xhs-note-crawling/python/py/Lib/site-packages/networkx/algorithms/community/lukes.py

"""Lukes Algorithm for exact optimal weighted tree partitioning."""

from copy import deepcopy
from functools import lru_cache
from random import choice

import networkx as nx
from networkx.utils import not_implemented_for

__all__ = ["lukes_partitioning"]

D_EDGE_W = "weight"
D_EDGE_VALUE = 1.0
D_NODE_W = "weight"
D_NODE_VALUE = 1
PKEY = "partitions"
CLUSTER_EVAL_CACHE_SIZE = 2048


def _split_n_from(n, min_size_of_first_part):
    # splits j in two parts of which the first is at least
    # the second argument
    assert n >= min_size_of_first_part
    for p1 in range(min_size_of_first_part, n + 1):
        yield p1, n - p1


@nx._dispatchable(node_attrs="node_weight", edge_attrs="edge_weight")
def lukes_partitioning(G, max_size, node_weight=None, edge_weight=None):
    """Optimal partitioning of a weighted tree using the Lukes algorithm.

    This algorithm partitions a connected, acyclic graph featuring integer
    node weights and float edge weights. The resulting clusters are such
    that the total weight of the nodes in each cluster does not exceed
    max_size and that the weight of the edges that are cut by the partition
    is minimum. The algorithm is based on [1]_.

    Parameters
    ----------
    G : NetworkX graph

    max_size : int
        Maximum weight a partition can have in terms of sum of
        node_weight for all nodes in the partition

    edge_weight : key
        Edge data key to use as weight. If None, the weights are all
        set to one.

    node_weight : key
        Node data key to use as weight. If None, the weights are all
        set to one. The data must be int.

    Returns
    -------
    partition : list
        A list of sets of nodes representing the clusters of the
        partition.

    Raises
    ------
    NotATree
        If G is not a tree.
    TypeError
        If any of the values of node_weight is not int.

    References
    ----------
    .. [1] Lukes, J. A. (1974).
       "Efficient Algorithm for the Partitioning of Trees."
       IBM Journal of Research and Development, 18(3), 217–224.

    """
    # First sanity check and tree preparation
    if not nx.is_tree(G):
        raise nx.NotATree("lukes_partitioning works only on trees")
    else:
        if nx.is_directed(G):
            root = [n for n, d in G.in_degree() if d == 0]
            assert len(root) == 1
            root = root[0]
            t_G = deepcopy(G)
        else:
            root = choice(list(G.nodes))
            # this has the desirable side effect of not inheriting attributes
            t_G = nx.dfs_tree(G, root)

    # Since we do not want to screw up the original graph,
    # if we have a blank attribute, we make a deepcopy
    if edge_weight is None or node_weight is None:
        safe_G = deepcopy(G)
        if edge_weight is None:
            nx.set_edge_attributes(safe_G, D_EDGE_VALUE, D_EDGE_W)
            edge_weight = D_EDGE_W
        if node_weight is None:
            nx.set_node_attributes(safe_G, D_NODE_VALUE, D_NODE_W)
            node_weight = D_NODE_W
    else:
        safe_G = G

    # Second sanity check
    # The values of node_weight MUST BE int.
    # I cannot see any room for duck typing without incurring serious
    # danger of subtle bugs.
    all_n_attr = nx.get_node_attributes(safe_G, node_weight).values()
    for x in all_n_attr:
        if not isinstance(x, int):
            raise TypeError(
                "lukes_partitioning needs integer "
                f"values for node_weight ({node_weight})"
            )

    # SUBROUTINES -----------------------
    # these functions are defined here for two reasons:
    # - brevity: we can leverage global "safe_G"
    # - caching: signatures are hashable

    @not_implemented_for("undirected")
    # this is intended to be called only on t_G
    def _leaves(gr):
        for x in gr.nodes:
            if not nx.descendants(gr, x):
                yield x

    @not_implemented_for("undirected")
    def _a_parent_of_leaves_only(gr):
        tleaves = set(_leaves(gr))
        for n in set(gr.nodes) - tleaves:
            if all(x in tleaves for x in nx.descendants(gr, n)):
                return n

    @lru_cache(CLUSTER_EVAL_CACHE_SIZE)
    def _value_of_cluster(cluster):
        valid_edges = [e for e in safe_G.edges if e[0] in cluster and e[1] in cluster]
        return sum(safe_G.edges[e][edge_weight] for e in valid_edges)

    def _value_of_partition(partition):
        return sum(_value_of_cluster(frozenset(c)) for c in partition)

    @lru_cache(CLUSTER_EVAL_CACHE_SIZE)
    def _weight_of_cluster(cluster):
        return sum(safe_G.nodes[n][node_weight] for n in cluster)

    def _pivot(partition, node):
        ccx = [c for c in partition if node in c]
        assert len(ccx) == 1
        return ccx[0]

    def _concatenate_or_merge(partition_1, partition_2, x, i, ref_weight):
        ccx = _pivot(partition_1, x)
        cci = _pivot(partition_2, i)
        merged_xi = ccx.union(cci)

        # We first check if we can do the merge.
        # If so, we do the actual calculations, otherwise we concatenate
        if _weight_of_cluster(frozenset(merged_xi)) <= ref_weight:
            cp1 = list(filter(lambda x: x != ccx, partition_1))
            cp2 = list(filter(lambda x: x != cci, partition_2))

            option_2 = [merged_xi] + cp1 + cp2
            return option_2, _value_of_partition(option_2)
        else:
            option_1 = partition_1 + partition_2
            return option_1, _value_of_partition(option_1)

    # INITIALIZATION -----------------------
    leaves = set(_leaves(t_G))
    for lv in leaves:
        t_G.nodes[lv][PKEY] = {}
        slot = safe_G.nodes[lv][node_weight]
        t_G.nodes[lv][PKEY][slot] = [{lv}]
        t_G.nodes[lv][PKEY][0] = [{lv}]

    for inner in [x for x in t_G.nodes if x not in leaves]:
        t_G.nodes[inner][PKEY] = {}
        slot = safe_G.nodes[inner][node_weight]
        t_G.nodes[inner][PKEY][slot] = [{inner}]
    nx._clear_cache(t_G)

    # CORE ALGORITHM -----------------------
    while True:
        x_node = _a_parent_of_leaves_only(t_G)
        weight_of_x = safe_G.nodes[x_node][node_weight]
        best_value = 0
        best_partition = None
        bp_buffer = {}
        x_descendants = nx.descendants(t_G, x_node)
        for i_node in x_descendants:
            for j in range(weight_of_x, max_size + 1):
                for a, b in _split_n_from(j, weight_of_x):
                    if (
                        a not in t_G.nodes[x_node][PKEY]
                        or b not in t_G.nodes[i_node][PKEY]
                    ):
                        # it's not possible to form this particular weight sum
                        continue

                    part1 = t_G.nodes[x_node][PKEY][a]
                    part2 = t_G.nodes[i_node][PKEY][b]
                    part, value = _concatenate_or_merge(part1, part2, x_node, i_node, j)

                    if j not in bp_buffer or bp_buffer[j][1] < value:
                        # we annotate in the buffer the best partition for j
                        bp_buffer[j] = part, value

                    # we also keep track of the overall best partition
                    if best_value <= value:
                        best_value = value
                        best_partition = part

            # as illustrated in Lukes, once we finished a child, we can
            # discharge the partitions we found into the graph
            # (the key phrase is make all x == x')
            # so that they are used by the subsequent children
            for w, (best_part_for_vl, vl) in bp_buffer.items():
                t_G.nodes[x_node][PKEY][w] = best_part_for_vl
            bp_buffer.clear()

        # the absolute best partition for this node
        # across all weights has to be stored at 0
        t_G.nodes[x_node][PKEY][0] = best_partition
        t_G.remove_nodes_from(x_descendants)

        if x_node == root:
            # the 0-labeled partition of root
            # is the optimal one for the whole tree
            return t_G.nodes[root][PKEY][0]