Respect include_transformed argument of find_MAP and fit_laplace

pymc_extras/inference/laplace_approx/find_map.py

@@ -326,7 +326,7 @@ def find_MAP(
     )
 
     raveled_optimized = RaveledVars(optimizer_result.x, initial_params.point_map_info)
-    unobserved_vars = get_default_varnames(model.unobserved_value_vars, include_transformed)
+    unobserved_vars = get_default_varnames(model.unobserved_value_vars, include_transformed=True)
     unobserved_vars_values = model.compile_fn(unobserved_vars, mode="FAST_COMPILE")(
         DictToArrayBijection.rmap(raveled_optimized)
     )
@@ -335,7 +335,7 @@ def find_MAP(
         var.name: value for var, value in zip(unobserved_vars, unobserved_vars_values)
     }
 
-    idata = map_results_to_inference_data(optimized_point, frozen_model)
+    idata = map_results_to_inference_data(optimized_point, frozen_model, include_transformed)
     idata = add_fit_to_inference_data(idata, raveled_optimized, H_inv)
     idata = add_optimizer_result_to_inference_data(
         idata, optimizer_result, method, raveled_optimized, model

pymc_extras/inference/laplace_approx/idata.py

@@ -59,6 +59,7 @@ def make_unpacked_variable_names(names: list[str], model: pm.Model) -> list[str]
 def map_results_to_inference_data(
     map_point: dict[str, float | int | np.ndarray],
     model: pm.Model | None = None,
+    include_transformed: bool = True,
 ):
     """
     Add the MAP point to an InferenceData object in the posterior group.
@@ -68,13 +69,13 @@ def map_results_to_inference_data(
 
     Parameters
     ----------
-    idata: az.InferenceData
-        An InferenceData object to which the MAP point will be added.
     map_point: dict
         A dictionary containing the MAP point estimates for each variable. The keys should be the variable names, and
         the values should be the corresponding MAP estimates.
     model: Model, optional
         A PyMC model. If None, the model is taken from the current model context.
+    include_transformed: bool
+        Whether to return transformed (unconstrained) variables in the constrained_posterior group. Default is True.
 
     Returns
     -------
@@ -118,7 +119,7 @@ def map_results_to_inference_data(
         dims=dims,
     )
 
-    if unconstrained_names:
+    if unconstrained_names and include_transformed:
         unconstrained_posterior = az.from_dict(
             posterior={
                 k: np.expand_dims(v, (0, 1))

pymc_extras/inference/laplace_approx/laplace.py

@@ -302,7 +302,7 @@ def fit_laplace(
     ----------
     model : pm.Model
         The PyMC model to be fit. If None, the current model context is used.
-    method : str
+    optimize_method : str
         The optimization method to use. Valid choices are: Nelder-Mead, Powell, CG, BFGS, L-BFGS-B, TNC, SLSQP,
         trust-constr, dogleg, trust-ncg, trust-exact, trust-krylov, and basinhopping.
 
@@ -441,9 +441,11 @@ def fit_laplace(
             .rename({"temp_chain": "chain", "temp_draw": "draw"})
         )
 
-        idata.unconstrained_posterior = unstack_laplace_draws(
-            new_posterior.laplace_approximation.values, model, chains=chains, draws=draws
-        )
+        if include_transformed:
+            idata.unconstrained_posterior = unstack_laplace_draws(
+                new_posterior.laplace_approximation.values, model, chains=chains, draws=draws
+            )
+
         idata.posterior = new_posterior.drop_vars(
             ["laplace_approximation", "unpacked_variable_names"]
         )

tests/inference/laplace_approx/test_find_map.py

@@ -133,12 +133,19 @@ def compute_z(x):
     ],
 )
 @pytest.mark.parametrize(
-    "backend, gradient_backend",
-    [("jax", "jax"), ("jax", "pytensor")],
+    "backend, gradient_backend, include_transformed",
+    [("jax", "jax", True), ("jax", "pytensor", False)],
     ids=str,
 )
 def test_find_MAP(
-    method, use_grad, use_hess, use_hessp, backend, gradient_backend: GradientBackend, rng
+    method,
+    use_grad,
+    use_hess,
+    use_hessp,
+    backend,
+    gradient_backend: GradientBackend,
+    include_transformed,
+    rng,
 ):
     pytest.importorskip("jax")
 
@@ -154,12 +161,12 @@ def test_find_MAP(
             use_hessp=use_hessp,
             progressbar=False,
             gradient_backend=gradient_backend,
+            include_transformed=include_transformed,
             compile_kwargs={"mode": backend.upper()},
             maxiter=5,
         )
 
     assert hasattr(idata, "posterior")
-    assert hasattr(idata, "unconstrained_posterior")
     assert hasattr(idata, "fit")
     assert hasattr(idata, "optimizer_result")
     assert hasattr(idata, "observed_data")
@@ -169,9 +176,13 @@ def test_find_MAP(
     assert posterior["mu"].shape == ()
     assert posterior["sigma"].shape == ()
 
-    unconstrained_posterior = idata.unconstrained_posterior.squeeze(["chain", "draw"])
-    assert "sigma_log__" in unconstrained_posterior
-    assert unconstrained_posterior["sigma_log__"].shape == ()
+    if include_transformed:
+        assert hasattr(idata, "unconstrained_posterior")
+        unconstrained_posterior = idata.unconstrained_posterior.squeeze(["chain", "draw"])
+        assert "sigma_log__" in unconstrained_posterior
+        assert unconstrained_posterior["sigma_log__"].shape == ()
+    else:
+        assert not hasattr(idata, "unconstrained_posterior")
 
 
 @pytest.mark.parametrize(

tests/inference/laplace_approx/test_laplace.py

@@ -83,7 +83,10 @@ def test_fit_laplace_basic(mode, gradient_backend: GradientBackend):
     np.testing.assert_allclose(idata.fit["covariance_matrix"].values, bda_cov, rtol=1e-3, atol=1e-3)
 
 
-def test_fit_laplace_coords(rng):
+@pytest.mark.parametrize(
+    "include_transformed", [True, False], ids=["include_transformed", "no_transformed"]
+)
+def test_fit_laplace_coords(include_transformed, rng):
     coords = {"city": ["A", "B", "C"], "obs_idx": np.arange(100)}
     with pm.Model(coords=coords) as model:
         mu = pm.Normal("mu", mu=3, sigma=0.5, dims=["city"])
@@ -102,6 +105,7 @@ def test_fit_laplace_coords(rng):
             chains=1,
             draws=1000,
             optimizer_kwargs=dict(tol=1e-20),
+            include_transformed=include_transformed,
         )
 
     np.testing.assert_allclose(
@@ -120,6 +124,11 @@ def test_fit_laplace_coords(rng):
         "sigma_log__[C]",
     ]
 
+    assert hasattr(idata, "unconstrained_posterior") == include_transformed
+    if include_transformed:
+        assert "sigma_log__" in idata.unconstrained_posterior
+        assert "city" in idata.unconstrained_posterior.coords
+
 
 def test_fit_laplace_ragged_coords(rng):
     coords = {"city": ["A", "B", "C"], "feature": [0, 1], "obs_idx": np.arange(100)}