graphkit-learn/gklearn/kernels/marginalized.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun  3 22:22:57 2020

@author: ljia

@references:

	[1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between 
	labeled graphs. In Proceedings of the 20th International Conference on 
	Machine Learning, Washington, DC, United States, 2003.

	[2] 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.
"""

import sys
from multiprocessing import Pool
from tqdm import tqdm
import numpy as np
import networkx as nx
from gklearn.utils import SpecialLabel
from gklearn.utils.kernels import deltakernel
from gklearn.utils.parallel import parallel_gm, parallel_me
from gklearn.utils.utils import untotterTransformation
from gklearn.kernels import GraphKernel


class Marginalized(GraphKernel):
	
	def __init__(self, **kwargs):
		GraphKernel.__init__(self)
		self._node_labels = kwargs.get('node_labels', [])
		self._edge_labels = kwargs.get('edge_labels', [])
		self._p_quit = kwargs.get('p_quit', 0.5)
		self._n_iteration = kwargs.get('n_iteration', 10)
		self._remove_totters = kwargs.get('remove_totters', False)
		self._ds_infos = kwargs.get('ds_infos', {})
		self._n_iteration = int(self._n_iteration)


	def _compute_gm_series(self):
		self._add_dummy_labels(self._graphs)
		
		if self._remove_totters:
			if self._verbose >= 2:
				iterator = tqdm(self._graphs, desc='removing tottering', file=sys.stdout)
			else:
				iterator = self._graphs
			# @todo: this may not work.
			self._graphs = [untotterTransformation(G, self._node_labels, self._edge_labels) for G in iterator]
		
		# 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='Computing kernels', file=sys.stdout)
		else:
			iterator = itr
		for i, j in iterator:
			kernel = self._kernel_do(self._graphs[i], self._graphs[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)
		
		if self._remove_totters:
			pool = Pool(self._n_jobs)
			itr = 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
			remove_fun = self._wrapper_untotter
			if self._verbose >= 2:
				iterator = tqdm(pool.imap_unordered(remove_fun, itr, chunksize),
								desc='removing tottering', file=sys.stdout)
			else:
				iterator = pool.imap_unordered(remove_fun, itr, chunksize)
			for i, g in iterator:
				self._graphs[i] = g
			pool.close()
			pool.join()
		
		# compute Gram matrix.
		gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
		
		def init_worker(gn_toshare):
			global G_gn
			G_gn = gn_toshare
		do_fun = self._wrapper_kernel_do
		parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, 
					glbv=(self._graphs,), 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])
		
		if self._remove_totters:
			g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
			if self._verbose >= 2:
				iterator = tqdm(g_list, desc='removing tottering', file=sys.stdout)
			else:
				iterator = g_list
			# @todo: this may not work.
			g_list = [untotterTransformation(G, self._node_labels, self._edge_labels) for G in iterator]
				
		# compute kernel list.
		kernel_list = [None] * len(g_list)
		if self._verbose >= 2:
			iterator = tqdm(range(len(g_list)), desc='Computing kernels', file=sys.stdout)
		else:
			iterator = range(len(g_list))
		for i in iterator:
			kernel = self._kernel_do(g1, g_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])
		
		if self._remove_totters:
			g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
			pool = Pool(self._n_jobs)
			itr = 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
			remove_fun = self._wrapper_untotter
			if self._verbose >= 2:
				iterator = tqdm(pool.imap_unordered(remove_fun, itr, chunksize),
								desc='removing tottering', file=sys.stdout)
			else:
				iterator = pool.imap_unordered(remove_fun, itr, chunksize)
			for i, g in iterator:
				g_list[i] = g
			pool.close()
			pool.join()
		
		# compute kernel list.
		kernel_list = [None] * len(g_list)

		def init_worker(g1_toshare, g_list_toshare):
			global G_g1, G_g_list
			G_g1 = g1_toshare
			G_g_list = g_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=(g1, g_list), method='imap_unordered', 
			n_jobs=self._n_jobs, itr_desc='Computing kernels', verbose=self._verbose)
			
		return kernel_list
	
	
	def _wrapper_kernel_list_do(self, itr):
		return itr, self._kernel_do(G_g1, G_g_list[itr])
	
	
	def _compute_single_kernel_series(self, g1, g2):
		self._add_dummy_labels([g1] + [g2])
		if self._remove_totters:
			g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
			g2 = untotterTransformation(g2, self._node_labels, self._edge_labels)
		kernel = self._kernel_do(g1, g2)
		return kernel			
	
	
	def _kernel_do(self, g1, g2):
		"""Compute marginalized graph kernel between 2 graphs.
		
		Parameters
		----------
		g1, g2 : NetworkX graphs
			2 graphs between which the kernel is computed.
			
		Return
		------
		kernel : float
			Marginalized kernel between 2 graphs.
		"""
		# init parameters
		kernel = 0
		num_nodes_G1 = nx.number_of_nodes(g1)
		num_nodes_G2 = nx.number_of_nodes(g2)
		# the initial probability distribution in the random walks generating step
		# (uniform distribution over |G|)
		p_init_G1 = 1 / num_nodes_G1
		p_init_G2 = 1 / num_nodes_G2
	
		q = self._p_quit * self._p_quit
		r1 = q
	
	#	# initial R_inf
	#	# matrix to save all the R_inf for all pairs of nodes
	#	R_inf = np.zeros([num_nodes_G1, num_nodes_G2])
	#
	#	# Compute R_inf with a simple interative method
	#	for i in range(1, n_iteration):
	#		R_inf_new = np.zeros([num_nodes_G1, num_nodes_G2])
	#		R_inf_new.fill(r1)
	#
	#		# Compute R_inf for each pair of nodes
	#		for node1 in g1.nodes(data=True):
	#			neighbor_n1 = g1[node1[0]]
	#			# the transition probability distribution in the random walks
	#			# generating step (uniform distribution over the vertices adjacent
	#			# to the current vertex)
	#			if len(neighbor_n1) > 0:
	#				p_trans_n1 = (1 - p_quit) / len(neighbor_n1)
	#				for node2 in g2.nodes(data=True):
	#					neighbor_n2 = g2[node2[0]]
	#					if len(neighbor_n2) > 0:
	#						p_trans_n2 = (1 - p_quit) / len(neighbor_n2)
	#		
	#						for neighbor1 in neighbor_n1:
	#							for neighbor2 in neighbor_n2:
	#								t = p_trans_n1 * p_trans_n2 * \
	#									deltakernel(g1.node[neighbor1][node_label],
	#												g2.node[neighbor2][node_label]) * \
	#									deltakernel(
	#										neighbor_n1[neighbor1][edge_label],
	#										neighbor_n2[neighbor2][edge_label])
	#		
	#								R_inf_new[node1[0]][node2[0]] += t * R_inf[neighbor1][
	#									neighbor2]  # ref [1] equation (8)
	#		R_inf[:] = R_inf_new
	#
	#	# add elements of R_inf up and compute kernel
	#	for node1 in g1.nodes(data=True):
	#		for node2 in g2.nodes(data=True):
	#			s = p_init_G1 * p_init_G2 * deltakernel(
	#				node1[1][node_label], node2[1][node_label])
	#			kernel += s * R_inf[node1[0]][node2[0]]  # ref [1] equation (6)
		
		
		R_inf = {} # dict to save all the R_inf for all pairs of nodes
		# initial R_inf, the 1st iteration.
		for node1 in g1.nodes():
			for node2 in g2.nodes():
	#			R_inf[(node1[0], node2[0])] = r1
				if len(g1[node1]) > 0:
					if len(g2[node2]) > 0:
						R_inf[(node1, node2)] = r1
					else:
						R_inf[(node1, node2)] = self._p_quit
				else:
					if len(g2[node2]) > 0:
						R_inf[(node1, node2)] = self._p_quit
					else:
						R_inf[(node1, node2)] = 1
				
		# compute all transition probability first.
		t_dict = {}
		if self._n_iteration > 1:
			for node1 in g1.nodes():
				neighbor_n1 = g1[node1]
				# the transition probability distribution in the random walks
				# generating step (uniform distribution over the vertices adjacent
				# to the current vertex)
				if len(neighbor_n1) > 0:
					p_trans_n1 = (1 - self._p_quit) / len(neighbor_n1)
					for node2 in g2.nodes():
						neighbor_n2 = g2[node2]
						if len(neighbor_n2) > 0:
							p_trans_n2 = (1 - self._p_quit) / len(neighbor_n2)
							for neighbor1 in neighbor_n1:
								for neighbor2 in neighbor_n2:
									t_dict[(node1, node2, neighbor1, neighbor2)] = \
										p_trans_n1 * p_trans_n2 * \
										deltakernel(tuple(g1.nodes[neighbor1][nl] for nl in self._node_labels), tuple(g2.nodes[neighbor2][nl] for nl in self._node_labels)) * \
										deltakernel(tuple(neighbor_n1[neighbor1][el] for el in self._edge_labels), tuple(neighbor_n2[neighbor2][el] for el in self._edge_labels))
	
		# Compute R_inf with a simple interative method
		for i in range(2, self._n_iteration + 1):
			R_inf_old = R_inf.copy()
	
			# Compute R_inf for each pair of nodes
			for node1 in g1.nodes():
				neighbor_n1 = g1[node1]
				# the transition probability distribution in the random walks
				# generating step (uniform distribution over the vertices adjacent
				# to the current vertex)
				if len(neighbor_n1) > 0:
					for node2 in g2.nodes():
						neighbor_n2 = g2[node2]
						if len(neighbor_n2) > 0:   
							R_inf[(node1, node2)] = r1
							for neighbor1 in neighbor_n1:
								for neighbor2 in neighbor_n2:
									R_inf[(node1, node2)] += \
										(t_dict[(node1, node2, neighbor1, neighbor2)] * \
										R_inf_old[(neighbor1, neighbor2)])  # ref [1] equation (8)
	
		# add elements of R_inf up and compute kernel.
		for (n1, n2), value in R_inf.items():
			s = p_init_G1 * p_init_G2 * deltakernel(tuple(g1.nodes[n1][nl] for nl in self._node_labels), tuple(g2.nodes[n2][nl] for nl in self._node_labels))
			kernel += s * value  # ref [1] equation (6)
	
		return kernel
	
	
	def _wrapper_kernel_do(self, itr):
		i = itr[0]
		j = itr[1]
		return i, j, self._kernel_do(G_gn[i], G_gn[j])

	
	def _wrapper_untotter(self, i):
		return i, untotterTransformation(self._graphs[i], self._node_labels, self._edge_labels) # @todo: this may not work.
	
	
	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]