add test for inverse concatenation

Author: Kucharssim

bayesflow/adapters/adapter.py

@@ -91,14 +91,14 @@ def forward(
         stage : str, one of ["training", "validation", "inference"]
             The stage the function is called in.
         log_det_jac: bool, optional
-            Whether to return the log determinant jacobians of the transforms.
+            Whether to return the log determinant of the Jacobian of the transforms.
         **kwargs : dict
             Additional keyword arguments passed to each transform.
 
         Returns
         -------
         dict | tuple[dict, dict]
-            The transformed data or tuple of transformed data and log determinant jacobians.
+            The transformed data or tuple of transformed data and log determinant of the Jacobian.
         """
         data = data.copy()
         if not log_det_jac:
@@ -125,14 +125,14 @@ def inverse(
         stage : str, one of ["training", "validation", "inference"]
             The stage the function is called in.
         log_det_jac: bool, optional
-            Whether to return the log determinant jacobians of the transforms.
+            Whether to return the log determinant of the Jacobian of the transforms.
         **kwargs : dict
             Additional keyword arguments passed to each transform.
 
         Returns
         -------
         dict | tuple[dict, dict]
-            The transformed data or tuple of transformed data and log determinant jacobians.
+            The transformed data or tuple of transformed data and log determinant of the Jacobian.
         """
         data = data.copy()
         if not log_det_jac:
@@ -166,7 +166,7 @@ def __call__(
         Returns
         -------
         dict | tuple[dict, dict]
-            The transformed data or tuple of transformed data and log determinant jacobians.
+            The transformed data or tuple of transformed data and log determinant of the Jacobian.
         """
         if inverse:
             return self.inverse(data, stage=stage, **kwargs)

bayesflow/adapters/transforms/concatenate.py

@@ -131,7 +131,7 @@ def log_det_jac(
         if inverse:
             if log_det_jac.get(self.into) is not None:
                 raise ValueError(
-                    "Cannot obtain an inverse jacobian determinant of concatenation. "
+                    "Cannot obtain an inverse Jacobian of concatenation. "
                     "Transform your variables before you concatenate."
                 )
 

bayesflow/adapters/transforms/numpy_transform.py

@@ -74,4 +74,4 @@ def inverse(self, data: np.ndarray, **kwargs) -> np.ndarray:
         return self._inverse(data)
 
     def log_det_jac(self, data, inverse=False, **kwargs):
-        raise NotImplementedError("Log determinand jacobian of the numpy transforms are not implemented yet")
+        raise NotImplementedError("log determinant of the Jacobian of the numpy transforms are not implemented yet")

tests/test_adapters/test_adapters.py

@@ -263,3 +263,29 @@ def test_log_det_jac_inverse(adapter_log_det_jac_inverse, random_data):
 
     for key in forward_log_det_jac.keys():
         assert np.allclose(forward_log_det_jac[key], -inverse_log_det_jac[key])
+
+
+def test_log_det_jac_exceptions(random_data):
+    # Test cannot compute inverse log_det_jac
+    # e.g., when we apply a concat and then a transform that
+    # we cannot "unconcatenate" the log_det_jac
+    # (because the log_det_jac are summed, not concatenated)
+    adapter = bf.Adapter().concatenate(["p1", "p2", "p3"], into="p").sqrt("p")
+    transformed_data, log_det_jac = adapter(random_data, log_det_jac=True)
+
+    # test that inverse raises error
+    with pytest.raises(ValueError):
+        adapter(transformed_data, inverse=True, log_det_jac=True)
+
+    # test resolvable order: first transform, then concatenate
+    adapter = bf.Adapter().sqrt(["p1", "p2", "p3"]).concatenate(["p1", "p2", "p3"], into="p")
+
+    transformed_data, forward_log_det_jac = adapter(random_data, log_det_jac=True)
+    data, inverse_log_det_jac = adapter(transformed_data, inverse=True, log_det_jac=True)
+    inverse_log_det_jac = sum(inverse_log_det_jac.values())
+
+    # forward is the same regardless
+    assert np.allclose(forward_log_det_jac["p"], log_det_jac["p"])
+
+    # inverse works when concatenation is used after transforms
+    assert np.allclose(forward_log_det_jac["p"], -inverse_log_det_jac)