],self.expparams[i])
self.num_updater_bayes.update(self.outcomes[i],self.expparams[i])
bim_currents.append(self.updater_bayes.current_bim)
num_bim_currents.append(self.num_updater_bayes.current_bim)
bim_adaptives.append(self.updater_bayes.adaptive_bim)
num_bim_adaptives.append(self.num_updater_bayes.adaptive_bim)
bim_currents = np.array(bim_currents)
num_bim_currents = np.array(num_bim_currents)
bim_adaptives = np.array(bim_adaptives)
num_bim_adaptives = np.array(num_bim_adaptives)
#compare numerical and analytical bims
assert_almost_equal(bim_currents,num_bim_currents,2)
assert_almost_equal(bim_adaptives,num_bim_adaptives,2)
#verify that array copying of properties is working
assert not np.all(bim_currents == bim_currents[0,...])
assert not np.all(num_bim_currents == num_bim_currents[0,...])
assert not np.all(bim_adaptives == bim_adaptives[0,...])
assert not np.all(num_bim_adaptives == num_bim_adaptives[0,...])
#verify that BCRB is approximately reached
assert_almost_equal(self.updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.current_bim),2)
assert_almost_equal(self.updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.adaptive_bim),2)
assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.current_bim),2)
assert_almost_equal(self.num_updater_bayes.est_covariance_mtx(),np.linalg.inv(self.updater_bayes.adaptive_bim),2)