def setup_dr(self):
model, options, parameters, data, covariance, __, __, __, __ = gf.setup_mcmc_case_dr(
)
RDR = covariance._RDR
invR = covariance._invR
old_set = ParameterSet(theta=np.random.rand(2),
ss=np.array([10.2]),
sigma2=np.array([0.5]),
prior=np.array([0.5]))
new_set = ParameterSet(theta=np.random.rand(2),
ss=np.array([8.2]),
sigma2=np.array([0.5]),
prior=np.array([0.5]))
priorobj = PriorFunction(
priorfun=model.prior_function,
mu=parameters._thetamu[parameters._parind[:]],
sigma=parameters._thetasigma[parameters._parind[:]])
sosobj = SumOfSquares(model, data, parameters)
DR = DelayedRejection()
DR._initialize_dr_metrics(options=options)
accept, out_set, outbound = DR.run_delayed_rejection(
old_set=old_set,
new_set=new_set,
RDR=RDR,
ntry=2,
parameters=parameters,
invR=invR,
sosobj=sosobj,
priorobj=priorobj)
return accept, out_set, outbound
def test_dr_metrics(self):
DR = DelayedRejection()
__, options, __, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
DR._initialize_dr_metrics(options=options)
self.assertTrue(np.array_equal(DR.iacce,
np.zeros(options.ntry, dtype=int)),
msg='Arrays should match')
self.assertEqual(DR.dr_step_counter,
0,
msg='Counter initialized to zero')
def test_addbasic_rejection(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
RS = ResultsStructure()
RS.add_basic(nsimu=options.nsimu,
covariance=covariance,
parameters=parameters,
rejected=rejected,
simutime=0.001,
theta=chain[-1, :])
self.assertEqual(RS.results['total_rejected'],
10 * (options.nsimu**(-1)),
msg='rejection reported as fraction of nsimu')
self.assertEqual(RS.results['rejected_outside_bounds'],
2 * (options.nsimu**(-1)),
msg='rejection reported as fraction of nsimu')
def test_addbasic_true(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
RS = ResultsStructure()
RS.add_basic(nsimu=options.nsimu,
covariance=covariance,
parameters=parameters,
rejected=rejected,
simutime=0.001,
theta=chain[-1, :])
self.assertTrue(RS.basic,
msg='basic features added to result structure')
self.assertTrue(np.array_equal(RS.results['theta'], np.array([0, 0])),
msg='Last elements of chain are zero')
def test_resfilename_is_not_none(self):
__, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
RS = ResultsStructure()
RS.add_options(options=options)
RS.results['simulation_options']['results_filename'] = 'test'
filename = RS.determine_filename(
options=RS.results['simulation_options'])
self.assertEqual(filename, 'test', msg='Filename matches')
def test_resfilename_is_none(self):
__, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
RS = ResultsStructure()
RS.add_options(options=options)
RS.results['simulation_options']['results_filename'] = None
filename = RS.determine_filename(
options=RS.results['simulation_options'])
self.assertEqual(filename,
str('{}{}{}'.format(
RS.results['simulation_options']['datestr'], '_',
'mcmc_simulation.json')),
msg='Filename matches')
def test_addbasic_true(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
covariance._RDR = np.random.random_sample(size=(2, 2))
DR = DelayedRejection()
DR._initialize_dr_metrics(options)
DR.dr_step_counter = 12000
RS = ResultsStructure()
RS.add_basic(nsimu=options.nsimu,
covariance=covariance,
parameters=parameters,
rejected=rejected,
simutime=0.001,
theta=chain[-1, :])
self.assertTrue(RS.add_dram(drscale=options.drscale,
RDR=covariance._RDR,
total_rejected=rejected['total'],
drsettings=DR),
msg='basic features added to result structure')
self.assertTrue(np.array_equal(RS.results['RDR'], covariance._RDR),
msg='RDR matches')
self.assertEqual(RS.results['alpha_count'],
DR.dr_step_counter,
msg='Alpha count matches dr step counter')
def test_addbasic_false(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
DR = DelayedRejection()
DR._initialize_dr_metrics(options)
RS = ResultsStructure()
self.assertFalse(RS.add_dram(drscale=options.drscale,
RDR=covariance._RDR,
total_rejected=rejected['total'],
drsettings=DR),
msg='basic features not added to result structure')
def test_addbasic_covariance(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
covariance._R[0, 0] = 1.1
covariance._R[0, 1] = 2.3
RS = ResultsStructure()
RS.add_basic(nsimu=options.nsimu,
covariance=covariance,
parameters=parameters,
rejected=rejected,
simutime=0.001,
theta=chain[-1, :])
self.assertTrue(np.array_equal(RS.results['R'], covariance._R),
msg='Cholesky matches')
self.assertTrue(np.array_equal(
RS.results['qcov'], np.dot(covariance._R.transpose(),
covariance._R)),
msg='Covariance matches')
def test_allnames(self):
model, options, parameters, data, covariance, rejected, chain, s2chain, sschain = gf.setup_mcmc_case_dr(
)
RS = ResultsStructure()
RS.add_basic(nsimu=options.nsimu,
covariance=covariance,
parameters=parameters,
rejected=rejected,
simutime=0.001,
theta=chain[-1, :])
allnames = RS.results['allnames']
names = RS.results['names']
print(allnames)
print(names)
self.assertEqual(len(allnames),
len(names) + 1,
msg='Expect extra name in allnames')
self.assertEqual(allnames[-1],
'b2',
msg='Expect final parameter is b2')
