V0.2

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)
 

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])