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_family_and_response_type_checks(formula_str, non_real_cols, family,
priors):
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(cols)
with pytest.raises(Exception, match='not compatible'):
build_model_pre(formula, metadata, family, {})
def test_parameter_shapes(formula_str, non_real_cols, contrasts, family,
priors, expected, fitargs):
# Make dummy data.
N = 5
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
df = dummy_df(cols, N, allow_non_exhaustive=True)
# Define model, and generate a single posterior sample.
metadata = metadata_from_cols(cols)
model = define_model(formula_str, metadata, family, priors,
contrasts).gen(fitargs['backend'])
data = model.encode(df)
fit = model.run_algo('prior', data, num_samples=1, seed=None)
num_chains = fitargs.get('num_chains', 1)
# Check parameter sizes.
for parameter in parameters(fit.model_desc):
expected_param_shape = parameter.shape
samples = fit.get_param(parameter.name)
# A single sample is collected by each chain for all cases.
assert samples.shape == (num_chains, ) + expected_param_shape
samples_with_chain_dim = fit.get_param(parameter.name, True)
assert samples_with_chain_dim.shape == (num_chains,
1) + expected_param_shape
def test_marginals_fitted_smoke(fitargs, formula_str, non_real_cols, family,
contrasts):
N = 10
S = 4
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
df = dummy_df(cols, N)
model = brm(formula_str, df, family, [], contrasts)
fit = model.fit(**fitargs(S))
# Sanity check output for `marginals`.
arr = fit.marginals().array
num_coefs = len(scalar_parameter_names(fit.model_desc))
assert arr.shape == (num_coefs, 9) # num coefs x num stats
# Don't check finiteness of n_eff and r_hat, which are frequently
# nan with few samples
assert np.all(np.isfinite(arr[:, :-2]))
# Sanity check output of `fitted`.
def chk(arr, expected_shape):
assert np.all(np.isfinite(arr))
assert arr.shape == expected_shape
chk(fit.fitted(), (S, N))
chk(fit.fitted('linear'), (S, N))
chk(fit.fitted('response'), (S, N))
chk(fit.fitted('sample'), (S, N))
chk(fit.fitted(data=dummy_df(cols, N)), (S, N))
def test_mu_correctness(formula_str, cols, backend, expected):
df = dummy_df(expand_columns(parse(formula_str), cols), 10)
fit = brm(formula_str, df).prior(num_samples=1, backend=backend)
# Pick out the one (and only) sample drawn.
actual_mu = fit.fitted(what='linear')[0]
# `expected` is assumed to return a data frame.
expected_mu = expected(df, fit.get_scalar_param).to_numpy(np.float32)
assert np.allclose(actual_mu, expected_mu)
def test_scalar_parameter_names_smoke(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)
model = define_model(formula_str, metadata, family, priors, contrasts)
names = scalar_parameter_names(model.desc)
assert type(names) == list
def test_prior_checks(formula_str, non_real_cols, family, priors,
expected_error):
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(cols)
design_metadata = build_model_pre(formula, metadata, family, {})
with pytest.raises(Exception, match=expected_error):
build_prior_tree(design_metadata, priors)
def test_designmatrix(formula_str, df, metadata_cols, contrasts, expected):
metadata = metadata_from_cols(
metadata_cols) if metadata_cols is not None else metadata_from_df(df)
data = makedata(parse(formula_str), df, metadata, contrasts)
assert set(data.keys()) == set(expected.keys())
for k in expected.keys():
assert data[k].dtype == expected[k].dtype
assert_equal(data[k], expected[k])
def test_expectation_correctness(cols, family, expected, backend):
formula_str = 'y ~ 1 + x'
df = dummy_df(expand_columns(parse(formula_str), cols), 10)
fit = brm(formula_str, df, family=family).prior(num_samples=1,
backend=backend)
actual_expectation = fit.fitted(what='expectation')[0]
# We assume (since it's tested elsewhere) that `mu` is computed
# correctly by `fitted`. So given that, we check that `fitted`
# computes the correct expectation.
expected_expectation = expected(fit.fitted('linear')[0])
assert np.allclose(actual_expectation, expected_expectation)
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_fitted_on_new_data(N2):
S = 4
N = 10
formula_str = 'y ~ 1 + a'
# Using this contrast means `a` is coded as two columns rather
# than (the default) one. Because of this, it's crucial that
# `fitted` uses the contrast when coding *new data*. This test
# would fail if that didn't happen.
contrasts = {'a': np.array([[-1, -1], [1, 1]])}
cols = expand_columns(parse(formula_str), [Categorical('a', ['a0', 'a1'])])
df = dummy_df(cols, N)
fit = brm(formula_str, df, Normal,
contrasts=contrasts).fit(iter=S, backend=pyro_backend)
new_data = dummy_df(cols, N2, allow_non_exhaustive=True)
arr = fit.fitted(data=new_data)
assert np.all(np.isfinite(arr))
assert arr.shape == (S, N2)
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 test_parameter_shapes(formula_str, non_real_cols, contrasts, family,
priors, expected, fitargs):
# Make dummy data.
N = 5
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
df = dummy_df(cols, N)
# Define model, and generate a single posterior sample.
model = defm(formula_str, df, family, priors, contrasts)
fit = model.fit(**fitargs)
# Check parameter sizes.
for parameter in parameters(model.desc):
# Get the first (and only) sample.
samples = get_param(fit, parameter.name)
assert samples.shape[
0] == 1 # Check the test spec. only generated one sample.
p = samples[0]
shape = p.shape
expected_shape = parameter.shape
assert shape == expected_shape
def test_marginals_fitted_smoke(fitargs, formula_str, non_real_cols, family,
contrasts, N2):
N = 10
S = 4
cols = expand_columns(parse(formula_str), non_real_cols)
df = dummy_df(cols, N)
print(df)
fit = defm(formula_str, df, family, contrasts=contrasts).fit(**fitargs(S))
def chk(arr, expected_shape):
assert np.all(np.isfinite(arr))
assert arr.shape == expected_shape
num_coefs = len(scalar_parameter_names(fit.model_desc))
chk(marginals(fit).array, (num_coefs, 7)) # num coefs x num stats
chk(fitted(fit), (S, N))
chk(fitted(fit, 'linear'), (S, N))
chk(fitted(fit, 'response'), (S, N))
chk(fitted(fit, 'sample'), (S, N))
# Applying `fitted` to new data.
df2 = dummy_df(cols, N2)
print(df2)
chk(fitted(fit, data=df2), (S, N2))
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)
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))
def test_coef_names(formula_str, non_real_cols, expected_names):
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(cols)
assert coef_names(formula.terms, metadata, {}) == expected_names
def test_coding(formula_str, non_real_cols, expected_coding):
formula = parse(formula_str)
cols = expand_columns(formula, non_real_cols)
metadata = metadata_from_cols(cols)
assert code_terms(formula.terms, metadata) == expected_coding
def test_parser(formula_str, expected_formula):
formula = parse(formula_str)
assert formula == expected_formula
