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 generate_graph(G, pi_p_forward):
G_new_list = [G.copy()
] # all "best" graphs generated in this iteration.
# nx.draw_networkx(G)
# import matplotlib.pyplot as plt
# plt.show()
# print(pi_p_forward)
# update vertex labels.
# pre-compute h_i0 for each label.
# for label in get_node_labels(Gn, node_label):
# print(label)
# for nd in G.nodes(data=True):
# pass
if not ds_attrs['node_attr_dim']: # labels are symbolic
for ndi, (nd, _) in enumerate(G.nodes(data=True)):
h_i0_list = []
label_list = []
for label in node_label_set:
h_i0 = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if pi_i != node_ir and g.nodes[pi_i][
node_label] == label:
h_i0 += 1
h_i0_list.append(h_i0)
label_list.append(label)
# case when the node is to be removed.
if removeNodes:
h_i0_remove = 0 # @todo: maybe this can be added to the node_label_set above.
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if pi_i == node_ir:
h_i0_remove += 1
h_i0_list.append(h_i0_remove)
label_list.append(label_r)
# get the best labels.
idx_max = np.argwhere(
h_i0_list == np.max(h_i0_list)).flatten().tolist()
if allBestNodes: # choose all best graphs.
nlabel_best = [label_list[idx] for idx in idx_max]
# generate "best" graphs with regard to "best" node labels.
G_new_list_nd = []
for g in G_new_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now.
for nl in nlabel_best:
g_tmp = g.copy()
if nl == label_r:
g_tmp.remove_node(nd)
else:
g_tmp.nodes[nd][node_label] = nl
G_new_list_nd.append(g_tmp)
# nx.draw_networkx(g_tmp)
# import matplotlib.pyplot as plt
# plt.show()
# print(g_tmp.nodes(data=True))
# print(g_tmp.edges(data=True))
G_new_list = [ggg.copy() for ggg in G_new_list_nd]
else:
# choose one of the best randomly.
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
h_i0_max = h_i0_list[idx_max[idx_rdm]]
g_new = G_new_list[0]
if best_label == label_r:
g_new.remove_node(nd)
else:
g_new.nodes[nd][node_label] = best_label
G_new_list = [g_new]
else: # labels are non-symbolic
for ndi, (nd, _) in enumerate(G.nodes(data=True)):
Si_norm = 0
phi_i_bar = np.array(
[0.0 for _ in range(ds_attrs['node_attr_dim'])])
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if g.has_node(
pi_i
): #@todo: what if no g has node? phi_i_bar = 0?
Si_norm += 1
phi_i_bar += np.array([
float(itm) for itm in g.nodes[pi_i]['attributes']
])
phi_i_bar /= Si_norm
G_new_list[0].nodes[nd]['attributes'] = phi_i_bar
# for g in G_new_list:
# import matplotlib.pyplot as plt
# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# update edge labels and adjacency matrix.
if ds_attrs['edge_labeled']:
G_new_list_edge = []
for g_new in G_new_list:
nd_list = [n for n in g_new.nodes()]
g_tmp_list = [g_new.copy()]
for nd1i in range(nx.number_of_nodes(g_new)):
nd1 = nd_list[
nd1i] # @todo: not just edges, but all pairs of nodes
for nd2i in range(nd1i + 1, nx.number_of_nodes(g_new)):
nd2 = nd_list[nd2i]
# for nd1, nd2, _ in g_new.edges(data=True):
h_ij0_list = []
label_list = []
for label in edge_label_set:
h_ij0 = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
h_ij0_p = (g.has_node(pi_i)
and g.has_node(pi_j)
and g.has_edge(pi_i, pi_j)
and g.edges[pi_i, pi_j][edge_label]
== label)
h_ij0 += h_ij0_p
h_ij0_list.append(h_ij0)
label_list.append(label)
# get the best labels.
idx_max = np.argwhere(h_ij0_list == np.max(
h_ij0_list)).flatten().tolist()
if allBestEdges: # choose all best graphs.
elabel_best = [label_list[idx] for idx in idx_max]
h_ij0_max = [h_ij0_list[idx] for idx in idx_max]
# generate "best" graphs with regard to "best" node labels.
G_new_list_ed = []
for g_tmp in g_tmp_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now.
for idxl, el in enumerate(elabel_best):
g_tmp_copy = g_tmp.copy()
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(pi_j) and \
g.has_edge(pi_i, pi_j):
sij_norm += 1
if h_ij0_max[idxl] > len(Gn_median) * c_er / c_es + \
sij_norm * (1 - (c_er + c_ei) / c_es):
if not g_tmp_copy.has_edge(nd1, nd2):
g_tmp_copy.add_edge(nd1, nd2)
g_tmp_copy.edges[nd1, nd2][
edge_label] = elabel_best[idxl]
else:
if g_tmp_copy.has_edge(nd1, nd2):
g_tmp_copy.remove_edge(nd1, nd2)
G_new_list_ed.append(g_tmp_copy)
g_tmp_list = [ggg.copy() for ggg in G_new_list_ed]
else: # choose one of the best randomly.
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
h_ij0_max = h_ij0_list[idx_max[idx_rdm]]
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(
pi_j) and g.has_edge(pi_i, pi_j):
sij_norm += 1
if h_ij0_max > len(
Gn_median) * c_er / c_es + sij_norm * (
1 - (c_er + c_ei) / c_es):
if not g_new.has_edge(nd1, nd2):
g_new.add_edge(nd1, nd2)
g_new.edges[nd1, nd2][edge_label] = best_label
else:
# elif h_ij0_max < len(Gn_median) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es):
if g_new.has_edge(nd1, nd2):
g_new.remove_edge(nd1, nd2)
g_tmp_list = [g_new]
G_new_list_edge += g_tmp_list
G_new_list = [ggg.copy() for ggg in G_new_list_edge]
else: # if edges are unlabeled
# @todo: is this even right? G or g_tmp? check if the new one is right
# @todo: works only for undirected graphs.
for g_tmp in G_new_list:
nd_list = [n for n in g_tmp.nodes()]
for nd1i in range(nx.number_of_nodes(g_tmp)):
nd1 = nd_list[nd1i]
for nd2i in range(nd1i + 1, nx.number_of_nodes(g_tmp)):
nd2 = nd_list[nd2i]
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(
pi_j) and g.has_edge(pi_i, pi_j):
sij_norm += 1
if sij_norm > len(Gn_median) * c_er / (c_er + c_ei):
# @todo: should we consider if nd1 and nd2 in g_tmp?
# or just add the edge anyway?
if g_tmp.has_node(nd1) and g_tmp.has_node(nd2) \
and not g_tmp.has_edge(nd1, nd2):
g_tmp.add_edge(nd1, nd2)
else: # @todo: which to use?
# elif sij_norm < len(Gn_median) * c_er / (c_er + c_ei):
if g_tmp.has_edge(nd1, nd2):
g_tmp.remove_edge(nd1, nd2)
# do not change anything when equal.
# for i, g in enumerate(G_new_list):
# import matplotlib.pyplot as plt
# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG")
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# # find the best graph generated in this iteration and update pi_p.
# @todo: should we update all graphs generated or just the best ones?
dis_list, pi_forward_list = ged_median(G_new_list,
Gn_median,
params_ged=params_ged)
# @todo: should we remove the identical and connectivity check?
# Don't know which is faster.
if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0:
G_new_list, idx_list = remove_duplicates(G_new_list)
pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
dis_list = [dis_list[idx] for idx in idx_list]
# if connected == True:
# G_new_list, idx_list = remove_disconnected(G_new_list)
# pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
# idx_min_list = np.argwhere(dis_list == np.min(dis_list)).flatten().tolist()
# dis_min = dis_list[idx_min_tmp_list[0]]
# pi_forward_list = [pi_forward_list[idx] for idx in idx_min_list]
# G_new_list = [G_new_list[idx] for idx in idx_min_list]
# for g in G_new_list:
# import matplotlib.pyplot as plt
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
return G_new_list, pi_forward_list, dis_list
def iam_upgraded(
Gn_median,
Gn_candidate,
c_ei=3,
c_er=3,
c_es=1,
ite_max=50,
epsilon=0.001,
node_label='atom',
edge_label='bond_type',
connected=False,
removeNodes=True,
allBestInit=False,
allBestNodes=False,
allBestEdges=False,
allBestOutput=False,
params_ged={
'lib':
'gedlibpy',
'cost':
'CHEM_1',
'method':
'IPFP',
'edit_cost_constant': [],
'stabilizer':
None,
'algo_options':
'--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'
}):
"""See my name, then you know what I do.
"""
# Gn_median = Gn_median[0:10]
# Gn_median = [nx.convert_node_labels_to_integers(g) for g in Gn_median]
node_ir = np.inf # corresponding to the node remove and insertion.
label_r = 'thanksdanny' # the label for node remove. # @todo: make this label unrepeatable.
ds_attrs = get_dataset_attributes(
Gn_median + Gn_candidate,
attr_names=['edge_labeled', 'node_attr_dim', 'edge_attr_dim'],
edge_label=edge_label)
node_label_set = get_node_labels(Gn_median, node_label)
edge_label_set = get_edge_labels(Gn_median, edge_label)
def generate_graph(G, pi_p_forward):
G_new_list = [G.copy()
] # all "best" graphs generated in this iteration.
# nx.draw_networkx(G)
# import matplotlib.pyplot as plt
# plt.show()
# print(pi_p_forward)
# update vertex labels.
# pre-compute h_i0 for each label.
# for label in get_node_labels(Gn, node_label):
# print(label)
# for nd in G.nodes(data=True):
# pass
if not ds_attrs['node_attr_dim']: # labels are symbolic
for ndi, (nd, _) in enumerate(G.nodes(data=True)):
h_i0_list = []
label_list = []
for label in node_label_set:
h_i0 = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if pi_i != node_ir and g.nodes[pi_i][
node_label] == label:
h_i0 += 1
h_i0_list.append(h_i0)
label_list.append(label)
# case when the node is to be removed.
if removeNodes:
h_i0_remove = 0 # @todo: maybe this can be added to the node_label_set above.
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if pi_i == node_ir:
h_i0_remove += 1
h_i0_list.append(h_i0_remove)
label_list.append(label_r)
# get the best labels.
idx_max = np.argwhere(
h_i0_list == np.max(h_i0_list)).flatten().tolist()
if allBestNodes: # choose all best graphs.
nlabel_best = [label_list[idx] for idx in idx_max]
# generate "best" graphs with regard to "best" node labels.
G_new_list_nd = []
for g in G_new_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now.
for nl in nlabel_best:
g_tmp = g.copy()
if nl == label_r:
g_tmp.remove_node(nd)
else:
g_tmp.nodes[nd][node_label] = nl
G_new_list_nd.append(g_tmp)
# nx.draw_networkx(g_tmp)
# import matplotlib.pyplot as plt
# plt.show()
# print(g_tmp.nodes(data=True))
# print(g_tmp.edges(data=True))
G_new_list = [ggg.copy() for ggg in G_new_list_nd]
else:
# choose one of the best randomly.
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
h_i0_max = h_i0_list[idx_max[idx_rdm]]
g_new = G_new_list[0]
if best_label == label_r:
g_new.remove_node(nd)
else:
g_new.nodes[nd][node_label] = best_label
G_new_list = [g_new]
else: # labels are non-symbolic
for ndi, (nd, _) in enumerate(G.nodes(data=True)):
Si_norm = 0
phi_i_bar = np.array(
[0.0 for _ in range(ds_attrs['node_attr_dim'])])
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][ndi]
if g.has_node(
pi_i
): #@todo: what if no g has node? phi_i_bar = 0?
Si_norm += 1
phi_i_bar += np.array([
float(itm) for itm in g.nodes[pi_i]['attributes']
])
phi_i_bar /= Si_norm
G_new_list[0].nodes[nd]['attributes'] = phi_i_bar
# for g in G_new_list:
# import matplotlib.pyplot as plt
# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# update edge labels and adjacency matrix.
if ds_attrs['edge_labeled']:
G_new_list_edge = []
for g_new in G_new_list:
nd_list = [n for n in g_new.nodes()]
g_tmp_list = [g_new.copy()]
for nd1i in range(nx.number_of_nodes(g_new)):
nd1 = nd_list[
nd1i] # @todo: not just edges, but all pairs of nodes
for nd2i in range(nd1i + 1, nx.number_of_nodes(g_new)):
nd2 = nd_list[nd2i]
# for nd1, nd2, _ in g_new.edges(data=True):
h_ij0_list = []
label_list = []
for label in edge_label_set:
h_ij0 = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
h_ij0_p = (g.has_node(pi_i)
and g.has_node(pi_j)
and g.has_edge(pi_i, pi_j)
and g.edges[pi_i, pi_j][edge_label]
== label)
h_ij0 += h_ij0_p
h_ij0_list.append(h_ij0)
label_list.append(label)
# get the best labels.
idx_max = np.argwhere(h_ij0_list == np.max(
h_ij0_list)).flatten().tolist()
if allBestEdges: # choose all best graphs.
elabel_best = [label_list[idx] for idx in idx_max]
h_ij0_max = [h_ij0_list[idx] for idx in idx_max]
# generate "best" graphs with regard to "best" node labels.
G_new_list_ed = []
for g_tmp in g_tmp_list: # @todo: seems it can be simplified. The G_new_list will only contain 1 graph for now.
for idxl, el in enumerate(elabel_best):
g_tmp_copy = g_tmp.copy()
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(pi_j) and \
g.has_edge(pi_i, pi_j):
sij_norm += 1
if h_ij0_max[idxl] > len(Gn_median) * c_er / c_es + \
sij_norm * (1 - (c_er + c_ei) / c_es):
if not g_tmp_copy.has_edge(nd1, nd2):
g_tmp_copy.add_edge(nd1, nd2)
g_tmp_copy.edges[nd1, nd2][
edge_label] = elabel_best[idxl]
else:
if g_tmp_copy.has_edge(nd1, nd2):
g_tmp_copy.remove_edge(nd1, nd2)
G_new_list_ed.append(g_tmp_copy)
g_tmp_list = [ggg.copy() for ggg in G_new_list_ed]
else: # choose one of the best randomly.
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
h_ij0_max = h_ij0_list[idx_max[idx_rdm]]
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(
pi_j) and g.has_edge(pi_i, pi_j):
sij_norm += 1
if h_ij0_max > len(
Gn_median) * c_er / c_es + sij_norm * (
1 - (c_er + c_ei) / c_es):
if not g_new.has_edge(nd1, nd2):
g_new.add_edge(nd1, nd2)
g_new.edges[nd1, nd2][edge_label] = best_label
else:
# elif h_ij0_max < len(Gn_median) * c_er / c_es + sij_norm * (1 - (c_er + c_ei) / c_es):
if g_new.has_edge(nd1, nd2):
g_new.remove_edge(nd1, nd2)
g_tmp_list = [g_new]
G_new_list_edge += g_tmp_list
G_new_list = [ggg.copy() for ggg in G_new_list_edge]
else: # if edges are unlabeled
# @todo: is this even right? G or g_tmp? check if the new one is right
# @todo: works only for undirected graphs.
for g_tmp in G_new_list:
nd_list = [n for n in g_tmp.nodes()]
for nd1i in range(nx.number_of_nodes(g_tmp)):
nd1 = nd_list[nd1i]
for nd2i in range(nd1i + 1, nx.number_of_nodes(g_tmp)):
nd2 = nd_list[nd2i]
sij_norm = 0
for idx, g in enumerate(Gn_median):
pi_i = pi_p_forward[idx][nd1i]
pi_j = pi_p_forward[idx][nd2i]
if g.has_node(pi_i) and g.has_node(
pi_j) and g.has_edge(pi_i, pi_j):
sij_norm += 1
if sij_norm > len(Gn_median) * c_er / (c_er + c_ei):
# @todo: should we consider if nd1 and nd2 in g_tmp?
# or just add the edge anyway?
if g_tmp.has_node(nd1) and g_tmp.has_node(nd2) \
and not g_tmp.has_edge(nd1, nd2):
g_tmp.add_edge(nd1, nd2)
else: # @todo: which to use?
# elif sij_norm < len(Gn_median) * c_er / (c_er + c_ei):
if g_tmp.has_edge(nd1, nd2):
g_tmp.remove_edge(nd1, nd2)
# do not change anything when equal.
# for i, g in enumerate(G_new_list):
# import matplotlib.pyplot as plt
# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG")
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# # find the best graph generated in this iteration and update pi_p.
# @todo: should we update all graphs generated or just the best ones?
dis_list, pi_forward_list = ged_median(G_new_list,
Gn_median,
params_ged=params_ged)
# @todo: should we remove the identical and connectivity check?
# Don't know which is faster.
if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0:
G_new_list, idx_list = remove_duplicates(G_new_list)
pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
dis_list = [dis_list[idx] for idx in idx_list]
# if connected == True:
# G_new_list, idx_list = remove_disconnected(G_new_list)
# pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
# idx_min_list = np.argwhere(dis_list == np.min(dis_list)).flatten().tolist()
# dis_min = dis_list[idx_min_tmp_list[0]]
# pi_forward_list = [pi_forward_list[idx] for idx in idx_min_list]
# G_new_list = [G_new_list[idx] for idx in idx_min_list]
# for g in G_new_list:
# import matplotlib.pyplot as plt
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
return G_new_list, pi_forward_list, dis_list
def best_median_graphs(Gn_candidate, pi_all_forward, dis_all):
idx_min_list = np.argwhere(
dis_all == np.min(dis_all)).flatten().tolist()
dis_min = dis_all[idx_min_list[0]]
pi_forward_min_list = [pi_all_forward[idx] for idx in idx_min_list]
G_min_list = [Gn_candidate[idx] for idx in idx_min_list]
return G_min_list, pi_forward_min_list, dis_min
def iteration_proc(G, pi_p_forward, cur_sod):
G_list = [G]
pi_forward_list = [pi_p_forward]
old_sod = cur_sod * 2
sod_list = [cur_sod]
dis_list = [cur_sod]
# iterations.
itr = 0
# @todo: what if difference == 0?
# while itr < ite_max and (np.abs(old_sod - cur_sod) > epsilon or
# np.abs(old_sod - cur_sod) == 0):
while itr < ite_max and np.abs(old_sod - cur_sod) > epsilon:
# while itr < ite_max:
# for itr in range(0, 5): # the convergence condition?
print('itr_iam is', itr)
G_new_list = []
pi_forward_new_list = []
dis_new_list = []
for idx, g in enumerate(G_list):
# label_set = get_node_labels(Gn_median + [g], node_label)
G_tmp_list, pi_forward_tmp_list, dis_tmp_list = generate_graph(
g, pi_forward_list[idx])
G_new_list += G_tmp_list
pi_forward_new_list += pi_forward_tmp_list
dis_new_list += dis_tmp_list
# @todo: need to remove duplicates here?
G_list = [ggg.copy() for ggg in G_new_list]
pi_forward_list = [pitem.copy() for pitem in pi_forward_new_list]
dis_list = dis_new_list[:]
old_sod = cur_sod
cur_sod = np.min(dis_list)
sod_list.append(cur_sod)
itr += 1
# @todo: do we return all graphs or the best ones?
# get the best ones of the generated graphs.
G_list, pi_forward_list, dis_min = best_median_graphs(
G_list, pi_forward_list, dis_list)
if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0:
G_list, idx_list = remove_duplicates(G_list)
pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
# dis_list = [dis_list[idx] for idx in idx_list]
# import matplotlib.pyplot as plt
# for g in G_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
print('\nsods:', sod_list, '\n')
return G_list, pi_forward_list, dis_min, sod_list
def remove_duplicates(Gn):
"""Remove duplicate graphs from list.
"""
Gn_new = []
idx_list = []
for idx, g in enumerate(Gn):
dupl = False
for g_new in Gn_new:
if graph_isIdentical(g_new, g):
dupl = True
break
if not dupl:
Gn_new.append(g)
idx_list.append(idx)
return Gn_new, idx_list
def remove_disconnected(Gn):
"""Remove disconnected graphs from list.
"""
Gn_new = []
idx_list = []
for idx, g in enumerate(Gn):
if nx.is_connected(g):
Gn_new.append(g)
idx_list.append(idx)
return Gn_new, idx_list
###########################################################################
# phase 1: initilize.
# compute set-median.
dis_min = np.inf
dis_list, pi_forward_all = ged_median(Gn_candidate,
Gn_median,
params_ged=params_ged,
parallel=True)
print('finish computing GEDs.')
# find all smallest distances.
if allBestInit: # try all best init graphs.
idx_min_list = range(len(dis_list))
dis_min = dis_list
else:
idx_min_list = np.argwhere(
dis_list == np.min(dis_list)).flatten().tolist()
dis_min = [dis_list[idx_min_list[0]]] * len(idx_min_list)
idx_min_rdm = random.randint(0, len(idx_min_list) - 1)
idx_min_list = [idx_min_list[idx_min_rdm]]
sod_set_median = np.min(dis_min)
# phase 2: iteration.
G_list = []
dis_list = []
pi_forward_list = []
G_set_median_list = []
# sod_list = []
for idx_tmp, idx_min in enumerate(idx_min_list):
# print('idx_min is', idx_min)
G = Gn_candidate[idx_min].copy()
G_set_median_list.append(G.copy())
# list of edit operations.
pi_p_forward = pi_forward_all[idx_min]
# pi_p_backward = pi_all_backward[idx_min]
Gi_list, pi_i_forward_list, dis_i_min, sod_list = iteration_proc(
G, pi_p_forward, dis_min[idx_tmp])
G_list += Gi_list
dis_list += [dis_i_min] * len(Gi_list)
pi_forward_list += pi_i_forward_list
if ds_attrs['node_attr_dim'] == 0 and ds_attrs['edge_attr_dim'] == 0:
G_list, idx_list = remove_duplicates(G_list)
dis_list = [dis_list[idx] for idx in idx_list]
pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
if connected == True:
G_list_con, idx_list = remove_disconnected(G_list)
# if there is no connected graphs at all, then remain the disconnected ones.
if len(G_list_con) > 0: # @todo: ??????????????????????????
G_list = G_list_con
dis_list = [dis_list[idx] for idx in idx_list]
pi_forward_list = [pi_forward_list[idx] for idx in idx_list]
# import matplotlib.pyplot as plt
# for g in G_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# get the best median graphs
G_gen_median_list, pi_forward_min_list, sod_gen_median = best_median_graphs(
G_list, pi_forward_list, dis_list)
# for g in G_gen_median_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
if not allBestOutput:
# randomly choose one graph.
idx_rdm = random.randint(0, len(G_gen_median_list) - 1)
G_gen_median_list = [G_gen_median_list[idx_rdm]]
return G_gen_median_list, sod_gen_median, sod_list, G_set_median_list, sod_set_median
def test_gkiam_2combination():
from gk_iam import gk_iam_nearest_multi
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 = 20 # k nearest neighbors
epsilon = 1e-6
ged_cost = 'CHEM_1'
ged_method = 'IPFP'
saveGXL = 'gedlib'
c_ei = 1
c_er = 1
c_es = 1
# randomly select two molecules
np.random.seed(1)
idx_gi = [10, 11] # np.random.randint(0, len(Gn), 2)
g1 = Gn[idx_gi[0]].copy()
g2 = Gn[idx_gi[1]].copy()
# Gn[10] = []
# Gn[10] = []
# nx.draw(g1, labels=nx.get_node_attributes(g1, 'atom'), with_labels=True)
# plt.savefig("results/random_preimage/mutag10.png", format="PNG")
# plt.show()
# nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True)
# plt.savefig("results/random_preimage/mutag11.png", format="PNG")
# plt.show()
Gn_mix = [g.copy() for g in Gn]
Gn_mix.append(g1.copy())
Gn_mix.append(g2.copy())
# compute
# time0 = time.time()
# km = compute_kernel(Gn_mix, gkernel, True)
# time_km = time.time() - time0
# write Gram matrix to file and read it.
# np.savez('results/gram_matrix.gm', gm=km, gmtime=time_km)
gmfile = np.load('results/gram_matrix.gm.npz')
km = gmfile['gm']
time_km = gmfile['gmtime']
time_list = []
dis_ks_min_list = []
sod_gs_list = []
sod_gs_min_list = []
nb_updated_list = []
g_best = []
# for each alpha
for alpha in alpha_range:
print('\n-------------------------------------------------------\n')
print('alpha =', alpha)
time0 = time.time()
dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(
Gn, [g1, g2], [alpha, 1 - alpha],
range(len(Gn),
len(Gn) + 2),
km,
k,
r_max,
gkernel,
c_ei=c_ei,
c_er=c_er,
c_es=c_es,
epsilon=epsilon,
ged_cost=ged_cost,
ged_method=ged_method,
saveGXL=saveGXL)
time_total = time.time() - time0 + time_km
print('time: ', time_total)
time_list.append(time_total)
dis_ks_min_list.append(dhat)
g_best.append(ghat_list)
nb_updated_list.append(nb_updated)
# show best graphs and save them to file.
for idx, item in enumerate(alpha_range):
print('when alpha is', item, 'the shortest distance is',
dis_ks_min_list[idx])
print('one of the possible corresponding pre-images is')
nx.draw(g_best[idx][0],
labels=nx.get_node_attributes(g_best[idx][0], 'atom'),
with_labels=True)
plt.savefig('results/gk_iam/mutag_alpha' + str(item) + '.png',
format="PNG")
plt.show()
print(g_best[idx][0].nodes(data=True))
print(g_best[idx][0].edges(data=True))
# for g in g_best[idx]:
# draw_Letter_graph(g, savepath='results/gk_iam/')
## nx.draw_networkx(g)
## plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# compute the corresponding sod in graph space.
for idx, item in enumerate(alpha_range):
sod_tmp, _ = ged_median([g_best[0]], [g1, g2],
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('\nsods in graph space: ', sod_gs_list)
print('\nsmallest sod in graph space for each alpha: ', sod_gs_min_list)
print('\nsmallest distance in kernel space for each alpha: ',
dis_ks_min_list)
print('\nnumber of updates for each alpha: ', nb_updated_list)
print('\ntimes:', time_list)
def test_gkiam_2combination_all_pairs():
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 = False
# 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 = True
connected_iam = False
nb_update_mat = np.full((len(Gn), len(Gn)), np.inf)
# test on each pair of graphs.
# for idx1 in range(len(Gn) - 1, -1, -1):
# for idx2 in range(idx1, -1, -1):
for idx1 in range(187, 188):
for idx2 in range(167, 168):
g1 = Gn[idx1].copy()
g2 = Gn[idx2].copy()
# Gn[10] = []
# Gn[10] = []
nx.draw(g1,
labels=nx.get_node_attributes(g1, 'atom'),
with_labels=True)
plt.savefig("results/gk_iam/all_pairs/mutag187.png", format="PNG")
plt.show()
plt.clf()
nx.draw(g2,
labels=nx.get_node_attributes(g2, 'atom'),
with_labels=True)
plt.savefig("results/gk_iam/all_pairs/mutag167.png", format="PNG")
plt.show()
plt.clf()
###################################################################
# Gn_mix = [g.copy() for g in Gn]
# Gn_mix.append(g1.copy())
# Gn_mix.append(g2.copy())
#
# # compute
# time0 = time.time()
# km = compute_kernel(Gn_mix, gkernel, True)
# time_km = time.time() - time0
#
# # write Gram matrix to file and read it.
# np.savez('results/gram_matrix_uhpath_itr7_pq0.8.gm', gm=km, gmtime=time_km)
###################################################################
gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03.gm.npz')
km = gmfile['gm']
time_km = gmfile['gmtime']
# modify mixed gram matrix.
for i in range(len(Gn)):
km[i, len(Gn)] = km[i, idx1]
km[i, len(Gn) + 1] = km[i, idx2]
km[len(Gn), i] = km[i, idx1]
km[len(Gn) + 1, i] = km[i, idx2]
km[len(Gn), len(Gn)] = km[idx1, idx1]
km[len(Gn), len(Gn) + 1] = km[idx1, idx2]
km[len(Gn) + 1, len(Gn)] = km[idx2, idx1]
km[len(Gn) + 1, len(Gn) + 1] = km[idx2, idx2]
###################################################################
# # use only the two graphs in median set as candidates.
# Gn = [g1.copy(), g2.copy()]
# Gn_mix = Gn + [g1.copy(), g2.copy()]
# # compute
# time0 = time.time()
# km = compute_kernel(Gn_mix, gkernel, True)
# time_km = time.time() - time0
time_list = []
dis_ks_min_list = []
sod_gs_list = []
sod_gs_min_list = []
nb_updated_list = []
nb_updated_k_list = []
g_best = []
# for each alpha
for alpha in alpha_range:
print(
'\n-------------------------------------------------------\n'
)
print('alpha =', alpha)
time0 = time.time()
dhat, ghat_list, sod_ks, nb_updated, nb_updated_k = \
preimage_iam(Gn, [g1, g2],
[alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), 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={'ged_cost': ged_cost, 'ged_method': ged_method,
'saveGXL': saveGXL})
time_total = time.time() - time0 + time_km
print('time: ', time_total)
time_list.append(time_total)
dis_ks_min_list.append(dhat)
g_best.append(ghat_list)
nb_updated_list.append(nb_updated)
nb_updated_k_list.append(nb_updated_k)
# show best graphs and save them to file.
for idx, item in enumerate(alpha_range):
print('when alpha is', item, 'the shortest distance is',
dis_ks_min_list[idx])
print('one of the possible corresponding pre-images is')
nx.draw(g_best[idx][0],
labels=nx.get_node_attributes(g_best[idx][0], 'atom'),
with_labels=True)
plt.savefig('results/gk_iam/mutag' + str(idx1) + '_' +
str(idx2) + '_alpha' + str(item) + '.png',
format="PNG")
# plt.show()
plt.clf()
# print(g_best[idx][0].nodes(data=True))
# print(g_best[idx][0].edges(data=True))
# for g in g_best[idx]:
# draw_Letter_graph(g, savepath='results/gk_iam/')
## nx.draw_networkx(g)
## plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# compute the corresponding sod in graph space.
for idx, item in enumerate(alpha_range):
sod_tmp, _ = ged_median([g_best[0]], [g1, g2],
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('\nsods in graph space: ', sod_gs_list)
print('\nsmallest sod in graph space for each alpha: ',
sod_gs_min_list)
print('\nsmallest distance in kernel space for each alpha: ',
dis_ks_min_list)
print('\nnumber of updates of the best graph for each alpha: ',
nb_updated_list)
print(
'\nnumber of updates of the k nearest graphs for each alpha: ',
nb_updated_k_list)
print('\ntimes:', time_list)
nb_update_mat[idx1, idx2] = nb_updated_list[0]
str_fw = 'graphs %d and %d: %d.\n' % (idx1, idx2,
nb_updated_list[0])
with open('results/gk_iam/all_pairs/nb_updates.txt', 'r+') as file:
content = file.read()
file.seek(0, 0)
file.write(str_fw + content)
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)