### view env/lib/python3.9/site-packages/networkx/algorithms/community/lukes.py @ 0:4f3585e2f14bdraftdefaulttip

author shellac Mon, 22 Mar 2021 18:12:50 +0000
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"""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: int, min_size_of_first_part: int):
# 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

def lukes_partitioning(G, max_size: int, node_weight=None, edge_weight=None) -> list:

"""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 LUKES.

Parameters
----------
G : 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
----------
.. 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
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: frozenset):
valid_edges = [e for e in safe_G.edges if e in cluster and e in cluster]
return sum([safe_G.edges[e][edge_weight] for e in valid_edges])

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

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

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

def _concatenate_or_merge(partition_1: list, partition_2: list, x, i, ref_weigth):

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_weigth:
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] = dict()
slot = safe_G.nodes[lv][node_weight]
t_G.nodes[lv][PKEY][slot] = [{lv}]
t_G.nodes[lv][PKEY] = [{lv}]

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

# 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 = dict()
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].keys()
or b not in t_G.nodes[i_node][PKEY].keys()
):
# 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.keys() or bp_buffer[j] < 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] = 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]
```