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- #!/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
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