Naive approach for IID potential evaluation for score estimators

sbi/inference/posteriors/vector_field_posterior.py

@@ -404,7 +404,13 @@ def log_prob(
             `(len(θ),)`-shaped log posterior probability $\log p(\theta|x)$ for θ in the
             support of the prior, -∞ (corresponding to 0 probability) outside.
         """
-        self.potential_fn.set_x(self._x_else_default_x(x), **(ode_kwargs or {}))
+        x = self._x_else_default_x(x)
+        x = reshape_to_batch_event(x, self.vector_field_estimator.condition_shape)
+        is_iid = x.shape[0] > 1
+        self.potential_fn.set_x(
+            x,
+            x_is_iid=is_iid,
+        )
 
         theta = ensure_theta_batched(torch.as_tensor(theta))
         return self.potential_fn(

sbi/inference/potentials/vector_field_potential.py

@@ -1,7 +1,7 @@
 # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
 # under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
 
-from typing import Any, Dict, Literal, Optional, Tuple, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -115,10 +115,10 @@ def set_x(
         super().set_x(x_o, x_is_iid)
         self.iid_method = iid_method or self.iid_method
         self.iid_params = iid_params
-        # NOTE: Once IID potential evaluation is supported. This needs to be adapted.
-        # See #1450.
         if not x_is_iid and (self._x_o is not None):
             self.flow = self.rebuild_flow(**ode_kwargs)
+        elif self._x_o is not None:
+            self.flows = self.rebuild_flows_for_batch(**ode_kwargs)
 
     def __call__(
         self,
@@ -135,26 +135,6 @@ def __call__(
         Returns:
             The potential function, i.e., the log probability of the posterior.
         """
-        # TODO: incorporate iid setting. See issue #1450 and PR #1508
-        if self.x_is_iid:
-            if (
-                self.vector_field_estimator.MARGINALS_DEFINED
-                and self.vector_field_estimator.SCORE_DEFINED
-            ):
-                raise NotImplementedError(
-                    "Potential function evaluation in the "
-                    "IID setting is not yet supported"
-                    " for vector field based methods. "
-                    "Sampling does however work via `.sample`. "
-                    "If you intended to evaluate the posterior "
-                    "given a batch of (non-iid) "
-                    "x use `log_prob_batched`."
-                )
-            else:
-                raise NotImplementedError(
-                    "IID is not supported for this vector field estimator "
-                    "since the required methods (marginals or score) are not defined."
-                )
 
         theta = ensure_theta_batched(torch.as_tensor(theta))
         theta_density_estimator = reshape_to_sample_batch_event(
@@ -163,7 +143,31 @@ def __call__(
         self.vector_field_estimator.eval()
 
         with torch.set_grad_enabled(track_gradients):
-            log_probs = self.flow.log_prob(theta_density_estimator).squeeze(-1)
+            if self.x_is_iid:
+                assert self.prior is not None, (
+                    "Prior is required for evaluating log_prob with iid observations."
+                )
+                assert self.flows is not None, (
+                    "Flows for each iid x are required for evaluating log_prob."
+                )
+                n = self.x_o.shape[0]  # number of iid samples
+                iid_posteriors_prob = torch.sum(
+                    torch.stack(
+                        [
+                            flow.log_prob(theta_density_estimator).squeeze(-1)
+                            for flow in self.flows
+                        ],
+                        dim=0,
+                    ),
+                    dim=0,
+                )
+                # Apply the adjustment for iid observations i.e. we have to subtract
+                # (n-1) times the log prior.
+                log_probs = iid_posteriors_prob - (n - 1) * self.prior.log_prob(
+                    theta_density_estimator
+                ).squeeze(-1)
+            else:
+                log_probs = self.flow.log_prob(theta_density_estimator).squeeze(-1)
             # Force probability to be zero outside prior support.
             in_prior_support = within_support(self.prior, theta)
 
@@ -208,8 +212,8 @@ def gradient(
 
         if self._x_o is None:
             raise ValueError(
-                "No observed data x_o is available. Please reinitialize \
-                the potential or manually set self._x_o."
+                "No observed data x_o is available. Please reinitialize"
+                "the potential or manually set self._x_o."
             )
 
         with torch.set_grad_enabled(track_gradients):
@@ -249,6 +253,31 @@ def rebuild_flow(self, **kwargs) -> NormalizingFlow:
         flow = self.neural_ode(x_density_estimator, **kwargs)
         return flow
 
+    def rebuild_flows_for_batch(
+        self, atol: float = 1e-5, rtol: float = 1e-6, exact: bool = True
+    ) -> List[NormalizingFlow]:
+        """
+        Rebuilds the continuous normalizing flows for each iid in x_o. This is used when
+        a new default x_o is set, or to evaluate the log probs at higher precision.
+        """
+        if self._x_o is None:
+            raise ValueError(
+                "No observed data x_o is available. Please reinitialize \
+                the potential or manually set self._x_o."
+            )
+        flows = []
+        for i in range(self._x_o.shape[0]):
+            iid_x = self._x_o[i]
+            x_density_estimator = reshape_to_batch_event(
+                iid_x, event_shape=self.vector_field_estimator.condition_shape
+            )
+
+            flow = self.neural_ode(
+                condition=x_density_estimator, atol=atol, rtol=rtol, exact=exact
+            )
+            flows.append(flow)
+        return flows
+
 
 def vector_field_estimator_based_potential(
     vector_field_estimator: ConditionalVectorFieldEstimator,

tests/linearGaussian_vector_field_test.py

@@ -588,3 +588,49 @@ def simulator(theta):
 
     max_err = np.max(error)
     assert max_err < 0.0027
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("vector_field_type", ["ve", "vp", "fmpe"])
+@pytest.mark.parametrize("prior_type", ["gaussian"])
+@pytest.mark.parametrize("iid_batch_size", [1, 2])
+def test_iid_log_prob(vector_field_type, prior_type, iid_batch_size):
+    '''
+    Tests the log-probability computation of the score-based posterior.
+
+    '''
+
+    vector_field_trained_model = train_vector_field_model(vector_field_type, prior_type)
+
+    # Prior Gaussian
+    prior = vector_field_trained_model["prior"]
+    vf_estimator = vector_field_trained_model["estimator"]
+    inference = vector_field_trained_model["inference"]
+    likelihood_shift = vector_field_trained_model["likelihood_shift"]
+    likelihood_cov = vector_field_trained_model["likelihood_cov"]
+    prior_mean = vector_field_trained_model["prior_mean"]
+    prior_cov = vector_field_trained_model["prior_cov"]
+    num_dim = vector_field_trained_model["num_dim"]
+    num_posterior_samples = 1000
+
+    # Ground truth theta
+    theta_o = zeros(num_dim)
+    x_o = linear_gaussian(
+        theta_o.repeat(iid_batch_size, 1),
+        likelihood_shift=likelihood_shift,
+        likelihood_cov=likelihood_cov,
+    )
+    true_posterior = true_posterior_linear_gaussian_mvn_prior(
+        x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov
+    )
+
+    approx_posterior = inference.build_posterior(vf_estimator, prior=prior)
+    posterior_samples = true_posterior.sample((num_posterior_samples,))
+    true_prob = true_posterior.log_prob(posterior_samples)
+    approx_prob = approx_posterior.log_prob(posterior_samples, x=x_o)
+
+    diff = torch.abs(true_prob - approx_prob)
+    assert diff.mean() < 0.4, (
+        f"Probs diff: {diff.mean()} too big "
+        f"for number of samples {num_posterior_samples}"
+    )