Emit FutureWarning when pm.Data(mutable=...) is not specified

pymc/backends/arviz.py

@@ -454,7 +454,7 @@ def constant_data_to_xarray(self):
         """Convert constant data to xarray."""
         # For constant data, we are concerned only with deterministics and
         # data.  The constant data vars must be either pm.Data
-        # (TensorSharedVariable) or pm.Deterministic
+        # (TensorConstant/SharedVariable) or pm.Deterministic
         constant_data_vars = {}  # type: Dict[str, Var]
 
         def is_data(name, var) -> bool:

pymc/data.py

@@ -17,6 +17,7 @@
 import os
 import pkgutil
 import urllib.request
+import warnings
 
 from copy import copy
 from typing import Any, Dict, List, Optional, Sequence, Union
@@ -30,6 +31,7 @@
 from aesara.graph.basic import Apply
 from aesara.tensor.type import TensorType
 from aesara.tensor.var import TensorConstant, TensorVariable
+from packaging import version
 
 import pymc as pm
 
@@ -555,7 +557,7 @@ def Data(
     *,
     dims: Optional[Sequence[str]] = None,
     export_index_as_coords=False,
-    mutable: bool = True,
+    mutable: Optional[bool] = None,
     **kwargs,
 ) -> Union[SharedVariable, TensorConstant]:
     """Data container that registers a data variable with the model.
@@ -570,6 +572,11 @@ def Data(
         The name for this variable
     value: {List, np.ndarray, pd.Series, pd.Dataframe}
         A value to associate with this variable
+    mutable : bool, optional
+        Switches between creating a ``SharedVariable`` (``mutable=True``, default)
+        vs. creating a ``TensorConstant`` (``mutable=False``).
+        Consider using ``pm.ConstantData`` or ``pm.MutableData`` as less verbose
+        alternatives to ``pm.Data(..., mutable=...)``.
     dims: {str, tuple of str}, optional, default=None
         Dimension names of the random variables (as opposed to the shapes of these
         random variables). Use this when `value` is a pandas Series or DataFrame. The
@@ -592,7 +599,7 @@ def Data(
     >>> observed_data = [mu + np.random.randn(20) for mu in true_mu]
 
     >>> with pm.Model() as model:
-    ...     data = pm.Data('data', observed_data[0])
+    ...     data = pm.MutableData('data', observed_data[0])
     ...     mu = pm.Normal('mu', 0, 10)
     ...     pm.Normal('y', mu=mu, sigma=1, observed=data)
 
@@ -626,6 +633,18 @@ def Data(
     # `pandas_to_array` takes care of parameter `value` and
     # transforms it to something digestible for Aesara.
     arr = pandas_to_array(value)
+
+    if mutable is None:
+        current = version.Version(pm.__version__)
+        mutable = current.major == 4 and current.minor < 1
+        if mutable:
+            warnings.warn(
+                "The `mutable` kwarg was not specified. Currently it defaults to `pm.Data(mutable=True)`,"
+                " which is equivalent to using `pm.MutableData()`."
+                " In v4.1.0 the default will change to `pm.Data(mutable=False)`, equivalent to `pm.ConstantData`."
+                " Set `pm.Data(..., mutable=False/True)`, or use `pm.ConstantData`/`pm.MutableData`.",
+                FutureWarning,
+            )
     if mutable:
         x = aesara.shared(arr, name, **kwargs)
     else:

pymc/model.py

@@ -1114,7 +1114,7 @@ def set_data(
     ):
         """Changes the values of a data variable in the model.
 
-        In contrast to pm.Data().set_value, this method can also
+        In contrast to pm.MutableData().set_value, this method can also
         update the corresponding coordinates.
 
         Parameters
@@ -1131,7 +1131,8 @@ def set_data(
         shared_object = self[name]
         if not isinstance(shared_object, SharedVariable):
             raise TypeError(
-                f"The variable `{name}` must be a `SharedVariable` (e.g. `pymc.Data`) to allow updating. "
+                f"The variable `{name}` must be a `SharedVariable`"
+                " (created through `pm.MutableData()` or `pm.Data(mutable=True)`) to allow updating. "
                 f"The current type is: {type(shared_object)}"
             )
 
@@ -1156,7 +1157,7 @@ def set_data(
             length_changed = new_length != old_length
 
             # Reject resizing if we already know that it would create shape problems.
-            # NOTE: If there are multiple pm.Data containers sharing this dim, but the user only
+            # NOTE: If there are multiple pm.MutableData containers sharing this dim, but the user only
             #       changes the values for one of them, they will run into shape problems nonetheless.
             length_belongs_to = length_tensor.owner.inputs[0].owner.inputs[0]
             if not isinstance(length_belongs_to, SharedVariable) and length_changed:
@@ -1735,8 +1736,8 @@ def set_data(new_data, model=None):
 
         >>> import pymc as pm
         >>> with pm.Model() as model:
-        ...     x = pm.Data('x', [1., 2., 3.])
-        ...     y = pm.Data('y', [1., 2., 3.])
+        ...     x = pm.MutableData('x', [1., 2., 3.])
+        ...     y = pm.MutableData('y', [1., 2., 3.])
         ...     beta = pm.Normal('beta', 0, 1)
         ...     obs = pm.Normal('obs', x * beta, 1, observed=y)
         ...     idata = pm.sample(1000, tune=1000)

pymc/model_graph.py

@@ -133,10 +133,14 @@ def _make_node(self, var_name, graph, *, formatting: str = "plain"):
             shape = "octagon"
             style = "filled"
             label = f"{var_name}\n~\nPotential"
-        elif isinstance(v, (SharedVariable, TensorConstant)):
+        elif isinstance(v, TensorConstant):
             shape = "box"
             style = "rounded, filled"
-            label = f"{var_name}\n~\nData"
+            label = f"{var_name}\n~\nConstantData"
+        elif isinstance(v, SharedVariable):
+            shape = "box"
+            style = "rounded, filled"
+            label = f"{var_name}\n~\nMutableData"
         elif v.owner and isinstance(v.owner.op, RandomVariable):
             shape = "ellipse"
             if hasattr(v.tag, "observations"):

pymc/sampling.py

@@ -42,6 +42,7 @@
 import xarray
 
 from aesara.compile.mode import Mode
+from aesara.graph.basic import Constant
 from aesara.tensor.sharedvar import SharedVariable
 from arviz import InferenceData
 from fastprogress.fastprogress import progress_bar
@@ -1728,7 +1729,7 @@ def sample_posterior_predictive(
             for rv in walk_model(vars_to_sample, walk_past_rvs=True)
             if rv not in vars_to_sample
             and rv in model.named_vars.values()
-            and not isinstance(rv, SharedVariable)
+            and not isinstance(rv, (Constant, SharedVariable))
         ]
         if inputs_and_names:
             inputs, input_names = zip(*inputs_and_names)
@@ -1739,7 +1740,7 @@ def sample_posterior_predictive(
         input_names = [
             n
             for n in _trace.varnames
-            if n not in output_names and not isinstance(model[n], SharedVariable)
+            if n not in output_names and not isinstance(model[n], (Constant, SharedVariable))
         ]
         inputs = [model[n] for n in input_names]
 
@@ -2067,7 +2068,7 @@ def sample_prior_predictive(
             names.append(rv_var.name)
             vars_to_sample.append(rv_var)
 
-    inputs = [i for i in inputvars(vars_to_sample) if not isinstance(i, SharedVariable)]
+    inputs = [i for i in inputvars(vars_to_sample) if not isinstance(i, (Constant, SharedVariable))]
 
     sampler_fn = compile_pymc(
         inputs, vars_to_sample, allow_input_downcast=True, accept_inplace=True, mode=mode

pymc/tests/test_data_container.py

@@ -17,6 +17,7 @@
 import pytest
 
 from aesara import shared
+from aesara.compile.sharedvalue import SharedVariable
 from aesara.tensor.sharedvar import ScalarSharedVariable
 from aesara.tensor.var import TensorVariable
 
@@ -32,7 +33,7 @@ class TestData(SeededTest):
     def test_deterministic(self):
         data_values = np.array([0.5, 0.4, 5, 2])
         with pm.Model() as model:
-            X = pm.Data("X", data_values)
+            X = pm.MutableData("X", data_values)
             pm.Normal("y", 0, 1, observed=X)
             model.logp(model.recompute_initial_point())
 
@@ -43,7 +44,7 @@ def test_sample(self):
         x_pred = np.linspace(-3, 3, 200, dtype="float32")
 
         with pm.Model():
-            x_shared = pm.Data("x_shared", x)
+            x_shared = pm.MutableData("x_shared", x)
             b = pm.Normal("b", 0.0, 10.0)
             pm.Normal("obs", b * x_shared, np.sqrt(1e-2), observed=y)
 
@@ -95,8 +96,8 @@ def test_sample_posterior_predictive_after_set_data(self):
 
     def test_sample_after_set_data(self):
         with pm.Model() as model:
-            x = pm.Data("x", [1.0, 2.0, 3.0])
-            y = pm.Data("y", [1.0, 2.0, 3.0])
+            x = pm.MutableData("x", [1.0, 2.0, 3.0])
+            y = pm.MutableData("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 10.0)
             pm.Normal("obs", beta * x, np.sqrt(1e-2), observed=y)
             pm.sample(
@@ -131,8 +132,8 @@ def test_shared_data_as_index(self):
         See https://github.com/pymc-devs/pymc/issues/3813
         """
         with pm.Model() as model:
-            index = pm.Data("index", [2, 0, 1, 0, 2])
-            y = pm.Data("y", [1.0, 2.0, 3.0, 2.0, 1.0])
+            index = pm.MutableData("index", [2, 0, 1, 0, 2])
+            y = pm.MutableData("y", [1.0, 2.0, 3.0, 2.0, 1.0])
             alpha = pm.Normal("alpha", 0, 1.5, size=3)
             pm.Normal("obs", alpha[index], np.sqrt(1e-2), observed=y)
 
@@ -163,7 +164,7 @@ def test_shared_data_as_rv_input(self):
         See https://github.com/pymc-devs/pymc/issues/3842
         """
         with pm.Model() as m:
-            x = pm.Data("x", [1.0, 2.0, 3.0])
+            x = pm.MutableData("x", [1.0, 2.0, 3.0])
             y = pm.Normal("y", mu=x, size=(2, 3))
             assert y.eval().shape == (2, 3)
             idata = pm.sample(
@@ -221,7 +222,7 @@ def test_shared_scalar_as_rv_input(self):
 
     def test_creation_of_data_outside_model_context(self):
         with pytest.raises((IndexError, TypeError)) as error:
-            pm.Data("data", [1.1, 2.2, 3.3])
+            pm.ConstantData("data", [1.1, 2.2, 3.3])
         error.match("No model on context stack")
 
     def test_set_data_to_non_data_container_variables(self):
@@ -244,8 +245,8 @@ def test_set_data_to_non_data_container_variables(self):
     @pytest.mark.xfail(reason="Depends on ModelGraph")
     def test_model_to_graphviz_for_model_with_data_container(self):
         with pm.Model() as model:
-            x = pm.Data("x", [1.0, 2.0, 3.0])
-            y = pm.Data("y", [1.0, 2.0, 3.0])
+            x = pm.ConstantData("x", [1.0, 2.0, 3.0])
+            y = pm.MutableData("y", [1.0, 2.0, 3.0])
             beta = pm.Normal("beta", 0, 10.0)
             obs_sigma = floatX(np.sqrt(1e-2))
             pm.Normal("obs", beta * x, obs_sigma, observed=y)
@@ -262,12 +263,14 @@ def test_model_to_graphviz_for_model_with_data_container(self):
                 pm.model_to_graphviz(model, formatting=formatting)
 
         exp_without = [
-            'x [label="x\n~\nData" shape=box style="rounded, filled"]',
+            'x [label="x\n~\nConstantData" shape=box style="rounded, filled"]',
+            'y [label="x\n~\nMutableData" shape=box style="rounded, filled"]',
             'beta [label="beta\n~\nNormal"]',
             'obs [label="obs\n~\nNormal" style=filled]',
         ]
         exp_with = [
-            'x [label="x\n~\nData" shape=box style="rounded, filled"]',
+            'x [label="x\n~\nConstantData" shape=box style="rounded, filled"]',
+            'y [label="x\n~\nMutableData" shape=box style="rounded, filled"]',
             'beta [label="beta\n~\nNormal(mu=0.0, sigma=10.0)"]',
             f'obs [label="obs\n~\nNormal(mu=f(f(beta), x), sigma={obs_sigma})" style=filled]',
         ]
@@ -290,7 +293,7 @@ def test_explicit_coords(self):
         }
         # pass coordinates explicitly, use numpy array in Data container
         with pm.Model(coords=coords) as pmodel:
-            pm.Data("observations", data, dims=("rows", "columns"))
+            pm.MutableData("observations", data, dims=("rows", "columns"))
 
         assert "rows" in pmodel.coords
         assert pmodel.coords["rows"] == ("R1", "R2", "R3", "R4", "R5")
@@ -310,7 +313,7 @@ def test_symbolic_coords(self):
         Their lengths are then automatically linked to the corresponding Tensor dimension.
         """
         with pm.Model() as pmodel:
-            intensity = pm.Data("intensity", np.ones((2, 3)), dims=("row", "column"))
+            intensity = pm.MutableData("intensity", np.ones((2, 3)), dims=("row", "column"))
             assert "row" in pmodel.dim_lengths
             assert "column" in pmodel.dim_lengths
             assert isinstance(pmodel.dim_lengths["row"], TensorVariable)
@@ -327,7 +330,7 @@ def test_no_resize_of_implied_dimensions(self):
             # Imply a dimension through RV params
             pm.Normal("n", mu=[1, 2, 3], dims="city")
             # _Use_ the dimension for a data variable
-            inhabitants = pm.Data("inhabitants", [100, 200, 300], dims="city")
+            inhabitants = pm.MutableData("inhabitants", [100, 200, 300], dims="city")
 
             # Attempting to re-size the dimension through the data variable would
             # cause shape problems in InferenceData conversion, because the RV remains (3,).
@@ -343,7 +346,7 @@ def test_implicit_coords_series(self):
             name="sales",
         )
         with pm.Model() as pmodel:
-            pm.Data("sales", ser_sales, dims="date", export_index_as_coords=True)
+            pm.ConstantData("sales", ser_sales, dims="date", export_index_as_coords=True)
 
         assert "date" in pmodel.coords
         assert len(pmodel.coords["date"]) == 22
@@ -360,7 +363,9 @@ def test_implicit_coords_dataframe(self):
 
         # infer coordinates from index and columns of the DataFrame
         with pm.Model() as pmodel:
-            pm.Data("observations", df_data, dims=("rows", "columns"), export_index_as_coords=True)
+            pm.ConstantData(
+                "observations", df_data, dims=("rows", "columns"), export_index_as_coords=True
+            )
 
         assert "rows" in pmodel.coords
         assert "columns" in pmodel.coords
@@ -370,23 +375,30 @@ def test_data_kwargs(self):
         strict_value = True
         allow_downcast_value = False
         with pm.Model():
-            data = pm.Data(
-                "data",
+            data = pm.MutableData(
+                "mdata",
                 value=[[1.0], [2.0], [3.0]],
                 strict=strict_value,
                 allow_downcast=allow_downcast_value,
             )
         assert data.container.strict is strict_value
         assert data.container.allow_downcast is allow_downcast_value
 
+    def test_data_mutable_default_warning(self):
+        with pm.Model():
+            with pytest.warns(FutureWarning, match="`mutable` kwarg was not specified"):
+                data = pm.Data("x", [1, 2, 3])
+            assert isinstance(data, SharedVariable)
+        pass
+
 
 def test_data_naming():
     """
     This is a test for issue #3793 -- `Data` objects in named models are
     not given model-relative names.
     """
     with pm.Model("named_model") as model:
-        x = pm.Data("x", [1.0, 2.0, 3.0])
+        x = pm.ConstantData("x", [1.0, 2.0, 3.0])
         y = pm.Normal("y")
     assert y.name == "named_model_y"
     assert x.name == "named_model_x"