New translations structural_sp.py (Chinese Simplified)

lang/zh/gklearn/kernels/structural_sp.py

@@ -0,0 +1,405 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 30 11:59:57 2020
+
+@author: ljia
+
+@references: 
+
+    [1] Suard F, Rakotomamonjy A, Bensrhair A. Kernel on Bag of Paths For 
+    Measuring Similarity of Shapes. InESANN 2007 Apr 25 (pp. 355-360).
+"""
+import sys
+from itertools import product
+# from functools import partial
+from multiprocessing import Pool
+from tqdm import tqdm
+# import networkx as nx
+import numpy as np
+from gklearn.utils.parallel import parallel_gm, parallel_me
+from gklearn.utils.utils import get_shortest_paths
+from gklearn.kernels import GraphKernel
+
+
+class StructuralSP(GraphKernel):
+	
+	def __init__(self, **kwargs):
+		GraphKernel.__init__(self)
+		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', [])
+		self.__edge_weight = kwargs.get('edge_weight', None)
+		self.__node_kernels = kwargs.get('node_kernels', None)
+		self.__edge_kernels = kwargs.get('edge_kernels', None)
+		self.__compute_method = kwargs.get('compute_method', 'naive')
+		self.__ds_infos = kwargs.get('ds_infos', {})
+
+
+	def _compute_gm_series(self):
+		# get shortest paths of each graph in the graphs.
+		splist = []
+		if self._verbose >= 2:
+			iterator = tqdm(self._graphs, desc='getting sp graphs', file=sys.stdout)
+		else:
+			iterator = self._graphs
+		if self.__compute_method == 'trie':
+			for g in iterator:
+				splist.append(self.__get_sps_as_trie(g))
+		else:
+			for g in iterator:
+				splist.append(get_shortest_paths(g, self.__edge_weight, self.__ds_infos['directed']))
+		
+		# 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
+		if self.__compute_method == 'trie':
+			for i, j in iterator:
+				kernel = self.__ssp_do_trie(self._graphs[i], self._graphs[j], splist[i], splist[j])
+				gram_matrix[i][j] = kernel
+				gram_matrix[j][i] = kernel
+		else:
+			for i, j in iterator:
+				kernel = self.__ssp_do_naive(self._graphs[i], self._graphs[j], splist[i], splist[j])
+		#		if(kernel > 1):
+		#			print("error here ")
+				gram_matrix[i][j] = kernel
+				gram_matrix[j][i] = kernel
+				
+		return gram_matrix
+			
+			
+	def _compute_gm_imap_unordered(self):
+		# get shortest paths of each graph in the graphs.
+		splist = [None] * len(self._graphs)
+		pool = Pool(self._n_jobs)
+		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
+		# get shortest path graphs of self._graphs
+		if self.__compute_method == 'trie':
+			get_sps_fun = self._wrapper_get_sps_trie	
+		else:
+			get_sps_fun = self._wrapper_get_sps_naive   
+		if self.verbose >= 2:
+			iterator = tqdm(pool.imap_unordered(get_sps_fun, itr, chunksize),
+							desc='getting shortest paths', file=sys.stdout)
+		else:
+			iterator = pool.imap_unordered(get_sps_fun, itr, chunksize)
+		for i, sp in iterator:
+			splist[i] = sp
+		pool.close()
+		pool.join()
+		
+		# compute Gram matrix.
+		gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
+
+		def init_worker(spl_toshare, gs_toshare):
+			global G_spl, G_gs
+			G_spl = spl_toshare
+			G_gs = gs_toshare	 
+		if self.__compute_method == 'trie':	   
+			do_fun = self.__wrapper_ssp_do_trie
+		else:  
+			do_fun = self._wrapper_ssp_do_naive  
+		parallel_gm(do_fun, gram_matrix, self._graphs, init_worker=init_worker, 
+							glbv=(splist, self._graphs), n_jobs=self._n_jobs, verbose=self._verbose)
+			
+		return gram_matrix
+	
+	
+	def _compute_kernel_list_series(self, g1, g_list):
+		# get shortest paths of g1 and each graph in g_list.
+		sp1 = get_shortest_paths(g1, self.__edge_weight, self.__ds_infos['directed'])
+		splist = []
+		if self._verbose >= 2:
+			iterator = tqdm(g_list, desc='getting sp graphs', file=sys.stdout)
+		else:
+			iterator = g_list
+		if self.__compute_method == 'trie':
+			for g in iterator:
+				splist.append(self.__get_sps_as_trie(g))
+		else:
+			for g in iterator:
+				splist.append(get_shortest_paths(g, self.__edge_weight, self.__ds_infos['directed']))
+		
+		# 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))
+		if self.__compute_method == 'trie':
+			for i in iterator:
+				kernel = self.__ssp_do_trie(g1, g_list[i], sp1, splist[i])
+				kernel_list[i] = kernel
+		else:
+			for i in iterator:
+				kernel = self.__ssp_do_naive(g1, g_list[i], sp1, splist[i])
+				kernel_list[i] = kernel
+				
+		return kernel_list
+	
+	
+	def _compute_kernel_list_imap_unordered(self, g1, g_list):
+		# get shortest paths of g1 and each graph in g_list.
+		sp1 = get_shortest_paths(g1, self.__edge_weight, self.__ds_infos['directed'])
+		splist = [None] * len(g_list)
+		pool = Pool(self._n_jobs)
+		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
+		# get shortest path graphs of g_list
+		if self.__compute_method == 'trie':
+			get_sps_fun = self._wrapper_get_sps_trie	
+		else:
+			get_sps_fun = self._wrapper_get_sps_naive   
+		if self.verbose >= 2:
+			iterator = tqdm(pool.imap_unordered(get_sps_fun, itr, chunksize),
+							desc='getting shortest paths', file=sys.stdout)
+		else:
+			iterator = pool.imap_unordered(get_sps_fun, itr, chunksize)
+		for i, sp in iterator:
+			splist[i] = sp
+		pool.close()
+		pool.join()
+		
+		# compute Gram matrix.
+		kernel_list = [None] * len(g_list)
+
+		def init_worker(sp1_toshare, spl_toshare, g1_toshare, gl_toshare):
+			global G_sp1, G_spl, G_g1, G_gl
+			G_sp1 = sp1_toshare
+			G_spl = spl_toshare
+			G_g1 = g1_toshare	 
+			G_gl = gl_toshare	 
+		if self.__compute_method == 'trie':	   
+			do_fun = self.__wrapper_ssp_do_trie
+		else: 	 
+			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=(sp1, splist, 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.__ssp_do_naive(G_g1, G_gl[itr], G_sp1, G_spl[itr])
+
+	
+	
+	def _compute_single_kernel_series(self, g1, g2):
+		sp1 = get_shortest_paths(g1, self.__edge_weight, self.__ds_infos['directed'])
+		sp2 = get_shortest_paths(g2, self.__edge_weight, self.__ds_infos['directed'])
+		if self.__compute_method == 'trie':
+			kernel = self.__ssp_do_trie(g1, g2, sp1, sp2)
+		else:
+			kernel = self.__ssp_do_naive(g1, g2, sp1, sp2)
+		return kernel			
+		
+	
+	def _wrapper_get_sps_naive(self, itr_item):
+		g = itr_item[0]
+		i = itr_item[1]
+		return i, get_shortest_paths(g, self.__edge_weight, self.__ds_infos['directed'])
+	
+	
+	def __ssp_do_naive(self, g1, g2, spl1, spl2):
+	
+		kernel = 0
+	
+		# First, compute shortest path matrices, method borrowed from FCSP.
+		vk_dict = self.__get_all_node_kernels(g1, g2)
+		# Then, compute kernels between all pairs of edges, which is an idea of
+		# extension of FCSP. It suits sparse graphs, which is the most case we
+		# went though. For dense graphs, this would be slow.
+		ek_dict = self.__get_all_edge_kernels(g1, g2)
+	
+		# compute graph kernels
+		if vk_dict:
+			if ek_dict:
+				for p1, p2 in product(spl1, spl2):
+					if len(p1) == len(p2):
+						kpath = vk_dict[(p1[0], p2[0])]
+						if kpath:
+							for idx in range(1, len(p1)):
+								kpath *= vk_dict[(p1[idx], p2[idx])] * \
+									ek_dict[((p1[idx-1], p1[idx]),
+											 (p2[idx-1], p2[idx]))]
+								if not kpath:
+									break
+							kernel += kpath  # add up kernels of all paths
+			else:
+				for p1, p2 in product(spl1, spl2):
+					if len(p1) == len(p2):
+						kpath = vk_dict[(p1[0], p2[0])]
+						if kpath:
+							for idx in range(1, len(p1)):
+								kpath *= vk_dict[(p1[idx], p2[idx])]
+								if not kpath:
+									break
+							kernel += kpath  # add up kernels of all paths
+		else:
+			if ek_dict:
+				for p1, p2 in product(spl1, spl2):
+					if len(p1) == len(p2):
+						if len(p1) == 0:
+							kernel += 1
+						else:
+							kpath = 1
+							for idx in range(0, len(p1) - 1):
+								kpath *= ek_dict[((p1[idx], p1[idx+1]),
+												  (p2[idx], p2[idx+1]))]
+								if not kpath:
+									break
+							kernel += kpath  # add up kernels of all paths
+			else:
+				for p1, p2 in product(spl1, spl2):
+					if len(p1) == len(p2):
+						kernel += 1
+		try:
+			kernel = kernel / (len(spl1) * len(spl2))  # calculate mean average
+		except ZeroDivisionError:
+			print(spl1, spl2)
+			print(g1.nodes(data=True))
+			print(g1.edges(data=True))
+			raise Exception
+	
+		# # ---- exact implementation of the Fast Computation of Shortest Path Kernel (FCSP), reference [2], sadly it is slower than the current implementation
+		# # compute vertex kernel matrix
+		# 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]
+		#				 Kmatrix += kn1 + kn2
+		return kernel
+	
+	
+	def _wrapper_ssp_do_naive(self, itr):
+		i = itr[0]
+		j = itr[1]
+		return i, j, self.__ssp_do_naive(G_gs[i], G_gs[j], G_spl[i], G_spl[j])
+	
+	
+	def __get_all_node_kernels(self, g1, g2):
+		# compute shortest path matrices, 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:
+				pass
+			
+		return vk_dict
+	
+	
+	def __get_all_edge_kernels(self, g1, g2):
+		# compute kernels between all pairs of edges, which is an idea of
+		# extension of FCSP. It suits sparse graphs, which is the most case we
+		# went though. For dense graphs, this would be slow.
+		ek_dict = {}  # dict of edge kernels
+		if len(self.__edge_labels) > 0:
+			# edge symb and non-synb labeled
+			if len(self.__edge_attrs) > 0:
+				ke = self.__edge_kernels['mix']
+				for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)):
+					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]
+					ek_temp = ke(e1_labels, e2_labels, e1_attrs, e2_attrs)
+					ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ek_temp
+					ek_dict[((e1[1], e1[0]), (e2[0], e2[1]))] = ek_temp
+					ek_dict[((e1[0], e1[1]), (e2[1], e2[0]))] = ek_temp
+					ek_dict[((e1[1], e1[0]), (e2[1], e2[0]))] = ek_temp
+			# edge symb labeled
+			else:
+				ke = self.__edge_kernels['symb']
+				for e1 in g1.edges(data=True):
+					for e2 in g2.edges(data=True):
+						e1_labels = [e1[2][el] for el in self.__edge_labels]
+						e2_labels = [e2[2][el] for el in self.__edge_labels]
+						ek_temp = ke(e1_labels, e2_labels)
+						ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ek_temp
+						ek_dict[((e1[1], e1[0]), (e2[0], e2[1]))] = ek_temp
+						ek_dict[((e1[0], e1[1]), (e2[1], e2[0]))] = ek_temp
+						ek_dict[((e1[1], e1[0]), (e2[1], e2[0]))] = ek_temp
+		else:
+			# edge non-synb labeled
+			if len(self.__edge_attrs) > 0:
+				ke = self.__edge_kernels['nsymb']
+				for e1 in g1.edges(data=True):
+					for e2 in g2.edges(data=True):
+						e1_attrs = [e1[2][ea] for ea in self.__edge_attrs]
+						e2_attrs = [e2[2][ea] for ea in self.__edge_attrs]
+						ek_temp = ke(e1_attrs, e2_attrs)
+						ek_dict[((e1[0], e1[1]), (e2[0], e2[1]))] = ek_temp
+						ek_dict[((e1[1], e1[0]), (e2[0], e2[1]))] = ek_temp
+						ek_dict[((e1[0], e1[1]), (e2[1], e2[0]))] = ek_temp
+						ek_dict[((e1[1], e1[0]), (e2[1], e2[0]))] = ek_temp
+			# edge unlabeled
+			else:
+				pass
+			
+			return ek_dict