def test_numpyro_codegen(N, formula_str, non_real_cols, contrasts, family,
priors, expected):
# Make dummy data.
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(cols)
desc = makedesc(formula, metadata, family, priors, code_lengths(contrasts))
# Generate model function and data.
modelfn = numpyro_backend.gen(desc).fn
df = dummy_df(cols, N)
data = data_from_numpy(numpyro_backend,
makedata(formula, df, metadata, contrasts))
# Check sample sites.
rng = random.PRNGKey(0)
trace = numpyro.trace(numpyro.seed(modelfn, rng)).get_trace(**data)
expected_sites = [site for (site, _, _) in expected]
sample_sites = [
name for name, node in trace.items() if not node['is_observed']
]
assert set(sample_sites) == set(expected_sites)
for (site, family_name, maybe_params) in expected:
numpyro_family_name = dict(LKJ='LKJCholesky').get(
family_name, family_name)
fn = trace[site]['fn']
params = maybe_params or default_params[family_name]
assert type(fn).__name__ == numpyro_family_name
for (name, expected_val) in params.items():
if family_name == 'LKJ':
assert name == 'eta'
name = 'concentration'
val = fn.__getattribute__(name)
assert_equal(val._value, np.broadcast_to(expected_val, val.shape))
def test_pyro_codegen(N, formula_str, non_real_cols, contrasts, family, priors,
expected):
# Make dummy data.
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
# Generate the model from the column information rather than from
# the metadata extracted from `df`. Since N is small, the metadata
# extracted from `df` might loose information compared to the full
# metadata derived from `cols` (e.g. levels of a categorical
# column) leading to unexpected results. e.g. Missing levels might
# cause correlations not to be modelled, even thought they ought
# to be given the full metadata.
metadata = metadata_from_cols(cols)
desc = makedesc(formula, metadata, family, priors, code_lengths(contrasts))
# Generate model function and data.
modelfn = pyro_backend.gen(desc).fn
df = dummy_df(cols, N)
data = data_from_numpy(pyro_backend,
makedata(formula, df, metadata, contrasts))
# Check sample sites.
trace = poutine.trace(modelfn).get_trace(**data)
expected_sites = [site for (site, _, _) in expected]
assert set(trace.stochastic_nodes) - {'obs'} == set(expected_sites)
for (site, family_name, maybe_params) in expected:
pyro_family_name = dict(LKJ='LKJCorrCholesky').get(
family_name, family_name)
fn = unwrapfn(trace.nodes[site]['fn'])
params = maybe_params or default_params[family_name]
assert type(fn).__name__ == pyro_family_name
for (name, expected_val) in params.items():
val = fn.__getattribute__(name)
assert_equal(val, torch.tensor(expected_val).expand(val.shape))
def test_expected_response_codegen(response_meta, family, args, expected,
backend):
formula = parse('y ~ 1')
desc = makedesc(formula, metadata_from_cols([response_meta]), family, [],
{})
def expected_response(*args):
backend_args = [backend.from_numpy(arg) for arg in args]
fn = backend.gen(desc).expected_response_fn
return backend.to_numpy(fn(*backend_args))
assert np.allclose(expected_response(*args), expected)
def test_sampling_from_prior_smoke(N, backend, formula_str, non_real_cols,
contrasts, family, priors, expected):
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(
cols
) # Use full metadata for same reason given in comment in codegen test.
desc = makedesc(formula, metadata, family, priors, code_lengths(contrasts))
model = backend.gen(desc)
df = dummy_df(cols, N, allow_non_exhaustive=True)
data = data_from_numpy(backend, makedata(formula, df, metadata, contrasts))
samples = backend.prior(data, model, num_samples=10, seed=None)
assert type(samples) == Samples
def test_scalar_param_map_consistency():
formula = parse('y ~ 1 + x1 + (1 + x2 + b | a) + (1 + x1 | a:b)')
non_real_cols = [
Categorical('a', ['a1', 'a2', 'a3']),
Categorical('b', ['b1', 'b2', 'b3']),
]
cols = expand_columns(formula, non_real_cols)
desc = makedesc(formula, metadata_from_cols(cols), Normal, [], {})
params = parameters(desc)
spmap = scalar_parameter_map(desc)
# Check that each entry in the map points to a unique parameter
# position.
param_and_indices_set = set(param_and_indices
for (_, param_and_indices) in spmap)
assert len(param_and_indices_set) == len(spmap)
# Ensure that we have enough entries in the map to cover all of
# the scalar parameters. (The L_i parameters have a funny status.
# We consider them to be parameters, but not scalar parameters.
# This is not planned, rather things just evolved this way. It
# does makes some sense though, since we usually look at R_i
# instead.)
num_scalar_params = sum(
np.product(shape) for name, shape in params
if not name.startswith('L_'))
assert num_scalar_params == len(spmap)
# Check that all indices are valid. (i.e. Within the shape of the
# parameter.)
for scalar_param_name, (param_name, indices) in spmap:
ss = [shape for (name, shape) in params if name == param_name]
assert len(ss) == 1
param_shape = ss[0]
assert len(indices) == len(param_shape)
assert all(i < s for (i, s) in zip(indices, param_shape))