Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.similarity

# -*- coding: utf-8 -*-
#    Copyright (C) 2010 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
#
# Author:  Andrey Paramonov <paramon@acdlabs.ru>
""" Functions measuring similarity using graph edit distance.

The graph edit distance is the number of edge/node changes needed
to make two graphs isomorphic.

The default algorithm/implementation is sub-optimal for some graphs.
The problem of finding the exact Graph Edit Distance (GED) is NP-hard
so it is often slow. If the simple interface graph_edit_distance
takes too long for your graph, try optimize_graph_edit_distance
and/or optimize_edit_paths.

At the same time, I encourage capable people to investigate
alternative GED algorithms, in order to improve the choices available.
"""
from __future__ import print_function
import math
import networkx as nx
from operator import *
import sys

__author__ = 'Andrey Paramonov <paramon@acdlabs.ru>'

__all__ = [
'graph_edit_distance',
'optimal_edit_paths',
'optimize_graph_edit_distance',
'optimize_edit_paths'
]

def debug_print(*args, **kwargs):
print(*args, **kwargs)

[docs]def graph_edit_distance(G1, G2, node_match=None, edge_match=None,
node_subst_cost=None, node_del_cost=None,
node_ins_cost=None,
edge_subst_cost=None, edge_del_cost=None,
edge_ins_cost=None,
upper_bound=None):
"""Returns GED (graph edit distance) between graphs G1 and G2.

Graph edit distance is a graph similarity measure analogous to
Levenshtein distance for strings.  It is defined as minimum cost
of edit path (sequence of node and edge edit operations)
transforming graph G1 to graph isomorphic to G2.

Parameters
----------
G1, G2: graphs
The two graphs G1 and G2 must be of the same type.

node_match : callable
A function that returns True if node n1 in G1 and n2 in G2
should be considered equal during matching.

The function will be called like

node_match(G1.nodes[n1], G2.nodes[n2]).

That is, the function will receive the node attribute
dictionaries for n1 and n2 as inputs.

Ignored if node_subst_cost is specified.  If neither
node_match nor node_subst_cost are specified then node
attributes are not considered.

edge_match : callable
A function that returns True if the edge attribute dictionaries
for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
be considered equal during matching.

The function will be called like

edge_match(G1[u1][v1], G2[u2][v2]).

That is, the function will receive the edge attribute
dictionaries of the edges under consideration.

Ignored if edge_subst_cost is specified.  If neither
edge_match nor edge_subst_cost are specified then edge
attributes are not considered.

node_subst_cost, node_del_cost, node_ins_cost : callable
Functions that return the costs of node substitution, node
deletion, and node insertion, respectively.

The functions will be called like

node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
node_del_cost(G1.nodes[n1]),
node_ins_cost(G2.nodes[n2]).

That is, the functions will receive the node attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function node_subst_cost overrides node_match if specified.
If neither node_match nor node_subst_cost are specified then
default node substitution cost of 0 is used (node attributes
are not considered during matching).

If node_del_cost is not specified then default node deletion
cost of 1 is used.  If node_ins_cost is not specified then
default node insertion cost of 1 is used.

edge_subst_cost, edge_del_cost, edge_ins_cost : callable
Functions that return the costs of edge substitution, edge
deletion, and edge insertion, respectively.

The functions will be called like

edge_subst_cost(G1[u1][v1], G2[u2][v2]),
edge_del_cost(G1[u1][v1]),
edge_ins_cost(G2[u2][v2]).

That is, the functions will receive the edge attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function edge_subst_cost overrides edge_match if specified.
If neither edge_match nor edge_subst_cost are specified then
default edge substitution cost of 0 is used (edge attributes
are not considered during matching).

If edge_del_cost is not specified then default edge deletion
cost of 1 is used.  If edge_ins_cost is not specified then
default edge insertion cost of 1 is used.

upper_bound : numeric
Maximum edit distance to consider.  Return None if no edit
distance under or equal to upper_bound exists.

Examples
--------
>>> G1 = nx.cycle_graph(6)
>>> G2 = nx.wheel_graph(7)
>>> nx.graph_edit_distance(G1, G2)
7.0

See Also
--------
optimal_edit_paths, optimize_graph_edit_distance,

is_isomorphic (test for graph edit distance of 0)

References
----------
.. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
Martineau. An Exact Graph Edit Distance Algorithm for Solving
Pattern Recognition Problems. 4th International Conference on
Pattern Recognition Applications and Methods 2015, Jan 2015,
Lisbon, Portugal. 2015,
<10.5220/0005209202710278>. <hal-01168816>
https://hal.archives-ouvertes.fr/hal-01168816

"""
bestcost = None
for vertex_path, edge_path, cost in \
optimize_edit_paths(G1, G2, node_match, edge_match,
node_subst_cost, node_del_cost, node_ins_cost,
edge_subst_cost, edge_del_cost, edge_ins_cost,
upper_bound, True):
#assert bestcost is None or cost < bestcost
bestcost = cost
return bestcost

[docs]def optimal_edit_paths(G1, G2, node_match=None, edge_match=None,
node_subst_cost=None, node_del_cost=None,
node_ins_cost=None,
edge_subst_cost=None, edge_del_cost=None,
edge_ins_cost=None,
upper_bound=None):
"""Returns all minimum-cost edit paths transforming G1 to G2.

Graph edit path is a sequence of node and edge edit operations
transforming graph G1 to graph isomorphic to G2.  Edit operations
include substitutions, deletions, and insertions.

Parameters
----------
G1, G2: graphs
The two graphs G1 and G2 must be of the same type.

node_match : callable
A function that returns True if node n1 in G1 and n2 in G2
should be considered equal during matching.

The function will be called like

node_match(G1.nodes[n1], G2.nodes[n2]).

That is, the function will receive the node attribute
dictionaries for n1 and n2 as inputs.

Ignored if node_subst_cost is specified.  If neither
node_match nor node_subst_cost are specified then node
attributes are not considered.

edge_match : callable
A function that returns True if the edge attribute dictionaries
for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
be considered equal during matching.

The function will be called like

edge_match(G1[u1][v1], G2[u2][v2]).

That is, the function will receive the edge attribute
dictionaries of the edges under consideration.

Ignored if edge_subst_cost is specified.  If neither
edge_match nor edge_subst_cost are specified then edge
attributes are not considered.

node_subst_cost, node_del_cost, node_ins_cost : callable
Functions that return the costs of node substitution, node
deletion, and node insertion, respectively.

The functions will be called like

node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
node_del_cost(G1.nodes[n1]),
node_ins_cost(G2.nodes[n2]).

That is, the functions will receive the node attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function node_subst_cost overrides node_match if specified.
If neither node_match nor node_subst_cost are specified then
default node substitution cost of 0 is used (node attributes
are not considered during matching).

If node_del_cost is not specified then default node deletion
cost of 1 is used.  If node_ins_cost is not specified then
default node insertion cost of 1 is used.

edge_subst_cost, edge_del_cost, edge_ins_cost : callable
Functions that return the costs of edge substitution, edge
deletion, and edge insertion, respectively.

The functions will be called like

edge_subst_cost(G1[u1][v1], G2[u2][v2]),
edge_del_cost(G1[u1][v1]),
edge_ins_cost(G2[u2][v2]).

That is, the functions will receive the edge attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function edge_subst_cost overrides edge_match if specified.
If neither edge_match nor edge_subst_cost are specified then
default edge substitution cost of 0 is used (edge attributes
are not considered during matching).

If edge_del_cost is not specified then default edge deletion
cost of 1 is used.  If edge_ins_cost is not specified then
default edge insertion cost of 1 is used.

upper_bound : numeric
Maximum edit distance to consider.

Returns
-------
edit_paths : list of tuples (node_edit_path, edge_edit_path)
node_edit_path : list of tuples (u, v)
edge_edit_path : list of tuples ((u1, v1), (u2, v2))

cost : numeric
Optimal edit path cost (graph edit distance).

Examples
--------
>>> G1 = nx.cycle_graph(6)
>>> G2 = nx.wheel_graph(7)
>>> paths, cost = nx.optimal_edit_paths(G1, G2)
>>> len(paths)
84
>>> cost
7.0

See Also
--------
graph_edit_distance, optimize_edit_paths

References
----------
.. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
Martineau. An Exact Graph Edit Distance Algorithm for Solving
Pattern Recognition Problems. 4th International Conference on
Pattern Recognition Applications and Methods 2015, Jan 2015,
Lisbon, Portugal. 2015,
<10.5220/0005209202710278>. <hal-01168816>
https://hal.archives-ouvertes.fr/hal-01168816

"""
paths = list()
bestcost = None
for vertex_path, edge_path, cost in \
optimize_edit_paths(G1, G2, node_match, edge_match,
node_subst_cost, node_del_cost, node_ins_cost,
edge_subst_cost, edge_del_cost, edge_ins_cost,
upper_bound, False):
#assert bestcost is None or cost <= bestcost
if bestcost is not None and cost < bestcost:
paths = list()
paths.append((vertex_path, edge_path))
bestcost = cost
return paths, bestcost

[docs]def optimize_graph_edit_distance(G1, G2, node_match=None, edge_match=None,
node_subst_cost=None, node_del_cost=None,
node_ins_cost=None,
edge_subst_cost=None, edge_del_cost=None,
edge_ins_cost=None,
upper_bound=None):
"""Returns consecutive approximations of GED (graph edit distance)
between graphs G1 and G2.

Graph edit distance is a graph similarity measure analogous to
Levenshtein distance for strings.  It is defined as minimum cost
of edit path (sequence of node and edge edit operations)
transforming graph G1 to graph isomorphic to G2.

Parameters
----------
G1, G2: graphs
The two graphs G1 and G2 must be of the same type.

node_match : callable
A function that returns True if node n1 in G1 and n2 in G2
should be considered equal during matching.

The function will be called like

node_match(G1.nodes[n1], G2.nodes[n2]).

That is, the function will receive the node attribute
dictionaries for n1 and n2 as inputs.

Ignored if node_subst_cost is specified.  If neither
node_match nor node_subst_cost are specified then node
attributes are not considered.

edge_match : callable
A function that returns True if the edge attribute dictionaries
for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
be considered equal during matching.

The function will be called like

edge_match(G1[u1][v1], G2[u2][v2]).

That is, the function will receive the edge attribute
dictionaries of the edges under consideration.

Ignored if edge_subst_cost is specified.  If neither
edge_match nor edge_subst_cost are specified then edge
attributes are not considered.

node_subst_cost, node_del_cost, node_ins_cost : callable
Functions that return the costs of node substitution, node
deletion, and node insertion, respectively.

The functions will be called like

node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
node_del_cost(G1.nodes[n1]),
node_ins_cost(G2.nodes[n2]).

That is, the functions will receive the node attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function node_subst_cost overrides node_match if specified.
If neither node_match nor node_subst_cost are specified then
default node substitution cost of 0 is used (node attributes
are not considered during matching).

If node_del_cost is not specified then default node deletion
cost of 1 is used.  If node_ins_cost is not specified then
default node insertion cost of 1 is used.

edge_subst_cost, edge_del_cost, edge_ins_cost : callable
Functions that return the costs of edge substitution, edge
deletion, and edge insertion, respectively.

The functions will be called like

edge_subst_cost(G1[u1][v1], G2[u2][v2]),
edge_del_cost(G1[u1][v1]),
edge_ins_cost(G2[u2][v2]).

That is, the functions will receive the edge attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function edge_subst_cost overrides edge_match if specified.
If neither edge_match nor edge_subst_cost are specified then
default edge substitution cost of 0 is used (edge attributes
are not considered during matching).

If edge_del_cost is not specified then default edge deletion
cost of 1 is used.  If edge_ins_cost is not specified then
default edge insertion cost of 1 is used.

upper_bound : numeric
Maximum edit distance to consider.

Returns
-------
Generator of consecutive approximations of graph edit distance.

Examples
--------
>>> G1 = nx.cycle_graph(6)
>>> G2 = nx.wheel_graph(7)
>>> for v in nx.optimize_graph_edit_distance(G1, G2):
...     minv = v
>>> minv
7.0

See Also
--------
graph_edit_distance, optimize_edit_paths

References
----------
.. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
Martineau. An Exact Graph Edit Distance Algorithm for Solving
Pattern Recognition Problems. 4th International Conference on
Pattern Recognition Applications and Methods 2015, Jan 2015,
Lisbon, Portugal. 2015,
<10.5220/0005209202710278>. <hal-01168816>
https://hal.archives-ouvertes.fr/hal-01168816
"""
for vertex_path, edge_path, cost in \
optimize_edit_paths(G1, G2, node_match, edge_match,
node_subst_cost, node_del_cost, node_ins_cost,
edge_subst_cost, edge_del_cost, edge_ins_cost,
upper_bound, True):
yield cost

[docs]def optimize_edit_paths(G1, G2, node_match=None, edge_match=None,
node_subst_cost=None, node_del_cost=None,
node_ins_cost=None,
edge_subst_cost=None, edge_del_cost=None,
edge_ins_cost=None,
upper_bound=None, strictly_decreasing=True):
"""GED (graph edit distance) calculation: advanced interface.

Graph edit path is a sequence of node and edge edit operations
transforming graph G1 to graph isomorphic to G2.  Edit operations
include substitutions, deletions, and insertions.

Graph edit distance is defined as minimum cost of edit path.

Parameters
----------
G1, G2: graphs
The two graphs G1 and G2 must be of the same type.

node_match : callable
A function that returns True if node n1 in G1 and n2 in G2
should be considered equal during matching.

The function will be called like

node_match(G1.nodes[n1], G2.nodes[n2]).

That is, the function will receive the node attribute
dictionaries for n1 and n2 as inputs.

Ignored if node_subst_cost is specified.  If neither
node_match nor node_subst_cost are specified then node
attributes are not considered.

edge_match : callable
A function that returns True if the edge attribute dictionaries
for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
be considered equal during matching.

The function will be called like

edge_match(G1[u1][v1], G2[u2][v2]).

That is, the function will receive the edge attribute
dictionaries of the edges under consideration.

Ignored if edge_subst_cost is specified.  If neither
edge_match nor edge_subst_cost are specified then edge
attributes are not considered.

node_subst_cost, node_del_cost, node_ins_cost : callable
Functions that return the costs of node substitution, node
deletion, and node insertion, respectively.

The functions will be called like

node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
node_del_cost(G1.nodes[n1]),
node_ins_cost(G2.nodes[n2]).

That is, the functions will receive the node attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function node_subst_cost overrides node_match if specified.
If neither node_match nor node_subst_cost are specified then
default node substitution cost of 0 is used (node attributes
are not considered during matching).

If node_del_cost is not specified then default node deletion
cost of 1 is used.  If node_ins_cost is not specified then
default node insertion cost of 1 is used.

edge_subst_cost, edge_del_cost, edge_ins_cost : callable
Functions that return the costs of edge substitution, edge
deletion, and edge insertion, respectively.

The functions will be called like

edge_subst_cost(G1[u1][v1], G2[u2][v2]),
edge_del_cost(G1[u1][v1]),
edge_ins_cost(G2[u2][v2]).

That is, the functions will receive the edge attribute
dictionaries as inputs.  The functions are expected to return
positive numeric values.

Function edge_subst_cost overrides edge_match if specified.
If neither edge_match nor edge_subst_cost are specified then
default edge substitution cost of 0 is used (edge attributes
are not considered during matching).

If edge_del_cost is not specified then default edge deletion
cost of 1 is used.  If edge_ins_cost is not specified then
default edge insertion cost of 1 is used.

upper_bound : numeric
Maximum edit distance to consider.

strictly_decreasing : bool
If True, return consecutive approximations of strictly
decreasing cost.  Otherwise, return all edit paths of cost
less than or equal to the previous minimum cost.

Returns
-------
Generator of tuples (node_edit_path, edge_edit_path, cost)
node_edit_path : list of tuples (u, v)
edge_edit_path : list of tuples ((u1, v1), (u2, v2))
cost : numeric

See Also
--------
graph_edit_distance, optimize_graph_edit_distance, optimal_edit_paths

References
----------
.. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
Martineau. An Exact Graph Edit Distance Algorithm for Solving
Pattern Recognition Problems. 4th International Conference on
Pattern Recognition Applications and Methods 2015, Jan 2015,
Lisbon, Portugal. 2015,
<10.5220/0005209202710278>. <hal-01168816>
https://hal.archives-ouvertes.fr/hal-01168816

"""
# TODO: support DiGraph

import numpy as np
from scipy.optimize import linear_sum_assignment

class CostMatrix:
def __init__(self, C, lsa_row_ind, lsa_col_ind, ls):
#assert C.shape[0] == len(lsa_row_ind)
#assert C.shape[1] == len(lsa_col_ind)
#assert len(lsa_row_ind) == len(lsa_col_ind)
#assert set(lsa_row_ind) == set(range(len(lsa_row_ind)))
#assert set(lsa_col_ind) == set(range(len(lsa_col_ind)))
#assert ls == C[lsa_row_ind, lsa_col_ind].sum()
self.C = C
self.lsa_row_ind = lsa_row_ind
self.lsa_col_ind = lsa_col_ind
self.ls = ls

def make_CostMatrix(C, m, n):
#assert(C.shape == (m + n, m + n))
lsa_row_ind, lsa_col_ind = linear_sum_assignment(C)

# Fixup dummy assignments:
# each substitution i<->j should have dummy assignment m+j<->n+i
# NOTE: fast reduce of Cv relies on it
#assert len(lsa_row_ind) == len(lsa_col_ind)
indexes = zip(range(len(lsa_row_ind)), lsa_row_ind, lsa_col_ind)
subst_ind = list(k for k, i, j in indexes if i < m and j < n)
indexes = zip(range(len(lsa_row_ind)), lsa_row_ind, lsa_col_ind)
dummy_ind = list(k for k, i, j in indexes if i >= m and j >= n)
#assert len(subst_ind) == len(dummy_ind)
lsa_row_ind[dummy_ind] = lsa_col_ind[subst_ind] + m
lsa_col_ind[dummy_ind] = lsa_row_ind[subst_ind] + n

return CostMatrix(C, lsa_row_ind, lsa_col_ind,
C[lsa_row_ind, lsa_col_ind].sum())

def extract_C(C, i, j, m, n):
#assert(C.shape == (m + n, m + n))
row_ind = [k in i or k - m in j for k in range(m + n)]
col_ind = [k in j or k - n in i for k in range(m + n)]
return C[row_ind, :][:, col_ind]

def reduce_C(C, i, j, m, n):
#assert(C.shape == (m + n, m + n))
row_ind = [k not in i and k - m not in j for k in range(m + n)]
col_ind = [k not in j and k - n not in i for k in range(m + n)]
return C[row_ind, :][:, col_ind]

def reduce_ind(ind, i):
#assert set(ind) == set(range(len(ind)))
rind = ind[[k not in i for k in ind]]
for k in set(i):
rind[rind >= k] -= 1
return rind

def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=[]):
"""
Parameters:
u, v: matched vertices, u=None or v=None for
deletion/insertion
pending_g, pending_h: lists of edges not yet mapped
Ce: CostMatrix of pending edge mappings
matched_uv: partial vertex edit path
list of tuples (u, v) of previously matched vertex
mappings u<->v, u=None or v=None for
deletion/insertion

Returns:
list of (i, j): indices of edge mappings g<->h
localCe: local CostMatrix of edge mappings
(basically submatrix of Ce at cross of rows i, cols j)
"""
M = len(pending_g)
N = len(pending_h)
#assert Ce.C.shape == (M + N, M + N)

g_ind = [i for i in range(M) if pending_g[i][:2] == (u, u) or
any(pending_g[i][:2] in ((p, u), (u, p))
for p, q in matched_uv)]
h_ind = [j for j in range(N) if pending_h[j][:2] == (v, v) or
any(pending_h[j][:2] in ((q, v), (v, q))
for p, q in matched_uv)]
m = len(g_ind)
n = len(h_ind)

if m or n:
C = extract_C(Ce.C, g_ind, h_ind, M, N)
#assert C.shape == (m + n, m + n)

# Forbid structurally invalid matches
# NOTE: inf remembered from Ce construction
for k, i in zip(range(m), g_ind):
g = pending_g[i][:2]
for l, j in zip(range(n), h_ind):
h = pending_h[j][:2]
if nx.is_directed(G1) or nx.is_directed(G2):
if any(g == (p, u) and h == (q, v) or
g == (u, p) and h == (v, q)
for p, q in matched_uv):
continue
else:
if any(g in ((p, u), (u, p)) and h in ((q, v), (v, q))
for p, q in matched_uv):
continue
if g == (u, u):
continue
if h == (v, v):
continue
C[k, l] = inf

localCe = make_CostMatrix(C, m, n)
ij = list((g_ind[k] if k < m else M + h_ind[l],
h_ind[l] if l < n else N + g_ind[k])
for k, l in zip(localCe.lsa_row_ind, localCe.lsa_col_ind)
if k < m or l < n)

else:
ij = []
localCe = CostMatrix(np.empty((0, 0)), [], [], 0)

return ij, localCe

def reduce_Ce(Ce, ij, m, n):
if len(ij):
i, j = zip(*ij)
m_i = m - sum(1 for t in i if t < m)
n_j = n - sum(1 for t in j if t < n)
return make_CostMatrix(reduce_C(Ce.C, i, j, m, n), m_i, n_j)
else:
return Ce

def get_edit_ops(matched_uv, pending_u, pending_v, Cv,
pending_g, pending_h, Ce, matched_cost):
"""
Parameters:
matched_uv: partial vertex edit path
list of tuples (u, v) of vertex mappings u<->v,
u=None or v=None for deletion/insertion
pending_u, pending_v: lists of vertices not yet mapped
Cv: CostMatrix of pending vertex mappings
pending_g, pending_h: lists of edges not yet mapped
Ce: CostMatrix of pending edge mappings
matched_cost: cost of partial edit path

Returns:
sequence of
(i, j): indices of vertex mapping u<->v
Cv_ij: reduced CostMatrix of pending vertex mappings
(basically Cv with row i, col j removed)
list of (x, y): indices of edge mappings g<->h
Ce_xy: reduced CostMatrix of pending edge mappings
(basically Ce with rows x, cols y removed)
cost: total cost of edit operation
NOTE: most promising ops first
"""
m = len(pending_u)
n = len(pending_v)
#assert Cv.C.shape == (m + n, m + n)

# 1) a vertex mapping from optimal linear sum assignment
i, j = min((k, l) for k, l in zip(Cv.lsa_row_ind, Cv.lsa_col_ind)
if k < m or l < n)
xy, localCe = match_edges(pending_u[i] if i < m else None,
pending_v[j] if j < n else None,
pending_g, pending_h, Ce, matched_uv)
Ce_xy = reduce_Ce(Ce, xy, len(pending_g), len(pending_h))
#assert Ce.ls <= localCe.ls + Ce_xy.ls
if prune(matched_cost + Cv.ls + localCe.ls + Ce_xy.ls):
pass
else:
# get reduced Cv efficiently
Cv_ij = CostMatrix(reduce_C(Cv.C, (i,), (j,), m, n),
reduce_ind(Cv.lsa_row_ind, (i, m + j)),
reduce_ind(Cv.lsa_col_ind, (j, n + i)),
Cv.ls - Cv.C[i, j])
yield (i, j), Cv_ij, xy, Ce_xy, Cv.C[i, j] + localCe.ls

# 2) other candidates, sorted by lower-bound cost estimate
other = list()
fixed_i, fixed_j = i, j
if m <= n:
candidates = ((t, fixed_j) for t in range(m + n)
if t != fixed_i and (t < m or t == m + fixed_j))
else:
candidates = ((fixed_i, t) for t in range(m + n)
if t != fixed_j and (t < n or t == n + fixed_i))
for i, j in candidates:
if prune(matched_cost + Cv.C[i, j] + Ce.ls):
continue
Cv_ij = make_CostMatrix(reduce_C(Cv.C, (i,), (j,), m, n),
m - 1 if i < m else m,
n - 1 if j < n else n)
#assert Cv.ls <= Cv.C[i, j] + Cv_ij.ls
if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + Ce.ls):
continue
xy, localCe = match_edges(pending_u[i] if i < m else None,
pending_v[j] if j < n else None,
pending_g, pending_h, Ce, matched_uv)
if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + localCe.ls):
continue
Ce_xy = reduce_Ce(Ce, xy, len(pending_g), len(pending_h))
#assert Ce.ls <= localCe.ls + Ce_xy.ls
if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + localCe.ls +
Ce_xy.ls):
continue
other.append(((i, j), Cv_ij, xy, Ce_xy, Cv.C[i, j] + localCe.ls))

# yield from
for t in sorted(other, key=lambda t: t[4] + t[1].ls + t[3].ls):
yield t

def get_edit_paths(matched_uv, pending_u, pending_v, Cv,
matched_gh, pending_g, pending_h, Ce, matched_cost):
"""
Parameters:
matched_uv: partial vertex edit path
list of tuples (u, v) of vertex mappings u<->v,
u=None or v=None for deletion/insertion
pending_u, pending_v: lists of vertices not yet mapped
Cv: CostMatrix of pending vertex mappings
matched_gh: partial edge edit path
list of tuples (g, h) of edge mappings g<->h,
g=None or h=None for deletion/insertion
pending_g, pending_h: lists of edges not yet mapped
Ce: CostMatrix of pending edge mappings
matched_cost: cost of partial edit path

Returns:
sequence of (vertex_path, edge_path, cost)
vertex_path: complete vertex edit path
list of tuples (u, v) of vertex mappings u<->v,
u=None or v=None for deletion/insertion
edge_path: complete edge edit path
list of tuples (g, h) of edge mappings g<->h,
g=None or h=None for deletion/insertion
cost: total cost of edit path
NOTE: path costs are non-increasing
"""
#debug_print('matched-uv:', matched_uv)
#debug_print('matched-gh:', matched_gh)
#debug_print('matched-cost:', matched_cost)
#debug_print('pending-u:', pending_u)
#debug_print('pending-v:', pending_v)
#debug_print(Cv.C)
#assert list(sorted(G1.nodes)) == list(sorted(list(u for u, v in matched_uv if u is not None) + pending_u))
#assert list(sorted(G2.nodes)) == list(sorted(list(v for u, v in matched_uv if v is not None) + pending_v))
#debug_print('pending-g:', pending_g)
#debug_print('pending-h:', pending_h)
#debug_print(Ce.C)
#assert list(sorted(G1.edges)) == list(sorted(list(g for g, h in matched_gh if g is not None) + pending_g))
#assert list(sorted(G2.edges)) == list(sorted(list(h for g, h in matched_gh if h is not None) + pending_h))
#debug_print()

if prune(matched_cost + Cv.ls + Ce.ls):
return

if not max(len(pending_u), len(pending_v)):
#assert not len(pending_g)
#assert not len(pending_h)
# path completed!
#assert matched_cost <= maxcost.value
maxcost.value = min(maxcost.value, matched_cost)
yield matched_uv, matched_gh, matched_cost

else:
edit_ops = get_edit_ops(matched_uv, pending_u, pending_v, Cv,
pending_g, pending_h, Ce, matched_cost)
for ij, Cv_ij, xy, Ce_xy, edit_cost in edit_ops:
i, j = ij
#assert Cv.C[i, j] + sum(Ce.C[t] for t in xy) == edit_cost
if prune(matched_cost + edit_cost + Cv_ij.ls + Ce_xy.ls):
continue

# dive deeper
u = pending_u.pop(i) if i < len(pending_u) else None
v = pending_v.pop(j) if j < len(pending_v) else None
matched_uv.append((u, v))
for x, y in xy:
len_g = len(pending_g)
len_h = len(pending_h)
matched_gh.append((pending_g[x] if x < len_g else None,
pending_h[y] if y < len_h else None))
sortedx = list(sorted(x for x, y in xy))
sortedy = list(sorted(y for x, y in xy))
G = list((pending_g.pop(x) if x < len(pending_g) else None)
for x in reversed(sortedx))
H = list((pending_h.pop(y) if y < len(pending_h) else None)
for y in reversed(sortedy))

# yield from
for t in get_edit_paths(matched_uv, pending_u, pending_v,
Cv_ij,
matched_gh, pending_g, pending_h,
Ce_xy,
matched_cost + edit_cost):
yield t

# backtrack
if u is not None:
pending_u.insert(i, u)
if v is not None:
pending_v.insert(j, v)
matched_uv.pop()
for x, g in zip(sortedx, reversed(G)):
if g is not None:
pending_g.insert(x, g)
for y, h in zip(sortedy, reversed(H)):
if h is not None:
pending_h.insert(y, h)
for t in xy:
matched_gh.pop()

# Initialization

pending_u = list(G1.nodes)
pending_v = list(G2.nodes)

# cost matrix of vertex mappings
m = len(pending_u)
n = len(pending_v)
C = np.zeros((m + n, m + n))
if node_subst_cost:
C[0:m, 0:n] = np.array([node_subst_cost(G1.nodes[u], G2.nodes[v])
for u in pending_u for v in pending_v]
).reshape(m, n)
elif node_match:
C[0:m, 0:n] = np.array([1 - int(node_match(G1.nodes[u], G2.nodes[v]))
for u in pending_u for v in pending_v]
).reshape(m, n)
else:
# all zeroes
pass
#assert not min(m, n) or C[0:m, 0:n].min() >= 0
if node_del_cost:
del_costs = [node_del_cost(G1.nodes[u]) for u in pending_u]
else:
del_costs = [1] * len(pending_u)
#assert not m or min(del_costs) >= 0
if node_ins_cost:
ins_costs = [node_ins_cost(G2.nodes[v]) for v in pending_v]
else:
ins_costs = [1] * len(pending_v)
#assert not n or min(ins_costs) >= 0
inf = C[0:m, 0:n].sum() + sum(del_costs) + sum(ins_costs) + 1
C[0:m, n:n + m] = np.array([del_costs[i] if i == j else inf
for i in range(m) for j in range(m)]
).reshape(m, m)
C[m:m + n, 0:n] = np.array([ins_costs[i] if i == j else inf
for i in range(n) for j in range(n)]
).reshape(n, n)
Cv = make_CostMatrix(C, m, n)
#debug_print('Cv: {} x {}'.format(m, n))
#debug_print(Cv.C)

pending_g = list(G1.edges)
pending_h = list(G2.edges)

# cost matrix of edge mappings
m = len(pending_g)
n = len(pending_h)
C = np.zeros((m + n, m + n))
if edge_subst_cost:
C[0:m, 0:n] = np.array([edge_subst_cost(G1.edges[g], G2.edges[h])
for g in pending_g for h in pending_h]
).reshape(m, n)
elif edge_match:
C[0:m, 0:n] = np.array([1 - int(edge_match(G1.edges[g], G2.edges[h]))
for g in pending_g for h in pending_h]
).reshape(m, n)
else:
# all zeroes
pass
#assert not min(m, n) or C[0:m, 0:n].min() >= 0
if edge_del_cost:
del_costs = [edge_del_cost(G1.edges[g]) for g in pending_g]
else:
del_costs = [1] * len(pending_g)
#assert not m or min(del_costs) >= 0
if edge_ins_cost:
ins_costs = [edge_ins_cost(G2.edges[h]) for h in pending_h]
else:
ins_costs = [1] * len(pending_h)
#assert not n or min(ins_costs) >= 0
inf = C[0:m, 0:n].sum() + sum(del_costs) + sum(ins_costs) + 1
C[0:m, n:n + m] = np.array([del_costs[i] if i == j else inf
for i in range(m) for j in range(m)]
).reshape(m, m)
C[m:m + n, 0:n] = np.array([ins_costs[i] if i == j else inf
for i in range(n) for j in range(n)]
).reshape(n, n)
Ce = make_CostMatrix(C, m, n)
#debug_print('Ce: {} x {}'.format(m, n))
#debug_print(Ce.C)
#debug_print()

class MaxCost:
def __init__(self):
# initial upper-bound estimate
# NOTE: should work for empty graph
self.value = Cv.C.sum() + Ce.C.sum() + 1
maxcost = MaxCost()

def prune(cost):
if upper_bound is not None:
if cost > upper_bound:
return True
if cost > maxcost.value:
return True
elif strictly_decreasing and cost >= maxcost.value:
return True

# Now go!

for vertex_path, edge_path, cost in \
get_edit_paths([], pending_u, pending_v, Cv,
[], pending_g, pending_h, Ce, 0):
#assert sorted(G1.nodes) == sorted(u for u, v in vertex_path if u is not None)
#assert sorted(G2.nodes) == sorted(v for u, v in vertex_path if v is not None)
#assert sorted(G1.edges) == sorted(g for g, h in edge_path if g is not None)
#assert sorted(G2.edges) == sorted(h for g, h in edge_path if h is not None)
#print(vertex_path, edge_path, cost, file = sys.stderr)
#assert cost == maxcost.value
yield list(vertex_path), list(edge_path), cost

def setup_module(module):
"""Fixture for nose tests."""
from nose import SkipTest
try:
import numpy
except:
raise SkipTest("NumPy not available")
try:
import scipy
except:
raise SkipTest("SciPy not available")