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 = 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_constants=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()
node_labels = ged_env.get_all_node_labels()
edge_labels = ged_env.get_all_edge_labels()
node_label_costs = label_costs_to_matrix(self._node_label_costs, len(node_labels))
edge_label_costs = label_costs_to_matrix(self._edge_label_costs, len(edge_labels))
ged_env.set_label_costs(node_label_costs, edge_label_costs)
set_median_id = ged_env.add_graph('set_median')
gen_median_id = ged_env.add_graph('gen_median')
ged_env.init(init_type=self._ged_options['init_option'])
# Set up the madian graph estimator.
self._mge = MedianGraphEstimatorCML(ged_env, constant_node_costs(self._ged_options['edit_cost']))
self._mge.set_refine_method(self._ged_options['method'], 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)
self._mge.set_descent_method(ged_options['method'], 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 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
