graphkit-learn/gklearn/preimage/random_preimage_generator.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 29 14:29:52 2020

@author: ljia
"""

import numpy as np
import time
import random
import sys
import tqdm
import multiprocessing
import networkx as nx
from gklearn.preimage import PreimageGenerator
from gklearn.preimage.utils import compute_k_dis
from gklearn.utils import Timer
from gklearn.utils.utils import get_graph_kernel_by_name
# from gklearn.utils.dataset import Dataset

class RandomPreimageGenerator(PreimageGenerator):
	
	def __init__(self, dataset=None):
		PreimageGenerator.__init__(self, dataset=dataset)
		# arguments to set.
		self.__k = 5 # number of nearest neighbors of phi in D_N.
		self.__r_max = 10 # maximum number of iterations.
		self.__l = 500 # numbers of graphs generated for each graph in D_k U {g_i_hat}.
		self.__alphas = None # weights of linear combinations of points in kernel space.
		self.__parallel = True
		self.__n_jobs = multiprocessing.cpu_count()
		self.__time_limit_in_sec = 0 # @todo
		self.__max_itrs = 100 # @todo
		# values to compute.
		self.__runtime_generate_preimage = None
		self.__runtime_total = None
		self.__preimage = None
		self.__best_from_dataset = None
		self.__k_dis_preimage = None
		self.__k_dis_dataset = None
		self.__itrs = 0
		self.__converged = False # @todo
		self.__num_updates = 0
		# values that can be set or to be computed.
		self.__gram_matrix_unnorm = None
		self.__runtime_precompute_gm = None

		
	def set_options(self, **kwargs):
		self._kernel_options = kwargs.get('kernel_options', {})
		self._graph_kernel = kwargs.get('graph_kernel', None)
		self._verbose = kwargs.get('verbose', 2)
		self.__k = kwargs.get('k', 5)
		self.__r_max = kwargs.get('r_max', 10)
		self.__l = kwargs.get('l', 500)
		self.__alphas = kwargs.get('alphas', None)
		self.__parallel = kwargs.get('parallel', True)
		self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count())
		self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0)
		self.__max_itrs = kwargs.get('max_itrs', 100)
		self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None)
		self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None)
		
		
	def run(self):
		self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], 
						  node_labels=self._dataset.node_labels,
						  edge_labels=self._dataset.edge_labels, 
						  node_attrs=self._dataset.node_attrs,
						  edge_attrs=self._dataset.edge_attrs,
						  ds_infos=self._dataset.get_dataset_infos(keys=['directed']),
						  kernel_options=self._kernel_options)
		
		# record start time.
		start = time.time()
		
		# 1. precompute gram matrix.
		if self.__gram_matrix_unnorm is None:
			gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options)
			self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm
			end_precompute_gm = time.time()
			self.__runtime_precompute_gm = end_precompute_gm - start
		else:
			if self.__runtime_precompute_gm is None:
				raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.')
			self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm
			if self._kernel_options['normalize']:
				self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm))
			else:
				self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm)
			end_precompute_gm = time.time()
			start -= self.__runtime_precompute_gm
			
		# 2. compute k nearest neighbors of phi in D_N.
		if self._verbose >= 2:
			print('\nstart computing k nearest neighbors of phi in D_N...\n')
		D_N = self._dataset.graphs
		if self.__alphas is None:
			self.__alphas = [1 / len(D_N)] * len(D_N)
		k_dis_list = [] # distance between g_star and each graph.
		term3 = 0
		for i1, a1 in enumerate(self.__alphas):
			for i2, a2 in enumerate(self.__alphas):
				term3 += a1 * a2 * self._graph_kernel.gram_matrix[i1, i2]
		for idx in range(len(D_N)):
			k_dis_list.append(compute_k_dis(idx, range(0, len(D_N)), self.__alphas, self._graph_kernel.gram_matrix, term3=term3, withterm3=True))
			
		# sort.
		sort_idx = np.argsort(k_dis_list)
		dis_gs = [k_dis_list[idis] for idis in sort_idx[0:self.__k]] # the k shortest distances.
		nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist())
		g0hat_list = [D_N[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in D_N
		self.__best_from_dataset = g0hat_list[0] # get the first best graph if there are muitlple.
		self.__k_dis_dataset = dis_gs[0]
		
		if self.__k_dis_dataset == 0: # get the exact pre-image.
			end_generate_preimage = time.time()
			self.__runtime_generate_preimage = end_generate_preimage - end_precompute_gm
			self.__runtime_total = end_generate_preimage - start
			self.__preimage = self.__best_from_dataset.copy()	
			self.__k_dis_preimage = self.__k_dis_dataset
			if self._verbose:
				print()
				print('=============================================================================')
				print('The exact pre-image is found from the input dataset.')
				print('-----------------------------------------------------------------------------')
				print('Distance in kernel space for the best graph from dataset and for preimage:', self.__k_dis_dataset)
				print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm)
				print('Time to generate pre-images:', self.__runtime_generate_preimage)
				print('Total time:', self.__runtime_total)
				print('=============================================================================')
				print()
			return
		
		dhat = dis_gs[0] # the nearest distance
		Gk = [D_N[ig].copy() for ig in sort_idx[0:self.__k]] # the k nearest neighbors
		Gs_nearest = [nx.convert_node_labels_to_integers(g) for g in Gk] # [g.copy() for g in Gk]
		
		# 3. start iterations.
		if self._verbose >= 2:
			print('starting iterations...')
		gihat_list = []
		dihat_list = []
		r = 0
		dis_of_each_itr = [dhat]
		while r < self.__r_max:
			print('\n- r =', r)
			found = False
			dis_bests = dis_gs + dihat_list
			
			# compute numbers of nodes to be inserted/deleted.
			# @todo what if the log is negetive? how to choose alpha (scalar)?
			fdgs_list = np.array(dis_bests)
			if np.min(fdgs_list) < 1:
				fdgs_list /= np.min(dis_bests)
			fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))]
			if np.min(fdgs_list) < 1:
				fdgs_list = np.array(fdgs_list) + 1
				
			for ig, gs in enumerate(Gs_nearest + gihat_list):
				if self._verbose >= 2:
					print('-- computing', ig + 1, 'graphs out of', len(Gs_nearest) + len(gihat_list))
				for trail in range(0, self.__l):
					if self._verbose >= 2:
						print('---', trail + 1, 'trail out of', self.__l)

					# add and delete edges.
					gtemp = gs.copy()
					np.random.seed() # @todo: may not work for possible parallel.
					# which edges to change.
					# @todo: should we use just half of the adjacency matrix for undirected graphs?
					nb_vpairs = nx.number_of_nodes(gs) * (nx.number_of_nodes(gs) - 1)
					# @todo: what if fdgs is bigger than nb_vpairs?
					idx_change = random.sample(range(nb_vpairs), fdgs_list[ig] if 
											   fdgs_list[ig] < nb_vpairs else nb_vpairs)
					for item in idx_change:
						node1 = int(item / (nx.number_of_nodes(gs) - 1))
						node2 = (item - node1 * (nx.number_of_nodes(gs) - 1))
						if node2 >= node1: # skip the self pair.
							node2 += 1
						# @todo: is the randomness correct?
						if not gtemp.has_edge(node1, node2):
							gtemp.add_edge(node1, node2)
						else:
							gtemp.remove_edge(node1, node2)
							
					# compute new distances.
					kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, D_N, **self._kernel_options)
					kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options)
					kernels_to_gtmp = [kernels_to_gtmp[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gtmp) for i in range(len(kernels_to_gtmp))] # normalize 
					# @todo: not correct kernel value
					gram_with_gtmp = np.concatenate((np.array([kernels_to_gtmp]), np.copy(self._graph_kernel.gram_matrix)), axis=0)
					gram_with_gtmp = np.concatenate((np.array([[1] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1)
					dnew = compute_k_dis(0, range(1, 1 + len(D_N)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True)
		
					# get the better graph preimage.
					if dnew <= dhat: # @todo: the new distance is smaller or also equal?
						if dnew < dhat:
							if self._verbose >= 2:
								print('trail =', str(trail))
								print('\nI am smaller!')
								print('index (as in D_k U {gihat} =', str(ig))
								print('distance:', dhat, '->', dnew)
							self.__num_updates += 1
						elif dnew == dhat:
							if self._verbose >= 2:
								print('I am equal!') 
						dhat = dnew
						gnew = gtemp.copy()
						found = True # found better graph.
						  
			if found:
				r = 0
				gihat_list = [gnew]
				dihat_list = [dhat]
			else:
				r += 1
				
			dis_of_each_itr.append(dhat)
			self.__itrs += 1
			if self._verbose >= 2:
				print('Total number of iterations is', self.__itrs)
				print('The preimage is updated', self.__num_updates, 'times.')
				print('The shortest distances for previous iterations are', dis_of_each_itr)
				
			
			
		# get results and print.
		end_generate_preimage = time.time()
		self.__runtime_generate_preimage = end_generate_preimage - end_precompute_gm
		self.__runtime_total = end_generate_preimage - start
		self.__preimage = (g0hat_list[0] if len(gihat_list) == 0 else gihat_list[0])
		self.__k_dis_preimage = dhat
		if self._verbose:
			print()
			print('=============================================================================')
			print('Finished generalization of preimages.')
			print('-----------------------------------------------------------------------------')
			print('Distance in kernel space for the best graph from dataset:', self.__k_dis_dataset)
			print('Distance in kernel space for the preimage:', self.__k_dis_preimage)
			print('Total number of iterations for optimizing:', self.__itrs)
			print('Total number of updating preimage:', self.__num_updates)
			print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm)
			print('Time to generate pre-images:', self.__runtime_generate_preimage)
			print('Total time:', self.__runtime_total)
			print('=============================================================================')
			print()		


	def get_results(self):
		results = {}
		results['runtime_precompute_gm'] = self.__runtime_precompute_gm
		results['runtime_generate_preimage'] = self.__runtime_generate_preimage
		results['runtime_total'] = self.__runtime_total
		results['k_dis_dataset'] = self.__k_dis_dataset
		results['k_dis_preimage'] = self.__k_dis_preimage
		results['itrs'] = self.__itrs
		results['num_updates'] = self.__num_updates
		return results


	def __termination_criterion_met(self, converged, timer, itr, itrs_without_update):
		if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False):
# 			if self.__state == AlgorithmState.TERMINATED:
# 				self.__state = AlgorithmState.INITIALIZED
			return True
		return converged or (itrs_without_update > self.__max_itrs_without_update if self.__max_itrs_without_update >= 0 else False)
		
	
	@property
	def preimage(self):
		return self.__preimage
	
	
	@property
	def best_from_dataset(self):
		return self.__best_from_dataset
	
		
	@property
	def gram_matrix_unnorm(self):
		return self.__gram_matrix_unnorm
	
	@gram_matrix_unnorm.setter
	def gram_matrix_unnorm(self, value):
		self.__gram_matrix_unnorm = value