Add class WeisfeilerLehman.

gklearn/kernels/__init__.py

@@ -12,3 +12,4 @@ from gklearn.kernels.structural_sp import StructuralSP
 from gklearn.kernels.shortest_path import ShortestPath
 from gklearn.kernels.path_up_to_h import PathUpToH
 from gklearn.kernels.treelet import Treelet
+from gklearn.kernels.weisfeiler_lehman import WeisfeilerLehman

gklearn/preimage/experiments/xp_median_preimage.py

@@ -196,6 +196,66 @@ def xp_median_preimage_9_3():
 		print('fit method:', fit_method, '\n')
 		mpg_options['fit_method'] = fit_method
 		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
+		
+		
+def xp_median_preimage_9_4():
+	"""xp 9_4: MAO, WeisfeilerLehman, using CONSTANT.
+	"""
+	# set parameters.
+	ds_name = 'MAO' #
+	mpg_options = {'fit_method': 'k-graphs',
+				   'init_ecc': [4, 4, 2, 1, 1, 1], #
+				   'ds_name': ds_name,
+				   'parallel': True, # False
+				   'time_limit_in_sec': 0,
+				   'max_itrs': 100, # 
+				   'max_itrs_without_update': 3,
+				   'epsilon_residual': 0.01,
+				   'epsilon_ec': 0.1,
+				   'verbose': 2}
+	kernel_options = {'name': 'WeisfeilerLehman',
+				      'height': 6,
+					  'base_kernel': 'subtree',
+ 					  'parallel': 'imap_unordered', 
+                      # 'parallel': None, 
+					  'n_jobs': multiprocessing.cpu_count(),
+					  'normalize': True,
+					  'verbose': 2}
+	ged_options = {'method': 'IPFP',
+				   'initialization_method': 'RANDOM', # 'NODE'
+				   'initial_solutions': 10, # 1
+				   'edit_cost': 'CONSTANT', # 
+				   'attr_distance': 'euclidean',
+				   'ratio_runs_from_initial_solutions': 1,
+				   'threads': multiprocessing.cpu_count(),
+				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
+	mge_options = {'init_type': 'MEDOID',
+				   'random_inits': 10,
+				   'time_limit': 600,
+				   'verbose': 2,
+				   'refine': False}
+	save_results = True
+	dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/'
+	irrelevant_labels = None #
+	edge_required = False #
+	
+	# print settings.
+	print('parameters:')
+	print('dataset name:', ds_name)
+	print('mpg_options:', mpg_options)
+	print('kernel_options:', kernel_options)
+	print('ged_options:', ged_options)
+	print('mge_options:', mge_options)
+	print('save_results:', save_results)
+	print('irrelevant_labels:', irrelevant_labels)
+	print()
+	
+	# generate preimages.
+	for fit_method in ['k-graphs', 'expert'] + ['random'] * 5:
+		print('\n-------------------------------------')
+		print('fit method:', fit_method, '\n')
+		mpg_options['fit_method'] = fit_method
+		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
 
 
 def xp_median_preimage_8_1():
@@ -383,6 +443,66 @@ def xp_median_preimage_8_3():
 		mpg_options['fit_method'] = fit_method
 		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
 		
+		
+def xp_median_preimage_8_4():
+	"""xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT.
+	"""
+	# set parameters.
+	ds_name = 'Monoterpenoides' #
+	mpg_options = {'fit_method': 'k-graphs',
+				   'init_ecc': [4, 4, 2, 1, 1, 1], #
+				   'ds_name': ds_name,
+				   'parallel': True, # False
+				   'time_limit_in_sec': 0,
+				   'max_itrs': 100, # 
+				   'max_itrs_without_update': 3,
+				   'epsilon_residual': 0.01,
+				   'epsilon_ec': 0.1,
+				   'verbose': 2}
+	kernel_options = {'name': 'WeisfeilerLehman',
+				      'height': 4,
+					  'base_kernel': 'subtree',
+ 					  'parallel': 'imap_unordered', 
+                      # 'parallel': None, 
+					  'n_jobs': multiprocessing.cpu_count(),
+					  'normalize': True,
+					  'verbose': 2}
+	ged_options = {'method': 'IPFP',
+				   'initialization_method': 'RANDOM', # 'NODE'
+				   'initial_solutions': 10, # 1
+				   'edit_cost': 'CONSTANT', # 
+				   'attr_distance': 'euclidean',
+				   'ratio_runs_from_initial_solutions': 1,
+				   'threads': multiprocessing.cpu_count(),
+				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
+	mge_options = {'init_type': 'MEDOID',
+				   'random_inits': 10,
+				   'time_limit': 600,
+				   'verbose': 2,
+				   'refine': False}
+	save_results = True
+	dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/'
+	irrelevant_labels = None #
+	edge_required = False #
+	
+	# print settings.
+	print('parameters:')
+	print('dataset name:', ds_name)
+	print('mpg_options:', mpg_options)
+	print('kernel_options:', kernel_options)
+	print('ged_options:', ged_options)
+	print('mge_options:', mge_options)
+	print('save_results:', save_results)
+	print('irrelevant_labels:', irrelevant_labels)
+	print()
+	
+	# generate preimages.
+	for fit_method in ['k-graphs', 'expert'] + ['random'] * 5:
+		print('\n-------------------------------------')
+		print('fit method:', fit_method, '\n')
+		mpg_options['fit_method'] = fit_method
+		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
+		
 
 def xp_median_preimage_7_1():
 	"""xp 7_1: MUTAG, StructuralSP, using CONSTANT.
@@ -568,6 +688,66 @@ def xp_median_preimage_7_3():
 		print('fit method:', fit_method, '\n')
 		mpg_options['fit_method'] = fit_method
 		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
+		
+		
+def xp_median_preimage_7_4():
+	"""xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT.
+	"""
+	# set parameters.
+	ds_name = 'MUTAG' #
+	mpg_options = {'fit_method': 'k-graphs',
+				   'init_ecc': [4, 4, 2, 1, 1, 1], #
+				   'ds_name': ds_name,
+				   'parallel': True, # False
+				   'time_limit_in_sec': 0,
+				   'max_itrs': 100, # 
+				   'max_itrs_without_update': 3,
+				   'epsilon_residual': 0.01,
+				   'epsilon_ec': 0.1,
+				   'verbose': 2}
+	kernel_options = {'name': 'WeisfeilerLehman',
+				      'height': 1,
+					  'base_kernel': 'subtree',
+ 					  'parallel': 'imap_unordered', 
+                      # 'parallel': None, 
+					  'n_jobs': multiprocessing.cpu_count(),
+					  'normalize': True,
+					  'verbose': 2}
+	ged_options = {'method': 'IPFP',
+				   'initialization_method': 'RANDOM', # 'NODE'
+				   'initial_solutions': 10, # 1
+				   'edit_cost': 'CONSTANT', # 
+				   'attr_distance': 'euclidean',
+				   'ratio_runs_from_initial_solutions': 1,
+				   'threads': multiprocessing.cpu_count(),
+				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
+	mge_options = {'init_type': 'MEDOID',
+				   'random_inits': 10,
+				   'time_limit': 600,
+				   'verbose': 2,
+				   'refine': False}
+	save_results = True
+	dir_save = '../results/xp_median_preimage/' + ds_name + '.' + kernel_options['name'] + '/'
+	irrelevant_labels = None #
+	edge_required = False #
+	
+	# print settings.
+	print('parameters:')
+	print('dataset name:', ds_name)
+	print('mpg_options:', mpg_options)
+	print('kernel_options:', kernel_options)
+	print('ged_options:', ged_options)
+	print('mge_options:', mge_options)
+	print('save_results:', save_results)
+	print('irrelevant_labels:', irrelevant_labels)
+	print()
+	
+	# generate preimages.
+	for fit_method in ['k-graphs', 'expert'] + ['random'] * 5:
+		print('\n-------------------------------------')
+		print('fit method:', fit_method, '\n')
+		mpg_options['fit_method'] = fit_method
+		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
 
 
 def xp_median_preimage_6_1():
@@ -1432,6 +1612,9 @@ if __name__ == "__main__":
 
 	#### xp 7_3: MUTAG, Treelet, using CONSTANT.
  	# xp_median_preimage_7_3()
+
+	#### xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT.
+# 	xp_median_preimage_7_4()
 	 
     #### xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT.
 # 	xp_median_preimage_8_1()
@@ -1442,6 +1625,9 @@ if __name__ == "__main__":
 	#### xp 8_3: Monoterpenoides, Treelet, using CONSTANT.
 # 	xp_median_preimage_8_3()
 
+	#### xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT.
+# 	xp_median_preimage_8_4()
+
 	#### xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only.
 # 	xp_median_preimage_9_1()
 
@@ -1449,4 +1635,7 @@ if __name__ == "__main__":
  	# xp_median_preimage_9_2()
 
 	#### xp 9_3: MAO, Treelet, using CONSTANT.
-	xp_median_preimage_9_3()
+# 	xp_median_preimage_9_3()
+
+	#### xp 9_4: MAO, WeisfeilerLehman, using CONSTANT.
+	xp_median_preimage_9_4()

gklearn/preimage/median_preimage_generator.py

@@ -751,8 +751,14 @@ class MedianPreimageGenerator(PreimageGenerator):
 								  edge_labels=self._dataset.edge_labels,
 								  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
 								  **self._kernel_options)
+		elif self._kernel_options['name'] == 'WeisfeilerLehman':
+			from gklearn.kernels import WeisfeilerLehman
+			self._graph_kernel = WeisfeilerLehman(node_labels=self._dataset.node_labels,
+								  edge_labels=self._dataset.edge_labels,
+								  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
+								  **self._kernel_options)
 		else:
-			raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet".')
+			raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WeisfeilerLehman".')
 			
 			
 # 	def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]):

gklearn/tests/test_graph_kernels.py

@@ -233,7 +233,7 @@ def test_PathUpToH(ds_name, parallel, k_func, compute_method):
 	
 @pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS'])
 @pytest.mark.parametrize('parallel', ['imap_unordered', None])
-def test_treeletkernel(ds_name, parallel):
+def test_Treelet(ds_name, parallel):
 	"""Test treelet kernel.
 	"""
 	from gklearn.kernels import Treelet
@@ -258,28 +258,30 @@ def test_treeletkernel(ds_name, parallel):
 		assert False, exception
 		
 		
-# @pytest.mark.parametrize('ds_name', ['Acyclic'])
-# #@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge'])
-# @pytest.mark.parametrize('base_kernel', ['subtree'])
-# @pytest.mark.parametrize('parallel', ['imap_unordered', None])
-# def test_weisfeilerlehmankernel(ds_name, parallel, base_kernel):
-#	 """Test Weisfeiler-Lehman kernel.
-#	 """
-#	 from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel
+@pytest.mark.parametrize('ds_name', ['Acyclic'])
+#@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge'])
+@pytest.mark.parametrize('base_kernel', ['subtree'])
+@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_WeisfeilerLehman(ds_name, parallel, base_kernel):
+	"""Test Weisfeiler-Lehman kernel.
+	"""
+	from gklearn.kernels import WeisfeilerLehman
 	
-#	 Gn, y = chooseDataset(ds_name)
+	dataset = chooseDataset(ds_name)
 
-#	 try:
-#		 Kmatrix, run_time = weisfeilerlehmankernel(Gn, 
-#													node_label='atom', 
-#													edge_label='bond_type',
-#													height=2,
-#													base_kernel=base_kernel,
-#													parallel=parallel,
-#													n_jobs=multiprocessing.cpu_count(), 
-#													verbose=True)
-#	 except Exception as exception:
-#		 assert False, exception
+	try:
+		graph_kernel = WeisfeilerLehman(node_labels=dataset.node_labels,
+					  edge_labels=dataset.edge_labels,
+					  ds_infos=dataset.get_dataset_infos(keys=['directed']),
+					  height=2, base_kernel=base_kernel)
+		gram_matrix, run_time = graph_kernel.compute(dataset.graphs,
+			parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True)
+		kernel_list, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1:],
+			parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True)
+		kernel, run_time = graph_kernel.compute(dataset.graphs[0], dataset.graphs[1],
+			parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True)
+	except Exception as exception:
+		assert False, exception
 		
 
 if __name__ == "__main__":