bayesflow.benchmarks.sir module

bayesflow.benchmarks.sir module#


Generates a random draw from a 2-dimensional (independent) lognormal prior which represents the contact and recovery rate parameters of a basic SIR model.

rngnp.random.Generator or None, default: None

An optional random number generator to use.

thetanp.ndarray of shape (2,)

A single draw from the 2-dimensional prior.

bayesflow.benchmarks.sir.simulator(theta, N=1000000.0, T=160, I0=1.0, R0=0.0, subsample=10, total_count=1000, scale_by_total=True, rng=None)[source]#

Runs a basic SIR model simulation for T time steps and returns subsample evenly spaced points from the simulated trajectory, given disease parameters (contact and recovery rate) theta.

See, Benchmark Task T.9.

Note, that the simulator will scale the outputs between 0 and 1.

thetanp.ndarray of shape (2,)

The 2-dimensional vector of disease parameters.

Nfloat, optional, default: 1e6 = 1 000 000

The size of the simulated population.

TT, optional, default: 160

The duration (time horizon) of the simulation.

I0float, optional, default: 1.

The number of initially infected individuals.

R0float, optional, default: 0.

The number of initially recovered individuals.

subsampleint or None, optional, default: 10

The number of evenly spaced time points to return. If None, no subsampling will be performed and all T timepoints will be returned.

total_countint, optional, default: 1000

The N parameter of the binomial noise distribution. Used just for scaling the data and magnifying the effect of noise, such that max infected == total_count.

scale_by_totalbool, optional, default: True

Scales the outputs by total_count if set to True.

rngnp.random.Generator or None, default: None

An optional random number generator to use.

xnp.ndarray of shape (subsample,) or (T,) if subsample=None

The time series of simulated infected individuals. A trailing dimension of 1 should be added by a BayesFlow configurator if the data is (properly) to be treated as time series.

bayesflow.benchmarks.sir.configurator(forward_dict, mode='posterior', as_summary_condition=False)[source]#

Configures simulator outputs for use in BayesFlow training.