#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jun 1 17:02:51 2020 @author: ljia """ import numpy as np from gklearn.utils import Dataset import csv import os import os.path from gklearn.preimage import RandomPreimageGenerator from gklearn.utils import split_dataset_by_target from gklearn.utils.graphfiles import saveGXL def generate_random_preimages_by_class(ds_name, rpg_options, kernel_options, save_results=True, save_preimages=True, load_gm='auto', dir_save='', irrelevant_labels=None, edge_required=False, cut_range=None): # 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) datasets = split_dataset_by_target(dataset_all) if save_results: # create result files. print('creating output files...') fn_output_detail, fn_output_summary = __init_output_file_preimage(ds_name, kernel_options['name'], dir_save) dis_k_dataset_list = [] dis_k_preimage_list = [] time_precompute_gm_list = [] time_generate_list = [] time_total_list = [] itrs_list = [] num_updates_list = [] if load_gm == 'auto': 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_list = [item for item in gmfile['gram_matrix_unnorm_list']] time_precompute_gm_list = gmfile['run_time_list'].tolist() else: gram_matrix_unnorm_list = [] time_precompute_gm_list = [] elif not load_gm: gram_matrix_unnorm_list = [] time_precompute_gm_list = [] else: gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' gmfile = np.load(gm_fname, allow_pickle=True) # @todo: may not be safe. gram_matrix_unnorm_list = [item for item in gmfile['gram_matrix_unnorm_list']] time_precompute_gm_list = gmfile['run_time_list'].tolist() print('starting generating preimage for each class of target...') idx_offset = 0 for idx, dataset in enumerate(datasets): target = dataset.targets[0] print('\ntarget =', target, '\n') # if target != 1: # continue num_graphs = len(dataset.graphs) if num_graphs < 2: print('\nnumber of graphs = ', num_graphs, ', skip.\n') idx_offset += 1 continue # 2. set parameters. print('2. initializing mpg and setting parameters...') if load_gm: if gmfile_exist: rpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_list[idx - idx_offset] rpg_options['runtime_precompute_gm'] = time_precompute_gm_list[idx - idx_offset] rpg = RandomPreimageGenerator() rpg.dataset = dataset 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() # 4. save results (and median graphs). print('4. saving results (and preimages)...') # write result detail. if save_results: print('writing results to files...') f_detail = open(dir_save + fn_output_detail, 'a') csv.writer(f_detail).writerow([ds_name, kernel_options['name'], num_graphs, target, 1, results['k_dis_dataset'], results['k_dis_preimage'], results['runtime_precompute_gm'], results['runtime_generate_preimage'], results['runtime_total'], results['itrs'], results['num_updates']]) f_detail.close() # compute result summary. dis_k_dataset_list.append(results['k_dis_dataset']) dis_k_preimage_list.append(results['k_dis_preimage']) time_precompute_gm_list.append(results['runtime_precompute_gm']) time_generate_list.append(results['runtime_generate_preimage']) time_total_list.append(results['runtime_total']) itrs_list.append(results['itrs']) num_updates_list.append(results['num_updates']) # write result summary for each letter. f_summary = open(dir_save + fn_output_summary, 'a') csv.writer(f_summary).writerow([ds_name, kernel_options['name'], num_graphs, target, results['k_dis_dataset'], results['k_dis_preimage'], results['runtime_precompute_gm'], results['runtime_generate_preimage'], results['runtime_total'], results['itrs'], results['num_updates']]) f_summary.close() # save median graphs. if save_preimages: if not os.path.exists(dir_save + 'preimages/'): os.makedirs(dir_save + 'preimages/') print('Saving preimages to files...') fn_best_dataset = dir_save + 'preimages/g_best_dataset.' + 'nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) saveGXL(rpg.best_from_dataset, fn_best_dataset + '.gxl', method='default', node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) fn_preimage = dir_save + 'preimages/g_preimage.' + 'nbg' + str(num_graphs) + '.y' + str(target) + '.repeat' + str(1) saveGXL(rpg.preimage, fn_preimage + '.gxl', method='default', node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs) if (load_gm == 'auto' and not gmfile_exist) or not load_gm: gram_matrix_unnorm_list.append(rpg.gram_matrix_unnorm) # write result summary for each class. if save_results: dis_k_dataset_mean = np.mean(dis_k_dataset_list) dis_k_preimage_mean = np.mean(dis_k_preimage_list) time_precompute_gm_mean = np.mean(time_precompute_gm_list) time_generate_mean = np.mean(time_generate_list) time_total_mean = np.mean(time_total_list) itrs_mean = np.mean(itrs_list) num_updates_mean = np.mean(num_updates_list) f_summary = open(dir_save + fn_output_summary, 'a') csv.writer(f_summary).writerow([ds_name, kernel_options['name'], num_graphs, 'all', dis_k_dataset_mean, dis_k_preimage_mean, time_precompute_gm_mean, time_generate_mean, time_total_mean, itrs_mean, num_updates_mean]) f_summary.close() # write Gram matrices to file. if (load_gm == 'auto' and not gmfile_exist) or not load_gm: np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm_list=gram_matrix_unnorm_list, run_time_list=time_precompute_gm_list) print('\ncomplete.\n') def __init_output_file_preimage(ds_name, gkernel, dir_output): if not os.path.exists(dir_output): os.makedirs(dir_output) fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv' f_detail = open(dir_output + fn_output_detail, 'a') csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'num graphs', 'target', 'repeat', 'dis_k best from dataset', 'dis_k preimage', 'time precompute gm', 'time generate preimage', 'time total', 'itrs', 'num updates']) f_detail.close() fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.csv' f_summary = open(dir_output + fn_output_summary, 'a') csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'num graphs', 'target', 'dis_k best from dataset', 'dis_k preimage', 'time precompute gm', 'time generate preimage', 'time total', 'itrs', 'num updates']) f_summary.close() return fn_output_detail, fn_output_summary