#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jun 1 11:37:57 2020 @author: ljia """ import multiprocessing import numpy as np import networkx as nx import os from gklearn.utils.graphfiles import saveGXL from gklearn.preimage import RandomPreimageGenerator from gklearn.utils import Dataset dir_root = '../results/xp_random_preimage_generation/' def xp_random_preimage_generation(kernel_name): """ Experiment similar to the one in Bakir's paper. A test to check if RandomPreimageGenerator class works correctly. Returns ------- None. """ alpha1_list = np.linspace(0, 1, 11) k_dis_datasets = [] k_dis_preimages = [] preimages = [] bests_from_dataset = [] for alpha1 in alpha1_list: print('alpha1 =', alpha1, ':\n') # set parameters. ds_name = 'MUTAG' rpg_options = {'k': 5, 'r_max': 10, # 'l': 500, 'alphas': None, 'parallel': True, 'verbose': 2} if kernel_name == 'PathUpToH': kernel_options = {'name': 'PathUpToH', 'depth': 2, # 'k_func': 'MinMax', # 'compute_method': 'trie', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 0} elif kernel_name == 'Marginalized': kernel_options = {'name': 'Marginalized', 'p_quit': 0.8, # 'n_iteration': 7, # 'remove_totters': False, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 0} edge_required = True irrelevant_labels = {'edge_labels': ['label_0']} cut_range = None # create/get Gram matrix. dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' if not os.path.exists(dir_save): os.makedirs(dir_save) gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' gmfile_exist = os.path.isfile(os.path.abspath(gm_fname)) if gmfile_exist: gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. gram_matrix_unnorm = gmfile['gram_matrix_unnorm'] time_precompute_gm = gmfile['run_time'] # 1. get dataset. print('1. getting dataset...') dataset_all = Dataset() dataset_all.load_predefined_dataset(ds_name) dataset_all.trim_dataset(edge_required=edge_required) if irrelevant_labels is not None: dataset_all.remove_labels(**irrelevant_labels) if cut_range is not None: dataset_all.cut_graphs(cut_range) # # add two "random" graphs. # g1 = nx.Graph() # g1.add_nodes_from(range(0, 16), label_0='0') # g1.add_nodes_from(range(16, 25), label_0='1') # g1.add_node(25, label_0='2') # g1.add_nodes_from([26, 27], label_0='3') # g1.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (5, 0), (4, 9), (12, 3), (10, 13), (13, 14), (14, 15), (15, 8), (0, 16), (1, 17), (2, 18), (12, 19), (11, 20), (13, 21), (15, 22), (7, 23), (6, 24), (14, 25), (25, 26), (25, 27)]) # g2 = nx.Graph() # g2.add_nodes_from(range(0, 12), label_0='0') # g2.add_nodes_from(range(12, 19), label_0='1') # g2.add_nodes_from([19, 20, 21], label_0='2') # g2.add_nodes_from([22, 23], label_0='3') # g2.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 19), (19, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 20), (20, 7), (5, 0), (4, 8), (0, 12), (1, 13), (2, 14), (9, 15), (10, 16), (11, 17), (6, 18), (3, 21), (21, 22), (21, 23)]) # dataset_all.load_graphs([g1, g2] + dataset_all.graphs, targets=None) # 2. initialize rpg and setting parameters. print('2. initializing rpg and setting parameters...') # nb_graphs = len(dataset_all.graphs) - 2 # rpg_options['alphas'] = [alpha1, 1 - alpha1] + [0] * nb_graphs nb_graphs = len(dataset_all.graphs) alphas = [0] * nb_graphs alphas[1] = alpha1 alphas[6] = 1 - alpha1 rpg_options['alphas'] = alphas if gmfile_exist: rpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm rpg_options['runtime_precompute_gm'] = time_precompute_gm rpg = RandomPreimageGenerator() rpg.dataset = dataset_all rpg.set_options(**rpg_options.copy()) rpg.kernel_options = kernel_options.copy() # 3. compute preimage. print('3. computing preimage...') rpg.run() results = rpg.get_results() k_dis_datasets.append(results['k_dis_dataset']) k_dis_preimages.append(results['k_dis_preimage']) bests_from_dataset.append(rpg.best_from_dataset) preimages.append(rpg.preimage) # 4. save results. # write Gram matrices to file. if not gmfile_exist: np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=rpg.gram_matrix_unnorm, run_time=results['runtime_precompute_gm']) # save graphs. fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3] saveGXL(rpg.best_from_dataset, fn_best_dataset + '.gxl', method='default', node_labels=dataset_all.node_labels, edge_labels=dataset_all.edge_labels, node_attrs=dataset_all.node_attrs, edge_attrs=dataset_all.edge_attrs) fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3] saveGXL(rpg.preimage, fn_preimage + '.gxl', method='default', node_labels=dataset_all.node_labels, edge_labels=dataset_all.edge_labels, node_attrs=dataset_all.node_attrs, edge_attrs=dataset_all.edge_attrs) # draw graphs. __draw_graph(rpg.best_from_dataset, fn_best_dataset) __draw_graph(rpg.preimage, fn_preimage) # save distances. np.savez(dir_save + 'distances.' + ds_name + '.' + kernel_options['name'], k_dis_datasets=k_dis_datasets, k_dis_preimages=k_dis_preimages) # plot results figure. __plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save) print('\ncomplete.\n') return k_dis_datasets, k_dis_preimages, bests_from_dataset, preimages def __draw_graph(graph, file_prefix): # import matplotlib # matplotlib.use('agg') import matplotlib.pyplot as plt plt.figure() pos = nx.spring_layout(graph) nx.draw(graph, pos, node_size=500, labels=nx.get_node_attributes(graph, 'label_0'), font_color='w', width=3, with_labels=True) plt.savefig(file_prefix + '.eps', format='eps', dpi=300) # plt.show() plt.clf() plt.close() def __plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save): import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1, figsize=(7, 4.5)) ind = np.arange(len(alpha1_list)) # the x locations for the groups width = 0.35 # the width of the bars: can also be len(x) sequence ax.bar(ind, k_dis_preimages, width, label='Reconstructed pre-image', zorder=3, color='#133AAC') ax.set_xlabel(r'$\alpha \in [0,1]$') ax.set_ylabel(r'$d(g_i,g^\star(\alpha))$') #ax.set_title('Runtime of the shortest path kernel on all datasets') plt.xticks(ind, [str(i)[0:3] for i in alpha1_list]) #ax.set_yticks(np.logspace(-16, -3, num=20, base=10)) #ax.set_ylim(bottom=1e-15) ax.grid(axis='y', zorder=0) ax.spines['top'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['right'].set_visible(False) ax.xaxis.set_ticks_position('none') ax.plot(ind, k_dis_datasets, 'b.-', label=r'Nearest neighbor in $D_N$', color='orange', zorder=4) ax.yaxis.set_ticks_position('none') fig.subplots_adjust(bottom=.2) fig.legend(loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) plt.savefig(dir_save + 'distances in kernel space.eps', format='eps', dpi=300, transparent=True, bbox_inches='tight') plt.show() plt.clf() plt.close() if __name__ == '__main__': # kernel_name = 'PathUpToH' kernel_name = 'Marginalized' k_dis_datasets, k_dis_preimages, bests_from_dataset, preimages = xp_random_preimage_generation(kernel_name) # # save graphs. # dir_save = dir_root + 'MUTAG.PathUpToH/' # for i, alpha1 in enumerate(np.linspace(0, 1, 11)): # fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3] # saveGXL(bests_from_dataset[i], fn_best_dataset + '.gxl', method='default', # node_labels=['label_0'], edge_labels=[], # node_attrs=[], edge_attrs=[]) # fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3] # saveGXL(preimages[i], fn_preimage + '.gxl', method='default', # node_labels=['label_0'], edge_labels=[], # node_attrs=[], edge_attrs=[]) # # draw graphs. # dir_save = dir_root + 'MUTAG.PathUpToH/' # for i, alpha1 in enumerate(np.linspace(0, 1, 11)): # fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3] # __draw_graph(bests_from_dataset[i], fn_best_dataset) # fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3] # __draw_graph(preimages[i], fn_preimage) # # plot results figure. # alpha1_list = np.linspace(0, 1, 11) # dir_save = dir_root + 'MUTAG.PathUpToH/' # __plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save) # k_dis_datasets = [0.0, # 0.08882515554098754, # 0.17765031108197632, # 0.2664754666229643, # 0.35530062216395264, # 0.44412577770494066, # 0.35530062216395236, # 0.2664754666229643, # 0.17765031108197632, # 0.08882515554098878, # 0.0] # k_dis_preimages = [0.0, # 0.08882515554098754, # 0.17765031108197632, # 0.2664754666229643, # 0.35530062216395264, # 0.44412577770494066, # 0.35530062216395236, # 0.2664754666229643, # 0.17765031108197632, # 0.08882515554098878, # 0.0]