def test_pdf_norm_methods():
"""Test pdf normalization methods standalone."""
# preparations
def _get_weighted_distances():
return pd.DataFrame({'distance': [1, 2, 3, 4], 'w': [2, 1, 1, 0]})
pdf_norm_args = dict(
kernel_val=42,
prev_pdf_norm=3.5,
get_weighted_distances=_get_weighted_distances,
prev_temp=10.3,
acceptance_rate=0.3,
)
# run functions
max_found = max(pdf_norm_args['get_weighted_distances']()['distance'])
assert pyabc.pdf_norm_max_found(**pdf_norm_args) == max_found
assert pyabc.pdf_norm_from_kernel(**pdf_norm_args) == 42
assert pyabc.ScaledPDFNorm()(**pdf_norm_args) == max_found
# test additional setups
pdf_norm_args['prev_pdf_norm'] = 4.5
pdf_norm_args['acceptance_rate'] = 0.05
assert pyabc.pdf_norm_max_found(**pdf_norm_args) == 4.5
offsetted_pdf = 4.5 - np.log(10) * 0.5 * pdf_norm_args['prev_temp']
assert pyabc.ScaledPDFNorm()(**pdf_norm_args) == offsetted_pdf
def test_pdf_norm_methods_integration():
"""Test integration of pdf normalization methods in ABCSMC."""
def model(par):
return {'s0': par['p0'] + np.array([0.3, 0.7])}
x_0 = {'s0': np.array([0.4, -0.6])}
for pdf_norm in [
pyabc.pdf_norm_max_found,
pyabc.pdf_norm_from_kernel,
pyabc.ScaledPDFNorm(),
]:
# just run
acceptor = pyabc.StochasticAcceptor(pdf_norm_method=pdf_norm)
eps = pyabc.Temperature()
distance = pyabc.IndependentNormalKernel(var=np.array([1, 1]))
prior = pyabc.Distribution(p0=pyabc.RV('uniform', -1, 2))
abc = pyabc.ABCSMC(model,
prior,
distance,
eps=eps,
acceptor=acceptor,
population_size=20)
abc.new(pyabc.create_sqlite_db_id(), x_0)
abc.run(max_nr_populations=3)