diff --git a/lang/zh/gklearn/preimage/experiments/xp_random_preimage_generation.py b/lang/zh/gklearn/preimage/experiments/xp_random_preimage_generation.py new file mode 100644 index 0000000..fc328ca --- /dev/null +++ b/lang/zh/gklearn/preimage/experiments/xp_random_preimage_generation.py @@ -0,0 +1,262 @@ +#!/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] \ No newline at end of file