linlin
5 years ago
1 changed files with 109 additions and 109 deletions
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lang/zh/gklearn/preimage/random_preimage_generator.py
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| @@ -26,43 +26,43 @@ 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 | |||
| self.__max_itrs = 20 | |||
| 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 | |||
| self._max_itrs = 20 | |||
| # 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 | |||
| 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 | |||
| 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', 20) | |||
| self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) | |||
| self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) | |||
| 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', 20) | |||
| self._gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) | |||
| self._runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) | |||
| def run(self): | |||
| @@ -78,65 +78,65 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| start = time.time() | |||
| # 1. precompute gram matrix. | |||
| if self.__gram_matrix_unnorm is None: | |||
| 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 | |||
| self._gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm | |||
| end_precompute_gm = time.time() | |||
| self.__runtime_precompute_gm = end_precompute_gm - start | |||
| self._runtime_precompute_gm = end_precompute_gm - start | |||
| else: | |||
| if self.__runtime_precompute_gm is None: | |||
| 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 | |||
| 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)) | |||
| 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) | |||
| self._graph_kernel.gram_matrix = np.copy(self._gram_matrix_unnorm) | |||
| end_precompute_gm = time.time() | |||
| start -= self.__runtime_precompute_gm | |||
| 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) | |||
| 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): | |||
| 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)) | |||
| 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. | |||
| 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] | |||
| 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. | |||
| 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 | |||
| 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('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 | |||
| 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. | |||
| @@ -146,12 +146,12 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| dihat_list = [] | |||
| r = 0 | |||
| dis_of_each_itr = [dhat] | |||
| if self.__parallel: | |||
| if self._parallel: | |||
| self._kernel_options['parallel'] = None | |||
| self.__itrs = 0 | |||
| self.__num_updates = 0 | |||
| timer = Timer(self.__time_limit_in_sec) | |||
| while not self.__termination_criterion_met(timer, self.__itrs, r): | |||
| self._itrs = 0 | |||
| self._num_updates = 0 | |||
| timer = Timer(self._time_limit_in_sec) | |||
| while not self._termination_criterion_met(timer, self._itrs, r): | |||
| print('\n- r =', r) | |||
| found = False | |||
| dis_bests = dis_gs + dihat_list | |||
| @@ -173,7 +173,7 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| nb_modif = 1 | |||
| for idx, nb in enumerate(range(nb_vpairs_min, nb_vpairs_min - fdgs_max, -1)): | |||
| nb_modif *= nb / (fdgs_max - idx) | |||
| while fdgs_max < nb_vpairs_min and nb_modif < self.__l: | |||
| while fdgs_max < nb_vpairs_min and nb_modif < self._l: | |||
| fdgs_max += 1 | |||
| nb_modif *= (nb_vpairs_min - fdgs_max + 1) / fdgs_max | |||
| nb_increase = int(fdgs_max - fdgs_max_old) | |||
| @@ -184,7 +184,7 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| 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)) | |||
| gnew, dhat, found = self.__generate_l_graphs(gs, fdgs_list[ig], dhat, ig, found, term3) | |||
| gnew, dhat, found = self._generate_l_graphs(gs, fdgs_list[ig], dhat, ig, found, term3) | |||
| if found: | |||
| r = 0 | |||
| @@ -194,51 +194,51 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| r += 1 | |||
| dis_of_each_itr.append(dhat) | |||
| self.__itrs += 1 | |||
| 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('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 | |||
| 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 generation 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('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 __generate_l_graphs(self, g_init, fdgs, dhat, ig, found, term3): | |||
| if self.__parallel: | |||
| gnew, dhat, found = self.__generate_l_graphs_parallel(g_init, fdgs, dhat, ig, found, term3) | |||
| def _generate_l_graphs(self, g_init, fdgs, dhat, ig, found, term3): | |||
| if self._parallel: | |||
| gnew, dhat, found = self._generate_l_graphs_parallel(g_init, fdgs, dhat, ig, found, term3) | |||
| else: | |||
| gnew, dhat, found = self.__generate_l_graphs_series(g_init, fdgs, dhat, ig, found, term3) | |||
| gnew, dhat, found = self._generate_l_graphs_series(g_init, fdgs, dhat, ig, found, term3) | |||
| return gnew, dhat, found | |||
| def __generate_l_graphs_series(self, g_init, fdgs, dhat, ig, found, term3): | |||
| def _generate_l_graphs_series(self, g_init, fdgs, dhat, ig, found, term3): | |||
| gnew = None | |||
| updated = False | |||
| for trial in range(0, self.__l): | |||
| for trial in range(0, self._l): | |||
| if self._verbose >= 2: | |||
| print('---', trial + 1, 'trial out of', self.__l) | |||
| print('---', trial + 1, 'trial out of', self._l) | |||
| gtemp, dnew = self.__do_trial(g_init, fdgs, term3, trial) | |||
| gtemp, dnew = self._do_trial(g_init, fdgs, term3, trial) | |||
| # get the better graph preimage. | |||
| if dnew <= dhat: # @todo: the new distance is smaller or also equal? | |||
| @@ -257,14 +257,14 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| found = True # found better or equally good graph. | |||
| if updated: | |||
| self.__num_updates += 1 | |||
| self._num_updates += 1 | |||
| return gnew, dhat, found | |||
| def __generate_l_graphs_parallel(self, g_init, fdgs, dhat, ig, found, term3): | |||
| def _generate_l_graphs_parallel(self, g_init, fdgs, dhat, ig, found, term3): | |||
| gnew = None | |||
| len_itr = self.__l | |||
| len_itr = self._l | |||
| gnew_list = [None] * len_itr | |||
| dnew_list = [None] * len_itr | |||
| itr = range(0, len_itr) | |||
| @@ -295,7 +295,7 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| print('I am smaller!') | |||
| print('index (as in D_k U {gihat}) =', str(ig)) | |||
| print('distance:', dhat, '->', dnew, '\n') | |||
| self.__num_updates += 1 | |||
| self._num_updates += 1 | |||
| else: | |||
| if self._verbose >= 2: | |||
| print('I am equal!') | |||
| @@ -308,11 +308,11 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| def _generate_graph_parallel(self, g_init, fdgs, term3, itr): | |||
| trial = itr | |||
| gtemp, dnew = self.__do_trial(g_init, fdgs, term3, trial) | |||
| gtemp, dnew = self._do_trial(g_init, fdgs, term3, trial) | |||
| return trial, gtemp, dnew | |||
| def __do_trial(self, g_init, fdgs, term3, trial): | |||
| def _do_trial(self, g_init, fdgs, term3, trial): | |||
| # add and delete edges. | |||
| gtemp = g_init.copy() | |||
| seed = (trial + int(time.time())) % (2 ** 32 - 1) | |||
| @@ -339,51 +339,51 @@ class RandomPreimageGenerator(PreimageGenerator): | |||
| kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, self._dataset.graphs, **self._kernel_options) | |||
| kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options) | |||
| if self._kernel_options['normalize']: | |||
| 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 | |||
| 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 | |||
| kernel_gtmp = 1 | |||
| # @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([[kernel_gtmp] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1) | |||
| dnew = compute_k_dis(0, range(1, 1 + len(self._dataset.graphs)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True) | |||
| dnew = compute_k_dis(0, range(1, 1 + len(self._dataset.graphs)), self._alphas, gram_with_gtmp, term3=term3, withterm3=True) | |||
| return gtemp, dnew | |||
| 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 | |||
| 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, timer, itr, r): | |||
| if timer.expired() or (itr >= self.__max_itrs if self.__max_itrs >= 0 else False): | |||
| # if self.__state == AlgorithmState.TERMINATED: | |||
| # self.__state = AlgorithmState.INITIALIZED | |||
| def _termination_criterion_met(self, timer, itr, r): | |||
| 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 (r >= self.__r_max if self.__r_max >= 0 else False) | |||
| # return converged or (itrs_without_update > self.__max_itrs_without_update if self.__max_itrs_without_update >= 0 else False) | |||
| return (r >= self._r_max if self._r_max >= 0 else False) | |||
| # 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 | |||
| return self._preimage | |||
| @property | |||
| def best_from_dataset(self): | |||
| return self.__best_from_dataset | |||
| return self._best_from_dataset | |||
| @property | |||
| def gram_matrix_unnorm(self): | |||
| return self.__gram_matrix_unnorm | |||
| return self._gram_matrix_unnorm | |||
| @gram_matrix_unnorm.setter | |||
| def gram_matrix_unnorm(self, value): | |||
| self.__gram_matrix_unnorm = value | |||
| self._gram_matrix_unnorm = value | |||