update node refactor more baby steps

Author: SamuelCarliles3

sklearn/tree/_tree.pyx

@@ -479,132 +479,7 @@ cdef class DepthFirstTreeBuilder(TreeBuilder):
             self._build_body(tree, splitter, &e, 1)
 
             e.target_stack = &e.builder_stack
-            while not e.target_stack.empty():
-                e.stack_record = e.target_stack.top()
-                e.target_stack.pop()
-
-                e.start = e.stack_record.start
-                e.end = e.stack_record.end
-                e.depth = e.stack_record.depth
-                e.parent = e.stack_record.parent
-                e.is_left = e.stack_record.is_left
-                e.parent_record.impurity = e.stack_record.impurity
-                e.parent_record.n_constant_features = e.stack_record.n_constant_features
-                e.parent_record.lower_bound = e.stack_record.lower_bound
-                e.parent_record.upper_bound = e.stack_record.upper_bound
-
-                e.n_node_samples = e.end - e.start
-                splitter.node_reset(e.start, e.end, &e.weighted_n_node_samples)
-
-                e.is_leaf = (e.depth >= e.max_depth or
-                           e.n_node_samples < e.min_samples_split or
-                           e.n_node_samples < 2 * e.min_samples_leaf or
-                           e.weighted_n_node_samples < 2 * e.min_weight_leaf)
-
-                if e.first:
-                    e.parent_record.impurity = splitter.node_impurity()
-                    e.first=0
-
-                # impurity == 0 with tolerance due to rounding errors
-                e.is_leaf = e.is_leaf or e.parent_record.impurity <= EPSILON
-
-                if not e.is_leaf:
-                    splitter.node_split(
-                        &e.parent_record,
-                        e.split,
-                    )
-
-                    # If EPSILON=0 in the below comparison, float precision
-                    # issues stop splitting, producing trees that are
-                    # dissimilar to v0.18
-                    e.is_leaf = (e.is_leaf or e.split.pos >= e.end or
-                               (e.split.improvement + EPSILON <
-                                e.min_impurity_decrease))
-
-                e.node_id = tree._add_node(
-                    e.parent, e.is_left, e.is_leaf, e.split,
-                    e.parent_record.impurity, e.n_node_samples,
-                    e.weighted_n_node_samples, e.split.missing_go_to_left
-                )
-
-                if e.node_id == INTPTR_MAX:
-                    e.rc = -1
-                    break
-
-                # Store value for all nodes, to facilitate tree/model
-                # inspection and interpretation
-                splitter.node_value(tree.value + e.node_id * tree.value_stride)
-                if splitter.with_monotonic_cst:
-                    splitter.clip_node_value(
-                        tree.value + e.node_id * tree.value_stride,
-                        e.parent_record.lower_bound,
-                        e.parent_record.upper_bound
-                    )
-
-                if not e.is_leaf:
-                    if (
-                        not splitter.with_monotonic_cst or
-                        splitter.monotonic_cst[e.split.feature] == 0
-                    ):
-                        # Split on a feature with no monotonicity constraint
-
-                        # Current bounds must always be propagated to both children.
-                        # If a monotonic constraint is active, bounds are used in
-                        # node value clipping.
-                        e.left_child_min = e.right_child_min = e.parent_record.lower_bound
-                        e.left_child_max = e.right_child_max = e.parent_record.upper_bound
-                    elif splitter.monotonic_cst[e.split.feature] == 1:
-                        # Split on a feature with monotonic increase constraint
-                        e.left_child_min = e.parent_record.lower_bound
-                        e.right_child_max = e.parent_record.upper_bound
-
-                        # Lower bound for right child and upper bound for left child
-                        # are set to the same value.
-                        e.middle_value = splitter.criterion.middle_value()
-                        e.right_child_min = e.middle_value
-                        e.left_child_max = e.middle_value
-                    else:  # i.e. splitter.monotonic_cst[e.split.feature] == -1
-                        # Split on a feature with monotonic decrease constraint
-                        e.right_child_min = e.parent_record.lower_bound
-                        e.left_child_max = e.parent_record.upper_bound
-
-                        # Lower bound for left child and upper bound for right child
-                        # are set to the same value.
-                        e.middle_value = splitter.criterion.middle_value()
-                        e.left_child_min = e.middle_value
-                        e.right_child_max = e.middle_value
-
-                    # Push right child on stack
-                    e.builder_stack.push({
-                        "start": e.split.pos,
-                        "end": e.end,
-                        "depth": e.depth + 1,
-                        "parent": e.node_id,
-                        "is_left": 0,
-                        "impurity": e.split.impurity_right,
-                        "n_constant_features": e.parent_record.n_constant_features,
-                        "lower_bound": e.right_child_min,
-                        "upper_bound": e.right_child_max,
-                    })
-
-                    # Push left child on stack
-                    e.builder_stack.push({
-                        "start": e.start,
-                        "end": e.split.pos,
-                        "depth": e.depth + 1,
-                        "parent": e.node_id,
-                        "is_left": 1,
-                        "impurity": e.split.impurity_left,
-                        "n_constant_features": e.parent_record.n_constant_features,
-                        "lower_bound": e.left_child_min,
-                        "upper_bound": e.left_child_max,
-                    })
-                elif e.store_leaf_values and e.is_leaf:
-                    # copy leaf values to leaf_values array
-                    splitter.node_samples(tree.value_samples[e.node_id])
-
-                if e.depth > e.max_depth_seen:
-                    e.max_depth_seen = e.depth
+            self._build_body(tree, splitter, &e, 0)
 
             if e.rc >= 0:
                 e.rc = tree._resize_c(tree.node_count)