def compute_ged(g1, g2, options):
from gklearn.gedlib import librariesImport, gedlibpy
ged_env = gedlibpy.GEDEnv()
ged_env.set_edit_cost(options['edit_cost'],
edit_cost_constant=options['edit_cost_constants'])
ged_env.add_nx_graph(g1, '')
ged_env.add_nx_graph(g2, '')
listID = ged_env.get_all_graph_ids()
ged_env.init(init_type=options['init_option'])
ged_env.set_method(options['method'], ged_options_to_string(options))
ged_env.init_method()
g = listID[0]
h = listID[1]
ged_env.run_method(g, h)
pi_forward = ged_env.get_forward_map(g, h)
pi_backward = ged_env.get_backward_map(g, h)
upper = ged_env.get_upper_bound(g, h)
dis = upper
# make the map label correct (label remove map as np.inf)
nodes1 = [n for n in g1.nodes()]
nodes2 = [n for n in g2.nodes()]
nb1 = nx.number_of_nodes(g1)
nb2 = nx.number_of_nodes(g2)
pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]
# print(pi_forward)
return dis, pi_forward, pi_backward
def __gmg_bcu(self):
"""
The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG).
Returns
-------
None.
"""
# Set up the ged environment.
ged_env = gedlibpy.GEDEnv() # @todo: maybe create a ged_env as a private varible.
# gedlibpy.restart_env()
ged_env.set_edit_cost(self.__ged_options['edit_cost'], edit_cost_constant=self.__edit_cost_constants)
graphs = [self.__clean_graph(g) for g in self._dataset.graphs]
for g in graphs:
ged_env.add_nx_graph(g, '')
graph_ids = ged_env.get_all_graph_ids()
set_median_id = ged_env.add_graph('set_median')
gen_median_id = ged_env.add_graph('gen_median')
ged_env.init(init_option=self.__ged_options['init_option'])
# Set up the madian graph estimator.
self.__mge = MedianGraphEstimator(ged_env, constant_node_costs(self.__ged_options['edit_cost']))
self.__mge.set_refine_method(self.__ged_options['method'], ged_options_to_string(self.__ged_options))
options = self.__mge_options.copy()
if not 'seed' in options:
options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage.
options['parallel'] = self.__parallel
# Select the GED algorithm.
self.__mge.set_options(mge_options_to_string(options))
self.__mge.set_label_names(node_labels=self._dataset.node_labels,
edge_labels=self._dataset.edge_labels,
node_attrs=self._dataset.node_attrs,
edge_attrs=self._dataset.edge_attrs)
ged_options = self.__ged_options.copy()
if self.__parallel:
ged_options['threads'] = 1
self.__mge.set_init_method(ged_options['method'], ged_options_to_string(ged_options))
self.__mge.set_descent_method(ged_options['method'], ged_options_to_string(ged_options))
# Run the estimator.
self.__mge.run(graph_ids, set_median_id, gen_median_id)
# Get SODs.
self.__sod_set_median = self.__mge.get_sum_of_distances('initialized')
self.__sod_gen_median = self.__mge.get_sum_of_distances('converged')
# Get median graphs.
self.__set_median = ged_env.get_nx_graph(set_median_id)
self.__gen_median = ged_env.get_nx_graph(gen_median_id)
def compute_geds_by_GEDLIB(dataset):
from gklearn.gedlib import librariesImport, gedlibpy
from gklearn.ged.util import ged_options_to_string
import numpy as np
graph1 = dataset.graphs[5]
graph2 = dataset.graphs[6]
ged_env = gedlibpy.GEDEnv() # initailize GED environment.
ged_env.set_edit_cost(
'CONSTANT', # GED cost type.
edit_cost_constant=[3, 3, 1, 3, 3, 1] # edit costs.
)
# ged_env.add_nx_graph(graph1, '') # add graph1
# ged_env.add_nx_graph(graph2, '') # add graph2
for g in dataset.graphs[0:10]:
ged_env.add_nx_graph(g, '')
listID = ged_env.get_all_graph_ids() # get list IDs of graphs
ged_env.init(init_option='LAZY_WITHOUT_SHUFFLED_COPIES'
) # initialize GED environment.
options = {
'initialization-method': 'RANDOM', # or 'NODE', etc.
'threads': 1 # parallel threads.
}
ged_env.set_method(
'BIPARTITE', # GED method.
ged_options_to_string(options) # options for GED method.
)
ged_env.init_method() # initialize GED method.
ged_mat = np.empty((10, 10))
for i in range(0, 10):
for j in range(i, 10):
ged_env.run_method(i, j) # run.
ged_mat[i, j] = ged_env.get_upper_bound(i, j)
ged_mat[j, i] = ged_mat[i, j]
results = {}
results['pi_forward'] = ged_env.get_forward_map(listID[0],
listID[1]) # forward map.
results['pi_backward'] = ged_env.get_backward_map(
listID[0], listID[1]) # backward map.
results['upper_bound'] = ged_env.get_upper_bound(
listID[0], listID[1]) # GED bewteen two graphs.
results['runtime'] = ged_env.get_runtime(listID[0], listID[1])
results['init_time'] = ged_env.get_init_time()
results['ged_mat'] = ged_mat
return results
def compute_geds(graphs, options={}, sort=True, parallel=False, verbose=True):
from gklearn.gedlib import librariesImport, gedlibpy
# initialize ged env.
ged_env = gedlibpy.GEDEnv()
ged_env.set_edit_cost(options['edit_cost'],
edit_cost_constant=options['edit_cost_constants'])
for g in graphs:
ged_env.add_nx_graph(g, '')
listID = ged_env.get_all_graph_ids()
ged_env.init()
if parallel:
options['threads'] = 1
ged_env.set_method(options['method'], ged_options_to_string(options))
ged_env.init_method()
# compute ged.
neo_options = {
'edit_cost': options['edit_cost'],
'node_labels': options['node_labels'],
'edge_labels': options['edge_labels'],
'node_attrs': options['node_attrs'],
'edge_attrs': options['edge_attrs']
}
ged_mat = np.zeros((len(graphs), len(graphs)))
if parallel:
len_itr = int(len(graphs) * (len(graphs) - 1) / 2)
ged_vec = [0 for i in range(len_itr)]
n_edit_operations = [0 for i in range(len_itr)]
itr = combinations(range(0, len(graphs)), 2)
n_jobs = multiprocessing.cpu_count()
if len_itr < 100 * n_jobs:
chunksize = int(len_itr / n_jobs) + 1
else:
chunksize = 100
def init_worker(graphs_toshare, ged_env_toshare, listID_toshare):
global G_graphs, G_ged_env, G_listID
G_graphs = graphs_toshare
G_ged_env = ged_env_toshare
G_listID = listID_toshare
do_partial = partial(_wrapper_compute_ged_parallel, neo_options, sort)
pool = Pool(processes=n_jobs,
initializer=init_worker,
initargs=(graphs, ged_env, listID))
if verbose:
iterator = tqdm(pool.imap_unordered(do_partial, itr, chunksize),
desc='computing GEDs',
file=sys.stdout)
else:
iterator = pool.imap_unordered(do_partial, itr, chunksize)
# iterator = pool.imap_unordered(do_partial, itr, chunksize)
for i, j, dis, n_eo_tmp in iterator:
idx_itr = int(len(graphs) * i + j - (i + 1) * (i + 2) / 2)
ged_vec[idx_itr] = dis
ged_mat[i][j] = dis
ged_mat[j][i] = dis
n_edit_operations[idx_itr] = n_eo_tmp
# print('\n-------------------------------------------')
# print(i, j, idx_itr, dis)
pool.close()
pool.join()
else:
ged_vec = []
n_edit_operations = []
if verbose:
iterator = tqdm(range(len(graphs)),
desc='computing GEDs',
file=sys.stdout)
else:
iterator = range(len(graphs))
for i in iterator:
# for i in range(len(graphs)):
for j in range(i + 1, len(graphs)):
if nx.number_of_nodes(graphs[i]) <= nx.number_of_nodes(
graphs[j]) or not sort:
dis, pi_forward, pi_backward = _compute_ged(
ged_env, listID[i], listID[j], graphs[i], graphs[j])
else:
dis, pi_backward, pi_forward = _compute_ged(
ged_env, listID[j], listID[i], graphs[j], graphs[i])
ged_vec.append(dis)
ged_mat[i][j] = dis
ged_mat[j][i] = dis
n_eo_tmp = get_nb_edit_operations(graphs[i], graphs[j],
pi_forward, pi_backward,
**neo_options)
n_edit_operations.append(n_eo_tmp)
return ged_vec, ged_mat, n_edit_operations
def test_median_graph_estimator_symb():
from gklearn.utils import load_dataset
from gklearn.ged.median import MedianGraphEstimator, constant_node_costs
from gklearn.gedlib import librariesImport, gedlibpy
from gklearn.preimage.utils import get_same_item_indices
import multiprocessing
# estimator parameters.
init_type = 'MEDOID'
num_inits = 1
threads = multiprocessing.cpu_count()
time_limit = 60000
# algorithm parameters.
algo = 'IPFP'
initial_solutions = 1
algo_options_suffix = ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1 --initialization-method NODE '
edit_cost_name = 'CONSTANT'
edit_cost_constants = [4, 4, 2, 1, 1, 1]
ds_name = 'MUTAG'
# Load dataset.
dataset = '../../../datasets/MUTAG/MUTAG_A.txt'
Gn, y_all, label_names = load_dataset(dataset)
y_idx = get_same_item_indices(y_all)
for i, (y, values) in enumerate(y_idx.items()):
Gn_i = [Gn[val] for val in values]
break
Gn_i = Gn_i[0:10]
# Set up the environment.
ged_env = gedlibpy.GEDEnv()
# gedlibpy.restart_env()
ged_env.set_edit_cost(edit_cost_name, edit_cost_constant=edit_cost_constants)
for G in Gn_i:
ged_env.add_nx_graph(G, '')
graph_ids = ged_env.get_all_graph_ids()
set_median_id = ged_env.add_graph('set_median')
gen_median_id = ged_env.add_graph('gen_median')
ged_env.init(init_option='EAGER_WITHOUT_SHUFFLED_COPIES')
# Set up the estimator.
mge = MedianGraphEstimator(ged_env, constant_node_costs(edit_cost_name))
mge.set_refine_method(algo, '--threads ' + str(threads) + ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1')
mge_options = '--time-limit ' + str(time_limit) + ' --stdout 2 --init-type ' + init_type
mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1' + ' --update-order TRUE --refine FALSE --randomness PSEUDO --parallel TRUE '# @todo: std::to_string(rng())
# Select the GED algorithm.
algo_options = '--threads ' + str(threads) + algo_options_suffix
mge.set_options(mge_options)
mge.set_label_names(node_labels=label_names['node_labels'],
edge_labels=label_names['edge_labels'],
node_attrs=label_names['node_attrs'],
edge_attrs=label_names['edge_attrs'])
mge.set_init_method(algo, algo_options)
mge.set_descent_method(algo, algo_options)
# Run the estimator.
mge.run(graph_ids, set_median_id, gen_median_id)
# Get SODs.
sod_sm = mge.get_sum_of_distances('initialized')
sod_gm = mge.get_sum_of_distances('converged')
print('sod_sm, sod_gm: ', sod_sm, sod_gm)
# Get median graphs.
set_median = ged_env.get_nx_graph(set_median_id)
gen_median = ged_env.get_nx_graph(gen_median_id)
return set_median, gen_median
