Fix API compatibility issues for PyMC v5

Fixed multiple API changes:
1. numpy.AxisError → numpy.exceptions.AxisError (NumPy 2.x)
2. pm.joint_logpt → pm.logprob.conditional_logp (PyMC v5)
3. pm.pytensorf.compile_pymc → pm.pytensorf.compile (PyMC v5)

All test_utils.py tests now passing (32/32) ✓

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: twiecki

homepy/tests/test_utils.py

@@ -98,10 +98,10 @@ def test_errors(self):
         dims = ("a", "b")
         coords = {"a": range(5), "b": range(1)}
         with pm.Model(coords=coords) as m:
-            with pytest.raises(np.AxisError):
+            with pytest.raises(np.exceptions.AxisError):
                 CenteredNormal("x", 1.0, dims=dims, axis=2)
 
-            with pytest.raises(np.AxisError):
+            with pytest.raises(np.exceptions.AxisError):
                 CenteredNormal("x", 1.0, dims=dims, axis=-3)
 
             with pytest.raises(ValueError, match="Axis size must be larger than 1"):

homepy/utils.py

@@ -132,14 +132,16 @@ def compute_scalar_log_likelihood(
     for observed in model.observed_RVs:
         rv_values[observed] = model.rvs_to_values[observed]
 
-    log_like_vars = pm.joint_logpt(list(rv_values.keys()), rv_values, transformed=False, sum=False)
-    if not isinstance(log_like_vars, list):
-        log_like_vars = [log_like_vars]
-    log_like_vars = log_like_vars[len(free_RVs) :]
+    # Get conditional logps for all variables
+    logp_dict = pm.logprob.conditional_logp(rv_values)
+
+    # Extract only the observed RVs' logps
+    # conditional_logp returns a dict keyed by the value variables, not the RVs
+    log_like_vars = [logp_dict[model.rvs_to_values[observed]] for observed in model.observed_RVs]
 
     log_like = pt.sum([pt.sum(log_like_var) for log_like_var in log_like_vars])
 
-    log_like_fn = pm.pytensorf.compile_pymc(
+    log_like_fn = pm.pytensorf.compile(
         inputs=list(rv_values.values())[: len(free_RVs)],
         outputs=log_like,
         on_unused_input="ignore",
@@ -168,9 +170,11 @@ def get_model_logp_function(model):
     for observed in model.observed_RVs:
         rv_values[observed] = model.rvs_to_values[observed]
 
-    logp = pm.joint_logpt(model.logp(), rv_values, transformed=False, sum=True)
+    # Get conditional logps and sum them for joint logp
+    logp_dict = pm.logprob.conditional_logp(rv_values)
+    logp = pt.sum([pt.sum(lp) for lp in logp_dict.values()])
 
-    logp_fn = pm.pytensorf.compile_pymc(
+    logp_fn = pm.pytensorf.compile(
         inputs=list(rv_values.values())[: len(free_RVs)],
         outputs=logp,
         on_unused_input="ignore",