def parse_nobal_ilp_sol(filename, genome_a, genome_b):
# build the master graph with matching and adjacency edges:
# 1st add capping genes:
add_capping_genes(genome_a, genome_b)
master_graph = nx.Graph()
# matching edges:
obj, edges = obj_and_matching_from_sol(filename)
for gene, c_a, c_b in edges:
master_graph.add_edge(("A", gene, c_a, Ext.HEAD), ("B", gene, c_b, Ext.HEAD))
master_graph.add_edge(("A", gene, c_a, Ext.TAIL), ("B", gene, c_b, Ext.TAIL))
# adjacency edges:
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, e_i), (g_j, copy_b, e_j) in genome.adjacency_iter_with_copies():
master_graph.add_edge((genome_name, g_i, copy_a, e_i), (genome_name, g_j, copy_b, e_j))
# now, graph should be a collection of cycles and paths, where path extremities are balancing extremities.
# We need to check those extremities to classify paths in AB-, AA- and BB-
# loop for each component:
G = nx.Graph()
ab_components = []
for comp in connected_components(master_graph):
degree_one = sorted([v for v in comp if master_graph.degree(v) == 1])
if len(degree_one) > 1:
assert len(degree_one) == 2
# if AA- or BB, add the balancing edge:
if degree_one[0][0] == degree_one[1][0]:
G.add_edge(*degree_one)
# add also the gene edges, from head to tail:
for genome, gene, copy, ext in degree_one:
G.add_edge((genome, gene, copy, Ext.HEAD), (genome, gene, copy, Ext.TAIL))
else:
# AB- component, save for later;
ab_components.append(degree_one)
# deal with AB pairing 2-by-2 arbitrarly:
for ab_i, ab_j in zip(*[iter(ab_components)] * 2):
G.add_edge(ab_i[0], ab_j[0])
G.add_edge(ab_i[1], ab_j[1])
# add also the gene edges, from head to tail:
for genome, gene, copy, ext in [ab_i[0], ab_i[1], ab_j[0], ab_j[1]]:
G.add_edge((genome, gene, copy, Ext.HEAD), (genome, gene, copy, Ext.TAIL))
# Find indels as connected components in A and B:
indels = {"A": 0, "B": 0}
for comp in connected_components(G):
genome, gene, copy, ext = list(comp)[0]
indels[genome] += 1
# log file:
gap, time = parse_log_file(filename.replace("sol", "log"))
# correct objective function, since each AB-component is counted as a full cycle in the ILP, but
# should only by half-cycle.
obj += len(ab_components) / 2
return {"dcj_distance": obj, "dup_a": indels["A"], "dup_b": indels["B"],
"time": time, "gap": gap}
def parse_ilp_sol(filename):
G = nx.Graph()
obj = 0
edges = []
with open(filename) as f:
for l in f:
m = obj_regexp.match(l.strip())
if m is not None:
obj = int(round(float(m.group(1)), 0))
m = balancing_regexp.match(l.strip())
if m is not None:
# print m.groups()
# print l
genome_1, gene_1, copy_1, ext_1, genome_2, gene_2, copy_2, ext_2, val = m.groups()
if float(val) >= 0.9: # sometimes variables with values like 1e-12, 0.99999 or 1.000000001 appear.
# connect head and tail:
G.add_edge((genome_1, gene_1, copy_1, Ext.HEAD), (genome_1, gene_1, copy_1, Ext.TAIL))
G.add_edge((genome_2, gene_2, copy_2, Ext.HEAD), (genome_2, gene_2, copy_2, Ext.TAIL))
# balancing edge:
G.add_edge((genome_1, gene_1, copy_1, ext_1), (genome_2, gene_2, copy_2, ext_2))
# Find indels as connected components in A and B:
indels = {"A": 0, "B": 0}
for comp in connected_components(G):
genome, gene, copy, ext = list(comp)[0]
indels[genome] += 1
# log file:
gap, time = parse_log_file(filename.replace("sol", "log"))
return {"dcj_distance": obj, "dup_a": indels["A"], "dup_b": indels["B"],
"time": time, "gap": gap}
def __init__(self, width, height):
self.width = width
self.height = height
self.g = grid_2d_graph(width, height)
for node, node_data in self.g.nodes(data=True):
node_data['barrier'] = False
for src, dst, edge_data in self.g.edges(data=True):
edge_data['weight'] = min_int
self.walls = set()
self.boxes = set()
self.bombs = {} # mapping from bomb to rounds
self.players = set()
self.enemies = set()
self.boost_remain = 0
self.boost_renew = 0
max_radius = distance((0, 0), (self.width-1, self.height-1))
self.in_boost_sequence = [False] * max_radius
self.radii = range(0, max_radius)
self.points = list(product(range(self.width), range(self.height)))
self.point_bomb_rounds = {p: max_int for p in self.points}
self.bomb_ranges_graph = Graph()
self.bomb_ranges_sub_graphs = connected_components(self.bomb_ranges_graph)
def calculate_features(queue,g_file, pairs):
print("Started!")
G = nx.read_graphml(graph_file)
gen = nxa.connected_components(G)
mainLst = next(gen)
G = G.subgraph(mainLst)
f = Featurator(G)
print("File: "+g_file)
print("Pairs: "+str(len(pairs)))
count = 0
for pair in pairs:
#queue.put(pair)
count += 1
#continue
h1 = pair[0]
h2 = pair[1]
res = f.get_feature_dict(h1,h2)
#res = dict()
res['pair'] = h1+h2 if h1 < h2 else h2+h1
res['h1'] = h1 if h1 < h2 else h2
res['h2'] = h2 if h1 < h2 else h1
# PUT PAIR IN QUEUE!
queue.put(res)
print("Calculated everyone! - "+str(count))
queue.put('done')
def calculate_features(queue, g_file, pairs):
print("Started!")
G = nx.read_graphml(graph_file)
gen = nxa.connected_components(G)
mainLst = next(gen)
G = G.subgraph(mainLst)
f = Featurator(G)
print("File: " + g_file)
print("Pairs: " + str(len(pairs)))
count = 0
for pair in pairs:
#queue.put(pair)
count += 1
#continue
h1 = pair[0]
h2 = pair[1]
res = f.get_feature_dict(h1, h2)
#res = dict()
res['pair'] = h1 + h2 if h1 < h2 else h2 + h1
res['h1'] = h1 if h1 < h2 else h2
res['h2'] = h2 if h1 < h2 else h1
# PUT PAIR IN QUEUE!
queue.put(res)
print("Calculated everyone! - " + str(count))
queue.put('done')
def update_bomb_rounds(self):
self.bomb_ranges_sub_graphs = connected_components(self.bomb_ranges_graph)
for sub_graph in self.bomb_ranges_sub_graphs:
rounds = max_int
for n in sub_graph:
if n in self.bombs and self.bombs[n] < rounds:
rounds = self.bombs[n]
for n in sub_graph:
self.point_bomb_rounds[n] = rounds
import csv
import sys
if len(sys.argv) < 3:
print("Usage: ./generate_csv.py graph_file output_file [#processes]")
sys.exit()
graph_file = sys.argv[1]
output_file = sys.argv[2]
processes = 1 if len(sys.argv) < 4 else int(sys.argv[3])
print("Loading graph file...")
G = nx.read_graphml(graph_file)
print("Obtaining largest connected component...")
gen = nxa.connected_components(G)
mainLst = next(gen)
G = G.subgraph(mainLst)
f = Featurator(G)
csv_fields = ['pair', 'h1', 'h2'] + f.feature_list()
csvfile = open(output_file, 'w')
writer = csv.DictWriter(csvfile, fieldnames=csv_fields)
writer.writeheader()
count = 0
pairs = [(h1, h2) for i, h1 in enumerate(G.nodes())
for j, h2 in enumerate(G.nodes()) if j > i]
import csv
import sys
if len(sys.argv) < 3:
print("Usage: ./generate_csv.py graph_file output_file [#processes]")
sys.exit()
graph_file = sys.argv[1]
output_file = sys.argv[2]
processes = 1 if len(sys.argv) < 4 else int(sys.argv[3])
print("Loading graph file...")
G = nx.read_graphml(graph_file)
print("Obtaining largest connected component...")
gen = nxa.connected_components(G)
mainLst = next(gen)
G = G.subgraph(mainLst)
f = Featurator(G)
csv_fields = ['pair','h1','h2'] + f.feature_list()
csvfile = open(output_file,'w')
writer = csv.DictWriter(csvfile,fieldnames=csv_fields)
writer.writeheader()
count = 0
pairs = [(h1, h2) for i,h1 in enumerate(G.nodes()) for j,h2 in enumerate(G.nodes()) if j > i]
def dcj_dupindel_ilp(genome_a, genome_b, output):
# copy genomes to possibly make some changes:
genome_a = copy.deepcopy(genome_a)
genome_b = copy.deepcopy(genome_b)
max_chromosomes = max(genome_a.n_chromosomes(), genome_b.n_chromosomes())
# add capping genes:
for genome in [genome_a, genome_b]:
for c in genome.chromosomes:
if not c.circular:
c.gene_order.append(0)
c.circular = True
for i in range(genome.n_chromosomes(), max_chromosomes):
genome.add_chromosome(DupChromosome([0], circular=True))
# count of each gene on each genome
gene_count = {"A": genome_a.gene_count(), "B": genome_b.gene_count()}
# for all genes ,the total "balanced" count:
total_gene_count = {g: max(gene_count["A"][g], gene_count["B"][g]) for g in
set(gene_count["A"].keys()).union(set(gene_count["B"].keys()))}
# define the y labels -> integer 1..n
y_label = define_y_label(total_gene_count)
# list of possible edges for each vertex:
edges = {}
for gene, copies in total_gene_count.iteritems():
for i in xrange(1, copies + 1):
edges[(gene, i)] = set(range(1, copies + 1))
# try to fix variables:
# Build the BP graph of fixed elements to try to find more variables to fix:
master_graph = nx.Graph()
# fixed vars:
y_fix = {}
z_fix = {}
balancing_fix = {"A": {}, "B": {}}
# add matching edges of genes with single copy:
for (gene, copy_a), set_y in edges.iteritems():
if len(set_y) == 1:
copy_b = list(set_y)[0]
for ext in [Ext.HEAD, Ext.TAIL]:
master_graph.add_edge(("A", gene, copy_a, ext), ("B", gene, copy_b, ext))
# add adjacency edges:
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, e_i), (g_j, copy_b, e_j) in adjacency_list(genome, total_gene_count):
master_graph.add_edge((genome_name, g_i, copy_a, e_i), (genome_name, g_j, copy_b, e_j))
# Search components to fix:
rescan = True
edges_to_add = []
vertices_to_remove = []
while rescan:
rescan = False
master_graph.add_edges_from(edges_to_add)
master_graph.remove_nodes_from(vertices_to_remove)
edges_to_add = []
vertices_to_remove = []
# check each connected component:
for comp in connected_components(master_graph):
# get degree-1 vertices:
degree_one = [v for v in comp if master_graph.degree(v) == 1]
# if two degree one vertices, it is a path;
if len(degree_one) == 2:
genome_i, g_i, copy_a, e_i = degree_one[0]
genome_j, g_j, copy_b, e_j = degree_one[1]
# 1 - check if nodes are balancing, to find AA-, BB- and AB- paths that can be fixed.
i_is_balancing = g_i != 0 and copy_a > gene_count[genome_i][g_i]
j_is_balancing = g_j != 0 and copy_b > gene_count[genome_j][g_j]
if i_is_balancing and j_is_balancing:
if genome_i == genome_j: # AA- or BB-path, close it
balancing_fix[genome_i][degree_one[0][1:]] = degree_one[1][1:]
balancing_fix[genome_i][degree_one[1][1:]] = degree_one[0][1:]
degree_one = []
else:
# TODO: deal with AB-components;
pass
# if the path has homologous genes at the ends, I can join:
elif genome_i != genome_j and g_i == g_j:
# invert to put genome A always in variables _i :
if genome_j == "A":
genome_i, g_i, copy_a, e_i, genome_j, g_j, copy_b, e_j = genome_j, g_j, copy_b, e_j, genome_i, g_i, copy_a, e_i
# check conflict, only add edge if ok:
if copy_b in edges[(g_i, copy_a)]:
edges[(g_i, copy_a)] = {copy_b}
# save edges to add to graph:
for ext in [Ext.HEAD, Ext.TAIL]:
edges_to_add.append((("A", g_i, copy_a, ext), ("B", g_i, copy_b, ext)))
# new edges, re-scan:
rescan = True
# remove possible edges from other copies:
for idx in xrange(1, total_gene_count[g_i] + 1):
if idx == copy_a:
continue
try:
# if not there already, exception is thrown, that' ok
edges[(g_i, idx)].remove(copy_b)
# Add new edges to graph, if the removal created degree 1 vertices:
if len(edges[(g_i, idx)]) == 1:
idx_c = list(edges[(g_i, idx)])[0]
for ext in [Ext.HEAD, Ext.TAIL]:
edges_to_add.append((("A", g_i, idx, ext), ("B", g_i, idx_c, ext)))
except KeyError:
pass
# if no degree one vertices, it is a cycle, I can fix the y_i:
elif len(degree_one) == 0:
# get indexes of the y_i:
indexes = [(v, y_label[vertex_name(*v)]) for v in comp]
min_label = min([x[1] for x in indexes])
for v, label in indexes:
y_fix[label] = min_label
z_fix[label] = 0
z_fix[min_label] = 1
vertices_to_remove.extend(comp)
# DRAW?
# nx.draw_circular(master_graph, font_size=8, width=0.5, node_shape="8", node_size=1, with_labels=True)
# nx.draw_spring(master_graph, font_size=8, width=0.5, node_shape="8", node_size=20, with_labels=True)
# nx.draw_spectral(master_graph, font_size=8, width=0.5, node_shape="8", node_size=20, with_labels=True)
# nx.draw_graphviz(master_graph, font_size=8, width=0.5, node_shape="8", node_size=20, with_labels=True)
# plt.savefig('graph.pdf', bbox_inches='tight')
# all fixed, generate ILP:
constraints = []
# consistency and matching 1-to-1
constraints.append("\ Matching and consistency constraints")
# sorting just to make it nicer looking:
for (gene, copy_a) in sorted(edges):
copy_set_b = edges[(gene, copy_a)]
if len(copy_set_b) > 1:
for copy_b in copy_set_b:
constraints.append("%s - %s = 0" % (
matching_edge_name(gene, copy_a, copy_b, Ext.TAIL),
matching_edge_name(gene, copy_a, copy_b, Ext.HEAD)))
constraints.append(
" + ".join([matching_edge_name(gene, copy_a, copy_b, Ext.TAIL) for copy_b in copy_set_b]) + " = 1")
constraints.append("\ Balancing:")
balancing_genes_A = {g: range(gene_count["A"][g] + 1, gene_count["B"][g] + 1) for g in total_gene_count.iterkeys()
if gene_count["A"][g] < gene_count["B"][g]}
balancing_genes_B = {g: range(gene_count["B"][g] + 1, gene_count["A"][g] + 1) for g in total_gene_count.iterkeys()
if gene_count["B"][g] < gene_count["A"][g]}
for genome, balancing in [("A", balancing_genes_A), ("B", balancing_genes_B)]:
constraints.append("\ Genome %s" % genome)
for gene_i, copy_i, ext_i in balancing_extremities(balancing):
# check if fixed:
if (gene_i, copy_i, ext_i) in balancing_fix[genome]:
gene_j, copy_j, ext_j = balancing_fix[genome][(gene_i, copy_i, ext_i)]
if (gene_i, copy_i, ext_i) < (gene_j, copy_j, ext_j):
constraints.append(
"%s = 1" % balancing_edge_name(genome, gene_i, copy_i, ext_i, gene_j, copy_j, ext_j))
# if not, matching 1-to-1:
else:
constraints.append(
" + ".join([balancing_edge_name(genome, gene_i, copy_i, ext_i, gene_j, copy_j, ext_j) for
gene_j, copy_j, ext_j in
balancing_extremities(balancing, exclude=balancing_fix[genome].keys()) if
(gene_i, copy_i, ext_i) != (gene_j, copy_j, ext_j)]) + " = 1")
constraints.append("\ Labelling")
#
# for each adjacency, fix label:
constraints.append("\\ Adjacency have the same label:")
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for i, j in adjacency_list(genome, total_gene_count):
v_i = vertex_name(genome_name, *i)
v_j = vertex_name(genome_name, *j)
# if already fixed, skip
if y_label[v_i] in y_fix and y_label[v_j] in y_fix:
continue
# if the edge is 0 for sure, also skip:
constraints.append("y_%s - y_%s = 0 \\ %s <-> %s " % (y_label[v_i], y_label[v_j], v_i, v_j))
#
constraints.append("\\ Matching edges with the same label:")
for (gene, copy_a) in sorted(edges):
copy_set_b = edges[(gene, copy_a)]
for ext in [Ext.HEAD, Ext.TAIL]:
y_i = y_label[vertex_name("A", gene, copy_a, ext)]
# if edge is set, just make the y_i's equal;
if len(copy_set_b) == 1:
y_j = y_label[vertex_name("B", gene, list(copy_set_b)[0], ext)]
# skip if this y_i's are already fixed
if y_i in y_fix and y_j in y_fix:
continue
constraints.append("y_%s - y_%s = 0 " % (y_i, y_j))
else:
# if edge not set, add both ineqs.
for copy_b in copy_set_b:
y_j = y_label[vertex_name("B", gene, copy_b, ext)]
constraints.append(
"y_%s - y_%s + %s %s <= %d" % (
y_i, y_j, y_i, matching_edge_name(gene, copy_a, copy_b, ext), y_i))
constraints.append(
"y_%s - y_%s + %s %s <= %d" % (
y_j, y_i, y_j, matching_edge_name(gene, copy_a, copy_b, ext), y_j))
constraints.append("\\ Balancing edges with same label:")
for genome, balancing in [("A", balancing_genes_A), ("B", balancing_genes_B)]:
constraints.append("\\ Genome %s" % genome)
for gene_i, copy_i, ext_i in balancing_extremities(balancing, exclude=balancing_fix[genome].keys()):
for gene_j, copy_j, ext_j in balancing_extremities(balancing, exclude=balancing_fix[genome].keys()):
if (gene_i, copy_i, ext_i) >= (gene_j, copy_j, ext_j):
continue
y_i = y_label[vertex_name(genome, gene_i, copy_i, ext_i)]
y_j = y_label[vertex_name(genome, gene_j, copy_j, ext_j)]
# should not have someone here if I'm excluding fixed edges:
if y_i in y_fix and y_j in y_fix:
continue
constraints.append("y_%s - y_%s + %s %s <= %d" % (
y_i, y_j, y_i, balancing_edge_name(genome, gene_i, copy_i, ext_i, gene_j, copy_j, ext_j), y_i))
constraints.append("y_%s - y_%s + %s %s <= %d" % (
y_j, y_i, y_j, balancing_edge_name(genome, gene_i, copy_i, ext_i, gene_j, copy_j, ext_j), y_j))
# z variables: since all cycles have to contains vertices from both genomes, we only add z variables
# for genome A, that have smallest labels, so a genome B z variable will never be =1.
constraints.append("\\ Z variables")
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if vertex[0] == "A":
# if i in z_fix and z_fix[i] == 0:
# continue
# if i in z_fix and z_fix[i] == 1:
# constraints.append("z_%s = 1" % i)
if i not in z_fix:
constraints.append("%d z_%s - y_%s <= 0" % (i, i, i))
#
# # number of genes, to fix distance:
constraints.append("n = %d" % (sum(total_gene_count.itervalues())))
# # number of fixed cycles
constraints.append("c = %d" % (sum(z_fix.itervalues())))
# for g in sorted(total_gene_count):
# print g,total_gene_count[g]
#
# # bounds:
bounds = []
for i in sorted(y_label.itervalues()):
if i not in y_fix:
bounds.append("y_%d <= %d" % (i, i))
#
# # variables:
binary = []
#
# # matching edges
# matching edges, skipping fixed pairs.
matching = ["\ match"]
# for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
for (gene, copy_a), copy_set_b in sorted(edges.items(), key=operator.itemgetter(0)):
if len(copy_set_b) > 1:
for copy_b in copy_set_b:
for ext in [Ext.HEAD, Ext.TAIL]:
matching.append(matching_edge_name(gene, copy_a, copy_b, ext))
print "%d matching edges" % len(matching)
# print "Potentially %d matching edges" % sum([2*x ** 2 for x in gene_count.itervalues()])
binary.extend(matching)
#
# balancing edges:
balancing_edges = [balancing_edge_name(genome, gene_i, copy_i, ext_i, gene_j, copy_j, ext_j) for genome, balancing
in [("A", balancing_genes_A), ("B", balancing_genes_B)] for gene_i, copy_i, ext_i in
balancing_extremities(balancing, exclude=balancing_fix[genome].keys()) for gene_j, copy_j, ext_j
in balancing_extremities(balancing, exclude=balancing_fix[genome].keys()) if
(gene_i, copy_i, ext_i) < (gene_j, copy_j, ext_j)]
print "%d balancing edges" % len(balancing_edges)
binary.extend(balancing_edges)
#
# z cycles:
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if i in z_fix: # and z_fix[i] == 0:
continue
if vertex[0] == "B":
continue
binary.append("z_%d" % i)
#
# # Y label are general:
# TODO: remove unused y' and z's from model. If y=1, it can be removed, just set z=1.
general = []
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if i not in y_fix:
general.append("y_%d" % i)
#
# # number of genes and fixed cycles:
general.append("n")
general.append("c")
# # objective function:
objective = ["obj: n - c - " + " - ".join(
["z_%d" % i for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)) if
vertex[0] == "A" and i not in z_fix])]
# write:
with open(output, "w") as f:
for header, lines in [("Minimize", objective), ("Subject to", constraints),
("Bounds", bounds), ("Binary", binary), ("General", general)]:
print >> f, header
print >> f, "\n".join(lines)
def solve_ilp(timelimit=60):
# import here, so only if actually solving we will need gurobi.
from gurobipy import read, GRB
# pycharm complains of gurobi commands, cannot see them from the import
model = read(filename)
# set some options:
# time limit in seconds:
model.params.timeLimit = timelimit
# not verbose:
# model.setParam('OutputFlag', False)
# MIP focus, from 0 to 3:
model.params.MIPFocus = 1 # best solutions, less focus on bounds.
model.optimize()
if model.status != GRB.Status.INFEASIBLE:
print('FINISHED: Best objective: %g' % model.objVal)
print('Optimization ended with status %d' % model.status)
model.write(filename + '.sol')
if model.status == GRB.INFEASIBLE:
model.computeIIS()
model.write("unfeasible.lp")
print('\nThe following constraint(s) cannot be satisfied:')
for c in model.getConstrs():
if c.IISConstr:
print('%s' % c.constrName)
else:
z = n = c = 0
solution_matching = collections.defaultdict(list)
matching_regexp = re.compile("x_A(\d+)_(\d+)h,B(\d+)_(\d+)h")
# get basic vars and matching:
for v in model.getVars():
if v.varName == "n":
n = v.x
elif v.varName == "c":
c = v.x
elif v.varName.startswith("z") and v.x >= 0.9:
z += 1
else:
m = matching_regexp.match(v.varName)
if m is not None and v.x == 1:
g_a, c_a, g_b, c_b = map(int, m.groups())
solution_matching[g_a].append((c_a, c_b))
from parse_orthology import build_correct_matching, parse_orthology_quality
correct_matching = build_correct_matching(genome_a, genome_b)
tp, fp, fn = parse_orthology_quality(solution_matching, correct_matching)
print "N: %d cycles:%d (%d fixed, %d from opt)" % (n, z + c, c, z)
print "Orthology. TP:%d FP:%d FN:%d" % (len(tp), len(fp), len(fn))
# print match_edges
# Now, analyse the BP graph, for the incomplete matching model, to find AA-, BB- and AB- components:
master_graph = nx.Graph()
# fixed vars:
# add matching edges of genes with single copy:
# for (gene, copy_a), copy_j in match_edges.iteritems():
for gene, pair_list in solution_matching.iteritems():
for copy_a, copy_b in pair_list:
for ext in [Ext.HEAD, Ext.TAIL]:
master_graph.add_edge(("A", gene, copy_a, ext), ("B", gene, copy_b, ext))
# add adjacency edges:
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, e_i), (g_j, copy_j, e_j) in genome.adjacency_iter_with_copies():
master_graph.add_edge((genome_name, g_i, copy_a, e_i), (genome_name, g_j, copy_j, e_j))
count = {"A": 0, "B": 0, "AB": 0}
c = 0
# print "C:", len([x for x in connected_components(master_graph)])
for comp in connected_components(master_graph):
degree_one = [v for v in comp if master_graph.degree(v) == 1]
if len(degree_one) == 0:
c += 1
else:
if len(degree_one) != 2:
import ipdb;
ipdb.set_trace()
if degree_one[0][0] == degree_one[1][0]:
count[degree_one[0][0]] += 1
else:
count["AB"] += 1
print count
if skip_balancing:
print "Corrected distance: %d" % (model.objVal + count["AB"] / 2)
return model
def dcj_dupindel_ilp(genome_a, genome_b, output, skip_balancing=False, fix_vars=True, solve=False, all_vs_all=False):
def solve_ilp(timelimit=60):
# import here, so only if actually solving we will need gurobi.
from gurobipy import read, GRB
# pycharm complains of gurobi commands, cannot see them from the import
model = read(filename)
# set some options:
# time limit in seconds:
model.params.timeLimit = timelimit
# not verbose:
# model.setParam('OutputFlag', False)
# MIP focus, from 0 to 3:
model.params.MIPFocus = 1 # best solutions, less focus on bounds.
model.optimize()
if model.status != GRB.Status.INFEASIBLE:
print('FINISHED: Best objective: %g' % model.objVal)
print('Optimization ended with status %d' % model.status)
model.write(filename + '.sol')
if model.status == GRB.INFEASIBLE:
model.computeIIS()
model.write("unfeasible.lp")
print('\nThe following constraint(s) cannot be satisfied:')
for c in model.getConstrs():
if c.IISConstr:
print('%s' % c.constrName)
else:
z = n = c = 0
solution_matching = collections.defaultdict(list)
matching_regexp = re.compile("x_A(\d+)_(\d+)h,B(\d+)_(\d+)h")
# get basic vars and matching:
for v in model.getVars():
if v.varName == "n":
n = v.x
elif v.varName == "c":
c = v.x
elif v.varName.startswith("z") and v.x >= 0.9:
z += 1
else:
m = matching_regexp.match(v.varName)
if m is not None and v.x == 1:
g_a, c_a, g_b, c_b = map(int, m.groups())
solution_matching[g_a].append((c_a, c_b))
from parse_orthology import build_correct_matching, parse_orthology_quality
correct_matching = build_correct_matching(genome_a, genome_b)
tp, fp, fn = parse_orthology_quality(solution_matching, correct_matching)
print "N: %d cycles:%d (%d fixed, %d from opt)" % (n, z + c, c, z)
print "Orthology. TP:%d FP:%d FN:%d" % (len(tp), len(fp), len(fn))
# print match_edges
# Now, analyse the BP graph, for the incomplete matching model, to find AA-, BB- and AB- components:
master_graph = nx.Graph()
# fixed vars:
# add matching edges of genes with single copy:
# for (gene, copy_a), copy_j in match_edges.iteritems():
for gene, pair_list in solution_matching.iteritems():
for copy_a, copy_b in pair_list:
for ext in [Ext.HEAD, Ext.TAIL]:
master_graph.add_edge(("A", gene, copy_a, ext), ("B", gene, copy_b, ext))
# add adjacency edges:
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, e_i), (g_j, copy_j, e_j) in genome.adjacency_iter_with_copies():
master_graph.add_edge((genome_name, g_i, copy_a, e_i), (genome_name, g_j, copy_j, e_j))
count = {"A": 0, "B": 0, "AB": 0}
c = 0
# print "C:", len([x for x in connected_components(master_graph)])
for comp in connected_components(master_graph):
degree_one = [v for v in comp if master_graph.degree(v) == 1]
if len(degree_one) == 0:
c += 1
else:
if len(degree_one) != 2:
import ipdb;
ipdb.set_trace()
if degree_one[0][0] == degree_one[1][0]:
count[degree_one[0][0]] += 1
else:
count["AB"] += 1
print count
if skip_balancing:
print "Corrected distance: %d" % (model.objVal + count["AB"] / 2)
return model
# copy genomes to possibly make some changes:
genome_a = copy.deepcopy(genome_a)
genome_b = copy.deepcopy(genome_b)
add_capping_genes(genome_a, genome_b)
# since the gene set might be different for each genome, find all genes:
all_genes = genome_a.gene_set().union(genome_b.gene_set())
# find all gene copies
gene_copies = build_gene_copies_dict(all_genes, genome_a, genome_b)
# count balancing genes:
bal = {
g: sum([len([c for c in gene_copies[g][gene].itervalues() if c == CopyType.BALANCING]) for gene in all_genes])
for g in ["A", "B"]}
print "Balancing genes:A=%(A)d, B=%(B)d" % bal
# define the y labels (vertex = genome,gene,copy,ext) -> integer 1..n
y_label = define_y_label(gene_copies)
# store all possible matchings (edges) from each family:
fixed_matching = {}
possible_matching = {}
for gene in all_genes:
# if only 1 copy, matching is fixed:
if len(gene_copies["A"][gene]) == 1:
# fix the matching, then remove from the available copies
copy_a, type_a = gene_copies["A"][gene].items()[0]
copy_j, type_b = gene_copies["B"][gene].items()[0]
fixed_matching[(gene, copy_a)] = copy_j
else:
possible_matching[gene] = {"A": {copy_i for copy_i, type_i in gene_copies["A"][gene].items()},
"B": {copy_i for copy_i, type_i in gene_copies["B"][gene].items()}}
# Build the BP graph of fixed matchings to try to find more variables to fix:
y_fix = {}
z_fix = {}
balancing_fix = {"A": {}, "B": {}}
if fix_vars:
master_graph = nx.Graph()
# fixed vars:
# add matching edges of genes with single copy:
for (gene, copy_a), copy_j in fixed_matching.iteritems():
for ext in [Ext.HEAD, Ext.TAIL]:
master_graph.add_edge(("A", gene, copy_a, ext), ("B", gene, copy_j, ext))
# add adjacency edges:
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, e_i), (g_j, copy_j, e_j) in genome.adjacency_iter_with_copies():
master_graph.add_edge((genome_name, g_i, copy_a, e_i), (genome_name, g_j, copy_j, e_j))
# Search components to fix:
rescan = True
edges_to_add = []
vertices_to_remove = []
ab_components = set()
while rescan:
rescan = False
# Pre-scan:
# add and remove edges detected from previous rounds:
master_graph.add_edges_from(edges_to_add)
master_graph.remove_nodes_from(vertices_to_remove)
edges_to_add = []
vertices_to_remove = []
# fix AB-components; while I have at least 2, join pairs arbitrarily:
while len(ab_components) > 1:
a_i, b_i = ab_components.pop()
a_j, b_j = ab_components.pop()
master_graph.add_edge(a_i, a_j)
balancing_fix["A"][a_i[1:]] = a_j[1:]
balancing_fix["A"][a_j[1:]] = a_i[1:]
master_graph.add_edge(b_i, b_j)
balancing_fix["B"][b_i[1:]] = b_j[1:]
balancing_fix["B"][b_j[1:]] = b_i[1:]
# Now I search for vertices that have only balancing vertices as matching
# candidates. If that is the case, I can fix them arbitrarly.
fix_only_bal = True
if fix_only_bal:
for gene in sorted(possible_matching):
set_a = possible_matching[gene]["A"]
set_b = possible_matching[gene]["B"]
if all([gene_copies["A"][gene][copy_a] == CopyType.BALANCING for copy_a in set_a]) or all(
[gene_copies["B"][gene][copy_b] == CopyType.BALANCING for copy_b in set_b]):
for copy_a, copy_b in zip(set_a, set_b):
fixed_matching[(gene, copy_a)] = copy_b
# save edges to add to graph:
for ext in [Ext.HEAD, Ext.TAIL]:
# edges_to_add.append((("A", gene, copy_a, ext), ("B", gene, copy_b, ext)))
master_graph.add_edge(("A", gene, copy_a, ext), ("B", gene, copy_b, ext))
rescan = True
# remove from possible matching:
del possible_matching[gene]
# now loop for each connected component, fixing cycles and trying to close paths to cycles when possible.
for comp in connected_components(master_graph):
# can only consider even components;
if len(comp) % 2 != 0:
continue
# get degree-1 vertices:
degree_one = [v for v in comp if master_graph.degree(v) == 1]
# if two degree one vertices, it is a path;
if len(degree_one) == 2:
genome_i, g_i, copy_a, e_i = degree_one[0]
genome_j, g_j, copy_j, e_j = degree_one[1]
# 1 - check if both nodes are balancing, to find AA-, BB- and AB- paths that can be fixed.
i_is_balancing = g_i != 0 and gene_copies[genome_i][g_i][copy_a] == CopyType.BALANCING
j_is_balancing = g_j != 0 and gene_copies[genome_j][g_j][copy_j] == CopyType.BALANCING
if i_is_balancing and j_is_balancing:
# open-path, both ends are balancing.
# If AA- or BB-path, close it to a cycle:
if genome_i == genome_j:
# fix the cycle:
fix_cycle_y_z(comp, y_label, y_fix, z_fix, vertices_to_remove)
# fix the balancing variables if we have them:
if not skip_balancing:
balancing_fix[genome_i][degree_one[0][1:]] = degree_one[1][1:]
balancing_fix[genome_i][degree_one[1][1:]] = degree_one[0][1:]
else:
# If not, it is AB-, add to the list to try to make pairs.
if skip_balancing: # if not using balancing edges, I can fix the AB directly, instead of
# doing the merge in pairs;
fix_cycle_y_z(comp, y_label, y_fix, z_fix, vertices_to_remove)
else:
# merge in pairs:
ab_components.add(tuple(sorted(degree_one)))
if len(ab_components) > 1:
rescan = True
# Not open path; then, check if the path has homologous extremities at both ends, so I can close
# to a path:
elif genome_i != genome_j and g_i == g_j and e_i == e_j:
# invert to put genome A always in variables _i :
if genome_j == "A":
genome_i, g_i, copy_a, e_i, genome_j, g_j, copy_j, e_j = genome_j, g_j, copy_j, e_j, genome_i, g_i, copy_a, e_i
# check conflict, only add edge if it's in the allowed edges:
if g_i in possible_matching and copy_a in possible_matching[g_i]["A"] and copy_j in \
possible_matching[g_i]["B"]:
# new edges, re-scan:
rescan = True
fix_new_matching(fixed_matching, edges_to_add, possible_matching, g_i, copy_a, copy_j)
# if there are no degree one vertices, it is a cycle; I can fix the y_i and z_i for this cycle:
elif len(degree_one) == 0:
fix_cycle_y_z(comp, y_label, y_fix, z_fix, vertices_to_remove)
rescan = True
# DRAW:
draw_bp = False
if draw_bp:
plot_bp('graph.pdf', master_graph, gene_copies, possible_matching)
# all fixed, generate ILP
# to make it easier to find the matching edges, specially when limiting edges from balancing genes,
# I will build a gene connections graph;
gene_connection = nx.DiGraph() # make it directed, so the vertex of A is always 1st on the edge tuple.
for gene in possible_matching.iterkeys():
set_a = possible_matching[gene]["A"]
set_b = possible_matching[gene]["B"]
# All vs all model:
if all_vs_all:
for copy_a in set_a:
for copy_b in set_b:
gene_connection.add_edge(("A", gene, copy_a), ("B", gene, copy_b))
else:
# try to minimise needed matching edges for balancing nodes:
real_a = [cp for cp in set_a if gene_copies["A"][gene][cp] == CopyType.REAL]
real_b = [cp for cp in set_b if gene_copies["B"][gene][cp] == CopyType.REAL]
# all real, then all-vs-all:
if len(real_a) == len(real_b):
for copy_a in set_a:
for copy_b in set_b:
gene_connection.add_edge(("A", gene, copy_a), ("B", gene, copy_b))
# a has balancing:
if len(real_a) < len(real_b):
balancing_a = [cp for cp in set_a if gene_copies["A"][gene][cp] == CopyType.BALANCING]
# the real in A match the real in B (which are all)
for copy_a in real_a:
for copy_b in set_b:
gene_connection.add_edge(("A", gene, copy_a), ("B", gene, copy_b))
# then, the balancing in A have len(real_a)+1 incident edges
list_b = list(set_b)
for idx, copy_a in enumerate(balancing_a):
for j in range(len(real_a) + 1):
gene_connection.add_edge(("A", gene, copy_a), ("B", gene, list_b[idx + j]))
# b has balancing:
else:
balancing_b = [cp for cp in set_b if gene_copies["B"][gene][cp] == CopyType.BALANCING]
# the real in B match the real in A (which are all)
for copy_b in real_b:
for copy_a in set_a:
gene_connection.add_edge(("A", gene, copy_a), ("B", gene, copy_b))
# then, the balancing in B have len(real_b)+1 incident edges
list_a = list(set_a)
for idx, copy_b in enumerate(balancing_b):
for j in range(len(real_b) + 1):
gene_connection.add_edge(("A", gene, list_a[idx + j]), ("B", gene, copy_b))
# Start building constraints:
constraints = []
# consistency and matching 1-to-1
# Fixed matching:
# sorting just to make it nicer looking:
constraints.append("\ Fixed matching:")
for (gene, copy_a), copy_b in sorted(fixed_matching.items(), key=lambda pair: pair[0]):
constraints.append("%s = 1" % matching_edge_name(gene, copy_a, copy_b, Ext.TAIL))
constraints.append("%s = 1" % matching_edge_name(gene, copy_a, copy_b, Ext.HEAD))
# HEAD TAIL consistency:
constraints.append("\ Head/Tail consistency:")
for (_, gene_a, copy_a), (_, gene_b, copy_b) in gene_connection.edges_iter():
constraints.append("%s - %s = 0" % (
matching_edge_name(gene_a, copy_a, copy_b, Ext.TAIL),
matching_edge_name(gene_a, copy_a, copy_b, Ext.HEAD)))
# 1 Matching per node :
constraints.append("\ Degree 1 per node (Matching):")
# for all vertices:
for v in gene_connection.nodes_iter():
# find the incident edges:
if v[0] == "A":
edges = gene_connection.out_edges_iter
else:
edges = gene_connection.in_edges_iter
incident = [matching_edge_name(gene_a, copy_a, copy_b, Ext.TAIL) for
(_, gene_a, copy_a), (_, gene_b, copy_b) in edges(v)]
# sum of incidents is 1:
constraints.append("%s = 1" % (" + ".join(incident)))
if not skip_balancing:
constraints.append("\ Balancing:")
for genome in ["A", "B"]:
constraints.append("\ Genome %s" % genome)
for gene_i, copy_a, ext_i in balancing_extremities(gene_copies[genome]):
# check if fixed:
if (gene_i, copy_a, ext_i) in balancing_fix[genome]:
gene_j, copy_j, ext_j = balancing_fix[genome][(gene_i, copy_a, ext_i)]
if (gene_i, copy_a, ext_i) < (gene_j, copy_j, ext_j):
constraints.append(
"%s = 1" % balancing_edge_name(genome, gene_i, copy_a, ext_i, gene_j, copy_j, ext_j))
# if not, matching 1-to-1:
else:
constraints.append(
" + ".join([balancing_edge_name(genome, gene_i, copy_a, ext_i, gene_j, copy_j, ext_j) for
gene_j, copy_j, ext_j in
balancing_extremities(gene_copies[genome], exclude=balancing_fix[genome].keys())
if
(gene_i, copy_a, ext_i) != (gene_j, copy_j, ext_j)]) + " = 1")
constraints.append("\ Labelling")
# for each adjacency, fix the label of adjacent genes:
constraints.append("\\ Adjacent nodes have the same label:")
for genome, genome_name in [(genome_a, "A"), (genome_b, "B")]:
for (g_i, copy_a, ext_i), (g_j, copy_j, ext_j) in genome.adjacency_iter_with_copies():
v_i = genome_name, g_i, copy_a, ext_i
v_j = genome_name, g_j, copy_j, ext_j
# if already fixed, skip
if y_label[v_i] in y_fix and y_label[v_j] in y_fix:
continue
# if the edge is 0 for sure, also skip:
constraints.append("y_%s - y_%s = 0 \\ %s <-> %s " % (y_label[v_i], y_label[v_j], v_i, v_j))
#
constraints.append("\\ Matching extremities have the same label:")
# if extremities are matched, but I don't know the y_i (cycle was not closed in the fixing phase),
# then I know that the y_i's of these extremities are equal:
constraints.append("\\ Fixed matching without fixed y_i:")
for (gene, copy_a) in sorted(fixed_matching):
copy_j = fixed_matching[(gene, copy_a)]
for ext in [Ext.HEAD, Ext.TAIL]:
y_i = y_label[("A", gene, copy_a, ext)]
y_j = y_label[("B", gene, copy_j, ext)]
# only add if this y_i's aren't already fixed
if y_i not in y_fix and y_j not in y_fix:
constraints.append("y_%s - y_%s = 0 " % (y_i, y_j))
# for the "open" matching, for each edge I add the "y fixing" restrictions, that force the y_i's
# to be equal whenever the edge variable is set to 1.
constraints.append("\\ Open matching:")
for (_, gene_a, copy_a), (_, gene_b, copy_b) in gene_connection.edges_iter():
for ext in [Ext.HEAD, Ext.TAIL]:
y_a = y_label[("A", gene_a, copy_a, ext)]
y_b = y_label[("B", gene_b, copy_b, ext)]
constraints.append(
"y_%s - y_%s + %s %s <= %d" % (
y_a, y_b, y_a, matching_edge_name(gene_a, copy_a, copy_b, ext), y_a))
constraints.append(
"y_%s - y_%s + %s %s <= %d" % (
y_b, y_a, y_b, matching_edge_name(gene_a, copy_a, copy_b, ext), y_b))
if not skip_balancing:
constraints.append("\\ Balancing edges have same label:")
for genome in ["A", "B"]:
constraints.append("\\ Genome %s" % genome)
for gene_i, copy_a, ext_i in balancing_extremities(gene_copies[genome],
exclude=balancing_fix[genome].keys()):
for gene_j, copy_j, ext_j in balancing_extremities(gene_copies[genome],
exclude=balancing_fix[genome].keys()):
if (gene_i, copy_a, ext_i) >= (gene_j, copy_j, ext_j):
continue
y_i = y_label[(genome, gene_i, copy_a, ext_i)]
y_j = y_label[(genome, gene_j, copy_j, ext_j)]
# should not have someone here if I'm excluding fixed edges:
if y_i in y_fix and y_j in y_fix:
continue
constraints.append("y_%s - y_%s + %s %s <= %d" % (
y_i, y_j, y_i, balancing_edge_name(genome, gene_i, copy_a, ext_i, gene_j, copy_j, ext_j), y_i))
constraints.append("y_%s - y_%s + %s %s <= %d" % (
y_j, y_i, y_j, balancing_edge_name(genome, gene_i, copy_a, ext_i, gene_j, copy_j, ext_j), y_j))
# z variables: since all cycles have to contains vertices from both genomes, we only add z variables
# for genome A, that have smallest labels, so a genome B z variable will never be =1.
constraints.append("\\ Z variables")
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if vertex[0] == "A":
if i not in z_fix:
constraints.append("%d z_%s - y_%s <= 0" % (i, i, i))
#
# # number of genes, to fix distance:
n_genes = sum([len(copies) for copies in gene_copies["A"].itervalues()])
constraints.append("n = %d" % n_genes)
# # number of fixed cycles
constraints.append("c = %d" % (sum(z_fix.itervalues())))
#
# # bounds:
bounds = []
for i in sorted(y_label.itervalues()):
if i not in y_fix:
bounds.append("y_%d <= %d" % (i, i))
#
# # variables:
binary = []
#
# # matching edges
matching = ["\ Fixed matching:"]
for (gene, copy_a), copy_b in fixed_matching.iteritems():
matching.append(matching_edge_name(gene, copy_a, copy_b, Ext.TAIL))
matching.append(matching_edge_name(gene, copy_a, copy_b, Ext.HEAD))
matching.append("\ Open matching:")
for (_, gene_a, copy_a), (_, gene_b, copy_b) in gene_connection.edges_iter():
for ext in [Ext.HEAD, Ext.TAIL]:
matching.append(matching_edge_name(gene_a, copy_a, copy_b, ext))
print "%d fixed matching edges" % (len(fixed_matching) * 2)
print "%d open matching edges" % (len(gene_connection.edges()) * 2)
binary.extend(matching)
if not skip_balancing:
# balancing edges:
balancing_edges = [balancing_edge_name(genome, gene_i, copy_a, ext_i, gene_j, copy_j, ext_j) for
genome
in ["A", "B"] for gene_i, copy_a, ext_i in
balancing_extremities(gene_copies[genome], exclude=balancing_fix[genome].keys()) for
gene_j, copy_j, ext_j
in balancing_extremities(gene_copies[genome], exclude=balancing_fix[genome].keys()) if
(gene_i, copy_a, ext_i) < (gene_j, copy_j, ext_j)]
print "%d balancing edges" % len(balancing_edges)
binary.extend(balancing_edges)
#
# z cycles:
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if i in z_fix: # and z_fix[i] == 0:
continue
if vertex[0] == "B":
continue
binary.append("z_%d" % i)
#
# # Y label are general:
# TODO: remove unused y' and z's from model. If y=1, it can be removed, just set z=1.
general = []
for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)):
if i not in y_fix:
general.append("y_%d" % i)
#
# # number of genes and fixed cycles:
general.append("n")
general.append("c")
# # objective function:
z_obj = " - ".join(["z_%d" % i for vertex, i in sorted(y_label.items(), key=operator.itemgetter(1)) if
vertex[0] == "A" and i not in z_fix])
objective = ["obj: n - c %s" % ("- " + z_obj if len(z_obj) > 0 else "")]
# write ILP:
with open(output, "w") as f:
for header, lines in [("Minimize", objective), ("Subject to", constraints),
("Bounds", bounds), ("Binary", binary), ("General", general)]:
print >> f, header
print >> f, "\n".join(lines)
if solve:
model = solve_ilp(timelimit=60)
return model
def plot_bp(filename, master_graph, gene_copies, possible_matching, simplified=True):
# add isolated vertices (balancing extremities that are not fixed already)
for genome_i in ["A", "B"]:
for gene_i, copy_i, ext_i in balancing_extremities(gene_copies[genome_i]):
if gene_i in possible_matching and copy_i in possible_matching[gene_i][genome_i]:
# if (gene_i, copy_i) not in fixed_matching:
# print "add bal", (genome_i, gene_i, copy_i, ext_i)
master_graph.add_node((genome_i, gene_i, copy_i, ext_i))
# simplified:
if simplified:
edges = []
vertices = []
for comp in connected_components(master_graph):
if len(comp) == 1:
vertices.append(comp.pop())
continue
degree_one = tuple([v for v in comp if master_graph.degree(v) == 1])
edges.append(degree_one)
master_graph = nx.Graph()
master_graph.add_edges_from(edges)
master_graph.add_nodes_from(vertices)
# Relabel nodes to make it easier to read:
mapping = {}
normal = []
balancing = []
be = {genome_i: list(balancing_extremities(gene_copies[genome_i])) for genome_i in ["A", "B"]}
for v in master_graph.nodes():
genome_i, gene_i, copy_i, ext_i = v
mapping[v] = "$%s%s_{(%s)}^%s$" % v
if (gene_i, copy_i, ext_i) in be[genome_i]:
balancing.append(mapping[v])
else:
normal.append(mapping[v])
master_graph = nx.relabel_nodes(master_graph, mapping)
# Graphviz position:
# pos = nx.nx_agraph.graphviz_layout(master_graph, prog="fdp")
# custom position:
x_pos = 0
y_pos = {"A": 1, "B": 0}
pos = {}
for comp in sorted(connected_components(master_graph), key=lambda c: (-len(c), min(c))):
last_v = None
for v in sort_component(master_graph, comp, fmt=False):
if last_v == v[1]:
x_pos += 1
last_v = v[1]
pos[v] = (x_pos, y_pos[v[1]])
x_pos += 1
if x_pos > 7:
x_pos = 0
y_pos["A"] -= 2
y_pos["B"] -= 2
# draw and save:
for nodelist, color in [(normal, "lightgray"), (balancing, "lightblue")]:
nx.draw(master_graph, pos, font_size=5, nodelist=nodelist, node_color=color, linewidths=0.1, width=0.5,
node_size=400,
with_labels=True)
plt.savefig(filename, bbox_inches='tight')
def getConnectedComponents(self, G):
return nalgos.connected_components(G)
from networkx import read_edgelist
import networkx as nx
G = read_edgelist('hartford_drug.edgelist')
print(G.number_of_nodes())
print(G.number_of_edges())
import matplotlib.pyplot as plt
nx.draw(G)
plt.show()
# 寻找社区/联通子图
from networkx.algorithms import number_connected_components, connected_components
print(number_connected_components(G))
for subG in connected_components(G):
print(subG)
# 获取联通子图的图结构
from networkx.algorithms import connected_component_subgraphs
for i, subG in enumerate(connected_component_subgraphs(G)):
print('G%s' % i, subG.number_of_nodes(), subG.number_of_edges())
# 通过三角计算强化社区发现
# 三角计数(triangles counts)和集束系数/聚类系数（clustering coefficient）衡量社区/子图的紧密程度
from networkx.algorithms import triangles, transitivity, average_clustering
# 三角计数
print(triangles(G))
# 平均三角计数
