"""
This module implements a wrapper around DeepWalk as a class. The class
contains a graph used as a basis for running the Deepwalk algorithm. It also
implements a method to run a given number of walks and save the walks as an
attribute of the instance.
"""
import time
import random
import logging
import functools
import networkx as nx
import multiprocessing
from gensim.models import Word2Vec
logger = logging.getLogger('genewalk.deepwalk')
default_walk_length = 10
default_niter = 100
[docs]class DeepWalk(object):
"""Perform DeepWalk (node2vec), i.e., unbiased random walk over nodes
on an undirected networkx MultiGraph.
Parameters
----------
graph : networkx.MultiGraph
A networkx multigraph to be used as the basis for DeepWalk.
walk_length : Optional[int]
The length of each random walk on the graph. Default: 10
niter : Optional[int]
The number of iterations for each node to run (this is multiplied by
the number of neighbors of the node when determining the overall number
of walks to start from a given node). Default: 100
Attributes
----------
walks : list
A list of walks.
"""
def __init__(self, graph, walk_length=default_walk_length,
niter=default_niter):
self.graph = graph
self.walks = []
self.wl = walk_length
self.niter = niter
self.model = None
[docs] def get_walks(self, workers=1):
"""Generates walks (sentences) sampled by an (unbiased) random walk
over the networkx MultiGraph.
Parameters
----------
workers : Optional[int]
The number of workers to use when running random walks. If greater
than 1, multiprocessing is used to speed up random walk generation.
Default: 1
"""
logger.info('Running random walks...')
self.walks = []
start = time.time()
nodes = nx.nodes(self.graph)
# In case we don't parallelize
if workers == 1:
for count, node in enumerate(nodes):
walks = run_walks_for_node(node, self.graph, self.niter,
self.wl)
self.walks.extend(walks)
if (count + 1) % 100 == 0:
logger.info('Walks for %d/%d nodes complete in %.2fs' %
(count + 1, len(nodes), time.time() - start))
# In case we parallelize
else:
pool = multiprocessing.Pool(workers)
start_nodes = get_start_nodes(self.graph, self.niter)
chunk_size = int(float(0.9*len(start_nodes))/workers)
walk_fun = functools.partial(run_single_walk,
graph=self.graph,
length=self.wl)
for count, res in enumerate(
pool.imap_unordered(walk_fun, start_nodes,
chunksize=chunk_size)):
self.walks.append(res)
if (count + 1) % chunk_size == 0:
logger.info('%d/%d walks complete in %.2fs' %
(count + 1, len(start_nodes),
time.time() - start))
logger.debug("Closing pool...")
pool.close()
logger.debug("Joining pool...")
pool.join()
logger.debug("Pool closed and joined.")
end = time.time()
logger.info('Running random walks done in %.2fs' % (end - start))
[docs] def word2vec(self, sg=1, vector_size=8, window=1, min_count=1, negative=5,
workers=1, sample=0):
"""Set the model based on Word2Vec
Source: https://radimrehurek.com/gensim/models/word2vec.html
Note that his function sets the model attribute if the DeepWalk object
and doesn't return a value.
Parameters
----------
sg : Optional[int] {1, 0}
Defines the training algorithm. If 1, skip-gram is employed;
otherwise, CBOW is used. For GeneWalk this is set to 1.
vector_size : Optional[int]
Dimensionality of the node vectors. Default for GeneWalk is 8.
window : Optional[int]
a.k.a. context size. Maximum distance between the current and
predicted word within a sentence. For GeneWalk this is set to 1
to assess directly connected nodes only.
min_count : Optional[int]
Ignores all words with total frequency lower than this. For
GeneWalk this is set to 0.
negative : Optional[int]
If > 0, negative sampling will be used, the int for negative
specifies how many "noise words" should be drawn (usually between
5-20). If set to 0, no negative sampling is used.
Default for GeneWalk is 5.
workers : Optional[int]
Use these many worker threads to train the model (=faster training
with multicore machines).
sample : Optional[float]
The threshold for configuring which higher-frequency words are
randomly downsampled, useful range is (0, 1e-5). parameter t in eq
5 Mikolov et al. For GeneWalk this is set to 0.
"""
logger.info('Generating node vectors...')
start = time.time()
self.model = Word2Vec(sentences=self.walks, sg=sg,
vector_size=vector_size,
window=window, min_count=min_count,
negative=negative, workers=workers,
sample=sample)
end = time.time()
logger.info('Generating node vectors done in %.2fs'
% (end - start))
[docs]def run_single_walk(start_node, graph, length):
"""Run a single random walk on a graph from a given start node.
Parameters
----------
graph : networks.MultiGraph
The graph on which the random walk is to be run.
start_node : str
The identifier of the node from which the random walk starts.
length : int
The length of the random walk.
Returns
-------
list of str
A path of the given length, with each element corresponding to a node
along the path.
"""
path = [start_node]
for i in range(1, length):
start_node = random.choice(list(graph[start_node]))
path.append(start_node)
return path
def get_start_nodes(graph, niter):
start_nodes = []
for node in nx.nodes(graph):
start_nodes += [node for _ in range(niter * len(graph[node]))]
return start_nodes
[docs]def run_walks_for_node(node, graph, niter, walk_length):
"""Run all random walks starting from a given node.
Parameters
----------
node : str
The identifier of the node from which the walks start.
graph : networks.MultiGraph
The graph on which the random walks are to be run.
niter : int
The number of iterations to run for gene nodes.
walk_length : int
The length of the walk.
Returns
-------
list of list of str
A list of random walks starting from the given node.
"""
walks = []
for _ in range(niter * len(graph[node])):
walk = run_single_walk(node, graph, walk_length)
walks.append(walk)
return walks
[docs]def run_walks(graph, **kwargs):
"""Run random walks and get node vectors on a given graph.
Parameters
----------
graph : networkx.MultiGraph
The graph on which random walks are going to be run and node vectors
calculated.
**kwargs
Key word arguments passed as the arguments of the DeepWalk constructor,
as well as the get_walks method and the word2vec method. See the
DeepWalk class documentation for more information on these.
Returns
-------
:py:class:`genewalk.deepwalk.DeepWalk`
A DeepWalk instance whose walks attribute contains the list
of random walks produced on the graph.
"""
dw_args = {'walk_length': kwargs.pop('walk_length', default_walk_length),
'niter': kwargs.pop('niter', default_niter)}
DW = DeepWalk(graph, **dw_args)
DW.get_walks(kwargs.get('workers', 1))
DW.word2vec(**kwargs)
return DW