OpenI
/
graphkit-learn
Not watched
1
0
Fork 0
Code
Releases 2 Wiki Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
Tree: e3985c5481
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'e3985c5481'
${ noResults }
graphkit-learn/preimage/test_fitDistance.py

114 lines
4.4 kB
Raw Blame History 

  1. #!/usr/bin/env python3

  2. # -*- coding: utf-8 -*-

  3. """

  4. Created on Thu Oct 24 11:50:56 2019

  5. 

  6. @author: ljia

  7. """

  8. from matplotlib import pyplot as plt

  9. import numpy as np

  10. 

  11. from pygraph.utils.graphfiles import loadDataset

  12. from utils import remove_edges

  13. from fitDistance import fit_GED_to_kernel_distance

  14. from utils import normalize_distance_matrix

  15. 

  16. def test_anycosts():

  17.     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',

  18.           'extra_params': {}}  # node/edge symb

  19.     Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])

  20. #    Gn = Gn[0:10]

  21.     remove_edges(Gn)

  22.     gkernel = 'marginalizedkernel'

  23.     itr_max = 10

  24.     edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list = \

  25.         fit_GED_to_kernel_distance(Gn, gkernel, itr_max)

  26.     total_time = np.sum(time_list)

  27.     print('\nedit_costs:', edit_costs)

  28.     print('\nresidual_list:', residual_list)

  29.     print('\nedit_cost_list:', edit_cost_list)

  30.     print('\ndistance matrix in kernel space:', dis_k_mat)

  31.     print('\nged matrix:', ged_mat)

  32.     print('total time:', total_time)

  33.     np.savez('results/fit_distance.any_costs.gm', edit_costs=edit_costs, 

  34.              residual_list=residual_list, edit_cost_list=edit_cost_list,

  35.              dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, 

  36.              total_time=total_time)

  37.     

  38.     # normalized distance matrices.

  39. #    gmfile = np.load('results/fit_distance.any_costs.gm.npz')

  40. #    edit_costs = gmfile['edit_costs']

  41. #    residual_list = gmfile['residual_list']

  42. #    edit_cost_list = gmfile['edit_cost_list']

  43. #    dis_k_mat = gmfile['dis_k_mat']

  44. #    ged_mat = gmfile['ged_mat']

  45. #    total_time = gmfile['total_time']

  46.     

  47.     norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)

  48.     plt.imshow(norm_dis_k_mat)

  49.     plt.colorbar()

  50.     plt.savefig('results/norm_dis_k_mat.any_costs' + '.eps', format='eps', dpi=300)

  51. #    plt.savefig('results/norm_dis_k_mat.any_costs' + '.jpg', format='jpg')

  52. #    plt.show()

  53.     plt.clf()

  54.     norm_ged_mat = normalize_distance_matrix(ged_mat)

  55.     plt.imshow(norm_ged_mat)

  56.     plt.colorbar()

  57.     plt.savefig('results/norm_ged_mat.any_costs' + '.eps', format='eps', dpi=300)

  58. #    plt.savefig('results/norm_ged_mat.any_costs' + '.jpg', format='jpg')

  59. #    plt.show()

  60.     plt.clf()

  61.     

  62. 

  63. def test_cs_leq_ci_plus_cr():

  64.     """c_vs <= c_vi + c_vr, c_es <= c_ei + c_er

  65.     """

  66.     ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',

  67.           'extra_params': {}}  # node/edge symb

  68.     Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])

  69. #    Gn = Gn[0:10]

  70.     remove_edges(Gn)

  71.     gkernel = 'marginalizedkernel'

  72.     itr_max = 10

  73.     edit_costs, residual_list, edit_cost_list, dis_k_mat, ged_mat, time_list = \

  74.         fit_GED_to_kernel_distance(Gn, gkernel, itr_max)

  75.     total_time = np.sum(time_list)

  76.     print('\nedit_costs:', edit_costs)

  77.     print('\nresidual_list:', residual_list)

  78.     print('\nedit_cost_list:', edit_cost_list)

  79.     print('\ndistance matrix in kernel space:', dis_k_mat)

  80.     print('\nged matrix:', ged_mat)

  81.     print('total time:', total_time)

  82.     np.savez('results/fit_distance.cs_leq_ci_plus_cr.gm', edit_costs=edit_costs, 

  83.              residual_list=residual_list, edit_cost_list=edit_cost_list,

  84.              dis_k_mat=dis_k_mat, ged_mat=ged_mat, time_list=time_list, 

  85.              total_time=total_time)

  86.     

  87.     # normalized distance matrices.

  88. #    gmfile = np.load('results/fit_distance.cs_leq_ci_plus_cr.gm.npz')

  89. #    edit_costs = gmfile['edit_costs']

  90. #    residual_list = gmfile['residual_list']

  91. #    edit_cost_list = gmfile['edit_cost_list']

  92. #    dis_k_mat = gmfile['dis_k_mat']

  93. #    ged_mat = gmfile['ged_mat']

  94. #    total_time = gmfile['total_time']

  95.     

  96.     norm_dis_k_mat = normalize_distance_matrix(dis_k_mat)

  97.     plt.imshow(norm_dis_k_mat)

  98.     plt.colorbar()

  99.     plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr' + '.eps', format='eps', dpi=300)

  100. #    plt.savefig('results/norm_dis_k_mat.cs_leq_ci_plus_cr' + '.jpg', format='jpg')

  101. #    plt.show()

  102.     plt.clf()

  103.     norm_ged_mat = normalize_distance_matrix(ged_mat)

  104.     plt.imshow(norm_ged_mat)

  105.     plt.colorbar()

  106.     plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr' + '.eps', format='eps', dpi=300)

  107. #    plt.savefig('results/norm_ged_mat.cs_leq_ci_plus_cr' + '.jpg', format='jpg')

  108. #    plt.show()

  109.     plt.clf()

  110.     

  111.     

  112. if __name__ == '__main__':

  113.     test_anycosts()

  114.     test_cs_leq_ci_plus_cr()