- """
- @author: linlin
- @references: Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
- """
-
- import sys
- import pathlib
- sys.path.insert(0, "../")
- from tqdm import tqdm
- import time
-
- import networkx as nx
- import numpy as np
-
- from pygraph.utils.utils import getSPGraph
- from pygraph.utils.graphdataset import get_dataset_attributes
-
-
- def spkernel(*args, node_label='atom', edge_weight=None, node_kernels=None):
- """Calculate shortest-path kernels between graphs.
-
- Parameters
- ----------
- Gn : List of NetworkX graph
- List of graphs between which the kernels are calculated.
- /
- G1, G2 : NetworkX graphs
- 2 graphs between which the kernel is calculated.
- edge_weight : string
- Edge attribute name corresponding to the edge weight.
- node_kernels: dict
- A dictionary of kernel functions for nodes, including 3 items: 'symb' for symbolic node labels, 'nsymb' for non-symbolic node labels, 'mix' for both labels. The first 2 functions take two node labels as parameters, and the 'mix' function takes 4 parameters, a symbolic and a non-symbolic label for each the two nodes. Each label is in form of 2-D dimension array (n_samples, n_features). Each function returns an number as the kernel value. Ignored when nodes are unlabeled.
-
- Return
- ------
- Kmatrix : Numpy matrix
- Kernel matrix, each element of which is the sp kernel between 2 praphs.
- """
- # pre-process
- Gn = args[0] if len(args) == 1 else [args[0], args[1]]
-
- Gn = [nx.to_directed(G) for G in Gn]
-
- weight = None
- if edge_weight == None:
- print('\n None edge weight specified. Set all weight to 1.\n')
- else:
- try:
- some_weight = list(
- nx.get_edge_attributes(Gn[0], edge_weight).values())[0]
- if isinstance(some_weight, float) or isinstance(some_weight, int):
- weight = edge_weight
- else:
- print(
- '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
- % edge_weight)
- except:
- print(
- '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
- % edge_weight)
- ds_attrs = get_dataset_attributes(
- Gn,
- attr_names=['node_labeled', 'node_attr_dim', 'is_directed'],
- node_label=node_label)
-
- # remove graphs with no edges, as no sp can be found in their structures, so the kernel between such a graph and itself will be zero.
- len_gn = len(Gn)
- Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0]
- idx = [G[0] for G in Gn]
- Gn = [G[1] for G in Gn]
- if len(Gn) != len_gn:
- print('\n %d graphs are removed as they don\'t contain edges.\n' %
- (len_gn - len(Gn)))
-
- start_time = time.time()
-
- # get shortest path graphs of Gn
- Gn = [
- getSPGraph(G, edge_weight=edge_weight)
- for G in tqdm(Gn, desc='getting sp graphs', file=sys.stdout)
- ]
-
- Kmatrix = np.zeros((len(Gn), len(Gn)))
- pbar = tqdm(
- total=((len(Gn) + 1) * len(Gn) / 2),
- desc='calculating kernels',
- file=sys.stdout)
- if ds_attrs['node_labeled']:
- # node symb and non-synb labeled
- if ds_attrs['node_attr_dim'] > 0:
- if ds_attrs['is_directed']:
- 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'] == e2[2]['cost']:
- kn = node_kernels['mix']
- try:
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn(n11[node_label], n21[
- node_label], [n11['attributes']],
- [n21['attributes']]) * kn(
- n12[node_label],
- n22[node_label],
- [n12['attributes']],
- [n22['attributes']])
- Kmatrix[i][j] += kn1
- except KeyError: # missing labels or attributes
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
-
- else:
- 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'] == e2[2]['cost']:
- kn = node_kernels['mix']
- try:
- # each edge walk is counted twice, starting from both its extreme nodes.
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn(n11[node_label], n21[
- node_label], [n11['attributes']],
- [n21['attributes']]) * kn(
- n12[node_label],
- n22[node_label],
- [n12['attributes']],
- [n22['attributes']])
- kn2 = kn(n11[node_label], n22[
- node_label], [n11['attributes']],
- [n22['attributes']]) * kn(
- n12[node_label],
- n21[node_label],
- [n12['attributes']],
- [n21['attributes']])
- Kmatrix[i][j] += kn1 + kn2
- except KeyError: # missing labels or attributes
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
- # node symb labeled
- else:
- if ds_attrs['is_directed']:
- 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'] == e2[2]['cost']:
- kn = node_kernels['symb']
- try:
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn(n11[node_label],
- n21[node_label]) * kn(
- n12[node_label],
- n22[node_label])
- Kmatrix[i][j] += kn1
- except KeyError: # missing labels
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
-
- else:
- 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'] == e2[2]['cost']:
- kn = node_kernels['symb']
- try:
- # each edge walk is counted twice, starting from both its extreme nodes.
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn(n11[node_label],
- n21[node_label]) * kn(
- n12[node_label],
- n22[node_label])
- kn2 = kn(n11[node_label],
- n22[node_label]) * kn(
- n12[node_label],
- n21[node_label])
- Kmatrix[i][j] += kn1 + kn2
- except KeyError: # missing labels
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
- else:
- # node non-synb labeled
- if ds_attrs['node_attr_dim'] > 0:
- if ds_attrs['is_directed']:
- 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'] == e2[2]['cost']:
- kn = node_kernels['nsymb']
- try:
- # each edge walk is counted twice, starting from both its extreme nodes.
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn([n11['attributes']],
- [n21['attributes']]) * kn(
- [n12['attributes']],
- [n22['attributes']])
- Kmatrix[i][j] += kn1
- except KeyError: # missing attributes
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
- else:
- 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'] == e2[2]['cost']:
- kn = node_kernels['nsymb']
- try:
- # each edge walk is counted twice, starting from both its extreme nodes.
- n11, n12, n21, n22 = Gn[i].nodes[e1[
- 0]], Gn[i].nodes[e1[1]], Gn[
- j].nodes[e2[0]], Gn[j].nodes[
- e2[1]]
- kn1 = kn([n11['attributes']],
- [n21['attributes']]) * kn(
- [n12['attributes']],
- [n22['attributes']])
- kn2 = kn([n11['attributes']],
- [n22['attributes']]) * kn(
- [n12['attributes']],
- [n21['attributes']])
- Kmatrix[i][j] += kn1 + kn2
- except KeyError: # missing attributes
- pass
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
-
- # node unlabeled
- else:
- 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'] == e2[2]['cost']:
- Kmatrix[i][j] += 1
- Kmatrix[j][i] = Kmatrix[i][j]
- pbar.update(1)
-
- run_time = time.time() - start_time
- print(
- "\n --- shortest path kernel matrix of size %d built in %s seconds ---"
- % (len(Gn), run_time))
-
- return Kmatrix, run_time, idx
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