From 79200d2bec04c8b843ad8862344ea0167e7e9cf9 Mon Sep 17 00:00:00 2001
From: ricardoV94 <ricardo.vieira1994@gmail.com>
Date: Tue, 8 Oct 2024 12:57:01 +0200
Subject: [PATCH] Do not use initval in test model

PRs https://github.com/pymc-devs/pymc/pull/7508 and https://github.com/pymc-devs/pymc/pull/7492 introduced incompatible changes but were not tested simultaneously.

Deepcopying the steps in the tests leads to deepcopying the model which uses `clone_model`, which in turn does not support initvals.
---
 tests/models.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/tests/models.py b/tests/models.py
index b66c1dc67d..fd45fb8bdb 100644
--- a/tests/models.py
+++ b/tests/models.py
@@ -18,7 +18,6 @@
 import pytensor
 import pytensor.tensor as pt
 
-from pytensor import config
 from pytensor.compile.ops import as_op
 
 import pymc as pm
@@ -30,9 +29,9 @@ def simple_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal("x", mu, tau=tau, size=2, initval=np.array([0.1, 0.1]).astype(config.floatX))
+        x = Normal("x", mu, tau=tau, size=2)
 
-    return model.initial_point(), model, (mu, tau**-0.5)
+    return {"x": np.array([0.1, 0.1], dtype=x.type.dtype)}, model, (mu, tau**-0.5)
 
 
 def another_simple_model():
@@ -46,11 +45,11 @@ def simple_categorical():
     p = np.array([0.1, 0.2, 0.3, 0.4])
     v = np.array([0.0, 1.0, 2.0, 3.0])
     with Model() as model:
-        Categorical("x", p, size=3, initval=[1, 2, 3])
+        x = Categorical("x", p, size=3)
 
     mu = np.dot(p, v)
     var = np.dot(p, (v - mu) ** 2)
-    return model.initial_point(), model, (mu, var)
+    return {"x": np.array([1, 2, 3], dtype=x.type.dtype)}, model, (mu, var)
 
 
 def multidimensional_model():
@@ -98,15 +97,14 @@ def mv_simple():
     p = np.array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
     tau = np.dot(p, p.T)
     with pm.Model() as model:
-        pm.MvNormal(
+        x = pm.MvNormal(
             "x",
             pt.constant(mu),
             tau=pt.constant(tau),
-            initval=np.array([0.1, 1.0, 0.8]),
         )
     H = tau
     C = np.linalg.inv(H)
-    return model.initial_point(), model, (mu, C)
+    return {"x": np.array([0.1, 1.0, 0.8], dtype=x.type.dtype)}, model, (mu, C)
 
 
 def mv_simple_coarse():