graphkit-learn/gklearn/tests/ged/test_ged_env.py

"""Tests of GEDEnv.
"""


def test_GEDEnv():
	"""Test GEDEnv.
	"""
	"""**1.   Get dataset.**"""

	from gklearn.utils import Dataset
	
	# Predefined dataset name, use dataset "MUTAG".
	ds_name = 'MUTAG'
	
	# Initialize a Dataset.
	dataset = Dataset()
	# Load predefined dataset "MUTAG".
	dataset.load_predefined_dataset(ds_name)
	graph1 = dataset.graphs[0]
	graph2 = dataset.graphs[1]
	
	"""**2.  Compute graph edit distance.**"""
	
	try:	
		from gklearn.ged.env import GEDEnv		
		
		ged_env = GEDEnv() # initailize GED environment.
		ged_env.set_edit_cost('CONSTANT', # GED cost type.
		                      edit_cost_constants=[3, 3, 1, 3, 3, 1] # edit costs.
							  )  
		ged_env.add_nx_graph(graph1, '') # add graph1
		ged_env.add_nx_graph(graph2, '') # add graph2
		listID = ged_env.get_all_graph_ids() # get list IDs of graphs
		ged_env.init(init_type='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.
		                   options # options for GED method.
						   )
		ged_env.init_method() # initialize GED method.
		
		ged_env.run_method(listID[0], listID[1]) # run.
		
		pi_forward = ged_env.get_forward_map(listID[0], listID[1]) # forward map.
		pi_backward = ged_env.get_backward_map(listID[0], listID[1]) # backward map.
		dis = ged_env.get_upper_bound(listID[0], listID[1])	# GED bewteen two graphs.
		
		import networkx as nx
		assert len(pi_forward) == nx.number_of_nodes(graph1), len(pi_backward) == nx.number_of_nodes(graph2)
	
	except Exception as exception:
		assert False, exception


if __name__ == "__main__":
	test_GEDEnv()