Add MedianGraphEstimator for cost matrices learning.

gklearn/ged/env/ged_env.py

@@ -637,6 +637,10 @@ class GEDEnv(object):
 		return [i for i in self.__internal_to_original_node_ids[graph_id].values()]		
 	
 	
+	def get_node_cost(self, node_label_1, node_label_2):
+		return self.__ged_data.node_cost(node_label_1, node_label_2)
+	
+	
 	def get_node_rel_cost(self, node_label_1, node_label_2):
 		"""
 	/*!
@@ -650,7 +654,7 @@ class GEDEnv(object):
 			node_label_1 = tuple(sorted(node_label_1.items(), key=lambda kv: kv[0]))
 		if isinstance(node_label_2, dict):
 			node_label_2 = tuple(sorted(node_label_2.items(), key=lambda kv: kv[0]))
-		return self.__ged_data._edit_cost.node_rel_cost_fun(node_label_1, node_label_2)
+		return self.__ged_data._edit_cost.node_rel_cost_fun(node_label_1, node_label_2) # @todo: may need to use node_cost() instead (or change node_cost() and modify ged_method for pre-defined cost matrices.)
 		
 		
 	def get_node_del_cost(self, node_label):
@@ -677,6 +681,10 @@ class GEDEnv(object):
 		if isinstance(node_label, dict):
 			node_label = tuple(sorted(node_label.items(), key=lambda kv: kv[0]))
 		return self.__ged_data._edit_cost.node_ins_cost_fun(node_label)
+	
+	
+	def get_edge_cost(self, edge_label_1, edge_label_2):
+		return self.__ged_data.edge_cost(edge_label_1, edge_label_2)
 		
 		
 	def get_edge_rel_cost(self, edge_label_1, edge_label_2):

gklearn/ged/median/__init__.py

@@ -1,3 +1,4 @@
 from gklearn.ged.median.median_graph_estimator import MedianGraphEstimator
 from gklearn.ged.median.median_graph_estimator_py import MedianGraphEstimatorPy
+from gklearn.ged.median.median_graph_estimator_cml import MedianGraphEstimatorCML
 from gklearn.ged.median.utils import constant_node_costs, mge_options_to_string

gklearn/ged/util/__init__.py

@@ -1,3 +1,3 @@
 from gklearn.ged.util.lsape_solver import LSAPESolver
 from gklearn.ged.util.util import compute_geds, ged_options_to_string
-from gklearn.ged.util.util import compute_geds_cml
+from gklearn.ged.util.util import compute_geds_cml, label_costs_to_matrix

gklearn/preimage/median_preimage_generator_cml.py

@@ -14,9 +14,10 @@ from gklearn.preimage import PreimageGenerator
 from gklearn.preimage.utils import compute_k_dis
 from gklearn.ged.env import GEDEnv
 from gklearn.ged.learning import CostMatricesLearner
-from gklearn.ged.median import MedianGraphEstimatorPy
+from gklearn.ged.median import MedianGraphEstimatorCML
 from gklearn.ged.median import constant_node_costs, mge_options_to_string
 from gklearn.utils.utils import get_graph_kernel_by_name
+from gklearn.ged.util import label_costs_to_matrix
 
 
 class MedianPreimageGeneratorCML(PreimageGenerator):
@@ -347,12 +348,19 @@ class MedianPreimageGeneratorCML(PreimageGenerator):
 		for g in graphs:
 			ged_env.add_nx_graph(g, '')
 		graph_ids = ged_env.get_all_graph_ids()
+	
+		node_labels = ged_env.get_all_node_labels()
+		edge_labels =  ged_env.get_all_edge_labels()
+		node_label_costs = label_costs_to_matrix(self.__node_label_costs, len(node_labels))
+		edge_label_costs = label_costs_to_matrix(self.__edge_label_costs, len(edge_labels))
+		ged_env.set_label_costs(node_label_costs, edge_label_costs)
+	
 		set_median_id = ged_env.add_graph('set_median')
 		gen_median_id = ged_env.add_graph('gen_median')
 		ged_env.init(init_type=self.__ged_options['init_option'])
 		
 		# Set up the madian graph estimator.
-		self.__mge = MedianGraphEstimatorPy(ged_env, constant_node_costs(self.__ged_options['edit_cost']))
+		self.__mge = MedianGraphEstimatorCML(ged_env, constant_node_costs(self.__ged_options['edit_cost']))
 		self.__mge.set_refine_method(self.__ged_options['method'], self.__ged_options)
 		options = self.__mge_options.copy()
 		if not 'seed' in options: