def test_Ensemble_data():
"""tests to check data from an ensemble"""
# Test that the ensemble of two identical models
# behalves like twice a single model.
# Only for the independent (ie. diagonal) case
# Note: this test would fail if a fixed contact_matrix
# is passed which happens to be the identity matrix
# The difference is that when specified as independent
# each model is booted independently
# If a contact matrix is specified, then the boot goal
# is the combined total of all models. So there will be a different starting point.
test_a = Model.open_file(path_model_2)
test_a.name = 'test_a'
test_b = Model.open_file(path_model_2)
test_b.name = 'test_b'
test_b.parameters['alpha_0'].set_value(0.7)
reference = Model.open_file(path_model_2)
reference.name = 'reference'
off_diag = Parameter('off_diagonal',
0.1,
parameter_min=0.,
parameter_max=1.,
description='off diagonal element of contact matrix')
off_diags = [off_diag]
test_ensemble = Ensemble('test_ensemble', reference)
test_ensemble.upload_models([test_a, test_b])
test_ensemble.define_cross_transmission('infection cycle',
'infected',
'susceptible',
'total',
'contagious',
'alpha',
contact_type='simple',
contact=off_diags)
n_days = 100
norm_day = 50
test_ensemble.reset()
test_ensemble.evolve_expectations(norm_day)
for key in test_ensemble.populations:
pop = test_ensemble.populations[key]
nu = pop.history[norm_day]
pop.history[norm_day] = int(round(nu))
pop.scale_future(1., expectations=False)
for model_name in test_ensemble.models:
model = test_ensemble.models[model_name]
for pop_name in model.populations:
pop = model.populations[pop_name]
nu = pop.history[norm_day]
pop.history[norm_day] = int(round(nu))
pop.scale_future(1., expectations=False)
test_ensemble.generate_data(n_days, norm_day)
for pop_name in test_ensemble.populations:
pop = test_ensemble.populations[pop_name]
if pop.show_sim:
ens_hist = test_ensemble.populations[pop_name].history
def test_point_estimate_local_death():
start_day = 80
end_day = 100
ref_2 = Model.open_file(
'/Users/karlen/pypm-local/models/covid19/USA/ny_4_2_0109.pypm')
sim_2 = Model.open_file(
'/Users/karlen/pypm-local/models/covid19/USA/ny_4_2_0109.pypm')
# do fit of recover_frac
for par_name in ref_2.parameters:
par = ref_2.parameters[par_name]
par.set_fixed()
for par_name in ['recover_frac']:
par = ref_2.parameters[par_name]
par.set_variable(None, None)
sim_2.reset()
sim_2.generate_data(end_day)
optimizer = Optimizer(ref_2,
'total deaths',
sim_2.populations['deaths'].history,
[start_day, end_day],
cumul_reset=True)
optimizer.reset_variables()
popt, pcov = optimizer.fit()
iii = 1
def test_point_estimates_repeated():
start_day = 12
end_day = 60
ref_2 = Model.open_file(path_model_2_2)
sim_2 = Model.open_file(path_model_2_2)
# do fit of alpha_0, alpha_1, cont_0
par_names = ['alpha_0', 'alpha_1', 'cont_0']
sums = {}
sum2s = {}
for par_name in par_names:
par = ref_2.parameters[par_name]
par.set_variable(None, None)
sums[par_name] = 0.
sum2s[par_name] = 0.
n_rep = 10
fit_stat_list = []
for i in range(n_rep):
sim_2.reset()
sim_2.generate_data(end_day)
optimizer = Optimizer(ref_2, 'total reported',
sim_2.populations['reported'].history,
[start_day, end_day])
optimizer.reset_variables()
popt, pcov = optimizer.fit()
fit_stat_list.append(optimizer.fit_statistics)
for par_name in par_names:
value = ref_2.parameters[par_name].get_value()
sums[par_name] += value
sum2s[par_name] += value**2
ass_std = {}
ass_std['alpha_0'] = 0.03
ass_std['alpha_1'] = 0.01
ass_std['cont_0'] = 10.
means = {}
std = {}
for par_name in par_names:
means[par_name] = sums[par_name] / n_rep
std[par_name] = np.sqrt(sum2s[par_name] / n_rep - means[par_name]**2)
assert std[par_name] < ass_std[par_name]
truth = ref_2.parameters[par_name].initial_value
assert np.abs((means[par_name] - truth) / std[par_name] /
np.sqrt(1. * n_rep)) < 3.
ndof = fit_stat_list[0]['ndof']
chi2_list = [fit_stat_list[i]['chi2'] for i in range(n_rep)]
chi2_mean = np.mean(chi2_list)
assert np.abs(chi2_mean - ndof) < 8.
acor_list = [fit_stat_list[i]['acor'] for i in range(n_rep)]
acor_mean = np.mean(acor_list)
assert np.abs(acor_mean) < 0.2
def test_Ensemble_properties_different():
"""tests to ensure the properties of Ensemble with different sub models"""
# Test that the ensemble of two identical models
# behalves like twice a single model.
# Only for the independent (ie. diagonal) case
# Note: this test would fail if a fixed contact_matrix
# is passed which happens to be the identity matrix
# The difference is that when specified as independent
# each model is booted independently
# If a contact matrix is specified, then the boot goal
# is the combined total of all models. So there will be a different starting point.
test_a = Model.open_file(path_model_2)
test_a.name = 'test_a'
test_b = Model.open_file(path_model_2)
test_b.name = 'test_b'
test_b.parameters['alpha_0'].set_value(0.7)
test_c = Model.open_file(path_model_2)
test_c.name = 'test_c'
test_d = Model.open_file(path_model_2)
test_d.name = 'test_d'
test_d.parameters['alpha_0'].set_value(0.7)
reference = Model.open_file(path_model_2)
reference.name = 'reference'
test_ensemble = Ensemble('test_ensemble', reference)
test_ensemble.upload_models([test_a, test_b])
test_ensemble.define_cross_transmission('infection cycle',
'infected',
'susceptible',
'total',
'contagious',
'alpha',
contact_type='diagonal')
n_days = 100
test_c.reset()
test_c.evolve_expectations(n_days)
test_d.reset()
test_d.evolve_expectations(n_days)
test_ensemble.reset()
test_ensemble.evolve_expectations(n_days)
for pop_name in test_ensemble.populations:
pop = test_ensemble.populations[pop_name]
if pop.show_sim:
ens_hist = test_ensemble.populations[pop_name].history
tc_hist = test_c.populations[pop_name].history
td_hist = test_d.populations[pop_name].history
for i in range(len(ens_hist)):
ratio = ens_hist[i] / (tc_hist[i] + td_hist[i])
assert np.abs(ratio - 1.) < 0.01
def test_Ensemble_properties_identical():
"""tests to ensure the properties of Ensemble"""
# Test that the ensemble of two identical models
# behalves like twice a single model.
# independent of the contact matrix
test_a = Model.open_file(path_model_2)
test_a.name = 'test_a'
test_b = Model.open_file(path_model_2)
test_b.name = 'test_b'
reference = Model.open_file(path_model_2)
reference.name = 'reference'
single = Model.open_file(path_model_2)
single.name = 'single'
test_ensemble = Ensemble('test_ensemble', reference)
test_ensemble.upload_models([test_a, test_b])
contacts = [
[[1., 0.0], [0.0, 1.]],
[[1., 1.0], [1.0, 1.]],
[[1., 0.5], [0.7, 1.]],
]
for contact in contacts:
test_ensemble.define_cross_transmission('infection cycle',
'infected',
'susceptible',
'total',
'contagious',
'alpha',
contact_type='fixed',
contact=contact)
n_days = 100
test_ensemble.reset()
test_ensemble.evolve_expectations(n_days)
single.reset()
single.evolve_expectations(n_days)
for pop_name in test_ensemble.populations:
pop = test_ensemble.populations[pop_name]
if pop.show_sim:
ens_hist = test_ensemble.populations[pop_name].history
single_hist = single.populations[pop_name].history
for i in range(len(ens_hist)):
ratio = ens_hist[i] / single_hist[i]
assert np.abs(ratio - 2.) < 0.001
def test_interval_maker():
hub_date = datetime.date(2020, 4, 1)
my_IntervalMaker = IntervalMaker("USA", hub_date)
categories = ['case', 'death', 'hospitalization']
n_period_dict = {'case': 5, 'death': 5, 'hospitalization': 30}
n_rep = 10
scale_std_alpha = 2.
model = Model.open_file(path_model_2_6)
if 'interval_maker' not in model.user_dict:
model.user_dict['interval_maker'] = {}
model.user_dict['interval_maker']['smearing parameters'] = [
'non_icu_hosp_frac', 'recover_frac'
]
model.parameters['non_icu_hosp_frac'].std_estimator = 0.002
model.parameters['recover_frac'].set_value(0.99)
model.parameters['recover_frac'].std_estimator = 0.01
my_IntervalMaker.get_quantiles(categories,
n_period_dict,
model,
n_rep=n_rep,
scale_std_alpha=scale_std_alpha,
back_up=21,
fall_back=True,
rescale=True)
for category in categories:
my_IntervalMaker.append_user_dict(category, model)
i = 1
def test_model_2_9():
ref_2_9 = Model.open_file(path_model_2_9)
ref_2_9.parameters['alpha_0'].set_value(0.35)
ref_2_9.transitions['outbreak_v'].enabled = True
ref_2_9.parameters['outbreak_v_time'].set_value(30)
ref_2_9.parameters['outbreak_v_number'].set_value(2.)
ref_2_9.parameters['alpha_0_v'].set_value(0.35)
ref_2_9.transitions['outbreak_w'].enabled = True
ref_2_9.parameters['outbreak_w_time'].set_value(60)
ref_2_9.parameters['outbreak_w_number'].set_value(4.)
ref_2_9.parameters['alpha_0_w'].set_value(0.45)
ref_2_9.transitions['vaccination_1'].enabled = True
ref_2_9.parameters['vacc_time_1'].set_value(80)
ref_2_9.parameters['vacc_number_1'].set_value(1000.)
# ref_2_9.reset()
# ref_2_9.evolve_expectations(200)
ref_2_9.reset()
ref_2_9.generate_data(200)
i = 1
def test_point_estimate_daily():
def delta(cumul):
diff = []
for i in range(1, len(cumul)):
diff.append(cumul[i] - cumul[i - 1])
# first daily value is repeated since val(t0-1) is unknown
diff.insert(0, diff[0])
return diff
start_day = 12
end_day = 60
ref_2 = Model.open_file(path_model_2_2)
sim_2 = Model.open_file(path_model_2_2)
# do fit of alpha_0, alpha_1, cont_0, trans_rate_1_time
for par_name in ['alpha_0', 'alpha_1', 'cont_0']:
par = ref_2.parameters[par_name]
par.set_variable(None, None)
par = ref_2.parameters['trans_rate_1_time']
par.set_variable(None, None)
par.set_min(13)
par.set_max(19)
sim_2.reset()
sim_2.generate_data(end_day)
daily_data = delta(sim_2.populations['reported'].history)
daily_data[47] = np.inf
optimizer = Optimizer(ref_2,
'daily reported',
daily_data, [start_day, end_day],
skip_data='42,45:48')
optimizer.reset_variables()
scan_dict = optimizer.i_fit()
assert ref_2.parameters['trans_rate_1_time'].get_value() in [15, 16, 17]
par = ref_2.parameters['trans_rate_1_time']
par.set_fixed()
popt, pcov = optimizer.fit()
assert np.abs(ref_2.parameters['alpha_0'].get_value() -
ref_2.parameters['alpha_0'].initial_value) < 0.06
assert np.abs(ref_2.parameters['alpha_1'].get_value() -
ref_2.parameters['alpha_1'].initial_value) < 0.02
assert np.abs(ref_2.parameters['cont_0'].get_value() -
ref_2.parameters['cont_0'].initial_value) < 20.
def test_Model_properties():
"""tests to ensure the properties of Model"""
ref_model_1 = Model.open_file(path_model_4_1)
ref_model_2 = Model.open_file(path_model_4_1)
for test_model in [ref_model_1, ref_model_2]:
# check simple scaling of initial contagious population
# Will not be exact due to bootstrap
EPS = 0.01
cont_0 = test_model.parameters['cont_0'].get_value()
test_model.reset()
test_model.evolve_expectations(200)
n_reported1 = test_model.populations['reported'].history[-1]
assert test_model.populations['contagious_o'].history[-1] < 10.
test_model.parameters['cont_0'].set_value(2 * cont_0)
test_model.reset()
test_model.evolve_expectations(200)
n_reported2 = test_model.populations['reported'].history[-1]
assert np.abs(n_reported2 - n_reported1 * 2) < EPS * n_reported1 * 2
# check that the mean of many data runs is near expectation
ref_model = Model.open_file(path_model_2)
ref_model.reset()
n_days = 60
ref_model.evolve_expectations(n_days)
sim_models = []
n_rep = 100
for i in range(n_rep):
sim_model = copy.deepcopy(ref_model)
sim_model.reset()
sim_model.generate_data(n_days)
sim_models.append(sim_model)
for pop_name in ref_model.populations:
pop = ref_model.populations[pop_name]
if pop.show_sim:
results = []
for sim_model in sim_models:
results.append(sim_model.populations[pop_name].history[-1])
mean = np.mean(np.array(results))
std = np.std(np.array(results))
error = std / np.sqrt(1. * n_rep)
expect = ref_model.populations[pop_name].history[-1]
assert np.abs(expect - mean) < 8. * error
def test_max_mult():
ref_2_5 = Model.open_file(path_model_2_5)
ref_2_5.transitions['vaccination'].enabled = True
ref_2_5.parameters['vaccination_number'].set_value(800000.)
ref_2_5.parameters['vaccination_time'].set_value(5)
ref_2_5.reset()
ref_2_5.boot(expectations=True)
ref_2_5.evolve_expectations(200)
i = 1
def test_trajectory():
ref_2 = Model.open_file(path_model_2_2)
trajectory = Trajectory(ref_2, 'contagious', 'trans_rate_1', [0.03, 0.75])
alpha_c = trajectory.get_alpha(0.)
delta_1 = trajectory.get_delta(0.1)
delta_2 = trajectory.get_delta(0.2)
assert np.abs(alpha_c - 0.152) < 0.001
assert np.abs(trajectory.get_delta(alpha_c)) < 0.00001
assert np.abs(delta_1 + 0.0513) < 0.0001
assert np.abs(delta_2 - 0.0389) < 0.0001
def test_model_2_5():
ref_2_5 = Model.open_file(path_model_2_5)
ref_2_3 = Model.open_file(path_model_2_3)
#ref_2_5.transitions['vaccination'].enabled = True
#ref_2_5.parameters['vaccination_number'].set_value(10000.)
ref_2_5.reset()
ref_2_5.evolve_expectations(200)
ref_2_3.reset()
ref_2_3.evolve_expectations(200)
for pop_name in ref_2_3.populations:
pop = ref_2_3.populations[pop_name]
if pop.show_sim:
print(pop_name)
hist_2_3 = ref_2_3.populations[pop_name].history
hist_2_5 = ref_2_5.populations[pop_name].history
for i in range(len(hist_2_3)):
if hist_2_3[i] > 0:
ratio = hist_2_5[i] / hist_2_3[i]
assert np.abs(ratio - 1.) < 0.001
def test_point_estimate_skip_zeros():
start_day = 12
end_day = 60
ref_2 = Model.open_file(path_model_2_2)
sim_2 = Model.open_file(path_model_2_2)
# do fit of alpha_1, trans_rate_1_time
for par_name in ['alpha_1']:
par = ref_2.parameters[par_name]
par.set_variable(None, None)
par = ref_2.parameters['trans_rate_1_time']
par.set_variable(None, None)
par.set_min(13)
par.set_max(19)
sim_2.reset()
rn_dict = sim_2.populations['reported'].get_report_noise()
rn_dict['report_days'].set_value(7)
sim_2.generate_data(end_day)
sim_2.populations['reported'].history[47] = np.inf
optimizer = Optimizer(ref_2,
'total reported',
sim_2.populations['reported'].history,
[start_day, end_day],
cumul_reset=True,
skip_data='42,45:48',
skip_zeros=True)
#optimizer = Optimizer(ref_2, 'total reported', sim_2.populations['reported'].history, [start_day, end_day],
# cumul_reset=True, skip_zeros=False)
optimizer.reset_variables()
scan_dict = optimizer.i_fit()
assert ref_2.parameters['trans_rate_1_time'].get_value() in [15, 16, 17]
par = ref_2.parameters['trans_rate_1_time']
par.set_fixed()
popt, pcov = optimizer.fit()
assert np.abs(ref_2.parameters['alpha_1'].get_value() -
ref_2.parameters['alpha_1'].initial_value) < 0.02
def test_model_2_8():
ref_2_8 = Model.open_file(path_model_2_8)
ref_2_8.transitions['outbreak_v'].enabled = True
ref_2_8.parameters['outbreak_v_time'].set_value(30)
ref_2_8.parameters['outbreak_v_number'].set_value(2.)
ref_2_8.parameters['alpha_0'].set_value(0.35)
# ref_2_8.reset()
# ref_2_8.evolve_expectations(200)
ref_2_8.reset()
ref_2_8.generate_data(200)
i = 1
def test_model_2_7():
ref_2_7 = Model.open_file(path_model_2_7)
ref_2_7.transitions['vaccination_1'].enabled = True
ref_2_7.transitions['vaccination_2'].enabled = True
ref_2_7.parameters['vacc_time_2'].set_value(80)
ref_2_7.parameters['vacc_number_2'].set_value(-5.)
# ref_2_7.reset()
# ref_2_7.evolve_expectations(200)
ref_2_7.reset()
ref_2_7.generate_data(200)
i = 1
def test_model_ks_4_2():
ks_4_2 = Model.open_file('ks_4_2_0116_problem.pypm')
ks_4_2.reset()
ks_4_2.evolve_expectations(720)
recent_oe_ve = [[ks_4_2.populations['os_susceptible'].history[i], ks_4_2.populations['ve_susceptible'].history[i]] \
for i in range(695,700)]
ks_4_2.reset()
ks_4_2.parameters['ve_frac'].set_value(0.801)
ks_4_2.evolve_expectations(720)
recent_oe_ve2 = [[ks_4_2.populations['os_susceptible'].history[i], ks_4_2.populations['ve_susceptible'].history[i]] \
for i in range(695,700)]
i = 1
def test_class_Delay():
"""tests to ensure the behaviour class Delay"""
test_model = Model('test_model')
EPS = 0.1
mean = 10.
std_dev = 4.
half_width = float(std_dev * np.sqrt(12.) / 2.)
k_vals = [1, 2, 3]
for delay_type in ['norm', 'uniform', 'erlang', 'gamma']:
k_s = [1]
if delay_type == 'erlang':
k_s = k_vals
for k in k_s:
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.),
'half_width': Parameter('hw', half_width, parameter_min=-100., parameter_max=100.),
'k': Parameter('k', k, parameter_type='int', parameter_min=1, parameter_max=100)
}
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
# The delay is created after the model: since it is not associated with a connector, the model
# does not know to call its update method. (The model does not have stand alone delays.)
test_delay = Delay('test_delay', delay_type, delay_parameters=delay_pars, model=test_model)
distribution = test_delay.future_expectations
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - 1.) < EPS
est_mean = sum_tp
assert np.abs(est_mean - mean) < EPS
est_sigma = np.sqrt(sum_ttp - sum_tp * sum_tp)
if delay_type != 'erlang':
assert np.abs(est_sigma - std_dev) < EPS
else:
assert np.abs(est_sigma - mean / np.sqrt(1. * k)) < EPS
def upload_model_files(self, model_filenames: list):
if not isinstance(model_filenames, list):
raise TypeError(
'Error in upload_model_files: argument must be a list of .pypm filenames'
)
for model_file in model_filenames:
if not isinstance(model_file, str):
raise TypeError(
'Error in upload_model_files: argument must be a list of .pypm filenames'
)
for model_filename in model_filenames:
self.__add_model(Model.open_file(model_filename))
def test_Model_copy_values_from():
ref_model_2 = Model.open_file(path_model_2)
alberta_model_2 = Model.open_file('ab_2_0514.pypm')
ref_model_2.copy_values_from(alberta_model_2)
ref_model_2.reset()
ref_model_2.evolve_expectations(80)
alberta_model_2.reset()
alberta_model_2.evolve_expectations(80)
for pop_name in ref_model_2.populations:
pop = ref_model_2.populations[pop_name]
# Bug fixed in ref_model_2, not in alberta_model_2 accounts for small differences in icu
if 'vent' not in pop.name and 'in_' not in pop.name:
ref_hist = pop.history
alberta_hist = alberta_model_2.populations[pop_name].history
for i in range(len(ref_hist)):
if np.abs(ref_hist[i] - alberta_hist[i]) > 0.0001:
print(pop.name, i, ref_hist[i], alberta_hist[i],
np.abs(ref_hist[i] - alberta_hist[i]))
assert np.abs(ref_hist[i] - alberta_hist[i]) < 0.001
def test_class_Population():
"""tests to ensure the behaviour class Population"""
init_value = 100
test_pop = Population('test population', init_value, description='For testing populations',
hidden=True, color='black', show_sim=False, report_noise=False,
report_noise_par=None)
assert test_pop.history[0] == init_value
model = Model.open_file(path_model_2_8)
test_pop.set_model(model)
incoming = 10
test_pop.update_future_fast(incoming)
assert test_pop.future[0] == incoming
test_pop.do_time_step()
assert test_pop.history[1] == init_value + incoming
assert len(test_pop.future) == 0 or test_pop.future == 0
scale_factor = 0.5
test_pop.scale_history(scale_factor)
assert test_pop.history[0] == init_value * scale_factor
assert test_pop.history[1] == (init_value + incoming) * scale_factor
# check that noise in reporting makes sense
# Expectations are not effected - data should be
# but the total reported should be the same after all reporting complete
noise_factor = Parameter('noise_factor', 0.3)
backlog_factor = Parameter('backlog_factor', 0.8)
# restart - back to initial value
for expectations in [True, False]:
test_pop.reset()
future = [0, 10, 100, 100, 100, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
future_sum = np.sum(np.array(future))
test_pop.set_report_noise(True, noise_factor, backlog_factor, None)
test_pop.future = future
for i in range(len(future) + 5):
test_pop.do_time_step(expectations=expectations)
assert test_pop.history[-1] == init_value + future_sum
report_days = Parameter('report_days', 127, parameter_min=-7, parameter_max=127, parameter_type='int')
for report_day_value in [127, 63, -1, -2, -5, -7]:
report_days.set_value(report_day_value)
test_pop.reset()
future = [0, 10, 100, 100, 100, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
future_sum = np.sum(np.array(future))
test_pop.set_report_noise(True, noise_factor, backlog_factor, report_days)
test_pop.future = future
for i in range(len(future) + 5):
test_pop.do_time_step(expectations=expectations)
assert test_pop.history[-1] == init_value + future_sum
def test_sim_gof_local():
start_day = 12
end_day = 60
ref_2 = Model.open_file(path_model_2_2)
sim_2 = Model.open_file(path_model_2_2)
# do fit of alpha_0, alpha_1, cont_0
par_names = ['alpha_1']
for par_name in par_names:
par = ref_2.parameters[par_name]
par.set_variable(None, None)
sim_2.reset()
sim_2.generate_data(end_day)
sim_2.populations['reported'].history[47] = np.inf
optimizer = Optimizer(ref_2,
'total reported',
sim_2.populations['reported'].history,
[start_day, end_day],
cumul_reset=True,
skip_data='42,45:48')
optimizer.reset_variables()
popt, pcov = optimizer.fit()
fit_statistics = optimizer.fit_statistics
optimizer.calc_chi2s = False
optimizer.calc_chi2f = True
n_rep = 10
optimizer.calc_sim_gof(n_rep)
fit_stat_list = optimizer.fit_stat_list
ndof = fit_stat_list[0]['ndof']
chi2_list = [fit_stat_list[i]['chi2'] for i in range(n_rep)]
chi2_mean = np.mean(chi2_list)
assert np.abs(chi2_mean - ndof) < 8.E6
acor_list = [fit_stat_list[i]['acor'] for i in range(n_rep)]
acor_mean = np.mean(acor_list)
assert np.abs(acor_mean) < 0.2
def test_class_Chain():
"""tests to ensure the behaviour class Chain"""
test_model = Model('test_model')
from_pop = Population('from_pop', 0.)
to_pop = Population('to_pop', 0.)
pop_a = Population('pop_a', 0.)
pop_b = Population('pop_b', 0.)
start = 100000
from_pop.future = [start]
mean = 10.
sigma = 2.
delay_pars = {'mean': Parameter('mean', mean, parameter_min=0.1, parameter_max=100.),
'sigma': Parameter('sigma', sigma, parameter_min=0.1, parameter_max=1000.)}
delay = Delay('delay', 'norm', delay_pars, test_model)
frac = 0.8
fraction = Parameter('frac', frac)
chain = []
chain.append(Propagator('prop_0', from_pop, pop_a, fraction, delay))
chain.append(Propagator('prop_1', pop_a, pop_b, fraction, delay))
test_chain = Chain('test_chain', from_pop, to_pop, chain, fraction, delay, test_model)
test_model.add_connector(test_chain)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_chain.update_expectation, test_chain.update_data]:
EPS = 0.02
if func == test_chain.update_data:
EPS = 0.2
to_pop.reset()
pop_a.reset()
pop_b.reset()
func()
for pop in [to_pop, pop_b]:
distribution = pop.future
total = start * (1. - frac ** 2) * frac
ave = mean
std_dev = sigma
if pop == pop_b:
total = start * frac ** 2
ave = 2. * mean
std_dev = np.sqrt(2.) * sigma
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - total) < EPS * total
est_mean = sum_tp / total
assert np.abs(est_mean - ave) < EPS * ave
est_sigma = np.sqrt(sum_ttp / total - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
def test_model_ny_4_2():
ny_4_2 = Model.open_file(
'/Users/karlen/pypm-local/models/covid19/USA/ny_4_2_0109.pypm')
ny_4_2.reset()
ny_4_2.evolve_expectations(40)
num = ny_4_2.populations['reported'].history[-1]
cont_0 = ny_4_2.parameters['cont_0'].get_value()
ny_4_2.parameters['cont_0'].set_value(2. * cont_0)
ny_4_2.reset()
ny_4_2.evolve_expectations(40)
num2 = ny_4_2.populations['reported'].history[-1]
if (ny_4_2.populations['contagious_o'].initial_value !=
ny_4_2.parameters['cont_0']):
ny_4_2.populations['contagious_o'].initial_value = ny_4_2.parameters[
'cont_0']
print('changed')
ny_4_2.reset()
ny_4_2.evolve_expectations(40)
num3 = ny_4_2.populations['reported'].history[-1]
i = 1
def test_class_Injector():
"""tests to ensure the behaviour class Injector"""
test_model = Model('test_model')
number = 50.
inject = Parameter('inject', number, parameter_min=0., parameter_max=1000.)
time = 5
trans_time = Parameter('time', time, parameter_type='int', parameter_min=0, parameter_max=1000)
to_pop = Population('to_pop', 0)
test_injector = Injector('injector', 'rel_days', trans_time, to_pop, inject, model=test_model)
test_model.add_transition(test_injector)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
to_pop.reset()
test_injector.take_action()
assert to_pop.future[0] == number
assert np.abs(test_injector.trigger_step - time / time_step) < 0.1
def test_class_Multiplier():
"""tests to ensure the behaviour class Multiplier"""
test_model = Model('test_model')
EPS = 1.
n1 = 50.
n2 = 20.
n3 = 2.
scale = 0.1
f_pops = [Population('f1_pop', n1), Population('f2_pop', n2), Population('f3_pop', n3)]
to_pop = Population('to_pop', 0.)
scale_par = Parameter('alpha', scale)
delay = Delay('fast', 'fast')
test_multiplier = Multiplier('test_multiplier', f_pops, to_pop, scale_par, delay, model=test_model)
test_model.add_connector(test_multiplier)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
# expectation:
expected = n1 * n2 / n3 * scale * time_step
to_pop.reset()
test_multiplier.set_distribution('poisson', None)
test_multiplier.update_expectation()
assert to_pop.future[0] == expected
# Poisson
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected)) < EPS
# Negative binomial
p_nb = 0.2
nbinom_par = Parameter('nb', p_nb)
test_multiplier.set_distribution('nbinom', nbinom_par)
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected / p_nb)) < EPS
def test_class_Splitter():
"""tests to ensure the behaviour class Splitter"""
test_model = Model('test_model')
start = 100000
from_pop = Population('from_pop', 0)
from_pop.future = [start]
to_pops = [Population('to_pop1', 0.), Population('to_pop2', 0.)]
fracs = [0.4, 0.6]
fraction = Parameter('frac', fracs[0])
mean = 10.
std_dev = 4.
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.)
}
test_delay = Delay('test_delay', 'norm', delay_parameters=delay_pars, model=test_model)
test_split = Splitter('test_prop', from_pop, to_pops, [fraction], test_delay)
test_model.add_connector(test_split)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_split.update_expectation, test_split.update_data]:
EPS = 0.01
if func == test_split.update_data:
EPS = 0.1
to_pops[0].reset()
to_pops[1].reset()
func()
total = 0
for i in range(2):
distribution = to_pops[i].future
frac = fracs[i]
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
total += sum_p
assert np.abs(sum_p - start * frac) < EPS * start * frac
est_mean = sum_tp / (start * frac)
assert np.abs(est_mean - mean) < EPS * mean
est_sigma = np.sqrt(sum_ttp / (start * frac) - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
assert np.abs(total - start) < 0.1
def test_linear_modifier():
ref_2_4 = Model.open_file(path_model_2_4)
trajectory = Trajectory(ref_2_4, 'contagious', 'trans_rate_1', [0.02, 2.0])
alpha_c = trajectory.get_alpha(0.)
ref_2_4.parameters['alpha_1'].set_value(alpha_c)
ref_2_4.parameters['to_icu_delay_mean'].set_value(0.5)
ref_2_4.parameters['to_icu_delay_sigma'].set_value(0.5)
ref_2_4.transitions['mod_icu_frac'].enabled = True
ref_2_4.parameters['icu_frac_time'].set_value(60)
ref_2_4.parameters['icu_frac_0'].set_value(0.1)
# the meaning of this parameter changed to the end_value
# ref_2_4.parameters['icu_frac_slope'].set_value(0.01)
ref_2_4.parameters['icu_frac_slope'].set_value(0.2)
ref_2_4.parameters['icu_frac_nstep'].set_value(10)
ref_2_4.reset()
ref_2_4.evolve_expectations(100)
pop_hist = ref_2_4.populations['icu admissions'].history
assert np.abs(pop_hist[55] - pop_hist[54] - pop_hist[50] +
pop_hist[49]) < 0.01
assert np.abs(pop_hist[75] - pop_hist[74] - pop_hist[70] +
pop_hist[69]) < 0.1
assert np.abs((pop_hist[75] - pop_hist[74]) /
(pop_hist[55] - pop_hist[54]) - 2.) < 0.1
def test_class_Propagator():
"""tests to ensure the behaviour class Propagator"""
test_model = Model('test_model')
start = 100000
from_pop = Population('from_pop', 0)
from_pop.future = [start]
to_pop = Population('to_pop', 0.)
frac = 0.4
fraction = Parameter('frac', frac)
mean = 10.
std_dev = 4.
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.)
}
test_delay = Delay('test_delay', 'norm', delay_parameters=delay_pars, model=test_model)
test_prop = Propagator('test_prop', from_pop, to_pop, fraction, test_delay)
test_model.add_connector(test_prop)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_prop.update_expectation, test_prop.update_data]:
EPS = 0.01
if func == test_prop.update_data:
EPS = 0.1
to_pop.reset()
func()
distribution = to_pop.future
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - start * frac) < EPS * start * frac
est_mean = sum_tp / (start * frac)
assert np.abs(est_mean - mean) < EPS * mean
est_sigma = np.sqrt(sum_ttp / (start * frac) - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
def test_class_Modifier():
"""tests to ensure the behaviour class Modifier"""
test_model = Model('test_model')
mod_time = Parameter('time', 5, parameter_type='int', parameter_min=0, parameter_max=1000)
par_val = 0.3
par_0_val = 0.5
par_1_val = 0.7
parameter = Parameter('par', par_val)
parameter_0 = Parameter('par_0', par_0_val)
parameter_1 = Parameter('par_1', par_1_val)
test_modifier = Modifier('test_modifier', 'rel_days', mod_time, parameter, parameter_0, parameter_1,
model=test_model)
test_model.add_transition(test_modifier)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
parameter.reset()
assert parameter.get_value() == par_val
test_modifier.take_action()
assert parameter.get_value() == par_1_val
test_modifier.reset()
assert parameter.get_value() == par_0_val
assert np.abs(test_modifier.trigger_step - 5 / time_step) < 0.1
def test_mixing_evolve_data():
ref_2_9 = Model.open_file(path_model_2_9)
ref_2_9.reset()
ref_2_9.generate_data(100, from_step=0, data_start=50)
reported = ref_2_9.populations['reported'].history
i = 1