New translations graph_files.py (Chinese Simplified)

lang/zh/gklearn/utils/graph_files.py

@@ -0,0 +1,833 @@
+""" Utilities function to manage graph files
+"""
+from os.path import dirname, splitext
+
+
+def load_dataset(filename, filename_targets=None, gformat=None, **kwargs):
+	"""Read graph data from filename and load them as NetworkX graphs.
+
+	Parameters
+	----------
+	filename : string
+		The name of the file from where the dataset is read.
+	filename_y : string
+		The name of file of the targets corresponding to graphs.
+	extra_params : dict
+		Extra parameters only designated to '.mat' format.
+
+	Return
+	------
+	data : List of NetworkX graph.
+
+	y : List
+
+	Targets corresponding to graphs.
+		
+	Notes
+	-----
+	This function supports following graph dataset formats:
+
+	'ds': load data from .ds file. See comments of function loadFromDS for a example.
+
+	'cxl': load data from Graph eXchange Language file (.cxl file). See 
+	`here <http://www.gupro.de/GXL/Introduction/background.html>`__ for detail.
+
+	'sdf': load data from structured data file (.sdf file). See 
+	`here <http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx>`__
+	for details.
+
+	'mat': Load graph data from a MATLAB (up to version 7.1) .mat file. See
+	README in `downloadable file <http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/>`__
+	for details.
+
+	'txt': Load graph data from a special .txt file. See
+	`here <https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets>`__
+	for details. Note here filename is the name of either .txt file in
+	the dataset directory.
+	"""
+	extension = splitext(filename)[1][1:]
+	if extension == "ds":
+		data, y, label_names = load_from_ds(filename, filename_targets)
+	elif extension == "cxl":
+		dir_dataset = kwargs.get('dirname_dataset', None)
+		data, y, label_names = load_from_xml(filename, dir_dataset)
+	elif extension == 'xml':
+		dir_dataset = kwargs.get('dirname_dataset', None)
+		data, y, label_names = load_from_xml(filename, dir_dataset)
+	elif extension == "mat":
+		order = kwargs.get('order')
+		data, y, label_names = load_mat(filename, order)
+	elif extension == 'txt':
+		data, y, label_names = load_tud(filename)
+
+	return data, y, label_names
+
+
+def save_dataset(Gn, y, gformat='gxl', group=None, filename='gfile', **kwargs):
+	"""Save list of graphs.
+	"""
+	import os
+	dirname_ds = os.path.dirname(filename)
+	if dirname_ds != '':
+		dirname_ds += '/'
+		if not os.path.exists(dirname_ds) :
+			os.makedirs(dirname_ds)
+				
+	if 'graph_dir' in kwargs:
+		graph_dir = kwargs['graph_dir'] + '/'
+		if not os.path.exists(graph_dir):
+			os.makedirs(graph_dir)
+		del kwargs['graph_dir']
+	else:
+		graph_dir = dirname_ds 
+			
+	if group == 'xml' and gformat == 'gxl':
+		with open(filename + '.xml', 'w') as fgroup:
+			fgroup.write("<?xml version=\"1.0\"?>")
+			fgroup.write("\n<!DOCTYPE GraphCollection SYSTEM \"http://www.inf.unibz.it/~blumenthal/dtd/GraphCollection.dtd\">")
+			fgroup.write("\n<GraphCollection>")
+			for idx, g in enumerate(Gn):
+				fname_tmp = "graph" + str(idx) + ".gxl"
+				save_gxl(g, graph_dir + fname_tmp, **kwargs)
+				fgroup.write("\n\t<graph file=\"" + fname_tmp + "\" class=\"" + str(y[idx]) + "\"/>")
+			fgroup.write("\n</GraphCollection>")
+			fgroup.close()
+
+
+def load_ct(filename): # @todo: this function is only tested on CTFile V2000; header not considered; only simple cases (atoms and bonds are considered.)
+	"""load data from a Chemical Table (.ct) file.
+
+	Notes
+	------
+	a typical example of data in .ct is like this:
+
+	3 2  <- number of nodes and edges
+
+	0.0000	0.0000	0.0000 C <- each line describes a node (x,y,z + label)
+
+	0.0000	0.0000	0.0000 C
+
+	0.0000	0.0000	0.0000 O
+
+	1  3  1  1 <- each line describes an edge : to, from, bond type, bond stereo
+
+	2  3  1  1
+	  
+	Check `CTFile Formats file <https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&ved=2ahUKEwivhaSdjsTlAhVhx4UKHczHA8gQFjAJegQIARAC&url=https%3A%2F%2Fwww.daylight.com%2Fmeetings%2Fmug05%2FKappler%2Fctfile.pdf&usg=AOvVaw1cDNrrmMClkFPqodlF2inS>`__
+	for detailed format discription.
+	"""
+	import networkx as nx
+	from os.path import basename
+	g = nx.Graph()
+	with open(filename) as f:
+		content = f.read().splitlines()
+		g = nx.Graph(name=str(content[0]), filename=basename(filename))  # set name of the graph
+
+		# read the counts line.
+		tmp = content[1].split(' ')
+		tmp = [x for x in tmp if x != '']
+		nb_atoms = int(tmp[0].strip())  # number of atoms
+		nb_bonds = int(tmp[1].strip())  # number of bonds
+		count_line_tags = ['number_of_atoms', 'number_of_bonds', 'number_of_atom_lists', '', 'chiral_flag', 'number_of_stext_entries', '', '', '', '', 'number_of_properties', 'CT_version']
+		i = 0
+		while i < len(tmp):
+			if count_line_tags[i] != '': # if not obsoleted
+				g.graph[count_line_tags[i]] = tmp[i].strip()
+			i += 1
+		
+		# read the atom block.
+		atom_tags = ['x', 'y', 'z', 'atom_symbol', 'mass_difference', 'charge', 'atom_stereo_parity', 'hydrogen_count_plus_1', 'stereo_care_box', 'valence', 'h0_designator', '', '', 'atom_atom_mapping_number', 'inversion_retention_flag', 'exact_change_flag']
+		for i in range(0, nb_atoms):
+			tmp = content[i + 2].split(' ')
+			tmp = [x for x in tmp if x != '']
+			g.add_node(i)
+			j = 0
+			while j < len(tmp):
+				if atom_tags[j] != '':
+					g.nodes[i][atom_tags[j]] = tmp[j].strip()
+				j += 1
+				
+		# read the bond block.
+		bond_tags = ['first_atom_number', 'second_atom_number', 'bond_type', 'bond_stereo', '', 'bond_topology', 'reacting_center_status']
+		for i in range(0, nb_bonds):
+			tmp = content[i + g.number_of_nodes() + 2].split(' ')
+			tmp = [x for x in tmp if x != '']
+			n1, n2 = int(tmp[0].strip()) - 1, int(tmp[1].strip()) - 1
+			g.add_edge(n1, n2)
+			j = 2
+			while j < len(tmp):
+				if bond_tags[j] != '':
+					g.edges[(n1, n2)][bond_tags[j]] = tmp[j].strip()
+				j += 1
+				
+	# get label names.
+	label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+	atom_symbolic = [0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, None, None, 1, 1, 1]
+	for nd in g.nodes():
+		for key in g.nodes[nd]:
+			if atom_symbolic[atom_tags.index(key)] == 1:
+				label_names['node_labels'].append(key)
+			else:
+				label_names['node_attrs'].append(key)
+		break
+	bond_symbolic = [None, None, 1, 1, None, 1, 1]
+	for ed in g.edges():
+		for key in g.edges[ed]:
+			if bond_symbolic[bond_tags.index(key)] == 1:
+				label_names['edge_labels'].append(key)
+			else:
+				label_names['edge_attrs'].append(key)
+		break
+		
+	return g, label_names
+
+
+def load_gxl(filename): # @todo: directed graphs.
+	from os.path import basename
+	import networkx as nx
+	import xml.etree.ElementTree as ET
+
+	tree = ET.parse(filename)
+	root = tree.getroot()
+	index = 0
+	g = nx.Graph(filename=basename(filename), name=root[0].attrib['id'])
+	dic = {}  # used to retrieve incident nodes of edges
+	for node in root.iter('node'):
+		dic[node.attrib['id']] = index
+		labels = {}
+		for attr in node.iter('attr'):
+			labels[attr.attrib['name']] = attr[0].text
+		g.add_node(index, **labels)
+		index += 1
+
+	for edge in root.iter('edge'):
+		labels = {}
+		for attr in edge.iter('attr'):
+			labels[attr.attrib['name']] = attr[0].text
+		g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels)
+		
+	# get label names.
+	label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+	for node in root.iter('node'):
+		for attr in node.iter('attr'):
+			if attr[0].tag == 'int': # @todo: this maybe wrong, and slow. 
+				label_names['node_labels'].append(attr.attrib['name'])
+			else:
+				label_names['node_attrs'].append(attr.attrib['name'])
+		break
+	for edge in root.iter('edge'):
+		for attr in edge.iter('attr'):
+			if attr[0].tag == 'int': # @todo: this maybe wrong, and slow. 
+				label_names['edge_labels'].append(attr.attrib['name'])
+			else:
+				label_names['edge_attrs'].append(attr.attrib['name'])
+		break
+
+	return g, label_names
+
+
+def save_gxl(graph, filename, method='default', node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]):
+	if method == 'default':
+		gxl_file = open(filename, 'w')
+		gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
+		gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
+		gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
+		if 'name' in graph.graph:
+			name = str(graph.graph['name'])
+		else:
+			name = 'dummy'
+		gxl_file.write("<graph id=\"" + name + "\" edgeids=\"false\" edgemode=\"undirected\">\n")
+		for v, attrs in graph.nodes(data=True):
+			gxl_file.write("<node id=\"_" + str(v) + "\">")
+			for l_name in node_labels:	
+				gxl_file.write("<attr name=\"" + l_name + "\"><int>" + 
+							   str(attrs[l_name]) + "</int></attr>")
+			for a_name in node_attrs:	
+				gxl_file.write("<attr name=\"" + a_name + "\"><float>" + 
+							   str(attrs[a_name]) + "</float></attr>")
+			gxl_file.write("</node>\n")
+		for v1, v2, attrs in graph.edges(data=True):
+			gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\">")
+			for l_name in edge_labels:	
+				gxl_file.write("<attr name=\"" + l_name + "\"><int>" + 
+							   str(attrs[l_name]) + "</int></attr>")
+			for a_name in edge_attrs:	
+				gxl_file.write("<attr name=\"" + a_name + "\"><float>" + 
+							   str(attrs[a_name]) + "</float></attr>")
+			gxl_file.write("</edge>\n")
+		gxl_file.write("</graph>\n")
+		gxl_file.write("</gxl>")
+		gxl_file.close()
+	elif method == 'benoit':
+		import xml.etree.ElementTree as ET
+		root_node = ET.Element('gxl')
+		attr = dict()
+		attr['id'] = str(graph.graph['name'])
+		attr['edgeids'] = 'true'
+		attr['edgemode'] = 'undirected'
+		graph_node = ET.SubElement(root_node, 'graph', attrib=attr)
+	
+		for v in graph:
+			current_node = ET.SubElement(graph_node, 'node', attrib={'id': str(v)})
+			for attr in graph.nodes[v].keys():
+				cur_attr = ET.SubElement(
+					current_node, 'attr', attrib={'name': attr})
+				cur_value = ET.SubElement(cur_attr,
+										  graph.nodes[v][attr].__class__.__name__)
+				cur_value.text = graph.nodes[v][attr]
+	
+		for v1 in graph:
+			for v2 in graph[v1]:
+				if (v1 < v2):  # Non oriented graphs
+					cur_edge = ET.SubElement(
+						graph_node,
+						'edge',
+						attrib={
+							'from': str(v1),
+							'to': str(v2)
+						})
+					for attr in graph[v1][v2].keys():
+						cur_attr = ET.SubElement(
+							cur_edge, 'attr', attrib={'name': attr})
+						cur_value = ET.SubElement(
+							cur_attr, graph[v1][v2][attr].__class__.__name__)
+						cur_value.text = str(graph[v1][v2][attr])
+	
+		tree = ET.ElementTree(root_node)
+		tree.write(filename)
+	elif method == 'gedlib':
+		# reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
+#		pass
+		gxl_file = open(filename, 'w')
+		gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
+		gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
+		gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
+		gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"true\" edgemode=\"undirected\">\n")
+		for v, attrs in graph.nodes(data=True):
+			gxl_file.write("<node id=\"_" + str(v) + "\">")
+			gxl_file.write("<attr name=\"" + "chem" + "\"><int>" + str(attrs['chem']) + "</int></attr>")
+			gxl_file.write("</node>\n")
+		for v1, v2, attrs in graph.edges(data=True):
+			gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\">")
+			gxl_file.write("<attr name=\"valence\"><int>" + str(attrs['valence']) + "</int></attr>")
+#			gxl_file.write("<attr name=\"valence\"><int>" + "1" + "</int></attr>")
+			gxl_file.write("</edge>\n")
+		gxl_file.write("</graph>\n")
+		gxl_file.write("</gxl>")
+		gxl_file.close()
+	elif method == 'gedlib-letter':
+		# reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
+		# and https://github.com/dbblumenthal/gedlib/blob/master/data/datasets/Letter/HIGH/AP1_0000.gxl
+		gxl_file = open(filename, 'w')
+		gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
+		gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
+		gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
+		gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"false\" edgemode=\"undirected\">\n")
+		for v, attrs in graph.nodes(data=True):
+			gxl_file.write("<node id=\"_" + str(v) + "\">")
+			gxl_file.write("<attr name=\"x\"><float>" + str(attrs['attributes'][0]) + "</float></attr>")
+			gxl_file.write("<attr name=\"y\"><float>" + str(attrs['attributes'][1]) + "</float></attr>")
+			gxl_file.write("</node>\n")
+		for v1, v2, attrs in graph.edges(data=True):
+			gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\"/>\n")
+		gxl_file.write("</graph>\n")
+		gxl_file.write("</gxl>")
+		gxl_file.close()
+
+
+# def loadSDF(filename):
+# 	"""load data from structured data file (.sdf file).
+
+# 	Notes
+# 	------
+# 	A SDF file contains a group of molecules, represented in the similar way as in MOL format.
+# 	Check `here <http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx>`__ for detailed structure.
+# 	"""
+# 	import networkx as nx
+# 	from os.path import basename
+# 	from tqdm import tqdm
+# 	import sys
+# 	data = []
+# 	with open(filename) as f:
+# 		content = f.read().splitlines()
+# 		index = 0
+# 		pbar = tqdm(total=len(content) + 1, desc='load SDF', file=sys.stdout)
+# 		while index < len(content):
+# 			index_old = index
+
+# 			g = nx.Graph(name=content[index].strip())  # set name of the graph
+
+# 			tmp = content[index + 3]
+# 			nb_nodes = int(tmp[:3])  # number of the nodes
+# 			nb_edges = int(tmp[3:6])  # number of the edges
+
+# 			for i in range(0, nb_nodes):
+# 				tmp = content[i + index + 4]
+# 				g.add_node(i, atom=tmp[31:34].strip())
+
+# 			for i in range(0, nb_edges):
+# 				tmp = content[i + index + g.number_of_nodes() + 4]
+# 				tmp = [tmp[i:i + 3] for i in range(0, len(tmp), 3)]
+# 				g.add_edge(
+# 					int(tmp[0]) - 1, int(tmp[1]) - 1, bond_type=tmp[2].strip())
+
+# 			data.append(g)
+
+# 			index += 4 + g.number_of_nodes() + g.number_of_edges()
+# 			while content[index].strip() != '$$$$':  # seperator
+# 				index += 1
+# 			index += 1
+
+# 			pbar.update(index - index_old)
+# 		pbar.update(1)
+# 		pbar.close()
+
+# 	return data
+
+
+def load_mat(filename, order): # @todo: need to be updated (auto order) or deprecated.
+	"""Load graph data from a MATLAB (up to version 7.1) .mat file.
+
+	Notes
+	------
+	A MAT file contains a struct array containing graphs, and a column vector lx containing a class label for each graph.
+	Check README in `downloadable file <http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/>`__ for detailed structure.
+	"""
+	from scipy.io import loadmat
+	import numpy as np
+	import networkx as nx
+	data = []
+	content = loadmat(filename)
+	# print(content)
+	# print('----')
+	for key, value in content.items():
+		if key[0] == 'l':  # class label
+			y = np.transpose(value)[0].tolist()
+			# print(y)
+		elif key[0] != '_':
+			# print(value[0][0][0])
+			# print()
+			# print(value[0][0][1])
+			# print()
+			# print(value[0][0][2])
+			# print()
+			# if len(value[0][0]) > 3:
+			#	 print(value[0][0][3])
+			# print('----')
+			# if adjacency matrix is not compressed / edge label exists
+			if order[1] == 0:
+				for i, item in enumerate(value[0]):
+					# print(item)
+					# print('------')
+					g = nx.Graph(name=i)  # set name of the graph
+					nl = np.transpose(item[order[3]][0][0][0])  # node label
+					# print(item[order[3]])
+					# print()
+					for index, label in enumerate(nl[0]):
+						g.add_node(index, label_1=str(label))
+					el = item[order[4]][0][0][0]  # edge label
+					for edge in el:
+						g.add_edge(edge[0] - 1, edge[1] - 1, label_1=str(edge[2]))
+					data.append(g)
+			else:
+# 				from scipy.sparse import csc_matrix
+				for i, item in enumerate(value[0]):
+					# print(item)
+					# print('------')
+					g = nx.Graph(name=i)  # set name of the graph
+					nl = np.transpose(item[order[3]][0][0][0])  # node label
+					# print(nl)
+					# print()
+					for index, label in enumerate(nl[0]):
+						g.add_node(index, label_1=str(label))
+					sam = item[order[0]]  # sparse adjacency matrix
+					index_no0 = sam.nonzero()
+					for col, row in zip(index_no0[0], index_no0[1]):
+						# print(col)
+						# print(row)
+						g.add_edge(col, row)
+					data.append(g)
+					# print(g.edges(data=True))
+					
+	label_names = {'node_labels': ['label_1'], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+	if order[1] == 0:
+		label_names['edge_labels'].append('label_1')
+		
+	return data, y, label_names
+
+
+def load_tud(filename):
+	"""Load graph data from TUD dataset files.
+
+	Notes
+	------
+	The graph data is loaded from separate files.
+	Check README in `downloadable file <http://tiny.cc/PK_MLJ_data>`__, 2018 for detailed structure.
+	"""
+	import networkx as nx
+	from os import listdir
+	from os.path import dirname, basename
+	
+	
+	def get_infos_from_readme(frm): # @todo: add README (cuniform), maybe node/edge label maps.
+		"""Get information from DS_label_readme.txt file.
+		"""
+		
+		def get_label_names_from_line(line):
+			"""Get names of labels/attributes from a line.
+			"""
+			str_names = line.split('[')[1].split(']')[0]
+			names = str_names.split(',')
+			names = [attr.strip() for attr in names]
+			return names
+		
+		
+		def get_class_label_map(label_map_strings):
+			label_map = {}
+			for string in label_map_strings:
+				integer, label = string.split('\t')
+				label_map[int(integer.strip())] = label.strip()
+			return label_map
+
+
+		label_names = {'node_labels': [], 'node_attrs': [], 
+					   'edge_labels': [], 'edge_attrs': []}
+		class_label_map = None
+		class_label_map_strings = []
+		with open(frm) as rm:
+			content_rm = rm.read().splitlines()
+		i = 0
+		while i < len(content_rm):
+			line = content_rm[i].strip()
+			# get node/edge labels and attributes.
+			if line.startswith('Node labels:'):
+				label_names['node_labels'] = get_label_names_from_line(line)
+			elif line.startswith('Node attributes:'):
+				label_names['node_attrs'] = get_label_names_from_line(line)
+			elif line.startswith('Edge labels:'):
+				label_names['edge_labels'] = get_label_names_from_line(line)
+			elif line.startswith('Edge attributes:'):
+				label_names['edge_attrs'] = get_label_names_from_line(line)
+			# get class label map.
+			elif line.startswith('Class labels were converted to integer values using this map:'):
+				i += 2
+				line = content_rm[i].strip()
+				while line != '' and i < len(content_rm):
+					class_label_map_strings.append(line)
+					i += 1
+					line = content_rm[i].strip()
+				class_label_map = get_class_label_map(class_label_map_strings)
+			i += 1
+				
+		return label_names, class_label_map
+
+	
+	# get dataset name.
+	dirname_dataset = dirname(filename)
+	filename = basename(filename)
+	fn_split = filename.split('_A')
+	ds_name = fn_split[0].strip()
+	
+	# load data file names
+	for name in listdir(dirname_dataset):
+		if ds_name + '_A' in name:
+			fam = dirname_dataset + '/' + name
+		elif ds_name + '_graph_indicator' in name:
+			fgi = dirname_dataset + '/' + name
+		elif ds_name + '_graph_labels' in name:
+			fgl = dirname_dataset + '/' + name
+		elif ds_name + '_node_labels' in name:
+			fnl = dirname_dataset + '/' + name
+		elif ds_name + '_edge_labels' in name:
+			fel = dirname_dataset + '/' + name
+		elif ds_name + '_edge_attributes' in name:
+			fea = dirname_dataset + '/' + name
+		elif ds_name + '_node_attributes' in name:
+			fna = dirname_dataset + '/' + name
+		elif ds_name + '_graph_attributes' in name:
+			fga = dirname_dataset + '/' + name
+		elif ds_name + '_label_readme' in name:
+			frm = dirname_dataset + '/' + name
+		# this is supposed to be the node attrs, make sure to put this as the last 'elif'
+		elif ds_name + '_attributes' in name:
+			fna = dirname_dataset + '/' + name
+			
+	# get labels and attributes names.
+	if 'frm' in locals():
+		label_names, class_label_map = get_infos_from_readme(frm)
+	else:
+		label_names = {'node_labels': [], 'node_attrs': [], 
+					   'edge_labels': [], 'edge_attrs': []}
+		class_label_map = None
+	
+	with open(fgi) as gi:
+		content_gi = gi.read().splitlines()  # graph indicator
+	with open(fam) as am:
+		content_am = am.read().splitlines()  # adjacency matrix
+	
+	# load targets.
+	if 'fgl' in locals():
+		with open(fgl) as gl:
+			content_targets = gl.read().splitlines()  # targets (classification)
+		targets = [float(i) for i in content_targets]
+	elif 'fga' in locals():
+		with open(fga) as ga:
+			content_targets = ga.read().splitlines()  # targets (regression)
+		targets = [int(i) for i in content_targets]
+	else:
+		raise Exception('Can not find targets file. Please make sure there is a "', ds_name, '_graph_labels.txt" or "', ds_name, '_graph_attributes.txt"', 'file in your dataset folder.')
+	if class_label_map is not None:
+		targets = [class_label_map[t] for t in targets]
+
+	# create graphs and add nodes
+	data = [nx.Graph(name=str(i)) for i in range(0, len(content_targets))]
+	if 'fnl' in locals():
+		with open(fnl) as nl:
+			content_nl = nl.read().splitlines()  # node labels
+		for idx, line in enumerate(content_gi):
+			# transfer to int first in case of unexpected blanks
+			data[int(line) - 1].add_node(idx)
+			labels = [l.strip() for l in content_nl[idx].split(',')]
+			if label_names['node_labels'] == []: # @todo: need fix bug.
+				for i, label in enumerate(labels):
+					l_name = 'label_' + str(i)
+					data[int(line) - 1].nodes[idx][l_name] = label
+					label_names['node_labels'].append(l_name)
+			else:
+				for i, l_name in enumerate(label_names['node_labels']):
+					data[int(line) - 1].nodes[idx][l_name] = labels[i]
+	else:
+		for i, line in enumerate(content_gi):
+			data[int(line) - 1].add_node(i)
+			
+	# add edges
+	for line in content_am:
+		tmp = line.split(',')
+		n1 = int(tmp[0]) - 1
+		n2 = int(tmp[1]) - 1
+		# ignore edge weight here.
+		g = int(content_gi[n1]) - 1
+		data[g].add_edge(n1, n2)
+
+	# add edge labels
+	if 'fel' in locals():
+		with open(fel) as el:
+			content_el = el.read().splitlines()
+		for idx, line in enumerate(content_el):
+			labels = [l.strip() for l in line.split(',')]
+			n = [int(i) - 1 for i in content_am[idx].split(',')]
+			g = int(content_gi[n[0]]) - 1
+			if label_names['edge_labels'] == []:
+				for i, label in enumerate(labels):
+					l_name = 'label_' + str(i)
+					data[g].edges[n[0], n[1]][l_name] = label
+					label_names['edge_labels'].append(l_name)
+			else:
+				for i, l_name in enumerate(label_names['edge_labels']):
+					data[g].edges[n[0], n[1]][l_name] = labels[i]
+
+	# add node attributes
+	if 'fna' in locals():
+		with open(fna) as na:
+			content_na = na.read().splitlines()
+		for idx, line in enumerate(content_na):
+			attrs = [a.strip() for a in line.split(',')]
+			g = int(content_gi[idx]) - 1
+			if label_names['node_attrs'] == []:
+				for i, attr in enumerate(attrs):
+					a_name = 'attr_' + str(i)
+					data[g].nodes[idx][a_name] = attr
+					label_names['node_attrs'].append(a_name)
+			else:
+				for i, a_name in enumerate(label_names['node_attrs']):
+					data[g].nodes[idx][a_name] = attrs[i]
+
+	# add edge attributes
+	if 'fea' in locals():
+		with open(fea) as ea:
+			content_ea = ea.read().splitlines()
+		for idx, line in enumerate(content_ea):
+			attrs = [a.strip() for a in line.split(',')]
+			n = [int(i) - 1 for i in content_am[idx].split(',')]
+			g = int(content_gi[n[0]]) - 1
+			if label_names['edge_attrs'] == []:
+				for i, attr in enumerate(attrs):
+					a_name = 'attr_' + str(i)
+					data[g].edges[n[0], n[1]][a_name] = attr
+					label_names['edge_attrs'].append(a_name)
+			else:
+				for i, a_name in enumerate(label_names['edge_attrs']):
+					data[g].edges[n[0], n[1]][a_name] = attrs[i]
+
+	return data, targets, label_names
+			
+
+def load_from_ds(filename, filename_targets):
+	"""Load data from .ds file.
+
+	Possible graph formats include:
+
+	'.ct': see function load_ct for detail.
+
+	'.gxl': see dunction load_gxl for detail.
+
+	Note these graph formats are checked automatically by the extensions of 
+	graph files.
+	"""		
+	dirname_dataset = dirname(filename)
+	data = []
+	y = []
+	label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+	with open(filename) as fn:
+		content = fn.read().splitlines()
+	extension = splitext(content[0].split(' ')[0])[1][1:]
+	if extension == 'ct':
+		load_file_fun = load_ct
+	elif extension == 'gxl' or extension == 'sdf': # @todo: .sdf not tested yet.
+		load_file_fun = load_gxl
+		
+	if filename_targets is None or filename_targets == '':
+		for i in range(0, len(content)):
+			tmp = content[i].split(' ')
+			# remove the '#'s in file names
+			g, l_names = load_file_fun(dirname_dataset + '/' + tmp[0].replace('#', '', 1))
+			data.append(g)
+			__append_label_names(label_names, l_names)
+			y.append(float(tmp[1]))
+	else:  # targets in a seperate file
+		for i in range(0, len(content)):
+			tmp = content[i]
+			# remove the '#'s in file names
+			g, l_names = load_file_fun(dirname_dataset + '/' + tmp.replace('#', '', 1))
+			data.append(g)
+			__append_label_names(label_names, l_names)
+		
+		with open(filename_targets) as fnt:
+			content_y = fnt.read().splitlines()
+		# assume entries in filename and filename_targets have the same order.
+		for item in content_y:
+			tmp = item.split(' ')
+			# assume the 3rd entry in a line is y (for Alkane dataset)
+			y.append(float(tmp[2]))
+			
+	return data, y, label_names
+
+
+# def load_from_cxl(filename):
+# 	import xml.etree.ElementTree as ET
+# 	
+# 	dirname_dataset = dirname(filename)
+# 	tree = ET.parse(filename)
+# 	root = tree.getroot()
+# 	data = []
+# 	y = []
+# 	for graph in root.iter('graph'):
+# 		mol_filename = graph.attrib['file']
+# 		mol_class = graph.attrib['class']
+# 		data.append(load_gxl(dirname_dataset + '/' + mol_filename))
+# 		y.append(mol_class)
+		
+		
+def load_from_xml(filename, dir_dataset=None):
+	import xml.etree.ElementTree as ET
+	
+	if dir_dataset is not None:
+		dir_dataset = dir_dataset
+	else:
+		dir_dataset = dirname(filename)
+	tree = ET.parse(filename)
+	root = tree.getroot()
+	data = []
+	y = []
+	label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
+	for graph in root.iter('graph'):
+		mol_filename = graph.attrib['file']
+		mol_class = graph.attrib['class']
+		g, l_names = load_gxl(dir_dataset + '/' + mol_filename)
+		data.append(g)
+		__append_label_names(label_names, l_names)
+		y.append(mol_class)
+		
+	return data, y, label_names
+
+
+def __append_label_names(label_names, new_names):
+	for key, val in label_names.items():
+		label_names[key] += [name for name in new_names[key] if name not in val]
+		
+			
+if __name__ == '__main__':	
+#	### Load dataset from .ds file.
+#	# .ct files.
+#	ds = {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds',
+#		'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'}
+#	Gn, y = loadDataset(ds['dataset'], filename_y=ds['dataset_y'])
+# 	ds_file = '../../datasets/Acyclic/dataset_bps.ds'  # node symb
+# 	Gn, targets, label_names = load_dataset(ds_file)
+# 	ds_file = '../../datasets/MAO/dataset.ds' # node/edge symb
+# 	Gn, targets, label_names = load_dataset(ds_file)
+##	ds = {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'} # unlabeled
+##	Gn, y = loadDataset(ds['dataset'])
+# 	print(Gn[1].graph)
+# 	print(Gn[1].nodes(data=True))
+# 	print(Gn[1].edges(data=True))
+# 	print(targets[1])
+	
+# 	# .gxl file.
+# 	ds_file = '../../datasets/monoterpenoides/dataset_10+.ds'  # node/edge symb
+# 	Gn, y, label_names = load_dataset(ds_file)
+# 	print(Gn[1].graph)
+# 	print(Gn[1].nodes(data=True))
+# 	print(Gn[1].edges(data=True))
+# 	print(y[1])
+
+	# .mat file.
+	ds_file = '../../datasets/MUTAG_mat/MUTAG.mat'
+	order = [0, 0, 3, 1, 2]
+	Gn, targets, label_names = load_dataset(ds_file, order=order)
+	print(Gn[1].graph)
+	print(Gn[1].nodes(data=True))
+	print(Gn[1].edges(data=True))
+	print(targets[1])
+
+#	### Convert graph from one format to another.
+#	# .gxl file.
+#	import networkx as nx
+#	ds = {'name': 'monoterpenoides', 
+#		  'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'}  # node/edge symb
+#	Gn, y = loadDataset(ds['dataset'])
+#	y = [int(i) for i in y]
+#	print(Gn[1].nodes(data=True))
+#	print(Gn[1].edges(data=True))
+#	print(y[1])
+#	# Convert a graph to the proper NetworkX format that can be recognized by library gedlib.
+#	Gn_new = []
+#	for G in Gn:
+#		G_new = nx.Graph()
+#		for nd, attrs in G.nodes(data=True):
+#			G_new.add_node(str(nd), chem=attrs['atom'])
+#		for nd1, nd2, attrs in G.edges(data=True):
+#			G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
+##			G_new.add_edge(str(nd1), str(nd2))
+#		Gn_new.append(G_new)
+#	print(Gn_new[1].nodes(data=True))
+#	print(Gn_new[1].edges(data=True))
+#	print(Gn_new[1])
+#	filename = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/generated_datsets/monoterpenoides/gxl/monoterpenoides'
+#	xparams = {'method': 'gedlib'}
+#	saveDataset(Gn, y, gformat='gxl', group='xml', filename=filename, xparams=xparams)
+	
+	# save dataset.
+#	ds = {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat',
+#		  'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}  # node/edge symb
+#	Gn, y = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
+#	saveDataset(Gn, y, group='xml', filename='temp/temp')
+	
+	# test - new way to add labels and attributes.
+#	dataset = '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt'
+# 	filename = '../../datasets/Fingerprint/Fingerprint_A.txt'
+#	dataset = '../../datasets/Letter-med/Letter-med_A.txt'
+#	dataset = '../../datasets/AIDS/AIDS_A.txt'
+#	dataset = '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'
+# 	Gn, targets, label_names = load_dataset(filename)
+	pass