Doc/remove mutabledata

docs/source/api/data.rst

@@ -6,8 +6,6 @@ Data
 .. autosummary::
    :toctree: generated/
 
-   ConstantData
-   MutableData
-   get_data
    Data
+   get_data
    Minibatch

pymc/gp/hsgp_approx.py

@@ -620,7 +620,7 @@ def prior_linearized(self, X: TensorLike):
         they may share the same basis.
 
         Correct results when using `prior_linearized` in tandem with
-        `pm.set_data` and `pm.MutableData` require that the `Xs` are
+        `pm.set_data` and `pm.Data` require that the `Xs` are
         zero-centered, so its mean must be subtracted.
 
         An example is given below.

pymc/sampling/forward.py

@@ -607,8 +607,8 @@ def sample_posterior_predictive(
 
         import pymc as pm
 
-        with pm.Model(coords_mutable={"trial": [0, 1, 2]}) as model:
-            x = pm.MutableData("x", [-1, 0, 1], dims=["trial"])
+        with pm.Model(coords={"trial": [0, 1, 2]}) as model:
+            x = pm.Data("x", [-1, 0, 1], dims=["trial"])
             beta = pm.Normal("beta")
             noise = pm.HalfNormal("noise")
             y = pm.Normal("y", mu=x * beta, sigma=noise, observed=[-2, 0, 3], dims=["trial"])
@@ -634,8 +634,8 @@ def sample_posterior_predictive(
 
     .. code-block:: python
 
-        with pm.Model(coords_mutable={"trial": [3, 4]}) as predictions_model:
-            x = pm.MutableData("x", [-2, 2], dims=["trial"])
+        with pm.Model(coords={"trial": [3, 4]}) as predictions_model:
+            x = pm.Data("x", [-2, 2], dims=["trial"])
             beta = pm.Normal("beta")
             noise = pm.HalfNormal("noise")
             y = pm.Normal("y", mu=x * beta, sigma=noise, dims=["trial"])
@@ -651,8 +651,8 @@ def sample_posterior_predictive(
 
     .. code-block:: python
 
-        with pm.Model(coords_mutable={"trial": [3, 4]}) as distinct_predictions_model:
-            x = pm.MutableData("x", [-2, 2], dims=["trial"])
+        with pm.Model(coords={"trial": [3, 4]}) as distinct_predictions_model:
+            x = pm.Data("x", [-2, 2], dims=["trial"])
             beta = pm.Normal("beta")
             noise = pm.HalfNormal("noise")
             extra_noise = pm.HalfNormal("extra_noise", sigma=noise)

tests/sampling/test_forward.py

@@ -1022,7 +1022,7 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             caplog.clear()
         elif kind == "Dataset":
             # Dataset has all MCMC posterior samples and the values of the coordinates. This
-            # enables it to see that the coordinates have not changed, but the MutableData is
+            # enables it to see that the coordinates have not changed, but the Data is
             # assumed volatile by default
             assert caplog.record_tuples == [
                 ("pymc.sampling.forward", logging.INFO, "Sampling: [b, y]")
@@ -1031,7 +1031,7 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
 
         original_offsets = model["offsets"].get_value()
         with model:
-            # Changing the MutableData values. This will only be picked up by InferenceData
+            # Changing the Data values. This will only be picked up by InferenceData
             pm.set_data({"offsets": original_offsets + 1})
             pm.sample_posterior_predictive(samples)
         if kind == "MultiTrace":
@@ -1072,7 +1072,7 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             caplog.clear()
 
         with model:
-            # Changing the mutable coordinate values, but not shape, and also changing MutableData.
+            # Changing the mutable coordinate values, but not shape, and also changing Data.
             # This will trigger resampling of all variables
             model.set_dim("name", new_length=3, coord_values=["A", "B", "D"])
             pm.set_data({"offsets": original_offsets + 1, "y_obs": np.zeros((10, 3))})