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1. Solve uncolsed file warning in graph_file.py. 2. Solve CXVPY * and @ warning in MPG.

v0.2.x
jajupmochi 5 years ago
parent
7293234d6c
commit
34506302ce
4 changed files with 3076 additions and 152 deletions
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  1. +0
    -109
      gklearn/preimage/experiments/xp_1nn.py
  2. +3022
    -0
      gklearn/preimage/experiments/xp_1nn_init10_trianglerule.py
  3. +30
    -30
      gklearn/preimage/median_preimage_generator.py
  4. +24
    -13
      gklearn/utils/graph_files.py

+ 0
- 109
gklearn/preimage/experiments/xp_1nn.py View File

@@ -1,109 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 11 14:15:11 2020

@author: ljia
"""
import functools
import multiprocessing
import os
import sys
import logging
from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
from gklearn.preimage import kernel_knn_cv

dir_root = '../results/xp_1nn.init1.no_triangle_rule.allow_zeros/'
num_random = 10
initial_solutions = 1
triangle_rule = False
allow_zeros = True
update_order = False
test_sizes = [0.9, 0.7] # , 0.5, 0.3, 0.1]

def xp_knn_1_1():
for test_size in test_sizes:
ds_name = 'Letter-high'
knn_options = {'n_neighbors': 1,
'n_splits': 30,
'test_size': test_size,
'verbose': True}
mpg_options = {'fit_method': 'k-graphs',
'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425],
'ds_name': ds_name,
'parallel': False, # False
'time_limit_in_sec': 0,
'max_itrs': 100,
'max_itrs_without_update': 3,
'epsilon_residual': 0.01,
'epsilon_ec': 0.1,
'allow_zeros': allow_zeros,
'triangle_rule': triangle_rule,
'verbose': 1}
mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
kernel_options = {'name': 'StructuralSP',
'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': 0}
ged_options = {'method': 'IPFP',
'initialization_method': 'RANDOM', # 'NODE'
'initial_solutions': initial_solutions, # 1
'edit_cost': 'LETTER2',
'attr_distance': 'euclidean',
'ratio_runs_from_initial_solutions': 1,
'threads': multiprocessing.cpu_count(),
'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
mge_options = {'init_type': 'MEDOID',
'random_inits': 10,
'time_limit': 0,
'verbose': 1,
'update_order': update_order,
'randomness': 'REAL',
'refine': False}
save_results = True
dir_save = dir_root + ds_name + '.' + kernel_options['name'] + '/' + ('update_order/' if update_order else '')
if not os.path.exists(dir_save):
os.makedirs(dir_save)
file_output = open(dir_save + 'output.txt', 'a')
# sys.stdout = file_output

# print settings.
print('parameters:')
print('dataset name:', ds_name)
print('mpg_options:', mpg_options)
print('kernel_options:', kernel_options)
print('ged_options:', ged_options)
print('mge_options:', mge_options)
print('save_results:', save_results)
for train_examples in ['k-graphs', 'expert', 'random', 'best-dataset', 'trainset']:
# for train_examples in ['expert']:
print('\n-------------------------------------')
print('train examples used:', train_examples, '\n')
mpg_options['fit_method'] = train_examples
# try:
kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, load_gm='auto', dir_save=dir_save, irrelevant_labels=None, edge_required=False, cut_range=None)
# except Exception as exp:
# print('An exception occured when running this experiment:')
# LOG_FILENAME = dir_save + 'error.txt'
# logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
# logging.exception('')
# print(repr(exp))


if __name__ == '__main__':
import pdb, traceback
try:
xp_knn_1_1()
except:
extype, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)

+ 3022
- 0
gklearn/preimage/experiments/xp_1nn_init10_trianglerule.py
File diff suppressed because it is too large
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+ 30
- 30
gklearn/preimage/median_preimage_generator.py View File

@@ -443,7 +443,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# # 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)
# 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.
@@ -454,7 +454,7 @@ class MedianPreimageGenerator(PreimageGenerator):
## 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)
## 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.
@@ -469,7 +469,7 @@ class MedianPreimageGenerator(PreimageGenerator):
if not self.__triangle_rule and self.__allow_zeros:
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -482,7 +482,7 @@ class MedianPreimageGenerator(PreimageGenerator):
elif self.__triangle_rule and self.__allow_zeros:
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -496,7 +496,7 @@ class MedianPreimageGenerator(PreimageGenerator):
elif not self.__triangle_rule and not self.__allow_zeros:
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)
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()
@@ -506,7 +506,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# # 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)
# 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)
@@ -519,7 +519,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# 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)
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]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
@@ -530,7 +530,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# 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)
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,
@@ -548,7 +548,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# 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)
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])]]
@@ -565,7 +565,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -578,7 +578,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -591,7 +591,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -604,7 +604,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -615,7 +615,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -630,7 +630,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -644,7 +644,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -658,7 +658,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -669,7 +669,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -678,7 +678,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -687,7 +687,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -696,7 +696,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -708,7 +708,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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]
@@ -719,7 +719,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
@@ -729,7 +729,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
@@ -739,7 +739,7 @@ class MedianPreimageGenerator(PreimageGenerator):
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)
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)
self.__execute_cvx(prob)
@@ -750,7 +750,7 @@ class MedianPreimageGenerator(PreimageGenerator):
elif self.__ged_options['edit_cost'] == 'CONSTANT': # @todo: node/edge may not labeled.
if not self.__triangle_rule and self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
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, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -762,7 +762,7 @@ class MedianPreimageGenerator(PreimageGenerator):
residual = np.sqrt(prob.value)
elif self.__triangle_rule and self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
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, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
@@ -776,7 +776,7 @@ class MedianPreimageGenerator(PreimageGenerator):
residual = np.sqrt(prob.value)
elif not self.__triangle_rule and not self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
@@ -784,7 +784,7 @@ class MedianPreimageGenerator(PreimageGenerator):
residual = np.sqrt(prob.value)
elif self.__triangle_rule and not self.__allow_zeros:
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat * x - dis_k_vec)
cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.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]
@@ -818,7 +818,7 @@ class MedianPreimageGenerator(PreimageGenerator):
# 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)
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,


+ 24
- 13
gklearn/utils/graph_files.py View File

@@ -494,7 +494,8 @@ def load_tud(filename):
'edge_labels': [], 'edge_attrs': []}
class_label_map = None
class_label_map_strings = []
content_rm = open(frm).read().splitlines()
with open(frm) as rm:
content_rm = rm.read().splitlines()
i = 0
while i < len(content_rm):
line = content_rm[i].strip()
@@ -558,16 +559,20 @@ def load_tud(filename):
label_names = {'node_labels': [], 'node_attrs': [],
'edge_labels': [], 'edge_attrs': []}
class_label_map = None

content_gi = open(fgi).read().splitlines() # graph indicator
content_am = open(fam).read().splitlines() # adjacency matrix
with open(fgi) as gi:
content_gi = gi.read().splitlines() # graph indicator
with open(fam) as am:
content_am = am.read().splitlines() # adjacency matrix
# load targets.
if 'fgl' in locals():
content_targets = open(fgl).read().splitlines() # targets (classification)
with open(fgl) as gl:
content_targets = gl.read().splitlines() # targets (classification)
targets = [float(i) for i in content_targets]
elif 'fga' in locals():
content_targets = open(fga).read().splitlines() # targets (regression)
with open(fga) as ga:
content_targets = ga.read().splitlines() # targets (regression)
targets = [int(i) for i in content_targets]
else:
raise Exception('Can not find targets file. Please make sure there is a "', ds_name, '_graph_labels.txt" or "', ds_name, '_graph_attributes.txt"', 'file in your dataset folder.')
@@ -577,7 +582,8 @@ def load_tud(filename):
# create graphs and add nodes
data = [nx.Graph(name=str(i)) for i in range(0, len(content_targets))]
if 'fnl' in locals():
content_nl = open(fnl).read().splitlines() # node labels
with open(fnl) as nl:
content_nl = nl.read().splitlines() # node labels
for idx, line in enumerate(content_gi):
# transfer to int first in case of unexpected blanks
data[int(line) - 1].add_node(idx)
@@ -605,7 +611,8 @@ def load_tud(filename):

# add edge labels
if 'fel' in locals():
content_el = open(fel).read().splitlines()
with open(fel) as el:
content_el = el.read().splitlines()
for idx, line in enumerate(content_el):
labels = [l.strip() for l in line.split(',')]
n = [int(i) - 1 for i in content_am[idx].split(',')]
@@ -621,7 +628,8 @@ def load_tud(filename):

# add node attributes
if 'fna' in locals():
content_na = open(fna).read().splitlines()
with open(fna) as na:
content_na = na.read().splitlines()
for idx, line in enumerate(content_na):
attrs = [a.strip() for a in line.split(',')]
g = int(content_gi[idx]) - 1
@@ -636,7 +644,8 @@ def load_tud(filename):

# add edge attributes
if 'fea' in locals():
content_ea = open(fea).read().splitlines()
with open(fea) as ea:
content_ea = ea.read().splitlines()
for idx, line in enumerate(content_ea):
attrs = [a.strip() for a in line.split(',')]
n = [int(i) - 1 for i in content_am[idx].split(',')]
@@ -669,7 +678,8 @@ def load_from_ds(filename, filename_targets):
data = []
y = []
label_names = {'node_labels': [], 'edge_labels': [], 'node_attrs': [], 'edge_attrs': []}
content = open(filename).read().splitlines()
with open(filename) as fn:
content = fn.read().splitlines()
extension = splitext(content[0].split(' ')[0])[1][1:]
if extension == 'ct':
load_file_fun = load_ct
@@ -691,8 +701,9 @@ def load_from_ds(filename, filename_targets):
g, l_names = load_file_fun(dirname_dataset + '/' + tmp.replace('#', '', 1))
data.append(g)
__append_label_names(label_names, l_names)
content_y = open(filename_targets).read().splitlines()
with open(filename_targets) as fnt:
content_y = fnt.read().splitlines()
# assume entries in filename and filename_targets have the same order.
for item in content_y:
tmp = item.split(' ')


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