def elemwise_logp(model, var):
terms = []
for v in model.basic_RVs:
v_logp = logpt(v)
if var in graph_inputs([v_logp]):
terms.append(v_logp)
return model.fn(at.add(*terms))
def check_vectortransform_elementwise_logp(self, model, vect_opt=0):
x = model.free_RVs[0]
x0 = x.tag.value_var
assert (x.ndim - 1) == logpt(x).ndim
pt = model.initial_point
array = np.random.randn(*pt[x0.name].shape)
transform = x0.tag.transform
logp_nojac = logpt(x, transform.backward(x, array), transformed=False)
jacob_det = transform.jacobian_det(x, aesara.shared(array))
assert logpt(x).ndim == jacob_det.ndim
# Hack to get relative tolerance
a = logpt(x, array.astype(aesara.config.floatX), jacobian=False).eval()
b = logp_nojac.eval()
close_to(a, b, np.abs(0.5 * (a + b) * tol))
def check_transform_elementwise_logp(self, model):
x = model.free_RVs[0]
x0 = x.tag.value_var
assert x.ndim == logpt(x).ndim
pt = model.initial_point
array = np.random.randn(*pt[x0.name].shape)
transform = x0.tag.transform
logp_notrans = logpt(x,
transform.backward(x, array),
transformed=False)
jacob_det = transform.jacobian_det(x, aesara.shared(array))
assert logpt(x).ndim == jacob_det.ndim
v1 = logpt(x, array, jacobian=False).eval()
v2 = logp_notrans.eval()
close_to(v1, v2, tol)
def test_shared_scalar_as_rv_input(self):
# See https://github.com/pymc-devs/pymc3/issues/3139
with pm.Model() as m:
shared_var = shared(5.0)
v = pm.Normal("v", mu=shared_var, size=1)
np.testing.assert_allclose(
logpt(v, np.r_[5.0]).eval(),
-0.91893853,
rtol=1e-5,
)
shared_var.set_value(10.0)
np.testing.assert_allclose(
logpt(v, np.r_[10.0]).eval(),
-0.91893853,
rtol=1e-5,
)
def _extract_log_likelihood(self, trace):
"""Compute log likelihood of each observation."""
if self.trace is None:
return None
if self.model is None:
return None
if self.log_likelihood is True:
cached = [(var, self.model.fn(logpt(var)))
for var in self.model.observed_RVs]
else:
cached = [(var, self.model.fn(logpt(var)))
for var in self.model.observed_RVs
if var.name in self.log_likelihood]
log_likelihood_dict = _DefaultTrace(len(trace.chains))
for var, log_like_fun in cached:
for k, chain in enumerate(trace.chains):
log_like_chain = [
self.log_likelihood_vals_point(point, var, log_like_fun)
for point in trace.points([chain])
]
log_likelihood_dict.insert(var.name, np.stack(log_like_chain),
k)
return log_likelihood_dict.trace_dict