Rename Model initial_values to rvs_to_initial_values

Author: ricardoV94

pymc/initial_point.py

@@ -133,7 +133,7 @@ def make_initial_point_fn(
 
     sdict_overrides = convert_str_to_rv_dict(model, overrides or {})
     initval_strats = {
-        **model.initial_values,
+        **model.rvs_to_initial_values,
         **sdict_overrides,
     }
 

pymc/model.py

@@ -550,15 +550,14 @@ def __init__(
         self.name = self._validate_name(name)
         self.check_bounds = check_bounds
 
-        self._initial_values: Dict[TensorVariable, Optional[Union[np.ndarray, Variable, str]]] = {}
-
         if self.parent is not None:
             self.named_vars = treedict(parent=self.parent.named_vars)
             self.named_vars_to_dims = treedict(parent=self.parent.named_vars_to_dims)
             self.values_to_rvs = treedict(parent=self.parent.values_to_rvs)
             self.rvs_to_values = treedict(parent=self.parent.rvs_to_values)
             self.rvs_to_transforms = treedict(parent=self.parent.rvs_to_transforms)
             self.rvs_to_total_sizes = treedict(parent=self.parent.rvs_to_total_sizes)
+            self.rvs_to_initial_values = treedict(parent=self.parent.rvs_to_initial_values)
             self.free_RVs = treelist(parent=self.parent.free_RVs)
             self.observed_RVs = treelist(parent=self.parent.observed_RVs)
             self.auto_deterministics = treelist(parent=self.parent.auto_deterministics)
@@ -573,6 +572,7 @@ def __init__(
             self.rvs_to_values = treedict()
             self.rvs_to_transforms = treedict()
             self.rvs_to_total_sizes = treedict()
+            self.rvs_to_initial_values = treedict()
             self.free_RVs = treelist()
             self.observed_RVs = treelist()
             self.auto_deterministics = treelist()
@@ -1128,15 +1128,18 @@ def initial_values(self) -> Dict[TensorVariable, Optional[Union[np.ndarray, Vari
         Keys are the random variables (as returned by e.g. ``pm.Uniform()``) and
         values are the numeric/symbolic initial values, strings denoting the strategy to get them, or None.
         """
-        return self._initial_values
+        warnings.warn(
+            "Model.initial_values is deprecated. Use Model.rvs_to_initial_values instead."
+        )
+        return self.rvs_to_initial_values
 
     def set_initval(self, rv_var, initval):
         """Sets an initial value (strategy) for a random variable."""
         if initval is not None and not isinstance(initval, (Variable, str)):
             # Convert scalars or array-like inputs to ndarrays
             initval = rv_var.type.filter(initval)
 
-        self.initial_values[rv_var] = initval
+        self.rvs_to_initial_values[rv_var] = initval
 
     def set_data(
         self,

pymc/tests/test_initial_point.py

@@ -86,7 +86,7 @@ def test_dependent_initvals(self):
             assert ip["B2_interval__"] == 0
 
             # Modify initval of L and re-evaluate
-            pmodel.initial_values[U] = 9.9
+            pmodel.rvs_to_initial_values[U] = 9.9
             ip = pmodel.initial_point(random_seed=0)
             assert ip["B1_interval__"] < 0
             assert ip["B2_interval__"] == 0
@@ -108,7 +108,7 @@ def test_nested_initvals(self):
         ip_vals = list(make_initial_point_fn(model=pmodel, return_transformed=False)(0).values())
         assert np.allclose(ip_vals, [1, 2, 4, 8, 16, 32], rtol=1e-3)
 
-        pmodel.initial_values[four] = 1
+        pmodel.rvs_to_initial_values[four] = 1
 
         ip_vals = list(make_initial_point_fn(model=pmodel, return_transformed=True)(0).values())
         assert np.allclose(np.exp(ip_vals), [1, 2, 4, 1, 2, 4], rtol=1e-3)

pymc/tests/test_model.py

@@ -586,11 +586,11 @@ def test_initial_point():
     with model:
         y = pm.Normal("y", initval=y_initval)
 
-    assert a in model.initial_values
-    assert x in model.initial_values
-    assert model.initial_values[b] == b_initval
+    assert a in model.rvs_to_initial_values
+    assert x in model.rvs_to_initial_values
+    assert model.rvs_to_initial_values[b] == b_initval
     assert model.initial_point(0)["b_interval__"] == b_initval_trans
-    assert model.initial_values[y] == y_initval
+    assert model.rvs_to_initial_values[y] == y_initval
 
 
 def test_point_logps():