def setUp(self):
self.d = Distribution(
**{
"a": RV("randint", low=0, high=3 + 1),
"b": Distribution(
**{
"b1": RV("randint", low=0, high=3 + 1),
"b2": RV("randint", low=0, high=3 + 1),
}
),
}
)
self.d_plus_one = Distribution(
**{
"a": RV("randint", low=1, high=1 + 1),
"b": Distribution(
**{
"b1": RV("randint", low=1, high=1 + 1),
"b2": RV("randint", low=1, high=1 + 1),
}
),
}
)
self.x_one = Parameter({"a": 1, "b": Parameter({"b1": 1, "b2": 1})})
self.x_zero = Parameter({"a": 0, "b": Parameter({"b1": 0, "b2": 0})})
self.x_two = Parameter({"a": 2, "b": Parameter({"b1": 2, "b2": 2})})
def test_info_weighted_pnorm_distance():
"""Just test the info weighted distance pipeline."""
db_file = create_sqlite_db_id()[len("sqlite:///"):]
scale_log_file = tempfile.mkstemp()[1]
info_log_file = tempfile.mkstemp()[1]
info_sample_log_file = tempfile.mkstemp()[1]
try:
def model(p):
return {
"s0": p["p0"] + np.random.normal(),
"s1": p["p1"] + np.random.normal(size=2),
}
prior = Distribution(p0=RV("uniform", 0, 1), p1=RV("uniform", 0, 10))
data = {"s0": 0.5, "s1": np.array([5, 5])}
for feature_normalization in ["mad", "std", "weights", "none"]:
distance = InfoWeightedPNormDistance(
predictor=LinearPredictor(),
fit_info_ixs={1, 3},
feature_normalization=feature_normalization,
scale_log_file=scale_log_file,
info_log_file=info_log_file,
info_sample_log_file=info_sample_log_file,
)
abc = ABCSMC(model, prior, distance, population_size=100)
abc.new("sqlite:///" + db_file, data)
abc.run(max_nr_populations=3)
finally:
if os.path.exists(db_file):
os.remove(db_file)
if os.path.exists(scale_log_file):
os.remove(scale_log_file)
if os.path.exists(info_log_file):
os.remove(info_log_file)
def test_wasserstein_distance():
"""Test Wasserstein and Sliced Wasserstein distances."""
n_sample = 11
def model_1d(p):
return {"y": np.random.normal(p["p0"], 1.0, size=n_sample)}
p_true = {"p0": -0.5}
y0 = model_1d(p_true)
p1 = {"p0": -0.55}
y1 = model_1d(p1)
p2 = {"p0": 3.55}
y2 = model_1d(p2)
class IdSumstat(Sumstat):
"""Identity summary statistic."""
def __call__(self, data: dict) -> np.ndarray:
# shape (n, dim)
return data["y"].reshape((-1, 1))
for p in [1, 2]:
for distance in [
WassersteinDistance(
sumstat=IdSumstat(),
p=p,
),
SlicedWassersteinDistance(
sumstat=IdSumstat(),
p=p,
),
]:
distance.initialize(x_0=y0)
# evaluate distance
dist = distance(y1, y0)
assert dist > 0
# sample from somewhere else
assert dist < distance(y2, y0)
# compare to ground truth
if isinstance(distance, SlicedWassersteinDistance):
continue
# weights
w = np.ones(shape=n_sample) / n_sample
dist_exp = sp_dist.minkowski(
np.sort(y1["y"].flatten()),
np.sort(y0["y"]).flatten(),
w=w,
p=p,
)
assert np.isclose(dist, dist_exp)
with pytest.raises(ValueError):
WassersteinDistance(sumstat=IdSumstat(), p=3)
# test integrated
prior = Distribution(p0=RV("norm", 0, 2))
db_file = tempfile.mkstemp(suffix=".db")[1]
try:
for distance in [
WassersteinDistance(sumstat=IdSumstat(), ),
SlicedWassersteinDistance(sumstat=IdSumstat(), ),
]:
abc = ABCSMC(model_1d, prior, distance, population_size=10)
abc.new("sqlite:///" + db_file, y0)
abc.run(max_nr_populations=3)
finally:
os.remove(db_file)
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()