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graphkit-learn
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New translations marginalized.py (French)

l10n_v0.2.x
linlin 5 years ago
parent
940a268b9f
commit
bd16c9964f
1 changed files with 45 additions and 45 deletions
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  1. +45
    -45
      lang/fr/gklearn/kernels/marginalized.py

+ 45
- 45
lang/fr/gklearn/kernels/marginalized.py View File

@@ -33,25 +33,25 @@ class Marginalized(GraphKernel):
def __init__(self, **kwargs):
GraphKernel.__init__(self)
self.__node_labels = kwargs.get('node_labels', [])
self.__edge_labels = kwargs.get('edge_labels', [])
self.__p_quit = kwargs.get('p_quit', 0.5)
self.__n_iteration = kwargs.get('n_iteration', 10)
self.__remove_totters = kwargs.get('remove_totters', False)
self.__ds_infos = kwargs.get('ds_infos', {})
self.__n_iteration = int(self.__n_iteration)
self._node_labels = kwargs.get('node_labels', [])
self._edge_labels = kwargs.get('edge_labels', [])
self._p_quit = kwargs.get('p_quit', 0.5)
self._n_iteration = kwargs.get('n_iteration', 10)
self._remove_totters = kwargs.get('remove_totters', False)
self._ds_infos = kwargs.get('ds_infos', {})
self._n_iteration = int(self._n_iteration)


def _compute_gm_series(self):
self.__add_dummy_labels(self._graphs)
self._add_dummy_labels(self._graphs)
if self.__remove_totters:
if self._remove_totters:
if self._verbose >= 2:
iterator = tqdm(self._graphs, desc='removing tottering', file=sys.stdout)
else:
iterator = self._graphs
# @todo: this may not work.
self._graphs = [untotterTransformation(G, self.__node_labels, self.__edge_labels) for G in iterator]
self._graphs = [untotterTransformation(G, self._node_labels, self._edge_labels) for G in iterator]
# compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
@@ -63,7 +63,7 @@ class Marginalized(GraphKernel):
else:
iterator = itr
for i, j in iterator:
kernel = self.__kernel_do(self._graphs[i], self._graphs[j])
kernel = self._kernel_do(self._graphs[i], self._graphs[j])
gram_matrix[i][j] = kernel
gram_matrix[j][i] = kernel # @todo: no directed graph considered?
@@ -71,9 +71,9 @@ class Marginalized(GraphKernel):
def _compute_gm_imap_unordered(self):
self.__add_dummy_labels(self._graphs)
self._add_dummy_labels(self._graphs)
if self.__remove_totters:
if self._remove_totters:
pool = Pool(self._n_jobs)
itr = range(0, len(self._graphs))
if len(self._graphs) < 100 * self._n_jobs:
@@ -105,16 +105,16 @@ class Marginalized(GraphKernel):
def _compute_kernel_list_series(self, g1, g_list):
self.__add_dummy_labels(g_list + [g1])
self._add_dummy_labels(g_list + [g1])
if self.__remove_totters:
g1 = untotterTransformation(g1, self.__node_labels, self.__edge_labels) # @todo: this may not work.
if self._remove_totters:
g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
if self._verbose >= 2:
iterator = tqdm(g_list, desc='removing tottering', file=sys.stdout)
else:
iterator = g_list
# @todo: this may not work.
g_list = [untotterTransformation(G, self.__node_labels, self.__edge_labels) for G in iterator]
g_list = [untotterTransformation(G, self._node_labels, self._edge_labels) for G in iterator]
# compute kernel list.
kernel_list = [None] * len(g_list)
@@ -123,17 +123,17 @@ class Marginalized(GraphKernel):
else:
iterator = range(len(g_list))
for i in iterator:
kernel = self.__kernel_do(g1, g_list[i])
kernel = self._kernel_do(g1, g_list[i])
kernel_list[i] = kernel
return kernel_list
def _compute_kernel_list_imap_unordered(self, g1, g_list):
self.__add_dummy_labels(g_list + [g1])
self._add_dummy_labels(g_list + [g1])
if self.__remove_totters:
g1 = untotterTransformation(g1, self.__node_labels, self.__edge_labels) # @todo: this may not work.
if self._remove_totters:
g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
pool = Pool(self._n_jobs)
itr = range(0, len(g_list))
if len(g_list) < 100 * self._n_jobs:
@@ -171,19 +171,19 @@ class Marginalized(GraphKernel):
def _wrapper_kernel_list_do(self, itr):
return itr, self.__kernel_do(G_g1, G_g_list[itr])
return itr, self._kernel_do(G_g1, G_g_list[itr])
def _compute_single_kernel_series(self, g1, g2):
self.__add_dummy_labels([g1] + [g2])
if self.__remove_totters:
g1 = untotterTransformation(g1, self.__node_labels, self.__edge_labels) # @todo: this may not work.
g2 = untotterTransformation(g2, self.__node_labels, self.__edge_labels)
kernel = self.__kernel_do(g1, g2)
self._add_dummy_labels([g1] + [g2])
if self._remove_totters:
g1 = untotterTransformation(g1, self._node_labels, self._edge_labels) # @todo: this may not work.
g2 = untotterTransformation(g2, self._node_labels, self._edge_labels)
kernel = self._kernel_do(g1, g2)
return kernel
def __kernel_do(self, g1, g2):
def _kernel_do(self, g1, g2):
"""Compute marginalized graph kernel between 2 graphs.
Parameters
@@ -205,7 +205,7 @@ class Marginalized(GraphKernel):
p_init_G1 = 1 / num_nodes_G1
p_init_G2 = 1 / num_nodes_G2
q = self.__p_quit * self.__p_quit
q = self._p_quit * self._p_quit
r1 = q
# # initial R_inf
@@ -260,36 +260,36 @@ class Marginalized(GraphKernel):
if len(g2[node2]) > 0:
R_inf[(node1, node2)] = r1
else:
R_inf[(node1, node2)] = self.__p_quit
R_inf[(node1, node2)] = self._p_quit
else:
if len(g2[node2]) > 0:
R_inf[(node1, node2)] = self.__p_quit
R_inf[(node1, node2)] = self._p_quit
else:
R_inf[(node1, node2)] = 1
# compute all transition probability first.
t_dict = {}
if self.__n_iteration > 1:
if self._n_iteration > 1:
for node1 in g1.nodes():
neighbor_n1 = g1[node1]
# the transition probability distribution in the random walks
# generating step (uniform distribution over the vertices adjacent
# to the current vertex)
if len(neighbor_n1) > 0:
p_trans_n1 = (1 - self.__p_quit) / len(neighbor_n1)
p_trans_n1 = (1 - self._p_quit) / len(neighbor_n1)
for node2 in g2.nodes():
neighbor_n2 = g2[node2]
if len(neighbor_n2) > 0:
p_trans_n2 = (1 - self.__p_quit) / len(neighbor_n2)
p_trans_n2 = (1 - self._p_quit) / len(neighbor_n2)
for neighbor1 in neighbor_n1:
for neighbor2 in neighbor_n2:
t_dict[(node1, node2, neighbor1, neighbor2)] = \
p_trans_n1 * p_trans_n2 * \
deltakernel(tuple(g1.nodes[neighbor1][nl] for nl in self.__node_labels), tuple(g2.nodes[neighbor2][nl] for nl in self.__node_labels)) * \
deltakernel(tuple(neighbor_n1[neighbor1][el] for el in self.__edge_labels), tuple(neighbor_n2[neighbor2][el] for el in self.__edge_labels))
deltakernel(tuple(g1.nodes[neighbor1][nl] for nl in self._node_labels), tuple(g2.nodes[neighbor2][nl] for nl in self._node_labels)) * \
deltakernel(tuple(neighbor_n1[neighbor1][el] for el in self._edge_labels), tuple(neighbor_n2[neighbor2][el] for el in self._edge_labels))
# Compute R_inf with a simple interative method
for i in range(2, self.__n_iteration + 1):
for i in range(2, self._n_iteration + 1):
R_inf_old = R_inf.copy()
# Compute R_inf for each pair of nodes
@@ -311,7 +311,7 @@ class Marginalized(GraphKernel):
# add elements of R_inf up and compute kernel.
for (n1, n2), value in R_inf.items():
s = p_init_G1 * p_init_G2 * deltakernel(tuple(g1.nodes[n1][nl] for nl in self.__node_labels), tuple(g2.nodes[n2][nl] for nl in self.__node_labels))
s = p_init_G1 * p_init_G2 * deltakernel(tuple(g1.nodes[n1][nl] for nl in self._node_labels), tuple(g2.nodes[n2][nl] for nl in self._node_labels))
kernel += s * value # ref [1] equation (6)
return kernel
@@ -320,19 +320,19 @@ class Marginalized(GraphKernel):
def _wrapper_kernel_do(self, itr):
i = itr[0]
j = itr[1]
return i, j, self.__kernel_do(G_gn[i], G_gn[j])
return i, j, self._kernel_do(G_gn[i], G_gn[j])

def _wrapper_untotter(self, i):
return i, untotterTransformation(self._graphs[i], self.__node_labels, self.__edge_labels) # @todo: this may not work.
return i, untotterTransformation(self._graphs[i], self._node_labels, self._edge_labels) # @todo: this may not work.
def __add_dummy_labels(self, Gn):
if len(self.__node_labels) == 0 or (len(self.__node_labels) == 1 and self.__node_labels[0] == SpecialLabel.DUMMY):
def _add_dummy_labels(self, Gn):
if len(self._node_labels) == 0 or (len(self._node_labels) == 1 and self._node_labels[0] == SpecialLabel.DUMMY):
for i in range(len(Gn)):
nx.set_node_attributes(Gn[i], '0', SpecialLabel.DUMMY)
self.__node_labels = [SpecialLabel.DUMMY]
if len(self.__edge_labels) == 0 or (len(self.__edge_labels) == 1 and self.__edge_labels[0] == SpecialLabel.DUMMY):
self._node_labels = [SpecialLabel.DUMMY]
if len(self._edge_labels) == 0 or (len(self._edge_labels) == 1 and self._edge_labels[0] == SpecialLabel.DUMMY):
for i in range(len(Gn)):
nx.set_edge_attributes(Gn[i], '0', SpecialLabel.DUMMY)
self.__edge_labels = [SpecialLabel.DUMMY]
self._edge_labels = [SpecialLabel.DUMMY]

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