Error
Traceback (most recent call last):
File "/stochss/stochss/handlers/util/scripts/start_job.py", line 120, in <module>
job.run(verbose=args.verbose)
File "/stochss/stochss/handlers/util/model_inference.py", line 958, in run
results = smc_abc_inference.infer(*infer_args, **infer_kwargs)
File "/opt/conda/lib/python3.8/site-packages/sciope/inference/smc_abc.py", line 220, in infer
kweights = self.perturbation_kernel.pdf(population, new_samples)
File "/opt/conda/lib/python3.8/site-packages/sciope/utilities/perturbationkernels/multivariate_normal.py", line 45, in pdf
pdfs.append(multivariate_normal.pdf(x, x0[i], self.cov))
File "/opt/conda/lib/python3.8/site-packages/scipy/stats/_multivariate.py", line 580, in pdf
params = self._process_parameters(mean, cov, allow_singular)
File "/opt/conda/lib/python3.8/site-packages/scipy/stats/_multivariate.py", line 417, in _process_parameters
psd = _PSD(cov, allow_singular=allow_singular)
File "/opt/conda/lib/python3.8/site-packages/scipy/stats/_multivariate.py", line 172, in __init__
raise np.linalg.LinAlgError(msg)
numpy.linalg.LinAlgError: When `allow_singular is False`, the input matrix must be symmetric positive definite.
Model
def create_genetic_toggle_switch(parameter_values=None):
model = gillespy2.Model(name='Genetic_Toggle_Switch')
model.volume = 1
# Variables
U = gillespy2.Species(name='U', initial_value=10, mode='discrete')
V = gillespy2.Species(name='V', initial_value=10, mode='discrete')
model.add_species([
U, V
])
# Parameters
alpha1 = gillespy2.Parameter(name='alpha1', expression='1')
alpha2 = gillespy2.Parameter(name='alpha2', expression='1')
beta = gillespy2.Parameter(name='beta', expression='2')
gamma = gillespy2.Parameter(name='gamma', expression='2')
mu = gillespy2.Parameter(name='mu', expression='1')
model.add_parameter([
alpha1, alpha2, beta, gamma, mu
])
# Reactions
cu = gillespy2.Reaction(
name='cu',
reactants={}, products={'U': 1},
propensity_function='alpha1/(1+pow(V,beta))',
ode_propensity_function='alpha1/(1+pow(V,beta))'
)
cv = gillespy2.Reaction(
name='cv',
reactants={}, products={'V': 1},
propensity_function='alpha2/(1+pow(U,gamma))',
ode_propensity_function='alpha2/(1+pow(U,gamma))'
)
du = gillespy2.Reaction(
name='du', rate='mu',
reactants={'U': 1}, products={}
)
dv = gillespy2.Reaction(
name='dv', rate='mu',
reactants={'V': 1}, products={}
)
model.add_reaction([
cu, cv, du, dv
])
# Timespan
tspan = gillespy2.TimeSpan.arange(1, t=100)
model.timespan(tspan)
return model
Inference
def configure_simulation(trajectories=1):
solver = gillespy2.SSACSolver(model=model, delete_directory=False)
kwargs = {
'number_of_trajectories': trajectories,
'seed': None,
'solver': solver,
}
return kwargs
kwargs = configure_simulation(100)
results = model.run(**kwargs)
unshaped_obs_data = results.to_array()
shaped_obs_data = unshaped_obs_data.swapaxes(1, 2)
obs_data = shaped_obs_data[:,1:, :]
print(obs_data.shape)
def process(raw_results):
return raw_results.to_array().swapaxes(1, 2)[:,1:, :]
def simulator(parameter_point):
model = create_genetic_toggle_switch()
labels = [
'alpha1', 'alpha2', 'beta', 'gamma', 'mu'
]
for ndx, parameter in enumerate(parameter_point):
model.listOfParameters[labels[ndx]].expression = str(parameter)
kwargs = configure_simulation()
raw_results = model.run(**kwargs)
return process(raw_results)
values = numpy.array([
1.0, 1.0, 2.0, 2.0, 1.0
])
parameter_names = [
'alpha1', 'alpha2', 'beta', 'gamma', 'mu'
]
lower_bounds = numpy.array([
0.5, 0.5, 1, 1, 0.5
])
upper_bounds = numpy.array([
1.5, 1.5, 3, 3, 1.5
])
uni_prior = uniform_prior.UniformPrior(lower_bounds, upper_bounds)
summ_func = identity.Identity()
eps_selector = RelativeEpsilonSelector(20, max_rounds=1)
c = Client()
smc_abc_inference = smc_abc.SMCABC(
obs_data, sim=simulator, prior_function=uni_prior, summaries_function=summ_func.compute
)
smc_abc_results = smc_abc_inference.infer(
num_samples=10, batch_size=10, eps_selector=eps_selector, chunk_size=10
)
