FIX Make decision tree pickles deterministic (#27580)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

doc/whats_new/v1.3.rst

@@ -2,6 +2,23 @@
 
 .. currentmodule:: sklearn
 
+.. _changes_1_3_2:
+
+Version 1.3.2
+=============
+
+**October 2023**
+
+Changelog
+---------
+
+:mod:`sklearn.tree`
+...................
+
+- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make
+  the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève <lesteve>`.
+
+
 .. _changes_1_3_1:
 
 Version 1.3.1

sklearn/tree/_tree.pyx

@@ -908,11 +908,13 @@ cdef class Tree:
         safe_realloc(&self.nodes, capacity)
         safe_realloc(&self.value, capacity * self.value_stride)
 
-        # value memory is initialised to 0 to enable classifier argmax
         if capacity > self.capacity:
+            # value memory is initialised to 0 to enable classifier argmax
             memset(<void*>(self.value + self.capacity * self.value_stride), 0,
                    (capacity - self.capacity) * self.value_stride *
                    sizeof(float64_t))
+            # node memory is initialised to 0 to ensure deterministic pickle (padding in Node struct)
+            memset(<void*>(self.nodes + self.capacity), 0, (capacity - self.capacity) * sizeof(Node))
 
         # if capacity smaller than node_count, adjust the counter
         if capacity < self.node_count:

sklearn/tree/tests/test_tree.py

@@ -2601,3 +2601,16 @@ def test_sample_weight_non_uniform(make_data, Tree):
     tree_samples_removed.fit(X[1::2, :], y[1::2])
 
     assert_allclose(tree_samples_removed.predict(X), tree_with_sw.predict(X))
+
+
+def test_deterministic_pickle():
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/27268
+    # Uninitialised memory would lead to the two pickle strings being different.
+    tree1 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+    tree2 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+
+    pickle1 = pickle.dumps(tree1)
+    pickle2 = pickle.dumps(tree2)
+
+    assert pickle1 == pickle2