apply ruff to tests and tutorials, ignore long lines.

Author: janfb

sbi/diagnostics/lc2st.py

@@ -12,6 +12,8 @@
 from torch import Tensor
 from tqdm import tqdm
 
+from sbi.utils.diagnostics_utils import remove_nans_and_infs
+
 
 class LC2ST:
     def __init__(
@@ -83,6 +85,8 @@ def __init__(
         [2] : https://github.com/sbi-dev/sbi/blob/main/sbi/utils/metrics.py
         """
 
+        thetas, xs = remove_nans_and_infs(thetas, xs)
+
         assert (
             thetas.shape[0] == xs.shape[0] == posterior_samples.shape[0]
         ), "Number of samples must match"

sbi/diagnostics/sbc.py

@@ -13,7 +13,10 @@
 from sbi.inference import DirectPosterior
 from sbi.inference.posteriors.base_posterior import NeuralPosterior
 from sbi.inference.posteriors.vi_posterior import VIPosterior
-from sbi.utils.diagnostics_utils import get_posterior_samples_on_batch
+from sbi.utils.diagnostics_utils import (
+    get_posterior_samples_on_batch,
+    remove_nans_and_infs,
+)
 from sbi.utils.metrics import c2st
 
 
@@ -54,6 +57,9 @@ def run_sbc(
         ranks: ranks of the ground truth parameters under the inferred
         dap_samples: samples from the data averaged posterior.
     """
+
+    thetas, xs = remove_nans_and_infs(thetas, xs)
+
     num_sbc_samples = thetas.shape[0]
 
     if num_sbc_samples < 100:

sbi/diagnostics/tarp.py

@@ -13,7 +13,10 @@
 from torch import Tensor
 
 from sbi.inference.posteriors.base_posterior import NeuralPosterior
-from sbi.utils.diagnostics_utils import get_posterior_samples_on_batch
+from sbi.utils.diagnostics_utils import (
+    get_posterior_samples_on_batch,
+    remove_nans_and_infs,
+)
 from sbi.utils.metrics import l2
 
 
@@ -61,6 +64,9 @@ def run_tarp(
         ecp: Expected coverage probability (``ecp``), see equation 4 of the paper
         alpha: credibility values, see equation 2 of the paper
     """
+
+    thetas, xs = remove_nans_and_infs(thetas, xs)
+
     num_tarp_samples, dim_theta = thetas.shape
 
     posterior_samples = get_posterior_samples_on_batch(

sbi/utils/diagnostics_utils.py

@@ -1,4 +1,5 @@
 import warnings
+from typing import Tuple
 
 import torch
 from joblib import Parallel, delayed
@@ -9,6 +10,7 @@
 from sbi.inference.posteriors.mcmc_posterior import MCMCPosterior
 from sbi.inference.posteriors.vi_posterior import VIPosterior
 from sbi.sbi_types import Shape
+from sbi.utils import handle_invalid_x
 
 
 def get_posterior_samples_on_batch(
@@ -91,3 +93,20 @@ def sample_fun(
         posterior_samples.shape[:2]
     }."""
     return posterior_samples
+
+
+def remove_nans_and_infs(thetas: Tensor, xs: Tensor) -> Tuple[Tensor, Tensor]:
+    """Remove NaNs and Infs from the data."""
+    is_valid_x, num_nans, num_infs = handle_invalid_x(xs)
+    if num_nans > 0 or num_infs > 0:
+        warnings.warn(
+            f"Found {num_nans} NaNs and {num_infs} Infs in the data. "
+            f"These will be ignored below. Beware that only {is_valid_x.sum()} "
+            f"/ {len(xs)} samples are left.",
+            stacklevel=2,
+        )
+
+    thetas = thetas[is_valid_x]
+    xs = xs[is_valid_x]
+
+    return thetas, xs

sbi/utils/sbiutils.py

@@ -387,7 +387,7 @@ def nle_nre_apt_msg_on_invalid_x(
 
     if num_nans + num_infs > 0:
         if exclude_invalid_x:
-            logging.warn(
+            logging.warning(
                 f"Found {num_nans} NaN simulations and {num_infs} Inf simulations."
                 f"These will be discarded from training due to "
                 f"`exclude_invalid_x=True`. Please be aware that this gives "

tests/inference_with_NaN_simulator_test.py

@@ -7,6 +7,7 @@
 from torch.distributions import MultivariateNormal
 
 from sbi import utils as utils
+from sbi.diagnostics import run_sbc
 from sbi.inference import (
     NPE_A,
     NPE_C,
@@ -113,6 +114,13 @@ def linear_gaussian_nan(
     # Compute the c2st and assert it is near chance level of 0.5.
     check_c2st(samples, target_samples, alg=f"{method}")
 
+    # run sbc
+    num_sbc_samples = 100
+    thetas = prior.sample((num_sbc_samples,))
+    xs = simulator(thetas)
+    ranks, daps = run_sbc(thetas, xs, posterior, num_posterior_samples=1000)
+    assert torch.isfinite(ranks).all()
+
 
 @pytest.mark.slow
 def test_inference_with_restriction_estimator():