New translations treelet.py (Chinese Simplified)

lang/zh/gklearn/kernels/treelet.py

@@ -0,0 +1,506 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr 13 18:02:46 2020
+
+@author: ljia
+
+@references: 
+
+    [1] Gaüzère B, Brun L, Villemin D. Two new graphs kernels in 
+    chemoinformatics. Pattern Recognition Letters. 2012 Nov 1;33(15):2038-47.
+"""
+
+import sys
+from multiprocessing import Pool
+from tqdm import tqdm
+import numpy as np
+import networkx as nx
+from collections import Counter
+from itertools import chain
+from gklearn.utils import SpecialLabel
+from gklearn.utils.parallel import parallel_gm, parallel_me
+from gklearn.utils.utils import find_all_paths, get_mlti_dim_node_attrs
+from gklearn.kernels import GraphKernel
+
+
+class Treelet(GraphKernel):
+	
+	def __init__(self, **kwargs):
+		GraphKernel.__init__(self)
+		self.__node_labels = kwargs.get('node_labels', [])
+		self.__edge_labels = kwargs.get('edge_labels', [])
+		self.__sub_kernel = kwargs.get('sub_kernel', None)
+		self.__ds_infos = kwargs.get('ds_infos', {})
+		if self.__sub_kernel is None:
+			raise Exception('Sub kernel not set.')
+
+
+	def _compute_gm_series(self):
+		self.__add_dummy_labels(self._graphs)
+		
+		# get all canonical keys of all graphs before calculating kernels to save 
+		# time, but this may cost a lot of memory for large dataset.
+		canonkeys = []
+		if self._verbose >= 2:
+			iterator = tqdm(self._graphs, desc='getting canonkeys', file=sys.stdout)
+		else:
+			iterator = self._graphs
+		for g in iterator:
+			canonkeys.append(self.__get_canonkeys(g))
+		
+		# compute Gram matrix.
+		gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
+		
+		from itertools import combinations_with_replacement
+		itr = combinations_with_replacement(range(0, len(self._graphs)), 2)
+		if self._verbose >= 2:
+			iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout)
+		else:
+			iterator = itr
+		for i, j in iterator:
+			kernel = self.__kernel_do(canonkeys[i], canonkeys[j])
+			gram_matrix[i][j] = kernel
+			gram_matrix[j][i] = kernel # @todo: no directed graph considered?
+				
+		return gram_matrix
+			
+			
+	def _compute_gm_imap_unordered(self):
+		self.__add_dummy_labels(self._graphs)
+		
+		# get all canonical keys of all graphs before calculating kernels to save 
+		# time, but this may cost a lot of memory for large dataset.
+		pool = Pool(self._n_jobs)
+		itr = zip(self._graphs, range(0, len(self._graphs)))
+		if len(self._graphs) < 100 * self._n_jobs:
+			chunksize = int(len(self._graphs) / self._n_jobs) + 1
+		else:
+			chunksize = 100
+		canonkeys = [[] for _ in range(len(self._graphs))]
+		get_fun = self._wrapper_get_canonkeys
+		if self._verbose >= 2:
+			iterator = tqdm(pool.imap_unordered(get_fun, itr, chunksize),
+							desc='getting canonkeys', file=sys.stdout)
+		else:
+			iterator = pool.imap_unordered(get_fun, itr, chunksize)
+		for i, ck in iterator:
+			canonkeys[i] = ck
+		pool.close()
+		pool.join()
+		
+		# compute Gram matrix.
+		gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
+		
+		def init_worker(canonkeys_toshare):
+			global G_canonkeys
+			G_canonkeys = canonkeys_toshare
+		do_fun = self._wrapper_kernel_do
+		parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, 
+					glbv=(canonkeys,), n_jobs=self._n_jobs, verbose=self._verbose)
+			
+		return gram_matrix
+	
+	
+	def _compute_kernel_list_series(self, g1, g_list):
+		self.__add_dummy_labels(g_list + [g1])
+		
+		# get all canonical keys of all graphs before calculating kernels to save 
+		# time, but this may cost a lot of memory for large dataset.
+		canonkeys_1 = self.__get_canonkeys(g1)
+		canonkeys_list = []
+		if self._verbose >= 2:
+			iterator = tqdm(g_list, desc='getting canonkeys', file=sys.stdout)
+		else:
+			iterator = g_list
+		for g in iterator:
+			canonkeys_list.append(self.__get_canonkeys(g))
+				
+		# compute kernel list.
+		kernel_list = [None] * len(g_list)
+		if self._verbose >= 2:
+			iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout)
+		else:
+			iterator = range(len(g_list))
+		for i in iterator:
+			kernel = self.__kernel_do(canonkeys_1, canonkeys_list[i])
+			kernel_list[i] = kernel
+				
+		return kernel_list
+	
+	
+	def _compute_kernel_list_imap_unordered(self, g1, g_list):
+		self.__add_dummy_labels(g_list + [g1])
+		
+		# get all canonical keys of all graphs before calculating kernels to save 
+		# time, but this may cost a lot of memory for large dataset.
+		canonkeys_1 = self.__get_canonkeys(g1)
+		canonkeys_list = [[] for _ in range(len(g_list))]
+		pool = Pool(self._n_jobs)
+		itr = zip(g_list, range(0, len(g_list)))
+		if len(g_list) < 100 * self._n_jobs:
+			chunksize = int(len(g_list) / self._n_jobs) + 1
+		else:
+			chunksize = 100
+		get_fun = self._wrapper_get_canonkeys
+		if self._verbose >= 2:
+			iterator = tqdm(pool.imap_unordered(get_fun, itr, chunksize),
+							desc='getting canonkeys', file=sys.stdout)
+		else:
+			iterator = pool.imap_unordered(get_fun, itr, chunksize)
+		for i, ck in iterator:
+			canonkeys_list[i] = ck
+		pool.close()
+		pool.join()
+		
+		# compute kernel list.
+		kernel_list = [None] * len(g_list)
+
+		def init_worker(ck_1_toshare, ck_list_toshare):
+			global G_ck_1, G_ck_list
+			G_ck_1 = ck_1_toshare
+			G_ck_list = ck_list_toshare
+		do_fun = self._wrapper_kernel_list_do
+		def func_assign(result, var_to_assign):	
+			var_to_assign[result[0]] = result[1]
+		itr = range(len(g_list))
+		len_itr = len(g_list)
+		parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr,
+			init_worker=init_worker, glbv=(canonkeys_1, canonkeys_list), method='imap_unordered', 
+			n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose)
+			
+		return kernel_list
+	
+	
+	def _wrapper_kernel_list_do(self, itr):
+		return itr, self.__kernel_do(G_ck_1, G_ck_list[itr])
+	
+	
+	def _compute_single_kernel_series(self, g1, g2):
+		self.__add_dummy_labels([g1] + [g2])
+		canonkeys_1 = self.__get_canonkeys(g1)
+		canonkeys_2 = self.__get_canonkeys(g2)
+		kernel = self.__kernel_do(canonkeys_1, canonkeys_2)
+		return kernel			
+	
+	
+	def __kernel_do(self, canonkey1, canonkey2):
+		"""Calculate treelet graph kernel between 2 graphs.
+		
+		Parameters
+		----------
+		canonkey1, canonkey2 : list
+			List of canonical keys in 2 graphs, where each key is represented by a string.
+			
+		Return
+		------
+		kernel : float
+			Treelet kernel between 2 graphs.
+		"""
+		keys = set(canonkey1.keys()) & set(canonkey2.keys()) # find same canonical keys in both graphs
+		vector1 = np.array([(canonkey1[key] if (key in canonkey1.keys()) else 0) for key in keys])
+		vector2 = np.array([(canonkey2[key] if (key in canonkey2.keys()) else 0) for key in keys]) 
+		kernel = self.__sub_kernel(vector1, vector2) 
+		return kernel
+	
+	
+	def _wrapper_kernel_do(self, itr):
+		i = itr[0]
+		j = itr[1]
+		return i, j, self.__kernel_do(G_canonkeys[i], G_canonkeys[j])
+	
+	
+	def __get_canonkeys(self, G):
+		"""Generate canonical keys of all treelets in a graph.
+		
+		Parameters
+		----------
+		G : NetworkX graphs
+			The graph in which keys are generated.
+			
+		Return
+		------
+		canonkey/canonkey_l : dict
+			For unlabeled graphs, canonkey is a dictionary which records amount of 
+			every tree pattern. For labeled graphs, canonkey_l is one which keeps 
+			track of amount of every treelet.
+		"""
+		patterns = {} # a dictionary which consists of lists of patterns for all graphlet.
+		canonkey = {} # canonical key, a dictionary which records amount of every tree pattern.
+	
+		### structural analysis ###
+		### In this section, a list of patterns is generated for each graphlet, 
+		### where every pattern is represented by nodes ordered by Morgan's 
+		### extended labeling.
+		# linear patterns
+		patterns['0'] = list(G.nodes())
+		canonkey['0'] = nx.number_of_nodes(G)
+		for i in range(1, 6): # for i in range(1, 6):
+			patterns[str(i)] = find_all_paths(G, i, self.__ds_infos['directed'])
+			canonkey[str(i)] = len(patterns[str(i)])
+	
+		# n-star patterns
+		patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3]
+		patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4]
+		patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5]		
+		# n-star patterns
+		canonkey['6'] = len(patterns['3star'])
+		canonkey['8'] = len(patterns['4star'])
+		canonkey['d'] = len(patterns['5star'])
+	
+		# pattern 7
+		patterns['7'] = [] # the 1st line of Table 1 in Ref [1]
+		for pattern in patterns['3star']:
+			for i in range(1, len(pattern)): # for each neighbor of node 0
+				if G.degree(pattern[i]) >= 2:
+					pattern_t = pattern[:]
+					# set the node with degree >= 2 as the 4th node
+					pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i]
+					for neighborx in G[pattern[i]]:
+						if neighborx != pattern[0]:
+							new_pattern = pattern_t + [neighborx]
+							patterns['7'].append(new_pattern)
+		canonkey['7'] = len(patterns['7'])
+	
+		# pattern 11
+		patterns['11'] = [] # the 4th line of Table 1 in Ref [1]
+		for pattern in patterns['4star']:
+			for i in range(1, len(pattern)):
+				if G.degree(pattern[i]) >= 2:
+					pattern_t = pattern[:]
+					pattern_t[i], pattern_t[4] = pattern_t[4], pattern_t[i]
+					for neighborx in G[pattern[i]]:
+						if neighborx != pattern[0]:
+							new_pattern = pattern_t + [neighborx]
+							patterns['11'].append(new_pattern)
+		canonkey['b'] = len(patterns['11'])
+	
+		# pattern 12
+		patterns['12'] = [] # the 5th line of Table 1 in Ref [1]
+		rootlist = [] # a list of root nodes, whose extended labels are 3
+		for pattern in patterns['3star']:
+			if pattern[0] not in rootlist: # prevent to count the same pattern twice from each of the two root nodes
+				rootlist.append(pattern[0])
+				for i in range(1, len(pattern)):
+					if G.degree(pattern[i]) >= 3:
+						rootlist.append(pattern[i])
+						pattern_t = pattern[:]
+						pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i]
+						for neighborx1 in G[pattern[i]]:
+							if neighborx1 != pattern[0]:
+								for neighborx2 in G[pattern[i]]:
+									if neighborx1 > neighborx2 and neighborx2 != pattern[0]:
+										new_pattern = pattern_t + [neighborx1] + [neighborx2]
+	#						 new_patterns = [ pattern + [neighborx1] + [neighborx2] for neighborx1 in G[pattern[i]] if neighborx1 != pattern[0] for neighborx2 in G[pattern[i]] if (neighborx1 > neighborx2 and neighborx2 != pattern[0]) ]
+										patterns['12'].append(new_pattern)
+		canonkey['c'] = int(len(patterns['12']) / 2)
+	
+		# pattern 9
+		patterns['9'] = [] # the 2nd line of Table 1 in Ref [1]
+		for pattern in patterns['3star']:
+			for pairs in [ [neighbor1, neighbor2] for neighbor1 in G[pattern[0]] if G.degree(neighbor1) >= 2 \
+				for neighbor2 in G[pattern[0]] if G.degree(neighbor2) >= 2 if neighbor1 > neighbor2]:
+				pattern_t = pattern[:]
+				# move nodes with extended labels 4 to specific position to correspond to their children
+				pattern_t[pattern_t.index(pairs[0])], pattern_t[2] = pattern_t[2], pattern_t[pattern_t.index(pairs[0])]
+				pattern_t[pattern_t.index(pairs[1])], pattern_t[3] = pattern_t[3], pattern_t[pattern_t.index(pairs[1])]
+				for neighborx1 in G[pairs[0]]:
+					if neighborx1 != pattern[0]:
+						for neighborx2 in G[pairs[1]]:
+							if neighborx2 != pattern[0]:
+								new_pattern = pattern_t + [neighborx1] + [neighborx2]
+								patterns['9'].append(new_pattern)
+		canonkey['9'] = len(patterns['9'])
+	
+		# pattern 10
+		patterns['10'] = [] # the 3rd line of Table 1 in Ref [1]
+		for pattern in patterns['3star']:		
+			for i in range(1, len(pattern)):
+				if G.degree(pattern[i]) >= 2:
+					for neighborx in G[pattern[i]]:
+						if neighborx != pattern[0] and G.degree(neighborx) >= 2:
+							pattern_t = pattern[:]
+							pattern_t[i], pattern_t[3] = pattern_t[3], pattern_t[i]
+							new_patterns = [ pattern_t + [neighborx] + [neighborxx] for neighborxx in G[neighborx] if neighborxx != pattern[i] ]
+							patterns['10'].extend(new_patterns)
+		canonkey['a'] = len(patterns['10'])
+	
+		### labeling information ###
+		### In this section, a list of canonical keys is generated for every 
+		### pattern obtained in the structural analysis section above, which is a 
+		### string corresponding to a unique treelet. A dictionary is built to keep
+		### track of the amount of every treelet.
+		if len(self.__node_labels) > 0 or len(self.__edge_labels) > 0:
+			canonkey_l = {} # canonical key, a dictionary which keeps track of amount of every treelet.
+	
+			# linear patterns
+			canonkey_t = Counter(get_mlti_dim_node_attrs(G, self.__node_labels))
+			for key in canonkey_t:
+				canonkey_l[('0', key)] = canonkey_t[key]
+	
+			for i in range(1, 6): # for i in range(1, 6):
+				treelet = []
+				for pattern in patterns[str(i)]:
+					canonlist = []
+					for idx, node in enumerate(pattern[:-1]):
+						canonlist.append(tuple(G.nodes[node][nl] for nl in self.__node_labels))
+						canonlist.append(tuple(G[node][pattern[idx+1]][el] for el in self.__edge_labels))
+					canonlist.append(tuple(G.nodes[pattern[-1]][nl] for nl in self.__node_labels))
+					canonkey_t = canonlist if canonlist < canonlist[::-1] else canonlist[::-1]
+					treelet.append(tuple([str(i)] + canonkey_t))
+				canonkey_l.update(Counter(treelet))
+	
+			# n-star patterns
+			for i in range(3, 6):
+				treelet = []
+				for pattern in patterns[str(i) + 'star']:
+					canonlist = []
+					for leaf in pattern[1:]:
+						nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+						elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels)
+						canonlist.append(tuple((nlabels, elabels)))
+					canonlist.sort()
+					canonlist = list(chain.from_iterable(canonlist))
+					canonkey_t = tuple(['d' if i == 5 else str(i * 2)] + 
+									   [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] 
+									   + canonlist)
+					treelet.append(canonkey_t)
+				canonkey_l.update(Counter(treelet))
+	
+			# pattern 7
+			treelet = []
+			for pattern in patterns['7']:
+				canonlist = []
+				for leaf in pattern[1:3]:
+					nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+					elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels)
+					canonlist.append(tuple((nlabels, elabels)))
+				canonlist.sort()
+				canonlist = list(chain.from_iterable(canonlist))
+				canonkey_t = tuple(['7'] 
+					   + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist 
+					   + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] 
+					   + [tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)]
+					   + [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] 
+					   + [tuple(G[pattern[4]][pattern[3]][el] for el in self.__edge_labels)])
+				treelet.append(canonkey_t)
+			canonkey_l.update(Counter(treelet))
+	
+			# pattern 11
+			treelet = []
+			for pattern in patterns['11']:
+				canonlist = []
+				for leaf in pattern[1:4]:
+					nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+					elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels)
+					canonlist.append(tuple((nlabels, elabels)))
+				canonlist.sort()
+				canonlist = list(chain.from_iterable(canonlist))
+				canonkey_t = tuple(['b'] 
+					   + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist 
+					   + [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] 
+					   + [tuple(G[pattern[4]][pattern[0]][el] for el in self.__edge_labels)]
+					   + [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels)] 
+					   + [tuple(G[pattern[5]][pattern[4]][el] for el in self.__edge_labels)])
+				treelet.append(canonkey_t)
+			canonkey_l.update(Counter(treelet))
+	
+			# pattern 10
+			treelet = []
+			for pattern in patterns['10']:
+				canonkey4 = [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels),
+				 tuple(G[pattern[5]][pattern[4]][el] for el in self.__edge_labels)]
+				canonlist = []
+				for leaf in pattern[1:3]:
+					nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+					elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels)
+					canonlist.append(tuple((nlabels, elabels)))
+				canonlist.sort()
+				canonkey0 = list(chain.from_iterable(canonlist))
+				canonkey_t = tuple(['a']
+					    + [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] 
+						+ [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels)] 
+						+ [tuple(G[pattern[4]][pattern[3]][el] for el in self.__edge_labels)] 
+						+ [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] 
+						+ [tuple(G[pattern[0]][pattern[3]][el] for el in self.__edge_labels)] 
+						+ canonkey4 + canonkey0)
+				treelet.append(canonkey_t)
+			canonkey_l.update(Counter(treelet))
+	
+			# pattern 12
+			treelet = []
+			for pattern in patterns['12']:
+				canonlist0 = []
+				for leaf in pattern[1:3]:
+					nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+					elabels = tuple(G[leaf][pattern[0]][el] for el in self.__edge_labels)
+					canonlist0.append(tuple((nlabels, elabels)))
+				canonlist0.sort()
+				canonlist0 = list(chain.from_iterable(canonlist0))
+				canonlist3 = []
+				for leaf in pattern[4:6]:
+					nlabels = tuple(G.nodes[leaf][nl] for nl in self.__node_labels)
+					elabels = tuple(G[leaf][pattern[3]][el] for el in self.__edge_labels)
+					canonlist3.append(tuple((nlabels, elabels)))
+				canonlist3.sort()
+				canonlist3 = list(chain.from_iterable(canonlist3))
+				
+				# 2 possible key can be generated from 2 nodes with extended label 3, 
+				# select the one with lower lexicographic order.
+				canonkey_t1 = tuple(['c'] 
+						+ [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] + canonlist0 
+						+ [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] 
+						+ [tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)] 
+						+ canonlist3)
+				canonkey_t2 = tuple(['c'] 
+						+ [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels)] + canonlist3 
+						+ [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] 
+						+ [tuple(G[pattern[0]][pattern[3]][el] for el in self.__edge_labels)] 
+						+ canonlist0)
+				treelet.append(canonkey_t1 if canonkey_t1 < canonkey_t2 else canonkey_t2)
+			canonkey_l.update(Counter(treelet))
+	
+			# pattern 9
+			treelet = []
+			for pattern in patterns['9']:
+				canonkey2 = [tuple(G.nodes[pattern[4]][nl] for nl in self.__node_labels),
+				  tuple(G[pattern[4]][pattern[2]][el] for el in self.__edge_labels)]
+				canonkey3 = [tuple(G.nodes[pattern[5]][nl] for nl in self.__node_labels),
+				  tuple(G[pattern[5]][pattern[3]][el] for el in self.__edge_labels)]
+				prekey2 = [tuple(G.nodes[pattern[2]][nl] for nl in self.__node_labels),
+			      tuple(G[pattern[2]][pattern[0]][el] for el in self.__edge_labels)]
+				prekey3 = [tuple(G.nodes[pattern[3]][nl] for nl in self.__node_labels), 
+			      tuple(G[pattern[3]][pattern[0]][el] for el in self.__edge_labels)]
+				if prekey2 + canonkey2 < prekey3 + canonkey3:
+					canonkey_t = [tuple(G.nodes[pattern[1]][nl] for nl in self.__node_labels)] \
+								 + [tuple(G[pattern[1]][pattern[0]][el] for el in self.__edge_labels)] \
+								 + prekey2 + prekey3 + canonkey2 + canonkey3
+				else:
+					canonkey_t = [tuple(G.nodes[pattern[1]][nl] for nl in self.__node_labels)] \
+								 + [tuple(G[pattern[1]][pattern[0]][el] for el in self.__edge_labels)] \
+								 + prekey3 + prekey2 + canonkey3 + canonkey2
+				treelet.append(tuple(['9']
+						  + [tuple(G.nodes[pattern[0]][nl] for nl in self.__node_labels)] 
+						  + canonkey_t))
+			canonkey_l.update(Counter(treelet))
+	
+			return canonkey_l
+	
+		return canonkey
+	
+	
+	def _wrapper_get_canonkeys(self, itr_item):
+		g = itr_item[0]
+		i = itr_item[1]
+		return i, self.__get_canonkeys(g)
+	
+	
+	def __add_dummy_labels(self, Gn):
+		if len(self.__node_labels) == 0 or (len(self.__node_labels) == 1 and self.__node_labels[0] == SpecialLabel.DUMMY):
+			for i in range(len(Gn)):
+				nx.set_node_attributes(Gn[i], '0', SpecialLabel.DUMMY)
+			self.__node_labels = [SpecialLabel.DUMMY]
+		if len(self.__edge_labels) == 0 or (len(self.__edge_labels) == 1 and self.__edge_labels[0] == SpecialLabel.DUMMY):
+			for i in range(len(Gn)):
+				nx.set_edge_attributes(Gn[i], '0', SpecialLabel.DUMMY)
+			self.__edge_labels = [SpecialLabel.DUMMY]