def build_forest()

in eland/ml/transformers/xgboost.py [0:0]

• 17 lines of code
• 6 McCabe index (conditional complexity)

    def build_forest(self) -> List[Tree]:
        """
        This builds out the forest of trees as described by XGBoost into a format
        supported by Elasticsearch

        :return: A list of Tree objects
        """
        tree_table: pd.DataFrame = self._model.trees_to_dataframe()
        transformed_trees = []
        curr_tree: Optional[Any] = None
        tree_nodes: List[TreeNode] = []
        for _, row in tree_table.iterrows():
            if row["Tree"] != curr_tree:
                if len(tree_nodes) > 0:
                    transformed_trees.append(self.build_tree(tree_nodes))
                curr_tree = row["Tree"]
                tree_nodes = []
            tree_nodes.append(self.build_tree_node(row, curr_tree))
            # add last tree
        if len(tree_nodes) > 0:
            transformed_trees.append(self.build_tree(tree_nodes))
        # We add this stump as XGBoost adds the base_score to the regression outputs
        if self._objective.partition(":")[0] in ["reg", "rank"]:
            transformed_trees.append(self.build_base_score_stump())
        return transformed_trees