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- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Thu Sep 5 15:59:00 2019
-
- @author: ljia
- """
-
- import numpy as np
- import networkx as nx
- import matplotlib.pyplot as plt
- import time
- import random
- #from tqdm import tqdm
-
- #import os
- import sys
- sys.path.insert(0, "../")
- from pygraph.utils.graphfiles import loadDataset
- from iam import iam_upgraded
- from utils import remove_edges, compute_kernel, get_same_item_indices
- from ged import ged_median
-
- ###############################################################################
- # tests on different numbers of median-sets.
-
- def test_iam_median_nb():
-
- ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
- 'extra_params': {}} # node/edge symb
- Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
- # Gn = Gn[0:50]
- remove_edges(Gn)
- gkernel = 'marginalizedkernel'
-
- # lmbda = 0.03 # termination probalility
- # r_max = 10 # iteration limit for pre-image.
- # alpha_range = np.linspace(0.5, 0.5, 1)
- # k = 5 # k nearest neighbors
- # epsilon = 1e-6
- # InitIAMWithAllDk = True
- # parameters for GED function
- ged_cost='CHEM_1'
- ged_method='IPFP'
- saveGXL='gedlib'
- # parameters for IAM function
- c_ei=1
- c_er=1
- c_es=1
- ite_max_iam = 50
- epsilon_iam = 0.001
- removeNodes = False
- connected_iam = False
-
- # number of graphs; we what to compute the median of these graphs.
- nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100]
-
- # find out all the graphs classified to positive group 1.
- idx_dict = get_same_item_indices(y_all)
- Gn = [Gn[i] for i in idx_dict[1]]
-
- # # compute Gram matrix.
- # time0 = time.time()
- # km = compute_kernel(Gn, gkernel, True)
- # time_km = time.time() - time0
- # # write Gram matrix to file.
- # np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km)
-
-
- time_list = []
- dis_ks_min_list = []
- sod_gs_list = []
- sod_gs_min_list = []
- nb_updated_list = []
- nb_updated_k_list = []
- g_best = []
- for nb_median in nb_median_range:
- print('\n-------------------------------------------------------')
- print('number of median graphs =', nb_median)
- random.seed(1)
- idx_rdm = random.sample(range(len(Gn)), nb_median)
- print('graphs chosen:', idx_rdm)
- Gn_median = [Gn[idx].copy() for idx in idx_rdm]
- Gn_candidate = [g.copy() for g in Gn_median]
-
- # for g in Gn_median:
- # nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
- ## plt.savefig("results/preimage_mix/mutag.png", format="PNG")
- # plt.show()
- # plt.clf()
-
- ###################################################################
- gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz')
- km_tmp = gmfile['gm']
- time_km = gmfile['gmtime']
- # modify mixed gram matrix.
- km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median))
- for i in range(len(Gn)):
- for j in range(i, len(Gn)):
- km[i, j] = km_tmp[i, j]
- km[j, i] = km[i, j]
- for i in range(len(Gn)):
- for j, idx in enumerate(idx_rdm):
- km[i, len(Gn) + j] = km[i, idx]
- km[len(Gn) + j, i] = km[i, idx]
- for i, idx1 in enumerate(idx_rdm):
- for j, idx2 in enumerate(idx_rdm):
- km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2]
-
- ###################################################################
- alpha_range = [1 / nb_median] * nb_median
- time0 = time.time()
- ghat_new_list, dis_min = iam_upgraded(Gn_median, Gn_candidate,
- c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam,
- epsilon=epsilon_iam, removeNodes=removeNodes,
- connected=connected_iam,
- params_ged={'ged_cost': ged_cost, 'ged_method': ged_method,
- 'saveGXL': saveGXL})
-
- time_total = time.time() - time0
- print('\ntime: ', time_total)
- time_list.append(time_total)
- print('\nsmallest distance in kernel space: ', dhat)
- dis_ks_min_list.append(dhat)
- g_best.append(ghat_list)
- print('\nnumber of updates of the best graph: ', nb_updated)
- nb_updated_list.append(nb_updated)
- print('\nnumber of updates of k nearest graphs: ', nb_updated_k)
- nb_updated_k_list.append(nb_updated_k)
-
- # show the best graph and save it to file.
- print('the shortest distance is', dhat)
- print('one of the possible corresponding pre-images is')
- nx.draw(ghat_list[0], labels=nx.get_node_attributes(ghat_list[0], 'atom'),
- with_labels=True)
- plt.show()
- plt.savefig('results/preimage_iam/mutag_median_nb' + str(nb_median) +
- '.png', format="PNG")
- plt.clf()
- # print(ghat_list[0].nodes(data=True))
- # print(ghat_list[0].edges(data=True))
-
- # compute the corresponding sod in graph space.
- sod_tmp, _ = ged_median([ghat_list[0]], Gn_median, ged_cost=ged_cost,
- ged_method=ged_method, saveGXL=saveGXL)
- sod_gs_list.append(sod_tmp)
- sod_gs_min_list.append(np.min(sod_tmp))
- print('\nsmallest sod in graph space: ', np.min(sod_tmp))
-
- print('\nsods in graph space: ', sod_gs_list)
- print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list)
- print('\nsmallest distance in kernel space for each set of median graphs: ',
- dis_ks_min_list)
- print('\nnumber of updates of the best graph for each set of median graphs by IAM: ',
- nb_updated_list)
- print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ',
- nb_updated_k_list)
- print('\ntimes:', time_list)
-
-
- ###############################################################################
-
-
- if __name__ == '__main__':
- ###############################################################################
- # tests on different numbers of median-sets.
- test_iam_median_nb()
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