Adding option to include transformed variables in InferenceData

pymc/backends/arviz.py

@@ -164,9 +164,11 @@ def __init__(
         dims: Optional[DimSpec] = None,
         model=None,
         save_warmup: Optional[bool] = None,
+        include_transformed: bool = False,
     ):
 
         self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup
+        self.include_transformed = include_transformed
         self.trace = trace
 
         # this permits us to get the model from command-line argument or from with model:
@@ -311,7 +313,9 @@ def _extract_log_likelihood(self, trace):
     @requires("trace")
     def posterior_to_xarray(self):
         """Convert the posterior to an xarray dataset."""
-        var_names = get_default_varnames(self.trace.varnames, include_transformed=False)
+        var_names = get_default_varnames(
+            self.trace.varnames, include_transformed=self.include_transformed
+        )
         data = {}
         data_warmup = {}
         for var_name in var_names:
@@ -539,6 +543,7 @@ def to_inference_data(
     dims: Optional[DimSpec] = None,
     model: Optional["Model"] = None,
     save_warmup: Optional[bool] = None,
+    include_transformed: bool = False,
 ) -> InferenceData:
     """Convert pymc data into an InferenceData object.
 
@@ -571,6 +576,9 @@ def to_inference_data(
     save_warmup : bool, optional
         Save warmup iterations InferenceData object. If not defined, use default
         defined by the rcParams.
+    include_transformed : bool, optional
+        Save the transformed parameters in the InferenceData object. By default, these are
+        not saved.
 
     Returns
     -------
@@ -588,6 +596,7 @@ def to_inference_data(
         dims=dims,
         model=model,
         save_warmup=save_warmup,
+        include_transformed=include_transformed,
     ).to_inference_data()
 
 

pymc/tests/backends/test_arviz.py

@@ -279,7 +279,7 @@ def test_autodetect_coords_from_model(self, use_context):
         np.testing.assert_array_equal(idata.observed_data.coords["date"], coords["date"])
         np.testing.assert_array_equal(idata.observed_data.coords["city"], coords["city"])
 
-    def test_ovewrite_model_coords_dims(self):
+    def test_overwrite_model_coords_dims(self):
         """Check coords and dims from model object can be partially overwritten."""
         dim1 = ["a", "b"]
         new_dim1 = ["c", "d"]
@@ -617,6 +617,23 @@ def test_variable_dimension_name_collision(self):
                 var = at.as_tensor([1, 2, 3])
                 pmodel.register_rv(var, name="time", dims=("time",))
 
+    def test_include_transformed(self):
+        with pm.Model():
+            pm.Uniform("p", 0, 1)
+
+            # First check that the default is to exclude the transformed variables
+            sample_kwargs = dict(tune=5, draws=7, chains=2, cores=1)
+            inference_data = pm.sample(**sample_kwargs, step=pm.Metropolis())
+            assert "p_interval__" not in inference_data.posterior
+
+            # Now check that they are included when requested
+            inference_data = pm.sample(
+                **sample_kwargs,
+                step=pm.Metropolis(),
+                idata_kwargs={"include_transformed": True},
+            )
+            assert "p_interval__" in inference_data.posterior
+
 
 class TestPyMCWarmupHandling:
     @pytest.mark.parametrize("save_warmup", [False, True])