Allow skipping covariance computation in find_MAP

pymc_extras/inference/laplace_approx/find_map.py

@@ -198,6 +198,7 @@ def find_MAP(
     include_transformed: bool = True,
     gradient_backend: GradientBackend = "pytensor",
     compile_kwargs: dict | None = None,
+    compute_hessian: bool = False,
     **optimizer_kwargs,
 ) -> (
     dict[str, np.ndarray]
@@ -239,6 +240,10 @@ def find_MAP(
         Whether to include transformed variable values in the returned dictionary. Defaults to True.
     gradient_backend: str, default "pytensor"
         Which backend to use to compute gradients. Must be one of "pytensor" or "jax".
+    compute_hessian: bool
+        If True, the inverse Hessian matrix at the optimum will be computed and included in the returned
+        InferenceData object. This is needed for the Laplace approximation, but can be computationally expensive for
+        high-dimensional problems. Defaults to False.
     compile_kwargs: dict, optional
         Additional options to pass to the ``pytensor.function`` function when compiling loss functions.
     **optimizer_kwargs
@@ -316,14 +321,17 @@ def find_MAP(
             **optimizer_kwargs,
         )
 
-    H_inv = _compute_inverse_hessian(
-        optimizer_result=optimizer_result,
-        optimal_point=None,
-        f_fused=f_fused,
-        f_hessp=f_hessp,
-        use_hess=use_hess,
-        method=method,
-    )
+    if compute_hessian:
+        H_inv = _compute_inverse_hessian(
+            optimizer_result=optimizer_result,
+            optimal_point=None,
+            f_fused=f_fused,
+            f_hessp=f_hessp,
+            use_hess=use_hess,
+            method=method,
+        )
+    else:
+        H_inv = None
 
     raveled_optimized = RaveledVars(optimizer_result.x, initial_params.point_map_info)
     unobserved_vars = get_default_varnames(model.unobserved_value_vars, include_transformed=True)

pymc_extras/inference/laplace_approx/idata.py

@@ -136,7 +136,10 @@ def map_results_to_inference_data(
 
 
 def add_fit_to_inference_data(
-    idata: az.InferenceData, mu: RaveledVars, H_inv: np.ndarray, model: pm.Model | None = None
+    idata: az.InferenceData,
+    mu: RaveledVars,
+    H_inv: np.ndarray | None,
+    model: pm.Model | None = None,
 ) -> az.InferenceData:
     """
     Add the mean vector and covariance matrix of the Laplace approximation to an InferenceData object.
@@ -147,7 +150,7 @@ def add_fit_to_inference_data(
         An InferenceData object containing the approximated posterior samples.
     mu: RaveledVars
         The MAP estimate of the model parameters.
-    H_inv: np.ndarray
+    H_inv: np.ndarray, optional
         The inverse Hessian matrix of the log-posterior evaluated at the MAP estimate.
     model: Model, optional
         A PyMC model. If None, the model is taken from the current model context.

pymc_extras/inference/laplace_approx/laplace.py

@@ -389,6 +389,7 @@ def fit_laplace(
         include_transformed=include_transformed,
         gradient_backend=gradient_backend,
         compile_kwargs=compile_kwargs,
+        compute_hessian=True,
         **optimizer_kwargs,
     )
 

tests/inference/laplace_approx/test_find_map.py

@@ -133,8 +133,8 @@ def compute_z(x):
     ],
 )
 @pytest.mark.parametrize(
-    "backend, gradient_backend, include_transformed",
-    [("jax", "jax", True), ("jax", "pytensor", False)],
+    "backend, gradient_backend, include_transformed, compute_hessian",
+    [("jax", "jax", True, True), ("jax", "pytensor", False, False)],
     ids=str,
 )
 def test_find_MAP(
@@ -145,6 +145,7 @@ def test_find_MAP(
     backend,
     gradient_backend: GradientBackend,
     include_transformed,
+    compute_hessian,
     rng,
 ):
     pytest.importorskip("jax")
@@ -164,6 +165,7 @@ def test_find_MAP(
             include_transformed=include_transformed,
             compile_kwargs={"mode": backend.upper()},
             maxiter=5,
+            compute_hessian=compute_hessian,
         )
 
     assert hasattr(idata, "posterior")
@@ -184,6 +186,8 @@ def test_find_MAP(
     else:
         assert not hasattr(idata, "unconstrained_posterior")
 
+    assert ("covariance_matrix" in idata.fit) == compute_hessian
+
 
 def test_find_map_outside_model_context():
     """