def test_uniform_acceptor():
"""Test the uniform acceptor."""
def dist(x, x_0):
return sum(abs(x[key] - x_0[key]) for key in x_0)
distance = pyabc.SimpleFunctionDistance(dist)
acceptor = pyabc.UniformAcceptor()
eps = pyabc.ListEpsilon([1, 4, 2])
x = {'s0': 1.5}
x_0 = {'s0': 0}
ret = acceptor(distance_function=distance,
eps=eps,
x=x,
x_0=x_0,
t=2,
par=None)
assert ret.accept
# now let's test again, including previous time points
acceptor = pyabc.UniformAcceptor(use_complete_history=True)
ret = acceptor(distance_function=distance,
eps=eps,
x=x,
x_0=x_0,
t=2,
par=None)
assert not ret.accept
def __init__(self,
get_acceptor=None,
get_transition=None,
get_eps=None,
n_acc: int = 1000,
n_pop: int = 20,
eps_min: float = 0.0,
min_acc_rate: float = 0.0,
id_=None):
if get_acceptor is None:
get_acceptor = lambda: pyabc.UniformAcceptor()
self.get_acceptor = get_acceptor
if get_transition is None:
get_transition = lambda: pyabc.MultivariateNormalTransition()
self.get_transition = get_transition
if get_eps is None:
get_eps = lambda: pyabc.MedianEpsilon()
self.get_eps = get_eps
self.n_acc = n_acc
self.n_pop = n_pop
self.eps_min = eps_min
self.min_acc_rate = min_acc_rate
self.id = id_
and comparing that to the true analysis performed using either a noisy model,
or a stochastic acceptor.
"""
import pyabc
import sys
from study_abc_noise.model import NonIdAblePrioredModelVars
from study_abc_noise.util import create_sampler, get_timestamp
from study_abc_noise.vars import AnalysisVars, Task
mv = NonIdAblePrioredModelVars()
# create analysis settings
list_analysis_vars = []
for acceptor, id_ in [
(pyabc.UniformAcceptor(), "deterministic"),
(pyabc.UniformAcceptor(), "noisy_model"),
(pyabc.StochasticAcceptor(temp_schemes=[
pyabc.acceptor.scheme_acceptance_rate, pyabc.acceptor.scheme_decay
]), "stochastic_acceptor")
]:
list_analysis_vars.append(
AnalysisVars(get_acceptor=lambda acceptor=acceptor: acceptor, id_=id_))
# create tasks
tasks = []
for analysis_vars in list_analysis_vars:
tasks.append(Task.from_vars(analysis_vars, mv, 0))
# overwrite deterministic setting
tasks[0].model = mv.get_model()
tasks[0].eps_min = 0.0
muA=pyabc.RV("uniform", lim.lb, lim.interval_length))
# %% Define ABC-SMC model
distanceP2_adpt = pyabc.AdaptivePNormDistance(
p=2,
scale_function=pyabc.distance.root_mean_square_deviation,
factors=factors)
# distanceP2 = pyabc.PNormDistance(p=2)
# kernel1 = pyabc.IndependentNormalKernel(var=1.0 ** 2)
# Measure distance and set it as minimum epsilon
# min_eps = distanceP2(obs_data_noisy, obs_data_raw)
# acceptor1 = pyabc.StochasticAcceptor()
acceptor_adpt = pyabc.UniformAcceptor(use_complete_history=True)
eps0 = pyabc.MedianEpsilon(50)
# eps1 = pyabc.Temperature()
# eps_fixed = pyabc.epsilon.ListEpsilon([50, 46, 43, 40, 37, 34, 31, 29, 27, 25,
# 23, 21, 19, 17, 15, 14, 13, 12, 11, 10])
# transition0 = pyabc.transition.LocalTransition(k=50, k_fraction=None)
# transition1 = pyabc.transition.GridSearchCV()
sampler0 = pyabc.sampler.MulticoreEvalParallelSampler(n_procs=48)
abc = pyabc.ABCSMC(
models=solver.non_noisy_model,
parameter_priors=paraPrior,
acceptor=acceptor_adpt,