Merge branch 'main' of https://github.com/pymc-devs/pymc-bart

Author: aloctavodia

.pre-commit-config.yaml

@@ -12,14 +12,14 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.4
+    rev: v0.6.1
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]
       - id: ruff-format
         args: ["--line-length=100"]
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.0
+    rev: v1.11.1
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

pymc_bart/__init__.py

@@ -36,7 +36,7 @@
     "plot_pdp",
     "plot_variable_importance",
 ]
-__version__ = "0.5.14"
+__version__ = "0.6.0"
 
 
 pm.STEP_METHODS = list(pm.STEP_METHODS) + [PGBART]

pymc_bart/pgbart.py

@@ -138,6 +138,11 @@ def __init__(  # noqa: PLR0915
         else:
             self.X = self.bart.X
 
+        if isinstance(self.bart.Y, Variable):
+            self.Y = self.bart.Y.eval()
+        else:
+            self.Y = self.bart.Y
+
         self.missing_data = np.any(np.isnan(self.X))
         self.m = self.bart.m
         self.response = self.bart.response
@@ -166,26 +171,26 @@ def __init__(  # noqa: PLR0915
             if rule is ContinuousSplitRule:
                 self.X[:, idx] = jitter_duplicated(self.X[:, idx], np.nanstd(self.X[:, idx]))
 
-        init_mean = self.bart.Y.mean()
+        init_mean = self.Y.mean()
         self.num_observations = self.X.shape[0]
         self.num_variates = self.X.shape[1]
         self.available_predictors = list(range(self.num_variates))
 
         # if data is binary
         self.leaf_sd = np.ones((self.trees_shape, self.leaves_shape))
 
-        y_unique = np.unique(self.bart.Y)
+        y_unique = np.unique(self.Y)
         if y_unique.size == 2 and np.all(y_unique == [0, 1]):
             self.leaf_sd *= 3 / self.m**0.5
         else:
-            self.leaf_sd *= self.bart.Y.std() / self.m**0.5
+            self.leaf_sd *= self.Y.std() / self.m**0.5
 
         self.running_sd = [
             RunningSd((self.leaves_shape, self.num_observations)) for _ in range(self.trees_shape)
         ]
 
         self.sum_trees = np.full(
-            (self.trees_shape, self.leaves_shape, self.bart.Y.shape[0]), init_mean
+            (self.trees_shape, self.leaves_shape, self.Y.shape[0]), init_mean
         ).astype(config.floatX)
         self.sum_trees_noi = self.sum_trees - init_mean
         self.a_tree = Tree.new_tree(