rename in docs

Author: williambdean

pymc/distributions/distribution.py

@@ -405,7 +405,7 @@ def update(self, node: Apply) -> dict[Variable, Variable]:
 
         Returns a dictionary with the symbolic expressions required for correct updating
         of random state input variables repeated function evaluations. This is used by
-        `pytensorf.compile_pymc`.
+        `pytensorf.compile`.
         """
         return collect_default_updates_inner_fgraph(node)
 

pymc/logprob/basic.py

@@ -144,7 +144,7 @@ def logp(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs) ->
         print(rv_logp.eval({value: 0.9, mu: 0.0}))  # -1.32393853
 
         # Compile a function for repeated evaluations
-        rv_logp_fn = pm.compile_pymc([value, mu], rv_logp)
+        rv_logp_fn = pm.compile([value, mu], rv_logp)
         print(rv_logp_fn(value=0.9, mu=0.0))  # -1.32393853
 
 
@@ -166,7 +166,7 @@ def logp(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs) ->
         print(exp_rv_logp.eval({value: 0.9, mu: 0.0}))  # -0.81912844
 
         # Compile a function for repeated evaluations
-        exp_rv_logp_fn = pm.compile_pymc([value, mu], exp_rv_logp)
+        exp_rv_logp_fn = pm.compile([value, mu], exp_rv_logp)
         print(exp_rv_logp_fn(value=0.9, mu=0.0))  # -0.81912844
 
 
@@ -244,7 +244,7 @@ def logcdf(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs)
         print(rv_logcdf.eval({value: 0.9, mu: 0.0}))  # -0.2034146
 
         # Compile a function for repeated evaluations
-        rv_logcdf_fn = pm.compile_pymc([value, mu], rv_logcdf)
+        rv_logcdf_fn = pm.compile([value, mu], rv_logcdf)
         print(rv_logcdf_fn(value=0.9, mu=0.0))  # -0.2034146
 
 
@@ -266,7 +266,7 @@ def logcdf(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs)
         print(exp_rv_logcdf.eval({value: 0.9, mu: 0.0}))  # -0.78078813
 
         # Compile a function for repeated evaluations
-        exp_rv_logcdf_fn = pm.compile_pymc([value, mu], exp_rv_logcdf)
+        exp_rv_logcdf_fn = pm.compile([value, mu], exp_rv_logcdf)
         print(exp_rv_logcdf_fn(value=0.9, mu=0.0))  # -0.78078813
 
 
@@ -349,7 +349,7 @@ def logccdf(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs)
         print(rv_logccdf.eval({value: 0.9, mu: 0.0}))  # -1.5272506
 
         # Compile a function for repeated evaluations
-        rv_logccdf_fn = pm.compile_pymc([value, mu], rv_logccdf)
+        rv_logccdf_fn = pm.compile([value, mu], rv_logccdf)
         print(rv_logccdf_fn(value=0.9, mu=0.0))  # -1.5272506
 
     """
@@ -410,7 +410,7 @@ def icdf(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs) ->
         print(rv_icdf.eval({value: 0.9, mu: 0.0}))  # 1.28155157
 
         # Compile a function for repeated evaluations
-        rv_icdf_fn = pm.compile_pymc([value, mu], rv_icdf)
+        rv_icdf_fn = pm.compile([value, mu], rv_icdf)
         print(rv_icdf_fn(value=0.9, mu=0.0))  # 1.28155157
 
 
@@ -432,7 +432,7 @@ def icdf(rv: Variable, value: Variable | TensorLike, warn_rvs=True, **kwargs) ->
         print(exp_rv_icdf.eval({value: 0.9, mu: 0.0}))  # 3.60222448
 
         # Compile a function for repeated evaluations
-        exp_rv_icdf_fn = pm.compile_pymc([value, mu], exp_rv_icdf)
+        exp_rv_icdf_fn = pm.compile([value, mu], exp_rv_icdf)
         print(exp_rv_icdf_fn(value=0.9, mu=0.0))  # 3.60222448
 
     """

pymc/model/core.py

@@ -1638,7 +1638,7 @@ def compile_fn(
             Whether to wrap the compiled function in a PointFunc, which takes a Point
             dictionary with model variable names and values as input.
         Other keyword arguments :
-            Any other keyword argument is sent to :py:func:`pymc.pytensorf.compile_pymc`.
+            Any other keyword argument is sent to :py:func:`pymc.pytensorf.compile`.
 
         Returns
         -------

pymc/sampling/forward.py

@@ -294,7 +294,7 @@ def draw(
     random_seed : int, RandomState or numpy_Generator, optional
         Seed for the random number generator.
     **kwargs : dict, optional
-        Keyword arguments for :func:`pymc.pytensorf.compile_pymc`.
+        Keyword arguments for :func:`pymc.pytensorf.compile`.
 
     Returns
     -------
@@ -410,7 +410,7 @@ def sample_prior_predictive(
     idata_kwargs : dict, optional
         Keyword arguments for :func:`pymc.to_inference_data`
     compile_kwargs: dict, optional
-        Keyword arguments for :func:`pymc.pytensorf.compile_pymc`.
+        Keyword arguments for :func:`pymc.pytensorf.compile`.
     samples : int
         Number of samples from the prior predictive to generate. Deprecated in favor of `draws`.
 
@@ -580,7 +580,7 @@ def sample_posterior_predictive(
         Keyword arguments for :func:`pymc.to_inference_data` if ``predictions=False`` or to
         :func:`pymc.predictions_to_inference_data` otherwise.
     compile_kwargs: dict, optional
-        Keyword arguments for :func:`pymc.pytensorf.compile_pymc`.
+        Keyword arguments for :func:`pymc.pytensorf.compile`.
 
     Returns
     -------