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import torch
import torch.nn as nn
from torch.nn import init
from torch.autograd import Variable
import numpy as np
import time
import random
from sklearn.metrics import f1_score
from collections import defaultdict
#from graphsage.encoders import Encoder
#from graphsage.aggregators import MeanAggregator
"""
Simple supervised GraphSAGE model as well as examples running the model
on the Cora and Pubmed datasets.
"""
class MeanAggregator(nn.Module):
"""
Aggregates a node's embeddings using mean of neighbors' embeddings
"""
def __init__(self, features, cuda=False, gcn=False):
"""
Initializes the aggregator for a specific graph.
features -- function mapping LongTensor of node ids to FloatTensor of feature values.
cuda -- whether to use GPU
gcn --- whether to perform concatenation GraphSAGE-style, or add self-loops GCN-style
"""
super(MeanAggregator, self).__init__()
self.features = features
self.cuda = cuda
self.gcn = gcn
def forward(self, nodes, to_neighs, num_sample=10):
"""
nodes --- list of nodes in a batch
to_neighs --- list of sets, each set is the set of neighbors for node in batch
num_sample --- number of neighbors to sample. No sampling if None.
"""
# Local pointers to functions (speed hack)
_set = set
if not num_sample is None:
_sample = random.sample
samp_neighs = [_set(_sample(to_neigh,
num_sample,
)) if len(to_neigh) >= num_sample else to_neigh for to_neigh in to_neighs]
else:
samp_neighs = to_neighs
if self.gcn:
samp_neighs = [samp_neigh + set([nodes[i]]) for i, samp_neigh in enumerate(samp_neighs)]
unique_nodes_list = list(set.union(*samp_neighs))
# print ("\n unl's size=",len(unique_nodes_list))
unique_nodes = {n:i for i,n in enumerate(unique_nodes_list)}
mask = Variable(torch.zeros(len(samp_neighs), len(unique_nodes)))
column_indices = [unique_nodes[n] for samp_neigh in samp_neighs for n in samp_neigh]
row_indices = [i for i in range(len(samp_neighs)) for j in range(len(samp_neighs[i]))]
mask[row_indices, column_indices] = 1
if self.cuda:
mask = mask.cuda()
num_neigh = mask.sum(1, keepdim=True)
mask = mask.div(num_neigh)
if self.cuda:
embed_matrix = self.features(torch.LongTensor(unique_nodes_list).cuda())
else:
embed_matrix = self.features(torch.LongTensor(unique_nodes_list))
to_feats = mask.mm(embed_matrix)
return to_feats
class Encoder(nn.Module):
"""
Encodes a node's using 'convolutional' GraphSage approach
"""
def __init__(self, features, feature_dim,
embed_dim, adj_lists, aggregator,
num_sample=10,
base_model=None, gcn=False, cuda=False,
feature_transform=False):
super(Encoder, self).__init__()
self.features = features
self.feat_dim = feature_dim
self.adj_lists = adj_lists
self.aggregator = aggregator
self.num_sample = num_sample
if base_model != None:
self.base_model = base_model
self.gcn = gcn
self.embed_dim = embed_dim
self.cuda = cuda
self.aggregator.cuda = cuda
self.weight = nn.Parameter(
torch.FloatTensor(embed_dim, self.feat_dim if self.gcn else 2 * self.feat_dim))
init.xavier_uniform(self.weight)
def forward(self, nodes):
"""
Generates embeddings for a batch of nodes.
nodes -- list of nodes
"""
neigh_feats = self.aggregator.forward(nodes, [self.adj_lists[int(node)] for node in nodes],
self.num_sample)
if not self.gcn:
if self.cuda:
self_feats = self.features(torch.LongTensor(nodes).cuda())
else:
self_feats = self.features(torch.LongTensor(nodes))
combined = torch.cat([self_feats, neigh_feats], dim=1)
else:
combined = neigh_feats
combined = F.relu(self.weight.mm(combined.t()))
return combined
class SupervisedGraphSage(nn.Module):
def __init__(self, num_classes, enc):
super(SupervisedGraphSage, self).__init__()
self.enc = enc
self.xent = nn.CrossEntropyLoss()
self.weight = nn.Parameter(torch.FloatTensor(num_classes, enc.embed_dim))
init.xavier_uniform(self.weight)
def forward(self, nodes):
embeds = self.enc(nodes)
scores = self.weight.mm(embeds)
return scores.t()
def loss(self, nodes, labels):
scores = self.forward(nodes)
return self.xent(scores, labels.squeeze())
def load_cora():
num_nodes = 2708
num_feats = 1433
feat_data = np.zeros((num_nodes, num_feats))
labels = np.empty((num_nodes,1), dtype=np.int64)
node_map = {}
label_map = {}
with open("../cora/cora.content") as fp:
for i,line in enumerate(fp):
info = line.strip().split()
feat_data[i,:] = [float(x) for x in info[1:-1]]
node_map[info[0]] = i
if not info[-1] in label_map:
label_map[info[-1]] = len(label_map)
labels[i] = label_map[info[-1]]
adj_lists = defaultdict(set)
with open("../cora/cora.cites") as fp:
for i,line in enumerate(fp):
info = line.strip().split()
paper1 = node_map[info[0]]
paper2 = node_map[info[1]]
adj_lists[paper1].add(paper2)
adj_lists[paper2].add(paper1)
return feat_data, labels, adj_lists
def run_cora():
np.random.seed(1)
random.seed(1)
num_nodes = 2708
feat_data, labels, adj_lists = load_cora()
features = nn.Embedding(2708, 1433)
features.weight = nn.Parameter(torch.FloatTensor(feat_data), requires_grad=False)
# features.cuda()
agg1 = MeanAggregator(features, cuda=True)
enc1 = Encoder(features, 1433, 128, adj_lists, agg1, gcn=True, cuda=False)
agg2 = MeanAggregator(lambda nodes : enc1(nodes).t(), cuda=False)
enc2 = Encoder(lambda nodes : enc1(nodes).t(), enc1.embed_dim, 128, adj_lists, agg2,
base_model=enc1, gcn=True, cuda=False)
enc1.num_samples = 5
enc2.num_samples = 5
graphsage = SupervisedGraphSage(7, enc2)
# graphsage.cuda()
rand_indices = np.random.permutation(num_nodes)
test = rand_indices[:1000]
val = rand_indices[1000:1500]
train = list(rand_indices[1500:])
optimizer = torch.optim.SGD(filter(lambda p : p.requires_grad, graphsage.parameters()), lr=0.7)
times = []
for batch in range(100):
batch_nodes = train[:256]
random.shuffle(train)
start_time = time.time()
optimizer.zero_grad()
loss = graphsage.loss(batch_nodes,
Variable(torch.LongTensor(labels[np.array(batch_nodes)])))
loss.backward()
optimizer.step()
end_time = time.time()
times.append(end_time-start_time)
print (batch, loss.item())
val_output = graphsage.forward(val)
print ("Validation F1:", f1_score(labels[val], val_output.data.numpy().argmax(axis=1), average="micro"))
print ("Average batch time:", np.mean(times))
if __name__ == "__main__":
run_cora()
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