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4 changes: 2 additions & 2 deletions pymc_extras/inference/laplace_approx/laplace.py
Original file line number Diff line number Diff line change
Expand Up @@ -226,9 +226,9 @@ def model_to_laplace_approx(
else:
dims = (*batch_dims, *[f"{name}_dim_{i}" for i in range(batched_rv.ndim - 2)])
initval = initial_point.get(name, None)
dim_shapes = initval.shape if initval is not None else batched_rv.type.shape[2:]
dim_shapes = initval.shape if initval is not None else batched_rv.shape.eval()[2:]
laplace_model.add_coords(
{name: np.arange(shape) for name, shape in zip(dims[2:], dim_shapes)}
{name: pt.arange(shape) for name, shape in zip(dims[2:], dim_shapes)}
)

pm.Deterministic(name, batched_rv, dims=dims)
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38 changes: 38 additions & 0 deletions tests/inference/laplace_approx/test_laplace.py
Original file line number Diff line number Diff line change
Expand Up @@ -193,6 +193,44 @@ def test_fit_laplace_ragged_coords(rng):
assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all()


def test_fit_laplace_no_data_or_deterministic_dims(rng):
coords = {"city": ["A", "B", "C"], "feature": [0, 1], "obs_idx": np.arange(100)}
with pm.Model(coords=coords) as ragged_dim_model:
X = pm.Data("X", np.ones((100, 2)))
beta = pm.Normal(
"beta", mu=[[-100.0, 100.0], [-100.0, 100.0], [-100.0, 100.0]], dims=["city", "feature"]
)
mu = pm.Deterministic("mu", (X[:, None, :] * beta[None]).sum(axis=-1))
sigma = pm.Normal("sigma", mu=1.5, sigma=0.5, dims=["city"])

obs = pm.Normal(
"obs",
mu=mu,
sigma=sigma,
observed=rng.normal(loc=3, scale=1.5, size=(100, 3)),
dims=["obs_idx", "city"],
)

idata = fit_laplace(
optimize_method="Newton-CG",
progressbar=False,
use_grad=True,
use_hessp=True,
)

# These should have been dropped when the laplace idata was created
assert "laplace_approximation" not in list(idata.posterior.data_vars.keys())
assert "unpacked_var_names" not in list(idata.posterior.coords.keys())

assert idata["posterior"].beta.shape[-2:] == (3, 2)
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shouldn't we test we get the expected sample dims as well?

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Yes, this is definitely a good idea. I added assert statements to test for sampling dims and I also consolidated the test with test_fit_laplace_ragged_coords because there was a lot of code duplicated.

assert idata["posterior"].sigma.shape[-1:] == (3,)

# Check that everything got unraveled correctly -- feature 0 should be strictly negative, feature 1
# strictly positive
assert (idata["posterior"].beta.sel(feature=0).to_numpy() < 0).all()
assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all()


def test_model_with_nonstandard_dimensionality(rng):
y_obs = np.concatenate(
[rng.normal(-1, 2, size=150), rng.normal(3, 1, size=350), rng.normal(5, 4, size=50)]
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