Remove deprecated model methods

Author: ricardoV94

pymc/model/core.py

@@ -886,27 +886,11 @@ def unobserved_value_vars(self):
 
         return vars + untransformed_vars + deterministics
 
-    @property
-    def disc_vars(self):
-        warnings.warn(
-            "Model.disc_vars has been deprecated. Use Model.discrete_value_vars instead.",
-            FutureWarning,
-        )
-        return self.discrete_value_vars
-
     @property
     def discrete_value_vars(self):
         """All the discrete value variables in the model"""
         return list(typefilter(self.value_vars, discrete_types))
 
-    @property
-    def cont_vars(self):
-        warnings.warn(
-            "Model.cont_vars has been deprecated. Use Model.continuous_value_vars instead.",
-            FutureWarning,
-        )
-        return self.continuous_value_vars
-
     @property
     def continuous_value_vars(self):
         """All the continuous value variables in the model"""
@@ -935,18 +919,6 @@ def unobserved_RVs(self):
         """
         return self.free_RVs + self.deterministics
 
-    @property
-    def RV_dims(self) -> Dict[str, Tuple[Union[str, None], ...]]:
-        """Tuples of dimension names for specific model variables.
-
-        Entries in the tuples may be ``None``, if the RV dimension was not given a name.
-        """
-        warnings.warn(
-            "Model.RV_dims is deprecated. Use Model.named_vars_to_dims instead.",
-            FutureWarning,
-        )
-        return self.named_vars_to_dims
-
     @property
     def coords(self) -> Dict[str, Union[Tuple, None]]:
         """Coordinate values for model dimensions."""
@@ -1090,18 +1062,6 @@ def initial_point(self, random_seed: SeedSequenceSeed = None) -> Dict[str, np.nd
         fn = make_initial_point_fn(model=self, return_transformed=True)
         return Point(fn(random_seed), model=self)
 
-    @property
-    def initial_values(self) -> Dict[TensorVariable, Optional[Union[np.ndarray, Variable, str]]]:
-        """Maps transformed variables to initial value placeholders.
-
-        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.
-        """
-        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)):

tests/model/test_core.py

@@ -705,17 +705,17 @@ def test_set_initval():
         alpha = pm.HalfNormal("alpha", initval=100)
         value = pm.NegativeBinomial("value", mu=mu, alpha=alpha)
 
-    assert np.array_equal(model.initial_values[mu], np.array([[100.0]]))
-    np.testing.assert_array_equal(model.initial_values[alpha], np.array(100))
-    assert model.initial_values[value] is None
+    assert np.array_equal(model.rvs_to_initial_values[mu], np.array([[100.0]]))
+    np.testing.assert_array_equal(model.rvs_to_initial_values[alpha], np.array(100))
+    assert model.rvs_to_initial_values[value] is None
 
     # `Flat` cannot be sampled, so let's make sure that doesn't break initial
     # value computations
     with pm.Model() as model:
         x = pm.Flat("x")
         y = pm.Normal("y", x, 1)
 
-    assert y in model.initial_values
+    assert y in model.rvs_to_initial_values
 
 
 def test_datalogp_multiple_shapes():
@@ -974,18 +974,6 @@ def test_set_data_constant_shape_error():
         pmodel.set_data("y", np.arange(10))
 
 
-def test_model_deprecation_warning():
-    with pm.Model() as m:
-        x = pm.Normal("x", 0, 1, size=2)
-        y = pm.LogNormal("y", 0, 1, size=2)
-
-    with pytest.warns(FutureWarning):
-        m.disc_vars
-
-    with pytest.warns(FutureWarning):
-        m.cont_vars
-
-
 @pytest.mark.parametrize("jacobian", [True, False])
 def test_model_logp(jacobian):
     with pm.Model() as m: