Add exp: compare GEDEnv with the GEDLIB version.

gklearn/experiments/ged/check_results_of_ged_env.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jun 25 11:31:46 2020
+
+@author: ljia
+"""
+
+def xp_check_results_of_GEDEnv():
+	"""Compare results of GEDEnv to GEDLIB.
+	"""
+	"""**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)
+
+	results1 = compute_geds_by_GEDEnv(dataset)
+	results2 = compute_geds_by_GEDLIB(dataset)
+	
+	# Show results.
+	import pprint
+	pp = pprint.PrettyPrinter(indent=4) # pretty print
+	print('Restuls using GEDEnv:')
+	pp.pprint(results1)
+	print()
+	print('Restuls using GEDLIB:')
+	pp.pprint(results2)
+	
+	return results1, results2
+	
+	
+def compute_geds_by_GEDEnv(dataset):
+	from gklearn.ged.env import GEDEnv		
+	import numpy as np
+	
+	graph1 = dataset.graphs[0]
+	graph2 = dataset.graphs[1]
+		
+	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.
+						  )
+	for g in dataset.graphs[0:10]:
+		ged_env.add_nx_graph(g, '')
+# 	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 = {'threads': 1 # parallel threads.
+			   }
+	ged_env.set_method('BIPARTITE', # GED method.
+	                   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_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
+		
+	
+if __name__ == '__main__':
+	results1, results2 = xp_check_results_of_GEDEnv()

gklearn/ged/util/lsape_solver.py

@@ -61,9 +61,9 @@ class LSAPESolver(object):
 		"""
 		self.clear_solution()
 		if self.__solve_optimally:
-			row_id, col_id = linear_sum_assignment(self.__cost_matrix) # @todo: only hungarianLSAPE ('ECBP') can be used.
-			self.__row_to_col_assignments[0] = col_id
-			self.__col_to_row_assignments[0] = np.argsort(col_id) # @todo: might be slow, can use row_id
+			row_ind, col_ind = linear_sum_assignment(self.__cost_matrix) # @todo: only hungarianLSAPE ('ECBP') can be used.
+			self.__row_to_col_assignments[0] = col_ind
+			self.__col_to_row_assignments[0] = np.argsort(col_ind) # @todo: might be slow, can use row_ind
 			self.__compute_cost_from_assignments()
 			if num_solutions > 1:
 				pass # @todo: