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- """
- @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, "../")
-
-
- import networkx as nx
- import numpy as np
- import time
-
- from pygraph.utils.utils import getSPGraph
-
-
- def spkernel(*args, edge_weight = 'bond_type'):
- """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 corresponding to the edge weight. The default edge weight is bond_type.
-
- Return
- ------
- Kmatrix/kernel : Numpy matrix/float
- Kernel matrix, each element of which is the sp kernel between 2 praphs. / SP kernel between 2 graphs.
- """
- Gn = args[0] if len(args) == 1 else [args[0], args[1]] # arrange all graphs in a list
- Kmatrix = np.zeros((len(Gn), len(Gn)))
-
- start_time = time.time()
-
- Gn = [ getSPGraph(G, edge_weight = edge_weight) for G in args[0] ] # get shortest path graphs of Gn
-
- for i in range(0, len(Gn)):
- for j in range(i, len(Gn)):
- # kernel_t = [ 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])) \
- # for e1 in Sn[i].edges(data = True) for e2 in Sn[j].edges(data = True) ]
- # Kmatrix[i][j] = np.sum(kernel_t)
- # Kmatrix[j][i] = Kmatrix[i][j]
-
- 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]
-
- run_time = time.time() - start_time
- print("--- shortest path kernel matrix of size %d built in %s seconds ---" % (len(Gn), run_time))
-
- return Kmatrix, run_time
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