From f02ec9362be78bfcff91996d995eb6f7edf290e3 Mon Sep 17 00:00:00 2001 From: jajupmochi Date: Tue, 7 Apr 2020 17:04:34 +0200 Subject: [PATCH 1/2] add class ShortestPath. --- gklearn/kernels/shortest_path.py | 259 +++++++++++++++++++++++++++++++ 1 file changed, 259 insertions(+) create mode 100644 gklearn/kernels/shortest_path.py diff --git a/gklearn/kernels/shortest_path.py b/gklearn/kernels/shortest_path.py new file mode 100644 index 0000000000..eabe0a825c --- /dev/null +++ b/gklearn/kernels/shortest_path.py @@ -0,0 +1,259 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 7 15:24:58 2020 + +@author: ljia +""" + +import sys +from itertools import product +# from functools import partial +from multiprocessing import Pool +from tqdm import tqdm +import numpy as np +from gklearn.utils.parallel import parallel_gm, parallel_me +from gklearn.utils.utils import getSPGraph +from gklearn.kernels import GraphKernel + + +class ShortestPath(GraphKernel): + + def __init__(self, **kwargs): + GraphKernel.__init__(self) + self.__node_labels = kwargs.get('node_labels', []) + self.__node_attrs = kwargs.get('node_attrs', []) + self.__edge_weight = kwargs.get('edge_weight', None) + self.__node_kernels = kwargs.get('node_kernels', None) + self.__ds_infos = kwargs.get('ds_infos', {}) + + + def _compute_gm_series(self): + # get shortest path graph of each graph. + if self._verbose >= 2: + iterator = tqdm(self._graphs, desc='getting sp graphs', file=sys.stdout) + else: + iterator = self._graphs + self._graphs = [getSPGraph(g, edge_weight=self.__edge_weight) for g in iterator] + + # compute Gram matrix. + gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + + 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.__sp_do_(self._graphs[i], self._graphs[j]) + gram_matrix[i][j] = kernel + gram_matrix[j][i] = kernel + + return gram_matrix + + + def _compute_gm_imap_unordered(self): + # get shortest path graph of each graph. + pool = Pool(self._n_jobs) + get_sp_graphs_fun = self._wrapper_get_sp_graphs + itr = zip(self._graphs, range(0, len(self._graphs))) + if len(self._graphs) < 100 * self._n_jobs: + chunksize = int(len(self._graphs) / self._n_jobs) + 1 + else: + chunksize = 100 + if self._verbose >= 2: + iterator = tqdm(pool.imap_unordered(get_sp_graphs_fun, itr, chunksize), + desc='getting sp graphs', file=sys.stdout) + else: + iterator = pool.imap_unordered(get_sp_graphs_fun, itr, chunksize) + for i, g in iterator: + self._graphs[i] = g + pool.close() + pool.join() + + # compute Gram matrix. + gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) + + def init_worker(gs_toshare): + global G_gs + G_gs = gs_toshare + do_fun = self._wrapper_sp_do + parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, + glbv=(self._graphs,), n_jobs=self._n_jobs, verbose=self._verbose) + + return gram_matrix + + + def _compute_kernel_list_series(self, g1, g_list): + # get shortest path graphs of g1 and each graph in g_list. + g1 = getSPGraph(g1, edge_weight=self.__edge_weight) + if self._verbose >= 2: + iterator = tqdm(g_list, desc='getting sp graphs', file=sys.stdout) + else: + iterator = g_list + g_list = [getSPGraph(g, edge_weight=self.__edge_weight) for g in iterator] + + # compute kernel list. + kernel_list = [None] * len(g_list) + 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.__sp_do(g1, g_list[i]) + kernel_list[i] = kernel + + return kernel_list + + + def _compute_kernel_list_imap_unordered(self, g1, g_list): + # get shortest path graphs of g1 and each graph in g_list. + g1 = getSPGraph(g1, edge_weight=self.__edge_weight) + pool = Pool(self._n_jobs) + get_sp_graphs_fun = self._wrapper_get_sp_graphs + itr = zip(g_list, range(0, len(g_list))) + if len(g_list) < 100 * self._n_jobs: + chunksize = int(len(g_list) / self._n_jobs) + 1 + else: + chunksize = 100 + if self._verbose >= 2: + iterator = tqdm(pool.imap_unordered(get_sp_graphs_fun, itr, chunksize), + desc='getting sp graphs', file=sys.stdout) + else: + iterator = pool.imap_unordered(get_sp_graphs_fun, itr, chunksize) + for i, g in iterator: + g_list[i] = g + pool.close() + pool.join() + + # compute Gram matrix. + kernel_list = [None] * len(g_list) + + def init_worker(g1_toshare, gl_toshare): + global G_g1, G_gl + G_g1 = g1_toshare + G_gl = gl_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=(g1, g_list), method='imap_unordered', n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose) + + return kernel_list + + + def _wrapper_kernel_list_do(self, itr): + return itr, self.__sp_do(G_g1, G_gl[itr]) + + + def _compute_single_kernel_series(self, g1, g2): + g1 = getSPGraph(g1, edge_weight=self.__edge_weight) + g2 = getSPGraph(g2, edge_weight=self.__edge_weight) + kernel = self.__sp_do(g1, g2) + return kernel + + + def _wrapper_get_sp_graphs(self, itr_item): + g = itr_item[0] + i = itr_item[1] + return i, getSPGraph(g, edge_weight=self.__edge_weight) + + + def __sp_do(self, g1, g2): + + kernel = 0 + + # compute shortest path matrices first, method borrowed from FCSP. + vk_dict = {} # shortest path matrices dict + if len(self.__node_labels) > 0: + # node symb and non-synb labeled + if len(self.__node_attrs) > 0: + kn = self.__node_kernels['mix'] + for n1, n2 in product( + g1.nodes(data=True), g2.nodes(data=True)): + n1_labels = [n1[1][nl] for nl in self.__node_labels] + n2_labels = [n2[1][nl] for nl in self.__node_labels] + n1_attrs = [n1[1][na] for na in self.__node_attrs] + n2_attrs = [n2[1][na] for na in self.__node_attrs] + vk_dict[(n1[0], n2[0])] = kn(n1_labels, n2_labels, n1_attrs, n2_attrs) + # node symb labeled + else: + kn = self.__node_kernels['symb'] + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + n1_labels = [n1[1][nl] for nl in self.__node_labels] + n2_labels = [n2[1][nl] for nl in self.__node_labels] + vk_dict[(n1[0], n2[0])] = kn(n1_labels, n2_labels) + else: + # node non-synb labeled + if len(self.__node_attrs) > 0: + kn = self.__node_kernels['nsymb'] + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + n1_attrs = [n1[1][na] for na in self.__node_attrs] + n2_attrs = [n2[1][na] for na in self.__node_attrs] + vk_dict[(n1[0], n2[0])] = kn(n1_attrs, n2_attrs) + # node unlabeled + else: + for e1, e2 in product( + g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + kernel += 1 + return kernel + + # compute graph kernels + if self.__ds_infos['directed']: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + nk11, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[(e1[1], e2[1])] + kn1 = nk11 * nk22 + kernel += kn1 + else: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + # each edge walk is counted twice, starting from both its extreme nodes. + nk11, nk12, nk21, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[( + e1[0], e2[1])], vk_dict[(e1[1], e2[0])], vk_dict[(e1[1], e2[1])] + kn1 = nk11 * nk22 + kn2 = nk12 * nk21 + kernel += kn1 + kn2 + + # # ---- exact implementation of the Fast Computation of Shortest Path Kernel (FCSP), reference [2], sadly it is slower than the current implementation + # # compute vertex kernels + # try: + # vk_mat = np.zeros((nx.number_of_nodes(g1), + # nx.number_of_nodes(g2))) + # g1nl = enumerate(g1.nodes(data=True)) + # g2nl = enumerate(g2.nodes(data=True)) + # for i1, n1 in g1nl: + # for i2, n2 in g2nl: + # vk_mat[i1][i2] = kn( + # n1[1][node_label], n2[1][node_label], + # [n1[1]['attributes']], [n2[1]['attributes']]) + + # range1 = range(0, len(edge_w_g[i])) + # range2 = range(0, len(edge_w_g[j])) + # for i1 in range1: + # x1 = edge_x_g[i][i1] + # y1 = edge_y_g[i][i1] + # w1 = edge_w_g[i][i1] + # for i2 in range2: + # x2 = edge_x_g[j][i2] + # y2 = edge_y_g[j][i2] + # w2 = edge_w_g[j][i2] + # ke = (w1 == w2) + # if ke > 0: + # kn1 = vk_mat[x1][x2] * vk_mat[y1][y2] + # kn2 = vk_mat[x1][y2] * vk_mat[y1][x2] + # kernel += kn1 + kn2 + + return kernel + + + def _wrapper_sp_do(self, itr): + i = itr[0] + j = itr[1] + return i, j, self.__sp_do(G_gs[i], G_gs[j]) \ No newline at end of file From 7a16cfb9cfd001f54786add16e349b23d36bf6d9 Mon Sep 17 00:00:00 2001 From: jajupmochi Date: Tue, 7 Apr 2020 17:08:26 +0200 Subject: [PATCH 2/2] update versions of requirements in README.md. --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 491a1ede50..886aa6bfe2 100644 --- a/README.md +++ b/README.md @@ -8,14 +8,14 @@ A python package for graph kernels, graph edit distances and graph pre-image pro ## Requirements -* python==3.6.5 -* numpy==1.15.2 -* scipy==1.1.0 -* matplotlib==3.0.0 -* networkx==2.2 -* scikit-learn==0.20.0 -* tabulate==0.8.2 -* tqdm==4.26.0 +* python==3.6.9 +* numpy>=1.15.2 +* scipy>=1.1.0 +* matplotlib>=3.0.0 +* networkx>=2.2 +* scikit-learn>=0.20.0 +* tabulate>=0.8.2 +* tqdm>=4.26.0 * control==0.8.0 (for generalized random walk kernels only) * slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example)