graphkit-learn/gklearn/experiments/ged/stability/edit_costs.repeats.ratios.IPFP.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct  20 17:48:02 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]. The minimum solution from given numbers of repeats are computed.

import os
import multiprocessing
import pickle
import logging
from gklearn.ged.util import compute_geds
import numpy as np
import time
from utils import get_dataset
import sys


def xp_compute_ged_matrix(dataset, ds_name, repeats, ratio, trial):
		
	save_file_suffix = '.' + ds_name + '.repeats_' + str(repeats) + '.ratio_' + "{:.2f}".format(ratio) + '.trial_' + str(trial)
	
	"""**1.   Get dataset.**"""
	dataset = get_dataset(ds_name)

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

	# Parameters for GED computation.
	ged_options = {'method': 'IPFP',  # use IPFP huristic.
				   'initialization_method': 'RANDOM',  # or 'NODE', etc.
				   # when bigger than 1, then the method is considered mIPFP.
				   'initial_solutions': 1,
				   'edit_cost': 'CONSTANT',  # use CONSTANT cost.
				   # the distance between non-symbolic node/edge labels is computed by euclidean distance.
				   'attr_distance': 'euclidean',
				   'ratio_runs_from_initial_solutions': 1,
				   # parallel threads. Do not work if mpg_options['parallel'] = False.
				   'threads': multiprocessing.cpu_count(),
				   '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'] = dataset.node_labels
	options['edge_labels'] = dataset.edge_labels
	options['node_attrs'] = dataset.node_attrs
	options['edge_attrs'] = dataset.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(dataset.graphs, options=options, repeats=repeats, 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(dataset, ds_name, repeats, ratio):
	ged_mats = []
	runtimes = []
	for trial in range(1, 101):
		print()
		print('Trial:', trial)
		ged_mat, runtime = xp_compute_ged_matrix(dataset, ds_name, repeats, ratio, trial)
		ged_mats.append(ged_mat)
		runtimes.append(runtime)
		
# 	save_file_suffix = '.' + ds_name + '.repeats_' + str(repeats) + '.ratio_' + "{:.2f}".format(ratio)
# 	with open(save_dir + 'groups/ged_mats' + save_file_suffix + '.npy', 'wb') as f:
# 		np.save(f, np.array(ged_mats))
# 	with open(save_dir + 'groups/runtimes' + save_file_suffix + '.pkl', 'wb') as f:
# 		pickle.dump(runtime, f)
	
	
def results_for_a_dataset(ds_name):
	"""**1.   Get dataset.**"""
	dataset = get_dataset(ds_name)
	
	for repeats in [1, 20, 40, 60, 80, 100]:
		print()
		print('Repeats:', repeats)
		for ratio in [0.1, 0.3, 0.5, 0.7, 0.9, 1, 3, 5, 7, 9]:
			print()
			print('Ratio:', ratio)
			save_trials_as_group(dataset, ds_name, repeats, ratio)
		

if __name__ == '__main__':
	if len(sys.argv) > 1:
		ds_name_list = sys.argv[1:]
	else:
		ds_name_list = ['MAO', 'Monoterpenoides', 'MUTAG', 'AIDS_symb']
		
	save_dir = 'outputs/edit_costs.repeats.ratios.IPFP/'
	os.makedirs(save_dir, exist_ok=True)
	os.makedirs(save_dir + 'groups/', exist_ok=True)
		
	for ds_name in ds_name_list:
		print()
		print('Dataset:', ds_name)
		results_for_a_dataset(ds_name)