def __init__(self,
workdir = None,
wetlab=wet_lab.WetLab(),
policy=group_policy.Dorfman(),
sampler=smc.SmcSampler(),
cheap_sampler=lbp.LbpSampler(),
num_simulations = 1,
num_tests_per_cycle = 10,
max_test_cycles = 5,
max_group_size = 8,
prior_specificity = 0.97,
prior_sensitivity = 0.85,
prior_infection_rate = 0.05,
metrics_cls=metrics.Metrics,
export_metrics_every = 5):
self._wetlab = wetlab
self._policy = policy
self._samplers = [cheap_sampler, sampler]
self._max_test_cycles = max_test_cycles
self._num_simulations = num_simulations
self.state = state.State(self._wetlab.num_patients,
num_tests_per_cycle,
max_group_size,
prior_infection_rate,
prior_specificity,
prior_sensitivity)
self.metrics = metrics_cls(
workdir,
self._num_simulations,
self._max_test_cycles,
self._wetlab.num_patients,
self.state.num_tests_per_cycle)
self._export_every = export_metrics_every
def setUp(self):
super().setUp()
self.num_patients = 20
self.num_tests_per_cycle = 3
self.state = state.State(
self.num_patients, self.num_tests_per_cycle, max_group_size=4,
prior_infection_rate=0.30, prior_specificity=0.9, prior_sensitivity=0.7)
self.rng = jax.random.PRNGKey(0)
def setUp(self):
super().setUp()
self.rng = jax.random.PRNGKey(0)
self.state = state.State(num_patients=32,
num_tests_per_cycle=3,
max_group_size=5,
prior_infection_rate=0.05,
prior_specificity=0.95,
prior_sensitivity=0.75)
def test_exhaustive(self):
# Reduce the number of possible states.
exh_state = state.State(
num_patients=8, num_tests_per_cycle=3, max_group_size=5,
prior_infection_rate=0.05, prior_specificity=0.95,
prior_sensitivity=0.75)
sampler = exhaustive.ExhaustiveSampler()
self.assertIsNone(sampler.particles)
self.assertIsNone(sampler.particle_weights)
sampler.produce_sample(self.rng, exh_state)
self.assertIsNotNone(sampler.particles)
self.assertIsNotNone(sampler.particle_weights)
def test_act(self):
num_patients = 40
num_tests_per_cycle = 4
s = state.State(num_patients, num_tests_per_cycle,
max_group_size=5, prior_infection_rate=0.05,
prior_specificity=0.95, prior_sensitivity=0.80)
self.assertEqual(np.size(s.groups_to_test), 0)
self.assertEqual(self.policy.index, 0)
self.policy.act(self.rng, s)
self.assertGreater(np.size(s.groups_to_test), 0)
self.assertEqual(s.groups_to_test.shape[1], num_patients)
self.assertGreater(s.groups_to_test.shape[0], 0)
self.assertEqual(self.policy.index, 1)
def setUp(self):
super().setUp()
self.rng = jax.random.PRNGKey(0)
self.state = state.State(num_patients=72,
num_tests_per_cycle=3,
max_group_size=5,
prior_infection_rate=0.05,
prior_specificity=0.95,
prior_sensitivity=0.75)
self.num_groups = 4
self.rng, *rngs = jax.random.split(self.rng, 3)
self.groups = jax.random.uniform(
rngs[0], (self.num_groups, self.state.num_patients)) > 0.3
self.results = jax.random.uniform(rngs[1], (self.num_groups, )) > 0.2
def __init__(self,
workdir=None,
wetlab=wet_lab.WetLab(),
policy=group_policy.Dorfman(),
sampler=smc.SmcSampler(),
cheap_sampler=lbp.LbpSampler(),
num_simulations=1,
num_tests_per_cycle=10,
max_test_cycles=5,
max_group_size=8,
prior_specificity=0.97,
prior_sensitivity=0.85,
prior_infection_rate=0.05,
metrics_cls=metrics.Metrics,
export_metrics_every=5):
"""Initializes simulation.
Args:
workdir: where results will be stored.
wetlab: WetLab objet tasked with producing test results given groups.
policy: group testing policy, a sequence of algorithms tasked with
choosing groups to test. Can be adaptive to test environment (spec/sens)
of tests. Can be adaptive to previously tested groups. Can leverage
samplers to build information on what are the most likely disease status
among patients.
sampler: sampler that produces a posterior approximation. Instantiated by
default to SmcSampler that resamples at each iteration to fix cases
where the LBP sampler does not quite work the way it should.
cheap_sampler: LBP object to yield cheap approximation of marginal.
num_simulations: number of simulations run consecutively. Here randomness
can come from the group testing policy (if it uses randomness), as well
as diseased label, if WetLab.freeze_diseased is False.
num_tests_per_cycle: number of tests a testing machine can carry out in
the next testing cycle.
max_test_cycles: number of cycles in total that should be considered.
max_group_size: maximal size of groups, how many individuals can be pooled
prior_specificity: best guess one has prior to simulation of the testing
device's specificity per group size (or for all sizes, if singleton).
prior_sensitivity: best guess one has prior to simulation of the testing
device's sensitivity per group size (or for all sizes, if singleton).
prior_infection_rate: best guess of prior probability for patient to be
infected (same for all patients, if singleton)
metrics_cls: class of metrics object used to store results.
export_metrics_every: frequency of exports to file when carrying our
num_simulations results.
"""
self._wetlab = wetlab
self._policy = policy
self._samplers = [cheap_sampler, sampler]
self._max_test_cycles = max_test_cycles
self._num_simulations = num_simulations
self.state = state.State(self._wetlab.num_patients,
num_tests_per_cycle, max_group_size,
prior_infection_rate, prior_specificity,
prior_sensitivity)
self.metrics = metrics_cls(workdir, self._num_simulations,
self._max_test_cycles,
self._wetlab.num_patients,
self.state.num_tests_per_cycle)
self._export_every = export_metrics_every