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graphkit-learn
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Add parallel to RPG.

tags/v0.2.0
jajupmochi 5 years ago
parent
50da423a1c
commit
980dd1c9cf
1 changed files with 151 additions and 54 deletions
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  1. +151
    -54
      gklearn/preimage/random_preimage_generator.py

+ 151
- 54
gklearn/preimage/random_preimage_generator.py View File

@@ -10,9 +10,11 @@ import numpy as np
import time
import random
import sys
import tqdm
from tqdm import tqdm
import multiprocessing
import networkx as nx
from multiprocessing import Pool
from functools import partial
from gklearn.preimage import PreimageGenerator
from gklearn.preimage.utils import compute_k_dis
from gklearn.utils import Timer
@@ -144,12 +146,14 @@ class RandomPreimageGenerator(PreimageGenerator):
dihat_list = []
r = 0
dis_of_each_itr = [dhat]
if self.__parallel:
self._kernel_options['parallel'] = None
while r < self.__r_max:
print('\n- r =', r)
found = False
dis_bests = dis_gs + dihat_list
# compute numbers of nodes to be inserted/deleted.
# compute numbers of edges to be inserted/deleted.
# @todo what if the log is negetive? how to choose alpha (scalar)?
fdgs_list = np.array(dis_bests)
if np.min(fdgs_list) < 1:
@@ -161,54 +165,7 @@ class RandomPreimageGenerator(PreimageGenerator):
for ig, gs in enumerate(Gs_nearest + gihat_list):
if self._verbose >= 2:
print('-- computing', ig + 1, 'graphs out of', len(Gs_nearest) + len(gihat_list))
for trail in range(0, self.__l):
if self._verbose >= 2:
print('---', trail + 1, 'trail out of', self.__l)

# add and delete edges.
gtemp = gs.copy()
np.random.seed() # @todo: may not work for possible parallel.
# 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)
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)
else:
gtemp.remove_edge(node1, node2)
# compute new distances.
kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, D_N, **self._kernel_options)
kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options)
kernels_to_gtmp = [kernels_to_gtmp[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gtmp) for i in range(len(kernels_to_gtmp))] # normalize
# @todo: not correct kernel value
gram_with_gtmp = np.concatenate((np.array([kernels_to_gtmp]), np.copy(self._graph_kernel.gram_matrix)), axis=0)
gram_with_gtmp = np.concatenate((np.array([[1] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1)
dnew = compute_k_dis(0, range(1, 1 + len(D_N)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True)
# get the better graph preimage.
if dnew <= dhat: # @todo: the new distance is smaller or also equal?
if dnew < dhat:
if self._verbose >= 2:
print('trail =', str(trail))
print('\nI am smaller!')
print('index (as in D_k U {gihat} =', str(ig))
print('distance:', dhat, '->', dnew)
self.__num_updates += 1
elif dnew == dhat:
if self._verbose >= 2:
print('I am equal!')
dhat = dnew
gnew = gtemp.copy()
found = True # found better graph.
gnew, dhat, found = self.__generate_l_graphs(gs, fdgs_list[ig], dhat, ig, found, term3)
if found:
r = 0
@@ -220,10 +177,9 @@ class RandomPreimageGenerator(PreimageGenerator):
dis_of_each_itr.append(dhat)
self.__itrs += 1
if self._verbose >= 2:
print('Total number of iterations is', self.__itrs)
print('Total number of iterations is', self.__itrs, '.')
print('The preimage is updated', self.__num_updates, 'times.')
print('The shortest distances for previous iterations are', dis_of_each_itr)
print('The shortest distances for previous iterations are', dis_of_each_itr, '.')
# get results and print.
@@ -245,8 +201,149 @@ class RandomPreimageGenerator(PreimageGenerator):
print('Time to generate pre-images:', self.__runtime_generate_preimage)
print('Total time:', self.__runtime_total)
print('=============================================================================')
print()
print()
def __generate_l_graphs(self, g_init, fdgs, dhat, ig, found, term3):
if self.__parallel:
gnew, dhat, found = self.__generate_l_graphs_parallel(g_init, fdgs, dhat, ig, found, term3)
else:
gnew, dhat, found = self.__generate_l_graphs_series(g_init, fdgs, dhat, ig, found, term3)
return gnew, dhat, found
def __generate_l_graphs_series(self, g_init, fdgs, dhat, ig, found, term3):
gnew = None
for trail in range(0, self.__l):
if self._verbose >= 2:
print('---', trail + 1, 'trail out of', self.__l)

# add and delete edges.
gtemp = g_init.copy()
np.random.seed() # @todo: may not work for possible parallel.
# which edges to change.
# @todo: should we use just half of the adjacency matrix for undirected graphs?
nb_vpairs = nx.number_of_nodes(g_init) * (nx.number_of_nodes(g_init) - 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)
for item in idx_change:
node1 = int(item / (nx.number_of_nodes(g_init) - 1))
node2 = (item - node1 * (nx.number_of_nodes(g_init) - 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)
else:
gtemp.remove_edge(node1, node2)
# compute new distances.
kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, self._dataset.graphs, **self._kernel_options)
kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options)
kernels_to_gtmp = [kernels_to_gtmp[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gtmp) for i in range(len(kernels_to_gtmp))] # normalize
# @todo: not correct kernel value
gram_with_gtmp = np.concatenate((np.array([kernels_to_gtmp]), np.copy(self._graph_kernel.gram_matrix)), axis=0)
gram_with_gtmp = np.concatenate((np.array([[1] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1)
dnew = compute_k_dis(0, range(1, 1 + len(self._dataset.graphs)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True)

# get the better graph preimage.
if dnew <= dhat: # @todo: the new distance is smaller or also equal?
if dnew < dhat:
if self._verbose >= 2:
print('trail =', str(trail))
print('\nI am smaller!')
print('index (as in D_k U {gihat} =', str(ig))
print('distance:', dhat, '->', dnew)
self.__num_updates += 1
elif dnew == dhat:
if self._verbose >= 2:
print('I am equal!')
dhat = dnew
gnew = gtemp.copy()
found = True # found better graph.
return gnew, dhat, found
def __generate_l_graphs_parallel(self, g_init, fdgs, dhat, ig, found, term3):
gnew = None
len_itr = self.__l
gnew_list = [None] * len_itr
dnew_list = [None] * 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
do_fun = partial(self._generate_graph_parallel, g_init, fdgs, term3)
pool = Pool(processes=n_jobs)
if self._verbose >= 2:
iterator = tqdm(pool.imap_unordered(do_fun, itr, chunksize),
desc='Generating l graphs', file=sys.stdout)
else:
iterator = pool.imap_unordered(do_fun, itr, chunksize)
for idx, gnew, dnew in iterator:
gnew_list[idx] = gnew
dnew_list[idx] = dnew
pool.close()
pool.join()
# check if get the better graph preimage.
idx_min = np.argmin(dnew_list)
dnew = dnew_list[idx_min]
if dnew <= dhat: # @todo: the new distance is smaller or also equal?
if dnew < dhat:
if self._verbose >= 2:
print('\nI am smaller!')
print('index (as in D_k U {gihat} =', str(ig))
print('distance:', dhat, '->', dnew)
self.__num_updates += 1
elif dnew == dhat:
if self._verbose >= 2:
print('I am equal!')
dhat = dnew
gnew = gnew_list[idx_min]
found = True # found better graph.
return gnew, dhat, found
def _generate_graph_parallel(self, g_init, fdgs, term3, itr):
trail = itr

# add and delete edges.
gtemp = g_init.copy()
np.random.seed() # @todo: may not work for possible parallel.
# which edges to change.
# @todo: should we use just half of the adjacency matrix for undirected graphs?
nb_vpairs = nx.number_of_nodes(g_init) * (nx.number_of_nodes(g_init) - 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)
for item in idx_change:
node1 = int(item / (nx.number_of_nodes(g_init) - 1))
node2 = (item - node1 * (nx.number_of_nodes(g_init) - 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)
else:
gtemp.remove_edge(node1, node2)
# compute new distances.
kernels_to_gtmp, _ = self._graph_kernel.compute(gtemp, self._dataset.graphs, **self._kernel_options)
kernel_gtmp, _ = self._graph_kernel.compute(gtemp, gtemp, **self._kernel_options)
kernels_to_gtmp = [kernels_to_gtmp[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gtmp) for i in range(len(kernels_to_gtmp))] # normalize
# @todo: not correct kernel value
gram_with_gtmp = np.concatenate((np.array([kernels_to_gtmp]), np.copy(self._graph_kernel.gram_matrix)), axis=0)
gram_with_gtmp = np.concatenate((np.array([[1] + kernels_to_gtmp]).T, gram_with_gtmp), axis=1)
dnew = compute_k_dis(0, range(1, 1 + len(self._dataset.graphs)), self.__alphas, gram_with_gtmp, term3=term3, withterm3=True)
return trail, gtemp, dnew

def get_results(self):
results = {}


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