From 74f829f0cbe0d62435de75e4970288da6a52be9f Mon Sep 17 00:00:00 2001 From: linlin Date: Mon, 19 Oct 2020 15:25:02 +0200 Subject: [PATCH] New translations marginalized.py (Chinese Simplified) --- lang/zh/gklearn/kernels/marginalized.py | 90 ++++++++++++++++----------------- 1 file changed, 45 insertions(+), 45 deletions(-) diff --git a/lang/zh/gklearn/kernels/marginalized.py b/lang/zh/gklearn/kernels/marginalized.py index 499d51b..75355b1 100644 --- a/lang/zh/gklearn/kernels/marginalized.py +++ b/lang/zh/gklearn/kernels/marginalized.py @@ -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] \ No newline at end of file + self._edge_labels = [SpecialLabel.DUMMY] \ No newline at end of file