def test_scheme_acceptancerate():
scheme = pyabc.AcceptanceRateScheme()
temp = scheme(t=0, **scheme_args)
assert 1.0 < temp
# high acceptance probabilities
_scheme_args = copy.deepcopy(scheme_args)
# change normalization s.t. most have 1.0 acceptance rate
records = pd.DataFrame(_scheme_args['get_all_records']())
_scheme_args['pdf_norm'] = min(records['distance'])
temp = scheme(t=0, **_scheme_args)
assert temp == 1.0
def test_scheme_basic():
schemes = [
pyabc.AcceptanceRateScheme(),
pyabc.ExponentialDecayScheme(),
pyabc.PolynomialDecayScheme(),
pyabc.DalyScheme(),
pyabc.FrielPettittScheme(),
pyabc.EssScheme()
]
for scheme in schemes:
# call them
temp = scheme(t=0, **scheme_args)
assert 1.0 < temp and temp < np.inf
def __call__(
self, prev_pdf_norm, get_weighted_distances, prev_temp, acceptance_rate, **kwargs):
pdf_norm = pyabc.pdf_norm_max_found(prev_pdf_norm=prev_pdf_norm,
get_weighted_distances=get_weighted_distances)
print(" best: ", pdf_norm)
if prev_temp is None or (acceptance_rate >= 0.1 and not self.hit):
return pdf_norm
self.hit = True
temp = 0.6 * prev_temp
offset = temp * np.log(10)
used_norm = pdf_norm - offset
used_norm = max(prev_pdf_norm, used_norm)
print(" offsetted: ", pdf_norm - offset)
return used_norm
prior = pyabc.Distribution(**{key: pyabc.RV("uniform", a, b-a)
for key, (a,b) in limits.items()})
acceptor = pyabc.StochasticAcceptor(log_file="acc_log_v5_higherc.json", pdf_norm_method=PDFNorm())
temperature = pyabc.Temperature(schemes=[pyabc.AcceptanceRateScheme(), pyabc.ExpDecayFixedRatioScheme(alpha=0.6)])
kernel = pyabc.IndependentNormalKernel(keys=keys, var=noise_vector**2)
sampler = pyabc.sampler.RedisEvalParallelSampler(host="icb-mona", port=8776)
#sampler = pyabc.sampler.MulticoreEvalParallelSampler(daemon=False)
abc = pyabc.ABCSMC(model, prior, kernel, sampler=sampler,
acceptor=acceptor, eps=temperature, population_size=500)
db_path="sqlite:///tumor2d_stoch_acc_v5_higherc.db"
abc.new(db_path, noisy_data)
abc.run()
