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attri2vec.py
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attri2vec.py
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import random
from collections import defaultdict
import numpy as np
import gensim
from joblib import Parallel, delayed
from tqdm import tqdm
def parallel_generate_walks(d_graph, global_walk_length, num_walks, cpu_num, sampling_strategy=None,
num_walks_key=None, walk_length_key=None, neighbors_key=None, probabilities_key=None,
first_travel_key=None):
"""
Generates the random walks which will be used as the skip-gram input.
:return: List of walks. Each walk is a list of nodes.
"""
walks = list()
# print('parallel_generate_walks')
with tqdm(total=num_walks) as pbar:
pbar.set_description('Generating walks (CPU: {})'.format(cpu_num))
for n_walk in range(num_walks):
# print('cpu_num n_walk', cpu_num, n_walk)
# Update progress bar
pbar.update(1)
# Shuffle the nodes
shuffled_nodes = list(d_graph.keys())
random.shuffle(shuffled_nodes)
# Start a random walk from every node
for source in shuffled_nodes:
# Skip nodes with specific num_walks
if source in sampling_strategy and \
num_walks_key in sampling_strategy[source] and \
sampling_strategy[source][num_walks_key] <= n_walk:
continue
# Start walk
walk = [source]
# Calculate walk length
if source in sampling_strategy:
walk_length = sampling_strategy[source].get(walk_length_key, global_walk_length)
else:
walk_length = global_walk_length
# Perform walk
while len(walk) < walk_length:
walk_options = d_graph[walk[-1]].get(neighbors_key, None)
# Skip dead end nodes
if not walk_options:
break
if len(walk) == 1: # For the first step
probabilities = d_graph[walk[-1]][first_travel_key]
walk_to = np.random.choice(walk_options, size=1, p=probabilities)[0]
else:
probabilities = d_graph[walk[-1]][probabilities_key][walk[-2]]
walk_to = np.random.choice(walk_options, size=1, p=probabilities)[0]
walk.append(walk_to)
walk = list(map(str, walk)) # Convert all to strings
walks.append(walk)
# print('cpu_num n_walk end', cpu_num, n_walk)
# print('parallel_generate_walks end')
return walks
class Attri2Vec:
FIRST_TRAVEL_KEY = 'first_travel_key'
PROBABILITIES_KEY = 'probabilities'
NEIGHBORS_KEY = 'neighbors'
WEIGHT_KEY = 'weight'
NUM_WALKS_KEY = 'num_walks'
WALK_LENGTH_KEY = 'walk_length'
P_KEY = 'p'
Q_KEY = 'q'
R_KEY = 'r'
def __init__(self, graph, dimensions=128, walk_length=80, num_walks=10, p=1, q=1, r=1, weight_key='weight',
workers=1, sampling_strategy=None):
"""
Initiates the Node2Vec object, precomputes walking probabilities and generates the walks.
:param graph: Input graph
:type graph: Networkx Graph
:param dimensions: Embedding dimensions (default: 128)
:type dimensions: int
:param walk_length: Number of nodes in each walk (default: 80)
:type walk_length: int
:param num_walks: Number of walks per node (default: 10)
:type num_walks: int
:param p: Return hyper parameter (default: 1)
:type p: float
:param q: Inout parameter (default: 1)
:type q: float
:param r: Attribute parameter (default: 1)
:type r: float
:param weight_key: On weighted graphs, this is the key for the weight attribute (default: 'weight')
:type weight_key: str
:param workers: Number of workers for parallel execution (default: 1)
:type workers: int
:param sampling_strategy: Node specific sampling strategies, supports setting node specific 'q', 'p', 'num_walks' and 'walk_length'.
Use these keys exactly. If not set, will use the global ones which were passed on the object initialization
"""
self.graph = graph
self.dimensions = dimensions
self.walk_length = walk_length
self.num_walks = num_walks
self.p = p
self.q = q
self.r = r
self.weight_key = weight_key
self.workers = workers
if sampling_strategy is None:
self.sampling_strategy = {}
else:
self.sampling_strategy = sampling_strategy
self.d_graph = self._precompute_probabilities()
# print('after _precompute_probabilities')
self.walks = self._generate_walks()
def _precompute_probabilities(self):
"""
Precomputes transition probabilities for each node.
"""
d_graph = defaultdict(dict)
first_travel_done = set()
# print('_precompute_probabilities')
for source in tqdm(self.graph.nodes(), desc='Computing transition probabilities'):
# print('_precompute_probabilities', source)
# Init probabilities dict for first travel
if self.PROBABILITIES_KEY not in d_graph[source]:
d_graph[source][self.PROBABILITIES_KEY] = dict()
for current_node in self.graph.neighbors(source):
# Init probabilities dict
if self.PROBABILITIES_KEY not in d_graph[current_node]:
d_graph[current_node][self.PROBABILITIES_KEY] = dict()
unnormalized_weights = list()
first_travel_weights = list()
d_neighbors = list()
# Calculate unnormalized weights
for destination in self.graph.neighbors(current_node):
p = self.sampling_strategy[current_node].get(self.P_KEY, self.p)\
if current_node in self.sampling_strategy else self.p
q = self.sampling_strategy[current_node].get(self.Q_KEY, self.q)\
if current_node in self.sampling_strategy else self.q
r = self.sampling_strategy[current_node].get(self.R_KEY, self.r)\
if current_node in self.sampling_strategy else self.r
# if destination == source: # Backwards probability
# ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p
# elif destination in self.graph[source]: # If the neighbor is connected to the source
# ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)
# else:
# ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q
cns = str(current_node)
dns = str(destination)
# if cns.startswith('attri-') or dns.startswith('attri-'):
# if dns.startswith('attri-'):
# if cns.startswith('attri-'):
if cns.startswith('attri-') or dns.startswith('attri-'):
ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / r
else:
if destination == source: # Backwards probability
ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / p
elif destination in self.graph[source]: # If the neighbor is connected to the source
ss_weight = self.graph[current_node][destination].get(self.weight_key, 1)
else:
ss_weight = self.graph[current_node][destination].get(self.weight_key, 1) * 1 / q
# Assign the unnormalized sampling strategy weight, normalize during random walk
unnormalized_weights.append(ss_weight)
if current_node not in first_travel_done:
first_travel_weights.append(self.graph[current_node][destination].get(self.weight_key, 1))
d_neighbors.append(destination)
# Normalize
unnormalized_weights = np.array(unnormalized_weights)
d_graph[current_node][self.PROBABILITIES_KEY][
source] = unnormalized_weights / unnormalized_weights.sum()
if current_node not in first_travel_done:
unnormalized_weights = np.array(first_travel_weights)
d_graph[current_node][self.FIRST_TRAVEL_KEY] = unnormalized_weights / unnormalized_weights.sum()
first_travel_done.add(current_node)
# Save neighbors
d_graph[current_node][self.NEIGHBORS_KEY] = d_neighbors
# print('_precompute_probabilities end')
return d_graph
def _generate_walks(self):
"""
Generates the random walks which will be used as the skip-gram input.
:return: List of walks. Each walk is a list of nodes.
"""
# print('sublist')
flatten = lambda l: [item for sublist in l for item in sublist]
# Split num_walks for each worker
num_walks_lists = np.array_split(range(self.num_walks), self.workers)
# print('num_walks_lists', num_walks_lists)
walk_results = Parallel(n_jobs=self.workers)(delayed(parallel_generate_walks)(self.d_graph,
self.walk_length,
len(num_walks),
idx,
self.sampling_strategy,
self.NUM_WALKS_KEY,
self.WALK_LENGTH_KEY,
self.NEIGHBORS_KEY,
self.PROBABILITIES_KEY,
self.FIRST_TRAVEL_KEY) for
idx, num_walks
in enumerate(num_walks_lists, 1))
# print('walk_results', walk_results)
walks = flatten(walk_results)
# print('walks', walks)
return walks
def fit(self, **skip_gram_params):
"""
Creates the embeddings using gensim's Word2Vec.
:param skip_gram_params: Parameteres for gensim.models.Word2Vec - do not supply 'size' it is taken from the Node2Vec 'dimensions' parameter
:type skip_gram_params: dict
:return: A gensim word2vec model
"""
if 'workers' not in skip_gram_params:
skip_gram_params['workers'] = self.workers
if 'size' not in skip_gram_params:
skip_gram_params['size'] = self.dimensions
return gensim.models.Word2Vec(self.walks, **skip_gram_params)