- import sys
- import pathlib
- sys.path.insert(0, "../")
-
- import networkx as nx
- import numpy as np
- import time
-
- from pygraph.kernels.deltaKernel import deltakernel
-
- def pathkernel(*args, node_label = 'atom', edge_label = 'bond_type'):
- """Calculate mean average 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.
- 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.
-
- Return
- ------
- Kmatrix/Kernel : Numpy matrix/int
- Kernel matrix, each element of which is the path kernel between 2 praphs. / Path Kernel between 2 graphs.
-
- 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).
- """
- if len(args) == 1: # for a list of graphs
- Gn = args[0]
- Kmatrix = np.zeros((len(Gn), len(Gn)))
-
- start_time = time.time()
-
- for i in range(0, len(Gn)):
- for j in range(i, len(Gn)):
- Kmatrix[i][j] = _pathkernel_do(Gn[i], Gn[j], node_label, edge_label)
- Kmatrix[j][i] = Kmatrix[i][j]
-
- run_time = time.time() - start_time
- print("\n --- mean average path kernel matrix of size %d built in %s seconds ---" % (len(Gn), run_time))
-
- return Kmatrix, run_time
-
- else: # for only 2 graphs
- start_time = time.time()
-
- kernel = _pathkernel_do(args[0], args[1], node_label, edge_label)
-
- run_time = time.time() - start_time
- print("\n --- mean average path kernel built in %s seconds ---" % (run_time))
-
- return kernel, run_time
-
-
- def _pathkernel_do(G1, G2, node_label = 'atom', edge_label = 'bond_type'):
- """Calculate mean average path kernels between 2 graphs.
-
- Parameters
- ----------
- G1, G2 : NetworkX graphs
- 2 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.
-
- Return
- ------
- Kernel : int
- Path Kernel between 2 graphs.
- """
- # calculate shortest paths for both graphs
- sp1 = []
- num_nodes = G1.number_of_nodes()
- for node1 in range(num_nodes):
- for node2 in range(node1 + 1, num_nodes):
- sp1.append(nx.shortest_path(G1, node1, node2, weight = edge_label))
-
- sp2 = []
- num_nodes = G2.number_of_nodes()
- for node1 in range(num_nodes):
- for node2 in range(node1 + 1, num_nodes):
- sp2.append(nx.shortest_path(G2, node1, node2, weight = edge_label))
-
- # calculate kernel
- kernel = 0
- for path1 in sp1:
- for path2 in sp2:
- if len(path1) == len(path2):
- kernel_path = deltakernel(G1.node[path1[0]][node_label] == G2.node[path2[0]][node_label])
- if kernel_path:
- for i in range(1, len(path1)):
- # kernel = 1 if all corresponding nodes and edges in the 2 paths have same labels, otherwise 0
- kernel_path *= deltakernel(G1[path1[i - 1]][path1[i]][edge_label] == G2[path2[i - 1]][path2[i]][edge_label]) * deltakernel(G1.node[path1[i]][node_label] == G2.node[path2[i]][node_label])
- kernel += kernel_path # add up kernels of all paths
-
- kernel = kernel / (len(sp1) * len(sp2)) # calculate mean average
-
- return kernel
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