def test_initial_parameter_outside(self):
MP = ModelParameters()
MP.add_model_parameter('aa', 0, minimum = -10., maximum = 10)
MP.add_model_parameter('bb', 11, minimum = -10., maximum = 10)
MP._openparameterstructure(nbatch=1)
with self.assertRaises(SystemExit, msg = 'Initial value outside of bounds.'):
MP._check_initial_values_wrt_parameter_limits()
def test_not_all_adapt(self):
MP = ModelParameters()
parind, adapt, no_adapt = MP.setup_adaptation_indices(
adapt=np.array([False, True, True]),
parind=np.array([True, True, True]))
self.assertTrue(np.array_equal(parind, np.array([0, 1, 2])),
msg=str('Expect arrays to match: {} neq {}'.format(
parind, np.array([0, 1, 2]))))
self.assertTrue(np.array_equal(adapt, np.array([1, 2])),
msg=str('Expect arrays to match: {} neq {}'.format(
adapt, np.array([1, 2]))))
self.assertTrue(np.array_equal(no_adapt, np.array([True, False,
False])),
msg=str('Expect arrays to match: {} neq {}'.format(
no_adapt, np.array([True, False, False]))))
parind, adapt, no_adapt = MP.setup_adaptation_indices(
adapt=np.array([False, True, False]),
parind=np.array([True, True, True]))
self.assertTrue(np.array_equal(parind, np.array([0, 1, 2])),
msg=str('Expect arrays to match: {} neq {}'.format(
parind, np.array([0, 1, 2]))))
self.assertTrue(np.array_equal(adapt, np.array([1])),
msg=str('Expect arrays to match: {} neq {}'.format(
adapt, np.array([1]))))
self.assertTrue(np.array_equal(no_adapt, np.array([True, False,
True])),
msg=str('Expect arrays to match: {} neq {}'.format(
no_adapt, np.array([True, False, True]))))
def test_setup_prior_sigma(self):
MP = ModelParameters()
self.assertEqual(MP.setup_prior_sigma(sigma=np.array([0.1])),
np.array([0.1]),
msg='Expect = sigma')
self.assertEqual(MP.setup_prior_sigma(sigma=0.),
np.inf,
msg='Expect = inf')
def test_setup_prior_mu(self):
MP = ModelParameters()
self.assertEqual(MP.setup_prior_mu(mu=np.array([0.1]),
value=np.array([0.3])),
np.array([0.1]),
msg='Expect = mu')
self.assertEqual(MP.setup_prior_mu(mu=np.nan, value=np.array([0.3])),
np.array([0.3]),
msg='Expect = value')
def standard_check(self, theta0=1.0, name='$p_{0}$'):
MP = ModelParameters()
MP.add_model_parameter(name=name, theta0=theta0)
self.assertEqual(MP.parameters[0]['theta0'], theta0)
self.assertEqual(MP.parameters[0]['name'], name)
self.assertEqual(MP.parameters[0]['minimum'], -np.inf)
self.assertEqual(MP.parameters[0]['maximum'], np.inf)
self.assertEqual(MP.parameters[0]['prior_mu'], np.zeros([1]))
self.assertEqual(MP.parameters[0]['prior_sigma'], np.inf)
self.assertEqual(MP.parameters[0]['sample'], 1)
self.assertEqual(MP.parameters[0]['local'], 0)
def test_raises_error(self):
MP = ModelParameters()
MP.add_model_parameter('aa', 0, minimum=-10., maximum=10)
MP.add_model_parameter('bb', 5, minimum=100., maximum=10)
MP._openparameterstructure(nbatch=1)
with self.assertRaises(SystemExit, msg='Min > Max'):
MP._check_parameter_limits()
def test_scan_for_local_variables_with_sample_false(self):
MP = ModelParameters()
MP.add_model_parameter(name='m', theta0=0.2, local=0, sample=False)
MP.add_model_parameter(name='b', theta0=-0.5, local=1)
local = MP.scan_for_local_variables(nbatch=2, parameters=MP.parameters)
self.assertTrue(np.array_equal(local, np.array([1, 2])),
msg=str('Expect arrays to match: {} neq {}'.format(
local, np.array([1, 2]))))
def test_setup_adapting(self):
MP = ModelParameters()
self.assertTrue(MP.setup_adapting(adapt=True, sample=True),
msg='Expect True')
self.assertFalse(MP.setup_adapting(adapt=False, sample=True),
msg='Expect False')
self.assertFalse(MP.setup_adapting(adapt=True, sample=False),
msg='Expect False')
self.assertFalse(MP.setup_adapting(adapt=False, sample=False),
msg='Expect False')
def test_get_parameter_features(self):
MP = ModelParameters()
MP.add_model_parameter(name='a1', theta0=0.1, minimum=0, maximum=1)
MP.add_model_parameter(name='a2', theta0=0.2, minimum=0, maximum=1)
MP.add_model_parameter(name='a3', theta0=0.3, minimum=0, maximum=1)
npar, low_lim, upp_lim = get_parameter_features(MP.parameters)
self.assertEqual(npar, 3, msg='Expect to find 3 parameters')
self.assertTrue(np.array_equal(low_lim, np.zeros([3])),
msg=str('Expect low_lim all zero: {} neq {}'.format(
low_lim, np.zeros([3]))))
self.assertTrue(np.array_equal(upp_lim, np.ones([3])),
msg=str('Expect upp_lim all one: {} neq {}'.format(
upp_lim, np.ones([3]))))
def test_get_parameter_features_with_inf_bounds(self):
MP = ModelParameters()
MP.add_model_parameter(name='a1',
theta0=0.1,
minimum=-np.inf,
maximum=1)
MP.add_model_parameter(name='a2', theta0=0.2, minimum=0, maximum=1)
MP.add_model_parameter(name='a3',
theta0=0.3,
minimum=0,
maximum=np.inf)
npar, low_lim, upp_lim = get_parameter_features(MP.parameters)
expect_low_lim = np.array([0.1 - 100 * 0.1, 0, 0])
expect_upp_lim = np.array([1, 1, 0.3 + 100 * 0.3])
self.assertEqual(npar, 3, msg='Expect to find 3 parameters')
self.assertTrue(np.array_equal(low_lim, expect_low_lim),
msg=str('Expect low_lim finite: {} neq {}'.format(
low_lim, expect_low_lim)))
self.assertTrue(np.array_equal(upp_lim, expect_upp_lim),
msg=str('Expect upp_lim finite: {} neq {}'.format(
upp_lim, expect_upp_lim)))
def test_results_to_params(self):
MP = ModelParameters()
MP.add_model_parameter('aa', 0)
MP._openparameterstructure(nbatch=1)
# define minimal results dictionary
results = {
'parind': MP._parind,
'names': MP._names,
'local': MP._local,
'theta': [1.2]
}
# initialize default options
SO = SimulationOptions()
SO.define_simulation_options(verbosity=0)
MP.display_parameter_settings(verbosity=SO.verbosity,
no_adapt=MP._no_adapt)
MP._results_to_params(results, 1)
MP._openparameterstructure(nbatch=1)
MP.display_parameter_settings(verbosity=SO.verbosity,
no_adapt=MP._no_adapt)
self.assertEqual(MP.parameters[0]['theta0'], results['theta'][0])
def test_initial_parameter_okay(self):
MP = ModelParameters()
MP.add_model_parameter('aa', 0, minimum=-10., maximum=10)
MP.add_model_parameter('bb', 9, minimum=-10., maximum=10)
MP._openparameterstructure(nbatch=1)
self.assertTrue(MP._check_initial_values_wrt_parameter_limits())
def test_parameter_display_set_2(self):
MP = ModelParameters()
MP.add_model_parameter(name='m',
theta0=2.,
minimum=-10,
maximum=np.inf,
sample=True)
MP.add_model_parameter(name='b',
theta0=-5.,
minimum=-10,
maximum=100,
sample=False)
MP.add_model_parameter(name='b2',
theta0=-5.3e6,
minimum=-1e7,
maximum=1e6,
sample=True)
MP._openparameterstructure(nbatch=1)
MP.display_parameter_settings(verbosity=None, no_adapt=None)
'theta': [1.2]
}
# initialize default options
SO = SimulationOptions()
SO.define_simulation_options(verbosity=0)
MP.display_parameter_settings(verbosity=SO.verbosity,
no_adapt=MP._no_adapt)
MP._results_to_params(results, 1)
MP._openparameterstructure(nbatch=1)
MP.display_parameter_settings(verbosity=SO.verbosity,
no_adapt=MP._no_adapt)
self.assertEqual(MP.parameters[0]['theta0'], results['theta'][0])
# --------------------------
MP = ModelParameters()
class LessThanOrEqualToZero(unittest.TestCase):
def test_x_lt_0(self):
self.assertTrue(less_than_or_equal_to_zero(x=-1))
def test_x_eq_0(self):
self.assertTrue(less_than_or_equal_to_zero(x=0))
def test_x_gt_0(self):
self.assertFalse(less_than_or_equal_to_zero(x=1))
# --------------------------
class ReplaceListElements(unittest.TestCase):
