pm.MutableData -> pm.Data

Author: jessegrabowski

pymc_experimental/linearmodel.py

@@ -69,8 +69,8 @@ def build_model(self, X: pd.DataFrame, y: pd.Series):
 
         # Data array size can change but number of dimensions must stay the same.
         with pm.Model() as self.model:
-            x = pm.MutableData("x", np.zeros((1,)), dims="observation")
-            y_data = pm.MutableData("y_data", np.zeros((1,)), dims="observation")
+            x = pm.Data("x", np.zeros((1,)), dims="observation")
+            y_data = pm.Data("y_data", np.zeros((1,)), dims="observation")
 
             # priors
             intercept = pm.Normal(

pymc_experimental/statespace/core/statespace.py

@@ -291,7 +291,7 @@ def _print_data_requirements(self) -> None:
         out = out.rstrip()
 
         _log.info(
-            "The following MutableData variables should be assigned to the model inside a PyMC "
+            "The following Data variables should be assigned to the model inside a PyMC "
             f"model block: \n"
             f"{out}"
         )
@@ -366,7 +366,7 @@ def param_info(self) -> dict[str, dict[str, Any]]:
     @property
     def data_info(self) -> dict[str, dict[str, Any]]:
         """
-        Information about MutableData variables that need to be declared in the PyMC model block.
+        Information about Data variables that need to be declared in the PyMC model block.
 
         Returns a dictionary of data_name: dictionary of property-name:property description pairs. The return value is
         used by the ``_print_data_requirements`` method, to print a message telling users how to define the necessary
@@ -877,7 +877,7 @@ def build_statespace_graph(
             or a Pytensor tensor variable.
 
         register_data : bool, optional, default=True
-            If True, the observed data will be registered with PyMC as a pm.MutableData variable. In addition,
+            If True, the observed data will be registered with PyMC as a pm.Data variable. In addition,
             a "time" dim will be created an added to the model's coords.
 
         mode : Optional[str], optional, default=None

pymc_experimental/tests/model/test_marginal_model.py

@@ -609,7 +609,7 @@ def test_is_conditional_dependent_static_shape():
 def test_data_container():
     """Test that MarginalModel can handle Data containers."""
     with MarginalModel(coords_mutable={"obs": [0]}) as marginal_m:
-        x = pm.MutableData("x", 2.5)
+        x = pm.Data("x", 2.5)
         idx = pm.Bernoulli("idx", p=0.7, dims="obs")
         y = pm.Normal("y", idx * x, dims="obs")
 
@@ -618,7 +618,7 @@ def test_data_container():
     logp_fn = marginal_m.compile_logp()
 
     with pm.Model(coords_mutable={"obs": [0]}) as m_ref:
-        x = pm.MutableData("x", 2.5)
+        x = pm.Data("x", 2.5)
         y = pm.NormalMixture("y", w=[0.3, 0.7], mu=[0, x], dims="obs")
 
     ref_logp_fn = m_ref.compile_logp()

pymc_experimental/tests/statespace/test_distributions.py

@@ -172,7 +172,7 @@ def test_lgss_with_time_varying_inputs(output_name, rng):
     }
 
     with pm.Model(coords=coords):
-        exog_data = pm.MutableData("data_exog", X)
+        exog_data = pm.Data("data_exog", X)
         P0_diag = pm.Exponential("P0_diag", 1, shape=(mod.k_states,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
         initial_trend = pm.Normal("initial_trend", shape=(2,))

pymc_experimental/tests/statespace/test_statespace.py

@@ -117,7 +117,7 @@ def exog_pymc_mod(exog_ss_mod, rng):
     X = rng.normal(size=(100, 3)).astype(floatX)
 
     with pm.Model(coords=exog_ss_mod.coords) as m:
-        exog_data = pm.MutableData("data_exog", X)
+        exog_data = pm.Data("data_exog", X)
         initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
         P0_sigma = pm.Exponential("P0_sigma", 1)
         P0 = pm.Deterministic(

pymc_experimental/tests/statespace/test_structural.py

@@ -750,7 +750,7 @@ def test_filter_scans_time_varying_design_matrix(rng):
     mod = reg.build(verbose=False)
 
     with pm.Model(coords=mod.coords) as m:
-        data_exog = pm.MutableData("data_exog", data.values)
+        data_exog = pm.Data("data_exog", data.values)
 
         x0 = pm.Normal("x0", dims=["state"])
         P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])
@@ -781,7 +781,7 @@ def test_extract_components_from_idata(rng):
     mod = (ll + season + reg + me).build(verbose=False)
 
     with pm.Model(coords=mod.coords) as m:
-        data_exog = pm.MutableData("data_exog", data.values)
+        data_exog = pm.Data("data_exog", data.values)
 
         x0 = pm.Normal("x0", dims=["state"])
         P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])

pymc_experimental/tests/test_model_builder.py

@@ -100,8 +100,8 @@ def build_model(self, X: pd.DataFrame, y: pd.Series, model_config=None):
         with pm.Model(coords=coords) as self.model:
             if model_config is None:
                 model_config = self.model_config
-            x = pm.MutableData("x", self.X["input"].values)
-            y_data = pm.MutableData("y_data", self.y)
+            x = pm.Data("x", self.X["input"].values)
+            y_data = pm.Data("y_data", self.y)
 
             # prior parameters
             a_loc = model_config["a"]["loc"]