def test_rowwise_approx(three_var_model, parametric_grouped_approxes):
# add to inference that supports aevb
cls, kw = parametric_grouped_approxes
with three_var_model:
try:
approx = Approximation([cls([three_var_model.one], rowwise=True, **kw), Group(None, vfam='mf')])
inference = pm.KLqp(approx)
approx = inference.fit(3, obj_n_mc=2)
approx.sample(10)
approx.sample_node(
three_var_model.one
).eval()
except pm.opvi.BatchedGroupError:
pytest.skip('Does not support rowwise grouping')
def test_init_groups(three_var_model, raises, grouping):
with raises, three_var_model:
approxes, groups = zip(*grouping.items())
groups = [list(map(functools.partial(getattr, three_var_model), g))
if g is not None else None
for g in groups]
inited_groups = [a(group=g) for a, g in zip(approxes, groups)]
approx = Approximation(inited_groups)
for ig, g in zip(inited_groups, groups):
if g is None:
pass
else:
assert set(pm.util.get_transformed(z) for z in g) == set(ig.group)
else:
assert approx.ndim == three_var_model.ndim
def test_rowwise_approx(three_var_model, parametric_grouped_approxes):
# add to inference that supports aevb
cls, kw = parametric_grouped_approxes
with three_var_model:
try:
approx = Approximation([cls([three_var_model.one], rowwise=True, **kw), Group(None, vfam='mf')])
inference = pm.KLqp(approx)
approx = inference.fit(3, obj_n_mc=2)
approx.sample(10)
approx.sample_node(
three_var_model.one
).eval()
except pm.opvi.BatchedGroupError:
pytest.skip('Does not support rowwise grouping')
def three_var_aevb_approx(three_var_model, three_var_aevb_groups):
approx = Approximation(three_var_aevb_groups, model=three_var_model)
return approx
