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
from ged import GED, get_nb_edit_operations
from gklearn.utils import load_dataset
#from gklearn.ged.util import compute_ged
def compute_ged(Gi, Gj, edit_cost, method='BIPARTITE'):
"""
Compute GED between two graph according to edit_cost
"""
dis, pi_forward, pi_backward = GED(Gi,
Gj,
lib='gedlibpy',
cost='CONSTANT',
method=method,
edit_cost_constant=edit_cost,
stabilizer='min',
repeat=20)
n_eo_tmp = get_nb_edit_operations(Gi, Gj, pi_forward, pi_backward)
return dis, n_eo_tmp
G, y, _ = load_dataset('./data/simple_test.ds')
dis, _ = compute_ged(G[0], G[1], edit_cost=[3, 1, 1, 3, 1, 1])
print(dis)