wangwei
/
graphkit-learn
Not watched
1
0
Fork 0
Code
Releases 2 Wiki Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain
Browse Source

New translations randomWalkKernel.py (French)

l10n_v0.2.x
linlin 5 years ago
parent
c3de68e721
commit
c95e641e28
1 changed files with 43 additions and 43 deletions
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +43
    -43
      lang/fr/gklearn/kernels/randomWalkKernel.py

+ 43
- 43
lang/fr/gklearn/kernels/randomWalkKernel.py View File

@@ -37,15 +37,15 @@ def randomwalkkernel(*args,
n_jobs=None, n_jobs=None,
chunksize=None, chunksize=None,
verbose=True): verbose=True):
"""Calculate random walk graph kernels.
"""Compute random walk graph kernels.


Parameters Parameters
---------- ----------
Gn : List of NetworkX graph Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
List of graphs between which the kernels are computed.
G1, G2 : NetworkX graphs G1, G2 : NetworkX graphs
Two graphs between which the kernel is calculated.
Two graphs between which the kernel is computed.


compute_method : string compute_method : string
Method used to compute kernel. The Following choices are Method used to compute kernel. The Following choices are
@@ -125,7 +125,7 @@ def randomwalkkernel(*args,
Gn = [g.copy() for g in Gn] Gn = [g.copy() for g in Gn]


eweight = None eweight = None
if edge_weight == None:
if edge_weight is None:
if verbose: if verbose:
print('\n None edge weight specified. Set all weight to 1.\n') print('\n None edge weight specified. Set all weight to 1.\n')
else: else:
@@ -212,12 +212,12 @@ def randomwalkkernel(*args,


############################################################################### ###############################################################################
def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, chunksize, verbose=True): def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, chunksize, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using Sylvester method.
"""Compute walk graph kernels up to n between 2 graphs using Sylvester method.


Parameters Parameters
---------- ----------
G1, G2 : NetworkX graph G1, G2 : NetworkX graph
Graphs between which the kernel is calculated.
Graphs between which the kernel is computed.
node_label : string node_label : string
node attribute used as label. node attribute used as label.
edge_label : string edge_label : string
@@ -230,7 +230,7 @@ def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, chunksize, verbose=True
""" """
Kmatrix = np.zeros((len(Gn), len(Gn))) Kmatrix = np.zeros((len(Gn), len(Gn)))


if q == None:
if q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note # 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_list actually contains the transposes of the adjacency matrices.
A_wave_list = [ A_wave_list = [
@@ -245,7 +245,7 @@ def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, chunksize, verbose=True
# norm = A_tilde.sum(axis=0) # norm = A_tilde.sum(axis=0)
# norm[norm == 0] = 1 # norm[norm == 0] = 1
# A_wave_list.append(A_tilde / norm) # A_wave_list.append(A_tilde / norm)
if p == None: # p is uniform distribution as default.
if p is None: # p is uniform distribution as default.
def init_worker(Awl_toshare): def init_worker(Awl_toshare):
global G_Awl global G_Awl
G_Awl = Awl_toshare G_Awl = Awl_toshare
@@ -255,7 +255,7 @@ def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, chunksize, verbose=True
# pbar = tqdm( # pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2, # total=(1 + len(Gn)) * len(Gn) / 2,
# desc='calculating kernels',
# desc='Computing kernels',
# file=sys.stdout) # file=sys.stdout)
# for i in range(0, len(Gn)): # for i in range(0, len(Gn)):
# for j in range(i, len(Gn)): # for j in range(i, len(Gn)):
@@ -300,12 +300,12 @@ def _se_do(A_wave1, A_wave2, lmda):
############################################################################### ###############################################################################
def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels, def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
node_label, edge_label, eweight, n_jobs, chunksize, verbose=True): node_label, edge_label, eweight, n_jobs, chunksize, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using conjugate method.
"""Compute walk graph kernels up to n between 2 graphs using conjugate method.


Parameters Parameters
---------- ----------
G1, G2 : NetworkX graph G1, G2 : NetworkX graph
Graphs between which the kernel is calculated.
Graphs between which the kernel is computed.
node_label : string node_label : string
node attribute used as label. node attribute used as label.
edge_label : string edge_label : string
@@ -321,14 +321,14 @@ def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# if not ds_attrs['node_labeled'] and ds_attrs['node_attr_dim'] < 1 and \ # if not ds_attrs['node_labeled'] and ds_attrs['node_attr_dim'] < 1 and \
# not ds_attrs['edge_labeled'] and ds_attrs['edge_attr_dim'] < 1: # not ds_attrs['edge_labeled'] and ds_attrs['edge_attr_dim'] < 1:
# # this is faster from unlabeled graphs. @todo: why? # # this is faster from unlabeled graphs. @todo: why?
# if q == None:
# if q is None:
# # don't normalize adjacency matrices if q is a uniform vector. Note # # 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_list actually contains the transposes of the adjacency matrices.
# A_wave_list = [ # A_wave_list = [
# nx.adjacency_matrix(G, eweight).todense().transpose() for G in # nx.adjacency_matrix(G, eweight).todense().transpose() for G in
# tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout) # tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout)
# ] # ]
# if p == None: # p is uniform distribution as default.
# if p is None: # p is uniform distribution as default.
# def init_worker(Awl_toshare): # def init_worker(Awl_toshare):
# global G_Awl # global G_Awl
# G_Awl = Awl_toshare # G_Awl = Awl_toshare
@@ -336,23 +336,23 @@ def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, # parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
# glbv=(A_wave_list,), n_jobs=n_jobs) # glbv=(A_wave_list,), n_jobs=n_jobs)
# else: # else:
# reindex nodes using consecutive integers for convenience of kernel calculation.
# reindex nodes using consecutive integers for convenience of kernel computation.
Gn = [nx.convert_node_labels_to_integers( Gn = [nx.convert_node_labels_to_integers(
g, first_label=0, label_attribute='label_orignal') for g in (tqdm( g, first_label=0, label_attribute='label_orignal') for g in (tqdm(
Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)] Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)]
if p == None and q == None: # p and q are uniform distributions as default.
if p is None and q is None: # p and q are uniform distributions as default.
def init_worker(gn_toshare): def init_worker(gn_toshare):
global G_gn global G_gn
G_gn = gn_toshare G_gn = gn_toshare
do_partial = partial(wrapper_cg_labled_do, ds_attrs, node_kernels,
do_partial = partial(wrapper_cg_labeled_do, ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda) node_label, edge_kernels, edge_label, lmda)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose) glbv=(Gn,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose)
# pbar = tqdm( # pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2, # total=(1 + len(Gn)) * len(Gn) / 2,
# desc='calculating kernels',
# desc='Computing kernels',
# file=sys.stdout) # file=sys.stdout)
# for i in range(0, len(Gn)): # for i in range(0, len(Gn)):
# for j in range(i, len(Gn)): # for j in range(i, len(Gn)):
@@ -382,24 +382,24 @@ def _cg_unlabled_do(A_wave1, A_wave2, lmda):
return np.dot(q_times, x) return np.dot(q_times, x)




def wrapper_cg_labled_do(ds_attrs, node_kernels, node_label, edge_kernels,
def wrapper_cg_labeled_do(ds_attrs, node_kernels, node_label, edge_kernels,
edge_label, lmda, itr): edge_label, lmda, itr):
i = itr[0] i = itr[0]
j = itr[1] j = itr[1]
return i, j, _cg_labled_do(G_gn[i], G_gn[j], ds_attrs, node_kernels,
return i, j, _cg_labeled_do(G_gn[i], G_gn[j], ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda) node_label, edge_kernels, edge_label, lmda)




def _cg_labled_do(g1, g2, ds_attrs, node_kernels, node_label,
def _cg_labeled_do(g1, g2, ds_attrs, node_kernels, node_label,
edge_kernels, edge_label, lmda): edge_kernels, edge_label, lmda):
# Frist, compute kernels between all pairs of nodes, method borrowed
# Frist, compute kernels between all pairs of nodes using the method borrowed
# from FCSP. It is faster than directly computing all edge kernels # 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 # 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 # graphs compared, which is the most case we went though. For very
# sparse graphs, this would be slow. # sparse graphs, this would be slow.
vk_dict = computeVK(g1, g2, ds_attrs, node_kernels, node_label) vk_dict = computeVK(g1, g2, ds_attrs, node_kernels, node_label)
# Compute weight matrix of the direct product graph.
# Compute the weight matrix of the direct product graph.
w_times, w_dim = computeW(g1, g2, vk_dict, ds_attrs, w_times, w_dim = computeW(g1, g2, vk_dict, ds_attrs,
edge_kernels, edge_label) edge_kernels, edge_label)
# use uniform distribution if there is no prior knowledge. # use uniform distribution if there is no prior knowledge.
@@ -415,12 +415,12 @@ def _cg_labled_do(g1, g2, ds_attrs, node_kernels, node_label,
############################################################################### ###############################################################################
def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels, def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
node_label, edge_label, eweight, n_jobs, chunksize, verbose=True): node_label, edge_label, eweight, n_jobs, chunksize, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using Fixed-Point method.
"""Compute walk graph kernels up to n between 2 graphs using Fixed-Point method.


Parameters Parameters
---------- ----------
G1, G2 : NetworkX graph G1, G2 : NetworkX graph
Graphs between which the kernel is calculated.
Graphs between which the kernel is computed.
node_label : string node_label : string
node attribute used as label. node attribute used as label.
edge_label : string edge_label : string
@@ -438,17 +438,17 @@ def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# if not ds_attrs['node_labeled'] and ds_attrs['node_attr_dim'] < 1 and \ # if not ds_attrs['node_labeled'] and ds_attrs['node_attr_dim'] < 1 and \
# not ds_attrs['edge_labeled'] and ds_attrs['edge_attr_dim'] > 1: # not ds_attrs['edge_labeled'] and ds_attrs['edge_attr_dim'] > 1:
# # this is faster from unlabeled graphs. @todo: why? # # this is faster from unlabeled graphs. @todo: why?
# if q == None:
# if q is None:
# # don't normalize adjacency matrices if q is a uniform vector. Note # # 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_list actually contains the transposes of the adjacency matrices.
# A_wave_list = [ # A_wave_list = [
# nx.adjacency_matrix(G, eweight).todense().transpose() for G in # nx.adjacency_matrix(G, eweight).todense().transpose() for G in
# tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout) # tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout)
# ] # ]
# if p == None: # p is uniform distribution as default.
# if p is None: # p is uniform distribution as default.
# pbar = tqdm( # pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2, # total=(1 + len(Gn)) * len(Gn) / 2,
# desc='calculating kernels',
# desc='Computing kernels',
# file=sys.stdout) # file=sys.stdout)
# for i in range(0, len(Gn)): # for i in range(0, len(Gn)):
# for j in range(i, len(Gn)): # for j in range(i, len(Gn)):
@@ -464,33 +464,33 @@ def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# Kmatrix[j][i] = Kmatrix[i][j] # Kmatrix[j][i] = Kmatrix[i][j]
# pbar.update(1) # pbar.update(1)
# else: # else:
# reindex nodes using consecutive integers for convenience of kernel calculation.
# reindex nodes using consecutive integers for the convenience of kernel computation.
Gn = [nx.convert_node_labels_to_integers( Gn = [nx.convert_node_labels_to_integers(
g, first_label=0, label_attribute='label_orignal') for g in (tqdm( g, first_label=0, label_attribute='label_orignal') for g in (tqdm(
Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)] Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)]
if p == None and q == None: # p and q are uniform distributions as default.
if p is None and q is None: # p and q are uniform distributions as default.
def init_worker(gn_toshare): def init_worker(gn_toshare):
global G_gn global G_gn
G_gn = gn_toshare G_gn = gn_toshare
do_partial = partial(wrapper_fp_labled_do, ds_attrs, node_kernels,
do_partial = partial(wrapper_fp_labeled_do, ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda) node_label, edge_kernels, edge_label, lmda)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose) glbv=(Gn,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose)
return Kmatrix return Kmatrix




def wrapper_fp_labled_do(ds_attrs, node_kernels, node_label, edge_kernels,
def wrapper_fp_labeled_do(ds_attrs, node_kernels, node_label, edge_kernels,
edge_label, lmda, itr): edge_label, lmda, itr):
i = itr[0] i = itr[0]
j = itr[1] j = itr[1]
return i, j, _fp_labled_do(G_gn[i], G_gn[j], ds_attrs, node_kernels,
return i, j, _fp_labeled_do(G_gn[i], G_gn[j], ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda) node_label, edge_kernels, edge_label, lmda)




def _fp_labled_do(g1, g2, ds_attrs, node_kernels, node_label,
def _fp_labeled_do(g1, g2, ds_attrs, node_kernels, node_label,
edge_kernels, edge_label, lmda): edge_kernels, edge_label, lmda):
# Frist, compute kernels between all pairs of nodes, method borrowed
# Frist, compute kernels between all pairs of nodes using the method borrowed
# from FCSP. It is faster than directly computing all edge kernels # 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 # 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 # graphs compared, which is the most case we went though. For very
@@ -519,13 +519,13 @@ def func_fp(x, p_times, lmda, w_times):


############################################################################### ###############################################################################
def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, chunksize, verbose=True): def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, chunksize, verbose=True):
"""Calculate walk graph kernels up to n between 2 unlabeled graphs using
"""Compute walk graph kernels up to n between 2 unlabeled graphs using
spectral decomposition method. Labels will be ignored. spectral decomposition method. Labels will be ignored.


Parameters Parameters
---------- ----------
G1, G2 : NetworkX graph G1, G2 : NetworkX graph
Graphs between which the kernel is calculated.
Graphs between which the kernel is computed.
node_label : string node_label : string
node attribute used as label. node attribute used as label.
edge_label : string edge_label : string
@@ -538,7 +538,7 @@ def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, chunk
""" """
Kmatrix = np.zeros((len(Gn), len(Gn))) Kmatrix = np.zeros((len(Gn), len(Gn)))


if q == None:
if q is None:
# precompute the spectral decomposition of each graph. # precompute the spectral decomposition of each graph.
P_list = [] P_list = []
D_list = [] D_list = []
@@ -552,7 +552,7 @@ def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, chunk
P_list.append(ev) P_list.append(ev)
# P_inv_list = [p.T for p in P_list] # @todo: also works for directed graphs? # P_inv_list = [p.T for p in P_list] # @todo: also works for directed graphs?


if p == None: # p is uniform distribution as default.
if p is None: # p is uniform distribution as default.
q_T_list = [np.full((1, nx.number_of_nodes(G)), 1 / nx.number_of_nodes(G)) for G in Gn] q_T_list = [np.full((1, nx.number_of_nodes(G)), 1 / nx.number_of_nodes(G)) for G in Gn]
# q_T_list = [q.T for q in q_list] # q_T_list = [q.T for q in q_list]
def init_worker(q_T_toshare, P_toshare, D_toshare): def init_worker(q_T_toshare, P_toshare, D_toshare):
@@ -568,7 +568,7 @@ def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, chunk
# pbar = tqdm( # pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2, # total=(1 + len(Gn)) * len(Gn) / 2,
# desc='calculating kernels',
# desc='Computing kernels',
# file=sys.stdout) # file=sys.stdout)
# for i in range(0, len(Gn)): # for i in range(0, len(Gn)):
# for j in range(i, len(Gn)): # for j in range(i, len(Gn)):
@@ -605,12 +605,12 @@ def _sd_do(q_T1, q_T2, P1, P2, D1, D2, weight, sub_kernel):


############################################################################### ###############################################################################
def _randomwalkkernel_kron(G1, G2, node_label, edge_label): def _randomwalkkernel_kron(G1, G2, node_label, edge_label):
"""Calculate walk graph kernels up to n between 2 graphs using nearest Kronecker product approximation method.
"""Compute walk graph kernels up to n between 2 graphs using nearest Kronecker product approximation method.


Parameters Parameters
---------- ----------
G1, G2 : NetworkX graph G1, G2 : NetworkX graph
Graphs between which the kernel is calculated.
Graphs between which the kernel is computed.
node_label : string node_label : string
node attribute used as label. node attribute used as label.
edge_label : string edge_label : string
@@ -692,8 +692,8 @@ def computeVK(g1, g2, ds_attrs, node_kernels, node_label):




def computeW(g1, g2, vk_dict, ds_attrs, edge_kernels, edge_label): def computeW(g1, g2, vk_dict, ds_attrs, edge_kernels, edge_label):
'''Compute weight matrix of the direct product graph.
'''
"""Compute the weight matrix of the direct product graph.
"""
w_dim = nx.number_of_nodes(g1) * nx.number_of_nodes(g2) w_dim = nx.number_of_nodes(g1) * nx.number_of_nodes(g2)
w_times = np.zeros((w_dim, w_dim)) w_times = np.zeros((w_dim, w_dim))
if vk_dict: # node labeled if vk_dict: # node labeled


Write Preview
Loading…
Cancel
Save