graphkit-learn/gklearn/dataset/file_managers.py

""" Utilities function to manage graph files
"""
from os.path import dirname, splitext


class DataLoader():
	
	
	def __init__(self, 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_targets : string
			The name of file of the 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 the TUDataset. 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":
			self._graphs, self._targets, self._label_names = self.load_from_ds(filename, filename_targets)
		elif extension == "cxl":
			dir_dataset = kwargs.get('dirname_dataset', None)
			self._graphs, self._targets, self._label_names = self.load_from_xml(filename, dir_dataset)
		elif extension == 'xml':
			dir_dataset = kwargs.get('dirname_dataset', None)
			self._graphs, self._targets, self._label_names = self.load_from_xml(filename, dir_dataset)
		elif extension == "mat":
			order = kwargs.get('order')
			self._graphs, self._targets, self._label_names = self.load_mat(filename, order)
		elif extension == 'txt':
			self._graphs, self._targets, self._label_names = self.load_tud(filename)
		else:
			raise ValueError('The input file with the extension ".', extension, '" is not supported. The supported extensions includes: ".ds", ".cxl", ".xml", ".mat", ".txt".')
	
	
	def load_from_ds(self, 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 = self.load_ct
		elif extension == 'gxl' or extension == 'sdf': # @todo: .sdf not tested yet.
			load_file_fun = self.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)
				self._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)
				self._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_xml(self, 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 = self.load_gxl(dir_dataset + '/' + mol_filename)
			data.append(g)
			self._append_label_names(label_names, l_names)
			y.append(mol_class)
			
		return data, y, label_names
	
	
	def load_mat(self, 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)
		for key, value in content.items():
			if key[0] == 'l':  # class label
				y = np.transpose(value)[0].tolist()
			elif key[0] != '_':
				# if adjacency matrix is not compressed / edge label exists
				if order[1] == 0:
					for i, item in enumerate(value[0]):
						g = nx.Graph(name=i)  # set name of the graph
						nl = np.transpose(item[order[3]][0][0][0])  # node label
						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:
					for i, item in enumerate(value[0]):
						g = nx.Graph(name=i)  # set name of the graph
						nl = np.transpose(item[order[3]][0][0][0])  # node label
						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]):
							g.add_edge(col, row)
						data.append(g)
						
		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(self, 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_ct(self, 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(self, 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 _append_label_names(self, 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]
	
	
	@property
	def data(self):
		return self._graphs, self._targets, self._label_names
	
	
	@property
	def graphs(self):
		return self._graphs
	
	
	@property
	def targets(self):
		return self._targets
	
	
	@property
	def label_names(self):
		return self._label_names
	
	
class DataSaver():
	
	
	def __init__(self, graphs, targets=None, filename='gfile', gformat='gxl', group=None, **kwargs):
		"""Save list of graphs.
		"""
		import os
		dirname_ds = os.path.dirname(filename)
		if dirname_ds != '':
			dirname_ds += '/'
			os.makedirs(dirname_ds, exist_ok=True)
					
		if 'graph_dir' in kwargs:
			graph_dir = kwargs['graph_dir'] + '/'
			os.makedirs(graph_dir, exist_ok=True)
			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(graphs):
					fname_tmp = "graph" + str(idx) + ".gxl"
					self.save_gxl(g, graph_dir + fname_tmp, **kwargs)
					fgroup.write("\n\t<graph file=\"" + fname_tmp + "\" class=\"" + str(targets[idx]) + "\"/>")
				fgroup.write("\n</GraphCollection>")
				fgroup.close()


	def save_gxl(self, 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_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)
		
			
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]
	gloader = DataLoader(ds_file, order=order)
	Gn, targets, label_names = gloader.data
	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