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_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_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')
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')