def test_automatic_use_of_sort(self):
with pm.Model() as model:
s_g = pm.Simulator(
"s_g",
None,
params=None,
distance="gaussian_kernel",
sum_stat="mean",
observed=self.data,
)
s_w = pm.Simulator(
"s_w",
None,
params=None,
distance="wasserstein",
sum_stat="identity",
observed=self.data,
)
s_e = pm.Simulator(
"s_e",
None,
params=None,
distance="energy",
sum_stat="identity",
observed=self.data,
)
assert s_g.distribution.sum_stat is np.mean
assert s_w.distribution.sum_stat is np.sort
assert s_e.distribution.sum_stat is np.sort
def setup_class(self):
super().setup_class()
self.data = np.random.normal(loc=0, scale=1, size=1000)
def normal_sim(a, b):
return np.random.normal(a, b, 1000)
with pm.Model() as self.SMABC_test:
a = pm.Normal("a", mu=0, sigma=1)
b = pm.HalfNormal("b", sigma=1)
s = pm.Simulator(
"s", normal_sim, params=(a, b), sum_stat="sort", epsilon=1, observed=self.data
)
self.s = s
def quantiles(x):
return np.quantile(x, [0.25, 0.5, 0.75])
def abs_diff(eps, obs_data, sim_data):
return np.mean(np.abs((obs_data - sim_data) / eps))
with pm.Model() as self.SMABC_test2:
a = pm.Normal("a", mu=0, sigma=1)
b = pm.HalfNormal("b", sigma=1)
s = pm.Simulator(
"s",
normal_sim,
params=(a, b),
distance=abs_diff,
sum_stat=quantiles,
epsilon=1,
observed=self.data,
)
def setup_class(self):
super().setup_class()
self.data = np.sort(np.random.normal(loc=0, scale=1, size=1000))
def normal_sim(a, b):
return np.sort(np.random.normal(a, b, 1000))
with pm.Model() as self.SMABC_test:
a = pm.Normal("a", mu=0, sd=5)
b = pm.HalfNormal("b", sd=2)
s = pm.Simulator("s", normal_sim, observed=self.data)
def test_automatic_use_of_sort(self):
with pm.Model() as model:
s_k = pm.Simulator(
"s_k",
None,
params=None,
distance="kullback_leibler",
sum_stat="sort",
observed=self.data,
)
assert s_k.distribution.sum_stat is pm.distributions.simulator.identity
def test_named_models_are_unsupported(self):
def normal_sim(a, b):
return np.random.normal(a, b, 1000)
with pm.Model(name="NamedModel"):
a = pm.Normal("a", mu=0, sigma=1)
b = pm.HalfNormal("b", sigma=1)
c = pm.Potential("c", pm.math.switch(a > 0, 0, -np.inf))
s = pm.Simulator(
"s", normal_sim, params=(a, b), sum_stat="sort", epsilon=1, observed=self.data
)
with pytest.raises(NotImplementedError, match="named models"):
pm.sample_smc(draws=10, kernel="ABC")
