def remove_best_graph(ds_name,
mpg_options,
kernel_options,
ged_options,
mge_options,
save_results=True,
save_medians=True,
plot_medians=True,
load_gm='auto',
dir_save='',
irrelevant_labels=None,
edge_required=False,
cut_range=None):
"""Remove the best graph from the median set w.r.t. distance in kernel space, and to see if it is possible to generate the removed graph using the graphs left in the median set.
"""
# 1. get dataset.
print('1. getting dataset...')
dataset_all = Dataset()
dataset_all.load_predefined_dataset(ds_name)
dataset_all.trim_dataset(edge_required=edge_required)
if irrelevant_labels is not None:
dataset_all.remove_labels(**irrelevant_labels)
if cut_range is not None:
dataset_all.cut_graphs(cut_range)
datasets = split_dataset_by_target(dataset_all)
if save_results:
# create result files.
print('creating output files...')
fn_output_detail, fn_output_summary = __init_output_file(
ds_name, kernel_options['name'], mpg_options['fit_method'],
dir_save)
else:
fn_output_detail, fn_output_summary = None, None
# 2. compute/load Gram matrix a priori.
print('2. computing/loading Gram matrix...')
gram_matrix_unnorm_list, time_precompute_gm_list = __get_gram_matrix(
load_gm, dir_save, ds_name, kernel_options, datasets)
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_list = []
time_optimize_ec_list = []
time_generate_list = []
time_total_list = []
itrs_list = []
converged_list = []
num_updates_ecc_list = []
mge_decrease_order_list = []
mge_increase_order_list = []
mge_converged_order_list = []
nb_sod_sm2gm = [0, 0, 0]
nb_dis_k_sm2gm = [0, 0, 0]
nb_dis_k_gi2sm = [0, 0, 0]
nb_dis_k_gi2gm = [0, 0, 0]
dis_k_max_list = []
dis_k_min_list = []
dis_k_mean_list = []
best_dis_list = []
print('starting experiment for each class of target...')
idx_offset = 0
for idx, dataset in enumerate(datasets):
target = dataset.targets[0]
print('\ntarget =', target, '\n')
# if target != 1:
# continue
num_graphs = len(dataset.graphs)
if num_graphs < 2:
print('\nnumber of graphs = ', num_graphs, ', skip.\n')
idx_offset += 1
continue
# 3. get the best graph and remove it from median set.
print('3. getting and removing the best graph...')
gram_matrix_unnorm = gram_matrix_unnorm_list[idx - idx_offset]
best_index, best_dis, best_graph = __get_best_graph(
[g.copy() for g in dataset.graphs],
normalize_gram_matrix(gram_matrix_unnorm.copy()))
median_set_new = [
dataset.graphs[i] for i in range(len(dataset.graphs))
if i != best_index
]
num_graphs -= 1
if num_graphs == 1:
continue
best_dis_list.append(best_dis)
dataset.load_graphs(median_set_new, targets=None)
gram_matrix_unnorm_new = np.delete(gram_matrix_unnorm,
best_index,
axis=0)
gram_matrix_unnorm_new = np.delete(gram_matrix_unnorm_new,
best_index,
axis=1)
# 4. set parameters.
print('4. initializing mpg and setting parameters...')
mpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_new
mpg_options['runtime_precompute_gm'] = time_precompute_gm_list[
idx - idx_offset]
mpg = MedianPreimageGenerator()
mpg.dataset = dataset
mpg.set_options(**mpg_options.copy())
mpg.kernel_options = kernel_options.copy()
mpg.ged_options = ged_options.copy()
mpg.mge_options = mge_options.copy()
# 5. compute median preimage.
print('5. computing median preimage...')
mpg.run()
results = mpg.get_results()
# 6. compute pairwise kernel distances.
print('6. computing pairwise kernel distances...')
_, dis_k_max, dis_k_min, dis_k_mean = mpg.graph_kernel.compute_distance_matrix(
)
dis_k_max_list.append(dis_k_max)
dis_k_min_list.append(dis_k_min)
dis_k_mean_list.append(dis_k_mean)
# 7. save results (and median graphs).
print('7. saving results (and median graphs)...')
# write result detail.
if save_results:
print('writing results to files...')
sod_sm2gm = get_relations(
np.sign(results['sod_gen_median'] - results['sod_set_median']))
dis_k_sm2gm = get_relations(
np.sign(results['k_dis_gen_median'] -
results['k_dis_set_median']))
dis_k_gi2sm = get_relations(
np.sign(results['k_dis_set_median'] -
results['k_dis_dataset']))
dis_k_gi2gm = get_relations(
np.sign(results['k_dis_gen_median'] -
results['k_dis_dataset']))
f_detail = open(dir_save + fn_output_detail, 'a')
csv.writer(f_detail).writerow([
ds_name,
kernel_options['name'],
ged_options['edit_cost'],
ged_options['method'],
ged_options['attr_distance'],
mpg_options['fit_method'],
num_graphs,
target,
1,
results['sod_set_median'],
results['sod_gen_median'],
results['k_dis_set_median'],
results['k_dis_gen_median'],
results['k_dis_dataset'],
best_dis,
best_index,
sod_sm2gm,
dis_k_sm2gm,
dis_k_gi2sm,
dis_k_gi2gm,
results['edit_cost_constants'],
results['runtime_precompute_gm'],
results['runtime_optimize_ec'],
results['runtime_generate_preimage'],
results['runtime_total'],
results['itrs'],
results['converged'],
results['num_updates_ecc'],
results['mge']['num_decrease_order'] >
0, # @todo: not suitable for multi-start mge
results['mge']['num_increase_order'] > 0,
results['mge']['num_converged_descents'] > 0
])
f_detail.close()
# compute result summary.
sod_sm_list.append(results['sod_set_median'])
sod_gm_list.append(results['sod_gen_median'])
dis_k_sm_list.append(results['k_dis_set_median'])
dis_k_gm_list.append(results['k_dis_gen_median'])
dis_k_gi_min_list.append(results['k_dis_dataset'])
time_precompute_gm_list.append(results['runtime_precompute_gm'])
time_optimize_ec_list.append(results['runtime_optimize_ec'])
time_generate_list.append(results['runtime_generate_preimage'])
time_total_list.append(results['runtime_total'])
itrs_list.append(results['itrs'])
converged_list.append(results['converged'])
num_updates_ecc_list.append(results['num_updates_ecc'])
mge_decrease_order_list.append(
results['mge']['num_decrease_order'] > 0)
mge_increase_order_list.append(
results['mge']['num_increase_order'] > 0)
mge_converged_order_list.append(
results['mge']['num_converged_descents'] > 0)
# # SOD SM -> GM
if results['sod_set_median'] > results['sod_gen_median']:
nb_sod_sm2gm[0] += 1
# repeats_better_sod_sm2gm.append(1)
elif results['sod_set_median'] == results['sod_gen_median']:
nb_sod_sm2gm[1] += 1
elif results['sod_set_median'] < results['sod_gen_median']:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if results['k_dis_set_median'] > results['k_dis_gen_median']:
nb_dis_k_sm2gm[0] += 1
# repeats_better_dis_k_sm2gm.append(1)
elif results['k_dis_set_median'] == results['k_dis_gen_median']:
nb_dis_k_sm2gm[1] += 1
elif results['k_dis_set_median'] < results['k_dis_gen_median']:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if results['k_dis_dataset'] > results['k_dis_set_median']:
nb_dis_k_gi2sm[0] += 1
# repeats_better_dis_k_gi2sm.append(1)
elif results['k_dis_dataset'] == results['k_dis_set_median']:
nb_dis_k_gi2sm[1] += 1
elif results['k_dis_dataset'] < results['k_dis_set_median']:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if results['k_dis_dataset'] > results['k_dis_gen_median']:
nb_dis_k_gi2gm[0] += 1
# repeats_better_dis_k_gi2gm.append(1)
elif results['k_dis_dataset'] == results['k_dis_gen_median']:
nb_dis_k_gi2gm[1] += 1
elif results['k_dis_dataset'] < results['k_dis_gen_median']:
nb_dis_k_gi2gm[2] += 1
# write result summary for each letter.
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name,
kernel_options['name'],
ged_options['edit_cost'],
ged_options['method'],
ged_options['attr_distance'],
mpg_options['fit_method'],
num_graphs,
target,
results['sod_set_median'],
results['sod_gen_median'],
results['k_dis_set_median'],
results['k_dis_gen_median'],
results['k_dis_dataset'],
best_dis,
best_index,
sod_sm2gm,
dis_k_sm2gm,
dis_k_gi2sm,
dis_k_gi2gm,
results['runtime_precompute_gm'],
results['runtime_optimize_ec'],
results['runtime_generate_preimage'],
results['runtime_total'],
results['itrs'],
results['converged'],
results['num_updates_ecc'],
results['mge']['num_decrease_order'] >
0, # @todo: not suitable for multi-start mge
results['mge']['num_increase_order'] > 0,
results['mge']['num_converged_descents'] > 0,
nb_sod_sm2gm,
nb_dis_k_sm2gm,
nb_dis_k_gi2sm,
nb_dis_k_gi2gm
])
f_summary.close()
# save median graphs.
if save_medians:
if not os.path.exists(dir_save + 'medians/'):
os.makedirs(dir_save + 'medians/')
print('Saving median graphs to files...')
fn_pre_sm = dir_save + 'medians/set_median.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.set_median,
fn_pre_sm + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_pre_gm = dir_save + 'medians/gen_median.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.gen_median,
fn_pre_gm + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_best_dataset = dir_save + 'medians/g_best_dataset.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(best_graph,
fn_best_dataset + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_best_median_set = dir_save + 'medians/g_best_median_set.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.best_from_dataset,
fn_best_median_set + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
# plot median graphs.
if plot_medians and save_medians:
if ged_options['edit_cost'] == 'LETTER2' or ged_options[
'edit_cost'] == 'LETTER' or ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low':
draw_Letter_graph(mpg.set_median, fn_pre_sm)
draw_Letter_graph(mpg.gen_median, fn_pre_gm)
draw_Letter_graph(mpg.best_from_dataset, fn_best_dataset)
# write result summary for each letter.
if save_results:
sod_sm_mean = np.mean(sod_sm_list)
sod_gm_mean = np.mean(sod_gm_list)
dis_k_sm_mean = np.mean(dis_k_sm_list)
dis_k_gm_mean = np.mean(dis_k_gm_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
best_dis_mean = np.mean(best_dis_list)
time_precompute_gm_mean = np.mean(time_precompute_gm_list)
time_optimize_ec_mean = np.mean(time_optimize_ec_list)
time_generate_mean = np.mean(time_generate_list)
time_total_mean = np.mean(time_total_list)
itrs_mean = np.mean(itrs_list)
num_converged = np.sum(converged_list)
num_updates_ecc_mean = np.mean(num_updates_ecc_list)
num_mge_decrease_order = np.sum(mge_decrease_order_list)
num_mge_increase_order = np.sum(mge_increase_order_list)
num_mge_converged = np.sum(mge_converged_order_list)
sod_sm2gm_mean = get_relations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = get_relations(np.sign(dis_k_gm_mean -
dis_k_sm_mean))
dis_k_gi2sm_mean = get_relations(
np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = get_relations(
np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name, kernel_options['name'], ged_options['edit_cost'],
ged_options['method'], ged_options['attr_distance'],
mpg_options['fit_method'], num_graphs, 'all', sod_sm_mean,
sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, dis_k_gi_min_mean,
best_dis_mean, '-', sod_sm2gm_mean, dis_k_sm2gm_mean,
dis_k_gi2sm_mean, dis_k_gi2gm_mean, time_precompute_gm_mean,
time_optimize_ec_mean, time_generate_mean, time_total_mean,
itrs_mean, num_converged, num_updates_ecc_mean,
num_mge_decrease_order, num_mge_increase_order, num_mge_converged
])
f_summary.close()
# save total pairwise kernel distances.
dis_k_max = np.max(dis_k_max_list)
dis_k_min = np.min(dis_k_min_list)
dis_k_mean = np.mean(dis_k_mean_list)
print('The maximum pairwise distance in kernel space:', dis_k_max)
print('The minimum pairwise distance in kernel space:', dis_k_min)
print('The average pairwise distance in kernel space:', dis_k_mean)
print('\ncomplete.\n')
def generate_median_preimages_by_class(ds_name,
mpg_options,
kernel_options,
ged_options,
mge_options,
save_results=True,
save_medians=True,
plot_medians=True,
load_gm='auto',
dir_save='',
irrelevant_labels=None,
edge_required=False,
cut_range=None):
import os.path
from gklearn.preimage import MedianPreimageGenerator
from gklearn.utils import split_dataset_by_target
from gklearn.utils.graphfiles import saveGXL
# 1. get dataset.
print('1. getting dataset...')
dataset_all = Dataset()
dataset_all.load_predefined_dataset(ds_name)
dataset_all.trim_dataset(edge_required=edge_required)
if irrelevant_labels is not None:
dataset_all.remove_labels(**irrelevant_labels)
if cut_range is not None:
dataset_all.cut_graphs(cut_range)
datasets = split_dataset_by_target(dataset_all)
if save_results:
# create result files.
print('creating output files...')
fn_output_detail, fn_output_summary = _init_output_file_preimage(
ds_name, kernel_options['name'], mpg_options['fit_method'],
dir_save)
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_list = []
time_optimize_ec_list = []
time_generate_list = []
time_total_list = []
itrs_list = []
converged_list = []
num_updates_ecc_list = []
mge_decrease_order_list = []
mge_increase_order_list = []
mge_converged_order_list = []
nb_sod_sm2gm = [0, 0, 0]
nb_dis_k_sm2gm = [0, 0, 0]
nb_dis_k_gi2sm = [0, 0, 0]
nb_dis_k_gi2gm = [0, 0, 0]
dis_k_max_list = []
dis_k_min_list = []
dis_k_mean_list = []
if load_gm == 'auto':
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options[
'name'] + '.gm.npz'
gmfile_exist = os.path.isfile(os.path.abspath(gm_fname))
if gmfile_exist:
gmfile = np.load(gm_fname,
allow_pickle=True) # @todo: may not be safe.
gram_matrix_unnorm_list = [
item for item in gmfile['gram_matrix_unnorm_list']
]
time_precompute_gm_list = gmfile['run_time_list'].tolist()
else:
gram_matrix_unnorm_list = []
time_precompute_gm_list = []
elif not load_gm:
gram_matrix_unnorm_list = []
time_precompute_gm_list = []
else:
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options[
'name'] + '.gm.npz'
gmfile = np.load(gm_fname,
allow_pickle=True) # @todo: may not be safe.
gram_matrix_unnorm_list = [
item for item in gmfile['gram_matrix_unnorm_list']
]
time_precompute_gm_list = gmfile['run_time_list'].tolist()
# repeats_better_sod_sm2gm = []
# repeats_better_dis_k_sm2gm = []
# repeats_better_dis_k_gi2sm = []
# repeats_better_dis_k_gi2gm = []
print('starting generating preimage for each class of target...')
idx_offset = 0
for idx, dataset in enumerate(datasets):
target = dataset.targets[0]
print('\ntarget =', target, '\n')
# if target != 1:
# continue
num_graphs = len(dataset.graphs)
if num_graphs < 2:
print('\nnumber of graphs = ', num_graphs, ', skip.\n')
idx_offset += 1
continue
# 2. set parameters.
print('2. initializing mpg and setting parameters...')
if load_gm:
if gmfile_exist:
mpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_list[
idx - idx_offset]
mpg_options['runtime_precompute_gm'] = time_precompute_gm_list[
idx - idx_offset]
mpg = MedianPreimageGenerator()
mpg.dataset = dataset
mpg.set_options(**mpg_options.copy())
mpg.kernel_options = kernel_options.copy()
mpg.ged_options = ged_options.copy()
mpg.mge_options = mge_options.copy()
# 3. compute median preimage.
print('3. computing median preimage...')
mpg.run()
results = mpg.get_results()
# 4. compute pairwise kernel distances.
print('4. computing pairwise kernel distances...')
_, dis_k_max, dis_k_min, dis_k_mean = mpg.graph_kernel.compute_distance_matrix(
)
dis_k_max_list.append(dis_k_max)
dis_k_min_list.append(dis_k_min)
dis_k_mean_list.append(dis_k_mean)
# 5. save results (and median graphs).
print('5. saving results (and median graphs)...')
# write result detail.
if save_results:
print('writing results to files...')
sod_sm2gm = get_relations(
np.sign(results['sod_gen_median'] - results['sod_set_median']))
dis_k_sm2gm = get_relations(
np.sign(results['k_dis_gen_median'] -
results['k_dis_set_median']))
dis_k_gi2sm = get_relations(
np.sign(results['k_dis_set_median'] -
results['k_dis_dataset']))
dis_k_gi2gm = get_relations(
np.sign(results['k_dis_gen_median'] -
results['k_dis_dataset']))
f_detail = open(dir_save + fn_output_detail, 'a')
csv.writer(f_detail).writerow([
ds_name,
kernel_options['name'],
ged_options['edit_cost'],
ged_options['method'],
ged_options['attr_distance'],
mpg_options['fit_method'],
num_graphs,
target,
1,
results['sod_set_median'],
results['sod_gen_median'],
results['k_dis_set_median'],
results['k_dis_gen_median'],
results['k_dis_dataset'],
sod_sm2gm,
dis_k_sm2gm,
dis_k_gi2sm,
dis_k_gi2gm,
results['edit_cost_constants'],
results['runtime_precompute_gm'],
results['runtime_optimize_ec'],
results['runtime_generate_preimage'],
results['runtime_total'],
results['itrs'],
results['converged'],
results['num_updates_ecc'],
results['mge']['num_decrease_order'] >
0, # @todo: not suitable for multi-start mge
results['mge']['num_increase_order'] > 0,
results['mge']['num_converged_descents'] > 0
])
f_detail.close()
# compute result summary.
sod_sm_list.append(results['sod_set_median'])
sod_gm_list.append(results['sod_gen_median'])
dis_k_sm_list.append(results['k_dis_set_median'])
dis_k_gm_list.append(results['k_dis_gen_median'])
dis_k_gi_min_list.append(results['k_dis_dataset'])
time_precompute_gm_list.append(results['runtime_precompute_gm'])
time_optimize_ec_list.append(results['runtime_optimize_ec'])
time_generate_list.append(results['runtime_generate_preimage'])
time_total_list.append(results['runtime_total'])
itrs_list.append(results['itrs'])
converged_list.append(results['converged'])
num_updates_ecc_list.append(results['num_updates_ecc'])
mge_decrease_order_list.append(
results['mge']['num_decrease_order'] > 0)
mge_increase_order_list.append(
results['mge']['num_increase_order'] > 0)
mge_converged_order_list.append(
results['mge']['num_converged_descents'] > 0)
# # SOD SM -> GM
if results['sod_set_median'] > results['sod_gen_median']:
nb_sod_sm2gm[0] += 1
# repeats_better_sod_sm2gm.append(1)
elif results['sod_set_median'] == results['sod_gen_median']:
nb_sod_sm2gm[1] += 1
elif results['sod_set_median'] < results['sod_gen_median']:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if results['k_dis_set_median'] > results['k_dis_gen_median']:
nb_dis_k_sm2gm[0] += 1
# repeats_better_dis_k_sm2gm.append(1)
elif results['k_dis_set_median'] == results['k_dis_gen_median']:
nb_dis_k_sm2gm[1] += 1
elif results['k_dis_set_median'] < results['k_dis_gen_median']:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if results['k_dis_dataset'] > results['k_dis_set_median']:
nb_dis_k_gi2sm[0] += 1
# repeats_better_dis_k_gi2sm.append(1)
elif results['k_dis_dataset'] == results['k_dis_set_median']:
nb_dis_k_gi2sm[1] += 1
elif results['k_dis_dataset'] < results['k_dis_set_median']:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if results['k_dis_dataset'] > results['k_dis_gen_median']:
nb_dis_k_gi2gm[0] += 1
# repeats_better_dis_k_gi2gm.append(1)
elif results['k_dis_dataset'] == results['k_dis_gen_median']:
nb_dis_k_gi2gm[1] += 1
elif results['k_dis_dataset'] < results['k_dis_gen_median']:
nb_dis_k_gi2gm[2] += 1
# write result summary for each letter.
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name,
kernel_options['name'],
ged_options['edit_cost'],
ged_options['method'],
ged_options['attr_distance'],
mpg_options['fit_method'],
num_graphs,
target,
results['sod_set_median'],
results['sod_gen_median'],
results['k_dis_set_median'],
results['k_dis_gen_median'],
results['k_dis_dataset'],
sod_sm2gm,
dis_k_sm2gm,
dis_k_gi2sm,
dis_k_gi2gm,
results['runtime_precompute_gm'],
results['runtime_optimize_ec'],
results['runtime_generate_preimage'],
results['runtime_total'],
results['itrs'],
results['converged'],
results['num_updates_ecc'],
results['mge']['num_decrease_order'] >
0, # @todo: not suitable for multi-start mge
results['mge']['num_increase_order'] > 0,
results['mge']['num_converged_descents'] > 0,
nb_sod_sm2gm,
nb_dis_k_sm2gm,
nb_dis_k_gi2sm,
nb_dis_k_gi2gm
])
f_summary.close()
# save median graphs.
if save_medians:
os.makedirs(dir_save + 'medians/', exist_ok=True)
print('Saving median graphs to files...')
fn_pre_sm = dir_save + 'medians/set_median.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.set_median,
fn_pre_sm + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_pre_gm = dir_save + 'medians/gen_median.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.gen_median,
fn_pre_gm + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_best_dataset = dir_save + 'medians/g_best_dataset.' + mpg_options[
'fit_method'] + '.nbg' + str(num_graphs) + '.y' + str(
target) + '.repeat' + str(1)
saveGXL(mpg.best_from_dataset,
fn_best_dataset + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
# plot median graphs.
if plot_medians and save_medians:
if ged_options['edit_cost'] == 'LETTER2' or ged_options[
'edit_cost'] == 'LETTER' or ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low':
draw_Letter_graph(mpg.set_median, fn_pre_sm)
draw_Letter_graph(mpg.gen_median, fn_pre_gm)
draw_Letter_graph(mpg.best_from_dataset, fn_best_dataset)
if (load_gm == 'auto' and not gmfile_exist) or not load_gm:
gram_matrix_unnorm_list.append(mpg.gram_matrix_unnorm)
# write result summary for each class.
if save_results:
sod_sm_mean = np.mean(sod_sm_list)
sod_gm_mean = np.mean(sod_gm_list)
dis_k_sm_mean = np.mean(dis_k_sm_list)
dis_k_gm_mean = np.mean(dis_k_gm_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
time_precompute_gm_mean = np.mean(time_precompute_gm_list)
time_optimize_ec_mean = np.mean(time_optimize_ec_list)
time_generate_mean = np.mean(time_generate_list)
time_total_mean = np.mean(time_total_list)
itrs_mean = np.mean(itrs_list)
num_converged = np.sum(converged_list)
num_updates_ecc_mean = np.mean(num_updates_ecc_list)
num_mge_decrease_order = np.sum(mge_decrease_order_list)
num_mge_increase_order = np.sum(mge_increase_order_list)
num_mge_converged = np.sum(mge_converged_order_list)
sod_sm2gm_mean = get_relations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = get_relations(np.sign(dis_k_gm_mean -
dis_k_sm_mean))
dis_k_gi2sm_mean = get_relations(
np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = get_relations(
np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name, kernel_options['name'], ged_options['edit_cost'],
ged_options['method'], ged_options['attr_distance'],
mpg_options['fit_method'], num_graphs, 'all', sod_sm_mean,
sod_gm_mean, dis_k_sm_mean, dis_k_gm_mean, dis_k_gi_min_mean,
sod_sm2gm_mean, dis_k_sm2gm_mean, dis_k_gi2sm_mean,
dis_k_gi2gm_mean, time_precompute_gm_mean, time_optimize_ec_mean,
time_generate_mean, time_total_mean, itrs_mean, num_converged,
num_updates_ecc_mean, num_mge_decrease_order,
num_mge_increase_order, num_mge_converged
])
f_summary.close()
# save total pairwise kernel distances.
dis_k_max = np.max(dis_k_max_list)
dis_k_min = np.min(dis_k_min_list)
dis_k_mean = np.mean(dis_k_mean_list)
print('The maximum pairwise distance in kernel space:', dis_k_max)
print('The minimum pairwise distance in kernel space:', dis_k_min)
print('The average pairwise distance in kernel space:', dis_k_mean)
# write Gram matrices to file.
if (load_gm == 'auto' and not gmfile_exist) or not load_gm:
np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' +
kernel_options['name'] + '.gm',
gram_matrix_unnorm_list=gram_matrix_unnorm_list,
run_time_list=time_precompute_gm_list)
print('\ncomplete.\n')
def generate_random_preimages_by_class(ds_name,
rpg_options,
kernel_options,
save_results=True,
save_preimages=True,
load_gm='auto',
dir_save='',
irrelevant_labels=None,
edge_required=False,
cut_range=None):
# 1. get dataset.
print('1. getting dataset...')
dataset_all = Dataset()
dataset_all.load_predefined_dataset(ds_name)
dataset_all.trim_dataset(edge_required=edge_required)
if irrelevant_labels is not None:
dataset_all.remove_labels(**irrelevant_labels)
if cut_range is not None:
dataset_all.cut_graphs(cut_range)
datasets = split_dataset_by_target(dataset_all)
if save_results:
# create result files.
print('creating output files...')
fn_output_detail, fn_output_summary = _init_output_file_preimage(
ds_name, kernel_options['name'], dir_save)
dis_k_dataset_list = []
dis_k_preimage_list = []
time_precompute_gm_list = []
time_generate_list = []
time_total_list = []
itrs_list = []
num_updates_list = []
if load_gm == 'auto':
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options[
'name'] + '.gm.npz'
gmfile_exist = os.path.isfile(os.path.abspath(gm_fname))
if gmfile_exist:
gmfile = np.load(gm_fname,
allow_pickle=True) # @todo: may not be safe.
gram_matrix_unnorm_list = [
item for item in gmfile['gram_matrix_unnorm_list']
]
time_precompute_gm_list = gmfile['run_time_list'].tolist()
else:
gram_matrix_unnorm_list = []
time_precompute_gm_list = []
elif not load_gm:
gram_matrix_unnorm_list = []
time_precompute_gm_list = []
else:
gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options[
'name'] + '.gm.npz'
gmfile = np.load(gm_fname,
allow_pickle=True) # @todo: may not be safe.
gram_matrix_unnorm_list = [
item for item in gmfile['gram_matrix_unnorm_list']
]
time_precompute_gm_list = gmfile['run_time_list'].tolist()
print('starting generating preimage for each class of target...')
idx_offset = 0
for idx, dataset in enumerate(datasets):
target = dataset.targets[0]
print('\ntarget =', target, '\n')
# if target != 1:
# continue
num_graphs = len(dataset.graphs)
if num_graphs < 2:
print('\nnumber of graphs = ', num_graphs, ', skip.\n')
idx_offset += 1
continue
# 2. set parameters.
print('2. initializing mpg and setting parameters...')
if load_gm:
if gmfile_exist:
rpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm_list[
idx - idx_offset]
rpg_options['runtime_precompute_gm'] = time_precompute_gm_list[
idx - idx_offset]
rpg = RandomPreimageGenerator()
rpg.dataset = dataset
rpg.set_options(**rpg_options.copy())
rpg.kernel_options = kernel_options.copy()
# 3. compute preimage.
print('3. computing preimage...')
rpg.run()
results = rpg.get_results()
# 4. save results (and median graphs).
print('4. saving results (and preimages)...')
# write result detail.
if save_results:
print('writing results to files...')
f_detail = open(dir_save + fn_output_detail, 'a')
csv.writer(f_detail).writerow([
ds_name, kernel_options['name'], num_graphs, target, 1,
results['k_dis_dataset'], results['k_dis_preimage'],
results['runtime_precompute_gm'],
results['runtime_generate_preimage'], results['runtime_total'],
results['itrs'], results['num_updates']
])
f_detail.close()
# compute result summary.
dis_k_dataset_list.append(results['k_dis_dataset'])
dis_k_preimage_list.append(results['k_dis_preimage'])
time_precompute_gm_list.append(results['runtime_precompute_gm'])
time_generate_list.append(results['runtime_generate_preimage'])
time_total_list.append(results['runtime_total'])
itrs_list.append(results['itrs'])
num_updates_list.append(results['num_updates'])
# write result summary for each letter.
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name, kernel_options['name'], num_graphs, target,
results['k_dis_dataset'], results['k_dis_preimage'],
results['runtime_precompute_gm'],
results['runtime_generate_preimage'], results['runtime_total'],
results['itrs'], results['num_updates']
])
f_summary.close()
# save median graphs.
if save_preimages:
os.makedirs(dir_save + 'preimages/', exist_ok=True)
print('Saving preimages to files...')
fn_best_dataset = dir_save + 'preimages/g_best_dataset.' + 'nbg' + str(
num_graphs) + '.y' + str(target) + '.repeat' + str(1)
saveGXL(rpg.best_from_dataset,
fn_best_dataset + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
fn_preimage = dir_save + 'preimages/g_preimage.' + 'nbg' + str(
num_graphs) + '.y' + str(target) + '.repeat' + str(1)
saveGXL(rpg.preimage,
fn_preimage + '.gxl',
method='default',
node_labels=dataset.node_labels,
edge_labels=dataset.edge_labels,
node_attrs=dataset.node_attrs,
edge_attrs=dataset.edge_attrs)
if (load_gm == 'auto' and not gmfile_exist) or not load_gm:
gram_matrix_unnorm_list.append(rpg.gram_matrix_unnorm)
# write result summary for each class.
if save_results:
dis_k_dataset_mean = np.mean(dis_k_dataset_list)
dis_k_preimage_mean = np.mean(dis_k_preimage_list)
time_precompute_gm_mean = np.mean(time_precompute_gm_list)
time_generate_mean = np.mean(time_generate_list)
time_total_mean = np.mean(time_total_list)
itrs_mean = np.mean(itrs_list)
num_updates_mean = np.mean(num_updates_list)
f_summary = open(dir_save + fn_output_summary, 'a')
csv.writer(f_summary).writerow([
ds_name, kernel_options['name'], num_graphs, 'all',
dis_k_dataset_mean, dis_k_preimage_mean, time_precompute_gm_mean,
time_generate_mean, time_total_mean, itrs_mean, num_updates_mean
])
f_summary.close()
# write Gram matrices to file.
if (load_gm == 'auto' and not gmfile_exist) or not load_gm:
np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' +
kernel_options['name'] + '.gm',
gram_matrix_unnorm_list=gram_matrix_unnorm_list,
run_time_list=time_precompute_gm_list)
print('\ncomplete.\n')