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_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_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 define_model(formula_str, metadata, family=None, priors=None, contrasts=None):
assert type(formula_str) == str
assert type(metadata) == Metadata
assert family is None or type(family) == Family
assert priors is None or type(priors) == list
assert contrasts is None or type(contrasts) == dict
family = family or Normal
priors = priors or []
contrasts = contrasts or {}
# TODO: Consider accepting nested arrays as well as numpy arrays.
# (If we do, convert to numpy arrays here in `define_model`?)
assert all(type(val) == np.ndarray and len(val.shape) == 2 for val in contrasts.values())
formula = parse(formula_str)
desc = makedesc(formula, metadata, family, priors, code_lengths(contrasts))
return Model(formula, metadata, contrasts, desc)
def defm(formula_str, df, family=None, priors=None, contrasts=None):
assert type(formula_str) == str
assert type(df) == pd.DataFrame
assert family is None or type(family) == Family
assert priors is None or type(priors) == list
assert contrasts is None or type(contrasts) == dict
family = family or Normal
priors = priors or []
contrasts = contrasts or {}
# TODO: Consider accepting nested arrays as well as numpy arrays.
# (If we do, convert to numpy arrays here in `defm`?)
assert all(
type(val) == np.ndarray and len(val.shape) == 2
for val in contrasts.values())
formula = parse(formula_str)
# Perhaps design matrices ought to always have metadata (i.e.
# column names) associated with them, as in Patsy.
metadata = metadata_from_df(df)
desc = makedesc(formula, metadata, family, priors, code_lengths(contrasts))
data = makedata(formula, df, metadata, contrasts)
return DefmResult(formula, metadata, contrasts, desc, data)