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New translations fixed_point.py (Chinese Simplified)

l10n_v0.2.x
linlin 5 years ago
parent
3cb0cc0c37
commit
c0d165e3a3
1 changed files with 218 additions and 142 deletions
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      lang/zh/gklearn/kernels/fixed_point.py

+ 218
- 142
lang/zh/gklearn/kernels/fixed_point.py View File

@@ -14,61 +14,56 @@ import sys
from tqdm import tqdm
import numpy as np
import networkx as nx
from control import dlyap
from scipy import optimize
from gklearn.utils.parallel import parallel_gm, parallel_me
from gklearn.kernels import RandomWalk
from gklearn.kernels import RandomWalkMeta
from gklearn.utils.utils import compute_vertex_kernels


class FixedPoint(RandomWalk):

class FixedPoint(RandomWalkMeta):
def __init__(self, **kwargs):
RandomWalk.__init__(self, **kwargs)
super().__init__(**kwargs)
self._node_kernels = kwargs.get('node_kernels', None)
self._edge_kernels = kwargs.get('edge_kernels', None)
self._node_labels = kwargs.get('node_labels', [])
self._edge_labels = kwargs.get('edge_labels', [])
self._node_attrs = kwargs.get('node_attrs', [])
self._edge_attrs = kwargs.get('edge_attrs', [])

def _compute_gm_series(self):
self._check_edge_weight(self._graphs)
self._check_edge_weight(self._graphs, self._verbose)
self._check_graphs(self._graphs)
if self._verbose >= 2:
import warnings
warnings.warn('All labels are ignored.')
lmda = self._weight
# compute Gram matrix.
# Compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
if self._q == None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
# Reindex nodes using consecutive integers for the convenience of kernel computation.
if self._verbose >= 2:
iterator = tqdm(self._graphs, desc='Reindex vertices', file=sys.stdout)
else:
iterator = self._graphs
self._graphs = [nx.convert_node_labels_to_integers(g, first_label=0, label_attribute='label_orignal') for g in iterator]
if self._p is None and self._q is None: # p and q are uniform distributions as default.
from itertools import combinations_with_replacement
itr = combinations_with_replacement(range(0, len(self._graphs)), 2)
if self._verbose >= 2:
iterator = tqdm(self._graphs, desc='compute adjacency matrices', file=sys.stdout)
iterator = tqdm(itr, desc='Computing kernels', file=sys.stdout)
else:
iterator = self._graphs
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator]
# # normalized adjacency matrices
# A_wave_list = []
# for G in tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout):
# A_tilde = nx.adjacency_matrix(G, eweight).todense().transpose()
# norm = A_tilde.sum(axis=0)
# norm[norm == 0] = 1
# A_wave_list.append(A_tilde / norm)

if self._p == None: # p is uniform distribution as default.
from itertools import combinations_with_replacement
itr = combinations_with_replacement(range(0, len(self._graphs)), 2)
if self._verbose >= 2:
iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout)
else:
iterator = itr
for i, j in iterator:
kernel = self.__kernel_do(A_wave_list[i], A_wave_list[j], lmda)
gram_matrix[i][j] = kernel
gram_matrix[j][i] = kernel
else: # @todo
pass
iterator = itr
for i, j in iterator:
kernel = self._kernel_do(self._graphs[i], self._graphs[j], lmda)
gram_matrix[i][j] = kernel
gram_matrix[j][i] = kernel

else: # @todo
pass
@@ -76,36 +71,31 @@ class FixedPoint(RandomWalk):
def _compute_gm_imap_unordered(self):
self._check_edge_weight(self._graphs)
self._check_edge_weight(self._graphs, self._verbose)
self._check_graphs(self._graphs)
if self._verbose >= 2:
import warnings
warnings.warn('All labels are ignored.')
# compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
# Compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
if self._q == None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
if self._verbose >= 2:
iterator = tqdm(self._graphs, desc='compute adjacency matrices', file=sys.stdout)
else:
iterator = self._graphs
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel?

if self._p == None: # p is uniform distribution as default.
def init_worker(A_wave_list_toshare):
global G_A_wave_list
G_A_wave_list = A_wave_list_toshare
do_fun = self._wrapper_kernel_do
parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker,
glbv=(A_wave_list,), n_jobs=self._n_jobs, verbose=self._verbose)

else: # @todo
pass
# @todo: parallel this.
# Reindex nodes using consecutive integers for the convenience of kernel computation.
if self._verbose >= 2:
iterator = tqdm(self._graphs, desc='Reindex vertices', file=sys.stdout)
else:
iterator = self._graphs
self._graphs = [nx.convert_node_labels_to_integers(g, first_label=0, label_attribute='label_orignal') for g in iterator]
if self._p is None and self._q is None: # p and q are uniform distributions as default.

def init_worker(gn_toshare):
global G_gn
G_gn = gn_toshare
do_fun = self._wrapper_kernel_do
parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker,
glbv=(self._graphs,), n_jobs=self._n_jobs, verbose=self._verbose)

else: # @todo
pass
@@ -113,39 +103,33 @@ class FixedPoint(RandomWalk):
def _compute_kernel_list_series(self, g1, g_list):
self._check_edge_weight(g_list + [g1])
self._check_edge_weight(g_list + [g1], self._verbose)
self._check_graphs(g_list + [g1])
if self._verbose >= 2:
import warnings
warnings.warn('All labels are ignored.')
lmda = self._weight
# compute kernel list.
kernel_list = [None] * len(g_list)

# Reindex nodes using consecutive integers for the convenience of kernel computation.
g1 = nx.convert_node_labels_to_integers(g1, first_label=0, label_attribute='label_orignal')
if self._verbose >= 2:
iterator = tqdm(g_list, desc='Reindex vertices', file=sys.stdout)
else:
iterator = g_list
g_list = [nx.convert_node_labels_to_integers(g, first_label=0, label_attribute='label_orignal') for g in iterator]
if self._q == None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
if self._p is None and self._q is None: # p and q are uniform distributions as default.

if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout)
iterator = tqdm(range(len(g_list)), desc='Computing kernels', file=sys.stdout)
else:
iterator = range(len(g_list))
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator]

if self._p == None: # p is uniform distribution as default.
if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout)
else:
iterator = range(len(g_list))
for i in iterator:
kernel = self.__kernel_do(A_wave_1, A_wave_list[i], lmda)
kernel_list[i] = kernel
else: # @todo
pass
for i in iterator:
kernel = self._kernel_do(g1, g_list[i], lmda)
kernel_list[i] = kernel

else: # @todo
pass
@@ -153,43 +137,38 @@ class FixedPoint(RandomWalk):
def _compute_kernel_list_imap_unordered(self, g1, g_list):
self._check_edge_weight(g_list + [g1])
self._check_edge_weight(g_list + [g1], self._verbose)
self._check_graphs(g_list + [g1])
if self._verbose >= 2:
import warnings
warnings.warn('All labels are ignored.')
# compute kernel list.
kernel_list = [None] * len(g_list)
if self._q == None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout)
else:
iterator = range(len(g_list))
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel?
# Reindex nodes using consecutive integers for the convenience of kernel computation.
g1 = nx.convert_node_labels_to_integers(g1, first_label=0, label_attribute='label_orignal')
# @todo: parallel this.
if self._verbose >= 2:
iterator = tqdm(g_list, desc='Reindex vertices', file=sys.stdout)
else:
iterator = g_list
g_list = [nx.convert_node_labels_to_integers(g, first_label=0, label_attribute='label_orignal') for g in iterator]
if self._p is None and self._q is None: # p and q are uniform distributions as default.

if self._p == None: # p is uniform distribution as default.
def init_worker(A_wave_1_toshare, A_wave_list_toshare):
global G_A_wave_1, G_A_wave_list
G_A_wave_1 = A_wave_1_toshare
G_A_wave_list = A_wave_list_toshare
def init_worker(g1_toshare, g_list_toshare):
global G_g1, G_g_list
G_g1 = g1_toshare
G_g_list = g_list_toshare

do_fun = self._wrapper_kernel_list_do
def func_assign(result, var_to_assign):
var_to_assign[result[0]] = result[1]
itr = range(len(g_list))
len_itr = len(g_list)
parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr,
init_worker=init_worker, glbv=(A_wave_1, A_wave_list), method='imap_unordered',
n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose)
do_fun = self._wrapper_kernel_list_do
def func_assign(result, var_to_assign):
var_to_assign[result[0]] = result[1]
itr = range(len(g_list))
len_itr = len(g_list)
parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr,
init_worker=init_worker, glbv=(g1, g_list), method='imap_unordered',
n_jobs=self._n_jobs, itr_desc='Computing kernels', verbose=self._verbose)
else: # @todo
pass
else: # @todo
pass
@@ -197,49 +176,146 @@ class FixedPoint(RandomWalk):


def _wrapper_kernel_list_do(self, itr):
return itr, self._kernel_do(G_A_wave_1, G_A_wave_list[itr], self._weight)
return itr, self._kernel_do(G_g1, G_g_list[itr], self._weight)
def _compute_single_kernel_series(self, g1, g2):
self._check_edge_weight([g1] + [g2])
self._check_edge_weight([g1] + [g2], self._verbose)
self._check_graphs([g1] + [g2])
if self._verbose >= 2:
import warnings
warnings.warn('All labels are ignored.')
lmda = self._weight
if self._q == None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
A_wave_2 = nx.adjacency_matrix(g2, self._edge_weight).todense().transpose()
if self._p == None: # p is uniform distribution as default.
kernel = self.__kernel_do(A_wave_1, A_wave_2, lmda)
else: # @todo
pass
# Reindex nodes using consecutive integers for the convenience of kernel computation.
g1 = nx.convert_node_labels_to_integers(g1, first_label=0, label_attribute='label_orignal')
g2 = nx.convert_node_labels_to_integers(g2, first_label=0, label_attribute='label_orignal')
if self._p is None and self._q is None: # p and q are uniform distributions as default.
kernel = self._kernel_do(g1, g2, lmda)

else: # @todo
pass
return kernel
def __kernel_do(self, A_wave1, A_wave2, lmda):
def _kernel_do(self, g1, g2, lmda):
S = lmda * A_wave2
T_t = A_wave1
# Frist, compute kernels between all pairs of nodes using the method borrowed
# from FCSP. It is faster than directly computing all edge kernels
# when $d_1d_2>2$, where $d_1$ and $d_2$ are vertex degrees of the
# graphs compared, which is the most case we went though. For very
# sparse graphs, this would be slow.
vk_dict = self._compute_vertex_kernels(g1, g2)
# Compute the weight matrix of the direct product graph.
w_times, w_dim = self._compute_weight_matrix(g1, g2, vk_dict)
# use uniform distribution if there is no prior knowledge.
nb_pd = len(A_wave1) * len(A_wave2)
p_times_uni = 1 / nb_pd
M0 = np.full((len(A_wave2), len(A_wave1)), p_times_uni)
X = dlyap(S, T_t, M0)
X = np.reshape(X, (-1, 1), order='F')
p_times_uni = 1 / w_dim
p_times = np.full((w_dim, 1), p_times_uni)
x = optimize.fixed_point(self._func_fp, p_times, args=(p_times, lmda, w_times), xtol=1e-06, maxiter=1000)
# use uniform distribution if there is no prior knowledge.
q_times = np.full((1, nb_pd), p_times_uni)
return np.dot(q_times, X)
q_times = np.full((1, w_dim), p_times_uni)
return np.dot(q_times, x)
def _wrapper_kernel_do(self, itr):
i = itr[0]
j = itr[1]
return i, j, self.__kernel_do(G_A_wave_list[i], G_A_wave_list[j], self._weight)
return i, j, self._kernel_do(G_gn[i], G_gn[j], self._weight)
def _func_fp(self, x, p_times, lmda, w_times):
haha = w_times * x
haha = lmda * haha
haha = p_times + haha
return p_times + lmda * np.dot(w_times, x)
def _compute_vertex_kernels(self, g1, g2):
"""Compute vertex kernels between vertices of two graphs.
"""
return compute_vertex_kernels(g1, g2, self._node_kernels, node_labels=self._node_labels, node_attrs=self._node_attrs)
# @todo: move if out to make it faster.
# @todo: node/edge kernels use direct function rather than dicts.
def _compute_weight_matrix(self, g1, g2, vk_dict):
"""Compute the weight matrix of the direct product graph.
"""
# Define edge kernels.
def compute_ek_11(e1, e2, ke):
e1_labels = [e1[2][el] for el in self._edge_labels]
e2_labels = [e2[2][el] for el in self._edge_labels]
e1_attrs = [e1[2][ea] for ea in self._edge_attrs]
e2_attrs = [e2[2][ea] for ea in self._edge_attrs]
return ke(e1_labels, e2_labels, e1_attrs, e2_attrs)
def compute_ek_10(e1, e2, ke):
e1_labels = [e1[2][el] for el in self._edge_labels]
e2_labels = [e2[2][el] for el in self._edge_labels]
return ke(e1_labels, e2_labels)
def compute_ek_01(e1, e2, ke):
e1_attrs = [e1[2][ea] for ea in self._edge_attrs]
e2_attrs = [e2[2][ea] for ea in self._edge_attrs]
return ke(e1_attrs, e2_attrs)
def compute_ek_00(e1, e2, ke):
return 1
# Select the proper edge kernel.
if len(self._edge_labels) > 0:
# edge symb and non-synb labeled
if len(self._edge_attrs) > 0:
ke = self._edge_kernels['mix']
ek_temp = compute_ek_11
# edge symb labeled
else:
ke = self._edge_kernels['symb']
ek_temp = compute_ek_10
else:
# edge non-synb labeled
if len(self._edge_attrs) > 0:
ke = self._edge_kernels['nsymb']
ek_temp = compute_ek_01
# edge unlabeled
else:
ke = None
ek_temp = compute_ek_00 # @todo: check how much slower is this.
# Compute the weight matrix.
w_dim = nx.number_of_nodes(g1) * nx.number_of_nodes(g2)
w_times = np.zeros((w_dim, w_dim))
if vk_dict: # node labeled
if self._ds_infos['directed']:
for e1 in g1.edges(data=True):
for e2 in g2.edges(data=True):
w_idx = (e1[0] * nx.number_of_nodes(g2) + e2[0], e1[1] * nx.number_of_nodes(g2) + e2[1])
w_times[w_idx] = vk_dict[(e1[0], e2[0])] * ek_temp(e1, e2, ke) * vk_dict[(e1[1], e2[1])]
else: # undirected
for e1 in g1.edges(data=True):
for e2 in g2.edges(data=True):
w_idx = (e1[0] * nx.number_of_nodes(g2) + e2[0], e1[1] * nx.number_of_nodes(g2) + e2[1])
w_times[w_idx] = vk_dict[(e1[0], e2[0])] * ek_temp(e1, e2, ke) * vk_dict[(e1[1], e2[1])] + vk_dict[(e1[0], e2[1])] * ek_temp(e1, e2, ke) * vk_dict[(e1[1], e2[0])]
w_times[w_idx[1], w_idx[0]] = w_times[w_idx[0], w_idx[1]]
w_idx2 = (e1[0] * nx.number_of_nodes(g2) + e2[1], e1[1] * nx.number_of_nodes(g2) + e2[0])
w_times[w_idx2[0], w_idx2[1]] = w_times[w_idx[0], w_idx[1]]
w_times[w_idx2[1], w_idx2[0]] = w_times[w_idx[0], w_idx[1]]
else: # node unlabeled
if self._ds_infos['directed']:
for e1 in g1.edges(data=True):
for e2 in g2.edges(data=True):
w_idx = (e1[0] * nx.number_of_nodes(g2) + e2[0], e1[1] * nx.number_of_nodes(g2) + e2[1])
w_times[w_idx] = ek_temp(e1, e2, ke)
else: # undirected
for e1 in g1.edges(data=True):
for e2 in g2.edges(data=True):
w_idx = (e1[0] * nx.number_of_nodes(g2) + e2[0], e1[1] * nx.number_of_nodes(g2) + e2[1])
w_times[w_idx] = ek_temp(e1, e2, ke)
w_times[w_idx[1], w_idx[0]] = w_times[w_idx[0], w_idx[1]]
w_idx2 = (e1[0] * nx.number_of_nodes(g2) + e2[1], e1[1] * nx.number_of_nodes(g2) + e2[0])
w_times[w_idx2[0], w_idx2[1]] = w_times[w_idx[0], w_idx[1]]
w_times[w_idx2[1], w_idx2[0]] = w_times[w_idx[0], w_idx[1]]

return w_times, w_dim

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