#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import scipy.io as sio
import scipy.sparse as sp
import scipy.sparse.linalg as lg
from time import time
import six
from .static_graph_embedding import StaticGraphEmbedding
from gemben.utils import graph_util, plot_util
from gemben.evaluation import visualize_embedding as viz
[docs]class JaccardCoefficient(StaticGraphEmbedding):
"""`Jaccard Coefficient`_.
Jaccard Coefficient measures the probability that
two nodes :math:`i` and :math:`j` have a connection to node :math:`k`,
for a randomly selected node $k$ from the neighbors of :math:`i` and :math:`j` .
Args:
hyper_dict (object): Hyper parameters.
kwargs (dict): keyword arguments, form updating the parameters
Examples:
>>> from gemben.embedding.jc import JaccardCoefficient
>>> edge_f = 'data/karate.edgelist'
>>> G = graph_util.loadGraphFromEdgeListTxt(edge_f, directed=False)
>>> G = G.to_directed()
>>> res_pre = 'results/testKarate'
>>> graph_util.print_graph_stats(G)
>>> t1 = time()
>>> embedding = JaccardCoefficient(4, 0.01)
>>> embedding.learn_embedding(graph=G, edge_f=None,
is_weighted=True, no_python=True)
>>> print('Adamic Adar:Training time: %f' % (time() - t1))
.. _Jaccard Coefficient:
https://dl.acm.org/citation.cfm?id=576628
"""
def __init__(self, *hyper_dict, **kwargs):
''' Initialize the JaccardCoefficient class
Args:
d: dimension of the embedding
beta: higher order coefficient
'''
hyper_params = {
'method_name': 'jaccard_coefficient'
}
hyper_params.update(kwargs)
for key in hyper_params.keys():
self.__setattr__('_%s' % key, hyper_params[key])
for dictionary in hyper_dict:
for key in dictionary:
self.__setattr__('_%s' % key, dictionary[key])
[docs] def get_method_name(self):
return self._method_name
[docs] def get_method_summary(self):
return '%s_%d' % (self._method_name, self._d)
[docs] def learn_embedding(self, graph=None, edge_f=None,
is_weighted=False, no_python=False):
self._G = graph.to_undirected()
return None, 0
[docs] def get_embedding(self):
return self._X
[docs] def get_edge_weight(self, i, j):
aa_index = nx.jaccard_coefficient(self._G, [(i, j)])
return six.next(aa_index)[2]
[docs] def get_reconstructed_adj(self, X=None, node_l=None):
if X is not None:
node_num = X.shape[0]
self._X = X
else:
node_num = self._G.number_of_nodes()
adj_mtx_r = np.zeros((node_num, node_num))
for v_i in range(node_num):
for v_j in range(node_num):
if v_i == v_j:
continue
adj_mtx_r[v_i, v_j] = self.get_edge_weight(v_i, v_j)
return adj_mtx_r
if __name__ == '__main__':
# load Zachary's Karate graph
edge_f = 'data/karate.edgelist'
G = graph_util.loadGraphFromEdgeListTxt(edge_f, directed=False)
G = G.to_directed()
res_pre = 'results/testKarate'
graph_util.print_graph_stats(G)
t1 = time()
embedding = JaccardCoefficient(4, 0.01)
embedding.learn_embedding(graph=G, edge_f=None,
is_weighted=True, no_python=True)
print('Adamic Adar:\n\tTraining time: %f' % (time() - t1))