graphkit-learn/gklearn/preimage/experiments/xp_1nn_init10_trianglerule.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 15 10:50:46 2020

@author: ljia
"""
import multiprocessing
import functools
import sys
import os
import logging
from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
from gklearn.preimage import kernel_knn_cv
from gklearn.utils import compute_gram_matrices_by_class


dir_root = '../results/xp_1nn.init10.triangle_rule/'
initial_solutions = 10
triangle_rule = True
allow_zeros = False
update_order = False
test_sizes = [0.9, 0.7] # , 0.5, 0.3, 0.1]


def xp_median_preimage_14_1():
	"""xp 14_1: DD, PathUpToH, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'DD' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		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}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
	
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
	# 	# compute gram matrices for each class a priori.
	# 	print('Compute gram matrices for each class a priori.')
	# 	compute_gram_matrices_by_class(ds_name, kernel_options, save_results=save_results, dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required)
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_13_1():
	"""xp 13_1: PAH, StructuralSP, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
		
		
def xp_median_preimage_13_2():
	"""xp 13_2: PAH, ShortestPath, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '') # 
		irrelevant_labels = None # 
		edge_required = True #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']: # 
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_12_1():
	"""xp 12_1: PAH, StructuralSP, using NON_SYMBOLIC, unlabeled.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 0, 1, 1, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.unlabeled/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
		
		
def xp_median_preimage_12_2():
	"""xp 12_2: PAH, PathUpToH, using CONSTANT, unlabeled.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'PathUpToH',
						  'depth': 1, #
						  'k_func': 'MinMax', #
						  'compute_method': 'trie',
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.unlabeled/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
		

def xp_median_preimage_12_3():
	"""xp 12_3: PAH, Treelet, using CONSTANT, unlabeled.
	"""
	from gklearn.utils.kernels import gaussiankernel
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		pkernel = functools.partial(gaussiankernel, gamma=None) # @todo
		kernel_options = {'name': 'Treelet', #
					      'sub_kernel': pkernel,
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.unlabeled/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
		edge_required = False #

		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
		
		
def xp_median_preimage_12_4():
	"""xp 12_4: PAH, WeisfeilerLehman, using CONSTANT, unlabeled.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'WeisfeilerLehman',
					      'height': 14,
						  'base_kernel': 'subtree',
	 					  'parallel': 'imap_unordered', 
	                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.unlabeled/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
	# 	# compute gram matrices for each class a priori.
	# 	print('Compute gram matrices for each class a priori.')
	# 	compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
		
		
def xp_median_preimage_12_5():
	"""xp 12_5: PAH, ShortestPath, using NON_SYMBOLIC, unlabeled.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'PAH' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 0, 1, 1, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.unlabeled/' # 
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
		edge_required = True #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']: # 
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_9_1():
	"""xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MAO' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.symb/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_9_2():
	"""xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MAO' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'PathUpToH',
						  'depth': 9, #
						  'k_func': 'MinMax', #
						  'compute_method': 'trie',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.symb/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_9_3():
	"""xp 9_3: MAO, Treelet, using CONSTANT, symbolic only.
	"""
	for test_size in test_sizes:
		from gklearn.utils.kernels import polynomialkernel
		# set parameters.
		ds_name = 'MAO' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		pkernel = functools.partial(polynomialkernel, d=4, c=1e+7)
		kernel_options = {'name': 'Treelet', #
					      'sub_kernel': pkernel,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.symb/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_9_4():
	"""xp 9_4: MAO, WeisfeilerLehman, using CONSTANT, symbolic only.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MAO' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'WeisfeilerLehman',
					      'height': 6,
						  'base_kernel': 'subtree',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.symb/'
		irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
# 	# compute gram matrices for each class a priori.
# 	print('Compute gram matrices for each class a priori.')
# 	compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_8_1():
	"""xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Monoterpenoides' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_8_2():
	"""xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Monoterpenoides' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'PathUpToH',
						  'depth': 7, #
						  'k_func': 'MinMax', #
						  'compute_method': 'trie',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_8_3():
	"""xp 8_3: Monoterpenoides, Treelet, using CONSTANT.
	"""
	for test_size in test_sizes:
		from gklearn.utils.kernels import polynomialkernel
		# set parameters.
		ds_name = 'Monoterpenoides' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		pkernel = functools.partial(polynomialkernel, d=2, c=1e+5)
		kernel_options = {'name': 'Treelet',
					      'sub_kernel': pkernel,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_8_4():
	"""xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Monoterpenoides' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'WeisfeilerLehman',
					      'height': 4,
						  'base_kernel': 'subtree',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			

def xp_median_preimage_7_1():
	"""xp 7_1: MUTAG, StructuralSP, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MUTAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_7_2():
	"""xp 7_2: MUTAG, PathUpToH, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MUTAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		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}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_7_2:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_7_3():
	"""xp 7_3: MUTAG, Treelet, using CONSTANT.
	"""
	for test_size in test_sizes:
		from gklearn.utils.kernels import polynomialkernel
		# set parameters.
		ds_name = 'MUTAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		pkernel = functools.partial(polynomialkernel, d=3, c=1e+8)
		kernel_options = {'name': 'Treelet',
					      'sub_kernel': pkernel,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_7_4():
	"""xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'MUTAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [4, 4, 2, 1, 1, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100, # 
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		kernel_options = {'name': 'WeisfeilerLehman',
					      'height': 1,
						  'base_kernel': 'subtree',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'CONSTANT', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_6_1():
	"""xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'COIL-RAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_6_1:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
				
			
def xp_median_preimage_6_2():
	"""xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'COIL-RAG' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 1], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC', # 
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = True #

		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_6_2:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
				

def xp_median_preimage_5_1():
	"""xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'FRANKENSTEIN' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			

def xp_median_preimage_4_1():
	"""xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'COLORS-3' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3, 0], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'NON_SYMBOLIC',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = False #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_3_2():
	"""xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Fingerprint' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.525, 0.525, 0.01, 0.125, 0.125], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = {'edge_attrs': ['orient', 'angle']} #
		edge_required = True #

		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_3_1():
	"""xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Fingerprint' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.525, 0.525, 0.01, 0.125, 0.125], #
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = {'edge_attrs': ['orient', 'angle']} #
		edge_required = False #

		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			

def xp_median_preimage_2_1():
	"""xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'COIL-DEL' #
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [3, 3, 1, 3, 3],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
 					  'parallel': 'imap_unordered', 
                      # 'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '.node_attrs/'
		irrelevant_labels = {'edge_labels': ['valence']}
		edge_required = False

		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output
		
		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
# 	# compute gram matrices for each class a priori.
# 	print('Compute gram matrices for each class a priori.')
# 	compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running this experiment:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_1_1():
	"""xp 1_1: Letter-high, StructuralSP.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-high'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None
		edge_required = False
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_1_1:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			
			
def xp_median_preimage_1_2():
	"""xp 1_2: Letter-high, ShortestPath.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-high'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = True #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_1_2:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))


def xp_median_preimage_10_1():
	"""xp 10_1: Letter-med, StructuralSP.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-med'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None
		edge_required = False
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_10_1:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
				
			
def xp_median_preimage_10_2():
	"""xp 10_2: Letter-med, ShortestPath.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-med'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = True #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_10_2:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
				

def xp_median_preimage_11_1():
	"""xp 11_1: Letter-low, StructuralSP.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-low'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'StructuralSP',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'edge_kernels': sub_kernels, 
						  'compute_method': 'naive',
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None
		edge_required = False
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_11_1:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
				
			
def xp_median_preimage_11_2():
	"""xp 11_2: Letter-low, ShortestPath.
	"""
	for test_size in test_sizes:
		# set parameters.
		ds_name = 'Letter-low'
		knn_options = {'n_neighbors': 1,
					   'n_splits': 30,
					   'test_size': test_size,
					   'verbose': True}
		mpg_options = {'fit_method': 'k-graphs',
					   'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075],
					   'ds_name': ds_name,
					   'parallel': True, # False
					   'time_limit_in_sec': 0,
					   'max_itrs': 100,
					   'max_itrs_without_update': 3,
					   'epsilon_residual': 0.01,
					   'epsilon_ec': 0.1,
					   'allow_zeros': allow_zeros,
					   'triangle_rule': triangle_rule,
					   'verbose': 1}
		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
		sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
		kernel_options = {'name': 'ShortestPath',
						  'edge_weight': None,
						  'node_kernels': sub_kernels,
						  'parallel': 'imap_unordered', 
# 						  'parallel': None, 
						  'n_jobs': multiprocessing.cpu_count(),
						  'normalize': True,
						  'verbose': 0}
		ged_options = {'method': 'IPFP',
					   'initialization_method': 'RANDOM', # 'NODE'
					   'initial_solutions': initial_solutions, # 1
					   'edit_cost': 'LETTER2',
					   'attr_distance': 'euclidean',
					   'ratio_runs_from_initial_solutions': 1,
					   'threads': multiprocessing.cpu_count(),
					   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
		mge_options = {'init_type': 'MEDOID',
					   'random_inits': 10,
					   'time_limit': 0,
					   'verbose': 1,
					   'update_order': update_order,
					   'randomness': 'REAL',
					   'refine': False}
		save_results = True
		dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
		irrelevant_labels = None #
		edge_required = True #
		
		if not os.path.exists(dir_save):
			os.makedirs(dir_save)
		file_output = open(dir_save + 'output.txt', 'a')
		sys.stdout = file_output

		# print settings.
		print('parameters:')
		print('dataset name:', ds_name)
		print('knn_options:', knn_options)
		print('mpg_options:', mpg_options)
		print('kernel_options:', kernel_options)
		print('ged_options:', ged_options)
		print('mge_options:', mge_options)
		print('save_results:', save_results)
		print('irrelevant_labels:', irrelevant_labels)
		print()
		
		# generate preimages.
		for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
			print('\n-------------------------------------')
			print('train examples used:', train_examples, '\n')
			mpg_options['fit_method'] = train_examples
			try:
				kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=None)
			except Exception as exp:
				print('An exception occured when running experiment on xp_median_preimage_11_2:')
				LOG_FILENAME = dir_save + 'error.txt'
				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
				logging.exception('')
				print(repr(exp))
			

if __name__ == "__main__":
	
# 	#### xp 1_1: Letter-high, StructuralSP.
 	# xp_median_preimage_1_1()

# 	#### xp 1_2: Letter-high, ShortestPath.
 	# xp_median_preimage_1_2()

# 	#### xp 10_1: Letter-med, StructuralSP.
 	# xp_median_preimage_10_1()

# 	#### xp 10_2: Letter-med, ShortestPath.
 	# xp_median_preimage_10_2()

# 	#### xp 11_1: Letter-low, StructuralSP.
 	# xp_median_preimage_11_1()

# 	#### xp 11_2: Letter-low, ShortestPath.
 	# xp_median_preimage_11_2()
# 	
# 	#### xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs.
# # 	xp_median_preimage_2_1()
# 	
# 	#### xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs.
#  	# xp_median_preimage_3_1()

# 	#### xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs.
 	# xp_median_preimage_3_2()

# 	#### xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC.
# # 	xp_median_preimage_4_1()
# 	
# 	#### xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC.
# # 	xp_median_preimage_5_1()
# 	
# 	#### xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC.
 	# xp_median_preimage_6_1()

# 	#### xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC.
 	# xp_median_preimage_6_2()

# 	#### xp 7_1: MUTAG, StructuralSP, using CONSTANT.
 	# xp_median_preimage_7_1()

# 	#### xp 7_2: MUTAG, PathUpToH, using CONSTANT.
 	# xp_median_preimage_7_2()

# 	#### xp 7_3: MUTAG, Treelet, using CONSTANT.
 	# xp_median_preimage_7_3()

# 	#### xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT.
 	# xp_median_preimage_7_4()
# 	 
#     #### xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT.
 	# xp_median_preimage_8_1()

# 	#### xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT.
 	# xp_median_preimage_8_2()

# 	#### xp 8_3: Monoterpenoides, Treelet, using CONSTANT.
 	# xp_median_preimage_8_3()

# 	#### xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT.
 	# xp_median_preimage_8_4()

# 	#### xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only.
 	# xp_median_preimage_9_1()

# 	#### xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only.
 	# xp_median_preimage_9_2()

# 	#### xp 9_3: MAO, Treelet, using CONSTANT, symbolic only.
 	# xp_median_preimage_9_3()

# 	#### xp 9_4: MAO, WeisfeilerLehman, using CONSTANT, symbolic only.
 	# xp_median_preimage_9_4()

	#### xp 12_1: PAH, StructuralSP, using NON_SYMBOLIC, unlabeled.
 	# xp_median_preimage_12_1()

	#### xp 12_2: PAH, PathUpToH, using CONSTANT, unlabeled.
 	# xp_median_preimage_12_2()

	#### xp 12_3: PAH, Treelet, using CONSTANT, unlabeled.
 	# xp_median_preimage_12_3()

	#### xp 12_4: PAH, WeisfeilerLehman, using CONSTANT, unlabeled.
 	# xp_median_preimage_12_4()

	#### xp 12_5: PAH, ShortestPath, using NON_SYMBOLIC, unlabeled.
 	# xp_median_preimage_12_5()

	#### xp 13_1: PAH, StructuralSP, using NON_SYMBOLIC.
 	# xp_median_preimage_13_1()

	#### xp 13_2: PAH, ShortestPath, using NON_SYMBOLIC.
# 	xp_median_preimage_13_2()

	#### xp 14_1: DD, PathUpToH, using CONSTANT.
# 	xp_median_preimage_14_1()








# 	#### xp 1_1: Letter-high, StructuralSP.
 	xp_median_preimage_1_1()

# 	#### xp 1_2: Letter-high, ShortestPath.
 	xp_median_preimage_1_2()

# 	#### xp 10_1: Letter-med, StructuralSP.
 	xp_median_preimage_10_1()

# 	#### xp 10_2: Letter-med, ShortestPath.
 	xp_median_preimage_10_2()

# 	#### xp 11_1: Letter-low, StructuralSP.
 	xp_median_preimage_11_1()

# 	#### xp 11_2: Letter-low, ShortestPath.
 	xp_median_preimage_11_2()
	 
	#### xp 13_1: PAH, StructuralSP, using NON_SYMBOLIC.
 	xp_median_preimage_13_1()

	#### xp 13_2: PAH, ShortestPath, using NON_SYMBOLIC.
 	xp_median_preimage_13_2()
	 
# 	#### xp 7_2: MUTAG, PathUpToH, using CONSTANT.
 	xp_median_preimage_7_2()

# 	#### xp 7_3: MUTAG, Treelet, using CONSTANT.
 	xp_median_preimage_7_3()

# 	#### xp 7_4: MUTAG, WeisfeilerLehman, using CONSTANT.
 	xp_median_preimage_7_4()
# 	 
# 	#### xp 7_1: MUTAG, StructuralSP, using CONSTANT.
 	xp_median_preimage_7_1()

# 	#### xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only.
 	xp_median_preimage_9_2()

# 	#### xp 9_3: MAO, Treelet, using CONSTANT, symbolic only.
 	xp_median_preimage_9_3()

# 	#### xp 9_4: MAO, WeisfeilerLehman, using CONSTANT, symbolic only.
 	xp_median_preimage_9_4()
	 
# 	#### xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only.
 	xp_median_preimage_9_1()

	#### xp 12_1: PAH, StructuralSP, using NON_SYMBOLIC, unlabeled.
 	xp_median_preimage_12_1()

	#### xp 12_2: PAH, PathUpToH, using CONSTANT, unlabeled.
 	xp_median_preimage_12_2()

	#### xp 12_3: PAH, Treelet, using CONSTANT, unlabeled.
 	xp_median_preimage_12_3()

	#### xp 12_4: PAH, WeisfeilerLehman, using CONSTANT, unlabeled.
 	xp_median_preimage_12_4()

	#### xp 12_5: PAH, ShortestPath, using NON_SYMBOLIC, unlabeled.
 	xp_median_preimage_12_5()
	 
# 	#### xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC.
 	xp_median_preimage_6_1()
	 
# 	#### xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT.
 	xp_median_preimage_8_2()

# 	#### xp 8_3: Monoterpenoides, Treelet, using CONSTANT.
 	xp_median_preimage_8_3()

# 	#### xp 8_4: Monoterpenoides, WeisfeilerLehman, using CONSTANT.
 	xp_median_preimage_8_4()
	 
#     #### xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT.
 	xp_median_preimage_8_1()
# 	
# 	#### xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs.
 	xp_median_preimage_2_1()
# 	
# 	#### xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs.
#  	# xp_median_preimage_3_1()

# 	#### xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs.
 	# xp_median_preimage_3_2()

# 	#### xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC.
# # 	xp_median_preimage_4_1()
# 	
# 	#### xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC.
# # 	xp_median_preimage_5_1()
# 	
# 	#### xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC.
 	# xp_median_preimage_6_2()

	#### xp 14_1: DD, PathUpToH, using CONSTANT.
# 	xp_median_preimage_14_1()