def simple_est_setup_mean_only_summary(self):
# see jupyter notebook ex_docs_tests for more info and plots
### define network
net = me.Network('net_min_2_4')
net.structure([{
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'd',
'type': 'S -> E',
'reaction_steps': 2
}, {
'start': 'Y_t',
'end': 'Y_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 4
}])
### create data with known values
time_values = np.array([0.0, 20.0, 40.0])
data_mean = np.array([[[1., 0.67, 0.37], [0., 0.45, 1.74]],
[[0.01, 0.0469473, 0.04838822],
[0.01, 0.07188642, 0.1995514]]])
data_variance = np.array([[[0., 0.22333333, 0.23545455],
[0., 0.51262626, 4.03272727]],
[[0.01, 0.01631605, 0.01293869],
[0.01, 0.08878719, 0.68612036]]])
data_covariance = np.array([[[0., -0.30454545, -0.65030303]],
[[0.01, 0.0303608, 0.06645246]]])
data = me.Data('data_test_est_min_2_4')
data.load(['X_t', 'Y_t'],
time_values,
None,
'summary',
mean_data=data_mean,
var_data=data_variance,
cov_data=data_covariance)
### run estimation and return object
variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
initial_values_type = 'synchronous'
initial_values = {('X_t', ): 1.0, ('Y_t', ): 0.0}
theta_bounds = {'d': (0.0, 0.15), 'l': (0.0, 0.15)}
time_values = None
sim_mean_only = True
fit_mean_only = True
nlive = 1000 # 250 # 1000
tolerance = 0.01 # 0.1 (COARSE) # 0.05 # 0.01 (NORMAL)
bound = 'multi'
sample = 'unif'
est = me.Estimation('est_min_2_4', net, data)
est.estimate(variables, initial_values_type, initial_values,
theta_bounds, time_values, sim_mean_only, fit_mean_only,
nlive, tolerance, bound, sample)
return est
def test_get_credible_interval(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
samples = np.array([[0.2, 3.4], [0.4, 3.2], [0.25, 3.65]])
params_cred_res = np.array(est.get_credible_interval(samples))
params_cred_sol = np.array([[0.25, 0.2025, 0.3925],
[3.4, 3.21, 3.6375]])
np.testing.assert_allclose(params_cred_sol, params_cred_res)
def test_compute_bayesian_information_criterion(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
bic = est.compute_bayesian_information_criterion(15, 2, 35.49)
np.testing.assert_allclose(-65.56389959779558, bic)
bic = est.compute_bayesian_information_criterion(15.0, 2, 35.49)
np.testing.assert_allclose(-65.56389959779558, bic)
bic = est.compute_bayesian_information_criterion(15, 2.0, 35.49)
np.testing.assert_allclose(-65.56389959779558, bic)
bic = est.compute_bayesian_information_criterion(15.0, 2.0, 35.49)
np.testing.assert_allclose(-65.56389959779558, bic)
def test_initialise_net_theta_bounds(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
net_theta_bounds_res = est.initialise_net_theta_bounds(
['theta_0', 'theta_1', 'theta_2'], {
'theta_2': 'p3',
'theta_0': 'p1',
'theta_1': 'p2'
}, {
'p2': (0.0, 0.1),
'p3': (0.0, 0.2),
'p1': (0.0, 0.3)
})
net_theta_bounds_sol = np.array([[0., 0.3], [0., 0.1], [0., 0.2]])
np.testing.assert_allclose(net_theta_bounds_sol, net_theta_bounds_res)
def test_prior_transform(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
est.net_theta_bounds = np.array([[0., 0.15], [0., 0.15]])
theta_unit = np.array([0.03 / 0.15, 0.075 / 0.15])
theta_orig_res = est.prior_transform(theta_unit)
theta_orig_sol = np.array([0.03, 0.075])
np.testing.assert_allclose(theta_orig_sol, theta_orig_res)
est.net_theta_bounds = np.array([[0.5, 2.0], [0.1, 4.0]])
theta_unit = np.array([0.8, 0.2])
theta_orig_res = est.prior_transform(theta_unit)
theta_orig_sol = np.array(
[0.8 * (2.0 - 0.5) + 0.5, 0.2 * (4.0 - 0.1) + 0.1])
np.testing.assert_allclose(theta_orig_sol, theta_orig_res)
def test_compute_log_likelihood_norm_mean_only_true(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
mean_data = np.array([[[1., 0.67, 0.37], [0., 0.45, 1.74]],
[[0.01, 0.0469473, 0.04838822],
[0.01, 0.07188642, 0.1995514]]])
var_data = np.array([[[0., 0.22333333, 0.23545455],
[0., 0.51262626, 4.03272727]],
[[0.01, 0.01631605, 0.01293869],
[0.01, 0.08878719, 0.68612036]]])
cov_data = np.array([[[0., -0.30454545, -0.65030303]],
[[0.01, 0.0303608, 0.06645246]]])
norm_res = est.compute_log_likelihood_norm(mean_data, var_data,
cov_data, True)
norm_sol = 14.028288976285737
np.testing.assert_allclose(norm_sol, norm_res)
def simple_est_setup_mean_only(self):
# see jupyter notebook ex_docs_tests for more info and plots
### define network
net = me.Network('net_min_2_4')
net.structure([{
'start': 'X_t',
'end': 'Y_t',
'rate_symbol': 'd',
'type': 'S -> E',
'reaction_steps': 2
}, {
'start': 'Y_t',
'end': 'Y_t',
'rate_symbol': 'l',
'type': 'S -> S + S',
'reaction_steps': 4
}])
### create data with known values
num_iter = 100
initial_values_type = 'synchronous'
initial_values = {'X_t': 1, 'Y_t': 0}
theta_values = {'d': 0.03, 'l': 0.07}
time_values = np.array([0.0, 20.0, 40.0])
variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
sim = me.Simulation(net)
res_list = list()
for __ in range(num_iter):
res_list.append(
sim.simulate('gillespie',
variables,
theta_values,
time_values,
initial_values_type,
initial_gillespie=initial_values)[1])
sims = np.array(res_list)
data = me.Data('data_test_est_min_2_4')
data.load(['X_t', 'Y_t'],
time_values,
sims,
bootstrap_samples=10000,
basic_sigma=1 / num_iter)
# overwrite with fixed values (from a num_iter = 100 simulation)
data.data_mean = np.array([[[1., 0.67, 0.37], [0., 0.45, 1.74]],
[[0.01, 0.0469473, 0.04838822],
[0.01, 0.07188642, 0.1995514]]])
data.data_variance = np.array([[[0., 0.22333333, 0.23545455],
[0., 0.51262626, 4.03272727]],
[[0.01, 0.01631605, 0.01293869],
[0.01, 0.08878719, 0.68612036]]])
data.data_covariance = np.array([[[0., -0.30454545, -0.65030303]],
[[0.01, 0.0303608, 0.06645246]]])
### run estimation and return object
variables = {'X_t': ('X_t', ), 'Y_t': ('Y_t', )}
initial_values_type = 'synchronous'
initial_values = {('X_t', ): 1.0, ('Y_t', ): 0.0}
theta_bounds = {'d': (0.0, 0.15), 'l': (0.0, 0.15)}
time_values = None
sim_mean_only = True
fit_mean_only = True
nlive = 1000 # 250 # 1000
tolerance = 0.01 # 0.1 (COARSE) # 0.05 # 0.01 (NORMAL)
bound = 'multi'
sample = 'unif'
est = me.Estimation('est_min_2_4', net, data)
est.estimate(variables, initial_values_type, initial_values,
theta_bounds, time_values, sim_mean_only, fit_mean_only,
nlive, tolerance, bound, sample)
return est
def test_compute_log_evidence_from_bic(self):
net = me.Network('net_test')
data = me.Data('data_test')
est = me.Estimation('est_test', net, data)
log_evid_from_bic = est.compute_log_evidence_from_bic(-65.5)
np.testing.assert_allclose(32.75, log_evid_from_bic)