shapiq.explainer.tree.conversion.edges#
This module contains the conversion functions to parse a tree model into the edge representation. The edge representation is used by the TreeSHAP-IQ algorithm to compute the interaction values of a tree-based model.
Functions
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Extracts edge information recursively from the tree information. |
- shapiq.explainer.tree.conversion.edges.create_edge_tree(children_left, children_right, features, node_sample_weight, values, n_nodes, n_features, max_interaction, subset_updates_pos_store)[source]#
Extracts edge information recursively from the tree information.
Parses the tree recursively to create an edge-based representation of the tree. It precalculates the p_e and p_e_ancestors of the interaction subsets up to order ‘max_interaction’.
- Parameters:
children_left (np.ndarray[int]) – The left children of each node. Leaf nodes are denoted with -1.
children_right (np.ndarray[int]) – The right children of each node. Leaf nodes are denoted with -1.
features (np.ndarray[int]) – The feature used for splitting at each node. Leaf nodes have the value -2.
node_sample_weight (np.ndarray[float]) – The sample weights of the tree.
values (np.ndarray[float]) – The output values at the leaf values of the tree.
n_nodes (int) – The number of nodes in the tree.
n_features (int) – The number of features of the dataset.
max_interaction (int, optional) – The maximum interaction order to be computed. An interaction order of 1 corresponds to the Shapley value. Any value higher than 1 computes the Shapley interactions values up to that order. Defaults to 1 (i.e. SV).
subset_updates_pos_store (dict[int, np.ndarray[int]]) – A dictionary containing the interaction subsets for each feature given an interaction order.
- Returns:
A dataclass containing the edge information of the tree.
- Return type: