graphkit-learn/gklearn/utils/utils.py

import networkx as nx
import numpy as np
from copy import deepcopy
#from itertools import product

# from tqdm import tqdm


def getSPLengths(G1):
	sp = nx.shortest_path(G1)
	distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))
	for i in sp.keys():
		for j in sp[i].keys():
			distances[i, j] = len(sp[i][j]) - 1
	return distances


def getSPGraph(G, edge_weight=None):
	"""Transform graph G to its corresponding shortest-paths graph.

	Parameters
	----------
	G : NetworkX graph
		The graph to be tramsformed.
	edge_weight : string
		edge attribute corresponding to the edge weight.

	Return
	------
	S : NetworkX graph
		The shortest-paths graph corresponding to G.

	Notes
	------
	For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.

	References
	----------
	.. [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
	"""
	return floydTransformation(G, edge_weight=edge_weight)


def floydTransformation(G, edge_weight=None):
	"""Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.

	Parameters
	----------
	G : NetworkX graph
		The graph to be tramsformed.
	edge_weight : string
		edge attribute corresponding to the edge weight. The default edge weight is bond_type.

	Return
	------
	S : NetworkX graph
		The shortest-paths graph corresponding to G.

	References
	----------
	.. [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
	"""
	spMatrix = nx.floyd_warshall_numpy(G, weight=edge_weight)
	S = nx.Graph()
	S.add_nodes_from(G.nodes(data=True))
	ns = list(G.nodes())
	for i in range(0, G.number_of_nodes()):
		for j in range(i + 1, G.number_of_nodes()):
			if spMatrix[i, j] != np.inf:
				S.add_edge(ns[i], ns[j], cost=spMatrix[i, j])
	return S


def get_shortest_paths(G, weight, directed):
	"""Get all shortest paths of a graph.

	Parameters
	----------
	G : NetworkX graphs
		The graphs whose paths are calculated.
	weight : string/None
		edge attribute used as weight to calculate the shortest path.
	directed: boolean
		Whether graph is directed.

	Return
	------
	sp : list of list
		List of shortest paths of the graph, where each path is represented by a list of nodes.
	"""
	from itertools import combinations
	sp = []
	for n1, n2 in combinations(G.nodes(), 2):
		try:
			spltemp = list(nx.all_shortest_paths(G, n1, n2, weight=weight))
		except nx.NetworkXNoPath:  # nodes not connected
			pass
		else:
			sp += spltemp
			# each edge walk is counted twice, starting from both its extreme nodes.
			if not directed:
				sp += [sptemp[::-1] for sptemp in spltemp]
				
	# add single nodes as length 0 paths.
	sp += [[n] for n in G.nodes()]
	return sp


def untotterTransformation(G, node_label, edge_label):
	"""Transform graph G according to Mahé et al.'s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.

	Parameters
	----------
	G : NetworkX graph
		The graph to be tramsformed.
	node_label : string
		node attribute used as label. The default node label is 'atom'.
	edge_label : string
		edge attribute used as label. The default edge label is 'bond_type'.

	Return
	------
	gt : NetworkX graph
		The transformed graph corresponding to G.

	References
	----------
	.. [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.
	"""
	# arrange all graphs in a list
	G = G.to_directed()
	gt = nx.Graph()
	gt.graph = G.graph
	gt.add_nodes_from(G.nodes(data=True))
	for edge in G.edges():
		gt.add_node(edge)
		gt.nodes[edge].update({node_label: G.nodes[edge[1]][node_label]})
		gt.add_edge(edge[0], edge)
		gt.edges[edge[0], edge].update({
			edge_label:
			G[edge[0]][edge[1]][edge_label]
		})
		for neighbor in G[edge[1]]:
			if neighbor != edge[0]:
				gt.add_edge(edge, (edge[1], neighbor))
				gt.edges[edge, (edge[1], neighbor)].update({
					edge_label:
					G[edge[1]][neighbor][edge_label]
				})
	# nx.draw_networkx(gt)
	# plt.show()

	# relabel nodes using consecutive integers for convenience of kernel calculation.
	gt = nx.convert_node_labels_to_integers(
		gt, first_label=0, label_attribute='label_orignal')
	return gt


def direct_product(G1, G2, node_label, edge_label):
	"""Return the direct/tensor product of directed graphs G1 and G2.

	Parameters
	----------
	G1, G2 : NetworkX graph
		The original graphs.
	node_label : string
		node attribute used as label. The default node label is 'atom'.
	edge_label : string
		edge attribute used as label. The default edge label is 'bond_type'.

	Return
	------
	gt : NetworkX graph
		The direct product graph of G1 and G2.

	Notes
	-----
	This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.

	References
	----------
	.. [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.
	"""
	# arrange all graphs in a list
	from itertools import product
	# G = G.to_directed()
	gt = nx.DiGraph()
	# add nodes
	for u, v in product(G1, G2):
		if G1.nodes[u][node_label] == G2.nodes[v][node_label]:
			gt.add_node((u, v))
			gt.nodes[(u, v)].update({node_label: G1.nodes[u][node_label]})
	# add edges, faster for sparse graphs (no so many edges), which is the most case for now.
	for (u1, v1), (u2, v2) in product(G1.edges, G2.edges):
		if (u1, u2) in gt and (
				v1, v2
		) in gt and G1.edges[u1, v1][edge_label] == G2.edges[u2,
															 v2][edge_label]:
			gt.add_edge((u1, u2), (v1, v2))
			gt.edges[(u1, u2), (v1, v2)].update({
				edge_label:
				G1.edges[u1, v1][edge_label]
			})

	# # add edges, faster for dense graphs (a lot of edges, complete graph would be super).
	# for u, v in product(gt, gt):
	#	 if (u[0], v[0]) in G1.edges and (
	#			 u[1], v[1]
	#	 ) in G2.edges and G1.edges[u[0],
	#								v[0]][edge_label] == G2.edges[u[1],
	#															  v[1]][edge_label]:
	#		 gt.add_edge((u[0], u[1]), (v[0], v[1]))
	#		 gt.edges[(u[0], u[1]), (v[0], v[1])].update({
	#			 edge_label:
	#			 G1.edges[u[0], v[0]][edge_label]
	#		 })

	# relabel nodes using consecutive integers for convenience of kernel calculation.
	# gt = nx.convert_node_labels_to_integers(
	#	 gt, first_label=0, label_attribute='label_orignal')
	return gt


def graph_deepcopy(G):
	"""Deep copy a graph, including deep copy of all nodes, edges and 
	attributes of the graph, nodes and edges.
	
	Note
	----
	It is the same as the NetworkX function graph.copy(), as far as I know.
	"""
	# add graph attributes.
	labels = {}
	for k, v in G.graph.items():
		labels[k] = deepcopy(v)
	if G.is_directed():
		G_copy = nx.DiGraph(**labels)
	else:
		G_copy = nx.Graph(**labels)
		
	# add nodes	
	for nd, attrs in G.nodes(data=True):
		labels = {}
		for k, v in attrs.items():
			labels[k] = deepcopy(v)
		G_copy.add_node(nd, **labels)
		
	# add edges.
	for nd1, nd2, attrs in G.edges(data=True):
		labels = {}
		for k, v in attrs.items():
			labels[k] = deepcopy(v)
		G_copy.add_edge(nd1, nd2, **labels)
	
	return G_copy


def graph_isIdentical(G1, G2):
	"""Check if two graphs are identical, including: same nodes, edges, node
	labels/attributes, edge labels/attributes.
	
	Notes
	-----
	1. The type of graphs has to be the same.

	2. Global/Graph attributes are neglected as they may contain names for graphs.
	"""
	# check nodes.
	nlist1 = [n for n in G1.nodes(data=True)]
	nlist2 = [n for n in G2.nodes(data=True)]
	if not nlist1 == nlist2:
		return False
	# check edges.
	elist1 = [n for n in G1.edges(data=True)]
	elist2 = [n for n in G2.edges(data=True)]
	if not elist1 == elist2:
		return False
	# check graph attributes.
	
	return True


def get_node_labels(Gn, node_label):
	"""Get node labels of dataset Gn.
	"""
	nl = set()
	for G in Gn:
		nl = nl | set(nx.get_node_attributes(G, node_label).values())
	return nl


def get_edge_labels(Gn, edge_label):
	"""Get edge labels of dataset Gn.
	"""
	el = set()
	for G in Gn:
		el = el | set(nx.get_edge_attributes(G, edge_label).values())
	return el


def get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attrs=None, edge_attrs=None, ds_infos=None, kernel_options={}):
	if name == 'structuralspkernel':
		from gklearn.kernels import StructuralSP
		graph_kernel = StructuralSP(node_labels=node_labels, edge_labels=edge_labels, 
								  node_attrs=node_attrs, edge_attrs=edge_attrs,
								  ds_infos=ds_infos, **kernel_options)
	return graph_kernel


def compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save='', irrelevant_labels=None):
	from gklearn.utils import Dataset, split_dataset_by_target
	
	# 1. get dataset.
	print('1. getting dataset...')
	dataset_all = Dataset()
	dataset_all.load_predefined_dataset(ds_name)
	if not irrelevant_labels is None:
		dataset_all.remove_labels(**irrelevant_labels)
# 	dataset_all.cut_graphs(range(0, 10))
	datasets = split_dataset_by_target(dataset_all)
	
	gram_matrix_unnorm_list = []
	run_time_list = []
	
	print('start generating preimage for each class of target...')
	for idx, dataset in enumerate(datasets):
		target = dataset.targets[0]
		print('\ntarget =', target, '\n')
		
		# 2. initialize graph kernel.
		print('2. initializing graph kernel and setting parameters...')
		graph_kernel = get_graph_kernel_by_name(kernel_options['name'], 
										  node_labels=dataset.node_labels,
										  edge_labels=dataset.edge_labels, 
										  node_attrs=dataset.node_attrs,
										  edge_attrs=dataset.edge_attrs,
										  ds_infos=dataset.get_dataset_infos(keys=['directed']),
										  kernel_options=kernel_options)

		# 3. compute gram matrix.
		print('3. computing gram matrix...')
		gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options)
		gram_matrix_unnorm = graph_kernel.gram_matrix_unnorm
		
		gram_matrix_unnorm_list.append(gram_matrix_unnorm)
		run_time_list.append(run_time)
				
	# 4. save results.
	print()
	print('4. saving results...')
	if save_results:
		np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=run_time_list)	

	print('\ncomplete.')