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rearrange gklearn/preimage directory.

v0.2.x
jajupmochi 5 years ago
parent
57cc316e51
commit
75e42f1838
26 changed files with 0 additions and 10582 deletions
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  1. +0
    -170
      gklearn/preimage/find_best_k.py
  2. +0
    -430
      gklearn/preimage/fitDistance.py
  3. +0
    -467
      gklearn/preimage/ged.py
  4. +0
    -775
      gklearn/preimage/iam.py
  5. +0
    -114
      gklearn/preimage/knn.py
  6. +0
    -6
      gklearn/preimage/libs.py
  7. +0
    -218
      gklearn/preimage/median.py
  8. +0
    -201
      gklearn/preimage/median_benoit.py
  9. +0
    -215
      gklearn/preimage/median_linlin.py
  10. +0
    -201
      gklearn/preimage/pathfrequency.py
  11. +0
    -705
      gklearn/preimage/preimage_iam.py
  12. +0
    -309
      gklearn/preimage/preimage_random.py
  13. +0
    -122
      gklearn/preimage/python_code.py
  14. +0
    -83
      gklearn/preimage/test.py
  15. +0
    -648
      gklearn/preimage/test_fitDistance.py
  16. +0
    -520
      gklearn/preimage/test_ged.py
  17. +0
    -964
      gklearn/preimage/test_iam.py
  18. +0
    -462
      gklearn/preimage/test_k_closest_graphs.py
  19. +0
    -69
      gklearn/preimage/test_median_preimage_generator.py
  20. +0
    -686
      gklearn/preimage/test_others.py
  21. +0
    -620
      gklearn/preimage/test_preimage_iam.py
  22. +0
    -539
      gklearn/preimage/test_preimage_mix.py
  23. +0
    -398
      gklearn/preimage/test_preimage_random.py
  24. +0
    -935
      gklearn/preimage/xp_fit_method.py
  25. +0
    -476
      gklearn/preimage/xp_letter_h.py
  26. +0
    -249
      gklearn/preimage/xp_monoterpenoides.py

+ 0
- 170
gklearn/preimage/find_best_k.py View File

@@ -1,170 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 9 11:54:32 2020

@author: ljia
"""
import numpy as np
import random
import csv

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs

def find_best_k():
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
gkernel = 'treeletkernel'
node_label = 'atom'
edge_label = 'bond_type'
ds_name = 'mono'
dir_output = 'results/test_find_best_k/'
repeats = 50
k_list = range(2, 11)
fit_method = 'k-graphs'
# fitted on the whole dataset - treelet - mono
edit_costs = [0.1268873773592978, 0.004084633224249829, 0.0897581955378986, 0.15328856114451297, 0.3109956881625734, 0.0]
# create result files.
fn_output_detail = 'results_detail.' + fit_method + '.csv'
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'repeat', 'median set', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM'])
f_detail.close()
fn_output_summary = 'results_summary.csv'
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM',
'# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM',
'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM',
'repeats better dis_k gi -> GM'])
f_summary.close()
random.seed(1)
rdn_seed_list = random.sample(range(0, repeats * 100), repeats)
for k in k_list:
print('\n--------- k =', k, '----------')
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_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]
repeats_better_sod_sm2gm = []
repeats_better_dis_k_sm2gm = []
repeats_better_dis_k_gi2sm = []
repeats_better_dis_k_gi2gm = []
for repeat in range(repeats):
print('\nrepeat =', repeat)
random.seed(rdn_seed_list[repeat])
median_set_idx = random.sample(range(0, len(Gn)), k)
print('median set: ', median_set_idx)
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \
= median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k,
fit_method='k-graphs',
edit_costs=edit_costs,
group_min=median_set_idx,
parallel=False)
# write result detail.
sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm))
dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm))
dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min))
dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min))
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k, repeat,
median_set_idx, sod_sm, sod_gm, dis_k_sm, dis_k_gm,
dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm,
dis_k_gi2gm])
f_detail.close()
# compute result summary.
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
dis_k_sm_list.append(dis_k_sm)
dis_k_gm_list.append(dis_k_gm)
dis_k_gi_min_list.append(dis_k_gi_min)
# # SOD SM -> GM
if sod_sm > sod_gm:
nb_sod_sm2gm[0] += 1
repeats_better_sod_sm2gm.append(repeat)
elif sod_sm == sod_gm:
nb_sod_sm2gm[1] += 1
elif sod_sm < sod_gm:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if dis_k_sm > dis_k_gm:
nb_dis_k_sm2gm[0] += 1
repeats_better_dis_k_sm2gm.append(repeat)
elif dis_k_sm == dis_k_gm:
nb_dis_k_sm2gm[1] += 1
elif dis_k_sm < dis_k_gm:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if dis_k_gi_min > dis_k_sm:
nb_dis_k_gi2sm[0] += 1
repeats_better_dis_k_gi2sm.append(repeat)
elif dis_k_gi_min == dis_k_sm:
nb_dis_k_gi2sm[1] += 1
elif dis_k_gi_min < dis_k_sm:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if dis_k_gi_min > dis_k_gm:
nb_dis_k_gi2gm[0] += 1
repeats_better_dis_k_gi2gm.append(repeat)
elif dis_k_gi_min == dis_k_gm:
nb_dis_k_gi2gm[1] += 1
elif dis_k_gi_min < dis_k_gm:
nb_dis_k_gi2gm[2] += 1
# write result summary.
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)
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k,
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, nb_sod_sm2gm,
nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm,
repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm,
repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm])
f_summary.close()
print('\ncomplete.')
return


def getRelations(sign):
if sign == -1:
return 'better'
elif sign == 0:
return 'same'
elif sign == 1:
return 'worse'


if __name__ == '__main__':
find_best_k()

+ 0
- 430
gklearn/preimage/fitDistance.py View File

@@ -1,430 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 16 14:20:06 2019

@author: ljia
"""
import numpy as np
from tqdm import tqdm
from itertools import combinations_with_replacement, combinations
import multiprocessing
from multiprocessing import Pool
from functools import partial
import time
import random
import sys

from scipy import optimize
from scipy.optimize import minimize
import cvxpy as cp

from gklearn.preimage.ged import GED, get_nb_edit_operations, get_nb_edit_operations_letter, get_nb_edit_operations_nonsymbolic
from gklearn.preimage.utils import kernel_distance_matrix

def fit_GED_to_kernel_distance(Gn, node_label, edge_label, gkernel, itr_max,
params_ged={'lib': 'gedlibpy', 'cost': 'CONSTANT',
'method': 'IPFP', 'stabilizer': None},
init_costs=[3, 3, 1, 3, 3, 1],
dataset='monoterpenoides', Kmatrix=None,
parallel=True):
# dataset = dataset.lower()
# c_vi, c_vr, c_vs, c_ei, c_er, c_es or parts of them.
# random.seed(1)
# cost_rdm = random.sample(range(1, 10), 6)
# init_costs = cost_rdm + [0]
# init_costs = cost_rdm
# init_costs = [3, 3, 1, 3, 3, 1]
# init_costs = [i * 0.01 for i in cost_rdm] + [0]
# init_costs = [0.2, 0.2, 0.2, 0.2, 0.2, 0]
# init_costs = [0, 0, 0.9544, 0.026, 0.0196, 0]
# init_costs = [0.008429912251810438, 0.025461055985319694, 0.2047320869225948, 0.004148727085832133, 0.0, 0]
# idx_cost_nonzeros = [i for i, item in enumerate(edit_costs) if item != 0]
# compute distances in feature space.
dis_k_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label,
Kmatrix=Kmatrix, gkernel=gkernel)
dis_k_vec = []
for i in range(len(dis_k_mat)):
# for j in range(i, len(dis_k_mat)):
for j in range(i + 1, len(dis_k_mat)):
dis_k_vec.append(dis_k_mat[i, j])
dis_k_vec = np.array(dis_k_vec)
# init ged.
print('\ninitial:')
time0 = time.time()
params_ged['dataset'] = dataset
params_ged['edit_cost_constant'] = init_costs
ged_vec_init, ged_mat, n_edit_operations = compute_geds(Gn, params_ged,
parallel=parallel)
residual_list = [np.sqrt(np.sum(np.square(np.array(ged_vec_init) - dis_k_vec)))]
time_list = [time.time() - time0]
edit_cost_list = [init_costs]
nb_cost_mat = np.array(n_edit_operations)
nb_cost_mat_list = [nb_cost_mat]
print('edit_costs:', init_costs)
print('residual_list:', residual_list)
for itr in range(itr_max):
print('\niteration', itr)
time0 = time.time()
# "fit" geds to distances in feature space by tuning edit costs using the
# Least Squares Method.
np.savez('results/xp_fit_method/fit_data_debug' + str(itr) + '.gm',
nb_cost_mat=nb_cost_mat, dis_k_vec=dis_k_vec,
n_edit_operations=n_edit_operations, ged_vec_init=ged_vec_init,
ged_mat=ged_mat)
edit_costs_new, residual = update_costs(nb_cost_mat, dis_k_vec,
dataset=dataset, cost=params_ged['cost'])
for i in range(len(edit_costs_new)):
if -1e-9 <= edit_costs_new[i] <= 1e-9:
edit_costs_new[i] = 0
if edit_costs_new[i] < 0:
raise ValueError('The edit cost is negative.')
# for i in range(len(edit_costs_new)):
# if edit_costs_new[i] < 0:
# edit_costs_new[i] = 0

# compute new GEDs and numbers of edit operations.
params_ged['edit_cost_constant'] = edit_costs_new # np.array([edit_costs_new[0], edit_costs_new[1], 0.75])
ged_vec, ged_mat, n_edit_operations = compute_geds(Gn, params_ged,
parallel=parallel)
residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - dis_k_vec))))
time_list.append(time.time() - time0)
edit_cost_list.append(edit_costs_new)
nb_cost_mat = np.array(n_edit_operations)
nb_cost_mat_list.append(nb_cost_mat)
print('edit_costs:', edit_costs_new)
print('residual_list:', residual_list)
return edit_costs_new, residual_list, edit_cost_list, dis_k_mat, ged_mat, \
time_list, nb_cost_mat_list


def compute_geds(Gn, params_ged, parallel=False):
edit_cost_name = params_ged['cost']
if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2':
get_nb_eo = get_nb_edit_operations_letter
elif edit_cost_name == 'NON_SYMBOLIC':
get_nb_eo = get_nb_edit_operations_nonsymbolic
else:
get_nb_eo = get_nb_edit_operations
ged_mat = np.zeros((len(Gn), len(Gn)))
if parallel:
# print('parallel')
# len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
len_itr = int(len(Gn) * (len(Gn) - 1) / 2)
ged_vec = [0 for i in range(len_itr)]
n_edit_operations = [0 for i in range(len_itr)]
# itr = combinations_with_replacement(range(0, len(Gn)), 2)
itr = combinations(range(0, len(Gn)), 2)
n_jobs = multiprocessing.cpu_count()
if len_itr < 100 * n_jobs:
chunksize = int(len_itr / n_jobs) + 1
else:
chunksize = 100
def init_worker(gn_toshare):
global G_gn
G_gn = gn_toshare
do_partial = partial(_wrapper_compute_ged_parallel, params_ged, get_nb_eo)
pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn,))
iterator = tqdm(pool.imap_unordered(do_partial, itr, chunksize),
desc='computing GEDs', file=sys.stdout)
# iterator = pool.imap_unordered(do_partial, itr, chunksize)
for i, j, dis, n_eo_tmp in iterator:
idx_itr = int(len(Gn) * i + j - (i + 1) * (i + 2) / 2)
ged_vec[idx_itr] = dis
ged_mat[i][j] = dis
ged_mat[j][i] = dis
n_edit_operations[idx_itr] = n_eo_tmp
# print('\n-------------------------------------------')
# print(i, j, idx_itr, dis)
pool.close()
pool.join()
else:
ged_vec = []
n_edit_operations = []
for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout):
# for i in range(len(Gn)):
for j in range(i + 1, len(Gn)):
dis, pi_forward, pi_backward = GED(Gn[i], Gn[j], **params_ged)
ged_vec.append(dis)
ged_mat[i][j] = dis
ged_mat[j][i] = dis
n_eo_tmp = get_nb_eo(Gn[i], Gn[j], pi_forward, pi_backward)
n_edit_operations.append(n_eo_tmp)
return ged_vec, ged_mat, n_edit_operations

def _wrapper_compute_ged_parallel(params_ged, get_nb_eo, itr):
i = itr[0]
j = itr[1]
dis, n_eo_tmp = _compute_ged_parallel(G_gn[i], G_gn[j], params_ged, get_nb_eo)
return i, j, dis, n_eo_tmp


def _compute_ged_parallel(g1, g2, params_ged, get_nb_eo):
dis, pi_forward, pi_backward = GED(g1, g2, **params_ged)
n_eo_tmp = get_nb_eo(g1, g2, pi_forward, pi_backward) # [0,0,0,0,0,0]
return dis, n_eo_tmp


def update_costs(nb_cost_mat, dis_k_vec, dataset='monoterpenoides',
cost='CONSTANT', rw_constraints='inequality'):
# if dataset == 'Letter-high':
if cost == 'LETTER':
pass
# # method 1: set alpha automatically, just tune c_vir and c_eir by
# # LMS using cvxpy.
# alpha = 0.5
# coeff = 100 # np.max(alpha * nb_cost_mat[:,4] / dis_k_vec)
## if np.count_nonzero(nb_cost_mat[:,4]) == 0:
## alpha = 0.75
## else:
## alpha = np.min([dis_k_vec / c_vs for c_vs in nb_cost_mat[:,4] if c_vs != 0])
## alpha = alpha * 0.99
# param_vir = alpha * (nb_cost_mat[:,0] + nb_cost_mat[:,1])
# param_eir = (1 - alpha) * (nb_cost_mat[:,4] + nb_cost_mat[:,5])
# nb_cost_mat_new = np.column_stack((param_vir, param_eir))
# dis_new = coeff * dis_k_vec - alpha * nb_cost_mat[:,3]
#
# x = cp.Variable(nb_cost_mat_new.shape[1])
# cost = cp.sum_squares(nb_cost_mat_new * x - dis_new)
# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]]
# prob = cp.Problem(cp.Minimize(cost), constraints)
# prob.solve()
# edit_costs_new = x.value
# edit_costs_new = np.array([edit_costs_new[0], edit_costs_new[1], alpha])
# residual = np.sqrt(prob.value)
# # method 2: tune c_vir, c_eir and alpha by nonlinear programming by
# # scipy.optimize.minimize.
# w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1]
# w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5]
# w2 = nb_cost_mat[:,3]
# w3 = dis_k_vec
# func_min = lambda x: np.sum((w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \
# + w2 * x[2] - w3 * x[3]) ** 2)
# bounds = ((0, None), (0., None), (0.5, 0.5), (0, None))
# res = minimize(func_min, [0.9, 1.7, 0.75, 10], bounds=bounds)
# edit_costs_new = res.x[0:3]
# residual = res.fun
# method 3: tune c_vir, c_eir and alpha by nonlinear programming using cvxpy.
# # method 4: tune c_vir, c_eir and alpha by QP function
# # scipy.optimize.least_squares. An initial guess is required.
# w0 = nb_cost_mat[:,0] + nb_cost_mat[:,1]
# w1 = nb_cost_mat[:,4] + nb_cost_mat[:,5]
# w2 = nb_cost_mat[:,3]
# w3 = dis_k_vec
# func = lambda x: (w0 * x[0] * x[3] + w1 * x[1] * (1 - x[2]) \
# + w2 * x[2] - w3 * x[3]) ** 2
# res = optimize.root(func, [0.9, 1.7, 0.75, 100])
# edit_costs_new = res.x
# residual = None
elif cost == 'LETTER2':
# # 1. if c_vi != c_vr, c_ei != c_er.
# nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
# x = cp.Variable(nb_cost_mat_new.shape[1])
# cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
## # 1.1 no constraints.
## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]]
# # 1.2 c_vs <= c_vi + c_vr.
# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
## # 2. if c_vi == c_vr, c_ei == c_er.
## nb_cost_mat_new = nb_cost_mat[:,[0,3,4]]
## nb_cost_mat_new[:,0] += nb_cost_mat[:,1]
## nb_cost_mat_new[:,2] += nb_cost_mat[:,5]
## x = cp.Variable(nb_cost_mat_new.shape[1])
## cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
## # 2.1 no constraints.
## constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])]]
### # 2.2 c_vs <= c_vi + c_vr.
### constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
### np.array([2.0, -1.0, 0.0]).T@x >= 0.0]
#
# prob = cp.Problem(cp.Minimize(cost_fun), constraints)
# prob.solve()
# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]]
# edit_costs_new = np.array(edit_costs_new)
# residual = np.sqrt(prob.value)
if rw_constraints == 'inequality':
# c_vs <= c_vi + c_vr.
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.001 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
try:
prob.solve(verbose=True)
except MemoryError as error0:
print('\nUsing solver "OSQP" caused a memory error.')
print('the original error message is\n', error0)
print('solver status: ', prob.status)
print('trying solver "CVXOPT" instead...\n')
try:
prob.solve(solver=cp.CVXOPT, verbose=True)
except Exception as error1:
print('\nAn error occured when using solver "CVXOPT".')
print('the original error message is\n', error1)
print('solver status: ', prob.status)
print('trying solver "MOSEK" instead. Notice this solver is commercial and a lisence is required.\n')
prob.solve(solver=cp.MOSEK, verbose=True)
else:
print('solver status: ', prob.status)
else:
print('solver status: ', prob.status)
print()
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif rw_constraints == '2constraints':
# c_vs <= c_vi + c_vr and c_vi == c_vr, c_ei == c_er.
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0,
np.array([1.0, -1.0, 0.0, 0.0, 0.0]).T@x == 0.0,
np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif rw_constraints == 'no-constraint':
# no constraint.
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = x.value
residual = np.sqrt(prob.value)
# elif method == 'inequality_modified':
# # c_vs <= c_vi + c_vr.
# nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
# x = cp.Variable(nb_cost_mat_new.shape[1])
# cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
# prob = cp.Problem(cp.Minimize(cost_fun), constraints)
# prob.solve()
# # use same costs for insertion and removal rather than the fitted costs.
# edit_costs_new = [x.value[0], x.value[0], x.value[1], x.value[2], x.value[2]]
# edit_costs_new = np.array(edit_costs_new)
# residual = np.sqrt(prob.value)
elif cost == 'NON_SYMBOLIC':
is_n_attr = np.count_nonzero(nb_cost_mat[:,2])
is_e_attr = np.count_nonzero(nb_cost_mat[:,5])
if dataset == 'SYNTHETICnew':
# nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
nb_cost_mat_new = nb_cost_mat[:,[2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
# constraints = [x >= [0.0 for i in range(nb_cost_mat_new.shape[1])],
# np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0]
# constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])]]
constraints = [x >= [0.0001 for i in range(nb_cost_mat_new.shape[1])],
np.array([0.0, 1.0, -1.0]).T@x == 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
# print(x.value)
edit_costs_new = np.concatenate((np.array([0.0, 0.0]), x.value,
np.array([0.0])))
residual = np.sqrt(prob.value)
elif rw_constraints == 'inequality':
# c_vs <= c_vi + c_vr.
if is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif is_n_attr and not is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.001 for i in range(nb_cost_mat_new.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
print(x.value)
edit_costs_new = np.concatenate((x.value, np.array([0.0])))
residual = np.sqrt(prob.value)
elif not is_n_attr and is_e_attr:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4,5]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])],
np.array([0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]), x.value[2:]))
residual = np.sqrt(prob.value)
else:
nb_cost_mat_new = nb_cost_mat[:,[0,1,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = np.concatenate((x.value[0:2], np.array([0.0]),
x.value[2:], np.array([0.0])))
residual = np.sqrt(prob.value)
else:
# # method 1: simple least square method.
# edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec,
# rcond=None)
# # method 2: least square method with x_i >= 0.
# edit_costs_new, residual = optimize.nnls(nb_cost_mat, dis_k_vec)
# method 3: solve as a quadratic program with constraints.
# P = np.dot(nb_cost_mat.T, nb_cost_mat)
# q_T = -2 * np.dot(dis_k_vec.T, nb_cost_mat)
# G = -1 * np.identity(nb_cost_mat.shape[1])
# h = np.array([0 for i in range(nb_cost_mat.shape[1])])
# A = np.array([1 for i in range(nb_cost_mat.shape[1])])
# b = 1
# x = cp.Variable(nb_cost_mat.shape[1])
# prob = cp.Problem(cp.Minimize(cp.quad_form(x, P) + q_T@x),
# [G@x <= h])
# prob.solve()
# edit_costs_new = x.value
# residual = prob.value - np.dot(dis_k_vec.T, dis_k_vec)
# G = -1 * np.identity(nb_cost_mat.shape[1])
# h = np.array([0 for i in range(nb_cost_mat.shape[1])])
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])],
# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
edit_costs_new = x.value
residual = np.sqrt(prob.value)
# method 4:
return edit_costs_new, residual


if __name__ == '__main__':
print('check test_fitDistance.py')

+ 0
- 467
gklearn/preimage/ged.py View File

@@ -1,467 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 17 18:44:59 2019

@author: ljia
"""
import numpy as np
import networkx as nx
from tqdm import tqdm
import sys
import multiprocessing
from multiprocessing import Pool
from functools import partial

#from gedlibpy_linlin import librariesImport, gedlibpy
from gklearn.gedlib import librariesImport, gedlibpy

def GED(g1, g2, dataset='monoterpenoides', lib='gedlibpy', cost='CHEM_1', method='IPFP',
edit_cost_constant=[], algo_options='', stabilizer='min', repeat=50):
"""
Compute GED for 2 graphs.
"""
# dataset = dataset.lower()
if lib == 'gedlibpy':
gedlibpy.restart_env()
gedlibpy.add_nx_graph(convertGraph(g1, cost), "")
gedlibpy.add_nx_graph(convertGraph(g2, cost), "")

listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost(cost, edit_cost_constant=edit_cost_constant)
gedlibpy.init()
gedlibpy.set_method(method, algo_options)
gedlibpy.init_method()

g = listID[0]
h = listID[1]
if stabilizer is None:
gedlibpy.run_method(g, h)
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
upper = gedlibpy.get_upper_bound(g, h)
lower = gedlibpy.get_lower_bound(g, h)
elif stabilizer == 'mean':
# @todo: to be finished...
upper_list = [np.inf] * repeat
for itr in range(repeat):
gedlibpy.run_method(g, h)
upper_list[itr] = gedlibpy.get_upper_bound(g, h)
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
lower = gedlibpy.get_lower_bound(g, h)
upper = np.mean(upper_list)
elif stabilizer == 'median':
if repeat % 2 == 0:
repeat += 1
upper_list = [np.inf] * repeat
pi_forward_list = [0] * repeat
pi_backward_list = [0] * repeat
for itr in range(repeat):
gedlibpy.run_method(g, h)
upper_list[itr] = gedlibpy.get_upper_bound(g, h)
pi_forward_list[itr] = gedlibpy.get_forward_map(g, h)
pi_backward_list[itr] = gedlibpy.get_backward_map(g, h)
lower = gedlibpy.get_lower_bound(g, h)
upper = np.median(upper_list)
idx_median = upper_list.index(upper)
pi_forward = pi_forward_list[idx_median]
pi_backward = pi_backward_list[idx_median]
elif stabilizer == 'min':
upper = np.inf
for itr in range(repeat):
gedlibpy.run_method(g, h)
upper_tmp = gedlibpy.get_upper_bound(g, h)
if upper_tmp < upper:
upper = upper_tmp
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
lower = gedlibpy.get_lower_bound(g, h)
if upper == 0:
break
elif stabilizer == 'max':
upper = 0
for itr in range(repeat):
gedlibpy.run_method(g, h)
upper_tmp = gedlibpy.get_upper_bound(g, h)
if upper_tmp > upper:
upper = upper_tmp
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
lower = gedlibpy.get_lower_bound(g, h)
elif stabilizer == 'gaussian':
pass
dis = upper
elif lib == 'gedlib-bash':
import time
import random
import os
from gklearn.utils.graphfiles import saveDataset
tmp_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/'
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
fn_collection = tmp_dir + 'collection.' + str(time.time()) + str(random.randint(0, 1e9))
xparams = {'method': 'gedlib', 'graph_dir': fn_collection}
saveDataset([g1, g2], ['dummy', 'dummy'], gformat='gxl', group='xml',
filename=fn_collection, xparams=xparams)
command = 'GEDLIB_HOME=\'/media/ljia/DATA/research-repo/codes/others/gedlib/gedlib2\'\n'
command += 'LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$GEDLIB_HOME/lib\n'
command += 'export LD_LIBRARY_PATH\n'
command += 'cd \'' + os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/bin\'\n'
command += './ged_for_python_bash monoterpenoides ' + fn_collection \
+ ' \'' + algo_options + '\' '
for ec in edit_cost_constant:
command += str(ec) + ' '
# output = os.system(command)
stream = os.popen(command)
output = stream.readlines()
# print(output)
dis = float(output[0].strip())
runtime = float(output[1].strip())
size_forward = int(output[2].strip())
pi_forward = [int(item.strip()) for item in output[3:3+size_forward]]
pi_backward = [int(item.strip()) for item in output[3+size_forward:]]

# print(dis)
# print(runtime)
# print(size_forward)
# print(pi_forward)
# print(pi_backward)
# make the map label correct (label remove map as np.inf)
nodes1 = [n for n in g1.nodes()]
nodes2 = [n for n in g2.nodes()]
nb1 = nx.number_of_nodes(g1)
nb2 = nx.number_of_nodes(g2)
pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]
# print(pi_forward)
return dis, pi_forward, pi_backward


def convertGraph(G, cost):
"""Convert a graph to the proper NetworkX format that can be
recognized by library gedlibpy.
"""
G_new = nx.Graph()
if cost == 'LETTER' or cost == 'LETTER2':
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), x=str(attrs['attributes'][0]),
y=str(attrs['attributes'][1]))
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2))
elif cost == 'NON_SYMBOLIC':
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd))
for a_name in G.graph['node_attrs']:
G_new.nodes[str(nd)][a_name] = str(attrs[a_name])
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2))
for a_name in G.graph['edge_attrs']:
G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name])
else:
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), chem=attrs['atom'])
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
# G_new.add_edge(str(nd1), str(nd2))
return G_new


def GED_n(Gn, lib='gedlibpy', cost='CHEM_1', method='IPFP',
edit_cost_constant=[], stabilizer='min', repeat=50):
"""
Compute GEDs for a group of graphs.
"""
if lib == 'gedlibpy':
def convertGraph(G):
"""Convert a graph to the proper NetworkX format that can be
recognized by library gedlibpy.
"""
G_new = nx.Graph()
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), chem=attrs['atom'])
for nd1, nd2, attrs in G.edges(data=True):
# G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
G_new.add_edge(str(nd1), str(nd2))
return G_new
gedlibpy.restart_env()
gedlibpy.add_nx_graph(convertGraph(g1), "")
gedlibpy.add_nx_graph(convertGraph(g2), "")

listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost(cost, edit_cost_constant=edit_cost_constant)
gedlibpy.init()
gedlibpy.set_method(method, "")
gedlibpy.init_method()

g = listID[0]
h = listID[1]
if stabilizer is None:
gedlibpy.run_method(g, h)
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
upper = gedlibpy.get_upper_bound(g, h)
lower = gedlibpy.get_lower_bound(g, h)
elif stabilizer == 'min':
upper = np.inf
for itr in range(repeat):
gedlibpy.run_method(g, h)
upper_tmp = gedlibpy.get_upper_bound(g, h)
if upper_tmp < upper:
upper = upper_tmp
pi_forward = gedlibpy.get_forward_map(g, h)
pi_backward = gedlibpy.get_backward_map(g, h)
lower = gedlibpy.get_lower_bound(g, h)
if upper == 0:
break
dis = upper
# make the map label correct (label remove map as np.inf)
nodes1 = [n for n in g1.nodes()]
nodes2 = [n for n in g2.nodes()]
nb1 = nx.number_of_nodes(g1)
nb2 = nx.number_of_nodes(g2)
pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]
return dis, pi_forward, pi_backward


def ged_median(Gn, Gn_median, verbose=False, params_ged={'lib': 'gedlibpy',
'cost': 'CHEM_1', 'method': 'IPFP', 'edit_cost_constant': [],
'algo_options': '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1',
'stabilizer': None}, parallel=False):
if parallel:
len_itr = int(len(Gn))
pi_forward_list = [[] for i in range(len_itr)]
dis_list = [0 for i in range(len_itr)]
itr = range(0, len_itr)
n_jobs = multiprocessing.cpu_count()
if len_itr < 100 * n_jobs:
chunksize = int(len_itr / n_jobs) + 1
else:
chunksize = 100
def init_worker(gn_toshare, gn_median_toshare):
global G_gn, G_gn_median
G_gn = gn_toshare
G_gn_median = gn_median_toshare
do_partial = partial(_compute_ged_median, params_ged)
pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(Gn, Gn_median))
if verbose:
iterator = tqdm(pool.imap_unordered(do_partial, itr, chunksize),
desc='computing GEDs', file=sys.stdout)
else:
iterator = pool.imap_unordered(do_partial, itr, chunksize)
for i, dis_sum, pi_forward in iterator:
pi_forward_list[i] = pi_forward
dis_list[i] = dis_sum
# print('\n-------------------------------------------')
# print(i, j, idx_itr, dis)
pool.close()
pool.join()
else:
dis_list = []
pi_forward_list = []
for idx, G in tqdm(enumerate(Gn), desc='computing median distances',
file=sys.stdout) if verbose else enumerate(Gn):
dis_sum = 0
pi_forward_list.append([])
for G_p in Gn_median:
dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p,
**params_ged)
pi_forward_list[idx].append(pi_tmp_forward)
dis_sum += dis_tmp
dis_list.append(dis_sum)
return dis_list, pi_forward_list


def _compute_ged_median(params_ged, itr):
# print(itr)
dis_sum = 0
pi_forward = []
for G_p in G_gn_median:
dis_tmp, pi_tmp_forward, pi_tmp_backward = GED(G_gn[itr], G_p,
**params_ged)
pi_forward.append(pi_tmp_forward)
dis_sum += dis_tmp
return itr, dis_sum, pi_forward


def get_nb_edit_operations(g1, g2, forward_map, backward_map):
"""Compute the number of each edit operations.
"""
n_vi = 0
n_vr = 0
n_vs = 0
n_ei = 0
n_er = 0
n_es = 0
nodes1 = [n for n in g1.nodes()]
for i, map_i in enumerate(forward_map):
if map_i == np.inf:
n_vr += 1
elif g1.node[nodes1[i]]['atom'] != g2.node[map_i]['atom']:
n_vs += 1
for map_i in backward_map:
if map_i == np.inf:
n_vi += 1
# idx_nodes1 = range(0, len(node1))
edges1 = [e for e in g1.edges()]
nb_edges2_cnted = 0
for n1, n2 in edges1:
idx1 = nodes1.index(n1)
idx2 = nodes1.index(n2)
# one of the nodes is removed, thus the edge is removed.
if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf:
n_er += 1
# corresponding edge is in g2.
elif (forward_map[idx1], forward_map[idx2]) in g2.edges():
nb_edges2_cnted += 1
# edge labels are different.
if g2.edges[((forward_map[idx1], forward_map[idx2]))]['bond_type'] \
!= g1.edges[(n1, n2)]['bond_type']:
n_es += 1
elif (forward_map[idx2], forward_map[idx1]) in g2.edges():
nb_edges2_cnted += 1
# edge labels are different.
if g2.edges[((forward_map[idx2], forward_map[idx1]))]['bond_type'] \
!= g1.edges[(n1, n2)]['bond_type']:
n_es += 1
# corresponding nodes are in g2, however the edge is removed.
else:
n_er += 1
n_ei = nx.number_of_edges(g2) - nb_edges2_cnted
return n_vi, n_vr, n_vs, n_ei, n_er, n_es


def get_nb_edit_operations_letter(g1, g2, forward_map, backward_map):
"""Compute the number of each edit operations.
"""
n_vi = 0
n_vr = 0
n_vs = 0
sod_vs = 0
n_ei = 0
n_er = 0
nodes1 = [n for n in g1.nodes()]
for i, map_i in enumerate(forward_map):
if map_i == np.inf:
n_vr += 1
else:
n_vs += 1
diff_x = float(g1.nodes[nodes1[i]]['x']) - float(g2.nodes[map_i]['x'])
diff_y = float(g1.nodes[nodes1[i]]['y']) - float(g2.nodes[map_i]['y'])
sod_vs += np.sqrt(np.square(diff_x) + np.square(diff_y))
for map_i in backward_map:
if map_i == np.inf:
n_vi += 1
# idx_nodes1 = range(0, len(node1))
edges1 = [e for e in g1.edges()]
nb_edges2_cnted = 0
for n1, n2 in edges1:
idx1 = nodes1.index(n1)
idx2 = nodes1.index(n2)
# one of the nodes is removed, thus the edge is removed.
if forward_map[idx1] == np.inf or forward_map[idx2] == np.inf:
n_er += 1
# corresponding edge is in g2. Edge label is not considered.
elif (forward_map[idx1], forward_map[idx2]) in g2.edges() or \
(forward_map[idx2], forward_map[idx1]) in g2.edges():
nb_edges2_cnted += 1
# corresponding nodes are in g2, however the edge is removed.
else:
n_er += 1
n_ei = nx.number_of_edges(g2) - nb_edges2_cnted
return n_vi, n_vr, n_vs, sod_vs, n_ei, n_er


def get_nb_edit_operations_nonsymbolic(g1, g2, forward_map, backward_map):
"""Compute the number of each edit operations.
"""
n_vi = 0
n_vr = 0
n_vs = 0
sod_vs = 0
n_ei = 0
n_er = 0
n_es = 0
sod_es = 0
nodes1 = [n for n in g1.nodes()]
for i, map_i in enumerate(forward_map):
if map_i == np.inf:
n_vr += 1
else:
n_vs += 1
sum_squares = 0
for a_name in g1.graph['node_attrs']:
diff = float(g1.nodes[nodes1[i]][a_name]) - float(g2.nodes[map_i][a_name])
sum_squares += np.square(diff)
sod_vs += np.sqrt(sum_squares)
for map_i in backward_map:
if map_i == np.inf:
n_vi += 1
# idx_nodes1 = range(0, len(node1))
edges1 = [e for e in g1.edges()]
for n1, n2 in edges1:
idx1 = nodes1.index(n1)
idx2 = nodes1.index(n2)
n1_g2 = forward_map[idx1]
n2_g2 = forward_map[idx2]
# one of the nodes is removed, thus the edge is removed.
if n1_g2 == np.inf or n2_g2 == np.inf:
n_er += 1
# corresponding edge is in g2.
elif (n1_g2, n2_g2) in g2.edges():
n_es += 1
sum_squares = 0
for a_name in g1.graph['edge_attrs']:
diff = float(g1.edges[n1, n2][a_name]) - float(g2.nodes[n1_g2, n2_g2][a_name])
sum_squares += np.square(diff)
sod_es += np.sqrt(sum_squares)
elif (n2_g2, n1_g2) in g2.edges():
n_es += 1
sum_squares = 0
for a_name in g1.graph['edge_attrs']:
diff = float(g1.edges[n2, n1][a_name]) - float(g2.nodes[n2_g2, n1_g2][a_name])
sum_squares += np.square(diff)
sod_es += np.sqrt(sum_squares)
# corresponding nodes are in g2, however the edge is removed.
else:
n_er += 1
n_ei = nx.number_of_edges(g2) - n_es
return n_vi, n_vr, sod_vs, n_ei, n_er, sod_es


if __name__ == '__main__':
print('check test_ged.py')

+ 0
- 775
gklearn/preimage/iam.py View File

@@ -1,775 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 26 11:49:12 2019

Iterative alternate minimizations using GED.
@author: ljia
"""
import numpy as np
import random
import networkx as nx
from tqdm import tqdm

from gklearn.utils.graphdataset import get_dataset_attributes
from gklearn.utils.utils import graph_isIdentical, get_node_labels, get_edge_labels
from gklearn.preimage.ged import GED, ged_median


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 iam_bash(Gn_names, edit_cost_constant, cost='CONSTANT', initial_solutions=1,
dataset='monoterpenoides',
graph_dir=''):
"""Compute the iam by c++ implementation (gedlib) through bash.
"""
import os
import time

def createCollectionFile(Gn_names, y, filename):
"""Create collection file.
"""
dirname_ds = os.path.dirname(filename)
if dirname_ds != '':
dirname_ds += '/'
if not os.path.exists(dirname_ds) :
os.makedirs(dirname_ds)
with open(filename + '.xml', 'w') as fgroup:
fgroup.write("<?xml version=\"1.0\"?>")
fgroup.write("\n<!DOCTYPE GraphCollection SYSTEM \"http://www.inf.unibz.it/~blumenthal/dtd/GraphCollection.dtd\">")
fgroup.write("\n<GraphCollection>")
for idx, fname in enumerate(Gn_names):
fgroup.write("\n\t<graph file=\"" + fname + "\" class=\"" + str(y[idx]) + "\"/>")
fgroup.write("\n</GraphCollection>")
fgroup.close()

tmp_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/'
fn_collection = tmp_dir + 'collection.' + str(time.time()) + str(random.randint(0, 1e9))
createCollectionFile(Gn_names, ['dummy'] * len(Gn_names), fn_collection)
# fn_collection = tmp_dir + 'collection_for_debug'
# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/gxl'
# if dataset == 'Letter-high' or dataset == 'Fingerprint':
# dataset = 'letter'
command = 'GEDLIB_HOME=\'/media/ljia/DATA/research-repo/codes/Linlin/gedlib\'\n'
command += 'LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$GEDLIB_HOME/lib\n'
command += 'export LD_LIBRARY_PATH\n'
command += 'cd \'' + os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/bin\'\n'
command += './iam_for_python_bash ' + dataset + ' ' + fn_collection \
+ ' \'' + graph_dir + '\' ' + ' ' + cost + ' ' + str(initial_solutions) + ' '
if edit_cost_constant is None:
command += 'None'
else:
for ec in edit_cost_constant:
command += str(ec) + ' '
# output = os.system(command)
stream = os.popen(command)

output = stream.readlines()
# print(output)
sod_sm = float(output[0].strip())
sod_gm = float(output[1].strip())
fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl'
fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl'
return sod_sm, sod_gm, fname_sm, fname_gm



###############################################################################
# Old implementations.
def iam(Gn, c_ei=3, c_er=3, c_es=1, node_label='atom', edge_label='bond_type',
connected=True):
"""See my name, then you know what I do.
"""
# Gn = Gn[0:10]
Gn = [nx.convert_node_labels_to_integers(g) for g in Gn]
# phase 1: initilize.
# compute set-median.
dis_min = np.inf
pi_p = []
pi_all = []
for idx1, G_p in enumerate(Gn):
dist_sum = 0
pi_all.append([])
for idx2, G_p_prime in enumerate(Gn):
dist_tmp, pi_tmp, _ = GED(G_p, G_p_prime)
pi_all[idx1].append(pi_tmp)
dist_sum += dist_tmp
if dist_sum < dis_min:
dis_min = dist_sum
G = G_p.copy()
idx_min = idx1
# list of edit operations.
pi_p = pi_all[idx_min]
# phase 2: iteration.
ds_attrs = get_dataset_attributes(Gn, attr_names=['edge_labeled', 'node_attr_dim'],
edge_label=edge_label)
for itr in range(0, 10): # @todo: the convergence condition?
G_new = G.copy()
# 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 nd, _ in G.nodes(data=True):
h_i0_list = []
label_list = []
for label in get_node_labels(Gn, node_label):
h_i0 = 0
for idx, g in enumerate(Gn):
pi_i = pi_p[idx][nd]
if g.has_node(pi_i) and g.nodes[pi_i][node_label] == label:
h_i0 += 1
h_i0_list.append(h_i0)
label_list.append(label)
# choose one of the best randomly.
idx_max = np.argwhere(h_i0_list == np.max(h_i0_list)).flatten().tolist()
idx_rdm = random.randint(0, len(idx_max) - 1)
G_new.nodes[nd][node_label] = label_list[idx_max[idx_rdm]]
else: # labels are non-symbolic
for nd, _ in 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):
pi_i = pi_p[idx][nd]
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.nodes[nd]['attributes'] = phi_i_bar
# update edge labels and adjacency matrix.
if ds_attrs['edge_labeled']:
for nd1, nd2, _ in G.edges(data=True):
h_ij0_list = []
label_list = []
for label in get_edge_labels(Gn, edge_label):
h_ij0 = 0
for idx, g in enumerate(Gn):
pi_i = pi_p[idx][nd1]
pi_j = pi_p[idx][nd2]
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)
# choose one of the best randomly.
idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist()
h_ij0_max = h_ij0_list[idx_max[0]]
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn):
pi_i = pi_p[idx][nd1]
pi_j = pi_p[idx][nd2]
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) * 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:
if G_new.has_edge(nd1, nd2):
G_new.remove_edge(nd1, nd2)
else: # if edges are unlabeled
for nd1, nd2, _ in G.edges(data=True):
sij_norm = 0
for idx, g in enumerate(Gn):
pi_i = pi_p[idx][nd1]
pi_j = pi_p[idx][nd2]
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) * c_er / (c_er + c_ei):
if not G_new.has_edge(nd1, nd2):
G_new.add_edge(nd1, nd2)
else:
if G_new.has_edge(nd1, nd2):
G_new.remove_edge(nd1, nd2)
G = G_new.copy()
# update pi_p
pi_p = []
for idx1, G_p in enumerate(Gn):
dist_tmp, pi_tmp, _ = GED(G, G_p)
pi_p.append(pi_tmp)
return G

# --------------------------- These are tests --------------------------------#
def test_iam_with_more_graphs_as_init(Gn, G_candidate, c_ei=3, c_er=3, c_es=1,
node_label='atom', edge_label='bond_type'):
"""See my name, then you know what I do.
"""
# Gn = Gn[0:10]
Gn = [nx.convert_node_labels_to_integers(g) for g in Gn]
# phase 1: initilize.
# compute set-median.
dis_min = np.inf
# pi_p = []
pi_all_forward = []
pi_all_backward = []
for idx1, G_p in tqdm(enumerate(G_candidate), desc='computing GEDs', file=sys.stdout):
dist_sum = 0
pi_all_forward.append([])
pi_all_backward.append([])
for idx2, G_p_prime in enumerate(Gn):
dist_tmp, pi_tmp_forward, pi_tmp_backward = GED(G_p, G_p_prime)
pi_all_forward[idx1].append(pi_tmp_forward)
pi_all_backward[idx1].append(pi_tmp_backward)
dist_sum += dist_tmp
if dist_sum <= dis_min:
dis_min = dist_sum
G = G_p.copy()
idx_min = idx1
# list of edit operations.
pi_p_forward = pi_all_forward[idx_min]
pi_p_backward = pi_all_backward[idx_min]
# phase 2: iteration.
ds_attrs = get_dataset_attributes(Gn + [G], attr_names=['edge_labeled', 'node_attr_dim'],
edge_label=edge_label)
label_set = get_node_labels(Gn + [G], node_label)
for itr in range(0, 10): # @todo: the convergence condition?
G_new = G.copy()
# 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 nd in G.nodes():
h_i0_list = []
label_list = []
for label in label_set:
h_i0 = 0
for idx, g in enumerate(Gn):
pi_i = pi_p_forward[idx][nd]
if g.has_node(pi_i) and g.nodes[pi_i][node_label] == label:
h_i0 += 1
h_i0_list.append(h_i0)
label_list.append(label)
# choose one of the best randomly.
idx_max = np.argwhere(h_i0_list == np.max(h_i0_list)).flatten().tolist()
idx_rdm = random.randint(0, len(idx_max) - 1)
G_new.nodes[nd][node_label] = label_list[idx_max[idx_rdm]]
else: # labels are non-symbolic
for nd in G.nodes():
Si_norm = 0
phi_i_bar = np.array([0.0 for _ in range(ds_attrs['node_attr_dim'])])
for idx, g in enumerate(Gn):
pi_i = pi_p_forward[idx][nd]
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.nodes[nd]['attributes'] = phi_i_bar
# update edge labels and adjacency matrix.
if ds_attrs['edge_labeled']:
for nd1, nd2, _ in G.edges(data=True):
h_ij0_list = []
label_list = []
for label in get_edge_labels(Gn, edge_label):
h_ij0 = 0
for idx, g in enumerate(Gn):
pi_i = pi_p_forward[idx][nd1]
pi_j = pi_p_forward[idx][nd2]
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)
# choose one of the best randomly.
idx_max = np.argwhere(h_ij0_list == np.max(h_ij0_list)).flatten().tolist()
h_ij0_max = h_ij0_list[idx_max[0]]
idx_rdm = random.randint(0, len(idx_max) - 1)
best_label = label_list[idx_max[idx_rdm]]
# check whether a_ij is 0 or 1.
sij_norm = 0
for idx, g in enumerate(Gn):
pi_i = pi_p_forward[idx][nd1]
pi_j = pi_p_forward[idx][nd2]
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) * 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:
if G_new.has_edge(nd1, nd2):
G_new.remove_edge(nd1, nd2)
else: # if edges are unlabeled
# @todo: works only for undirected graphs.
for nd1 in range(nx.number_of_nodes(G)):
for nd2 in range(nd1 + 1, nx.number_of_nodes(G)):
sij_norm = 0
for idx, g in enumerate(Gn):
pi_i = pi_p_forward[idx][nd1]
pi_j = pi_p_forward[idx][nd2]
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) * c_er / (c_er + c_ei):
if not G_new.has_edge(nd1, nd2):
G_new.add_edge(nd1, nd2)
elif sij_norm < len(Gn) * c_er / (c_er + c_ei):
if G_new.has_edge(nd1, nd2):
G_new.remove_edge(nd1, nd2)
# do not change anything when equal.
G = G_new.copy()
# update pi_p
pi_p_forward = []
for G_p in Gn:
dist_tmp, pi_tmp_forward, pi_tmp_backward = GED(G, G_p)
pi_p_forward.append(pi_tmp_forward)
return G


###############################################################################

if __name__ == '__main__':
from gklearn.utils.graphfiles import loadDataset
ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',
'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}} # node/edge symb
# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
# 'extra_params': {}} # node nsymb
# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds',
# 'extra_params': {}}
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])

iam(Gn)

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- 114
gklearn/preimage/knn.py View File

@@ -1,114 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 10 13:22:04 2020

@author: ljia
"""
import numpy as np
#import matplotlib.pyplot as plt
from tqdm import tqdm
import random
#import csv
from shutil import copyfile
import os

from gklearn.preimage.iam import iam_bash
from gklearn.utils.graphfiles import loadDataset, loadGXL
from gklearn.preimage.ged import GED
from gklearn.preimage.utils import get_same_item_indices

def test_knn():
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
# gkernel = 'treeletkernel'
# node_label = 'atom'
# edge_label = 'bond_type'
# ds_name = 'mono'
dir_output = 'results/knn/'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'
k_nn = 1
percent = 0.1
repeats = 50
edit_cost_constant = [3, 3, 1, 3, 3, 1]
# get indices by classes.
y_idx = get_same_item_indices(y_all)
sod_sm_list_list
for repeat in range(0, repeats):
print('\n---------------------------------')
print('repeat =', repeat)
accuracy_sm_list = []
accuracy_gm_list = []
sod_sm_list = []
sod_gm_list = []
random.seed(repeat)
set_median_list = []
gen_median_list = []
train_y_set = []
for y, values in y_idx.items():
print('\ny =', y)
size_median_set = int(len(values) * percent)
median_set_idx = random.sample(values, size_median_set)
print('median set: ', median_set_idx)
# compute set median and gen median using IAM (C++ through bash).
# Gn_median = [Gn[idx] for idx in median_set_idx]
group_fnames = [Gn[g].graph['filename'] for g in median_set_idx]
sod_sm, sod_gm, fname_sm, fname_gm = iam_bash(group_fnames, edit_cost_constant,
graph_dir=graph_dir)
print('sod_sm, sod_gm:', sod_sm, sod_gm)
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
fname_sm_new = dir_output + 'medians/set_median.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl'
copyfile(fname_sm, fname_sm_new)
fname_gm_new = dir_output + 'medians/gen_median.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl'
copyfile(fname_gm, fname_gm_new)
set_median_list.append(loadGXL(fname_sm_new))
gen_median_list.append(loadGXL(fname_gm_new))
train_y_set.append(int(y))
print(sod_sm, sod_gm)
# do 1-nn.
test_y_set = [int(y) for y in y_all]
accuracy_sm = knn(set_median_list, train_y_set, Gn, test_y_set, k=k_nn, distance='ged')
accuracy_gm = knn(set_median_list, train_y_set, Gn, test_y_set, k=k_nn, distance='ged')
accuracy_sm_list.append(accuracy_sm)
accuracy_gm_list.append(accuracy_gm)
print('current accuracy sm and gm:', accuracy_sm, accuracy_gm)
# output
accuracy_sm_mean = np.mean(accuracy_sm_list)
accuracy_gm_mean = np.mean(accuracy_gm_list)
print('\ntotal average accuracy sm and gm:', accuracy_sm_mean, accuracy_gm_mean)

def knn(train_set, train_y_set, test_set, test_y_set, k=1, distance='ged'):
if k == 1 and distance == 'ged':
algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'
params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP',
'algo_options': algo_options, 'stabilizer': None}
accuracy = 0
for idx_test, g_test in tqdm(enumerate(test_set), desc='computing 1-nn',
file=sys.stdout):
dis = np.inf
for idx_train, g_train in enumerate(train_set):
dis_cur, _, _ = GED(g_test, g_train, **params_ged)
if dis_cur < dis:
dis = dis_cur
test_y_cur = train_y_set[idx_train]
if test_y_cur == test_y_set[idx_test]:
accuracy += 1
accuracy = accuracy / len(test_set)
return accuracy


if __name__ == '__main__':
test_knn()

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- 6
gklearn/preimage/libs.py View File

@@ -1,6 +0,0 @@
import sys
import pathlib

# insert gedlibpy library.
sys.path.insert(0, "../../../")
from gedlibpy import librariesImport, gedlibpy

+ 0
- 218
gklearn/preimage/median.py View File

@@ -1,218 +0,0 @@
import sys
sys.path.insert(0, "../")
#import pathlib
import numpy as np
import networkx as nx
import time
from gedlibpy import librariesImport, gedlibpy
#import script
sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/")
import gklearn
from gklearn.utils.graphfiles import loadDataset
def replace_graph_in_env(script, graph, old_id, label='median'):
"""
Replace a graph in script
If old_id is -1, add a new graph to the environnemt
"""
if(old_id > -1):
script.PyClearGraph(old_id)
new_id = script.PyAddGraph(label)
for i in graph.nodes():
script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib
for e in graph.edges:
script.PyAddEdge(new_id, str(e[0]),str(e[1]), {})
script.PyInitEnv()
script.PySetMethod("IPFP", "")
script.PyInitMethod()
return new_id
#Dessin median courrant
def draw_Letter_graph(graph, savepath=''):
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['attributes'][0]),
float(graph.node[n]['attributes'][1])])
nx.draw_networkx(graph, pos)
if savepath != '':
plt.savefig(savepath + str(time.time()) + '.eps', format='eps', dpi=300)
plt.show()
plt.clf()
#compute new mappings
def update_mappings(script,median_id,listID):
med_distances = {}
med_mappings = {}
sod = 0
for i in range(0,len(listID)):
script.PyRunMethod(median_id,listID[i])
med_distances[i] = script.PyGetUpperBound(median_id,listID[i])
med_mappings[i] = script.PyGetForwardMap(median_id,listID[i])
sod += med_distances[i]
return med_distances, med_mappings, sod
def calcul_Sij(all_mappings, all_graphs,i,j):
s_ij = 0
for k in range(0,len(all_mappings)):
cur_graph = all_graphs[k]
cur_mapping = all_mappings[k]
size_graph = cur_graph.order()
if ((cur_mapping[i] < size_graph) and
(cur_mapping[j] < size_graph) and
(cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)):
s_ij += 1
return s_ij
# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings):
# from scipy.stats.mstats import gmean
# for i in median.nodes():
# for k in listIdSet:
# vectors = [] #np.zeros((len(listIdSet),2))
# if(k != median_id):
# phi_i = mappings[k][i]
# if(phi_i < dataset[k].order()):
# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])])
# new_labels = gmean(vectors)
# median.node[i]['x'] = str(new_labels[0])
# median.node[i]['y'] = str(new_labels[1])
# return median
def update_median_nodes(median,dataset,mappings):
#update node attributes
for i in median.nodes():
nb_sub=0
mean_label = {'x' : 0, 'y' : 0}
for k in range(0,len(mappings)):
phi_i = mappings[k][i]
if ( phi_i < dataset[k].order() ):
nb_sub += 1
mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x'])
mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y'])
median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub))
median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub))
return median
def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425):
#for letter high, ceir = 1.7, alpha = 0.75
size_dataset = len(dataset)
ratio_cei_cer = cer/(cei + cer)
threshold = size_dataset*ratio_cei_cer
order_graph_median = median.order()
for i in range(0,order_graph_median):
for j in range(i+1,order_graph_median):
s_ij = calcul_Sij(mappings,dataset,i,j)
if(s_ij > threshold):
median.add_edge(i,j)
else:
if(median.has_edge(i,j)):
median.remove_edge(i,j)
return median
def compute_median(script, listID, dataset,verbose=False):
"""Compute a graph median of a dataset according to an environment
Parameters
script : An gedlib initialized environnement
listID (list): a list of ID in script: encodes the dataset
dataset (list): corresponding graphs in networkX format. We assume that graph
listID[i] corresponds to dataset[i]
Returns:
A networkX graph, which is the median, with corresponding sod
"""
print(len(listID))
median_set_index, median_set_sod = compute_median_set(script, listID)
print(median_set_index)
print(median_set_sod)
sods = []
#Ajout median dans environnement
set_median = dataset[median_set_index].copy()
median = dataset[median_set_index].copy()
cur_med_id = replace_graph_in_env(script,median,-1)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite_max = 50
old_sod = cur_sod * 2
ite = 0
epsilon = 0.001
best_median
while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )):
median = update_median_nodes(median,dataset, med_mappings)
median = update_median_edges(dataset,med_mappings,median)
cur_med_id = replace_graph_in_env(script,median,cur_med_id)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite += 1
return median, cur_sod, sods, set_median
draw_Letter_graph(median)
def compute_median_set(script,listID):
'Returns the id in listID corresponding to median set'
#Calcul median set
N=len(listID)
map_id_to_index = {}
map_index_to_id = {}
for i in range(0,len(listID)):
map_id_to_index[listID[i]] = i
map_index_to_id[i] = listID[i]
distances = np.zeros((N,N))
for i in listID:
for j in listID:
script.PyRunMethod(i,j)
distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j)
median_set_index = np.argmin(np.sum(distances,0))
sod = np.min(np.sum(distances,0))
return median_set_index, sod
if __name__ == "__main__":
#Chargement du dataset
script.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml')
script.PySetEditCost("LETTER")
script.PyInitEnv()
script.PySetMethod("IPFP", "")
script.PyInitMethod()
dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl")
listID = script.PyGetAllGraphIds()
median, sod = compute_median(script,listID,dataset,verbose=True)
print(sod)
draw_Letter_graph(median)
#if __name__ == '__main__':
# # test draw_Letter_graph
# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
# 'extra_params': {}} # node nsymb
# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
# print(y_all)
# for g in Gn:
# draw_Letter_graph(g)

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- 201
gklearn/preimage/median_benoit.py View File

@@ -1,201 +0,0 @@
import sys
import pathlib
import numpy as np
import networkx as nx
import librariesImport
import script
sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/")
import gklearn
def replace_graph_in_env(script, graph, old_id, label='median'):
"""
Replace a graph in script
If old_id is -1, add a new graph to the environnemt
"""
if(old_id > -1):
script.PyClearGraph(old_id)
new_id = script.PyAddGraph(label)
for i in graph.nodes():
script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib
for e in graph.edges:
script.PyAddEdge(new_id, str(e[0]),str(e[1]), {})
script.PyInitEnv()
script.PySetMethod("IPFP", "")
script.PyInitMethod()
return new_id
#Dessin median courrant
def draw_Letter_graph(graph):
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])])
nx.draw_networkx(graph,pos)
plt.show()
#compute new mappings
def update_mappings(script,median_id,listID):
med_distances = {}
med_mappings = {}
sod = 0
for i in range(0,len(listID)):
script.PyRunMethod(median_id,listID[i])
med_distances[i] = script.PyGetUpperBound(median_id,listID[i])
med_mappings[i] = script.PyGetForwardMap(median_id,listID[i])
sod += med_distances[i]
return med_distances, med_mappings, sod
def calcul_Sij(all_mappings, all_graphs,i,j):
s_ij = 0
for k in range(0,len(all_mappings)):
cur_graph = all_graphs[k]
cur_mapping = all_mappings[k]
size_graph = cur_graph.order()
if ((cur_mapping[i] < size_graph) and
(cur_mapping[j] < size_graph) and
(cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)):
s_ij += 1
return s_ij
# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings):
# from scipy.stats.mstats import gmean
# for i in median.nodes():
# for k in listIdSet:
# vectors = [] #np.zeros((len(listIdSet),2))
# if(k != median_id):
# phi_i = mappings[k][i]
# if(phi_i < dataset[k].order()):
# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])])
# new_labels = gmean(vectors)
# median.node[i]['x'] = str(new_labels[0])
# median.node[i]['y'] = str(new_labels[1])
# return median
def update_median_nodes(median,dataset,mappings):
#update node attributes
for i in median.nodes():
nb_sub=0
mean_label = {'x' : 0, 'y' : 0}
for k in range(0,len(mappings)):
phi_i = mappings[k][i]
if ( phi_i < dataset[k].order() ):
nb_sub += 1
mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x'])
mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y'])
median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub))
median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub))
return median
def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425):
#for letter high, ceir = 1.7, alpha = 0.75
size_dataset = len(dataset)
ratio_cei_cer = cer/(cei + cer)
threshold = size_dataset*ratio_cei_cer
order_graph_median = median.order()
for i in range(0,order_graph_median):
for j in range(i+1,order_graph_median):
s_ij = calcul_Sij(mappings,dataset,i,j)
if(s_ij > threshold):
median.add_edge(i,j)
else:
if(median.has_edge(i,j)):
median.remove_edge(i,j)
return median
def compute_median(script, listID, dataset,verbose=False):
"""Compute a graph median of a dataset according to an environment
Parameters
script : An gedlib initialized environnement
listID (list): a list of ID in script: encodes the dataset
dataset (list): corresponding graphs in networkX format. We assume that graph
listID[i] corresponds to dataset[i]
Returns:
A networkX graph, which is the median, with corresponding sod
"""
print(len(listID))
median_set_index, median_set_sod = compute_median_set(script, listID)
print(median_set_index)
print(median_set_sod)
sods = []
#Ajout median dans environnement
set_median = dataset[median_set_index].copy()
median = dataset[median_set_index].copy()
cur_med_id = replace_graph_in_env(script,median,-1)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite_max = 50
old_sod = cur_sod * 2
ite = 0
epsilon = 0.001
best_median
while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )):
median = update_median_nodes(median,dataset, med_mappings)
median = update_median_edges(dataset,med_mappings,median)
cur_med_id = replace_graph_in_env(script,median,cur_med_id)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite += 1
return median, cur_sod, sods, set_median
draw_Letter_graph(median)
def compute_median_set(script,listID):
'Returns the id in listID corresponding to median set'
#Calcul median set
N=len(listID)
map_id_to_index = {}
map_index_to_id = {}
for i in range(0,len(listID)):
map_id_to_index[listID[i]] = i
map_index_to_id[i] = listID[i]
distances = np.zeros((N,N))
for i in listID:
for j in listID:
script.PyRunMethod(i,j)
distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j)
median_set_index = np.argmin(np.sum(distances,0))
sod = np.min(np.sum(distances,0))
return median_set_index, sod
if __name__ == "__main__":
#Chargement du dataset
script.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml')
script.PySetEditCost("LETTER")
script.PyInitEnv()
script.PySetMethod("IPFP", "")
script.PyInitMethod()
dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl")
listID = script.PyGetAllGraphIds()
median, sod = compute_median(script,listID,dataset,verbose=True)
print(sod)
draw_Letter_graph(median)

+ 0
- 215
gklearn/preimage/median_linlin.py View File

@@ -1,215 +0,0 @@
import sys
import pathlib
import numpy as np
import networkx as nx
from gedlibpy import librariesImport, gedlibpy
sys.path.insert(0, "/home/bgauzere/dev/optim-graphes/")
import gklearn
def replace_graph_in_env(script, graph, old_id, label='median'):
"""
Replace a graph in script
If old_id is -1, add a new graph to the environnemt
"""
if(old_id > -1):
script.PyClearGraph(old_id)
new_id = script.PyAddGraph(label)
for i in graph.nodes():
script.PyAddNode(new_id,str(i),graph.node[i]) # !! strings are required bt gedlib
for e in graph.edges:
script.PyAddEdge(new_id, str(e[0]),str(e[1]), {})
script.PyInitEnv()
script.PySetMethod("IPFP", "")
script.PyInitMethod()
return new_id
#Dessin median courrant
def draw_Letter_graph(graph):
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])])
nx.draw_networkx(graph,pos)
plt.show()
#compute new mappings
def update_mappings(script,median_id,listID):
med_distances = {}
med_mappings = {}
sod = 0
for i in range(0,len(listID)):
script.PyRunMethod(median_id,listID[i])
med_distances[i] = script.PyGetUpperBound(median_id,listID[i])
med_mappings[i] = script.PyGetForwardMap(median_id,listID[i])
sod += med_distances[i]
return med_distances, med_mappings, sod
def calcul_Sij(all_mappings, all_graphs,i,j):
s_ij = 0
for k in range(0,len(all_mappings)):
cur_graph = all_graphs[k]
cur_mapping = all_mappings[k]
size_graph = cur_graph.order()
if ((cur_mapping[i] < size_graph) and
(cur_mapping[j] < size_graph) and
(cur_graph.has_edge(cur_mapping[i], cur_mapping[j]) == True)):
s_ij += 1
return s_ij
# def update_median_nodes_L1(median,listIdSet,median_id,dataset, mappings):
# from scipy.stats.mstats import gmean
# for i in median.nodes():
# for k in listIdSet:
# vectors = [] #np.zeros((len(listIdSet),2))
# if(k != median_id):
# phi_i = mappings[k][i]
# if(phi_i < dataset[k].order()):
# vectors.append([float(dataset[k].node[phi_i]['x']),float(dataset[k].node[phi_i]['y'])])
# new_labels = gmean(vectors)
# median.node[i]['x'] = str(new_labels[0])
# median.node[i]['y'] = str(new_labels[1])
# return median
def update_median_nodes(median,dataset,mappings):
#update node attributes
for i in median.nodes():
nb_sub=0
mean_label = {'x' : 0, 'y' : 0}
for k in range(0,len(mappings)):
phi_i = mappings[k][i]
if ( phi_i < dataset[k].order() ):
nb_sub += 1
mean_label['x'] += 0.75*float(dataset[k].node[phi_i]['x'])
mean_label['y'] += 0.75*float(dataset[k].node[phi_i]['y'])
median.node[i]['x'] = str((1/0.75)*(mean_label['x']/nb_sub))
median.node[i]['y'] = str((1/0.75)*(mean_label['y']/nb_sub))
return median
def update_median_edges(dataset, mappings, median, cei=0.425,cer=0.425):
#for letter high, ceir = 1.7, alpha = 0.75
size_dataset = len(dataset)
ratio_cei_cer = cer/(cei + cer)
threshold = size_dataset*ratio_cei_cer
order_graph_median = median.order()
for i in range(0,order_graph_median):
for j in range(i+1,order_graph_median):
s_ij = calcul_Sij(mappings,dataset,i,j)
if(s_ij > threshold):
median.add_edge(i,j)
else:
if(median.has_edge(i,j)):
median.remove_edge(i,j)
return median
def compute_median(script, listID, dataset,verbose=False):
"""Compute a graph median of a dataset according to an environment
Parameters
script : An gedlib initialized environnement
listID (list): a list of ID in script: encodes the dataset
dataset (list): corresponding graphs in networkX format. We assume that graph
listID[i] corresponds to dataset[i]
Returns:
A networkX graph, which is the median, with corresponding sod
"""
print(len(listID))
median_set_index, median_set_sod = compute_median_set(script, listID)
print(median_set_index)
print(median_set_sod)
sods = []
#Ajout median dans environnement
set_median = dataset[median_set_index].copy()
median = dataset[median_set_index].copy()
cur_med_id = replace_graph_in_env(script,median,-1)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite_max = 50
old_sod = cur_sod * 2
ite = 0
epsilon = 0.001
best_median
while((ite < ite_max) and (np.abs(old_sod - cur_sod) > epsilon )):
median = update_median_nodes(median,dataset, med_mappings)
median = update_median_edges(dataset,med_mappings,median)
cur_med_id = replace_graph_in_env(script,median,cur_med_id)
med_distances, med_mappings, cur_sod = update_mappings(script,cur_med_id,listID)
sods.append(cur_sod)
if(verbose):
print(cur_sod)
ite += 1
return median, cur_sod, sods, set_median
draw_Letter_graph(median)
def compute_median_set(script,listID):
'Returns the id in listID corresponding to median set'
#Calcul median set
N=len(listID)
map_id_to_index = {}
map_index_to_id = {}
for i in range(0,len(listID)):
map_id_to_index[listID[i]] = i
map_index_to_id[i] = listID[i]
distances = np.zeros((N,N))
for i in listID:
for j in listID:
script.PyRunMethod(i,j)
distances[map_id_to_index[i],map_id_to_index[j]] = script.PyGetUpperBound(i,j)
median_set_index = np.argmin(np.sum(distances,0))
sod = np.min(np.sum(distances,0))
return median_set_index, sod
def _convertGraph(G):
"""Convert a graph to the proper NetworkX format that can be
recognized by library gedlibpy.
"""
G_new = nx.Graph()
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), chem=attrs['atom'])
# G_new.add_node(str(nd), x=str(attrs['attributes'][0]),
# y=str(attrs['attributes'][1]))
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
# G_new.add_edge(str(nd1), str(nd2))
return G_new
if __name__ == "__main__":
#Chargement du dataset
gedlibpy.PyLoadGXLGraph('/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/', '/home/bgauzere/dev/gedlib/data/collections/Letter_Z.xml')
gedlibpy.PySetEditCost("LETTER")
gedlibpy.PyInitEnv()
gedlibpy.PySetMethod("IPFP", "")
gedlibpy.PyInitMethod()
dataset,my_y = gklearn.utils.graphfiles.loadDataset("/home/bgauzere/dev/gedlib/data/datasets/Letter/HIGH/Letter_Z.cxl")
listID = gedlibpy.PyGetAllGraphIds()
median, sod = compute_median(gedlibpy,listID,dataset,verbose=True)
print(sod)
draw_Letter_graph(median)

+ 0
- 201
gklearn/preimage/pathfrequency.py View File

@@ -1,201 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 20 10:12:15 2019

inferring a graph grom path frequency.
@author: ljia
"""
#import numpy as np
import networkx as nx
from scipy.spatial.distance import hamming
import itertools

def SISF(K, v):
if output:
return output
else:
return 'no solution'

def SISF_M(K, v):
return output


def GIPF_tree(v_obj, K=1, alphabet=[0, 1]):
if K == 1:
n_graph = v_obj[0] + v_obj[1]
D_T, father_idx = getDynamicTable(n_graph, alphabet)
# get the vector the closest to v_obj.
if v_obj not in D_T:
print('no exact solution')
dis_lim = 1 / len(v_obj) # the possible shortest distance.
dis_min = 1.0 # minimum proportional distance
v_min = v_obj
for vc in D_T:
if vc[0] + vc[1] == n_graph:
# print(vc)
dis = hamming(vc, v_obj)
if dis < dis_min:
dis_min = dis
v_min = vc
if dis_min <= dis_lim:
break
v_obj = v_min
# obtain required graph by traceback procedure.
return getObjectGraph(v_obj, D_T, father_idx, alphabet), v_obj
def GIPF_M(K, v):
return G


def getDynamicTable(n_graph, alphabet=[0, 1]):
# init. When only one node exists.
D_T = {(1, 0, 0, 0, 0, 0): 1, (0, 1, 0, 0, 0, 0): 1, (0, 0, 1, 0, 0, 0): 0,
(0, 0, 0, 1, 0, 0): 0, (0, 0, 0, 0, 1, 0): 0, (0, 0, 0, 0, 0, 1): 0,}
D_T = [(1, 0, 0, 0, 0, 0), (0, 1, 0, 0, 0, 0)]
father_idx = [-1, -1] # index of each vector's father
# add possible vectors.
for idx, v in enumerate(D_T):
if v[0] + v[1] < n_graph:
D_T.append((v[0] + 1, v[1], v[2] + 2, v[3], v[4], v[5]))
D_T.append((v[0] + 1, v[1], v[2], v[3] + 1, v[4] + 1, v[5]))
D_T.append((v[0], v[1] + 1, v[2], v[3] + 1, v[4] + 1, v[5]))
D_T.append((v[0], v[1] + 1, v[2], v[3], v[4], v[5] + 2))
father_idx += [idx, idx, idx, idx]
# D_T = itertools.chain([(1, 0, 0, 0, 0, 0)], [(0, 1, 0, 0, 0, 0)])
# father_idx = itertools.chain([-1], [-1]) # index of each vector's father
# # add possible vectors.
# for idx, v in enumerate(D_T):
# if v[0] + v[1] < n_graph:
# D_T = itertools.chain(D_T, [(v[0] + 1, v[1], v[2] + 2, v[3], v[4], v[5])])
# D_T = itertools.chain(D_T, [(v[0] + 1, v[1], v[2], v[3] + 1, v[4] + 1, v[5])])
# D_T = itertools.chain(D_T, [(v[0], v[1] + 1, v[2], v[3] + 1, v[4] + 1, v[5])])
# D_T = itertools.chain(D_T, [(v[0], v[1] + 1, v[2], v[3], v[4], v[5] + 2)])
# father_idx = itertools.chain(father_idx, [idx, idx, idx, idx])
return D_T, father_idx


def getObjectGraph(v_obj, D_T, father_idx, alphabet=[0, 1]):
g_obj = nx.Graph()
# do vector traceback.
v_tb = [list(v_obj)] # traceback vectors.
v_tb_idx = [D_T.index(v_obj)] # indices of traceback vectors.
while v_tb_idx[-1] > 1:
idx_pre = father_idx[v_tb_idx[-1]]
v_tb_idx.append(idx_pre)
v_tb.append(list(D_T[idx_pre]))
v_tb = v_tb[::-1] # reverse
# v_tb_idx = v_tb_idx[::-1]

# construct tree.
v_c = v_tb[0] # current vector.
if v_c[0] == 1:
g_obj.add_node(0, node_label=alphabet[0])
else:
g_obj.add_node(0, node_label=alphabet[1])
for vct in v_tb[1:]:
if vct[0] - v_c[0] == 1:
if vct[2] - v_c[2] == 2: # transfer 1
label1 = alphabet[0]
label2 = alphabet[0]
else: # transfer 2
label1 = alphabet[1]
label2 = alphabet[0]
else:
if vct[3] - v_c[3] == 1: # transfer 3
label1 = alphabet[0]
label2 = alphabet[1]
else: # transfer 4
label1 = alphabet[1]
label2 = alphabet[1]
for nd, attr in g_obj.nodes(data=True):
if attr['node_label'] == label1:
nb_node = nx.number_of_nodes(g_obj)
g_obj.add_node(nb_node, node_label=label2)
g_obj.add_edge(nd, nb_node)
break
v_c = vct
return g_obj


import random
def hierarchy_pos(G, root=None, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5):

'''
From Joel's answer at https://stackoverflow.com/a/29597209/2966723.
Licensed under Creative Commons Attribution-Share Alike

If the graph is a tree this will return the positions to plot this in a
hierarchical layout.

G: the graph (must be a tree)

root: the root node of current branch
- if the tree is directed and this is not given,
the root will be found and used
- if the tree is directed and this is given, then
the positions will be just for the descendants of this node.
- if the tree is undirected and not given,
then a random choice will be used.

width: horizontal space allocated for this branch - avoids overlap with other branches

vert_gap: gap between levels of hierarchy

vert_loc: vertical location of root

xcenter: horizontal location of root
'''
if not nx.is_tree(G):
raise TypeError('cannot use hierarchy_pos on a graph that is not a tree')

if root is None:
if isinstance(G, nx.DiGraph):
root = next(iter(nx.topological_sort(G))) #allows back compatibility with nx version 1.11
else:
root = random.choice(list(G.nodes))

def _hierarchy_pos(G, root, width=1., vert_gap = 0.2, vert_loc = 0, xcenter = 0.5, pos = None, parent = None):
'''
see hierarchy_pos docstring for most arguments

pos: a dict saying where all nodes go if they have been assigned
parent: parent of this branch. - only affects it if non-directed

'''

if pos is None:
pos = {root:(xcenter,vert_loc)}
else:
pos[root] = (xcenter, vert_loc)
children = list(G.neighbors(root))
if not isinstance(G, nx.DiGraph) and parent is not None:
children.remove(parent)
if len(children)!=0:
dx = width/len(children)
nextx = xcenter - width/2 - dx/2
for child in children:
nextx += dx
pos = _hierarchy_pos(G,child, width = dx, vert_gap = vert_gap,
vert_loc = vert_loc-vert_gap, xcenter=nextx,
pos=pos, parent = root)
return pos


return _hierarchy_pos(G, root, width, vert_gap, vert_loc, xcenter)


if __name__ == '__main__':
v_obj = (6, 4, 10, 3, 3, 2)
# v_obj = (6, 5, 10, 3, 3, 2)
tree_obj, v_obj = GIPF_tree(v_obj)
print('One closest vector is', v_obj)
# plot
pos = hierarchy_pos(tree_obj, 0)
node_labels = nx.get_node_attributes(tree_obj, 'node_label')
nx.draw(tree_obj, pos=pos, labels=node_labels, with_labels=True)

+ 0
- 705
gklearn/preimage/preimage_iam.py View File

@@ -1,705 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 30 17:07:43 2019

A graph pre-image method combining iterative pre-image method in reference [1]
and the iterative alternate minimizations (IAM) in reference [2].
@author: ljia
@references:
[1] Gökhan H Bakir, Alexander Zien, and Koji Tsuda. Learning to and graph
pre-images. In Joint Pattern Re ognition Symposium , pages 253-261. Springer, 2004.
[2] Generalized median graph via iterative alternate minimization.
"""
import sys
import numpy as np
from tqdm import tqdm
import networkx as nx
import matplotlib.pyplot as plt
import random

from iam import iam_upgraded
from utils import dis_gstar, compute_kernel


def preimage_iam(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max,
gkernel, epsilon=0.001, InitIAMWithAllDk=False,
params_iam={'c_ei': 1, 'c_er': 1, 'c_es': 1,
'ite_max': 50, 'epsilon': 0.001,
'removeNodes': True, 'connected': False},
params_ged={'lib': 'gedlibpy', 'cost': 'CHEM_1', 'method': 'IPFP',
'edit_cost_constant': [], 'stabilizer': 'min',
'repeat': 50}):
"""This function constructs graph pre-image by the iterative pre-image
framework in reference [1], algorithm 1, where the step of generating new
graphs randomly is replaced by the IAM algorithm in reference [2].
notes
-----
Every time a set of n better graphs is acquired, their distances in kernel space are
compared with the k nearest ones, and the k nearest distances from the k+n
distances will be used as the new ones.
"""
# compute k nearest neighbors of phi in DN.
dis_all = [] # distance between g_star and each graph.
term3 = 0
for i1, a1 in enumerate(alpha):
for i2, a2 in enumerate(alpha):
term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]]
for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout):
dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3)
dis_all.append(dtemp)
# sort
sort_idx = np.argsort(dis_all)
dis_k = [dis_all[idis] for idis in sort_idx[0:k]] # the k shortest distances
nb_best = len(np.argwhere(dis_k == dis_k[0]).flatten().tolist())
ghat_list = [Gn_init[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN
if dis_k[0] == 0: # the exact pre-image.
print('The exact pre-image is found from the input dataset.')
return 0, ghat_list, 0, 0
dhat = dis_k[0] # the nearest distance
# for g in ghat_list:
# draw_Letter_graph(g)
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors
# for gi in Gk:
# nx.draw(gi, labels=nx.get_node_attributes(gi, 'atom'), with_labels=True)
## nx.draw_networkx(gi)
# plt.show()
## draw_Letter_graph(g)
# print(gi.nodes(data=True))
# print(gi.edges(data=True))
# i = 1
r = 0
itr_total = 0
dis_of_each_itr = [dhat]
found = False
nb_updated = 0
nb_updated_k = 0
while r < r_max:# and not found: # @todo: if not found?# and np.abs(old_dis - cur_dis) > epsilon:
print('\n-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-')
print('Current preimage iteration =', r)
print('Total preimage iteration =', itr_total, '\n')
found = False
Gn_nearest_median = [g.copy() for g in Gk]
if InitIAMWithAllDk: # each graph in D_k is used to initialize IAM.
ghat_new_list = []
for g_tmp in Gk:
Gn_nearest_init = [g_tmp.copy()]
ghat_new_list_tmp, _, _ = iam_upgraded(Gn_nearest_median,
Gn_nearest_init, params_ged=params_ged, **params_iam)
ghat_new_list += ghat_new_list_tmp
else: # only the best graph in D_k is used to initialize IAM.
Gn_nearest_init = [g.copy() for g in Gk]
ghat_new_list, _, _ = iam_upgraded(Gn_nearest_median, Gn_nearest_init,
params_ged=params_ged, **params_iam)

# for g in g_tmp_list:
# nx.draw_networkx(g)
# plt.show()
# draw_Letter_graph(g)
# print(g.nodes(data=True))
# print(g.edges(data=True))
# 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, knew, withterm3=False))
for idx_g, ghat_new in enumerate(ghat_new_list):
dhat_new = dhat_new_list[idx_g]
# if the new distance is smaller than the max of D_k.
if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon:
# check if the new distance is the same as one in D_k.
is_duplicate = False
for dis_tmp in dis_k[1:-1]:
if np.abs(dhat_new - dis_tmp) < epsilon:
is_duplicate = True
print('IAM: duplicate k nearest graph generated.')
break
if not is_duplicate:
if np.abs(dhat_new - dhat) < epsilon:
print('IAM: I am equal!')
# dhat = dhat_new
# ghat_list = [ghat_new.copy()]
else:
print('IAM: we got better k nearest neighbors!')
nb_updated_k += 1
print('the k nearest neighbors are updated',
nb_updated_k, 'times.')
dis_k = [dhat_new] + dis_k[0:k-1] # add the new nearest distance.
Gk = [ghat_new.copy()] + Gk[0:k-1] # add the corresponding graph.
sort_idx = np.argsort(dis_k)
dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances.
Gk = [Gk[idx] for idx in sort_idx[0:k]]
if dhat_new < dhat:
print('IAM: I have smaller distance!')
print(str(dhat) + '->' + str(dhat_new))
dhat = dhat_new
ghat_list = [Gk[0].copy()]
r = 0
nb_updated += 1
print('the graph is updated', nb_updated, 'times.')
nx.draw(Gk[0], labels=nx.get_node_attributes(Gk[0], 'atom'),
with_labels=True)
## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG")
plt.show()
found = True
if not found:
r += 1

dis_of_each_itr.append(dhat)
itr_total += 1
print('\nthe k shortest distances are', dis_k)
print('the shortest distances for previous iterations are', dis_of_each_itr)
print('\n\nthe graph is updated', nb_updated, 'times.')
print('\nthe k nearest neighbors are updated', nb_updated_k, 'times.')
print('distances in kernel space:', dis_of_each_itr, '\n')
return dhat, ghat_list, dis_of_each_itr[-1], nb_updated, nb_updated_k




def preimage_iam_random_mix(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max,
l_max, gkernel, epsilon=0.001,
InitIAMWithAllDk=False, InitRandomWithAllDk=True,
params_iam={'c_ei': 1, 'c_er': 1, 'c_es': 1,
'ite_max': 50, 'epsilon': 0.001,
'removeNodes': True, 'connected': False},
params_ged={'lib': 'gedlibpy', 'cost': 'CHEM_1',
'method': 'IPFP', 'edit_cost_constant': [],
'stabilizer': 'min', 'repeat': 50}):
"""This function constructs graph pre-image by the iterative pre-image
framework in reference [1], algorithm 1, where new graphs are generated
randomly and by the IAM algorithm in reference [2].
notes
-----
Every time a set of n better graphs is acquired, their distances in kernel space are
compared with the k nearest ones, and the k nearest distances from the k+n
distances will be used as the new ones.
"""
Gn_init = [nx.convert_node_labels_to_integers(g) for g in Gn_init]
# compute k nearest neighbors of phi in DN.
dis_all = [] # distance between g_star and each graph.
term3 = 0
for i1, a1 in enumerate(alpha):
for i2, a2 in enumerate(alpha):
term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]]
for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout):
dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3)
dis_all.append(dtemp)
# sort
sort_idx = np.argsort(dis_all)
dis_k = [dis_all[idis] for idis in sort_idx[0:k]] # the k shortest distances
nb_best = len(np.argwhere(dis_k == dis_k[0]).flatten().tolist())
ghat_list = [Gn_init[idx].copy() for idx in sort_idx[0:nb_best]] # the nearest neighbors of psi in DN
if dis_k[0] == 0: # the exact pre-image.
print('The exact pre-image is found from the input dataset.')
return 0, ghat_list, 0, 0
dhat = dis_k[0] # the nearest distance
# for g in ghat_list:
# draw_Letter_graph(g)
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors
# for gi in Gk:
# nx.draw(gi, labels=nx.get_node_attributes(gi, 'atom'), with_labels=True)
## nx.draw_networkx(gi)
# plt.show()
## draw_Letter_graph(g)
# print(gi.nodes(data=True))
# print(gi.edges(data=True))
r = 0
itr_total = 0
dis_of_each_itr = [dhat]
nb_updated_iam = 0
nb_updated_k_iam = 0
nb_updated_random = 0
nb_updated_k_random = 0
# is_iam_duplicate = False
while r < r_max: # and not found: # @todo: if not found?# and np.abs(old_dis - cur_dis) > epsilon:
print('\n-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-')
print('Current preimage iteration =', r)
print('Total preimage iteration =', itr_total, '\n')
found_iam = False

Gn_nearest_median = [g.copy() for g in Gk]
if InitIAMWithAllDk: # each graph in D_k is used to initialize IAM.
ghat_new_list = []
for g_tmp in Gk:
Gn_nearest_init = [g_tmp.copy()]
ghat_new_list_tmp, _ = iam_upgraded(Gn_nearest_median,
Gn_nearest_init, params_ged=params_ged, **params_iam)
ghat_new_list += ghat_new_list_tmp
else: # only the best graph in D_k is used to initialize IAM.
Gn_nearest_init = [g.copy() for g in Gk]
ghat_new_list, _ = iam_upgraded(Gn_nearest_median, Gn_nearest_init,
params_ged=params_ged, **params_iam)

# for g in g_tmp_list:
# nx.draw_networkx(g)
# plt.show()
# draw_Letter_graph(g)
# print(g.nodes(data=True))
# print(g.edges(data=True))
# 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, knew, withterm3=False))
# find the new k nearest graphs.
for idx_g, ghat_new in enumerate(ghat_new_list):
dhat_new = dhat_new_list[idx_g]
# if the new distance is smaller than the max of D_k.
if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon:
# check if the new distance is the same as one in D_k.
is_duplicate = False
for dis_tmp in dis_k[1:-1]:
if np.abs(dhat_new - dis_tmp) < epsilon:
is_duplicate = True
print('IAM: duplicate k nearest graph generated.')
break
if not is_duplicate:
if np.abs(dhat_new - dhat) < epsilon:
print('IAM: I am equal!')
# dhat = dhat_new
# ghat_list = [ghat_new.copy()]
else:
print('IAM: we got better k nearest neighbors!')
nb_updated_k_iam += 1
print('the k nearest neighbors are updated',
nb_updated_k_iam, 'times.')
dis_k = [dhat_new] + dis_k[0:k-1] # add the new nearest distance.
Gk = [ghat_new.copy()] + Gk[0:k-1] # add the corresponding graph.
sort_idx = np.argsort(dis_k)
dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances.
Gk = [Gk[idx] for idx in sort_idx[0:k]]
if dhat_new < dhat:
print('IAM: I have smaller distance!')
print(str(dhat) + '->' + str(dhat_new))
dhat = dhat_new
ghat_list = [Gk[0].copy()]
r = 0
nb_updated_iam += 1
print('the graph is updated by IAM', nb_updated_iam,
'times.')
nx.draw(Gk[0], labels=nx.get_node_attributes(Gk[0], 'atom'),
with_labels=True)
## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG")
plt.show()
found_iam = True
# when new distance is not smaller than the max of D_k, use random generation.
if not found_iam:
print('Distance not better, switching to random generation now.')
print(str(dhat) + '->' + str(dhat_new))
if InitRandomWithAllDk: # use all k nearest graphs as the initials.
init_list = [g_init.copy() for g_init in Gk]
else: # use just the nearest graph as the initial.
init_list = [Gk[0].copy()]
# number of edges to be changed.
if len(init_list) == 1:
# @todo what if the log is negetive? how to choose alpha (scalar)? seems fdgs is always 1.
# fdgs = dhat_new
fdgs = nb_updated_random + 1
if fdgs < 1:
fdgs = 1
fdgs = int(np.ceil(np.log(fdgs)))
if fdgs < 1:
fdgs += 1
# fdgs = nb_updated_random + 1 # @todo:
fdgs_list = [fdgs]
else:
# @todo what if the log is negetive? how to choose alpha (scalar)?
fdgs_list = np.array(dis_k[:])
if np.min(fdgs_list) < 1:
fdgs_list /= dis_k[0]
fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))]
if np.min(fdgs_list) < 1:
fdgs_list = np.array(fdgs_list) + 1
l = 0
found_random = False
while l < l_max and not found_random:
for idx_g, g_tmp in enumerate(init_list):
# add and delete edges.
ghat_new = nx.convert_node_labels_to_integers(g_tmp.copy())
# @todo: should we use just half of the adjacency matrix for undirected graphs?
nb_vpairs = nx.number_of_nodes(ghat_new) * (nx.number_of_nodes(ghat_new) - 1)
np.random.seed()
# which edges to change.
# @todo: what if fdgs is bigger than nb_vpairs?
idx_change = random.sample(range(nb_vpairs), fdgs_list[idx_g] if
fdgs_list[idx_g] < nb_vpairs else nb_vpairs)
# idx_change = np.random.randint(0, nx.number_of_nodes(gs) *
# (nx.number_of_nodes(gs) - 1), fdgs)
for item in idx_change:
node1 = int(item / (nx.number_of_nodes(ghat_new) - 1))
node2 = (item - node1 * (nx.number_of_nodes(ghat_new) - 1))
if node2 >= node1: # skip the self pair.
node2 += 1
# @todo: is the randomness correct?
if not ghat_new.has_edge(node1, node2):
ghat_new.add_edge(node1, node2)
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(ghat_new)
# plt.show()
else:
ghat_new.remove_edge(node1, node2)
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(ghat_new)
# plt.show()
# nx.draw_networkx(ghat_new)
# plt.show()
# compute distance between \psi and the new generated graph.
knew = compute_kernel([ghat_new] + Gn_median, gkernel, verbose=False)
dhat_new = dis_gstar(0, range(1, len(Gn_median) + 1),
alpha, knew, withterm3=False)
# @todo: the new distance is smaller or also equal?
if dhat_new < dis_k[-1] and np.abs(dhat_new - dis_k[-1]) >= epsilon:
# check if the new distance is the same as one in D_k.
is_duplicate = False
for dis_tmp in dis_k[1:-1]:
if np.abs(dhat_new - dis_tmp) < epsilon:
is_duplicate = True
print('Random: duplicate k nearest graph generated.')
break
if not is_duplicate:
if np.abs(dhat_new - dhat) < epsilon:
print('Random: I am equal!')
# dhat = dhat_new
# ghat_list = [ghat_new.copy()]
else:
print('Random: we got better k nearest neighbors!')
print('l =', str(l))
nb_updated_k_random += 1
print('the k nearest neighbors are updated by random generation',
nb_updated_k_random, 'times.')
dis_k = [dhat_new] + dis_k # add the new nearest distances.
Gk = [ghat_new.copy()] + Gk # add the corresponding graphs.
sort_idx = np.argsort(dis_k)
dis_k = [dis_k[idx] for idx in sort_idx[0:k]] # the new k nearest distances.
Gk = [Gk[idx] for idx in sort_idx[0:k]]
if dhat_new < dhat:
print('\nRandom: I am smaller!')
print('l =', str(l))
print(dhat, '->', dhat_new)
dhat = dhat_new
ghat_list = [ghat_new.copy()]
r = 0
nb_updated_random += 1
print('the graph is updated by random generation',
nb_updated_random, 'times.')
nx.draw(ghat_new, labels=nx.get_node_attributes(ghat_new, 'atom'),
with_labels=True)
## plt.savefig("results/gk_iam/simple_two/xx" + str(i) + ".png", format="PNG")
plt.show()
found_random = True
break
l += 1
if not found_random: # l == l_max:
r += 1
dis_of_each_itr.append(dhat)
itr_total += 1
print('\nthe k shortest distances are', dis_k)
print('the shortest distances for previous iterations are', dis_of_each_itr)
print('\n\nthe graph is updated by IAM', nb_updated_iam, 'times, and by random generation',
nb_updated_random, 'times.')
print('\nthe k nearest neighbors are updated by IAM', nb_updated_k_iam,
'times, and by random generation', nb_updated_k_random, 'times.')
print('distances in kernel space:', dis_of_each_itr, '\n')
return dhat, ghat_list, dis_of_each_itr[-1], \
nb_updated_iam, nb_updated_random, nb_updated_k_iam, nb_updated_k_random


###############################################################################
# Old implementations.
#def gk_iam(Gn, alpha):
# """This function constructs graph pre-image by the iterative pre-image
# framework in reference [1], algorithm 1, where the step of generating new
# graphs randomly is replaced by the IAM algorithm in reference [2].
#
# notes
# -----
# Every time a better graph is acquired, the older one is replaced by it.
# """
# pass
# # compute k nearest neighbors of phi in DN.
# dis_list = [] # distance between g_star and each graph.
# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout):
# dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) *
# k_g2_list[ig]) + (alpha * alpha * k_list[idx1] + alpha *
# (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha *
# k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2])
# dis_list.append(dtemp)
#
# # sort
# sort_idx = np.argsort(dis_list)
# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]]
# g0hat = Gn[sort_idx[0]] # the nearest neighbor of phi in DN
# if dis_gs[0] == 0: # the exact pre-image.
# print('The exact pre-image is found from the input dataset.')
# return 0, g0hat
# dhat = dis_gs[0] # the nearest distance
# Gk = [Gn[ig] for ig in sort_idx[0:k]] # the k nearest neighbors
# gihat_list = []
#
## i = 1
# r = 1
# while r < r_max:
# print('r =', r)
## found = False
# Gs_nearest = Gk + gihat_list
# g_tmp = iam(Gs_nearest)
#
# # compute distance between \psi and the new generated graph.
# knew = marginalizedkernel([g_tmp, g1, g2], node_label='atom', edge_label=None,
# p_quit=lmbda, n_iteration=20, remove_totters=False,
# n_jobs=multiprocessing.cpu_count(), verbose=False)
# dnew = knew[0][0, 0] - 2 * (alpha * knew[0][0, 1] + (1 - alpha) *
# knew[0][0, 2]) + (alpha * alpha * k_list[idx1] + alpha *
# (1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha *
# k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2])
# if dnew <= dhat: # the new distance is smaller
# print('I am smaller!')
# dhat = dnew
# g_new = g_tmp.copy() # found better graph.
# gihat_list = [g_new]
# dis_gs.append(dhat)
# r = 0
# else:
# r += 1
#
# ghat = ([g0hat] if len(gihat_list) == 0 else gihat_list)
#
# return dhat, ghat


#def gk_iam_nearest(Gn, alpha, idx_gi, Kmatrix, k, r_max):
# """This function constructs graph pre-image by the iterative pre-image
# framework in reference [1], algorithm 1, where the step of generating new
# graphs randomly is replaced by the IAM algorithm in reference [2].
#
# notes
# -----
# Every time a better graph is acquired, its distance in kernel space is
# compared with the k nearest ones, and the k nearest distances from the k+1
# distances will be used as the new ones.
# """
# # compute k nearest neighbors of phi in DN.
# dis_list = [] # distance between g_star and each graph.
# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout):
# dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix)
## dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) *
## k_g2_list[ig]) + (alpha * alpha * k_list[0] + alpha *
## (1 - alpha) * k_g2_list[0] + (1 - alpha) * alpha *
## k_g1_list[6] + (1 - alpha) * (1 - alpha) * k_list[6])
# dis_list.append(dtemp)
#
# # sort
# sort_idx = np.argsort(dis_list)
# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances
# g0hat = Gn[sort_idx[0]] # the nearest neighbor of phi in DN
# if dis_gs[0] == 0: # the exact pre-image.
# print('The exact pre-image is found from the input dataset.')
# return 0, g0hat
# dhat = dis_gs[0] # the nearest distance
# ghat = g0hat.copy()
# Gk = [Gn[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors
# for gi in Gk:
# nx.draw_networkx(gi)
# plt.show()
# print(gi.nodes(data=True))
# print(gi.edges(data=True))
# Gs_nearest = Gk.copy()
## gihat_list = []
#
## i = 1
# r = 1
# while r < r_max:
# print('r =', r)
## found = False
## Gs_nearest = Gk + gihat_list
## g_tmp = iam(Gs_nearest)
# g_tmp = test_iam_with_more_graphs_as_init(Gs_nearest, Gs_nearest, c_ei=1, c_er=1, c_es=1)
# nx.draw_networkx(g_tmp)
# plt.show()
# print(g_tmp.nodes(data=True))
# print(g_tmp.edges(data=True))
#
# # compute distance between \psi and the new generated graph.
# gi_list = [Gn[i] for i in idx_gi]
# knew = compute_kernel([g_tmp] + gi_list, 'untilhpathkernel', False)
# dnew = dis_gstar(0, range(1, len(gi_list) + 1), alpha, knew)
#
## dnew = knew[0, 0] - 2 * (alpha[0] * knew[0, 1] + alpha[1] *
## knew[0, 2]) + (alpha[0] * alpha[0] * k_list[0] + alpha[0] *
## alpha[1] * k_g2_list[0] + alpha[1] * alpha[0] *
## k_g1_list[1] + alpha[1] * alpha[1] * k_list[1])
# if dnew <= dhat and g_tmp != ghat: # the new distance is smaller
# print('I am smaller!')
# print(str(dhat) + '->' + str(dnew))
## nx.draw_networkx(ghat)
## plt.show()
## print('->')
## nx.draw_networkx(g_tmp)
## plt.show()
#
# dhat = dnew
# g_new = g_tmp.copy() # found better graph.
# ghat = g_tmp.copy()
# dis_gs.append(dhat) # add the new nearest distance.
# Gs_nearest.append(g_new) # add the corresponding graph.
# sort_idx = np.argsort(dis_gs)
# dis_gs = [dis_gs[idx] for idx in sort_idx[0:k]] # the new k nearest distances.
# Gs_nearest = [Gs_nearest[idx] for idx in sort_idx[0:k]]
# r = 0
# else:
# r += 1
#
# return dhat, ghat


#def gk_iam_nearest_multi(Gn, alpha, idx_gi, Kmatrix, k, r_max):
# """This function constructs graph pre-image by the iterative pre-image
# framework in reference [1], algorithm 1, where the step of generating new
# graphs randomly is replaced by the IAM algorithm in reference [2].
#
# notes
# -----
# Every time a set of n better graphs is acquired, their distances in kernel space are
# compared with the k nearest ones, and the k nearest distances from the k+n
# distances will be used as the new ones.
# """
# Gn_median = [Gn[idx].copy() for idx in idx_gi]
# # compute k nearest neighbors of phi in DN.
# dis_list = [] # distance between g_star and each graph.
# for ig, g in tqdm(enumerate(Gn), desc='computing distances', file=sys.stdout):
# dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix)
## dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) *
## k_g2_list[ig]) + (alpha * alpha * k_list[0] + alpha *
## (1 - alpha) * k_g2_list[0] + (1 - alpha) * alpha *
## k_g1_list[6] + (1 - alpha) * (1 - alpha) * k_list[6])
# dis_list.append(dtemp)
#
# # sort
# sort_idx = np.argsort(dis_list)
# dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances
# nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist())
# g0hat_list = [Gn[idx] for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN
# if dis_gs[0] == 0: # the exact pre-image.
# print('The exact pre-image is found from the input dataset.')
# return 0, g0hat_list
# dhat = dis_gs[0] # the nearest distance
# ghat_list = [g.copy() for g in g0hat_list]
# for g in ghat_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# Gk = [Gn[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors
# for gi in Gk:
# nx.draw_networkx(gi)
# plt.show()
# print(gi.nodes(data=True))
# print(gi.edges(data=True))
# Gs_nearest = Gk.copy()
## gihat_list = []
#
## i = 1
# r = 1
# while r < r_max:
# print('r =', r)
## found = False
## Gs_nearest = Gk + gihat_list
## g_tmp = iam(Gs_nearest)
# g_tmp_list = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations(
# Gn_median, Gs_nearest, c_ei=1, c_er=1, c_es=1)
# for g in g_tmp_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
#
# # compute distance between \psi and the new generated graphs.
# gi_list = [Gn[i] for i in idx_gi]
# knew = compute_kernel(g_tmp_list + gi_list, 'marginalizedkernel', False)
# dnew_list = []
# for idx, g_tmp in enumerate(g_tmp_list):
# dnew_list.append(dis_gstar(idx, range(len(g_tmp_list),
# len(g_tmp_list) + len(gi_list) + 1), alpha, knew))
#
## dnew = knew[0, 0] - 2 * (alpha[0] * knew[0, 1] + alpha[1] *
## knew[0, 2]) + (alpha[0] * alpha[0] * k_list[0] + alpha[0] *
## alpha[1] * k_g2_list[0] + alpha[1] * alpha[0] *
## k_g1_list[1] + alpha[1] * alpha[1] * k_list[1])
#
# # find the new k nearest graphs.
# dis_gs = dnew_list + dis_gs # add the new nearest distances.
# Gs_nearest = [g.copy() for g in g_tmp_list] + Gs_nearest # add the corresponding graphs.
# sort_idx = np.argsort(dis_gs)
# if len([i for i in sort_idx[0:k] if i < len(dnew_list)]) > 0:
# print('We got better k nearest neighbors! Hurray!')
# dis_gs = [dis_gs[idx] for idx in sort_idx[0:k]] # the new k nearest distances.
# print(dis_gs[-1])
# Gs_nearest = [Gs_nearest[idx] for idx in sort_idx[0:k]]
# nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist())
# if len([i for i in sort_idx[0:nb_best] if i < len(dnew_list)]) > 0:
# print('I have smaller or equal distance!')
# dhat = dis_gs[0]
# print(str(dhat) + '->' + str(dhat))
# idx_best_list = np.argwhere(dnew_list == dhat).flatten().tolist()
# ghat_list = [g_tmp_list[idx].copy() for idx in idx_best_list]
# for g in ghat_list:
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
# r = 0
# else:
# r += 1
#
# return dhat, ghat_list

+ 0
- 309
gklearn/preimage/preimage_random.py View File

@@ -1,309 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 6 16:03:11 2019

pre-image
@author: ljia
"""

import sys
import numpy as np
import random
from tqdm import tqdm
import networkx as nx
import matplotlib.pyplot as plt

from gklearn.preimage.utils import compute_kernel, dis_gstar


def preimage_random(Gn_init, Gn_median, alpha, idx_gi, Kmatrix, k, r_max, l, gkernel):
Gn_init = [nx.convert_node_labels_to_integers(g) for g in Gn_init]
# compute k nearest neighbors of phi in DN.
dis_list = [] # distance between g_star and each graph.
term3 = 0
for i1, a1 in enumerate(alpha):
for i2, a2 in enumerate(alpha):
term3 += a1 * a2 * Kmatrix[idx_gi[i1], idx_gi[i2]]
for ig, g in tqdm(enumerate(Gn_init), desc='computing distances', file=sys.stdout):
dtemp = dis_gstar(ig, idx_gi, alpha, Kmatrix, term3=term3)
dis_list.append(dtemp)
# print(np.max(dis_list))
# print(np.min(dis_list))
# print(np.min([item for item in dis_list if item != 0]))
# print(np.mean(dis_list))
# sort
sort_idx = np.argsort(dis_list)
dis_gs = [dis_list[idis] for idis in sort_idx[0:k]] # the k shortest distances
nb_best = len(np.argwhere(dis_gs == dis_gs[0]).flatten().tolist())
g0hat_list = [Gn_init[idx] for idx in sort_idx[0:nb_best]] # the nearest neighbors of phi in DN
if dis_gs[0] == 0: # the exact pre-image.
print('The exact pre-image is found from the input dataset.')
return 0, g0hat_list[0], 0
dhat = dis_gs[0] # the nearest distance
# ghat_list = [g.copy() for g in g0hat_list]
# for g in ghat_list:
# draw_Letter_graph(g)
# nx.draw_networkx(g)
# plt.show()
# print(g.nodes(data=True))
# print(g.edges(data=True))
Gk = [Gn_init[ig].copy() for ig in sort_idx[0:k]] # the k nearest neighbors
# for gi in Gk:
## nx.draw_networkx(gi)
## plt.show()
# draw_Letter_graph(g)
# print(gi.nodes(data=True))
# print(gi.edges(data=True))
Gs_nearest = [g.copy() for g in Gk]
gihat_list = []
dihat_list = []
# i = 1
r = 0
# sod_list = [dhat]
# found = False
dis_of_each_itr = [dhat]
nb_updated = 0
g_best = []
while r < r_max:
print('\nr =', r)
print('itr for gk =', nb_updated, '\n')
found = False
dis_bests = dis_gs + dihat_list
# @todo what if the log is negetive? how to choose alpha (scalar)?
fdgs_list = np.array(dis_bests)
if np.min(fdgs_list) < 1:
fdgs_list /= np.min(dis_bests)
fdgs_list = [int(item) for item in np.ceil(np.log(fdgs_list))]
if np.min(fdgs_list) < 1:
fdgs_list = np.array(fdgs_list) + 1
for ig, gs in enumerate(Gs_nearest + gihat_list):
# nx.draw_networkx(gs)
# plt.show()
for trail in range(0, l):
# for trail in tqdm(range(0, l), desc='l loops', file=sys.stdout):
# add and delete edges.
gtemp = gs.copy()
np.random.seed()
# which edges to change.
# @todo: should we use just half of the adjacency matrix for undirected graphs?
nb_vpairs = nx.number_of_nodes(gs) * (nx.number_of_nodes(gs) - 1)
# @todo: what if fdgs is bigger than nb_vpairs?
idx_change = random.sample(range(nb_vpairs), fdgs_list[ig] if
fdgs_list[ig] < nb_vpairs else nb_vpairs)
# idx_change = np.random.randint(0, nx.number_of_nodes(gs) *
# (nx.number_of_nodes(gs) - 1), fdgs)
for item in idx_change:
node1 = int(item / (nx.number_of_nodes(gs) - 1))
node2 = (item - node1 * (nx.number_of_nodes(gs) - 1))
if node2 >= node1: # skip the self pair.
node2 += 1
# @todo: is the randomness correct?
if not gtemp.has_edge(node1, node2):
gtemp.add_edge(node1, node2)
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
else:
gtemp.remove_edge(node1, node2)
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
# compute distance between \psi and the new generated graph.
# knew = marginalizedkernel([gtemp, g1, g2], node_label='atom', edge_label=None,
# p_quit=lmbda, n_iteration=20, remove_totters=False,
# n_jobs=multiprocessing.cpu_count(), verbose=False)
knew = compute_kernel([gtemp] + Gn_median, gkernel, verbose=False)
dnew = dis_gstar(0, range(1, len(Gn_median) + 1), alpha, knew,
withterm3=False)
if dnew <= dhat: # @todo: the new distance is smaller or also equal?
if dnew < dhat:
print('\nI am smaller!')
print('ig =', str(ig), ', l =', str(trail))
print(dhat, '->', dnew)
nb_updated += 1
elif dnew == dhat:
print('I am equal!')
# nx.draw_networkx(gtemp)
# plt.show()
# print(gtemp.nodes(data=True))
# print(gtemp.edges(data=True))
dhat = dnew
gnew = gtemp.copy()
found = True # found better graph.
if found:
r = 0
gihat_list = [gnew]
dihat_list = [dhat]
else:
r += 1
dis_of_each_itr.append(dhat)
print('the shortest distances for previous iterations are', dis_of_each_itr)
# dis_best.append(dhat)
g_best = (g0hat_list[0] if len(gihat_list) == 0 else gihat_list[0])
print('distances in kernel space:', dis_of_each_itr, '\n')
return dhat, g_best, nb_updated
# return 0, 0, 0


if __name__ == '__main__':
from gklearn.utils.graphfiles import loadDataset
# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
# 'extra_params': {}} # node/edge symb
ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
'extra_params': {}} # node nsymb
# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds',
# 'extra_params': {}}
# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',
# 'extra_params': {}} # node symb
DN, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
#DN = DN[0:10]
lmbda = 0.03 # termination probalility
r_max = 3 # 10 # iteration limit.
l = 500
alpha_range = np.linspace(0.5, 0.5, 1)
#alpha_range = np.linspace(0.1, 0.9, 9)
k = 10 # 5 # k nearest neighbors
# randomly select two molecules
#np.random.seed(1)
#idx1, idx2 = np.random.randint(0, len(DN), 2)
#g1 = DN[idx1]
#g2 = DN[idx2]
idx1 = 0
idx2 = 6
g1 = DN[idx1]
g2 = DN[idx2]
# compute
k_list = [] # kernel between each graph and itself.
k_g1_list = [] # kernel between each graph and g1
k_g2_list = [] # kernel between each graph and g2
for ig, g in tqdm(enumerate(DN), desc='computing self kernels', file=sys.stdout):
# ktemp = marginalizedkernel([g, g1, g2], node_label='atom', edge_label=None,
# p_quit=lmbda, n_iteration=20, remove_totters=False,
# n_jobs=multiprocessing.cpu_count(), verbose=False)
ktemp = compute_kernel([g, g1, g2], 'untilhpathkernel', verbose=False)
k_list.append(ktemp[0, 0])
k_g1_list.append(ktemp[0, 1])
k_g2_list.append(ktemp[0, 2])
g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
# compute k nearest neighbors of phi in DN.
dis_list = [] # distance between g_star and each graph.
for ig, g in tqdm(enumerate(DN), desc='computing distances', file=sys.stdout):
dtemp = k_list[ig] - 2 * (alpha * k_g1_list[ig] + (1 - alpha) *
k_g2_list[ig]) + (alpha * alpha * k_list[idx1] + alpha *
(1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha *
k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2])
dis_list.append(np.sqrt(dtemp))
# sort
sort_idx = np.argsort(dis_list)
dis_gs = [dis_list[idis] for idis in sort_idx[0:k]]
g0hat = DN[sort_idx[0]] # the nearest neighbor of phi in DN
if dis_gs[0] == 0: # the exact pre-image.
print('The exact pre-image is found from the input dataset.')
g_pimg = g0hat
break
dhat = dis_gs[0] # the nearest distance
Dk = [DN[ig] for ig in sort_idx[0:k]] # the k nearest neighbors
gihat_list = []
i = 1
r = 1
while r < r_max:
print('r =', r)
found = False
for ig, gs in enumerate(Dk + gihat_list):
# nx.draw_networkx(gs)
# plt.show()
# @todo what if the log is negetive?
fdgs = int(np.abs(np.ceil(np.log(alpha * dis_gs[ig]))))
for trail in tqdm(range(0, l), desc='l loop', file=sys.stdout):
# add and delete edges.
gtemp = gs.copy()
np.random.seed()
# which edges to change.
# @todo: should we use just half of the adjacency matrix for undirected graphs?
nb_vpairs = nx.number_of_nodes(gs) * (nx.number_of_nodes(gs) - 1)
# @todo: what if fdgs is bigger than nb_vpairs?
idx_change = random.sample(range(nb_vpairs), fdgs if fdgs < nb_vpairs else nb_vpairs)
# idx_change = np.random.randint(0, nx.number_of_nodes(gs) *
# (nx.number_of_nodes(gs) - 1), fdgs)
for item in idx_change:
node1 = int(item / (nx.number_of_nodes(gs) - 1))
node2 = (item - node1 * (nx.number_of_nodes(gs) - 1))
if node2 >= node1: # skip the self pair.
node2 += 1
# @todo: is the randomness correct?
if not gtemp.has_edge(node1, node2):
# @todo: how to update the bond_type? 0 or 1?
gtemp.add_edges_from([(node1, node2, {'bond_type': 1})])
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
else:
gtemp.remove_edge(node1, node2)
# nx.draw_networkx(gs)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
# nx.draw_networkx(gtemp)
# plt.show()
# compute distance between phi and the new generated graph.
# knew = marginalizedkernel([gtemp, g1, g2], node_label='atom', edge_label=None,
# p_quit=lmbda, n_iteration=20, remove_totters=False,
# n_jobs=multiprocessing.cpu_count(), verbose=False)
knew = compute_kernel([gtemp, g1, g2], 'untilhpathkernel', verbose=False)
dnew = np.sqrt(knew[0, 0] - 2 * (alpha * knew[0, 1] + (1 - alpha) *
knew[0, 2]) + (alpha * alpha * k_list[idx1] + alpha *
(1 - alpha) * k_g2_list[idx1] + (1 - alpha) * alpha *
k_g1_list[idx2] + (1 - alpha) * (1 - alpha) * k_list[idx2]))
if dnew < dhat: # @todo: the new distance is smaller or also equal?
print('I am smaller!')
print(dhat, '->', dnew)
nx.draw_networkx(gtemp)
plt.show()
print(gtemp.nodes(data=True))
print(gtemp.edges(data=True))
dhat = dnew
gnew = gtemp.copy()
found = True # found better graph.
r = 0
elif dnew == dhat:
print('I am equal!')
if found:
gihat_list = [gnew]
dis_gs.append(dhat)
else:
r += 1
dis_best.append(dhat)
g_best += ([g0hat] if len(gihat_list) == 0 else gihat_list)
for idx, item in enumerate(alpha_range):
print('when alpha is', item, 'the shortest distance is', dis_best[idx])
print('the corresponding pre-image is')
nx.draw_networkx(g_best[idx])
plt.show()

+ 0
- 122
gklearn/preimage/python_code.py View File

@@ -1,122 +0,0 @@
elif opt_name == 'random-inits':
try:
num_random_inits_ = std::stoul(opt_val)
desired_num_random_inits_ = num_random_inits_

except:
raise Error('Invalid argument "' + opt_val + '" for option random-inits. Usage: options = "[--random-inits <convertible to int greater 0>]"')

if num_random_inits_ <= 0:
raise Error('Invalid argument "' + opt_val + '" for option random-inits. Usage: options = "[--random-inits <convertible to int greater 0>]"')

}
elif opt_name == 'randomness':
if opt_val == 'PSEUDO':
use_real_randomness_ = False

elif opt_val == 'REAL':
use_real_randomness_ = True

else:
raise Error('Invalid argument "' + opt_val + '" for option randomness. Usage: options = "[--randomness REAL|PSEUDO] [...]"')

}
elif opt_name == 'stdout':
if opt_val == '0':
print_to_stdout_ = 0

elif opt_val == '1':
print_to_stdout_ = 1

elif opt_val == '2':
print_to_stdout_ = 2

else:
raise Error('Invalid argument "' + opt_val + '" for option stdout. Usage: options = "[--stdout 0|1|2] [...]"')

}
elif opt_name == 'refine':
if opt_val == 'TRUE':
refine_ = True

elif opt_val == 'FALSE':
refine_ = False

else:
raise Error('Invalid argument "' + opt_val + '" for option refine. Usage: options = "[--refine TRUE|FALSE] [...]"')

}
elif opt_name == 'time-limit':
try:
time_limit_in_sec_ = std::stod(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option time-limit. Usage: options = "[--time-limit <convertible to double>] [...]')

}
elif opt_name == 'max-itrs':
try:
max_itrs_ = std::stoi(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option max-itrs. Usage: options = "[--max-itrs <convertible to int>] [...]')

}
elif opt_name == 'max-itrs-without-update':
try:
max_itrs_without_update_ = std::stoi(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option max-itrs-without-update. Usage: options = "[--max-itrs-without-update <convertible to int>] [...]')

}
elif opt_name == 'seed':
try:
seed_ = std::stoul(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option seed. Usage: options = "[--seed <convertible to int greater equal 0>] [...]')

}
elif opt_name == 'epsilon':
try:
epsilon_ = std::stod(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option epsilon. Usage: options = "[--epsilon <convertible to double greater 0>] [...]')

if epsilon_ <= 0:
raise Error('Invalid argument "' + opt_val + '" for option epsilon. Usage: options = "[--epsilon <convertible to double greater 0>] [...]')

}
elif opt_name == 'inits-increase-order':
try:
num_inits_increase_order_ = std::stoul(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option inits-increase-order. Usage: options = "[--inits-increase-order <convertible to int greater 0>]"')

if num_inits_increase_order_ <= 0:
raise Error('Invalid argument "' + opt_val + '" for option inits-increase-order. Usage: options = "[--inits-increase-order <convertible to int greater 0>]"')

}
elif opt_name == 'init-type-increase-order':
init_type_increase_order_ = opt_val
if opt_val != 'CLUSTERS' and opt_val != 'K-MEANS++':
raise Exception(std::string('Invalid argument ') + opt_val + ' for option init-type-increase-order. Usage: options = "[--init-type-increase-order CLUSTERS|K-MEANS++] [...]"')

}
elif opt_name == 'max-itrs-increase-order':
try:
max_itrs_increase_order_ = std::stoi(opt_val)

except:
raise Error('Invalid argument "' + opt_val + '" for option max-itrs-increase-order. Usage: options = "[--max-itrs-increase-order <convertible to int>] [...]')

}
else:
std::string valid_options('[--init-type <arg>] [--random-inits <arg>] [--randomness <arg>] [--seed <arg>] [--stdout <arg>] ')
valid_options += '[--time-limit <arg>] [--max-itrs <arg>] [--epsilon <arg>] '
valid_options += '[--inits-increase-order <arg>] [--init-type-increase-order <arg>] [--max-itrs-increase-order <arg>]'
raise Error(std::string('Invalid option "') + opt_name + '". Usage: options = "' + valid_options + '"')


+ 0
- 83
gklearn/preimage/test.py View File

@@ -1,83 +0,0 @@
#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/gedlibpy/lib/fann/:/export/home/lambertn/Documents/gedlibpy/lib/libsvm.3.22:/export/home/lambertn/Documents/gedlibpy/lib/nomad

#Pour que "import script" trouve les librairies qu'a besoin GedLib
#Equivalent à définir la variable d'environnement LD_LIBRARY_PATH sur un bash
import gedlibpy.librariesImport
from gedlibpy import gedlibpy
import networkx as nx


def init() :
print("List of Edit Cost Options : ")
for i in gedlibpy.list_of_edit_cost_options :
print (i)
print("")

print("List of Method Options : ")
for j in gedlibpy.list_of_method_options :
print (j)
print("")

print("List of Init Options : ")
for k in gedlibpy.list_of_init_options :
print (k)
print("")
def test():
gedlibpy.load_GXL_graphs('include/gedlib-master/data/datasets/Mutagenicity/data/', 'collections/MUTA_10.xml')
listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost("CHEM_1")
gedlibpy.init()
gedlibpy.set_method("IPFP", "")
gedlibpy.init_method()
g = listID[0]
h = listID[1]
gedlibpy.run_method(g, h)
print("Node Map : ", gedlibpy.get_node_map(g,h))
print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h))
print("Assignment Matrix : ")
print(gedlibpy.get_assignment_matrix(g, h))
print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g,h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h)))


def convertGraph(G):
G_new = nx.Graph()
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), chem=attrs['atom'])
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
return G_new


def testNxGrapĥ():
from gklearn.utils.graphfiles import loadDataset
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'])
gedlibpy.restart_env()
for graph in Gn:
g_new = convertGraph(graph)
gedlibpy.add_nx_graph(g_new, "")
listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost("CHEM_1")
gedlibpy.init()
gedlibpy.set_method("IPFP", "")
gedlibpy.init_method()

print(listID)
g = listID[0]
h = listID[1]

gedlibpy.run_method(g, h)

print("Node Map : ", gedlibpy.get_node_map(g, h))
print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h))
print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g, h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h)))

#test()
init()
#testNxGrapĥ()

+ 0
- 648
gklearn/preimage/test_fitDistance.py View File

@@ -1,648 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 24 11:50:56 2019

@author: ljia
"""
from matplotlib import pyplot as plt
import numpy as np
from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.utils import remove_edges
from gklearn.preimage.fitDistance import fit_GED_to_kernel_distance
from gklearn.preimage.utils import normalize_distance_matrix


def test_update_costs():
from preimage.fitDistance import update_costs
import cvxpy as cp
ds = np.load('results/xp_fit_method/fit_data_debug4.gm.npz')
nb_cost_mat = ds['nb_cost_mat']
dis_k_vec = ds['dis_k_vec']
n_edit_operations = ds['n_edit_operations']
ged_vec_init = ds['ged_vec_init']
ged_mat = ds['ged_mat']
nb_cost_mat_new = nb_cost_mat[:,[2,3,4]]
x = cp.Variable(nb_cost_mat_new.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat_new * x - dis_k_vec)
# constraints = [x >= [0.000 for i in range(nb_cost_mat_new.shape[1])],
# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0]
# constraints = [x >= [0.000 for i in range(nb_cost_mat_new.shape[1])],
# np.array([1.0, 1.0, -1.0, 0.0, 0.0]).T@x >= 0.0,
# np.array([0.0, 0.0, 0.0, 1.0, -1.0]).T@x == 0.0]
constraints = [x >= [0.00 for i in range(nb_cost_mat_new.shape[1])],
np.array([0.0, 1.0, -1.0]).T@x == 0.0]
# constraints = [x >= [0.00000 for i in range(nb_cost_mat_new.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
prob.solve()
print(x.value)
edit_costs_new = np.concatenate((x.value, np.array([0.0])))
residual = np.sqrt(prob.value)


def median_paper_clcpc_python_best():
"""c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with
python invoking the c++ code by bash command (with updated library).
"""
# ds = {'name': 'monoterpenoides',
# 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
# _, y_all = loadDataset(ds['dataset'])
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
itr_max = 6
algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'
params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP',
'algo_options': algo_options, 'stabilizer': None}
y_all = ['3', '1', '4', '6', '7', '8', '9', '2']
repeats = 50
collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/'
graph_dir = collection_path + 'gxl/'
fn_edit_costs_output = 'results/median_paper/edit_costs_output.python_init40.k10.txt'

for y in y_all:
for repeat in range(repeats):
edit_costs_output_file = open(fn_edit_costs_output, 'a')
collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml'
Gn, _ = loadDataset(collection_file, extra_params=graph_dir)
edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
nb_cost_mat_list = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
gkernel, itr_max, params_ged=params_ged,
parallel=True)
total_time = np.sum(time_list)
# print('\nedit_costs:', edit_costs)
# print('\nresidual_list:', residual_list)
# print('\nedit_cost_list:', edit_cost_list)
# print('\ndistance matrix in kernel space:', dis_k_mat)
# print('\nged matrix:', ged_mat)
# print('\ntotal time:', total_time)
# print('\nnb_cost_mat:', nb_cost_mat_list[-1])
np.savez('results/median_paper/fit_distance.clcpc.python_init40.monot.elabeled.uhpkernel.y'
+ y + '.repeat' + str(repeat) + '.k10..gm',
edit_costs=edit_costs,
residual_list=residual_list, edit_cost_list=edit_cost_list,
dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
total_time=total_time, nb_cost_mat_list=nb_cost_mat_list)
for ec in edit_costs:
edit_costs_output_file.write(str(ec) + ' ')
edit_costs_output_file.write('\n')
edit_costs_output_file.close()
# # normalized distance matrices.
# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
# nb_cost_mat_list = gmfile['nb_cost_mat_list']
nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat)
print(nb_consistent, nb_inconsistent, ratio_consistent)
# norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
# plt.imshow(norm_dis_k_mat)
# plt.colorbar()
# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
#
# norm_ged_mat = normalize_distance_matrix(ged_mat)
# plt.imshow(norm_ged_mat)
# plt.colorbar()
# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
#
# norm_diff = norm_ged_mat - norm_dis_k_mat
# plt.imshow(norm_diff)
# plt.colorbar()
# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_best.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
# # draw_count_bar(norm_diff)


def median_paper_clcpc_python_bash_cpp():
"""c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with
python invoking the c++ code by bash command (with updated library).
"""
# ds = {'name': 'monoterpenoides',
# 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
# _, y_all = loadDataset(ds['dataset'])
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
itr_max = 20
algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5'
params_ged = {'lib': 'gedlib-bash', 'cost': 'CONSTANT', 'method': 'IPFP',
'algo_options': algo_options}
y_all = ['3', '1', '4', '6', '7', '8', '9', '2']
repeats = 50
collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/'
graph_dir = collection_path + 'gxl/'
fn_edit_costs_output = 'results/median_paper/edit_costs_output.txt'

for y in y_all:
for repeat in range(repeats):
edit_costs_output_file = open(fn_edit_costs_output, 'a')
collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml'
Gn, _ = loadDataset(collection_file, extra_params=graph_dir)
edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
gkernel, itr_max, params_ged=params_ged,
parallel=False)
total_time = np.sum(time_list)
# print('\nedit_costs:', edit_costs)
# print('\nresidual_list:', residual_list)
# print('\nedit_cost_list:', edit_cost_list)
# print('\ndistance matrix in kernel space:', dis_k_mat)
# print('\nged matrix:', ged_mat)
# print('\ntotal time:', total_time)
# print('\nnb_cost_mat:', nb_cost_mat_list[-1])
np.savez('results/median_paper/fit_distance.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
+ y + '.repeat' + str(repeat) + '.gm',
edit_costs=edit_costs,
residual_list=residual_list, edit_cost_list=edit_cost_list,
dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
total_time=total_time, nb_cost_mat_list=nb_cost_mat_list,
coef_dk=coef_dk)
for ec in edit_costs:
edit_costs_output_file.write(str(ec) + ' ')
edit_costs_output_file.write('\n')
edit_costs_output_file.close()
# # normalized distance matrices.
# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
# nb_cost_mat_list = gmfile['nb_cost_mat_list']
# coef_dk = gmfile['coef_dk']
nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat)
print(nb_consistent, nb_inconsistent, ratio_consistent)
# norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
# plt.imshow(norm_dis_k_mat)
# plt.colorbar()
# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/norm_dis_k_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
#
# norm_ged_mat = normalize_distance_matrix(ged_mat)
# plt.imshow(norm_ged_mat)
# plt.colorbar()
# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/norm_ged_mat.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
#
# norm_diff = norm_ged_mat - norm_dis_k_mat
# plt.imshow(norm_diff)
# plt.colorbar()
# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.eps', format='eps', dpi=300)
# plt.savefig('results/median_paper/diff_mat_norm_ged_dis_k.clcpc.python_bash_cpp.monot.elabeled.uhpkernel.y'
# + y + '.repeat' + str(repeat) + '.png', format='png')
# # plt.show()
# plt.clf()
# # draw_count_bar(norm_diff)





def test_cs_leq_ci_plus_cr_python_bash_cpp():
"""c_vs <= c_vi + c_vr, c_es <= c_ei + c_er with ged computation with
python invoking the c++ code by bash command (with updated library).
"""
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:10]
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
itr_max = 10
algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5'
params_ged = {'lib': 'gedlib-bash', 'cost': 'CONSTANT', 'method': 'IPFP',
'algo_options': algo_options}
edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
gkernel, itr_max, params_ged=params_ged,
parallel=False)
total_time = np.sum(time_list)
print('\nedit_costs:', edit_costs)
print('\nresidual_list:', residual_list)
print('\nedit_cost_list:', edit_cost_list)
print('\ndistance matrix in kernel space:', dis_k_mat)
print('\nged matrix:', ged_mat)
print('\ntotal time:', total_time)
print('\nnb_cost_mat:', nb_cost_mat_list[-1])
np.savez('results/fit_distance.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel.gm',
edit_costs=edit_costs,
residual_list=residual_list, edit_cost_list=edit_cost_list,
dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
total_time=total_time, nb_cost_mat_list=nb_cost_mat_list,
coef_dk=coef_dk)
# 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[0:10]
## remove_edges(Gn)
# gkernel = 'untilhpathkernel'
# node_label = 'atom'
# edge_label = 'bond_type'
# itr_max = 10
# edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
# nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
# gkernel, itr_max)
# total_time = np.sum(time_list)
# print('\nedit_costs:', edit_costs)
# print('\nresidual_list:', residual_list)
# print('\nedit_cost_list:', edit_cost_list)
# print('\ndistance matrix in kernel space:', dis_k_mat)
# print('\nged matrix:', ged_mat)
# print('\ntotal time:', total_time)
# print('\nnb_cost_mat:', nb_cost_mat_list[-1])
# np.savez('results/fit_distance.cs_leq_ci_plus_cr.mutag.elabeled.uhpkernel.gm',
# edit_costs=edit_costs,
# residual_list=residual_list, edit_cost_list=edit_cost_list,
# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, coef_dk)
# # normalized distance matrices.
# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.monot.elabeled.uhpkernel.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
# nb_cost_mat_list = gmfile['nb_cost_mat_list']
# coef_dk = gmfile['coef_dk']
nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat)
print(nb_consistent, nb_inconsistent, ratio_consistent)
# dis_k_sub = pairwise_substitution(dis_k_mat)
# ged_sub = pairwise_substitution(ged_mat)
# np.savez('results/sub_dis_mat.cs_leq_ci_plus_cr.gm',
# dis_k_sub=dis_k_sub, ged_sub=ged_sub)
norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
plt.imshow(norm_dis_k_mat)
plt.colorbar()
plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
norm_ged_mat = normalize_distance_matrix(ged_mat)
plt.imshow(norm_ged_mat)
plt.colorbar()
plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
norm_diff = norm_ged_mat - norm_dis_k_mat
plt.imshow(norm_diff)
plt.colorbar()
plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.python_bash_cpp.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
# draw_count_bar(norm_diff)


def test_anycosts():
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[0:10]
remove_edges(Gn)
gkernel = 'marginalizedkernel'
itr_max = 10
edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, gkernel, itr_max)
total_time = np.sum(time_list)
print('\nedit_costs:', edit_costs)
print('\nresidual_list:', residual_list)
print('\nedit_cost_list:', edit_cost_list)
print('\ndistance matrix in kernel space:', dis_k_mat)
print('\nged matrix:', ged_mat)
print('\ntotal time:', total_time)
print('\nnb_cost_mat:', nb_cost_mat_list[-1])
np.savez('results/fit_distance.any_costs.gm', edit_costs=edit_costs,
residual_list=residual_list, edit_cost_list=edit_cost_list,
dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
total_time=total_time, nb_cost_mat_list=nb_cost_mat_list)
# # normalized distance matrices.
# gmfile = np.load('results/fit_distance.any_costs.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
## nb_cost_mat_list = gmfile['nb_cost_mat_list']
norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
plt.imshow(norm_dis_k_mat)
plt.colorbar()
plt.savefig('results/norm_dis_k_mat.any_costs' + '.eps', format='eps', dpi=300)
# plt.savefig('results/norm_dis_k_mat.any_costs' + '.png', format='png')
# plt.show()
plt.clf()
norm_ged_mat = normalize_distance_matrix(ged_mat)
plt.imshow(norm_ged_mat)
plt.colorbar()
plt.savefig('results/norm_ged_mat.any_costs' + '.eps', format='eps', dpi=300)
# plt.savefig('results/norm_ged_mat.any_costs' + '.png', format='png')
# plt.show()
plt.clf()
norm_diff = norm_ged_mat - norm_dis_k_mat
plt.imshow(norm_diff)
plt.colorbar()
plt.savefig('results/diff_mat_norm_ged_dis_k.any_costs' + '.eps', format='eps', dpi=300)
# plt.savefig('results/diff_mat_norm_ged_dis_k.any_costs' + '.png', format='png')
# plt.show()
plt.clf()
# draw_count_bar(norm_diff)

def test_cs_leq_ci_plus_cr():
"""c_vs <= c_vi + c_vr, c_es <= c_ei + c_er
"""
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:10]
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
itr_max = 10
edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
gkernel, itr_max,
fitkernel='gaussian')
total_time = np.sum(time_list)
print('\nedit_costs:', edit_costs)
print('\nresidual_list:', residual_list)
print('\nedit_cost_list:', edit_cost_list)
print('\ndistance matrix in kernel space:', dis_k_mat)
print('\nged matrix:', ged_mat)
print('\ntotal time:', total_time)
print('\nnb_cost_mat:', nb_cost_mat_list[-1])
np.savez('results/fit_distance.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel.gm',
edit_costs=edit_costs,
residual_list=residual_list, edit_cost_list=edit_cost_list,
dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
total_time=total_time, nb_cost_mat_list=nb_cost_mat_list,
coef_dk=coef_dk)
# 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[0:10]
## remove_edges(Gn)
# gkernel = 'untilhpathkernel'
# node_label = 'atom'
# edge_label = 'bond_type'
# itr_max = 10
# edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list, \
# nb_cost_mat_list, coef_dk = fit_GED_to_kernel_distance(Gn, node_label, edge_label,
# gkernel, itr_max)
# total_time = np.sum(time_list)
# print('\nedit_costs:', edit_costs)
# print('\nresidual_list:', residual_list)
# print('\nedit_cost_list:', edit_cost_list)
# print('\ndistance matrix in kernel space:', dis_k_mat)
# print('\nged matrix:', ged_mat)
# print('\ntotal time:', total_time)
# print('\nnb_cost_mat:', nb_cost_mat_list[-1])
# np.savez('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.mutag.elabeled.uhpkernel.gm',
# edit_costs=edit_costs,
# residual_list=residual_list, edit_cost_list=edit_cost_list,
# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list, coef_dk)
# # normalized distance matrices.
# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.monot.elabeled.uhpkernel.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
# nb_cost_mat_list = gmfile['nb_cost_mat_list']
# coef_dk = gmfile['coef_dk']
nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat)
print(nb_consistent, nb_inconsistent, ratio_consistent)
# dis_k_sub = pairwise_substitution(dis_k_mat)
# ged_sub = pairwise_substitution(ged_mat)
# np.savez('results/sub_dis_mat.cs_leq_ci_plus_cr.cost_leq_1en2.gm',
# dis_k_sub=dis_k_sub, ged_sub=ged_sub)
norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
plt.imshow(norm_dis_k_mat)
plt.colorbar()
plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
norm_ged_mat = normalize_distance_matrix(ged_mat)
plt.imshow(norm_ged_mat)
plt.colorbar()
plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
norm_diff = norm_ged_mat - norm_dis_k_mat
plt.imshow(norm_diff)
plt.colorbar()
plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.eps', format='eps', dpi=300)
plt.savefig('results/diff_mat_norm_ged_dis_k.cs_leq_ci_plus_cr.gaussian.cost_leq_1en2.monot.elabeled.uhpkernel'
+ '.png', format='png')
# plt.show()
plt.clf()
# draw_count_bar(norm_diff)
def test_unfitted():
"""unfitted.
"""
from fitDistance import compute_geds
from utils import kernel_distance_matrix
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:10]
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'

# 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[0:10]
## remove_edges(Gn)
# gkernel = 'marginalizedkernel'

dis_k_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, gkernel=gkernel)
ged_all, ged_mat, n_edit_operations = compute_geds(Gn, [3, 3, 1, 3, 3, 1],
[0, 1, 2, 3, 4, 5], parallel=True)
print('\ndistance matrix in kernel space:', dis_k_mat)
print('\nged matrix:', ged_mat)
# np.savez('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en2.gm', edit_costs=edit_costs,
# residual_list=residual_list, edit_cost_list=edit_cost_list,
# dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list,
# total_time=total_time, nb_cost_mat_list=nb_cost_mat_list)
# normalized distance matrices.
# gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.cost_leq_1en3.gm.npz')
# edit_costs = gmfile['edit_costs']
# residual_list = gmfile['residual_list']
# edit_cost_list = gmfile['edit_cost_list']
# dis_k_mat = gmfile['dis_k_mat']
# ged_mat = gmfile['ged_mat']
# total_time = gmfile['total_time']
# nb_cost_mat_list = gmfile['nb_cost_mat_list']
nb_consistent, nb_inconsistent, ratio_consistent = pairwise_substitution_consistence(dis_k_mat, ged_mat)
print(nb_consistent, nb_inconsistent, ratio_consistent)
norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)
plt.imshow(norm_dis_k_mat)
plt.colorbar()
plt.savefig('results/norm_dis_k_mat.unfitted.MUTAG' + '.eps', format='eps', dpi=300)
plt.savefig('results/norm_dis_k_mat.unfitted.MUTAG' + '.png', format='png')
# plt.show()
plt.clf()
norm_ged_mat = normalize_distance_matrix(ged_mat)
plt.imshow(norm_ged_mat)
plt.colorbar()
plt.savefig('results/norm_ged_mat.unfitted.MUTAG' + '.eps', format='eps', dpi=300)
plt.savefig('results/norm_ged_mat.unfitted.MUTAG' + '.png', format='png')
# plt.show()
plt.clf()
norm_diff = norm_ged_mat - norm_dis_k_mat
plt.imshow(norm_diff)
plt.colorbar()
plt.savefig('results/diff_mat_norm_ged_dis_k.unfitted.MUTAG' + '.eps', format='eps', dpi=300)
plt.savefig('results/diff_mat_norm_ged_dis_k.unfitted.MUTAG' + '.png', format='png')
# plt.show()
plt.clf()
draw_count_bar(norm_diff)
def pairwise_substitution_consistence(mat1, mat2):
"""
"""
nb_consistent = 0
nb_inconsistent = 0
# the matrix is considered symmetric.
upper_tri1 = mat1[np.triu_indices_from(mat1)]
upper_tri2 = mat2[np.tril_indices_from(mat2)]
for i in tqdm(range(len(upper_tri1)), desc='computing consistence', file=sys.stdout):
for j in range(i, len(upper_tri1)):
if np.sign(upper_tri1[i] - upper_tri1[j]) == np.sign(upper_tri2[i] - upper_tri2[j]):
nb_consistent += 1
else:
nb_inconsistent += 1
return nb_consistent, nb_inconsistent, nb_consistent / (nb_consistent + nb_inconsistent)


def pairwise_substitution(mat):
# the matrix is considered symmetric.
upper_tri = mat[np.triu_indices_from(mat)]
sub_list = []
for i in tqdm(range(len(upper_tri)), desc='computing', file=sys.stdout):
for j in range(i, len(upper_tri)):
sub_list.append(upper_tri[i] - upper_tri[j])
return sub_list
def draw_count_bar(norm_diff):
import pandas
from collections import Counter, OrderedDict
norm_diff_cnt = norm_diff.flatten()
norm_diff_cnt = norm_diff_cnt * 10
norm_diff_cnt = np.floor(norm_diff_cnt)
norm_diff_cnt = Counter(norm_diff_cnt)
norm_diff_cnt = OrderedDict(sorted(norm_diff_cnt.items()))
df = pandas.DataFrame.from_dict(norm_diff_cnt, orient='index')
df.plot(kind='bar')
if __name__ == '__main__':
# test_anycosts()
# test_cs_leq_ci_plus_cr()
# test_unfitted()
# test_cs_leq_ci_plus_cr_python_bash_cpp()
# median_paper_clcpc_python_bash_cpp()
# median_paper_clcpc_python_best()

# x = np.array([[1,2,3],[4,5,6],[7,8,9]])
# xx = pairwise_substitution(x)
test_update_costs()

+ 0
- 520
gklearn/preimage/test_ged.py View File

@@ -1,520 +0,0 @@
#export LD_LIBRARY_PATH=.:/export/home/lambertn/Documents/gedlibpy/lib/fann/:/export/home/lambertn/Documents/gedlibpy/lib/libsvm.3.22:/export/home/lambertn/Documents/gedlibpy/lib/nomad

#Pour que "import script" trouve les librairies qu'a besoin GedLib
#Equivalent à définir la variable d'environnement LD_LIBRARY_PATH sur un bash
#import gedlibpy_linlin.librariesImport
#from gedlibpy_linlin import gedlibpy
from libs import *
import networkx as nx
import numpy as np
from tqdm import tqdm
import sys


def test_NON_SYMBOLIC_cost():
"""Test edit cost LETTER2.
"""
from gklearn.preimage.ged import GED, get_nb_edit_operations_nonsymbolic, get_nb_edit_operations_letter
from gklearn.preimage.test_k_closest_graphs import reform_attributes
from gklearn.utils.graphfiles import loadDataset

dataset = '../../datasets/Letter-high/Letter-high_A.txt'
Gn, y_all = loadDataset(dataset)

g1 = Gn[200]
g2 = Gn[1780]
reform_attributes(g1)
reform_attributes(g2)

c_vi = 0.675
c_vr = 0.675
c_vs = 0.75
c_ei = 0.425
c_er = 0.425
c_es = 0

edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
dis, pi_forward, pi_backward = GED(g1, g2, lib='gedlibpy',
cost='NON_SYMBOLIC', method='IPFP', edit_cost_constant=edit_cost_constant,
algo_options='', stabilizer=None)
n_vi, n_vr, sod_vs, n_ei, n_er, sod_es = get_nb_edit_operations_nonsymbolic(g1, g2,
pi_forward, pi_backward)

print('# of operations:', n_vi, n_vr, sod_vs, n_ei, n_er, sod_es)
print('c_vi, c_vr, c_vs, c_ei, c_er:', c_vi, c_vr, c_vs, c_ei, c_er, c_es)
cost_computed = c_vi * n_vi + c_vr * n_vr + c_vs * sod_vs \
+ c_ei * n_ei + c_er * n_er + c_es * sod_es
print('dis (cost computed by GED):', dis)
print('cost computed by # of operations and edit cost constants:', cost_computed)


def test_LETTER2_cost():
"""Test edit cost LETTER2.
"""
from gklearn.preimage.ged import GED, get_nb_edit_operations_letter
from gklearn.preimage.test_k_closest_graphs import reform_attributes
from gklearn.utils.graphfiles import loadDataset

ds = {'dataset': 'cpp_ext/data/collections/Letter.xml',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir'])

g1 = Gn[200]
g2 = Gn[1780]
reform_attributes(g1)
reform_attributes(g2)

c_vi = 0.675
c_vr = 0.675
c_vs = 0.75
c_ei = 0.425
c_er = 0.425

edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er]
dis, pi_forward, pi_backward = GED(g1, g2, dataset='letter', lib='gedlibpy',
cost='LETTER2', method='IPFP', edit_cost_constant=edit_cost_constant,
algo_options='', stabilizer=None)
n_vi, n_vr, n_vs, sod_vs, n_ei, n_er = get_nb_edit_operations_letter(g1, g2,
pi_forward, pi_backward)

print('# of operations:', n_vi, n_vr, n_vs, sod_vs, n_ei, n_er)
print('c_vi, c_vr, c_vs, c_ei, c_er:', c_vi, c_vr, c_vs, c_ei, c_er)
cost_computed = c_vi * n_vi + c_vr * n_vr + c_vs * sod_vs \
+ c_ei * n_ei + c_er * n_er
print('dis (cost computed by GED):', dis)
print('cost computed by # of operations and edit cost constants:', cost_computed)



def test_get_nb_edit_operations_letter():
"""Test whether function preimage.ged.get_nb_edit_operations_letter returns
correct numbers of edit operations. The distance/cost computed by GED
should be the same as the cost computed by number of operations and edit
cost constants.
"""
from gklearn.preimage.ged import GED, get_nb_edit_operations_letter
from gklearn.preimage.test_k_closest_graphs import reform_attributes
from gklearn.utils.graphfiles import loadDataset

ds = {'dataset': 'cpp_ext/data/collections/Letter.xml',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir'])

g1 = Gn[200]
g2 = Gn[1780]
reform_attributes(g1)
reform_attributes(g2)

c_vir = 0.9
c_eir = 1.7
alpha = 0.75

edit_cost_constant = [c_vir, c_eir, alpha]
dis, pi_forward, pi_backward = GED(g1, g2, dataset='letter', lib='gedlibpy',
cost='LETTER', method='IPFP', edit_cost_constant=edit_cost_constant,
algo_options='', stabilizer=None)
n_vi, n_vr, n_vs, c_vs, n_ei, n_er = get_nb_edit_operations_letter(g1, g2,
pi_forward, pi_backward)

print('# of operations and costs:', n_vi, n_vr, n_vs, c_vs, n_ei, n_er)
print('c_vir, c_eir, alpha:', c_vir, c_eir, alpha)
cost_computed = alpha * c_vir * (n_vi + n_vr) \
+ alpha * c_vs \
+ (1 - alpha) * c_eir * (n_ei + n_er)
print('dis (cost computed by GED):', dis)
print('cost computed by # of operations and edit cost constants:', cost_computed)


def test_get_nb_edit_operations():
"""Test whether function preimage.ged.get_nb_edit_operations returns correct
numbers of edit operations. The distance/cost computed by GED should be the
same as the cost computed by number of operations and edit cost constants.
"""
from gklearn.preimage.ged import GED, get_nb_edit_operations
from gklearn.utils.graphfiles import loadDataset
import os

ds = {'dataset': '../../datasets/monoterpenoides/dataset_10+.ds',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])

g1 = Gn[20]
g2 = Gn[108]

c_vi = 3
c_vr = 3
c_vs = 1
c_ei = 3
c_er = 3
c_es = 1

edit_cost_constant = [c_vi, c_vr, c_vs, c_ei, c_er, c_es]
dis, pi_forward, pi_backward = GED(g1, g2, dataset='monoterpenoides', lib='gedlibpy',
cost='CONSTANT', method='IPFP', edit_cost_constant=edit_cost_constant,
algo_options='', stabilizer=None)
n_vi, n_vr, n_vs, n_ei, n_er, n_es = get_nb_edit_operations(g1, g2,
pi_forward, pi_backward)

print('# of operations and costs:', n_vi, n_vr, n_vs, n_ei, n_er, n_es)
print('edit costs:', c_vi, c_vr, c_vs, c_ei, c_er, c_es)
cost_computed = n_vi * c_vi + n_vr * c_vr + n_vs * c_vs \
+ n_ei * c_ei + n_er * c_er + n_es * c_es
print('dis (cost computed by GED):', dis)
print('cost computed by # of operations and edit cost constants:', cost_computed)


def test_ged_python_bash_cpp():
"""Test ged computation with python invoking the c++ code by bash command (with updated library).
"""
from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.ged import GED

data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/'
# collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml'
collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/monoterpenoides_3_20.xml'
graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/'

Gn, y = loadDataset(collection_file, extra_params=graph_dir)

algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'

for repeat in range(0, 3):
# Generate the result file.
ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_bash_' + str(repeat) + '_init40.3_20.txt'
# runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_min_' + str(repeat) + '.txt'

ged_file = open(ged_filename, 'a')
# runtime_file = open(runtime_filename, 'a')

ged_mat = np.empty((len(Gn), len(Gn)))
# runtime_mat = np.empty((len(Gn), len(Gn)))

for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout):
for j in range(len(Gn)):
print(i, j)
g1 = Gn[i]
g2 = Gn[j]
upper_bound, _, _ = GED(g1, g2, lib='gedlib-bash', cost='CONSTANT',
method='IPFP',
edit_cost_constant=[3.0, 3.0, 1.0, 3.0, 3.0, 1.0],
algo_options=algo_options)
# runtime = gedlibpy.get_runtime(g1, g2)
ged_mat[i][j] = upper_bound
# runtime_mat[i][j] = runtime

# Write to files.
ged_file.write(str(int(upper_bound)) + ' ')
# runtime_file.write(str(runtime) + ' ')

ged_file.write('\n')
# runtime_file.write('\n')

ged_file.close()
# runtime_file.close()

print('ged_mat')
print(ged_mat)
# print('runtime_mat:')
# print(runtime_mat)

return



def test_ged_best_settings_updated():
"""Test ged computation with best settings the same as in the C++ code (with updated library).
"""

data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/'
collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml'
# collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/monoterpenoides_3_20.xml'

graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/'

algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'

for repeat in range(0, 3):
# Generate the result file.
ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_updated_' + str(repeat) + '_init40.txt'
runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_updated_' + str(repeat) + '_init40.txt'

gedlibpy.restart_env()
gedlibpy.load_GXL_graphs(graph_dir, collection_file)
listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0])
gedlibpy.init()
gedlibpy.set_method("IPFP", algo_options)
gedlibpy.init_method()

ged_mat = np.empty((len(listID), len(listID)))
runtime_mat = np.empty((len(listID), len(listID)))

for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout):
ged_file = open(ged_filename, 'a')
runtime_file = open(runtime_filename, 'a')

for j in range(len(listID)):
g1 = listID[i]
g2 = listID[j]
gedlibpy.run_method(g1, g2)
upper_bound = gedlibpy.get_upper_bound(g1, g2)
runtime = gedlibpy.get_runtime(g1, g2)
ged_mat[i][j] = upper_bound
runtime_mat[i][j] = runtime

# Write to files.
ged_file.write(str(int(upper_bound)) + ' ')
runtime_file.write(str(runtime) + ' ')

ged_file.write('\n')
runtime_file.write('\n')

ged_file.close()
runtime_file.close()

print('ged_mat')
print(ged_mat)
print('runtime_mat:')
print(runtime_mat)

return


def test_ged_best_settings():
"""Test ged computation with best settings the same as in the C++ code.
"""

data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/'
collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml'
graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/'

algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5'

for repeat in range(0, 3):
# Generate the result file.
ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_best_settings_' + str(repeat) + '.txt'
runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_best_settings_' + str(repeat) + '.txt'

ged_file = open(ged_filename, 'a')
runtime_file = open(runtime_filename, 'a')

gedlibpy.restart_env()
gedlibpy.load_GXL_graphs(graph_dir, collection_file)
listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0])
gedlibpy.init()
gedlibpy.set_method("IPFP", algo_options)
gedlibpy.init_method()

ged_mat = np.empty((len(listID), len(listID)))
runtime_mat = np.empty((len(listID), len(listID)))

for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout):
for j in range(len(listID)):
g1 = listID[i]
g2 = listID[j]
gedlibpy.run_method(g1, g2)
upper_bound = gedlibpy.get_upper_bound(g1, g2)
runtime = gedlibpy.get_runtime(g1, g2)
ged_mat[i][j] = upper_bound
runtime_mat[i][j] = runtime

# Write to files.
ged_file.write(str(int(upper_bound)) + ' ')
runtime_file.write(str(runtime) + ' ')

ged_file.write('\n')
runtime_file.write('\n')

ged_file.close()
runtime_file.close()

print('ged_mat')
print(ged_mat)
print('runtime_mat:')
print(runtime_mat)

return



def test_ged_default():
"""Test ged computation with default settings.
"""

data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/'
collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml'
graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/'

for repeat in range(3):
# Generate the result file.
ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_default_' + str(repeat) + '.txt'
runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_default_' + str(repeat) + '.txt'

ged_file = open(ged_filename, 'a')
runtime_file = open(runtime_filename, 'a')

gedlibpy.restart_env()
gedlibpy.load_GXL_graphs(graph_dir, collection_file)
listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost('CONSTANT', [3.0, 3.0, 1.0, 3.0, 3.0, 1.0])
gedlibpy.init()
gedlibpy.set_method("IPFP", "")
gedlibpy.init_method()

ged_mat = np.empty((len(listID), len(listID)))
runtime_mat = np.empty((len(listID), len(listID)))

for i in tqdm(range(len(listID)), desc='computing GEDs', file=sys.stdout):
for j in range(len(listID)):
g1 = listID[i]
g2 = listID[j]
gedlibpy.run_method(g1, g2)
upper_bound = gedlibpy.get_upper_bound(g1, g2)
runtime = gedlibpy.get_runtime(g1, g2)
ged_mat[i][j] = upper_bound
runtime_mat[i][j] = runtime

# Write to files.
ged_file.write(str(int(upper_bound)) + ' ')
runtime_file.write(str(runtime) + ' ')

ged_file.write('\n')
runtime_file.write('\n')

ged_file.close()
runtime_file.close()

print('ged_mat')
print(ged_mat)
print('runtime_mat:')
print(runtime_mat)

return


def test_ged_min():
"""Test ged computation with the "min" stabilizer.
"""
from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.ged import GED

data_dir_prefix = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/'
collection_file = data_dir_prefix + 'generated_datsets/monoterpenoides/gxl/monoterpenoides.xml'
graph_dir = data_dir_prefix +'generated_datsets/monoterpenoides/gxl/'

Gn, y = loadDataset(collection_file, extra_params=graph_dir)

# algo_options = '--threads 6 --initial-solutions 10 --ratio-runs-from-initial-solutions .5'

for repeat in range(0, 3):
# Generate the result file.
ged_filename = data_dir_prefix + 'output/test_ged/ged_mat_python_min_' + str(repeat) + '.txt'
# runtime_filename = data_dir_prefix + 'output/test_ged/runtime_mat_python_min_' + str(repeat) + '.txt'

ged_file = open(ged_filename, 'a')
# runtime_file = open(runtime_filename, 'a')

ged_mat = np.empty((len(Gn), len(Gn)))
# runtime_mat = np.empty((len(Gn), len(Gn)))

for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout):
for j in range(len(Gn)):
g1 = Gn[i]
g2 = Gn[j]
upper_bound, _, _ = GED(g1, g2, lib='gedlibpy', cost='CONSTANT',
method='IPFP',
edit_cost_constant=[3.0, 3.0, 1.0, 3.0, 3.0, 1.0],
stabilizer='min', repeat=10)
# runtime = gedlibpy.get_runtime(g1, g2)
ged_mat[i][j] = upper_bound
# runtime_mat[i][j] = runtime

# Write to files.
ged_file.write(str(int(upper_bound)) + ' ')
# runtime_file.write(str(runtime) + ' ')

ged_file.write('\n')
# runtime_file.write('\n')

ged_file.close()
# runtime_file.close()

print('ged_mat')
print(ged_mat)
# print('runtime_mat:')
# print(runtime_mat)

return


def init() :
print("List of Edit Cost Options : ")
for i in gedlibpy.list_of_edit_cost_options :
print (i)
print("")

print("List of Method Options : ")
for j in gedlibpy.list_of_method_options :
print (j)
print("")

print("List of Init Options : ")
for k in gedlibpy.list_of_init_options :
print (k)
print("")




def convertGraph(G):
G_new = nx.Graph()
for nd, attrs in G.nodes(data=True):
G_new.add_node(str(nd), chem=attrs['atom'])
for nd1, nd2, attrs in G.edges(data=True):
G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])

return G_new


def testNxGrapĥ():
from gklearn.utils.graphfiles import loadDataset
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'])

gedlibpy.restart_env()
for graph in Gn:
g_new = convertGraph(graph)
gedlibpy.add_nx_graph(g_new, "")

listID = gedlibpy.get_all_graph_ids()
gedlibpy.set_edit_cost("CHEM_1")
gedlibpy.init()
gedlibpy.set_method("IPFP", "")
gedlibpy.init_method()

print(listID)
g = listID[0]
h = listID[1]

gedlibpy.run_method(g, h)

print("Node Map : ", gedlibpy.get_node_map(g, h))
print("Forward map : " , gedlibpy.get_forward_map(g, h), ", Backward map : ", gedlibpy.get_backward_map(g, h))
print ("Upper Bound = " + str(gedlibpy.get_upper_bound(g, h)) + ", Lower Bound = " + str(gedlibpy.get_lower_bound(g, h)) + ", Runtime = " + str(gedlibpy.get_runtime(g, h)))

if __name__ == '__main__':
# test_ged_default()
# test_ged_min()
# test_ged_best_settings()
# test_ged_best_settings_updated()
# test_ged_python_bash_cpp()
# test_get_nb_edit_operations()
# test_get_nb_edit_operations_letter()
# test_LETTER2_cost()
test_NON_SYMBOLIC_cost()


#init()
#testNxGrapĥ()

+ 0
- 964
gklearn/preimage/test_iam.py View File

@@ -1,964 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 15:59:00 2019

@author: ljia
"""

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import time
import random
#from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
#from gklearn.utils.logger2file import *
from gklearn.preimage.iam import iam_upgraded
from gklearn.preimage.utils import remove_edges, compute_kernel, get_same_item_indices, dis_gstar
#from gklearn.preimage.ged import ged_median


def test_iam_monoterpenoides_with_init40():
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
# 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 = 0.0001
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]
ged_stabilizer = None
# ged_repeat = 50
algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1'
params_ged = {'lib': 'gedlibpy', 'cost': ged_cost, 'method': ged_method,
'edit_cost_constant': edit_cost_constant,
'algo_options': algo_options,
'stabilizer': ged_stabilizer}

collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/'
graph_dir = collection_path + 'gxl/'
y_all = ['3', '1', '4', '6', '7', '8', '9', '2']
repeats = 50
# classify graphs according to classes.
time_list = []
dis_ks_min_list = []
dis_ks_set_median_list = []
sod_gs_list = []
g_best = []
sod_set_median_list = []
sod_list_list = []
for y in y_all:
print('\n-------------------------------------------------------')
print('class of y:', y)
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(repeats):
# load median set.
collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml'
Gn_median, _ = loadDataset(collection_file, extra_params=graph_dir)
Gn_candidate = [g.copy() for g in 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, node_label=node_label, edge_label=edge_label,
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/monoter_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))
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))




def test_iam_monoterpenoides():
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
# parameters for GED function from the IAM paper.
# fitted edit costs (Gaussian).
c_vi = 0.03620133402089074
c_vr = 0.0417574590207099
c_vs = 0.009992282328587499
c_ei = 0.08293120042342755
c_er = 0.09512220476358019
c_es = 0.09222529696841467
# # fitted edit costs (linear combinations).
# c_vi = 0.1749684054238749
# c_vr = 0.0734054228711457
# c_vs = 0.05017781726016715
# c_ei = 0.1869431164806936
# c_er = 0.32055856948274
# c_es = 0.2569469379247611
# # 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/monoter_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_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.')

###############################################################################
# tests on different numbers of median-sets.

def test_iam_median_nb():
ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
'extra_params': {}} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
# Gn = Gn[0:50]
remove_edges(Gn)
gkernel = 'marginalizedkernel'
lmbda = 0.03 # termination probalility
# # 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 = [len(Gn)]
# # compute Gram matrix.
# time0 = time.time()
# km = compute_kernel(Gn, gkernel, True)
# time_km = time.time() - time0
# # write Gram matrix to file.
# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km)
time_list = []
dis_ks_min_list = []
sod_gs_list = []
# sod_gs_min_list = []
# nb_updated_list = []
# nb_updated_k_list = []
g_best = []
for nb_median in nb_median_range:
print('\n-------------------------------------------------------')
print('number of median graphs =', nb_median)
random.seed(1)
idx_rdm = random.sample(range(len(Gn)), nb_median)
print('graphs chosen:', idx_rdm)
Gn_median = [Gn[idx].copy() for idx in idx_rdm]
Gn_candidate = [g.copy() for g in Gn]
# for g in Gn_median:
# nx.draw(g, labels=nx.get_node_attributes(g, 'atom'), with_labels=True)
## plt.savefig("results/preimage_mix/mutag.png", format="PNG")
# plt.show()
# plt.clf()
###################################################################
# gmfile = np.load('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm.npz')
# km_tmp = gmfile['gm']
# time_km = gmfile['gmtime']
# # modify mixed gram matrix.
# km = np.zeros((len(Gn) + nb_median, len(Gn) + nb_median))
# for i in range(len(Gn)):
# for j in range(i, len(Gn)):
# km[i, j] = km_tmp[i, j]
# km[j, i] = km[i, j]
# for i in range(len(Gn)):
# for j, idx in enumerate(idx_rdm):
# km[i, len(Gn) + j] = km[i, idx]
# km[len(Gn) + j, i] = km[i, idx]
# for i, idx1 in enumerate(idx_rdm):
# for j, idx2 in enumerate(idx_rdm):
# km[len(Gn) + i, len(Gn) + j] = km[idx1, idx2]
###################################################################
alpha_range = [1 / nb_median] * nb_median
time0 = time.time()
ghat_new_list, sod_min = iam_upgraded(Gn_median, Gn_candidate,
c_ei=c_ei, c_er=c_er, c_es=c_es, ite_max=ite_max_iam,
epsilon=epsilon_iam, 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(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.append(dhat_new_list[0])
g_best.append(ghat_new_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(ghat_new_list[0], labels=nx.get_node_attributes(ghat_new_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_min)
# sod_gs_min_list.append(np.min(sod_min))
print('\nsmallest sod in graph space: ', sod_min)
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_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)
###############################################################################

if __name__ == '__main__':
###############################################################################
# tests on different numbers of median-sets.
# test_iam_median_nb()
# test_iam_letter_h()
# test_iam_monoterpenoides()
# test_iam_mutag()
# test_iam_fitdistance()
# print("test log")
test_iam_monoterpenoides_with_init40()

+ 0
- 462
gklearn/preimage/test_k_closest_graphs.py View File

@@ -1,462 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 11:53:54 2019

@author: ljia
"""
import numpy as np
import math
import networkx as nx
import matplotlib.pyplot as plt
import time
import random
from tqdm import tqdm
from itertools import combinations, islice
import multiprocessing
from multiprocessing import Pool
from functools import partial

from gklearn.utils.graphfiles import loadDataset, loadGXL
#from gklearn.utils.logger2file import *
from gklearn.preimage.iam import iam_upgraded, iam_bash
from gklearn.preimage.utils import compute_kernel, dis_gstar, kernel_distance_matrix
from gklearn.preimage.fitDistance import fit_GED_to_kernel_distance
#from gklearn.preimage.ged import ged_median


def fit_edit_cost_constants(fit_method, edit_cost_name,
edit_cost_constants=None, initial_solutions=1,
Gn_median=None, node_label=None, edge_label=None,
gkernel=None, dataset=None, init_ecc=None,
Gn=None, Kmatrix_median=None):
"""fit edit cost constants.
"""
if fit_method == 'random': # random
if edit_cost_name == 'LETTER':
edit_cost_constants = random.sample(range(1, 10), 3)
edit_cost_constants = [item * 0.1 for item in edit_cost_constants]
elif edit_cost_name == 'LETTER2':
random.seed(time.time())
edit_cost_constants = random.sample(range(1, 10), 5)
# edit_cost_constants = [item * 0.1 for item in edit_cost_constants]
elif edit_cost_name == 'NON_SYMBOLIC':
edit_cost_constants = random.sample(range(1, 10), 6)
if Gn_median[0].graph['node_attrs'] == []:
edit_cost_constants[2] = 0
if Gn_median[0].graph['edge_attrs'] == []:
edit_cost_constants[5] = 0
else:
edit_cost_constants = random.sample(range(1, 10), 6)
print('edit cost constants used:', edit_cost_constants)
elif fit_method == 'expert': # expert
if init_ecc is None:
if edit_cost_name == 'LETTER':
edit_cost_constants = [0.9, 1.7, 0.75]
elif edit_cost_name == 'LETTER2':
edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425]
else:
edit_cost_constants = [3, 3, 1, 3, 3, 1]
else:
edit_cost_constants = init_ecc
elif fit_method == 'k-graphs':
itr_max = 6
if init_ecc is None:
if edit_cost_name == 'LETTER':
init_costs = [0.9, 1.7, 0.75]
elif edit_cost_name == 'LETTER2':
init_costs = [0.675, 0.675, 0.75, 0.425, 0.425]
elif edit_cost_name == 'NON_SYMBOLIC':
init_costs = [0, 0, 1, 1, 1, 0]
if Gn_median[0].graph['node_attrs'] == []:
init_costs[2] = 0
if Gn_median[0].graph['edge_attrs'] == []:
init_costs[5] = 0
else:
init_costs = [3, 3, 1, 3, 3, 1]
else:
init_costs = init_ecc
algo_options = '--threads 1 --initial-solutions ' \
+ str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1'
params_ged = {'lib': 'gedlibpy', 'cost': edit_cost_name, 'method': 'IPFP',
'algo_options': algo_options, 'stabilizer': None}
# fit on k-graph subset
edit_cost_constants, _, _, _, _, _, _ = fit_GED_to_kernel_distance(Gn_median,
node_label, edge_label, gkernel, itr_max, params_ged=params_ged,
init_costs=init_costs, dataset=dataset, Kmatrix=Kmatrix_median,
parallel=True)
elif fit_method == 'whole-dataset':
itr_max = 6
if init_ecc is None:
if edit_cost_name == 'LETTER':
init_costs = [0.9, 1.7, 0.75]
elif edit_cost_name == 'LETTER2':
init_costs = [0.675, 0.675, 0.75, 0.425, 0.425]
else:
init_costs = [3, 3, 1, 3, 3, 1]
else:
init_costs = init_ecc
algo_options = '--threads 1 --initial-solutions ' \
+ str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1'
params_ged = {'lib': 'gedlibpy', 'cost': edit_cost_name, 'method': 'IPFP',
'algo_options': algo_options, 'stabilizer': None}
# fit on all subset
edit_cost_constants, _, _, _, _, _, _ = fit_GED_to_kernel_distance(Gn,
node_label, edge_label, gkernel, itr_max, params_ged=params_ged,
init_costs=init_costs, dataset=dataset, parallel=True)
elif fit_method == 'precomputed':
pass
return edit_cost_constants


def compute_distances_to_true_median(Gn_median, fname_sm, fname_gm,
gkernel, edit_cost_name,
Kmatrix_median=None):
# reform graphs.
set_median = loadGXL(fname_sm)
gen_median = loadGXL(fname_gm)
# print(gen_median.nodes(data=True))
# print(gen_median.edges(data=True))
if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2' or edit_cost_name == 'NON_SYMBOLIC':
# dataset == 'Fingerprint':
# for g in Gn_median:
# reform_attributes(g)
reform_attributes(set_median, Gn_median[0].graph['node_attrs'],
Gn_median[0].graph['edge_attrs'])
reform_attributes(gen_median, Gn_median[0].graph['node_attrs'],
Gn_median[0].graph['edge_attrs'])
if edit_cost_name == 'LETTER' or edit_cost_name == 'LETTER2' or edit_cost_name == 'NON_SYMBOLIC':
node_label = None
edge_label = None
else:
node_label = 'chem'
edge_label = 'valence'
# compute Gram matrix for median set.
if Kmatrix_median is None:
Kmatrix_median = compute_kernel(Gn_median, gkernel, node_label, edge_label, False)
# compute distance in kernel space for set median.
kernel_sm = []
for G_median in Gn_median:
km_tmp = compute_kernel([set_median, G_median], gkernel, node_label, edge_label, False)
kernel_sm.append(km_tmp[0, 1])
Kmatrix_sm = np.concatenate((np.array([kernel_sm]), np.copy(Kmatrix_median)), axis=0)
Kmatrix_sm = np.concatenate((np.array([[km_tmp[0, 0]] + kernel_sm]).T, Kmatrix_sm), axis=1)
# Kmatrix_sm = compute_kernel([set_median] + Gn_median, gkernel,
# node_label, edge_label, False)
dis_k_sm = dis_gstar(0, range(1, 1+len(Gn_median)),
[1 / len(Gn_median)] * len(Gn_median), Kmatrix_sm, withterm3=False)
# print(gen_median.nodes(data=True))
# print(gen_median.edges(data=True))
# print(set_median.nodes(data=True))
# print(set_median.edges(data=True))
# compute distance in kernel space for generalized median.
kernel_gm = []
for G_median in Gn_median:
km_tmp = compute_kernel([gen_median, G_median], gkernel, node_label, edge_label, False)
kernel_gm.append(km_tmp[0, 1])
Kmatrix_gm = np.concatenate((np.array([kernel_gm]), np.copy(Kmatrix_median)), axis=0)
Kmatrix_gm = np.concatenate((np.array([[km_tmp[0, 0]] + kernel_gm]).T, Kmatrix_gm), axis=1)
# Kmatrix_gm = compute_kernel([gen_median] + Gn_median, gkernel,
# node_label, edge_label, False)
dis_k_gm = dis_gstar(0, range(1, 1+len(Gn_median)),
[1 / len(Gn_median)] * len(Gn_median), Kmatrix_gm, withterm3=False)
# compute distance in kernel space for each graph in median set.
dis_k_gi = []
for idx in range(len(Gn_median)):
dis_k_gi.append(dis_gstar(idx+1, range(1, 1+len(Gn_median)),
[1 / len(Gn_median)] * len(Gn_median), Kmatrix_gm, withterm3=False))

print('dis_k_sm:', dis_k_sm)
print('dis_k_gm:', dis_k_gm)
print('dis_k_gi:', dis_k_gi)
idx_dis_k_gi_min = np.argmin(dis_k_gi)
dis_k_gi_min = dis_k_gi[idx_dis_k_gi_min]
print('min dis_k_gi:', dis_k_gi_min)
return dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min


def median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k, fit_method,
graph_dir=None, initial_solutions=1,
edit_cost_constants=None, group_min=None,
dataset=None, edit_cost_name=None, init_ecc=None,
Kmatrix=None, parallel=True):
# dataset = dataset.lower()
# # compute distances in kernel space.
# dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label,
# Kmatrix=None, gkernel=gkernel)
# # ged.
# gmfile = np.load('results/test_k_closest_graphs/ged_mat.fit_on_whole_dataset.with_medians.gm.npz')
# ged_mat = gmfile['ged_mat']
# dis_mat = ged_mat[0:len(Gn), 0:len(Gn)]
# # choose k closest graphs
# time0 = time.time()
# sod_ks_min, group_min = get_closest_k_graphs(dis_mat, k, parallel)
# time_spent = time.time() - time0
# print('closest graphs:', sod_ks_min, group_min)
# print('time spent:', time_spent)
# group_min = (12, 13, 22, 29) # closest w.r.t path kernel
# group_min = (77, 85, 160, 171) # closest w.r.t ged
# group_min = (0,1,2,3,4,5,6,7,8,9,10,11) # closest w.r.t treelet kernel
Gn_median = [Gn[g].copy() for g in group_min]
if Kmatrix is not None:
Kmatrix_median = np.copy(Kmatrix[group_min,:])
Kmatrix_median = Kmatrix_median[:,group_min]
else:
Kmatrix_median = None

# 1. fit edit cost constants.
time0 = time.time()
edit_cost_constants = fit_edit_cost_constants(fit_method, edit_cost_name,
edit_cost_constants=edit_cost_constants, initial_solutions=initial_solutions,
Gn_median=Gn_median, node_label=node_label, edge_label=edge_label,
gkernel=gkernel, dataset=dataset, init_ecc=init_ecc,
Gn=Gn, Kmatrix_median=Kmatrix_median)
time_fitting = time.time() - time0
# 2. compute set median and gen median using IAM (C++ through bash).
print('\nstart computing set median and gen median using IAM (C++ through bash)...\n')
group_fnames = [Gn[g].graph['filename'] for g in group_min]
time0 = time.time()
sod_sm, sod_gm, fname_sm, fname_gm = iam_bash(group_fnames, edit_cost_constants,
cost=edit_cost_name, initial_solutions=initial_solutions,
graph_dir=graph_dir, dataset=dataset)
time_generating = time.time() - time0
print('\nmedians computed.\n')
# 3. compute distances to the true median.
print('\nstart computing distances to true median....\n')
Gn_median = [Gn[g].copy() for g in group_min]
dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min = \
compute_distances_to_true_median(Gn_median, fname_sm, fname_gm,
gkernel, edit_cost_name,
Kmatrix_median=Kmatrix_median)
idx_dis_k_gi_min = group_min[idx_dis_k_gi_min]
print('index min dis_k_gi:', idx_dis_k_gi_min)
print('sod_sm:', sod_sm)
print('sod_gm:', sod_gm)
# collect return values.
return (sod_sm, sod_gm), \
(dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min), \
(time_fitting, time_generating)


def reform_attributes(G, na_names=[], ea_names=[]):
if not na_names == []:
for node in G.nodes:
G.nodes[node]['attributes'] = [G.node[node][a_name] for a_name in na_names]
if not ea_names == []:
for edge in G.edges:
G.edges[edge]['attributes'] = [G.edge[edge][a_name] for a_name in ea_names]


def get_closest_k_graphs(dis_mat, k, parallel):
k_graph_groups = combinations(range(0, len(dis_mat)), k)
sod_ks_min = np.inf
if parallel:
len_combination = get_combination_length(len(dis_mat), k)
len_itr_max = int(len_combination if len_combination < 1e7 else 1e7)
# pos_cur = 0
graph_groups_slices = split_iterable(k_graph_groups, len_itr_max, len_combination)
for graph_groups_cur in graph_groups_slices:
# while True:
# graph_groups_cur = islice(k_graph_groups, pos_cur, pos_cur + len_itr_max)
graph_groups_cur_list = list(graph_groups_cur)
print('current position:', graph_groups_cur_list[0])
len_itr_cur = len(graph_groups_cur_list)
# if len_itr_cur < len_itr_max:
# break

itr = zip(graph_groups_cur_list, range(0, len_itr_cur))
sod_k_list = np.empty(len_itr_cur)
graphs_list = [None] * len_itr_cur
n_jobs = multiprocessing.cpu_count()
chunksize = int(len_itr_max / n_jobs + 1)
n_jobs = multiprocessing.cpu_count()
def init_worker(dis_mat_toshare):
global G_dis_mat
G_dis_mat = dis_mat_toshare
pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(dis_mat,))
# iterator = tqdm(pool.imap_unordered(_get_closest_k_graphs_parallel,
# itr, chunksize),
# desc='Choosing k closest graphs', file=sys.stdout)
iterator = pool.imap_unordered(_get_closest_k_graphs_parallel, itr, chunksize)
for graphs, i, sod_ks in iterator:
sod_k_list[i] = sod_ks
graphs_list[i] = graphs
pool.close()
pool.join()
arg_min = np.argmin(sod_k_list)
sod_ks_cur = sod_k_list[arg_min]
group_cur = graphs_list[arg_min]
if sod_ks_cur < sod_ks_min:
sod_ks_min = sod_ks_cur
group_min = group_cur
print('get closer graphs:', sod_ks_min, group_min)
else:
for items in tqdm(k_graph_groups, desc='Choosing k closest graphs', file=sys.stdout):
# if items[0] != itmp:
# itmp = items[0]
# print(items)
k_graph_pairs = combinations(items, 2)
sod_ks = 0
for i1, i2 in k_graph_pairs:
sod_ks += dis_mat[i1, i2]
if sod_ks < sod_ks_min:
sod_ks_min = sod_ks
group_min = items
print('get closer graphs:', sod_ks_min, group_min)
return sod_ks_min, group_min


def _get_closest_k_graphs_parallel(itr):
k_graph_pairs = combinations(itr[0], 2)
sod_ks = 0
for i1, i2 in k_graph_pairs:
sod_ks += G_dis_mat[i1, i2]

return itr[0], itr[1], sod_ks

def split_iterable(iterable, n, len_iter):
it = iter(iterable)
for i in range(0, len_iter, n):
piece = islice(it, n)
yield piece


def get_combination_length(n, k):
len_combination = 1
for i in range(n, n - k, -1):
len_combination *= i
return int(len_combination / math.factorial(k))


###############################################################################

def test_k_closest_graphs():
ds = {'name': 'monoterpenoides',
'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
# gkernel = 'untilhpathkernel'
# gkernel = 'weisfeilerlehmankernel'
gkernel = 'treeletkernel'
node_label = 'atom'
edge_label = 'bond_type'
k = 5
edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297]
# sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \
# = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k,
# 'precomputed', edit_costs=edit_costs,
## 'k-graphs',
# parallel=False)
#
# sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \
# = median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k,
# 'expert', parallel=False)
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \
= median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel, k,
'expert', parallel=False)
return


def test_k_closest_graphs_with_cv():
gkernel = 'untilhpathkernel'
node_label = 'atom'
edge_label = 'bond_type'
k = 4
y_all = ['3', '1', '4', '6', '7', '8', '9', '2']
repeats = 50
collection_path = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/monoterpenoides/'
graph_dir = collection_path + 'gxl/'
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_list = []
for y in y_all:
print('\n-------------------------------------------------------')
print('class of y:', y)
sod_sm_list.append([])
sod_gm_list.append([])
dis_k_sm_list.append([])
dis_k_gm_list.append([])
dis_k_gi_min_list.append([])
for repeat in range(repeats):
print('\nrepeat ', repeat)
collection_file = collection_path + 'monoterpenoides_' + y + '_' + str(repeat) + '.xml'
Gn, _ = loadDataset(collection_file, extra_params=graph_dir)
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min \
= median_on_k_closest_graphs(Gn, node_label, edge_label, gkernel,
k, 'whole-dataset', graph_dir=graph_dir,
parallel=False)
sod_sm_list[-1].append(sod_sm)
sod_gm_list[-1].append(sod_gm)
dis_k_sm_list[-1].append(dis_k_sm)
dis_k_gm_list[-1].append(dis_k_gm)
dis_k_gi_min_list[-1].append(dis_k_gi_min)
print('\nsods of the set median for this class:', sod_sm_list[-1])
print('\nsods of the gen median for this class:', sod_gm_list[-1])
print('\ndistances in kernel space of set median for this class:',
dis_k_sm_list[-1])
print('\ndistances in kernel space of gen median for this class:',
dis_k_gm_list[-1])
print('\ndistances in kernel space of min graph for this class:',
dis_k_gi_min_list[-1])
sod_sm_list[-1] = np.mean(sod_sm_list[-1])
sod_gm_list[-1] = np.mean(sod_gm_list[-1])
dis_k_sm_list[-1] = np.mean(dis_k_sm_list[-1])
dis_k_gm_list[-1] = np.mean(dis_k_gm_list[-1])
dis_k_gi_min_list[-1] = np.mean(dis_k_gi_min_list[-1])
print()
print('\nmean sods of the set median for each class:', sod_sm_list)
print('\nmean sods of the gen median for each class:', sod_gm_list)
print('\nmean distance in kernel space of set median for each class:',
dis_k_sm_list)
print('\nmean distances in kernel space of gen median for each class:',
dis_k_gm_list)
print('\nmean distances in kernel space of min graph for each class:',
dis_k_gi_min_list)
print('\nmean sods of the set median of all:', np.mean(sod_sm_list))
print('\nmean sods of the gen median of all:', np.mean(sod_gm_list))
print('\nmean distances in kernel space of set median of all:',
np.mean(dis_k_sm_list))
print('\nmean distances in kernel space of gen median of all:',
np.mean(dis_k_gm_list))
print('\nmean distances in kernel space of min graph of all:',
np.mean(dis_k_gi_min_list))
return

if __name__ == '__main__':
test_k_closest_graphs()
# test_k_closest_graphs_with_cv()

+ 0
- 69
gklearn/preimage/test_median_preimage_generator.py View File

@@ -1,69 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 27 17:30:55 2020

@author: ljia
"""
import multiprocessing
import functools
from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
from gklearn.preimage import MedianPreimageGenerator
from gklearn.utils import Dataset


def test_median_preimage_generator():
# 1. set parameters.
print('1. setting parameters...')
ds_name = 'Letter-high'
mpg = MedianPreimageGenerator()
mpg_options = {'fit_method': 'k-graphs',
'init_ecc': [3, 3, 1, 3, 3],
'ds_name': 'Letter-high',
'parallel': True,
'time_limit_in_sec': 0,
'max_itrs': 100,
'max_itrs_without_update': 3,
'epsilon_ratio': 0.01,
'verbose': 2}
mpg.set_options(**mpg_options)
mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
mpg.kernel_options = {'name': 'structuralspkernel',
'edge_weight': None,
'node_kernels': sub_kernels,
'edge_kernels': sub_kernels,
'compute_method': 'naive',
'parallel': 'imap_unordered',
# 'parallel': None,
'n_jobs': multiprocessing.cpu_count(),
'normalize': True,
'verbose': 2}
mpg.ged_options = {'method': 'IPFP',
'initial_solutions': 40,
'edit_cost': 'LETTER2',
'attr_distance': 'euclidean',
'ratio_runs_from_initial_solutions': 1,
'threads': multiprocessing.cpu_count(),
'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
mpg.mge_options = {'init_type': 'MEDOID',
'random_inits': 10,
'time_limit': 600,
'verbose': 2,
'refine': False}
# 2. get dataset.
print('2. getting dataset...')
mpg.dataset = Dataset()
mpg.dataset.load_predefined_dataset(ds_name)
mpg.dataset.cut_graphs(range(0, 10))
# 3. compute median preimage.
print('3. computing median preimage...')
mpg.run()
if __name__ == '__main__':
test_median_preimage_generator()

+ 0
- 686
gklearn/preimage/test_others.py View File

@@ -1,686 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 4 12:20:16 2019

@author: ljia
"""
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import time
from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.median import draw_Letter_graph
from gklearn.preimage.ged import GED, ged_median
from gklearn.preimage.utils import get_same_item_indices, compute_kernel, gram2distances, \
dis_gstar, remove_edges


# --------------------------- These are tests --------------------------------#
def test_who_is_the_closest_in_kernel_space(Gn):
idx_gi = [0, 6]
g1 = Gn[idx_gi[0]]
g2 = Gn[idx_gi[1]]
# create the "median" graph.
gnew = g2.copy()
gnew.remove_node(0)
nx.draw_networkx(gnew)
plt.show()
print(gnew.nodes(data=True))
Gn = [gnew] + Gn
# compute gram matrix
Kmatrix = compute_kernel(Gn, 'untilhpathkernel', True)
# the distance matrix
dmatrix = gram2distances(Kmatrix)
print(np.sort(dmatrix[idx_gi[0] + 1]))
print(np.argsort(dmatrix[idx_gi[0] + 1]))
print(np.sort(dmatrix[idx_gi[1] + 1]))
print(np.argsort(dmatrix[idx_gi[1] + 1]))
# for all g in Gn, compute (d(g1, g) + d(g2, g)) / 2
dis_median = [(dmatrix[i, idx_gi[0] + 1] + dmatrix[i, idx_gi[1] + 1]) / 2 for i in range(len(Gn))]
print(np.sort(dis_median))
print(np.argsort(dis_median))
return


def test_who_is_the_closest_in_GED_space(Gn):
idx_gi = [0, 6]
g1 = Gn[idx_gi[0]]
g2 = Gn[idx_gi[1]]
# create the "median" graph.
gnew = g2.copy()
gnew.remove_node(0)
nx.draw_networkx(gnew)
plt.show()
print(gnew.nodes(data=True))
Gn = [gnew] + Gn
# compute GEDs
ged_matrix = np.zeros((len(Gn), len(Gn)))
for i1 in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout):
for i2 in range(len(Gn)):
dis, _, _ = GED(Gn[i1], Gn[i2], lib='gedlib')
ged_matrix[i1, i2] = dis
print(np.sort(ged_matrix[idx_gi[0] + 1]))
print(np.argsort(ged_matrix[idx_gi[0] + 1]))
print(np.sort(ged_matrix[idx_gi[1] + 1]))
print(np.argsort(ged_matrix[idx_gi[1] + 1]))
# for all g in Gn, compute (GED(g1, g) + GED(g2, g)) / 2
dis_median = [(ged_matrix[i, idx_gi[0] + 1] + ged_matrix[i, idx_gi[1] + 1]) / 2 for i in range(len(Gn))]
print(np.sort(dis_median))
print(np.argsort(dis_median))
return


def test_will_IAM_give_the_median_graph_we_wanted(Gn):
idx_gi = [0, 6]
g1 = Gn[idx_gi[0]].copy()
g2 = Gn[idx_gi[1]].copy()
# del Gn[idx_gi[0]]
# del Gn[idx_gi[1] - 1]
g_median = test_iam_with_more_graphs_as_init([g1, g2], [g1, g2], c_ei=1, c_er=1, c_es=1)
# g_median = test_iam_with_more_graphs_as_init(Gn, Gn, c_ei=1, c_er=1, c_es=1)
nx.draw_networkx(g_median)
plt.show()
print(g_median.nodes(data=True))
print(g_median.edges(data=True))
def test_new_IAM_allGraph_deleteNodes(Gn):
idx_gi = [0, 6]
# g1 = Gn[idx_gi[0]].copy()
# g2 = Gn[idx_gi[1]].copy()

# g1 = nx.Graph(name='haha')
# g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'})])
# g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'})])
# g2 = nx.Graph(name='hahaha')
# g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'}),
# (3, {'atom': 'O'}), (4, {'atom': 'C'})])
# g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
# (2, 3, {'bond_type': '1'}), (3, 4, {'bond_type': '1'})])
g1 = nx.Graph(name='haha')
g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}),
(3, {'atom': 'S'}), (4, {'atom': 'S'})])
g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
(2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})])
g2 = nx.Graph(name='hahaha')
g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}),
(3, {'atom': 'O'}), (4, {'atom': 'O'})])
g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
(2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})])

# g2 = g1.copy()
# g2.add_nodes_from([(3, {'atom': 'O'})])
# g2.add_nodes_from([(4, {'atom': 'C'})])
# g2.add_edges_from([(1, 3, {'bond_type': '1'})])
# g2.add_edges_from([(3, 4, {'bond_type': '1'})])

# del Gn[idx_gi[0]]
# del Gn[idx_gi[1] - 1]
nx.draw_networkx(g1)
plt.show()
print(g1.nodes(data=True))
print(g1.edges(data=True))
nx.draw_networkx(g2)
plt.show()
print(g2.nodes(data=True))
print(g2.edges(data=True))
g_median = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations([g1, g2], [g1, g2], c_ei=1, c_er=1, c_es=1)
# g_median = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations(Gn, Gn, c_ei=1, c_er=1, c_es=1)
nx.draw_networkx(g_median)
plt.show()
print(g_median.nodes(data=True))
print(g_median.edges(data=True))
def test_the_simple_two(Gn, gkernel):
from gk_iam import gk_iam_nearest_multi
lmbda = 0.03 # termination probalility
r_max = 10 # recursions
l = 500
alpha_range = np.linspace(0.5, 0.5, 1)
k = 2 # k nearest neighbors
# randomly select two molecules
np.random.seed(1)
idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2)
g1 = Gn[idx_gi[0]]
g2 = Gn[idx_gi[1]]
Gn_mix = [g.copy() for g in Gn]
Gn_mix.append(g1.copy())
Gn_mix.append(g2.copy())
# g_tmp = iam([g1, g2])
# nx.draw_networkx(g_tmp)
# plt.show()
# compute
# k_list = [] # kernel between each graph and itself.
# k_g1_list = [] # kernel between each graph and g1
# k_g2_list = [] # kernel between each graph and g2
# for ig, g in tqdm(enumerate(Gn), desc='computing self kernels', file=sys.stdout):
# ktemp = compute_kernel([g, g1, g2], 'marginalizedkernel', False)
# k_list.append(ktemp[0][0, 0])
# k_g1_list.append(ktemp[0][0, 1])
# k_g2_list.append(ktemp[0][0, 2])
km = compute_kernel(Gn_mix, gkernel, True)
# k_list = np.diag(km) # kernel between each graph and itself.
# k_g1_list = km[idx_gi[0]] # kernel between each graph and g1
# k_g2_list = km[idx_gi[1]] # kernel between each graph and g2

g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
dhat, ghat_list = gk_iam_nearest_multi(Gn, [g1, g2], [alpha, 1 - alpha],
range(len(Gn), len(Gn) + 2), km,
k, r_max,gkernel)
dis_best.append(dhat)
g_best.append(ghat_list)
for idx, item in enumerate(alpha_range):
print('when alpha is', item, 'the shortest distance is', dis_best[idx])
print('the corresponding pre-images are')
for g in g_best[idx]:
nx.draw_networkx(g)
plt.show()
print(g.nodes(data=True))
print(g.edges(data=True))
def test_remove_bests(Gn, gkernel):
from gk_iam import gk_iam_nearest_multi
lmbda = 0.03 # termination probalility
r_max = 10 # recursions
l = 500
alpha_range = np.linspace(0.5, 0.5, 1)
k = 20 # k nearest neighbors
# randomly select two molecules
np.random.seed(1)
idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2)
g1 = Gn[idx_gi[0]]
g2 = Gn[idx_gi[1]]
# remove the best 2 graphs.
del Gn[idx_gi[0]]
del Gn[idx_gi[1] - 1]
# del Gn[8]
Gn_mix = [g.copy() for g in Gn]
Gn_mix.append(g1.copy())
Gn_mix.append(g2.copy())

# compute
km = compute_kernel(Gn_mix, gkernel, True)
g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
dhat, ghat_list = gk_iam_nearest_multi(Gn, [g1, g2], [alpha, 1 - alpha],
range(len(Gn), len(Gn) + 2), km,
k, r_max, gkernel)
dis_best.append(dhat)
g_best.append(ghat_list)
for idx, item in enumerate(alpha_range):
print('when alpha is', item, 'the shortest distance is', dis_best[idx])
print('the corresponding pre-images are')
for g in g_best[idx]:
draw_Letter_graph(g)
# nx.draw_networkx(g)
# plt.show()
print(g.nodes(data=True))
print(g.edges(data=True))
###############################################################################
# Tests on dataset Letter-H.
def test_gkiam_letter_h():
from gk_iam import gk_iam_nearest_multi
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, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
gkernel = 'structuralspkernel'
lmbda = 0.03 # termination probalility
r_max = 3 # recursions
# alpha_range = np.linspace(0.5, 0.5, 1)
k = 10 # k nearest neighbors
# classify graphs according to letters.
idx_dict = get_same_item_indices(y_all)
time_list = []
sod_ks_min_list = []
sod_gs_list = []
sod_gs_min_list = []
nb_updated_list = []
for letter in idx_dict:
print('\n-------------------------------------------------------\n')
Gn_let = [Gn[i].copy() for i in idx_dict[letter]]
Gn_mix = Gn_let + [g.copy() for g in Gn_let]
alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1)
# compute
time0 = time.time()
km = compute_kernel(Gn_mix, gkernel, True)
g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn_let,
Gn_let, [alpha] * len(Gn_let), range(len(Gn_let), len(Gn_mix)),
km, k, r_max, gkernel, c_ei=1.7, c_er=1.7, c_es=1.7,
ged_cost='LETTER', ged_method='IPFP', saveGXL='gedlib-letter')
dis_best.append(dhat)
g_best.append(ghat_list)
time_list.append(time.time() - time0)
# 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_best[idx])
print('the corresponding pre-images are')
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. (alpha range not considered.)
sod_tmp, _ = ged_median(g_best[0], Gn_let, ged_cost='LETTER',
ged_method='IPFP', saveGXL='gedlib-letter')
sod_gs_list.append(sod_tmp)
sod_gs_min_list.append(np.min(sod_tmp))
sod_ks_min_list.append(sod_ks)
nb_updated_list.append(nb_updated)
print('\nsods in graph space: ', sod_gs_list)
print('\nsmallest sod in graph space for each letter: ', sod_gs_min_list)
print('\nsmallest sod in kernel space for each letter: ', sod_ks_min_list)
print('\nnumber of updates for each letter: ', nb_updated_list)
print('\ntimes:', time_list)

#def compute_letter_median_by_average(Gn):
# return g_median

def test_iam_letter_h():
from iam import test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations
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, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
lmbda = 0.03 # termination probalility
# alpha_range = np.linspace(0.5, 0.5, 1)
# classify graphs according to letters.
idx_dict = get_same_item_indices(y_all)
time_list = []
sod_list = []
sod_min_list = []
for letter in idx_dict:
Gn_let = [Gn[i].copy() for i in idx_dict[letter]]
alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1)
# compute
g_best = []
dis_best = []
time0 = time.time()
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
ghat_list, dhat = test_iam_moreGraphsAsInit_tryAllPossibleBestGraphs_deleteNodesInIterations(
Gn_let, Gn_let, c_ei=1.7, c_er=1.7, c_es=1.7,
ged_cost='LETTER', ged_method='IPFP', saveGXL='gedlib-letter')
dis_best.append(dhat)
g_best.append(ghat_list)
time_list.append(time.time() - time0)
# 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_best[idx])
print('the corresponding pre-images are')
for g in g_best[idx]:
draw_Letter_graph(g, savepath='results/iam/')
# nx.draw_networkx(g)
# plt.show()
print(g.nodes(data=True))
print(g.edges(data=True))
# compute the corresponding sod in kernel space. (alpha range not considered.)
gkernel = 'structuralspkernel'
sod_tmp = []
Gn_mix = g_best[0] + Gn_let
km = compute_kernel(Gn_mix, gkernel, True)
for ig, g in tqdm(enumerate(g_best[0]), desc='computing kernel sod', file=sys.stdout):
dtemp = dis_gstar(ig, range(len(g_best[0]), len(Gn_mix)),
[alpha_range[0]] * len(Gn_let), km, withterm3=False)
sod_tmp.append(dtemp)
sod_list.append(sod_tmp)
sod_min_list.append(np.min(sod_tmp))
print('\nsods in kernel space: ', sod_list)
print('\nsmallest sod in kernel space for each letter: ', sod_min_list)
print('\ntimes:', time_list)
def test_random_preimage_letter_h():
from preimage_random import preimage_random
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
# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
# 'extra_params': {}} # node/edge symb
# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds',
# 'extra_params': {}}
# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',
# 'extra_params': {}} # node symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
gkernel = 'structuralspkernel'
# lmbda = 0.03 # termination probalility
r_max = 3 # 10 # recursions
l = 500
# alpha_range = np.linspace(0.5, 0.5, 1)
#alpha_range = np.linspace(0.1, 0.9, 9)
k = 10 # 5 # k nearest neighbors
# classify graphs according to letters.
idx_dict = get_same_item_indices(y_all)
time_list = []
sod_list = []
sod_min_list = []
for letter in idx_dict:
print('\n-------------------------------------------------------\n')
Gn_let = [Gn[i].copy() for i in idx_dict[letter]]
Gn_mix = Gn_let + [g.copy() for g in Gn_let]
alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1)
# compute
time0 = time.time()
km = compute_kernel(Gn_mix, gkernel, True)
g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
dhat, ghat_list = preimage_random(Gn_let, Gn_let, [alpha] * len(Gn_let),
range(len(Gn_let), len(Gn_mix)), km,
k, r_max, gkernel, c_ei=1.7,
c_er=1.7, c_es=1.7)
dis_best.append(dhat)
g_best.append(ghat_list)
time_list.append(time.time() - time0)
# 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_best[idx])
print('the corresponding pre-images are')
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. (alpha range not considered.)
sod_tmp, _ = ged_median(g_best[0], Gn_let)
sod_list.append(sod_tmp)
sod_min_list.append(np.min(sod_tmp))
print('\nsods in graph space: ', sod_list)
print('\nsmallest sod in graph space for each letter: ', sod_min_list)
print('\ntimes:', time_list)

def test_gkiam_mutag():
from gk_iam import gk_iam_nearest_multi
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, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
gkernel = 'structuralspkernel'
lmbda = 0.03 # termination probalility
r_max = 3 # recursions
# alpha_range = np.linspace(0.5, 0.5, 1)
k = 20 # k nearest neighbors
# classify graphs according to letters.
idx_dict = get_same_item_indices(y_all)
time_list = []
sod_ks_min_list = []
sod_gs_list = []
sod_gs_min_list = []
nb_updated_list = []
for letter in idx_dict:
print('\n-------------------------------------------------------\n')
Gn_let = [Gn[i].copy() for i in idx_dict[letter]]
Gn_mix = Gn_let + [g.copy() for g in Gn_let]
alpha_range = np.linspace(1 / len(Gn_let), 1 / len(Gn_let), 1)
# compute
time0 = time.time()
km = compute_kernel(Gn_mix, gkernel, True)
g_best = []
dis_best = []
# for each alpha
for alpha in alpha_range:
print('alpha =', alpha)
dhat, ghat_list, sod_ks, nb_updated = gk_iam_nearest_multi(Gn_let, Gn_let, [alpha] * len(Gn_let),
range(len(Gn_let), len(Gn_mix)), km,
k, r_max, gkernel, c_ei=1.7,
c_er=1.7, c_es=1.7)
dis_best.append(dhat)
g_best.append(ghat_list)
time_list.append(time.time() - time0)
# 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_best[idx])
print('the corresponding pre-images are')
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. (alpha range not considered.)
sod_tmp, _ = ged_median(g_best[0], Gn_let)
sod_gs_list.append(sod_tmp)
sod_gs_min_list.append(np.min(sod_tmp))
sod_ks_min_list.append(sod_ks)
nb_updated_list.append(nb_updated)
print('\nsods in graph space: ', sod_gs_list)
print('\nsmallest sod in graph space for each letter: ', sod_gs_min_list)
print('\nsmallest sod in kernel space for each letter: ', sod_ks_min_list)
print('\nnumber of updates for each letter: ', nb_updated_list)
print('\ntimes:', time_list)
###############################################################################
# Re-test.
def retest_the_simple_two():
from gk_iam import gk_iam_nearest_multi
# The two simple graphs.
# g1 = nx.Graph(name='haha')
# g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'})])
# g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'})])
# g2 = nx.Graph(name='hahaha')
# g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'O'}), (2, {'atom': 'C'}),
# (3, {'atom': 'O'}), (4, {'atom': 'C'})])
# g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
# (2, 3, {'bond_type': '1'}), (3, 4, {'bond_type': '1'})])
g1 = nx.Graph(name='haha')
g1.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}),
(3, {'atom': 'S'}), (4, {'atom': 'S'})])
g1.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
(2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})])
g2 = nx.Graph(name='hahaha')
g2.add_nodes_from([(0, {'atom': 'C'}), (1, {'atom': 'C'}), (2, {'atom': 'C'}),
(3, {'atom': 'O'}), (4, {'atom': 'O'})])
g2.add_edges_from([(0, 1, {'bond_type': '1'}), (1, 2, {'bond_type': '1'}),
(2, 3, {'bond_type': '1'}), (2, 4, {'bond_type': '1'})])
# # randomly select two molecules
# np.random.seed(1)
# idx_gi = [0, 6] # np.random.randint(0, len(Gn), 2)
# g1 = Gn[idx_gi[0]]
# g2 = Gn[idx_gi[1]]
# Gn_mix = [g.copy() for g in Gn]
# Gn_mix.append(g1.copy())
# Gn_mix.append(g2.copy())
Gn = [g1.copy(), g2.copy()]
remove_edges(Gn)
gkernel = 'marginalizedkernel'
lmbda = 0.03 # termination probalility
r_max = 10 # recursions
# l = 500
alpha_range = np.linspace(0.5, 0.5, 1)
k = 2 # k nearest neighbors
epsilon = 1e-6
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
c_ei=1
c_er=1
c_es=1
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 = []
sod_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)
sod_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', sod_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 sod in kernel space for each alpha: ', sod_ks_min_list)
print('\nnumber of updates for each alpha: ', nb_updated_list)
print('\ntimes:', time_list)

if __name__ == '__main__':
# ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
# 'extra_params': {}} # node/edge symb
# ds = {'name': 'Letter-high', 'dataset': '../datasets/Letter-high/Letter-high_A.txt',
# 'extra_params': {}} # node nsymb
# ds = {'name': 'Acyclic', 'dataset': '../datasets/monoterpenoides/trainset_9.ds',
# 'extra_params': {}}
# ds = {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',
# 'extra_params': {}} # node symb
# Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
# Gn = Gn[0:20]
# import networkx.algorithms.isomorphism as iso
# G1 = nx.MultiDiGraph()
# G2 = nx.MultiDiGraph()
# G1.add_nodes_from([1,2,3], fill='red')
# G2.add_nodes_from([10,20,30,40], fill='red')
# nx.add_path(G1, [1,2,3,4], weight=3, linewidth=2.5)
# nx.add_path(G2, [10,20,30,40], weight=3)
# nm = iso.categorical_node_match('fill', 'red')
# print(nx.is_isomorphic(G1, G2, node_match=nm))
#
# test_new_IAM_allGraph_deleteNodes(Gn)
# test_will_IAM_give_the_median_graph_we_wanted(Gn)
# test_who_is_the_closest_in_GED_space(Gn)
# test_who_is_the_closest_in_kernel_space(Gn)
# test_the_simple_two(Gn, 'untilhpathkernel')
# test_remove_bests(Gn, 'untilhpathkernel')
# test_gkiam_letter_h()
# test_iam_letter_h()
# test_random_preimage_letter_h
###############################################################################
# retests.
retest_the_simple_two()

+ 0
- 620
gklearn/preimage/test_preimage_iam.py View File

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 15:59:00 2019

@author: ljia
"""

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import time
import random
#from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.utils import remove_edges, compute_kernel, get_same_item_indices
from gklearn.preimage.ged import ged_median

from gklearn.preimage.preimage_iam import preimage_iam


###############################################################################
# tests on different values on grid of median-sets and k.

def test_preimage_iam_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.
# alpha_range = np.linspace(0.5, 0.5, 1)
# k = 5 # k nearest neighbors
epsilon = 1e-6
InitIAMWithAllDk = True
# parameters for GED function
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
# parameters for IAM function
c_ei=1
c_er=1
c_es=1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = True
connected_iam = False
# number of graphs; we what to compute the median of these graphs.
nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100]
# 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 = []
nb_updated_k_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([])
nb_updated_k_list.append([])
g_best.append([])
for k in k_range:
print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n')
print('k =', k)
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={'ged_cost': ged_cost, 'ged_method': ged_method,
'saveGXL': saveGXL})
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_list)
print('\nnumber of updates of the best graph by IAM: ', nb_updated)
nb_updated_list[idx_nb].append(nb_updated)
print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k)
nb_updated_k_list[idx_nb].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.savefig('results/preimage_iam/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_list[0]], 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('\nnumber of updates of k nearest graphs for each set of median graphs and k by IAM: ',
nb_updated_k_list)
print('\ntimes:', time_list)


###############################################################################
# tests on different numbers of median-sets.

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)

###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)

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_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)
###############################################################################

if __name__ == '__main__':
###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)
# test_gkiam_2combination()
# test_gkiam_2combination_all_pairs()
###############################################################################
# tests on different numbers of median-sets.
test_preimage_iam_median_nb()
###############################################################################
# tests on different values on grid of median-sets and k.
# test_preimage_iam_grid_k_median_nb()

+ 0
- 539
gklearn/preimage/test_preimage_mix.py View File

@@ -1,539 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 15:59:00 2019

@author: ljia
"""

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import time
import random
#from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.ged import ged_median
from gklearn.preimage.utils import compute_kernel, get_same_item_indices, remove_edges
from gklearn.preimage.preimage_iam import preimage_iam_random_mix

###############################################################################
# tests on different values on grid of median-sets and k.

def test_preimage_mix_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_max = 500 # update limit for random generation
# alpha_range = np.linspace(0.5, 0.5, 1)
# k = 5 # k nearest neighbors
epsilon = 1e-6
InitIAMWithAllDk = True
InitRandomWithAllDk = True
# parameters for GED function
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
# parameters for IAM function
c_ei=1
c_er=1
c_es=1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = True
connected_iam = False
# number of graphs; we what to compute the median of these graphs.
nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100]
# 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_iam = []
nb_updated_list_random = []
nb_updated_k_list_iam = []
nb_updated_k_list_random = []
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_iam.append([])
nb_updated_list_random.append([])
nb_updated_k_list_iam.append([])
nb_updated_k_list_random.append([])
g_best.append([])
for k in k_range:
print('\n++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n')
print('k =', k)
time0 = time.time()
dhat, ghat_list, dis_of_each_itr, nb_updated_iam, nb_updated_random, \
nb_updated_k_iam, nb_updated_k_random = \
preimage_iam_random_mix(Gn, Gn_median,
alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max,
l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk,
InitRandomWithAllDk=InitRandomWithAllDk,
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[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_list)
print('\nnumber of updates of the best graph by IAM: ', nb_updated_iam)
nb_updated_list_iam[idx_nb].append(nb_updated_iam)
print('\nnumber of updates of the best graph by random generation: ',
nb_updated_random)
nb_updated_list_random[idx_nb].append(nb_updated_random)
print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k_iam)
nb_updated_k_list_iam[idx_nb].append(nb_updated_k_iam)
print('\nnumber of updates of k nearest graphs by random generation: ',
nb_updated_k_random)
nb_updated_k_list_random[idx_nb].append(nb_updated_k_random)
# 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.savefig('results/preimage_mix/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_list[0]], 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_iam)
print('\nnumber of updates of the best graph for each set of median graphs and k by random generation: ',
nb_updated_list_random)
print('\nnumber of updates of k nearest graphs for each set of median graphs and k by IAM: ',
nb_updated_k_list_iam)
print('\nnumber of updates of k nearest graphs for each set of median graphs and k by random generation: ',
nb_updated_k_list_random)
print('\ntimes:', time_list)


###############################################################################
# tests on different numbers of median-sets.

def test_preimage_mix_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_max = 500 # update limit for random generation
# alpha_range = np.linspace(0.5, 0.5, 1)
k = 5 # k nearest neighbors
epsilon = 1e-6
InitIAMWithAllDk = True
InitRandomWithAllDk = True
# parameters for GED function
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
# parameters for IAM function
c_ei=1
c_er=1
c_es=1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = True
connected_iam = False
# number of graphs; we what to compute the median of these graphs.
nb_median_range = [2, 3, 4, 5, 10, 20, 30, 40, 50, 100]
# find out all the graphs classified to positive group 1.
idx_dict = get_same_item_indices(y_all)
Gn = [Gn[i] for i in idx_dict[1]]
# # compute Gram matrix.
# time0 = time.time()
# km = compute_kernel(Gn, gkernel, True)
# time_km = time.time() - time0
# # write Gram matrix to file.
# np.savez('results/gram_matrix_marg_itr10_pq0.03_mutag_positive.gm', gm=km, gmtime=time_km)
time_list = []
dis_ks_min_list = []
sod_gs_list = []
sod_gs_min_list = []
nb_updated_list_iam = []
nb_updated_list_random = []
nb_updated_k_list_iam = []
nb_updated_k_list_random = []
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_iam, nb_updated_random, \
nb_updated_k_iam, nb_updated_k_random = \
preimage_iam_random_mix(Gn, Gn_median,
alpha_range, range(len(Gn), len(Gn) + nb_median), km, k, r_max,
l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk,
InitRandomWithAllDk=InitRandomWithAllDk,
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)
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 by IAM: ', nb_updated_iam)
nb_updated_list_iam.append(nb_updated_iam)
print('\nnumber of updates of the best graph by random generation: ',
nb_updated_random)
nb_updated_list_random.append(nb_updated_random)
print('\nnumber of updates of k nearest graphs by IAM: ', nb_updated_k_iam)
nb_updated_k_list_iam.append(nb_updated_k_iam)
print('\nnumber of updates of k nearest graphs by random generation: ',
nb_updated_k_random)
nb_updated_k_list_random.append(nb_updated_k_random)
# 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.savefig('results/preimage_mix/mutag_median_nb' + str(nb_median) +
'.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_list[0]], Gn_median, ged_cost=ged_cost,
ged_method=ged_method, saveGXL=saveGXL)
sod_gs_list.append(sod_tmp)
sod_gs_min_list.append(np.min(sod_tmp))
print('\nsmallest sod in graph space: ', np.min(sod_tmp))
print('\nsods in graph space: ', sod_gs_list)
print('\nsmallest sod in graph space for each set of median graphs: ', sod_gs_min_list)
print('\nsmallest distance in kernel space for each set of median graphs: ',
dis_ks_min_list)
print('\nnumber of updates of the best graph for each set of median graphs by IAM: ',
nb_updated_list_iam)
print('\nnumber of updates of the best graph for each set of median graphs by random generation: ',
nb_updated_list_random)
print('\nnumber of updates of k nearest graphs for each set of median graphs by IAM: ',
nb_updated_k_list_iam)
print('\nnumber of updates of k nearest graphs for each set of median graphs by random generation: ',
nb_updated_k_list_random)
print('\ntimes:', time_list)

###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)

def test_preimage_mix_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.
l_max = 500 # update limit for random generation
alpha_range = np.linspace(0.5, 0.5, 1)
k = 5 # k nearest neighbors
epsilon = 1e-6
InitIAMWithAllDk = True
InitRandomWithAllDk = True
# parameters for GED function
ged_cost='CHEM_1'
ged_method='IPFP'
saveGXL='gedlib'
# parameters for IAM function
c_ei=1
c_er=1
c_es=1
ite_max_iam = 50
epsilon_iam = 0.001
removeNodes = True
connected_iam = False
nb_update_mat_iam = np.full((len(Gn), len(Gn)), np.inf)
nb_update_mat_random = 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/preimage_mix/mutag187.png", format="PNG")
plt.show()
plt.clf()
nx.draw(g2, labels=nx.get_node_attributes(g2, 'atom'), with_labels=True)
plt.savefig("results/preimage_mix/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_iam = []
nb_updated_list_random = []
nb_updated_k_list_iam = []
nb_updated_k_list_random = []
g_best = []
# for each alpha
for alpha in alpha_range:
print('\n-------------------------------------------------------\n')
print('alpha =', alpha)
time0 = time.time()
dhat, ghat_list, dis_of_each_itr, nb_updated_iam, nb_updated_random, \
nb_updated_k_iam, nb_updated_k_random = \
preimage_iam_random_mix(Gn, [g1, g2],
[alpha, 1 - alpha], range(len(Gn), len(Gn) + 2), km, k, r_max,
l_max, gkernel, epsilon=epsilon, InitIAMWithAllDk=InitIAMWithAllDk,
InitRandomWithAllDk=InitRandomWithAllDk,
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_iam.append(nb_updated_iam)
nb_updated_list_random.append(nb_updated_random)
nb_updated_k_list_iam.append(nb_updated_k_iam)
nb_updated_k_list_random.append(nb_updated_k_random)
# 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/preimage_mix/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 by IAM: ', nb_updated_list_iam)
print('\nnumber of updates of the best graph for each alpha by random generation: ',
nb_updated_list_random)
print('\nnumber of updates of k nearest graphs for each alpha by IAM: ',
nb_updated_k_list_iam)
print('\nnumber of updates of k nearest graphs for each alpha by random generation: ',
nb_updated_k_list_random)
print('\ntimes:', time_list)
nb_update_mat_iam[idx1, idx2] = nb_updated_list_iam[0]
nb_update_mat_random[idx1, idx2] = nb_updated_list_random[0]
str_fw = 'graphs %d and %d: %d times by IAM, %d times by random generation.\n' \
% (idx1, idx2, nb_updated_list_iam[0], nb_updated_list_random[0])
with open('results/preimage_mix/nb_updates.txt', 'r+') as file:
content = file.read()
file.seek(0, 0)
file.write(str_fw + content)
###############################################################################

if __name__ == '__main__':
###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)
# test_preimage_mix_2combination_all_pairs()
###############################################################################
# tests on different numbers of median-sets.
# test_preimage_mix_median_nb()
###############################################################################
# tests on different values on grid of median-sets and k.
test_preimage_mix_grid_k_median_nb()

+ 0
- 398
gklearn/preimage/test_preimage_random.py View File

@@ -1,398 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 5 15:59:00 2019

@author: ljia
"""

import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import time
import random
#from tqdm import tqdm

from gklearn.utils.graphfiles import loadDataset
from gklearn.preimage.preimage_random import preimage_random
from gklearn.preimage.ged import ged_median
from gklearn.preimage.utils import compute_kernel, get_same_item_indices, remove_edges


###############################################################################
# tests on different values on grid of median-sets and k.

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)



###############################################################################
# tests on different numbers of median-sets.

def test_preimage_random_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]
# 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 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, 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.append(time_total)
print('\nsmallest distance in kernel space: ', dhat)
dis_ks_min_list.append(dhat)
g_best.append(ghat)
print('\nnumber of updates of the best graph: ', nb_updated)
nb_updated_list.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) +
'.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.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: ',
nb_updated_list)
print('\ntimes:', time_list)

###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)
def test_random_preimage_2combination():
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[0:12]
remove_edges(Gn)
gkernel = 'marginalizedkernel'
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, gkernel=gkernel)
# print(dis_max, dis_min, dis_mean)
lmbda = 0.03 # termination probalility
r_max = 10 # iteration limit for pre-image.
l = 500
alpha_range = np.linspace(0, 1, 11)
k = 5 # k nearest neighbors
# randomly select two molecules
np.random.seed(1)
idx_gi = [187, 167] # np.random.randint(0, len(Gn), 2)
g1 = Gn[idx_gi[0]].copy()
g2 = Gn[idx_gi[1]].copy()
# 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())
#
## g_tmp = iam([g1, g2])
## nx.draw_networkx(g_tmp)
## plt.show()
#
# # compute
# time0 = time.time()
# km = compute_kernel(Gn_mix, gkernel, True)
# time_km = time.time() - time0
###################################################################
idx1 = idx_gi[0]
idx2 = idx_gi[1]
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]
###################################################################

time_list = []
nb_updated_list = []
g_best = []
dis_ks_min_list = []
# for each alpha
for alpha in alpha_range:
print('\n-------------------------------------------------------\n')
print('alpha =', alpha)
time0 = time.time()
dhat, ghat, nb_updated = preimage_random(Gn, [g1, g2], [alpha, 1 - alpha],
range(len(Gn), len(Gn) + 2), km,
k, r_max, l, gkernel)
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)
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], labels=nx.get_node_attributes(g_best[idx], 'atom'),
with_labels=True)
plt.show()
plt.savefig('results/random_preimage/mutag_alpha' + str(item) + '.png', format="PNG")
plt.clf()
print(g_best[idx].nodes(data=True))
print(g_best[idx].edges(data=True))
# # compute the corresponding sod in graph space. (alpha range not considered.)
# sod_tmp, _ = median_distance(g_best[0], Gn_let)
# sod_gs_list.append(sod_tmp)
# sod_gs_min_list.append(np.min(sod_tmp))
# sod_ks_min_list.append(sod_ks)
# nb_updated_list.append(nb_updated)
# 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)
###############################################################################

if __name__ == '__main__':
###############################################################################
# test on the combination of the two randomly chosen graphs. (the same as in the
# random pre-image paper.)
# test_random_preimage_2combination()
###############################################################################
# tests all algorithms on different numbers of median-sets.
test_preimage_random_median_nb()
###############################################################################
# tests all algorithms on different values on grid of median-sets and k.
# test_preimage_random_grid_k_median_nb()

+ 0
- 935
gklearn/preimage/xp_fit_method.py View File

@@ -1,935 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 14 15:39:29 2020

@author: ljia
"""
import numpy as np
import random
import csv
from shutil import copyfile
import networkx as nx
import matplotlib.pyplot as plt
import os
import time

from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL
from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes
from gklearn.preimage.utils import get_same_item_indices, kernel_distance_matrix, compute_kernel
from gklearn.preimage.find_best_k import getRelations


def get_dataset(ds_name):
if ds_name == 'Letter-high': # node non-symb
dataset = 'cpp_ext/data/collections/Letter.xml'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'
Gn, y_all = loadDataset(dataset, extra_params=graph_dir)
for G in Gn:
reform_attributes(G, na_names=['x', 'y'])
G.graph['node_labels'] = []
G.graph['edge_labels'] = []
G.graph['node_attrs'] = ['x', 'y']
G.graph['edge_attrs'] = []
elif ds_name == 'Letter-med': # node non-symb
dataset = 'cpp_ext/data/collections/Letter.xml'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/MED/'
Gn, y_all = loadDataset(dataset, extra_params=graph_dir)
for G in Gn:
reform_attributes(G, na_names=['x', 'y'])
G.graph['node_labels'] = []
G.graph['edge_labels'] = []
G.graph['node_attrs'] = ['x', 'y']
G.graph['edge_attrs'] = []
elif ds_name == 'Letter-low': # node non-symb
dataset = 'cpp_ext/data/collections/Letter.xml'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/LOW/'
Gn, y_all = loadDataset(dataset, extra_params=graph_dir)
for G in Gn:
reform_attributes(G, na_names=['x', 'y'])
G.graph['node_labels'] = []
G.graph['edge_labels'] = []
G.graph['node_attrs'] = ['x', 'y']
G.graph['edge_attrs'] = []
elif ds_name == 'Fingerprint':
# dataset = 'cpp_ext/data/collections/Fingerprint.xml'
# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/Fingerprint/node_attrs/'
# Gn, y_all = loadDataset(dataset, extra_params=graph_dir)
# for G in Gn:
# reform_attributes(G)
dataset = '../../datasets/Fingerprint/Fingerprint_A.txt'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/Fingerprint/node_attrs/'
Gn, y_all = loadDataset(dataset)
elif ds_name == 'SYNTHETIC':
pass
elif ds_name == 'SYNTHETICnew':
dataset = '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/SYNTHETICnew'
# dataset = '../../datasets/Letter-high/Letter-high_A.txt'
# graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'
Gn, y_all = loadDataset(dataset)
elif ds_name == 'Synthie':
pass
elif ds_name == 'COIL-DEL':
dataset = '../../datasets/COIL-DEL/COIL-DEL_A.txt'
graph_dir = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/generated_datsets/COIL-DEL/'
Gn, y_all = loadDataset(dataset)
elif ds_name == 'COIL-RAG':
pass
elif ds_name == 'COLORS-3':
pass
elif ds_name == 'FRANKENSTEIN':
pass
return Gn, y_all, graph_dir


def init_output_file(ds_name, gkernel, fit_method, dir_output):
# fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.csv'
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'edit cost',
'GED method', 'attr distance', 'fit method', 'k',
'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', 'fitting time', 'generating time', 'total time',
'median set'])
f_detail.close()
# fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.csv'
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'edit cost',
'GED method', 'attr distance', 'fit method', 'k',
'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', 'fitting time', 'generating time', 'total time',
'# SOD SM -> GM', '# dis_k SM -> GM',
'# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM',
'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM',
'repeats better dis_k gi -> GM'])
f_summary.close()
return fn_output_detail, fn_output_summary


def xp_fit_method_for_non_symbolic(parameters, save_results=True, initial_solutions=1,
Gn_data=None, k_dis_data=None, Kmatrix=None,
is_separate=False):
# 1. set parameters.
print('1. setting parameters...')
ds_name = parameters['ds_name']
gkernel = parameters['gkernel']
edit_cost_name = parameters['edit_cost_name']
ged_method = parameters['ged_method']
attr_distance = parameters['attr_distance']
fit_method = parameters['fit_method']
init_ecc = parameters['init_ecc']

node_label = None
edge_label = None
dir_output = 'results/xp_fit_method/'
# 2. get dataset.
print('2. getting dataset...')
if Gn_data is None:
Gn, y_all, graph_dir = get_dataset(ds_name)
else:
Gn = Gn_data[0]
y_all = Gn_data[1]
graph_dir = Gn_data[2]
# 3. compute kernel distance matrix.
print('3. computing kernel distance matrix...')
if k_dis_data is None:
dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None,
None, Kmatrix=Kmatrix, gkernel=gkernel)
else:
# dis_mat = k_dis_data[0]
# dis_max = k_dis_data[1]
# dis_min = k_dis_data[2]
# dis_mean = k_dis_data[3]
# print('pair distances - dis_max, dis_min, dis_mean:', dis_max, dis_min, dis_mean)
pass


if save_results:
# create result files.
print('creating output files...')
fn_output_detail, fn_output_summary = init_output_file(ds_name, gkernel,
fit_method, dir_output)

# start repeats.
repeats = 1
# k_list = range(2, 11)
k_list = [0]
# get indices by classes.
y_idx = get_same_item_indices(y_all)
random.seed(1)
rdn_seed_list = random.sample(range(0, repeats * 100), repeats)
for k in k_list:
# print('\n--------- k =', k, '----------')
sod_sm_mean_list = []
sod_gm_mean_list = []
dis_k_sm_mean_list = []
dis_k_gm_mean_list = []
dis_k_gi_min_mean_list = []
time_fitting_mean_list = []
time_generating_mean_list = []
time_total_mean_list = []
# 3. start generating and computing over targets.
print('4. starting generating and computing over targets......')
for i, (y, values) in enumerate(y_idx.items()):
# y = 'I'
# values = y_idx[y]
# values = values[0:10]
print('\ny =', y)
# if y.strip() == 'A':
# continue
k = len(values)
print('\n--------- k =', k, '----------')
if k < 2:
print('\nk = ', k, ', skip.\n')
continue
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_list = []
time_fitting_list = []
time_generating_list = []
time_total_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]
repeats_better_sod_sm2gm = []
repeats_better_dis_k_sm2gm = []
repeats_better_dis_k_gi2sm = []
repeats_better_dis_k_gi2gm = []
# get Gram matrix for this part of data.
if Kmatrix is not None:
if is_separate:
Kmatrix_sub = Kmatrix[i].copy()
else:
Kmatrix_sub = Kmatrix[values,:]
Kmatrix_sub = Kmatrix_sub[:,values]
else:
Kmatrix_sub = None
for repeat in range(repeats):
print('\nrepeat =', repeat)
random.seed(rdn_seed_list[repeat])
median_set_idx_idx = random.sample(range(0, len(values)), k)
median_set_idx = [values[idx] for idx in median_set_idx_idx]
print('median set: ', median_set_idx)
Gn_median = [Gn[g] for g in values]
# from notebooks.utils.plot_all_graphs import draw_Fingerprint_graph
# for Gn in Gn_median:
# draw_Fingerprint_graph(Gn, save=None)
# GENERATING & COMPUTING!!
res_sods, res_dis_ks, res_times = median_on_k_closest_graphs(Gn_median,
node_label, edge_label,
gkernel, k, fit_method=fit_method, graph_dir=graph_dir,
edit_cost_constants=None, group_min=median_set_idx_idx,
dataset=ds_name, initial_solutions=initial_solutions,
edit_cost_name=edit_cost_name, init_ecc=init_ecc,
Kmatrix=Kmatrix_sub, parallel=False)
sod_sm = res_sods[0]
sod_gm = res_sods[1]
dis_k_sm = res_dis_ks[0]
dis_k_gm = res_dis_ks[1]
dis_k_gi = res_dis_ks[2]
dis_k_gi_min = res_dis_ks[3]
idx_dis_k_gi_min = res_dis_ks[4]
time_fitting = res_times[0]
time_generating = res_times[1]
# write result detail.
sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm))
dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm))
dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min))
dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min))
if save_results:
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow([ds_name, gkernel,
edit_cost_name, ged_method, attr_distance,
fit_method, k, y, repeat,
sod_sm, sod_gm, dis_k_sm, dis_k_gm,
dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm,
dis_k_gi2gm, time_fitting, time_generating,
time_fitting + time_generating, median_set_idx])
f_detail.close()
# compute result summary.
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
dis_k_sm_list.append(dis_k_sm)
dis_k_gm_list.append(dis_k_gm)
dis_k_gi_min_list.append(dis_k_gi_min)
time_fitting_list.append(time_fitting)
time_generating_list.append(time_generating)
time_total_list.append(time_fitting + time_generating)
# # SOD SM -> GM
if sod_sm > sod_gm:
nb_sod_sm2gm[0] += 1
repeats_better_sod_sm2gm.append(repeat)
elif sod_sm == sod_gm:
nb_sod_sm2gm[1] += 1
elif sod_sm < sod_gm:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if dis_k_sm > dis_k_gm:
nb_dis_k_sm2gm[0] += 1
repeats_better_dis_k_sm2gm.append(repeat)
elif dis_k_sm == dis_k_gm:
nb_dis_k_sm2gm[1] += 1
elif dis_k_sm < dis_k_gm:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if dis_k_gi_min > dis_k_sm:
nb_dis_k_gi2sm[0] += 1
repeats_better_dis_k_gi2sm.append(repeat)
elif dis_k_gi_min == dis_k_sm:
nb_dis_k_gi2sm[1] += 1
elif dis_k_gi_min < dis_k_sm:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if dis_k_gi_min > dis_k_gm:
nb_dis_k_gi2gm[0] += 1
repeats_better_dis_k_gi2gm.append(repeat)
elif dis_k_gi_min == dis_k_gm:
nb_dis_k_gi2gm[1] += 1
elif dis_k_gi_min < dis_k_gm:
nb_dis_k_gi2gm[2] += 1
# save median graphs.
fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl'
fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat)
copyfile(fname_sm, fn_pre_sm_new + '.gxl')
fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl'
fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat)
copyfile(fname_gm, fn_pre_gm_new + '.gxl')
G_best_kernel = Gn_median[idx_dis_k_gi_min].copy()
# reform_attributes(G_best_kernel)
fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(y) + '.repeat' + str(repeat)
saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='default')
# plot median graphs.
if ds_name == 'Letter-high' or ds_name == 'Letter-med' or ds_name == 'Letter-low':
set_median = loadGXL(fn_pre_sm_new + '.gxl')
gen_median = loadGXL(fn_pre_gm_new + '.gxl')
draw_Letter_graph(set_median, fn_pre_sm_new)
draw_Letter_graph(gen_median, fn_pre_gm_new)
draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel)
# write result summary for each letter.
sod_sm_mean_list.append(np.mean(sod_sm_list))
sod_gm_mean_list.append(np.mean(sod_gm_list))
dis_k_sm_mean_list.append(np.mean(dis_k_sm_list))
dis_k_gm_mean_list.append(np.mean(dis_k_gm_list))
dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list))
time_fitting_mean_list.append(np.mean(time_fitting_list))
time_generating_mean_list.append(np.mean(time_generating_list))
time_total_mean_list.append(np.mean(time_total_list))
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1]))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1]))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel,
edit_cost_name, ged_method, attr_distance,
fit_method, k, y,
sod_sm_mean_list[-1], sod_gm_mean_list[-1],
dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1],
dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean,
dis_k_gi2sm_mean, dis_k_gi2gm_mean,
time_fitting_mean_list[-1], time_generating_mean_list[-1],
time_total_mean_list[-1], nb_sod_sm2gm,
nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm,
repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm,
repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm])
f_summary.close()

# write result summary for each letter.
sod_sm_mean = np.mean(sod_sm_mean_list)
sod_gm_mean = np.mean(sod_gm_mean_list)
dis_k_sm_mean = np.mean(dis_k_sm_mean_list)
dis_k_gm_mean = np.mean(dis_k_gm_mean_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
time_fitting_mean = np.mean(time_fitting_list)
time_generating_mean = np.mean(time_generating_list)
time_total_mean = np.mean(time_total_list)
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel,
edit_cost_name, ged_method, attr_distance,
fit_method, k, '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_fitting_mean, time_generating_mean, time_total_mean])
f_summary.close()
print('\ncomplete.')
#Dessin median courrant
def draw_Letter_graph(graph, file_prefix):
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])])
nx.draw_networkx(graph, pos)
plt.savefig(file_prefix + '.eps', format='eps', dpi=300)
# plt.show()
plt.clf()
def compute_gm_for_each_class(Gn, y_all, gkernel, parallel='imap_unordered', is_separate=True):
if is_separate:
print('the Gram matrix is computed for each class.')
y_idx = get_same_item_indices(y_all)
Kmatrix = []
run_time = []
k_dis_data = []
for i, (y, values) in enumerate(y_idx.items()):
print('The ', str(i), ' class:')
Gn_i = [Gn[val] for val in values]
time0 = time.time()
Kmatrix.append(compute_kernel(Gn_i, gkernel, None, None, True, parallel=parallel))
run_time.append(time.time() - time0)
k_dis_data.append(kernel_distance_matrix(Gn_i, None, None,
Kmatrix=Kmatrix[i], gkernel=gkernel, verbose=True))
np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
Kmatrix=Kmatrix, run_time=run_time, is_separate=is_separate)
dis_max = np.max([item[1] for item in k_dis_data])
dis_min = np.min([item[2] for item in k_dis_data])
dis_mean = np.mean([item[3] for item in k_dis_data])
print('pair distances - dis_max, dis_min, dis_mean:', dis_max, dis_min,
dis_mean)

else:
time0 = time.time()
Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel=parallel)
run_time = time.time() - time0
np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
Kmatrix=Kmatrix, run_time=run_time, is_separate=is_separate)
k_dis_data = kernel_distance_matrix(Gn, None, None,
Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
print('the Gram matrix is computed for the whole dataset.')
print('pair distances - dis_max, dis_min, dis_mean:', k_dis_data[1],
k_dis_data[2], k_dis_data[3])
print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean]
return Kmatrix, run_time, k_dis_data

if __name__ == "__main__":
# #### xp 1: Letter-high, spkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-high'
# gkernel = 'spkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
# # remove graphs without edges.
# Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0]
# idx = [G[0] for G in Gn]
# Gn = [G[1] for G in Gn]
# y_all = [y_all[i] for i in idx]
## Gn = Gn[0:50]
## y_all = y_all[0:50]
# # compute pair distances.
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=None, gkernel=gkernel, verbose=True)
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
# # fitting and computing.
# fit_methods = ['random', 'expert', 'k-graphs']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method}
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean])
# #### xp 2: Letter-high, sspkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-high'
# gkernel = 'structuralspkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
## Gn = Gn[0:50]
## y_all = y_all[0:50]
# # compute pair distances.
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=None, gkernel=gkernel, verbose=True)
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
# # fitting and computing.
# fit_methods = ['random', 'expert', 'k-graphs']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method}
# print('parameters: ', parameters)
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean])
# #### xp 3: SYNTHETICnew, sspkernel, using NON_SYMBOLIC.
# gmfile = np.load('results/xp_fit_method/Kmatrix.SYNTHETICnew.structuralspkernel.gm.npz')
# Kmatrix = gmfile['Kmatrix']
# run_time = gmfile['run_time']
# # normalization
# Kmatrix_diag = Kmatrix.diagonal().copy()
# for i in range(len(Kmatrix)):
# for j in range(i, len(Kmatrix)):
# Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
# Kmatrix[j][i] = Kmatrix[i][j]
## np.savez('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm',
## Kmatrix=Kmatrix, run_time=run_time)
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'SYNTHETICnew'
# gkernel = 'structuralspkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
# # remove graphs without nodes and edges.
# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0
# and nx.number_of_edges(G) != 0)]
# idx = [G[0] for G in Gn]
# Gn = [G[1] for G in Gn]
# y_all = [y_all[i] for i in idx]
## Gn = Gn[0:10]
## y_all = y_all[0:10]
# for G in Gn:
# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl'
# # compute pair distances.
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
# # fitting and computing.
# fit_methods = ['k-graphs', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'NON_SYMBOLIC',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method}
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=1,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
# ### xp 4: SYNTHETICnew, spkernel, using NON_SYMBOLIC.
# gmfile = np.load('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm.npz')
# Kmatrix = gmfile['Kmatrix']
# # normalization
# Kmatrix_diag = Kmatrix.diagonal().copy()
# for i in range(len(Kmatrix)):
# for j in range(i, len(Kmatrix)):
# Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
# Kmatrix[j][i] = Kmatrix[i][j]
# run_time = 21821.35
# np.savez('results/xp_fit_method/Kmatrix.SYNTHETICnew.spkernel.gm',
# Kmatrix=Kmatrix, run_time=run_time)
#
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'SYNTHETICnew'
# gkernel = 'spkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
## # remove graphs without nodes and edges.
## Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_node(G) != 0
## and nx.number_of_edges(G) != 0)]
## idx = [G[0] for G in Gn]
## Gn = [G[1] for G in Gn]
## y_all = [y_all[i] for i in idx]
## Gn = Gn[0:5]
## y_all = y_all[0:5]
# for G in Gn:
# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl'
#
# # compute/read Gram matrix and pair distances.
## Kmatrix = compute_kernel(Gn, gkernel, None, None, True)
## np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
## Kmatrix=Kmatrix)
# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
# Kmatrix = gmfile['Kmatrix']
# run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
# print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'NON_SYMBOLIC',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method}
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=1,
# Gn_data=[Gn, y_all, graph_dir],
# k_dis_data=[dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
# #### xp 5: Fingerprint, sspkernel, using LETTER2, only node attrs.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Fingerprint'
# gkernel = 'structuralspkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
# # remove graphs without nodes and edges.
# Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_nodes(G) != 0]
## and nx.number_of_edges(G) != 0)]
# idx = [G[0] for G in Gn]
# Gn = [G[1] for G in Gn]
# y_all = [y_all[i] for i in idx]
# y_idx = get_same_item_indices(y_all)
# # remove unused labels.
# for G in Gn:
# G.graph['edge_attrs'] = []
# for edge in G.edges:
# del G.edges[edge]['attributes']
# del G.edges[edge]['orient']
# del G.edges[edge]['angle']
## Gn = Gn[805:815]
## y_all = y_all[805:815]
# for G in Gn:
# G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl'
#
# # compute/read Gram matrix and pair distances.
## Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
## np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
## Kmatrix=Kmatrix)
# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
# Kmatrix = gmfile['Kmatrix']
## run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
## print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method,
# 'init_ecc': [1,1,1,1,1]} # [0.525, 0.525, 0.001, 0.125, 0.125]}
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
# #### xp 6: Letter-med, sspkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-med'
# gkernel = 'structuralspkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
## Gn = Gn[0:50]
## y_all = y_all[0:50]
#
# # compute/read Gram matrix and pair distances.
# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
# Kmatrix=Kmatrix)
## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
## Kmatrix = gmfile['Kmatrix']
## run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
## print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method,
# 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475]}
# print('parameters: ', parameters)
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
# #### xp 7: Letter-low, sspkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-low'
# gkernel = 'structuralspkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
## Gn = Gn[0:50]
## y_all = y_all[0:50]
#
# # compute/read Gram matrix and pair distances.
# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
# Kmatrix=Kmatrix)
## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
## Kmatrix = gmfile['Kmatrix']
## run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
## print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method,
# 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075]}
# print('parameters: ', parameters)
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
# #### xp 8: Letter-med, spkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-med'
# gkernel = 'spkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
# # remove graphs without nodes and edges.
# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0
# and nx.number_of_edges(G) != 0)]
# idx = [G[0] for G in Gn]
# Gn = [G[1] for G in Gn]
# y_all = [y_all[i] for i in idx]
## Gn = Gn[0:50]
## y_all = y_all[0:50]
#
# # compute/read Gram matrix and pair distances.
# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
# Kmatrix=Kmatrix)
## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
## Kmatrix = gmfile['Kmatrix']
## run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
## print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method,
# 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475]}
# print('parameters: ', parameters)
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)

# #### xp 9: Letter-low, spkernel.
# # load dataset.
# print('getting dataset and computing kernel distance matrix first...')
# ds_name = 'Letter-low'
# gkernel = 'spkernel'
# Gn, y_all, graph_dir = get_dataset(ds_name)
# # remove graphs without nodes and edges.
# Gn = [(idx, G) for idx, G in enumerate(Gn) if (nx.number_of_nodes(G) != 0
# and nx.number_of_edges(G) != 0)]
# idx = [G[0] for G in Gn]
# Gn = [G[1] for G in Gn]
# y_all = [y_all[i] for i in idx]
## Gn = Gn[0:50]
## y_all = y_all[0:50]
#
# # compute/read Gram matrix and pair distances.
# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
# Kmatrix=Kmatrix)
## gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
## Kmatrix = gmfile['Kmatrix']
## run_time = gmfile['run_time']
## Kmatrix = Kmatrix[[0,1,2,3,4],:]
## Kmatrix = Kmatrix[:,[0,1,2,3,4]]
## print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
## Kmatrix = np.zeros((len(Gn), len(Gn)))
## dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
#
# # fitting and computing.
# fit_methods = ['k-graphs', 'expert', 'random', 'random', 'random']
# for fit_method in fit_methods:
# print('\n-------------------------------------')
# print('fit method:', fit_method)
# parameters = {'ds_name': ds_name,
# 'gkernel': gkernel,
# 'edit_cost_name': 'LETTER2',
# 'ged_method': 'mIPFP',
# 'attr_distance': 'euclidean',
# 'fit_method': fit_method,
# 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075]}
# print('parameters: ', parameters)
# xp_fit_method_for_non_symbolic(parameters, save_results=True,
# initial_solutions=40,
# Gn_data = [Gn, y_all, graph_dir],
# k_dis_data = [dis_mat, dis_max, dis_min, dis_mean],
# Kmatrix=Kmatrix)
#### xp 5: COIL-DEL, sspkernel, using LETTER2, only node attrs.
# load dataset.
print('getting dataset and computing kernel distance matrix first...')
ds_name = 'COIL-DEL'
gkernel = 'structuralspkernel'
Gn, y_all, graph_dir = get_dataset(ds_name)
# remove graphs without nodes and edges.
Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_nodes(G) != 0]
# and nx.number_of_edges(G) != 0)]
idx = [G[0] for G in Gn]
Gn = [G[1] for G in Gn]
y_all = [y_all[i] for i in idx]
# remove unused labels.
for G in Gn:
G.graph['edge_labels'] = []
for edge in G.edges:
del G.edges[edge]['bond_type']
del G.edges[edge]['valence']
# Gn = Gn[805:815]
# y_all = y_all[805:815]
for G in Gn:
G.graph['filename'] = 'graph' + str(G.graph['name']) + '.gxl'
# compute/read Gram matrix and pair distances.
is_separate = True
Kmatrix, run_time, k_dis_data = compute_gm_for_each_class(Gn,
y_all,
gkernel,
parallel='imap_unordered',
is_separate=is_separate)
# Kmatrix = compute_kernel(Gn, gkernel, None, None, True, parallel='imap_unordered')
# np.savez('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm',
# Kmatrix=Kmatrix)
# gmfile = np.load('results/xp_fit_method/Kmatrix.' + ds_name + '.' + gkernel + '.gm.npz')
# Kmatrix = gmfile['Kmatrix']
# run_time = gmfile['run_time']
# Kmatrix = Kmatrix[[0,1,2,3,4],:]
# Kmatrix = Kmatrix[:,[0,1,2,3,4]]
# print('\nTime to compute Gram matrix for the whole dataset: ', run_time)
# dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None,
# Kmatrix=Kmatrix, gkernel=gkernel, verbose=True)
# Kmatrix = np.zeros((len(Gn), len(Gn)))
# dis_mat, dis_max, dis_min, dis_mean = 0, 0, 0, 0
# fitting and computing.
fit_methods = ['k-graphs', 'random', 'random', 'random']
for fit_method in fit_methods:
print('\n-------------------------------------')
print('fit method:', fit_method)
parameters = {'ds_name': ds_name,
'gkernel': gkernel,
'edit_cost_name': 'LETTER2',
'ged_method': 'mIPFP',
'attr_distance': 'euclidean',
'fit_method': fit_method,
'init_ecc': [3,3,1,3,3]} # [0.525, 0.525, 0.001, 0.125, 0.125]}
xp_fit_method_for_non_symbolic(parameters, save_results=True,
initial_solutions=40,
Gn_data=[Gn, y_all, graph_dir],
k_dis_data=k_dis_data,
Kmatrix=Kmatrix,
is_separate=is_separate)

+ 0
- 476
gklearn/preimage/xp_letter_h.py View File

@@ -1,476 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 14 15:39:29 2020

@author: ljia
"""
import numpy as np
import random
import csv
from shutil import copyfile
import networkx as nx
import matplotlib.pyplot as plt

from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL
from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes
from gklearn.preimage.utils import get_same_item_indices, kernel_distance_matrix
from gklearn.preimage.find_best_k import getRelations


def xp_letter_h_LETTER2_cost():
ds = {'dataset': 'cpp_ext/data/collections/Letter.xml',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir'])
dis_mat, dis_max, dis_min, dis_mean = kernel_distance_matrix(Gn, None, None, Kmatrix=None, gkernel='structuralspkernel')
for G in Gn:
reform_attributes(G)
# ds = {'name': 'Letter-high',
# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb
# Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
gkernel = 'structuralspkernel'
node_label = None
edge_label = None
ds_name = 'letter-h'
dir_output = 'results/xp_letter_h/'
save_results = True
cost = 'LETTER2'
repeats = 1
# k_list = range(2, 11)
k_list = [150]
fit_method = 'k-graphs'
# get indices by classes.
y_idx = get_same_item_indices(y_all)
if save_results:
# create result files.
fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', 'median set'])
f_detail.close()
fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM',
'# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM',
'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM',
'repeats better dis_k gi -> GM'])
f_summary.close()
random.seed(1)
rdn_seed_list = random.sample(range(0, repeats * 100), repeats)
for k in k_list:
print('\n--------- k =', k, '----------')
sod_sm_mean_list = []
sod_gm_mean_list = []
dis_k_sm_mean_list = []
dis_k_gm_mean_list = []
dis_k_gi_min_mean_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]
# repeats_better_sod_sm2gm = []
# repeats_better_dis_k_sm2gm = []
# repeats_better_dis_k_gi2sm = []
# repeats_better_dis_k_gi2gm = []
for i, (y, values) in enumerate(y_idx.items()):
print('\ny =', y)
# y = 'F'
# values = y_idx[y]
# values = values[0:10]
k = len(values)
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_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]
repeats_better_sod_sm2gm = []
repeats_better_dis_k_sm2gm = []
repeats_better_dis_k_gi2sm = []
repeats_better_dis_k_gi2gm = []
for repeat in range(repeats):
print('\nrepeat =', repeat)
random.seed(rdn_seed_list[repeat])
median_set_idx_idx = random.sample(range(0, len(values)), k)
median_set_idx = [values[idx] for idx in median_set_idx_idx]
print('median set: ', median_set_idx)
Gn_median = [Gn[g] for g in values]
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \
= median_on_k_closest_graphs(Gn_median, node_label, edge_label,
gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'],
edit_costs=None, group_min=median_set_idx_idx,
dataset='Letter', cost=cost, parallel=False)
# write result detail.
sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm))
dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm))
dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min))
dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min))
if save_results:
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k,
y, repeat,
sod_sm, sod_gm, dis_k_sm, dis_k_gm,
dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm,
dis_k_gi2gm, median_set_idx])
f_detail.close()
# compute result summary.
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
dis_k_sm_list.append(dis_k_sm)
dis_k_gm_list.append(dis_k_gm)
dis_k_gi_min_list.append(dis_k_gi_min)
# # SOD SM -> GM
if sod_sm > sod_gm:
nb_sod_sm2gm[0] += 1
repeats_better_sod_sm2gm.append(repeat)
elif sod_sm == sod_gm:
nb_sod_sm2gm[1] += 1
elif sod_sm < sod_gm:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if dis_k_sm > dis_k_gm:
nb_dis_k_sm2gm[0] += 1
repeats_better_dis_k_sm2gm.append(repeat)
elif dis_k_sm == dis_k_gm:
nb_dis_k_sm2gm[1] += 1
elif dis_k_sm < dis_k_gm:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if dis_k_gi_min > dis_k_sm:
nb_dis_k_gi2sm[0] += 1
repeats_better_dis_k_gi2sm.append(repeat)
elif dis_k_gi_min == dis_k_sm:
nb_dis_k_gi2sm[1] += 1
elif dis_k_gi_min < dis_k_sm:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if dis_k_gi_min > dis_k_gm:
nb_dis_k_gi2gm[0] += 1
repeats_better_dis_k_gi2gm.append(repeat)
elif dis_k_gi_min == dis_k_gm:
nb_dis_k_gi2gm[1] += 1
elif dis_k_gi_min < dis_k_gm:
nb_dis_k_gi2gm[2] += 1
# save median graphs.
fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl'
fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
copyfile(fname_sm, fn_pre_sm_new + '.gxl')
fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl'
fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
copyfile(fname_gm, fn_pre_gm_new + '.gxl')
G_best_kernel = Gn_median[idx_dis_k_gi_min].copy()
reform_attributes(G_best_kernel)
fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib-letter')
# plot median graphs.
set_median = loadGXL(fn_pre_sm_new + '.gxl')
gen_median = loadGXL(fn_pre_gm_new + '.gxl')
draw_Letter_graph(set_median, fn_pre_sm_new)
draw_Letter_graph(gen_median, fn_pre_gm_new)
draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel)
# write result summary for each letter.
sod_sm_mean_list.append(np.mean(sod_sm_list))
sod_gm_mean_list.append(np.mean(sod_gm_list))
dis_k_sm_mean_list.append(np.mean(dis_k_sm_list))
dis_k_gm_mean_list.append(np.mean(dis_k_gm_list))
dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list))
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1]))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1]))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y,
sod_sm_mean_list[-1], sod_gm_mean_list[-1],
dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1],
dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean,
dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm,
nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm,
repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm,
repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm])
f_summary.close()

# write result summary for each letter.
sod_sm_mean = np.mean(sod_sm_mean_list)
sod_gm_mean = np.mean(sod_gm_mean_list)
dis_k_sm_mean = np.mean(dis_k_sm_mean_list)
dis_k_gm_mean = np.mean(dis_k_gm_mean_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, '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])
f_summary.close()
print('\ncomplete.')


def xp_letter_h():
ds = {'dataset': 'cpp_ext/data/collections/Letter.xml',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir'])
for G in Gn:
reform_attributes(G)
# ds = {'name': 'Letter-high',
# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb
# Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
gkernel = 'structuralspkernel'
node_label = None
edge_label = None
ds_name = 'letter-h'
dir_output = 'results/xp_letter_h/'
save_results = False
repeats = 1
# k_list = range(2, 11)
k_list = [150]
fit_method = 'k-graphs'
# get indices by classes.
y_idx = get_same_item_indices(y_all)
if save_results:
# create result files.
fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', 'median set'])
f_detail.close()
fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM',
'# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM',
'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM',
'repeats better dis_k gi -> GM'])
f_summary.close()
random.seed(1)
rdn_seed_list = random.sample(range(0, repeats * 100), repeats)
for k in k_list:
print('\n--------- k =', k, '----------')
sod_sm_mean_list = []
sod_gm_mean_list = []
dis_k_sm_mean_list = []
dis_k_gm_mean_list = []
dis_k_gi_min_mean_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]
# repeats_better_sod_sm2gm = []
# repeats_better_dis_k_sm2gm = []
# repeats_better_dis_k_gi2sm = []
# repeats_better_dis_k_gi2gm = []
for i, (y, values) in enumerate(y_idx.items()):
print('\ny =', y)
# y = 'N'
# values = y_idx[y]
# values = values[0:10]
k = len(values)
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_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]
repeats_better_sod_sm2gm = []
repeats_better_dis_k_sm2gm = []
repeats_better_dis_k_gi2sm = []
repeats_better_dis_k_gi2gm = []
for repeat in range(repeats):
print('\nrepeat =', repeat)
random.seed(rdn_seed_list[repeat])
median_set_idx_idx = random.sample(range(0, len(values)), k)
median_set_idx = [values[idx] for idx in median_set_idx_idx]
print('median set: ', median_set_idx)
Gn_median = [Gn[g] for g in values]
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \
= median_on_k_closest_graphs(Gn_median, node_label, edge_label,
gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'],
edit_costs=None, group_min=median_set_idx_idx,
dataset='Letter', parallel=False)
# write result detail.
sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm))
dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm))
dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min))
dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min))
if save_results:
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k,
y, repeat,
sod_sm, sod_gm, dis_k_sm, dis_k_gm,
dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm,
dis_k_gi2gm, median_set_idx])
f_detail.close()
# compute result summary.
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
dis_k_sm_list.append(dis_k_sm)
dis_k_gm_list.append(dis_k_gm)
dis_k_gi_min_list.append(dis_k_gi_min)
# # SOD SM -> GM
if sod_sm > sod_gm:
nb_sod_sm2gm[0] += 1
repeats_better_sod_sm2gm.append(repeat)
elif sod_sm == sod_gm:
nb_sod_sm2gm[1] += 1
elif sod_sm < sod_gm:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if dis_k_sm > dis_k_gm:
nb_dis_k_sm2gm[0] += 1
repeats_better_dis_k_sm2gm.append(repeat)
elif dis_k_sm == dis_k_gm:
nb_dis_k_sm2gm[1] += 1
elif dis_k_sm < dis_k_gm:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if dis_k_gi_min > dis_k_sm:
nb_dis_k_gi2sm[0] += 1
repeats_better_dis_k_gi2sm.append(repeat)
elif dis_k_gi_min == dis_k_sm:
nb_dis_k_gi2sm[1] += 1
elif dis_k_gi_min < dis_k_sm:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if dis_k_gi_min > dis_k_gm:
nb_dis_k_gi2gm[0] += 1
repeats_better_dis_k_gi2gm.append(repeat)
elif dis_k_gi_min == dis_k_gm:
nb_dis_k_gi2gm[1] += 1
elif dis_k_gi_min < dis_k_gm:
nb_dis_k_gi2gm[2] += 1
# save median graphs.
fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl'
fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
copyfile(fname_sm, fn_pre_sm_new + '.gxl')
fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl'
fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
copyfile(fname_gm, fn_pre_gm_new + '.gxl')
G_best_kernel = Gn_median[idx_dis_k_gi_min].copy()
reform_attributes(G_best_kernel)
fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \
+ '.k' + str(int(k)) + '.y' + y + '.repeat' + str(repeat)
saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib-letter')
# plot median graphs.
set_median = loadGXL(fn_pre_sm_new + '.gxl')
gen_median = loadGXL(fn_pre_gm_new + '.gxl')
draw_Letter_graph(set_median, fn_pre_sm_new)
draw_Letter_graph(gen_median, fn_pre_gm_new)
draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel)
# write result summary for each letter.
sod_sm_mean_list.append(np.mean(sod_sm_list))
sod_gm_mean_list.append(np.mean(sod_gm_list))
dis_k_sm_mean_list.append(np.mean(dis_k_sm_list))
dis_k_gm_mean_list.append(np.mean(dis_k_gm_list))
dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list))
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1]))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1]))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y,
sod_sm_mean_list[-1], sod_gm_mean_list[-1],
dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1],
dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean,
dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm,
nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm,
repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm,
repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm])
f_summary.close()

# write result summary for each letter.
sod_sm_mean = np.mean(sod_sm_mean_list)
sod_gm_mean = np.mean(sod_gm_mean_list)
dis_k_sm_mean = np.mean(dis_k_sm_mean_list)
dis_k_gm_mean = np.mean(dis_k_gm_mean_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
if save_results:
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, '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])
f_summary.close()
print('\ncomplete.')
#Dessin median courrant
def draw_Letter_graph(graph, file_prefix):
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])])
nx.draw_networkx(graph, pos)
plt.savefig(file_prefix + '.eps', format='eps', dpi=300)
# plt.show()
plt.clf()

if __name__ == "__main__":
# xp_letter_h()
xp_letter_h_LETTER2_cost()

+ 0
- 249
gklearn/preimage/xp_monoterpenoides.py View File

@@ -1,249 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 16 11:03:11 2020

@author: ljia
"""

import numpy as np
import random
import csv
from shutil import copyfile
import networkx as nx
import matplotlib.pyplot as plt

from gklearn.utils.graphfiles import loadDataset, loadGXL, saveGXL
from gklearn.preimage.test_k_closest_graphs import median_on_k_closest_graphs, reform_attributes
from gklearn.preimage.utils import get_same_item_indices
from gklearn.preimage.find_best_k import getRelations

def xp_monoterpenoides():
import os

ds = {'dataset': '../../datasets/monoterpenoides/dataset_10+.ds',
'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb
Gn, y_all = loadDataset(ds['dataset'])
# ds = {'name': 'Letter-high',
# 'dataset': '../datasets/Letter-high/Letter-high_A.txt'} # node/edge symb
# Gn, y_all = loadDataset(ds['dataset'])
# Gn = Gn[0:50]
gkernel = 'treeletkernel'
node_label = 'atom'
edge_label = 'bond_type'
ds_name = 'monoterpenoides'
dir_output = 'results/xp_monoterpenoides/'
repeats = 1
# k_list = range(2, 11)
k_list = [0]
fit_method = 'k-graphs'
# get indices by classes.
y_idx = get_same_item_indices(y_all)
# create result files.
fn_output_detail = 'results_detail.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'repeat', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', 'median set'])
f_detail.close()
fn_output_summary = 'results_summary.' + ds_name + '.' + gkernel + '.' + fit_method + '.csv'
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow(['dataset', 'graph kernel', 'fit method', 'k',
'target', 'SOD SM', 'SOD GM', 'dis_k SM', 'dis_k GM',
'min dis_k gi', 'SOD SM -> GM', 'dis_k SM -> GM', 'dis_k gi -> SM',
'dis_k gi -> GM', '# SOD SM -> GM', '# dis_k SM -> GM',
'# dis_k gi -> SM', '# dis_k gi -> GM', 'repeats better SOD SM -> GM',
'repeats better dis_k SM -> GM', 'repeats better dis_k gi -> SM',
'repeats better dis_k gi -> GM'])
f_summary.close()
random.seed(1)
rdn_seed_list = random.sample(range(0, repeats * 100), repeats)
for k in k_list:
print('\n--------- k =', k, '----------')
sod_sm_mean_list = []
sod_gm_mean_list = []
dis_k_sm_mean_list = []
dis_k_gm_mean_list = []
dis_k_gi_min_mean_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]
# repeats_better_sod_sm2gm = []
# repeats_better_dis_k_sm2gm = []
# repeats_better_dis_k_gi2sm = []
# repeats_better_dis_k_gi2gm = []
for i, (y, values) in enumerate(y_idx.items()):
print('\ny =', y)
# y = 'I'
# values = y_idx[y]
k = len(values)
# k = kkk
sod_sm_list = []
sod_gm_list = []
dis_k_sm_list = []
dis_k_gm_list = []
dis_k_gi_min_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]
repeats_better_sod_sm2gm = []
repeats_better_dis_k_sm2gm = []
repeats_better_dis_k_gi2sm = []
repeats_better_dis_k_gi2gm = []
for repeat in range(repeats):
print('\nrepeat =', repeat)
random.seed(rdn_seed_list[repeat])
median_set_idx_idx = random.sample(range(0, len(values)), k)
median_set_idx = [values[idx] for idx in median_set_idx_idx]
print('median set: ', median_set_idx)
Gn_median = [Gn[g] for g in values]
sod_sm, sod_gm, dis_k_sm, dis_k_gm, dis_k_gi, dis_k_gi_min, idx_dis_k_gi_min \
= median_on_k_closest_graphs(Gn_median, node_label, edge_label,
gkernel, k, fit_method=fit_method, graph_dir=ds['graph_dir'],
edit_costs=None, group_min=median_set_idx_idx,
dataset=ds_name, parallel=False)
# write result detail.
sod_sm2gm = getRelations(np.sign(sod_gm - sod_sm))
dis_k_sm2gm = getRelations(np.sign(dis_k_gm - dis_k_sm))
dis_k_gi2sm = getRelations(np.sign(dis_k_sm - dis_k_gi_min))
dis_k_gi2gm = getRelations(np.sign(dis_k_gm - dis_k_gi_min))
f_detail = open(dir_output + fn_output_detail, 'a')
csv.writer(f_detail).writerow([ds_name, gkernel, fit_method, k,
y, repeat,
sod_sm, sod_gm, dis_k_sm, dis_k_gm,
dis_k_gi_min, sod_sm2gm, dis_k_sm2gm, dis_k_gi2sm,
dis_k_gi2gm, median_set_idx])
f_detail.close()
# compute result summary.
sod_sm_list.append(sod_sm)
sod_gm_list.append(sod_gm)
dis_k_sm_list.append(dis_k_sm)
dis_k_gm_list.append(dis_k_gm)
dis_k_gi_min_list.append(dis_k_gi_min)
# # SOD SM -> GM
if sod_sm > sod_gm:
nb_sod_sm2gm[0] += 1
repeats_better_sod_sm2gm.append(repeat)
elif sod_sm == sod_gm:
nb_sod_sm2gm[1] += 1
elif sod_sm < sod_gm:
nb_sod_sm2gm[2] += 1
# # dis_k SM -> GM
if dis_k_sm > dis_k_gm:
nb_dis_k_sm2gm[0] += 1
repeats_better_dis_k_sm2gm.append(repeat)
elif dis_k_sm == dis_k_gm:
nb_dis_k_sm2gm[1] += 1
elif dis_k_sm < dis_k_gm:
nb_dis_k_sm2gm[2] += 1
# # dis_k gi -> SM
if dis_k_gi_min > dis_k_sm:
nb_dis_k_gi2sm[0] += 1
repeats_better_dis_k_gi2sm.append(repeat)
elif dis_k_gi_min == dis_k_sm:
nb_dis_k_gi2sm[1] += 1
elif dis_k_gi_min < dis_k_sm:
nb_dis_k_gi2sm[2] += 1
# # dis_k gi -> GM
if dis_k_gi_min > dis_k_gm:
nb_dis_k_gi2gm[0] += 1
repeats_better_dis_k_gi2gm.append(repeat)
elif dis_k_gi_min == dis_k_gm:
nb_dis_k_gi2gm[1] += 1
elif dis_k_gi_min < dis_k_gm:
nb_dis_k_gi2gm[2] += 1
# save median graphs.
fname_sm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/set_median.gxl'
fn_pre_sm_new = dir_output + 'medians/set_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat)
copyfile(fname_sm, fn_pre_sm_new + '.gxl')
fname_gm = os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/output/tmp_ged/gen_median.gxl'
fn_pre_gm_new = dir_output + 'medians/gen_median.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat)
copyfile(fname_gm, fn_pre_gm_new + '.gxl')
G_best_kernel = Gn_median[idx_dis_k_gi_min].copy()
# reform_attributes(G_best_kernel)
fn_pre_g_best_kernel = dir_output + 'medians/g_best_kernel.' + fit_method \
+ '.k' + str(int(k)) + '.y' + str(int(y)) + '.repeat' + str(repeat)
saveGXL(G_best_kernel, fn_pre_g_best_kernel + '.gxl', method='gedlib')
# # plot median graphs.
# set_median = loadGXL(fn_pre_sm_new + '.gxl')
# gen_median = loadGXL(fn_pre_gm_new + '.gxl')
# draw_Letter_graph(set_median, fn_pre_sm_new)
# draw_Letter_graph(gen_median, fn_pre_gm_new)
# draw_Letter_graph(G_best_kernel, fn_pre_g_best_kernel)
# write result summary for each letter.
sod_sm_mean_list.append(np.mean(sod_sm_list))
sod_gm_mean_list.append(np.mean(sod_gm_list))
dis_k_sm_mean_list.append(np.mean(dis_k_sm_list))
dis_k_gm_mean_list.append(np.mean(dis_k_gm_list))
dis_k_gi_min_mean_list.append(np.mean(dis_k_gi_min_list))
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean_list[-1] - sod_sm_mean_list[-1]))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_sm_mean_list[-1]))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean_list[-1] - dis_k_gi_min_mean_list[-1]))
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, y,
sod_sm_mean_list[-1], sod_gm_mean_list[-1],
dis_k_sm_mean_list[-1], dis_k_gm_mean_list[-1],
dis_k_gi_min_mean_list[-1], sod_sm2gm_mean, dis_k_sm2gm_mean,
dis_k_gi2sm_mean, dis_k_gi2gm_mean, nb_sod_sm2gm,
nb_dis_k_sm2gm, nb_dis_k_gi2sm, nb_dis_k_gi2gm,
repeats_better_sod_sm2gm, repeats_better_dis_k_sm2gm,
repeats_better_dis_k_gi2sm, repeats_better_dis_k_gi2gm])
f_summary.close()

# write result summary for each letter.
sod_sm_mean = np.mean(sod_sm_mean_list)
sod_gm_mean = np.mean(sod_gm_mean_list)
dis_k_sm_mean = np.mean(dis_k_sm_mean_list)
dis_k_gm_mean = np.mean(dis_k_gm_mean_list)
dis_k_gi_min_mean = np.mean(dis_k_gi_min_list)
sod_sm2gm_mean = getRelations(np.sign(sod_gm_mean - sod_sm_mean))
dis_k_sm2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_sm_mean))
dis_k_gi2sm_mean = getRelations(np.sign(dis_k_sm_mean - dis_k_gi_min_mean))
dis_k_gi2gm_mean = getRelations(np.sign(dis_k_gm_mean - dis_k_gi_min_mean))
f_summary = open(dir_output + fn_output_summary, 'a')
csv.writer(f_summary).writerow([ds_name, gkernel, fit_method, k, '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])
f_summary.close()
print('\ncomplete.')
#Dessin median courrant
def draw_Letter_graph(graph, file_prefix):
plt.figure()
pos = {}
for n in graph.nodes:
pos[n] = np.array([float(graph.node[n]['x']),float(graph.node[n]['y'])])
nx.draw_networkx(graph, pos)
plt.savefig(file_prefix + '.eps', format='eps', dpi=300)
# plt.show()
plt.clf()

if __name__ == "__main__":
xp_monoterpenoides()

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