[Enhancement] Allow deciding whether or not to make a copy of input graphs in GraphKernel class.

gklearn/kernels/graph_kernel.py

@@ -77,8 +77,6 @@ class GraphKernel(BaseEstimator): #, ABC):
 		# Clear any prior attributes stored on the estimator, # @todo: unless warm_start is used;
 		self.clear_attributes()
 
-# 		X = check_array(X, accept_sparse=True)
-
 		# Validate parameters for the transformer.
 		self.validate_parameters()
 
@@ -386,35 +384,58 @@ class GraphKernel(BaseEstimator): #, ABC):
 		self.n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count())
 		self.normalize = kwargs.get('normalize', True)
 		self.verbose = kwargs.get('verbose', 2)
+		self.copy_graphs = kwargs.get('copy_graphs', True)
+		self.save_unnormed = kwargs.get('save_unnormed', True)
 		self.validate_parameters()
 
+		# If the inputs is a list of graphs.
 		if len(graphs) == 1:
 			if not isinstance(graphs[0], list):
 				raise Exception('Cannot detect graphs.')
 			elif len(graphs[0]) == 0:
 				raise Exception('The graph list given is empty. No computation was performed.')
 			else:
-				self._graphs = [g.copy() for g in graphs[0]] # @todo: might be very slow.
+				if self.copy_graphs:
+					self._graphs = [g.copy() for g in graphs[0]] # @todo: might be very slow.
+				else:
+					self._graphs = graphs
 				self._gram_matrix = self._compute_gram_matrix()
-				self._gram_matrix_unnorm = np.copy(self._gram_matrix)
+
+				if self.save_unnormed:
+					self._gram_matrix_unnorm = np.copy(self._gram_matrix)
 				if self.normalize:
 					self._gram_matrix = normalize_gram_matrix(self._gram_matrix)
 				return self._gram_matrix, self._run_time
 
 		elif len(graphs) == 2:
+			# If the inputs are two graphs.
 			if self.is_graph(graphs[0]) and self.is_graph(graphs[1]):
-				kernel = self._compute_single_kernel(graphs[0].copy(), graphs[1].copy())
+				if self.copy_graphs:
+					G0, G1 = graphs[0].copy(), graphs[1].copy()
+				else:
+					G0, G1 = graphs[0], graphs[1]
+				kernel = self._compute_single_kernel(G0, G1)
 				return kernel, self._run_time
+
+			# If the inputs are a graph and a list of graphs.
 			elif self.is_graph(graphs[0]) and isinstance(graphs[1], list):
-				g1 = graphs[0].copy()
-				g_list = [g.copy() for g in graphs[1]]
-				kernel_list = self._compute_kernel_list(g1, g_list)
+				if self.copy_graphs:
+					g1 = graphs[0].copy()
+					g_list = [g.copy() for g in graphs[1]]
+					kernel_list = self._compute_kernel_list(g1, g_list)
+				else:
+					kernel_list = self._compute_kernel_list(graphs[0], graphs[1])
 				return kernel_list, self._run_time
+
 			elif isinstance(graphs[0], list) and self.is_graph(graphs[1]):
-				g1 = graphs[1].copy()
-				g_list = [g.copy() for g in graphs[0]]
-				kernel_list = self._compute_kernel_list(g1, g_list)
+				if self.copy_graphs:
+					g1 = graphs[1].copy()
+					g_list = [g.copy() for g in graphs[0]]
+					kernel_list = self._compute_kernel_list(g1, g_list)
+				else:
+					kernel_list = self._compute_kernel_list(graphs[1], graphs[0])
 				return kernel_list, self._run_time
+
 			else:
 				raise Exception('Cannot detect graphs.')