def test_iam_fitdistance():
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'
node_label = 'atom'
edge_label = 'bond_type'
# lmbda = 0.03 # termination probalility
# # parameters for GED function
# c_vi = 0.037
# c_vr = 0.038
# c_vs = 0.075
# c_ei = 0.001
# c_er = 0.001
# c_es = 0.0
# ite_max_iam = 50
# epsilon_iam = 0.001
# removeNodes = False
# connected_iam = False
# # parameters for IAM function
# ged_cost = 'CONSTANT'
# ged_method = 'IPFP'
# edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
# ged_stabilizer = 'min'
# ged_repeat = 50
# params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method,
# 'edit_cost_constant': edit_cost_constant,
# 'stabilizer': ged_stabilizer, 'repeat': ged_repeat}
# parameters for GED function
c_vi = 4
c_vr = 4
c_vs = 2
c_ei = 1
c_er = 1
c_es = 1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = False
connected_iam = False
# parameters for IAM function
ged_cost = 'CHEM_1'
ged_method = 'IPFP'
edit_cost_constant = []
ged_stabilizer = 'min'
ged_repeat = 50
params_ged = {
'lib': 'gedlibpy',
'cost': ged_cost,
'method': ged_method,
'edit_cost_constant': edit_cost_constant,
'stabilizer': ged_stabilizer,
'repeat': ged_repeat
}
# 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]]
# 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]
nb_median_range = [10]
# # 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 = []
dis_ks_gen_median_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()
G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \
= 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, connected=connected_iam, removeNodes=removeNodes,
params_ged=params_ged)
time_total = time.time() - time0
print('\ntime: ', time_total)
time_list.append(time_total)
# compute distance between \psi and the new generated graphs.
knew = compute_kernel(G_gen_median_list + Gn_median, gkernel,
node_label, edge_label, False)
dhat_new_list = []
for idx, g_tmp in enumerate(G_gen_median_list):
# @todo: the term3 below could use the one at the beginning of the function.
dhat_new_list.append(
dis_gstar(idx,
range(len(G_gen_median_list),
len(G_gen_median_list) + len(Gn_median) + 1),
alpha_range,
knew,
withterm3=False))
print('\nsmallest distance in kernel space: ', dhat_new_list[0])
dis_ks_min_list.append(dhat_new_list[0])
g_best.append(G_gen_median_list[0])
# 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(G_gen_median_list[0],
labels=nx.get_node_attributes(G_gen_median_list[0], 'atom'),
with_labels=True)
plt.show()
# plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) +
# plt.savefig('results/iam/mutag_median_unfit2.nb' + str(nb_median) +
# '.png', format="PNG")
plt.clf()
# print(ghat_list[0].nodes(data=True))
# print(ghat_list[0].edges(data=True))
sod_gs_list.append(sod_gen_median)
# sod_gs_min_list.append(np.min(sod_gen_median))
print('\nsmallest sod in graph space: ', sod_gen_median)
print('\nsmallest sod of set median in graph space: ', sod_set_median)
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)
def test_iam_letter_h():
from median import draw_Letter_graph
ds = {
'name': 'Letter-high',
'dataset': '../datasets/Letter-high/Letter-high_A.txt',
'extra_params': {}
} # node nsymb
# ds = {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt',
# 'extra_params': {}} # node nsymb
# Gn = Gn[0:50]
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
gkernel = 'structuralspkernel'
# parameters for GED function from the IAM paper.
c_vi = 3
c_vr = 3
c_vs = 1
c_ei = 3
c_er = 3
c_es = 1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = False
connected_iam = False
# parameters for IAM function
# ged_cost = 'CONSTANT'
ged_cost = 'LETTER'
ged_method = 'IPFP'
# edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
edit_cost_constant = []
ged_stabilizer = 'min'
ged_repeat = 50
params_ged = {
'lib': 'gedlibpy',
'cost': ged_cost,
'method': ged_method,
'edit_cost_constant': edit_cost_constant,
'stabilizer': ged_stabilizer,
'repeat': ged_repeat
}
# classify graphs according to letters.
time_list = []
dis_ks_min_list = []
sod_gs_list = []
g_best = []
sod_set_median_list = []
idx_dict = get_same_item_indices(y_all)
for letter in idx_dict:
print('\n-------------------------------------------------------')
print('letter', letter)
Gn_let = [Gn[i].copy() for i in idx_dict[letter]]
time_list.append([])
dis_ks_min_list.append([])
sod_gs_list.append([])
g_best.append([])
sod_set_median_list.append([])
for repeat in range(50):
idx_rdm = random.sample(range(len(Gn_let)), 50)
print('graphs chosen:', idx_rdm)
Gn_median = [Gn_let[idx].copy() for idx in idx_rdm]
Gn_candidate = [g.copy() for g in Gn_median]
alpha_range = [1 / len(Gn_median)] * len(Gn_median)
time0 = time.time()
ghat_new_list, sod_min, sod_set_median = 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,
connected=connected_iam,
removeNodes=removeNodes,
params_ged=params_ged)
time_total = time.time() - time0
print('\ntime: ', time_total)
time_list[-1].append(time_total)
g_best[-1].append(ghat_new_list[0])
sod_set_median_list[-1].append(sod_set_median)
print('\nsmallest sod of the set median:', sod_set_median)
sod_gs_list[-1].append(sod_min)
print('\nsmallest sod in graph space:', sod_min)
# show the best graph and save it to file.
print('one of the possible corresponding pre-images is')
draw_Letter_graph(ghat_new_list[0],
savepath='results/iam/paper_compare/')
# compute distance between \psi and the new generated graphs.
knew = compute_kernel(ghat_new_list + Gn_median, gkernel, False)
dhat_new_list = []
for idx, g_tmp in enumerate(ghat_new_list):
# @todo: the term3 below could use the one at the beginning of the function.
dhat_new_list.append(
dis_gstar(idx,
range(len(ghat_new_list),
len(ghat_new_list) + len(Gn_median) + 1),
alpha_range,
knew,
withterm3=False))
print('\nsmallest distance in kernel space: ', dhat_new_list[0])
dis_ks_min_list[-1].append(dhat_new_list[0])
print('\nsods of the set median for this letter:',
sod_set_median_list[-1])
print('\nsods in graph space for this letter:', sod_gs_list[-1])
print('\nsmallest distances in kernel space for this letter:',
dis_ks_min_list[-1])
print('\ntimes for this letter:', time_list[-1])
sod_set_median_list[-1] = np.mean(sod_set_median_list[-1])
sod_gs_list[-1] = np.mean(sod_gs_list[-1])
dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1])
time_list[-1] = np.mean(time_list[-1])
print('\nmean sods of the set median for each letter:',
sod_set_median_list)
print('\nmean sods in graph space for each letter:', sod_gs_list)
print('\nmean smallest distances in kernel space for each letter:',
dis_ks_min_list)
print('\nmean times for each letter:', time_list)
print('\nmean sods of the set median of all:',
np.mean(sod_set_median_list))
print('\nmean sods in graph space of all:', np.mean(sod_gs_list))
print('\nmean smallest distances in kernel space of all:',
np.mean(dis_ks_min_list))
print('\nmean times of all:', np.mean(time_list))
def test_preimage_random_grid_k_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 = 5 # iteration limit for pre-image.
l = 500 # update limit for random generation
# alpha_range = np.linspace(0.5, 0.5, 1)
# k = 5 # k nearest neighbors
# parameters for GED function
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
# 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]
# number of nearest neighbors.
k_range = [5, 6, 7, 8, 9, 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 = []
g_best = []
for idx_nb, nb_median in enumerate(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]
# 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
time_list.append([])
dis_ks_min_list.append([])
sod_gs_list.append([])
sod_gs_min_list.append([])
nb_updated_list.append([])
g_best.append([])
for k in k_range:
print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n')
print('k =', k)
time0 = time.time()
dhat, ghat, nb_updated = preimage_random(Gn, Gn_median, alpha_range,
range(len(Gn), len(Gn) + nb_median), km, k, r_max, l, gkernel)
time_total = time.time() - time0 + time_km
print('time: ', time_total)
time_list[idx_nb].append(time_total)
print('\nsmallest distance in kernel space: ', dhat)
dis_ks_min_list[idx_nb].append(dhat)
g_best[idx_nb].append(ghat)
print('\nnumber of updates of the best graph: ', nb_updated)
nb_updated_list[idx_nb].append(nb_updated)
# 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, labels=nx.get_node_attributes(ghat, 'atom'),
with_labels=True)
plt.savefig('results/preimage_random/mutag_median_nb' + str(nb_median) +
'_k' + str(k) + '.png', format="PNG")
# plt.show()
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], Gn_median, ged_cost=ged_cost,
ged_method=ged_method, saveGXL=saveGXL)
sod_gs_list[idx_nb].append(sod_tmp)
sod_gs_min_list[idx_nb].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 and k: ',
sod_gs_min_list)
print('\nsmallest distance in kernel space for each set of median graphs and k: ',
dis_ks_min_list)
print('\nnumber of updates of the best graph for each set of median graphs and k by IAM: ',
nb_updated_list)
print('\ntimes:', time_list)
def test_iam_mutag():
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]
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
# parameters for GED function from the IAM paper.
# fitted edit costs.
c_vi = 0.03523843108436513
c_vr = 0.03347339739350128
c_vs = 0.06871290673612238
c_ei = 0.08591999846720685
c_er = 0.07962086440894103
c_es = 0.08596855855478233
# unfitted edit costs.
# c_vi = 3
# c_vr = 3
# c_vs = 1
# c_ei = 3
# c_er = 3
# c_es = 1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = False
connected_iam = False
# parameters for IAM function
# ged_cost = 'CONSTANT'
ged_cost = 'CONSTANT'
ged_method = 'IPFP'
edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
# edit_cost_constant = []
ged_stabilizer = 'min'
ged_repeat = 50
params_ged = {
'lib': 'gedlibpy',
'cost': ged_cost,
'method': ged_method,
'edit_cost_constant': edit_cost_constant,
'stabilizer': ged_stabilizer,
'repeat': ged_repeat
}
# classify graphs according to letters.
time_list = []
dis_ks_min_list = []
dis_ks_set_median_list = []
sod_gs_list = []
g_best = []
sod_set_median_list = []
sod_list_list = []
idx_dict = get_same_item_indices(y_all)
for y_class in idx_dict:
print('\n-------------------------------------------------------')
print('class of y:', y_class)
Gn_class = [Gn[i].copy() for i in idx_dict[y_class]]
time_list.append([])
dis_ks_min_list.append([])
dis_ks_set_median_list.append([])
sod_gs_list.append([])
g_best.append([])
sod_set_median_list.append([])
for repeat in range(50):
idx_rdm = random.sample(range(len(Gn_class)), 10)
print('graphs chosen:', idx_rdm)
Gn_median = [Gn_class[idx].copy() for idx in idx_rdm]
Gn_candidate = [g.copy() for g in Gn_median]
alpha_range = [1 / len(Gn_median)] * len(Gn_median)
time0 = time.time()
G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median \
= 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, connected=connected_iam, removeNodes=removeNodes,
params_ged=params_ged)
time_total = time.time() - time0
print('\ntime: ', time_total)
time_list[-1].append(time_total)
g_best[-1].append(G_gen_median_list[0])
sod_set_median_list[-1].append(sod_set_median)
print('\nsmallest sod of the set median:', sod_set_median)
sod_gs_list[-1].append(sod_gen_median)
print('\nsmallest sod in graph space:', sod_gen_median)
sod_list_list.append(sod_list)
# show the best graph and save it to file.
print('one of the possible corresponding pre-images is')
nx.draw(G_gen_median_list[0],
labels=nx.get_node_attributes(G_gen_median_list[0],
'atom'),
with_labels=True)
# plt.show()
# plt.savefig('results/iam/mutag_median.fit_costs2.001.nb' + str(nb_median) +
# plt.savefig('results/iam/paper_compare/mutag_y' + str(y_class) +
# '_repeat' + str(repeat) + '_' + str(time.time()) +
# '.png', format="PNG")
plt.clf()
# print(G_gen_median_list[0].nodes(data=True))
# print(G_gen_median_list[0].edges(data=True))
# compute distance between \psi and the set median graph.
knew_set_median = compute_kernel(G_set_median_list + Gn_median,
gkernel, node_label, edge_label,
False)
dhat_new_set_median_list = []
for idx, g_tmp in enumerate(G_set_median_list):
# @todo: the term3 below could use the one at the beginning of the function.
dhat_new_set_median_list.append(
dis_gstar(idx,
range(
len(G_set_median_list),
len(G_set_median_list) + len(Gn_median) + 1),
alpha_range,
knew_set_median,
withterm3=False))
print('\ndistance in kernel space of set median: ',
dhat_new_set_median_list[0])
dis_ks_set_median_list[-1].append(dhat_new_set_median_list[0])
# compute distance between \psi and the new generated graphs.
knew = compute_kernel(G_gen_median_list + Gn_median, gkernel,
node_label, edge_label, False)
dhat_new_list = []
for idx, g_tmp in enumerate(G_gen_median_list):
# @todo: the term3 below could use the one at the beginning of the function.
dhat_new_list.append(
dis_gstar(idx,
range(
len(G_gen_median_list),
len(G_gen_median_list) + len(Gn_median) + 1),
alpha_range,
knew,
withterm3=False))
print('\nsmallest distance in kernel space: ', dhat_new_list[0])
dis_ks_min_list[-1].append(dhat_new_list[0])
print('\nsods of the set median for this class:',
sod_set_median_list[-1])
print('\nsods in graph space for this class:', sod_gs_list[-1])
print('\ndistance in kernel space of set median for this class:',
dis_ks_set_median_list[-1])
print('\nsmallest distances in kernel space for this class:',
dis_ks_min_list[-1])
print('\ntimes for this class:', time_list[-1])
sod_set_median_list[-1] = np.mean(sod_set_median_list[-1])
sod_gs_list[-1] = np.mean(sod_gs_list[-1])
dis_ks_set_median_list[-1] = np.mean(dis_ks_set_median_list[-1])
dis_ks_min_list[-1] = np.mean(dis_ks_min_list[-1])
time_list[-1] = np.mean(time_list[-1])
print()
print('\nmean sods of the set median for each class:', sod_set_median_list)
print('\nmean sods in graph space for each class:', sod_gs_list)
print('\ndistances in kernel space of set median for each class:',
dis_ks_set_median_list)
print('\nmean smallest distances in kernel space for each class:',
dis_ks_min_list)
print('\nmean times for each class:', time_list)
print('\nmean sods of the set median of all:',
np.mean(sod_set_median_list))
print('\nmean sods in graph space of all:', np.mean(sod_gs_list))
print('\nmean distances in kernel space of set median of all:',
np.mean(dis_ks_set_median_list))
print('\nmean smallest distances in kernel space of all:',
np.mean(dis_ks_min_list))
print('\nmean times of all:', np.mean(time_list))
nb_better_sods = 0
nb_worse_sods = 0
nb_same_sods = 0
for sods in sod_list_list:
if sods[0] > sods[-1]:
nb_better_sods += 1
elif sods[0] < sods[-1]:
nb_worse_sods += 1
else:
nb_same_sods += 1
print('\n In', str(len(sod_list_list)), 'sod lists,', str(nb_better_sods),
'are getting better,', str(nb_worse_sods), 'are getting worse,',
str(nb_same_sods), 'are not changed; ',
str(nb_better_sods / len(sod_list_list)), 'sods are improved.')
def test_preimage_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 = 3 # 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 IAM function
# c_vi = 0.037
# c_vr = 0.038
# c_vs = 0.075
# c_ei = 0.001
# c_er = 0.001
# c_es = 0.0
c_vi = 4
c_vr = 4
c_vs = 2
c_ei = 1
c_er = 1
c_es = 1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = True
connected_iam = False
# parameters for GED function
# ged_cost='CHEM_1'
ged_cost = 'CONSTANT'
ged_method = 'IPFP'
edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
ged_stabilizer = 'min'
ged_repeat = 50
params_ged = {
'lib': 'gedlibpy',
'cost': ged_cost,
'method': ged_method,
'edit_cost_constant': edit_cost_constant,
'stabilizer': ged_stabilizer,
'repeat': ged_repeat
}
# 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]
nb_median_range = [2]
# 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]
# 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()
dhat, ghat_list, dis_of_each_itr, nb_updated, nb_updated_k = \
preimage_iam(Gn, Gn_median,
alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max,
gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk,
params_iam={'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=params_ged)
time_total = time.time() - time0 + time_km
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_cs.001_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,
params_ged=params_ged)
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)