add replace nan adapter

bayesflow/adapters/adapter.py

@@ -31,6 +31,7 @@
     Ungroup,
     RandomSubsample,
     Take,
+    NanToNum,
 )
 from .transforms.filter_transform import Predicate
 
@@ -956,3 +957,34 @@ def to_dict(self):
         transform = ToDict()
         self.transforms.append(transform)
         return self
+
+    def nan_to_num(
+        self,
+        keys: str | Sequence[str],
+        default_value: float = 0.0,
+        return_mask: bool = False,
+        mask_prefix: str = "mask",
+    ):
+        """
+        Append :py:class:`~bf.adapters.transforms.NanToNum` transform to the adapter.
+
+        Parameters
+        ----------
+        keys : str or sequence of str
+            The names of the variables to clean / mask.
+        default_value : float
+            Value to substitute wherever data is NaN. Defaults to 0.0.
+        return_mask : bool
+            If True, encode a binary missingness mask alongside the data. Defaults to False.
+        mask_prefix : str
+            Prefix for the mask key in the output dictionary. Defaults to 'mask_'. If the mask key already exists,
+            a ValueError is raised to avoid overwriting existing masks.
+        """
+        if isinstance(keys, str):
+            keys = [keys]
+
+        for key in keys:
+            self.transforms.append(
+                NanToNum(key=key, default_value=default_value, return_mask=return_mask, mask_prefix=mask_prefix)
+            )
+        return self

bayesflow/adapters/transforms/__init__.py

@@ -29,6 +29,7 @@
 from .random_subsample import RandomSubsample
 from .take import Take
 from .ungroup import Ungroup
+from .nan_to_num import NanToNum
 
 from ...utils._docs import _add_imports_to_all
 

bayesflow/adapters/transforms/nan_to_num.py

@@ -0,0 +1,91 @@
+import numpy as np
+
+from bayesflow.utils.serialization import serializable, serialize
+from .transform import Transform
+
+
+@serializable("bayesflow.adapters")
+class NanToNum(Transform):
+    """
+    Replace NaNs with a default value, and optionally encode a missing-data mask as a separate output key.
+
+    This is based on "Missing data in amortized simulation-based neural posterior estimation" by Wang et al. (2024).
+
+    Parameters
+    ----------
+    default_value : float
+        Value to substitute wherever data is NaN.
+    return_mask : bool, default=False
+        If True, a mask array will be returned under a new key.
+    mask_prefix : str, default='mask_'
+        Prefix for the mask key in the output dictionary.
+    """
+
+    def __init__(self, key: str, default_value: float = 0.0, return_mask: bool = False, mask_prefix: str = "mask"):
+        super().__init__()
+        self.key = key
+        self.default_value = default_value
+        self.return_mask = return_mask
+        self.mask_prefix = mask_prefix
+
+    def get_config(self) -> dict:
+        return serialize(
+            {
+                "key": self.key,
+                "default_value": self.default_value,
+                "return_mask": self.return_mask,
+                "mask_prefix": self.mask_prefix,
+            }
+        )
+
+    @property
+    def mask_key(self) -> str:
+        """
+        Key under which the mask will be stored in the output dictionary.
+        """
+        return f"{self.mask_prefix}_{self.key}"
+
+    def forward(self, data: dict[str, any], **kwargs) -> dict[str, any]:
+        """
+        Forward transform: fill NaNs and optionally output mask under 'mask_<key>'.
+        """
+        data = data.copy()
+
+        # Check if the mask key already exists in the data
+        if self.mask_key in data.keys():
+            raise ValueError(
+                f"Mask key '{self.mask_key}' already exists in the data. Please choose a different mask_prefix."
+            )
+
+        # Identify NaNs and fill with default value
+        mask = np.isnan(data[self.key])
+        data[self.key] = np.nan_to_num(data[self.key], copy=False, nan=self.default_value)
+
+        if not self.return_mask:
+            return data
+
+        # Prepare mask array (1 for valid, 0 for NaN)
+        mask_array = (~mask).astype(np.int8)
+
+        # Return both the filled data and the mask under separate keys
+        data[self.mask_key] = mask_array
+        return data
+
+    def inverse(self, data: dict[str, any], **kwargs) -> dict[str, any]:
+        """
+        Inverse transform: restore NaNs using the mask under 'mask_<key>'.
+        """
+        data = data.copy()
+
+        # Retrieve mask and values to reconstruct NaNs
+        values = data[self.key]
+
+        if not self.return_mask:
+            values[values == self.default_value] = np.nan  # we assume default_value is not in data
+        else:
+            mask_array = data[self.mask_key].astype(bool)
+            # Put NaNs where mask is 0
+            values[~mask_array] = np.nan
+
+        data[self.key] = values
+        return data

tests/test_adapters/test_adapters.py

@@ -298,6 +298,27 @@ def test_log_det_jac_exceptions(random_data):
     assert np.allclose(forward_log_det_jac["p"], -inverse_log_det_jac)
 
 
+def test_nan_to_num():
+    arr = {"test": np.array([1.0, np.nan, 3.0])}
+    # test without mask
+    transform = bf.Adapter().nan_to_num(keys="test", default_value=-1.0, return_mask=False)
+    out = transform.forward(arr)["test"]
+    np.testing.assert_array_equal(out, np.array([1.0, -1.0, 3.0]))
+
+    # test with mask
+    arr = {"test": np.array([1.0, np.nan, 3.0]), "test-2d": np.array([[1.0, np.nan], [np.nan, 4.0]])}
+    transform = bf.Adapter().nan_to_num(keys="test", default_value=0.0, return_mask=True)
+    out = transform.forward(arr)
+    np.testing.assert_array_equal(out["test"], np.array([1.0, 0.0, 3.0]))
+    np.testing.assert_array_equal(out["mask_test"], np.array([1.0, 0.0, 1.0]))
+
+    # test two-d array
+    transform = bf.Adapter().nan_to_num(keys="test-2d", default_value=0.5, return_mask=True, mask_prefix="new_mask")
+    out = transform.forward(arr)
+    np.testing.assert_array_equal(out["test-2d"], np.array([[1.0, 0.5], [0.5, 4.0]]))
+    np.testing.assert_array_equal(out["new_mask_test-2d"], np.array([[1, 0], [0, 1]]))
+
+
 def test_nnpe(random_data):
     # NNPE cannot be integrated into the adapter fixture and its tests since it modifies the input data
     # and therefore breaks existing allclose checks