graphkit-learn/gklearn/experiments/ged/stability/edit_costs.max_num_sols.N.bipartite.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct  20 11:48:02 2020

@author: ljia
"""	
# This script tests the influence of the ratios between node costs and edge costs on the stability of the GED computation, where the base edit costs are [1, 1, 1, 1, 1, 1].

import os
import multiprocessing
import pickle
import logging
from gklearn.ged.util import compute_geds
import time
import sys
from group_results import group_trials


def generate_graphs():
	from gklearn.utils.graph_synthesizer import GraphSynthesizer
	gsyzer = GraphSynthesizer()
	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=20, num_edges=20, num_node_labels=0, num_edge_labels=0, seed=None, directed=False)
	return graphs


def xp_compute_ged_matrix(graphs, N, max_num_solutions, ratio, trial):

	save_file_suffix = '.' + str(N) + '.mnum_sols_' + str(max_num_solutions) + '.ratio_' + "{:.2f}".format(ratio) + '.trial_' + str(trial)

	# Return if the file exists.
	if os.path.isfile(save_dir + 'ged_matrix' + save_file_suffix + '.pkl'):
		return None, None

	"""**2.  Set parameters.**"""

	# Parameters for GED computation.
	ged_options = {'method': 'BIPARTITE',  # use BIPARTITE huristic.
  				   # 'initialization_method': 'RANDOM',  # or 'NODE', etc. (for GEDEnv)
				   'lsape_model': 'ECBP',  # 
				   # ??when bigger than 1, then the method is considered mIPFP.
				   # the actual number of computed solutions might be smaller than the specified value 
				   'max_num_solutions': max_num_solutions,
				   'edit_cost': 'CONSTANT',  # use CONSTANT cost.
				   'greedy_method': 'BASIC',  # 
				   # the distance between non-symbolic node/edge labels is computed by euclidean distance.
				   'attr_distance': 'euclidean',
				   'optimal': True, # if TRUE, the option --greedy-method has no effect 
				   # parallel threads. Do not work if mpg_options['parallel'] = False.
				   'threads': multiprocessing.cpu_count(),
				   'centrality_method': 'NONE',
				   'centrality_weight': 0.7,
				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'
				   }
	
	edit_cost_constants = [i * ratio for i in [1, 1, 1]] + [1, 1, 1]
# 	edit_cost_constants = [item * 0.01 for item in edit_cost_constants]
# 	pickle.dump(edit_cost_constants, open(save_dir + "edit_costs" + save_file_suffix + ".pkl", "wb"))

	options = ged_options.copy()
	options['edit_cost_constants'] = edit_cost_constants
	options['node_labels'] = []
	options['edge_labels'] = []
	options['node_attrs'] = []
	options['edge_attrs'] = []
	parallel = True # if num_solutions == 1 else False
	
	"""**5.   Compute GED matrix.**"""
	ged_mat = 'error'
	runtime = 0
	try:
		time0 = time.time()
		ged_vec_init, ged_mat, n_edit_operations = compute_geds(graphs, options=options, repeats=1, parallel=parallel, verbose=True)
		runtime = time.time() - time0
	except Exception as exp:
		print('An exception occured when running this experiment:')
		LOG_FILENAME = save_dir + 'error.txt'
		logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
		logging.exception(save_file_suffix)
		print(repr(exp))
					
	"""**6. Get results.**"""
	
	with open(save_dir + 'ged_matrix' + save_file_suffix + '.pkl', 'wb') as f:
		pickle.dump(ged_mat, f)
	with open(save_dir + 'runtime' + save_file_suffix + '.pkl', 'wb') as f:
		pickle.dump(runtime, f)

	return ged_mat, runtime

	
def save_trials_as_group(graphs, N, max_num_solutions, ratio):
	# Return if the group file exists.
	name_middle = '.' + str(N) + '.mnum_sols_' + str(max_num_solutions) + '.ratio_' + "{:.2f}".format(ratio) + '.'
	name_group = save_dir + 'groups/ged_mats' +  name_middle + 'npy'
	if os.path.isfile(name_group):
		return
	
	ged_mats = []
	runtimes = []
	for trial in range(1, 101):
		print()
		print('Trial:', trial)
		ged_mat, runtime = xp_compute_ged_matrix(graphs, N, max_num_solutions, ratio, trial)
		ged_mats.append(ged_mat)
		runtimes.append(runtime)
		
	# Group trials and Remove single files.
	name_prefix = 'ged_matrix' + name_middle
	group_trials(save_dir, name_prefix, True, True, False)
	name_prefix = 'runtime' + name_middle
	group_trials(save_dir, name_prefix, True, True, False)


def results_for_a_ratio(ratio):
	
	for N in N_list:
		print()
		print('# of graphs:', N)
		for max_num_solutions in [1, 20, 40, 60, 80, 100]:
			print()
			print('Max # of solutions:', max_num_solutions)
			save_trials_as_group(graphs[:N], N, max_num_solutions, ratio)
				

if __name__ == '__main__':
	if len(sys.argv) > 1:
		N_list = [int(i) for i in sys.argv[1:]]
	else:
		N_list = [10, 50, 100]
		
	# Generate graphs.
	graphs = generate_graphs()
		
	save_dir = 'outputs/edit_costs.max_num_sols.N.bipartite/'
	os.makedirs(save_dir, exist_ok=True)
	os.makedirs(save_dir + 'groups/', exist_ok=True)
		
	for ratio in [10, 1, 0.1]:
		print()
		print('Ratio:', ratio)
		results_for_a_ratio(ratio)