Add experiment of random preimage.

gklearn/preimage/__init__.py

@@ -14,3 +14,4 @@ from gklearn.preimage.preimage_generator import PreimageGenerator
 from gklearn.preimage.median_preimage_generator import MedianPreimageGenerator
 from gklearn.preimage.random_preimage_generator import RandomPreimageGenerator
 from gklearn.preimage.kernel_knn_cv import kernel_knn_cv
+from gklearn.preimage.generate_random_preimages_by_class import generate_random_preimages_by_class

gklearn/preimage/generate_random_preimages_by_class.py

@@ -0,0 +1,188 @@
+#!/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

gklearn/preimage/utils.py

@@ -256,7 +256,7 @@ def generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged
 		if (load_gm == 'auto' and not gmfile_exist) or not load_gm:
 			gram_matrix_unnorm_list.append(mpg.gram_matrix_unnorm)
 
-	# write result summary for each letter. 
+	# write result summary for each class. 
 	if save_results:
 		sod_sm_mean = np.mean(sod_sm_list)
 		sod_gm_mean = np.mean(sod_gm_list)