expose progress bar in IS posterior.

sbi/inference/posteriors/importance_posterior.py

@@ -160,12 +160,18 @@ def sample(
         oversampling_factor: int = 32,
         max_sampling_batch_size: int = 10_000,
         sample_with: Optional[str] = None,
+        show_progress_bars: bool = False,
     ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
         """Return samples from the approximate posterior distribution.
 
         Args:
             sample_shape: _description_
             x: _description_
+            oversampling_factor: Number of proposed samples from which only one is
+                selected based on its importance weight.
+            max_sampling_batch_size: The batch size of samples being drawn from the
+                proposal at every iteration.
+            show_progress_bars: Whether to show a progressbar during sampling.
         """
         if sample_with is not None:
             raise ValueError(
@@ -181,6 +187,7 @@ def sample(
                 sample_shape,
                 oversampling_factor=oversampling_factor,
                 max_sampling_batch_size=max_sampling_batch_size,
+                show_progress_bars=show_progress_bars,
             )
         elif self.method == "importance":
             return self._importance_sample(sample_shape)
@@ -190,13 +197,15 @@ def sample(
     def _importance_sample(
         self,
         sample_shape: Shape = torch.Size(),
+        show_progress_bars: bool = False,
     ) -> Tuple[Tensor, Tensor]:
         """Returns samples from the proposal and log of their importance weights.
 
         Args:
             sample_shape: Desired shape of samples that are drawn from posterior.
             sample_with: This argument only exists to keep backward-compatibility with
                 `sbi` v0.17.2 or older. If it is set, we instantly raise an error.
+            show_progress_bars: Whether to show sampling progress monitor.
 
         Returns:
             Samples and logarithm of corresponding importance weights.
@@ -206,6 +215,7 @@ def _importance_sample(
             self.potential_fn,
             proposal=self.proposal,
             num_samples=num_samples,
+            show_progress_bars=show_progress_bars,
         )
 
         samples = samples.reshape((*sample_shape, -1)).to(self._device)

sbi/samplers/importance/importance_sampling.py

@@ -9,6 +9,7 @@ def importance_sample(
     potential_fn,
     proposal,
     num_samples: int = 1,
+    show_progress_bars: bool = False,
 ) -> Tuple[Tensor, Tensor]:
     """Returns samples from proposal and log(importance weights).
 
@@ -20,7 +21,11 @@ def importance_sample(
     Returns:
         Samples and logarithm of importance weights.
     """
-    samples = proposal.sample((num_samples,))
+    # Use progress bars when available (e.g., for multi-round proposals)
+    try:
+        samples = proposal.sample((num_samples,), show_progress_bar=show_progress_bars)
+    except TypeError:
+        samples = proposal.sample((num_samples,))
 
     potential_logprobs = potential_fn(samples)
     proposal_logprobs = proposal.log_prob(samples)

tests/linearGaussian_snle_test.py

@@ -502,4 +502,4 @@ def test_api_snl_sampling_methods(
         )
         posterior.train(max_num_iters=10)
 
-    posterior.sample(sample_shape=(num_samples,))
+    posterior.sample(sample_shape=(num_samples,), show_progress_bars=False)

tests/linearGaussian_snre_test.py

@@ -419,4 +419,4 @@ def test_api_sre_sampling_methods(
         )
         posterior.train(max_num_iters=10)
 
-    posterior.sample(sample_shape=(num_samples,))
+    posterior.sample(sample_shape=(num_samples,), show_progress_bars=False)