def test_pnormdistance():
abc = MockABC([{
's1': -1,
's2': -1,
's3': -1
}, {
's1': -1,
's2': 0,
's3': 1
}])
x_0 = {'s1': 0, 's2': 0, 's3': 1}
# first test that for PNormDistance, the weights stay constant
dist_f = PNormDistance()
dist_f.initialize(0, abc.sample_from_prior, x_0=x_0)
# call distance function, also to initialize w
d = dist_f(abc.sample_from_prior()[0], abc.sample_from_prior()[1], t=0)
expected = pow(1**2 + 2**2, 1 / 2)
assert expected == d
assert sum(
abs(a - b)
for a, b in zip(list(dist_f.weights[0].values()), [1, 1, 1])) < 0.01
def test_pnormdistance():
abc = MockABC([{
's1': -1,
's2': -1,
's3': -1
}, {
's1': -1,
's2': 0,
's3': 1
}])
x_0 = {'s1': 0, 's2': 0, 's3': 1}
# first test that for PNormDistance, the weights stay constant
dist_f = PNormDistance(p=2)
dist_f.initialize(0, abc.sample_from_prior, x_0=x_0, total_sims=0)
# call distance function, also to initialize w
d = dist_f(abc.sumstats[0], abc.sumstats[1], t=0)
expected = pow(0**2 + 1**2 + 2**2, 1 / 2)
assert expected == d
assert dist_f.fixed_weights[0] == 1
# maximum norm
dist_f = PNormDistance(p=np.inf)
dist_f.initialize(0, abc.sample_from_prior, x_0=x_0, total_sims=0)
d = dist_f(abc.sumstats[0], abc.sumstats[1], t=0)
assert d == 2
def __init__(
self,
models: Union[List[Model], Model, Callable],
parameter_priors: Union[List[Distribution],
Distribution, Callable],
distance_function: Union[Distance, Callable] = None,
population_size: Union[PopulationStrategy, int] = 100,
summary_statistics: Callable[[model_output], dict] = identity,
model_prior: RV = None,
model_perturbation_kernel: ModelPerturbationKernel = None,
transitions: Union[List[Transition], Transition] = None,
eps: Epsilon = None,
sampler: Sampler = None,
acceptor: Acceptor = None,
stop_if_only_single_model_alive: bool = False,
max_nr_recorded_particles: int = np.inf):
if not isinstance(models, list):
models = [models]
models = list(map(SimpleModel.assert_model, models))
self.models = models
if not isinstance(parameter_priors, list):
parameter_priors = [parameter_priors]
self.parameter_priors = parameter_priors
# sanity checks
if len(self.models) != len(self.parameter_priors):
raise AssertionError(
"Number models and number parameter priors have to agree.")
if distance_function is None:
distance_function = PNormDistance()
self.distance_function = to_distance(distance_function)
self.summary_statistics = summary_statistics
if model_prior is None:
model_prior = RV("randint", 0, len(self.models))
self.model_prior = model_prior
if model_perturbation_kernel is None:
model_perturbation_kernel = ModelPerturbationKernel(
len(self.models), probability_to_stay=.7)
self.model_perturbation_kernel = model_perturbation_kernel
if transitions is None:
transitions = [MultivariateNormalTransition()
for _ in self.models]
if not isinstance(transitions, list):
transitions = [transitions]
self.transitions = transitions # type: List[Transition]
if eps is None:
eps = MedianEpsilon(median_multiplier=1)
self.eps = eps
if isinstance(population_size, int):
population_size = ConstantPopulationSize(
population_size)
self.population_size = population_size
if sampler is None:
sampler = DefaultSampler()
self.sampler = sampler
if acceptor is None:
acceptor = UniformAcceptor()
self.acceptor = SimpleFunctionAcceptor.assert_acceptor(acceptor)
self.stop_if_only_single_model_alive = stop_if_only_single_model_alive
self.max_nr_recorded_particles = max_nr_recorded_particles
# will be set later
self.x_0 = None
self.history = None
self._initial_population = None
self.minimum_epsilon = None
self.max_nr_populations = None
self.min_acceptance_rate = None
self._sanity_check()