#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from keras.layers import Input, Lambda, merge, Subtract
from keras.models import Model, model_from_json
from keras.optimizers import SGD, Adam
from keras import backend as KBack
import tensorflow as tf
from time import time
import networkx as nx
from dynamicgem.embedding.dynamic_graph_embedding import DynamicGraphEmbedding
from dynamicgem.utils import graph_util, plot_util
from dynamicgem.evaluation import visualize_embedding as viz
from dynamicgem.utils.sdne_utils import *
from dynamicgem.graph_generation import SBM_graph
from dynamicgem.evaluation import evaluate_graph_reconstruction as gr
[docs]class DynGEM(DynamicGraphEmbedding):
"""Structural Deep Network Embedding
DynSDNE (also DynGEM) perfomr the dynamic network embedding
while utilizing Structural Deep Network Embedding (SDNE) with dynamically evolving graphs as input.
Args:
d (int): dimension of the embedding
beta (float): penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int): Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float): L1-reg hyperparameter
nu2 (float): L2-reg hyperparameter
K (float): number of hidden layers in encoder/decoder
rho (float): bounding ratio for number of units in consecutive layers (< 1)
n_aeunits (list) = List of embedding dimension for auto encoder layers
n_lstmunits= List of embedding dimension for lstm layers
n_iter (int): number of sgd iterations for first embedding (const)
xeta (float): sgd step size parameter
n_batch (int): minibatch size for SGD
modelfile (str): Files containing previous encoder and decoder models
weightfile (str): Files containing previous encoder and decoder weights
Examples:
>>> from dynamicgem.embedding.dynSDNE import DynSDNE
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
community_num,
length,
1,
node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embedding = DynSDNE(d=128, beta=5, alpha=0, nu1=1e-6, nu2=1e-6, K=3,
n_units=[500, 300], n_iter=20, xeta=0.01,
n_batch=500,
modelfile=['./intermediate/enc_model.json',
'./intermediate/dec_model.json'],
weightfile=['./intermediate/enc_weights.hdf5',
'./intermediate/dec_weights.hdf5'])
>>> embedding.learn_embedding(graph=graphs._graph, edge_f=None,
is_weighted=True, no_python=True)
"""
def __init__(self, *hyper_dict, **kwargs):
""" Initialize the DynSDNE class"""
hyper_params = {
'method_name': 'dynSDNE',
'actfn': 'relu',
'modelfile': None,
'weightfile': None,
'savefilesuffix': None
}
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):
"""Function to return the method name.
Returns:
String: Name of the method.
"""
return self._method_name
[docs] def get_method_summary(self):
"""Function to return the summary of the algorithm.
Returns:
String: Method summary
"""
return '%s_%d' % (self._method_name, self._d)
[docs] def learn_embedding(self, graph=None, edge_f=None,
is_weighted=False, no_python=False):
"""Learns the embedding of the nodes.
Attributes:
graph (Object): Networkx Graph Object
Returns:
List: Node embeddings and time taken by the algorithm
"""
###################################
# TensorFlow wizardry
config = tf.ConfigProto()
# Don't pre-allocate memory; allocate as-needed
config.gpu_options.allow_growth = True
# Only allow a total of half the GPU memory to be allocated
config.gpu_options.per_process_gpu_memory_fraction = 0.25
# Create a session with the above options specified.
KBack.tensorflow_backend.set_session(tf.Session(config=config))
###################################
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
S = nx.to_scipy_sparse_matrix(graph)
t1 = time()
S = (S + S.T) / 2
self._node_num = graph.number_of_nodes()
# Generate encoder, decoder and autoencoder
self._num_iter = self._n_iter
# If cannot use previous step information, initialize new models
self._encoder = get_encoder(self._node_num, self._d,
self._n_units,
self._nu1, self._nu2,
self._actfn)
self._decoder = get_decoder(self._node_num, self._d,
self._n_units,
self._nu1, self._nu2,
self._actfn)
self._autoencoder = get_autoencoder(self._encoder, self._decoder)
# Initialize self._model
# Input
x_in = Input(shape=(2 * self._node_num,), name='x_in')
x1 = Lambda(
lambda x: x[:, 0:self._node_num],
output_shape=(self._node_num,)
)(x_in)
x2 = Lambda(
lambda x: x[:, self._node_num:2 * self._node_num],
output_shape=(self._node_num,)
)(x_in)
# Process inputs
[x_hat1, y1] = self._autoencoder(x1)
[x_hat2, y2] = self._autoencoder(x2)
# Outputs
x_diff1 = Subtract()([x_hat1, x1])
x_diff2 = Subtract()([x_hat2, x2])
y_diff = Subtract()([y2, y1])
# Objectives
def weighted_mse_x(y_true, y_pred):
''' Hack: This fn doesn't accept additional arguments.
We use y_true to pass them.
y_pred: Contains x_hat - x
y_true: Contains [b, deg]
'''
return KBack.sum(
KBack.square(y_pred * y_true[:, 0:self._node_num]),
axis=-1) / y_true[:, self._node_num]
def weighted_mse_y(y_true, y_pred):
''' Hack: This fn doesn't accept additional arguments.
We use y_true to pass them.
y_pred: Contains y2 - y1
y_true: Contains s12
'''
min_batch_size = KBack.shape(y_true)[0]
return KBack.reshape(
KBack.sum(KBack.square(y_pred), axis=-1),
[min_batch_size, 1]
) * y_true
# Model
self._model = Model(input=x_in, output=[x_diff1, x_diff2, y_diff])
sgd = SGD(lr=self._xeta, decay=1e-5, momentum=0.99, nesterov=True)
# adam = Adam(lr=self._xeta, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
self._model.compile(
optimizer=sgd,
loss=[weighted_mse_x, weighted_mse_x, weighted_mse_y],
loss_weights=[1, 1, self._alpha]
)
self._model.fit_generator(
generator=batch_generator_sdne(S, self._beta, self._n_batch, True),
nb_epoch=self._num_iter,
samples_per_epoch=S.nonzero()[0].shape[0] // self._n_batch,
verbose=1
)
# Get embedding for all points
self._Y = model_batch_predictor(self._autoencoder, S, self._n_batch)
t2 = time()
# Save the autoencoder and its weights
if self._weightfile is not None:
saveweights(self._encoder, self._weightfile[0])
saveweights(self._decoder, self._weightfile[1])
if self._modelfile is not None:
savemodel(self._encoder, self._modelfile[0])
savemodel(self._decoder, self._modelfile[1])
if self._savefilesuffix is not None:
saveweights(
self._encoder,
'encoder_weights_' + self._savefilesuffix + '.hdf5'
)
saveweights(
self._decoder,
'decoder_weights_' + self._savefilesuffix + '.hdf5'
)
savemodel(
self._encoder,
'encoder_model_' + self._savefilesuffix + '.json'
)
savemodel(
self._decoder,
'decoder_model_' + self._savefilesuffix + '.json'
)
# Save the embedding
np.savetxt('embedding_' + self._savefilesuffix + '.txt', self._Y)
return self._Y, (t2 - t1)
[docs] def get_embedding(self, filesuffix=None):
"""Function to return the embeddings"""
return self._Y if filesuffix is None else np.loadtxt(
'embedding_' + filesuffix + '.txt'
)
[docs] def get_edge_weight(self, i, j, embed=None, filesuffix=None):
"""Function to get edge weight.
Attributes:
i (int): source node for the edge.
j (int): target node for the edge.
embed (Matrix): Embedding values of all the nodes.
filesuffix (str): File suffix to be used to load the embedding.
Returns:
Float: Weight of the given edge.
"""
if embed is None:
if filesuffix is None:
embed = self._Y
else:
embed = np.loadtxt('embedding_' + filesuffix + '.txt')
if i == j:
return 0
else:
S_hat = self.get_reconst_from_embed(embed[(i, j), :], filesuffix)
return (S_hat[i, j] + S_hat[j, i]) / 2
[docs] def get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None):
"""Function to reconstruct the adjacency list for the given node.
Attributes:
node_l (int): node for which the adjacency list will be created.
embed (Matrix): Embedding values of all the nodes.
filesuffix (str): File suffix to be used to load the embedding.
Returns:
List : Adjacency list of the given node.
"""
if embed is None:
if filesuffix is None:
embed = self._Y
else:
embed = np.loadtxt('embedding_' + filesuffix + '.txt')
S_hat = self.get_reconst_from_embed(embed, node_l, filesuffix)
return graphify(S_hat)
[docs] def get_reconst_from_embed(self, embed, node_l=None, filesuffix=None):
"""Function to reconstruct the graph from the embedding.
Attributes:
node_l (int): node for which the adjacency list will be created.
embed (Matrix): Embedding values of all the nodes.
filesuffix (str): File suffix to be used to load the embedding.
Returns:
List: REconstructed graph for the given nodes.
"""
if filesuffix is None:
if node_l is not None:
return self._decoder.predict(
embed,
batch_size=self._n_batch)[:, node_l]
else:
return self._decoder.predict(embed, batch_size=self._n_batch)
else:
try:
decoder = model_from_json(
open('decoder_model_' + filesuffix + '.json').read()
)
except:
print('Error reading file: {0}. Cannot load previous model'.format('decoder_model_'+filesuffix+'.json'))
exit()
try:
decoder.load_weights('decoder_weights_' + filesuffix + '.hdf5')
except:
print('Error reading file: {0}. Cannot load previous weights'.format('decoder_weights_'+filesuffix+'.hdf5'))
exit()
if node_l is not None:
return decoder.predict(embed, batch_size=self._n_batch)[:, node_l]
else:
return decoder.predict(embed, batch_size=self._n_batch)