Add class WLSubtree.

gklearn/ged/median/median_graph_estimator.py

@@ -392,7 +392,7 @@ class MedianGraphEstimator(object):
 				# Update the median. # @todo!!!!!!!!!!!!!!!!!!!!!!
 				median_modified = self.__update_median(graphs, median)
 				if not median_modified or self.__itrs[median_pos] == 0:
-					decreased_order = False
+					decreased_order = self.__decrease_order(graphs, median)
 					if not decreased_order or self.__itrs[median_pos] == 0:
 						increased_order = False
 						
@@ -742,6 +742,81 @@ class MedianGraphEstimator(object):
 		return node_maps_were_modified
 	
 	
+	def __decrease_order(self, graphs, median):
+		# Print information about current iteration
+		if self.__print_to_stdout == 2:
+			print('Trying to decrease order: ... ', end='')
+			
+		# Initialize ID of the node that is to be deleted.
+		id_deleted_node = None # @todo: or np.inf
+		decreased_order = False
+		
+		# Decrease the order as long as the best deletion delta is negative.
+		while self.__compute_best_deletion_delta(graphs, median, [id_deleted_node]) < -self.__epsilon:
+			decreased_order = True
+			self.__delete_node_from_median(id_deleted_node, median)
+			
+		# Print information about current iteration.
+		if self.__print_to_stdout == 2:
+			print('done.')
+			
+		# Return true iff the order was decreased.
+		return decreased_order
+	
+	
+	def __compute_best_deletion_delta(self, graphs, median, id_deleted_node):
+		best_delta = 0.0
+		
+		# Determine node that should be deleted (if any).
+		for i in range(0, nx.number_of_nodes(median)):
+			# Compute cost delta.
+			delta = 0.0
+			for graph_id, graph in graphs.items():
+				k = self.__get_node_image_from_map(self.__node_maps_from_median[graph_id], i)
+				if k == np.inf:
+					delta -= self.__node_del_cost
+				else:
+					delta += self.__node_ins_cost - self.__ged_env.get_node_rel_cost(median.nodes[i], graph.nodes[k])
+				for j, j_label in median[i]:
+					l = self.__get_node_image_from_map(self.__node_maps_from_median[graph_id], j)
+					if k == np.inf or l == np.inf:
+						delta -= self.__edge_del_cost
+					elif not graph.has_edge(k, l):
+						delta -= self.__edge_del_cost
+					else:
+						delta += self.__edge_ins_cost - self.__ged_env.get_edge_rel_cost(j_label, graph.edges[(k, l)])
+						
+			# Update best deletion delta.
+			if delta < best_delta - self.__epsilon:
+				best_delta = delta
+				id_deleted_node[0] = i
+				
+		return best_delta
+	
+	
+	def __delete_node_from_median(self, id_deleted_node, median):
+		# Update the nodes of the median.
+		median.remove_node(id_deleted_node) # @todo: test if it is right.
+		
+		# Update the node maps.
+		for _, node_map in self.__node_maps_from_median.items():
+			new_node_map = {nx.number_of_nodes(median): ''} # @todo
+			is_unassigned_target_node = ['', True]
+			for i in range(0, nx.number_of_nodes(median)):
+				if i != id_deleted_node:
+					new_i = (i if i < id_deleted_node else i - 1)
+					k = self.__get_node_image_from_map(node_map, i)
+					new_node_map["ds"] # @todo
+					if k != np.inf:
+						is_unassigned_target_node[k] = False
+			for k in range(0, ''):
+				if is_unassigned_target_node[k]:
+					new_node_map.sdf[]
+			node_map = new_node_map
+			
+		# Increase overall number of decreases.
+		self.__num_decrease_order += 1
+	
 	def __improve_sum_of_distances(self, timer):
 		pass
 	

gklearn/kernels/__init__.py

@@ -12,4 +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
+from gklearn.kernels.weisfeiler_lehman import WeisfeilerLehman, WLSubtree

gklearn/kernels/weisfeiler_lehman.py

@@ -472,4 +472,11 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
 		if len(self.__node_labels) == 0:
 			for G in Gn:
 				nx.set_node_attributes(G, '0', 'dummy')
-			self.__node_labels.append('dummy')
+			self.__node_labels.append('dummy')
+			
+			
+class WLSubtree(WeisfeilerLehman):
+	
+	def __init__(self, **kwargs):
+		kwargs['base_kernel'] = 'subtree'
+		super().__init__(**kwargs)

gklearn/tests/test_graph_kernels.py

@@ -260,20 +260,20 @@ def test_Treelet(ds_name, parallel):
 		
 @pytest.mark.parametrize('ds_name', ['Acyclic'])
 #@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge'])
-@pytest.mark.parametrize('base_kernel', ['subtree'])
+# @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.
+def test_WLSubtree(ds_name, parallel):
+	"""Test Weisfeiler-Lehman subtree kernel.
 	"""
-	from gklearn.kernels import WeisfeilerLehman
+	from gklearn.kernels import WLSubtree
 	
 	dataset = chooseDataset(ds_name)
 
 	try:
-		graph_kernel = WeisfeilerLehman(node_labels=dataset.node_labels,
+		graph_kernel = WLSubtree(node_labels=dataset.node_labels,
 					  edge_labels=dataset.edge_labels,
 					  ds_infos=dataset.get_dataset_infos(keys=['directed']),
-					  height=2, base_kernel=base_kernel)
+					  height=2)
 		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:],

gklearn/utils/utils.py

@@ -325,6 +325,12 @@ def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attr
 							  edge_labels=edge_labels,
 							  ds_infos=ds_infos,
 							  **kernel_options)
+	elif name == 'WLSubtree':
+		from gklearn.kernels import WLSubtree
+		graph_kernel = WLSubtree(node_labels=node_labels,
+							  edge_labels=edge_labels,
+							  ds_infos=ds_infos,
+							  **kernel_options)
 	elif name == 'WeisfeilerLehman':
 		from gklearn.kernels import WeisfeilerLehman
 		graph_kernel = WeisfeilerLehman(node_labels=node_labels,
@@ -332,7 +338,7 @@ def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attr
 							  ds_infos=ds_infos,
 							  **kernel_options)
 	else:
-		raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WeisfeilerLehman".')
+		raise Exception('The graph kernel given is not defined. Possible choices include: "StructuralSP", "ShortestPath", "PathUpToH", "Treelet", "WLSubtree", "WeisfeilerLehman".')
 
 	return graph_kernel