From 83ad1949b03e50c444e87ac38ee9fb7da1c0bbbe Mon Sep 17 00:00:00 2001 From: linlin Date: Tue, 6 Oct 2020 17:26:11 +0200 Subject: [PATCH] New translations weisfeilerLehmanKernel.py (Chinese Simplified) --- lang/zh/gklearn/kernels/weisfeilerLehmanKernel.py | 570 ++++++++++++++++++++++ 1 file changed, 570 insertions(+) create mode 100644 lang/zh/gklearn/kernels/weisfeilerLehmanKernel.py diff --git a/lang/zh/gklearn/kernels/weisfeilerLehmanKernel.py b/lang/zh/gklearn/kernels/weisfeilerLehmanKernel.py new file mode 100644 index 0000000..222f5c5 --- /dev/null +++ b/lang/zh/gklearn/kernels/weisfeilerLehmanKernel.py @@ -0,0 +1,570 @@ +""" +@author: linlin + +@references: + + [1] Shervashidze N, Schweitzer P, Leeuwen EJ, Mehlhorn K, Borgwardt KM. + Weisfeiler-lehman graph kernels. Journal of Machine Learning Research. + 2011;12(Sep):2539-61. +""" + +import sys +from collections import Counter +from functools import partial +import time +#from multiprocessing import Pool +from tqdm import tqdm + +import networkx as nx +import numpy as np + +#from gklearn.kernels.pathKernel import pathkernel +from gklearn.utils.graphdataset import get_dataset_attributes +from gklearn.utils.parallel import parallel_gm + +# @todo: support edge kernel, sp kernel, user-defined kernel. +def weisfeilerlehmankernel(*args, + node_label='atom', + edge_label='bond_type', + height=0, + base_kernel='subtree', + parallel=None, + n_jobs=None, + chunksize=None, + verbose=True): + """Calculate Weisfeiler-Lehman kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + + G1, G2 : NetworkX graphs + Two graphs between which the kernel is calculated. + + node_label : string + Node attribute used as label. The default node label is atom. + + edge_label : string + Edge attribute used as label. The default edge label is bond_type. + + height : int + Subtree height. + + base_kernel : string + Base kernel used in each iteration of WL kernel. Only default 'subtree' + kernel can be applied for now. + + parallel : None + Which paralleliztion method is applied to compute the kernel. No + parallelization can be applied for now. + + n_jobs : int + Number of jobs for parallelization. The default is to use all + computational cores. This argument is only valid when one of the + parallelization method is applied and can be ignored for now. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + + Notes + ----- + This function now supports WL subtree kernel only. + """ +# The default base +# kernel is subtree kernel. For user-defined kernel, base_kernel is the +# name of the base kernel function used in each iteration of WL kernel. +# This function returns a Numpy matrix, each element of which is the +# user-defined Weisfeiler-Lehman kernel between 2 praphs. + # pre-process + base_kernel = base_kernel.lower() + Gn = args[0] if len(args) == 1 else [args[0], args[1]] # arrange all graphs in a list + Gn = [g.copy() for g in Gn] + ds_attrs = get_dataset_attributes(Gn, attr_names=['node_labeled'], + node_label=node_label) + if not ds_attrs['node_labeled']: + for G in Gn: + nx.set_node_attributes(G, '0', 'atom') + + start_time = time.time() + + # for WL subtree kernel + if base_kernel == 'subtree': + Kmatrix = _wl_kernel_do(Gn, node_label, edge_label, height, parallel, n_jobs, chunksize, verbose) + + # for WL shortest path kernel + elif base_kernel == 'sp': + Kmatrix = _wl_spkernel_do(Gn, node_label, edge_label, height) + + # for WL edge kernel + elif base_kernel == 'edge': + Kmatrix = _wl_edgekernel_do(Gn, node_label, edge_label, height) + + # for user defined base kernel + else: + Kmatrix = _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel) + + run_time = time.time() - start_time + if verbose: + print("\n --- Weisfeiler-Lehman %s kernel matrix of size %d built in %s seconds ---" + % (base_kernel, len(args[0]), run_time)) + + return Kmatrix, run_time + + +def _wl_kernel_do(Gn, node_label, edge_label, height, parallel, n_jobs, chunksize, verbose): + """Calculate Weisfeiler-Lehman kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + wl height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) + + # initial for height = 0 + all_num_of_each_label = [] # number of occurence of each label in each graph in this iteration + + # for each graph + for G in Gn: + # get the set of original labels + labels_ori = list(nx.get_node_attributes(G, node_label).values()) + # number of occurence of each label in G + all_num_of_each_label.append(dict(Counter(labels_ori))) + + # calculate subtree kernel with the 0th iteration and add it to the final kernel + compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, chunksize, False) + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs +# all_labels_ori = set() # all unique orignal labels in all graphs in this iteration + all_num_of_each_label = [] # number of occurence of each label in G + +# # for each graph +# # ---- use pool.imap_unordered to parallel and track progress. ---- +# pool = Pool(n_jobs) +# itr = zip(Gn, range(0, len(Gn))) +# if len(Gn) < 100 * n_jobs: +# chunksize = int(len(Gn) / n_jobs) + 1 +# else: +# chunksize = 100 +# all_multisets_list = [[] for _ in range(len(Gn))] +## set_unique_list = [[] for _ in range(len(Gn))] +# get_partial = partial(wrapper_wl_iteration, node_label) +## if verbose: +## iterator = tqdm(pool.imap_unordered(get_partial, itr, chunksize), +## desc='wl iteration', file=sys.stdout) +## else: +# iterator = pool.imap_unordered(get_partial, itr, chunksize) +# for i, all_multisets in iterator: +# all_multisets_list[i] = all_multisets +## set_unique_list[i] = set_unique +## all_set_unique = all_set_unique | set(set_unique) +# pool.close() +# pool.join() + +# all_set_unique = set() +# for uset in all_multisets_list: +# all_set_unique = all_set_unique | set(uset) +# +# all_set_unique = list(all_set_unique) +## # a dictionary mapping original labels to new ones. +## set_compressed = {} +## for idx, uset in enumerate(all_set_unique): +## set_compressed.update({uset: idx}) +# +# for ig, G in enumerate(Gn): +# +## # a dictionary mapping original labels to new ones. +## set_compressed = {} +## # if a label occured before, assign its former compressed label, +## # else assign the number of labels occured + 1 as the compressed label. +## for value in set_unique_list[i]: +## if uset in all_set_unique: +## set_compressed.update({uset: all_set_compressed[value]}) +## else: +## set_compressed.update({value: str(num_of_labels_occured + 1)}) +## num_of_labels_occured += 1 +# +## all_set_compressed.update(set_compressed) +# +# # relabel nodes +# for idx, node in enumerate(G.nodes()): +# G.nodes[node][node_label] = all_set_unique.index(all_multisets_list[ig][idx]) +# +# # get the set of compressed labels +# labels_comp = list(nx.get_node_attributes(G, node_label).values()) +## all_labels_ori.update(labels_comp) +# all_num_of_each_label[ig] = dict(Counter(labels_comp)) + + + + +# all_set_unique = list(all_set_unique) + + + # @todo: parallel this part. + for idx, G in enumerate(Gn): + + all_multisets = [] + for node, attrs in G.nodes(data=True): + # Multiset-label determination. + multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] + # sorting each multiset + multiset.sort() + multiset = [attrs[node_label]] + multiset # add the prefix + all_multisets.append(tuple(multiset)) + + # label compression + set_unique = list(set(all_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, + # else assign the number of labels occured + 1 as the compressed label. + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({value: all_set_compressed[value]}) + else: + set_compressed.update({value: str(num_of_labels_occured + 1)}) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for idx, node in enumerate(G.nodes()): + G.nodes[node][node_label] = set_compressed[all_multisets[idx]] + + # get the set of compressed labels + labels_comp = list(nx.get_node_attributes(G, node_label).values()) +# all_labels_ori.update(labels_comp) + all_num_of_each_label.append(dict(Counter(labels_comp))) + + # calculate subtree kernel with h iterations and add it to the final kernel + compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, chunksize, False) + + return Kmatrix + + +def wl_iteration(G, node_label): + all_multisets = [] + for node, attrs in G.nodes(data=True): + # Multiset-label determination. + multiset = [G.nodes[neighbors][node_label] for neighbors in G[node]] + # sorting each multiset + multiset.sort() + multiset = [attrs[node_label]] + multiset # add the prefix + all_multisets.append(tuple(multiset)) +# # label compression +# set_unique = list(set(all_multisets)) # set of unique multiset labels + return all_multisets + +# # a dictionary mapping original labels to new ones. +# set_compressed = {} +# # if a label occured before, assign its former compressed label, +# # else assign the number of labels occured + 1 as the compressed label. +# for value in set_unique: +# if value in all_set_compressed.keys(): +# set_compressed.update({value: all_set_compressed[value]}) +# else: +# set_compressed.update({value: str(num_of_labels_occured + 1)}) +# num_of_labels_occured += 1 +# +# all_set_compressed.update(set_compressed) +# +# # relabel nodes +# for idx, node in enumerate(G.nodes()): +# G.nodes[node][node_label] = set_compressed[all_multisets[idx]] +# +# # get the set of compressed labels +# labels_comp = list(nx.get_node_attributes(G, node_label).values()) +# all_labels_ori.update(labels_comp) +# all_num_of_each_label.append(dict(Counter(labels_comp))) +# return + + +def wrapper_wl_iteration(node_label, itr_item): + g = itr_item[0] + i = itr_item[1] + all_multisets = wl_iteration(g, node_label) + return i, all_multisets + + +def compute_kernel_matrix(Kmatrix, all_num_of_each_label, Gn, parallel, n_jobs, chunksize, verbose): + """Compute kernel matrix using the base kernel. + """ + if parallel == 'imap_unordered': + # compute kernels. + def init_worker(alllabels_toshare): + global G_alllabels + G_alllabels = alllabels_toshare + do_partial = partial(wrapper_compute_subtree_kernel, Kmatrix) + parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, + glbv=(all_num_of_each_label,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose) + elif parallel == None: + for i in range(len(Kmatrix)): + for j in range(i, len(Kmatrix)): + Kmatrix[i][j] = compute_subtree_kernel(all_num_of_each_label[i], + all_num_of_each_label[j], Kmatrix[i][j]) + Kmatrix[j][i] = Kmatrix[i][j] + + +def compute_subtree_kernel(num_of_each_label1, num_of_each_label2, kernel): + """Compute the subtree kernel. + """ + labels = set(list(num_of_each_label1.keys()) + list(num_of_each_label2.keys())) + vector1 = np.array([(num_of_each_label1[label] + if (label in num_of_each_label1.keys()) else 0) + for label in labels]) + vector2 = np.array([(num_of_each_label2[label] + if (label in num_of_each_label2.keys()) else 0) + for label in labels]) + kernel += np.dot(vector1, vector2) + return kernel + + +def wrapper_compute_subtree_kernel(Kmatrix, itr): + i = itr[0] + j = itr[1] + return i, j, compute_subtree_kernel(G_alllabels[i], G_alllabels[j], Kmatrix[i][j]) + + +def _wl_spkernel_do(Gn, node_label, edge_label, height): + """Calculate Weisfeiler-Lehman shortest path kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + from gklearn.utils.utils import getSPGraph + + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + + Gn = [ getSPGraph(G, edge_weight = edge_label) for G in Gn ] # get shortest path graphs of Gn + + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2]['cost'] != 0 and e1[2]['cost'] == e2[2]['cost'] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + return Kmatrix + + + +def _wl_edgekernel_do(Gn, node_label, edge_label, height): + """Calculate Weisfeiler-Lehman edge kernels between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + + # initial for height = 0 + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate subtree kernel with h iterations and add it to the final kernel + for i in range(0, len(Gn)): + for j in range(i, len(Gn)): + for e1 in Gn[i].edges(data = True): + for e2 in Gn[j].edges(data = True): + if e1[2][edge_label] == e2[2][edge_label] and ((e1[0] == e2[0] and e1[1] == e2[1]) or (e1[0] == e2[1] and e1[1] == e2[0])): + Kmatrix[i][j] += 1 + Kmatrix[j][i] = Kmatrix[i][j] + + return Kmatrix + + +def _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel): + """Calculate Weisfeiler-Lehman kernels based on user-defined kernel between graphs. + + Parameters + ---------- + Gn : List of NetworkX graph + List of graphs between which the kernels are calculated. + node_label : string + node attribute used as label. + edge_label : string + edge attribute used as label. + height : int + subtree height. + base_kernel : string + Name of the base kernel function used in each iteration of WL kernel. This function returns a Numpy matrix, each element of which is the user-defined Weisfeiler-Lehman kernel between 2 praphs. + + Return + ------ + Kmatrix : Numpy matrix + Kernel matrix, each element of which is the Weisfeiler-Lehman kernel between 2 praphs. + """ + pass + # init. + height = int(height) + Kmatrix = np.zeros((len(Gn), len(Gn))) # init kernel + + # initial for height = 0 + Kmatrix = base_kernel(Gn, node_label, edge_label) + + # iterate each height + for h in range(1, height + 1): + all_set_compressed = {} # a dictionary mapping original labels to new ones in all graphs in this iteration + num_of_labels_occured = 0 # number of the set of letters that occur before as node labels at least once in all graphs + for G in Gn: # for each graph + set_multisets = [] + for node in G.nodes(data = True): + # Multiset-label determination. + multiset = [ G.node[neighbors][node_label] for neighbors in G[node[0]] ] + # sorting each multiset + multiset.sort() + multiset = node[1][node_label] + ''.join(multiset) # concatenate to a string and add the prefix + set_multisets.append(multiset) + + # label compression + set_unique = list(set(set_multisets)) # set of unique multiset labels + # a dictionary mapping original labels to new ones. + set_compressed = {} + # if a label occured before, assign its former compressed label, else assign the number of labels occured + 1 as the compressed label + for value in set_unique: + if value in all_set_compressed.keys(): + set_compressed.update({ value : all_set_compressed[value] }) + else: + set_compressed.update({ value : str(num_of_labels_occured + 1) }) + num_of_labels_occured += 1 + + all_set_compressed.update(set_compressed) + + # relabel nodes + for node in G.nodes(data = True): + node[1][node_label] = set_compressed[set_multisets[node[0]]] + + # calculate kernel with h iterations and add it to the final kernel + Kmatrix += base_kernel(Gn, node_label, edge_label) + + return Kmatrix