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sigat.py
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sigat.py
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#!/usr/bin/env python3
#-*- coding: utf-8 -*-
"""
@author: huangjunjie
@file: sigat.py
@time: 2018/12/10
"""
import sys
import os
import time
import random
import subprocess
from collections import defaultdict
import argparse
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from common import DATASET_NUM_DIC
#
from fea_extra import FeaExtra
OUTPUT_DIR = './embeddings/sigat'
if not os.path.exists('embeddings'):
os.mkdir('embeddings')
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('--devices', type=str, default='cpu', help='Devices')
parser.add_argument('--seed', type=int, default=13, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100, help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.0005, help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=0.0001, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--dataset', default='bitcoin_alpha', help='Dataset')
parser.add_argument('--dim', type=int, default=20, help='Embedding Dimension')
parser.add_argument('--fea_dim', type=int, default=20, help='Feature Embedding Dimension')
parser.add_argument('--batch_size', type=int, default=500, help='Batch Size')
parser.add_argument('--dropout', type=float, default=0.0, help='Dropout k')
parser.add_argument('--k', default=1, help='Folder k')
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
NEG_LOSS_RATIO = 1
INTERVAL_PRINT = 20
NUM_NODE = DATASET_NUM_DIC[args.dataset]
WEIGHT_DECAY = args.weight_decay
NODE_FEAT_SIZE = args.fea_dim
EMBEDDING_SIZE1 = args.dim
DEVICES = torch.device(args.devices)
LEARNING_RATE = args.lr
BATCH_SIZE = args.batch_size
EPOCHS = args.epochs
DROUPOUT = args.dropout
K = args.k
print(DEVICES)
class Encoder(nn.Module):
"""
Encode features to embeddings
"""
def __init__(self, features_lists, feature_dim, embed_dim, adj_lists, aggs):
super(Encoder, self).__init__()
self.features_lists = features_lists
self.feat_dim = feature_dim
self.adj_lists = adj_lists
self.aggs = aggs
self.embed_dim = embed_dim
for i, agg in enumerate(self.aggs):
self.add_module('agg_{}'.format(i), agg)
self.aggs[i] = agg.to(DEVICES)
def init_weights(m):
if type(m) == nn.Linear:
torch.nn.init.kaiming_normal_(m.weight)
m.bias.data.fill_(0.01)
self.nonlinear_layer = nn.Sequential(
nn.Linear(self.feat_dim * (len(adj_lists) + 1), self.feat_dim),
nn.Tanh(),
nn.Linear(self.feat_dim, self.embed_dim),
)
self.nonlinear_layer.apply(init_weights)
def forward(self, nodes):
"""
Generates embeddings for nodes.
"""
neigh_feats = [agg.forward(nodes, adj) for adj, agg in zip(self.adj_lists, self.aggs)]
self_feats = self.features_lists[0](torch.LongTensor(nodes).to(DEVICES))
combined = torch.cat([self_feats] + neigh_feats, 1)
combined = self.nonlinear_layer(combined)
return combined
class SpecialSpmmFunction(torch.autograd.Function):
"""Special function for only sparse region backpropataion layer."""
@staticmethod
def forward(ctx, indices, values, shape, b):
assert indices.requires_grad == False
a = torch.sparse_coo_tensor(indices, values, shape, device=DEVICES)
ctx.save_for_backward(a, b)
ctx.N = shape[0]
return torch.matmul(a, b)
@staticmethod
def backward(ctx, grad_output):
a, b = ctx.saved_tensors
grad_values = grad_b = None
if ctx.needs_input_grad[1]:
grad_a_dense = grad_output.matmul(b.t())
edge_idx = a._indices()[0, :] * ctx.N + a._indices()[1, :]
grad_values = grad_a_dense.view(-1)[edge_idx]
if ctx.needs_input_grad[3]:
grad_b = a.t().matmul(grad_output)
return None, grad_values, None, grad_b
class SpecialSpmm(nn.Module):
def forward(self, indices, values, shape, b):
return SpecialSpmmFunction.apply(indices, values, shape, b)
class AttentionAggregator(nn.Module):
def __init__(self, features, in_dim, out_dim, node_num, dropout_rate=DROUPOUT, slope_ratio=0.1):
super(AttentionAggregator, self).__init__()
self.features = features
self.in_dim = in_dim
self.out_dim = out_dim
self.dropout = nn.Dropout(dropout_rate)
self.slope_ratio = slope_ratio
self.a = nn.Parameter(torch.FloatTensor(out_dim * 2, 1))
nn.init.kaiming_normal_(self.a.data)
self.speical_spmm = SpecialSpmm()
self.out_linear_layer = nn.Linear(self.in_dim, self.out_dim)
self.unique_nodes_dict = np.zeros(node_num, dtype=np.int32)
def forward(self, nodes, adj):
"""
nodes --- list of nodes in a batch
adj --- sp.csr_matrix
"""
node_pku = np.array(nodes)
edges = np.array(adj[nodes, :].nonzero()).T
edges[:, 0] = node_pku[edges[:, 0]]
unique_nodes_list = np.unique(np.hstack((np.unique(edges), np.array(nodes))))
batch_node_num = len(unique_nodes_list)
# this dict can map new i to originial node id
self.unique_nodes_dict[unique_nodes_list] = np.arange(batch_node_num)
edges[:, 0] = self.unique_nodes_dict[edges[:, 0]]
edges[:, 1] = self.unique_nodes_dict[edges[:, 1]]
n2 = torch.LongTensor(unique_nodes_list).to(DEVICES)
new_embeddings = self.out_linear_layer(self.features(n2))
original_node_edge = np.array([self.unique_nodes_dict[nodes], self.unique_nodes_dict[nodes]]).T
edges = np.vstack((edges, original_node_edge))
edges = torch.LongTensor(edges).to(DEVICES)
edge_h_2 = torch.cat((new_embeddings[edges[:, 0], :], new_embeddings[edges[:, 1], :]), dim=1)
edges_h = torch.exp(F.leaky_relu(torch.einsum("ij,jl->il", [edge_h_2, self.a]), self.slope_ratio))
indices = edges
row_sum = self.speical_spmm(edges.t(), edges_h[:, 0], torch.Size((batch_node_num, batch_node_num)), torch.ones(size=(batch_node_num, 1)).to(DEVICES))
results = self.speical_spmm(edges.t(), edges_h[:, 0], torch.Size((batch_node_num, batch_node_num)), new_embeddings)
output_emb = results.div(row_sum)
return output_emb[self.unique_nodes_dict[nodes]]
class SiGAT(nn.Module):
def __init__(self, enc):
super(SiGAT, self).__init__()
self.enc = enc
def forward(self, nodes):
embeds = self.enc(nodes)
return embeds
def criterion(self, nodes, pos_neighbors, neg_neighbors):
pos_neighbors_list = [set.union(pos_neighbors[i]) for i in nodes]
neg_neighbors_list = [set.union(neg_neighbors[i]) for i in nodes]
unique_nodes_list = list(set.union(*pos_neighbors_list).union(*neg_neighbors_list).union(nodes))
unique_nodes_dict = {n: i for i, n in enumerate(unique_nodes_list)}
nodes_embs = self.enc(unique_nodes_list)
loss_total = 0
for index, node in enumerate(nodes):
z1 = nodes_embs[unique_nodes_dict[node], :]
pos_neigs = list([unique_nodes_dict[i] for i in pos_neighbors[node]])
neg_neigs = list([unique_nodes_dict[i] for i in neg_neighbors[node]])
pos_num = len(pos_neigs)
neg_num = len(neg_neigs)
if pos_num > 0:
pos_neig_embs = nodes_embs[pos_neigs, :]
loss_pku = -1 * torch.sum(F.logsigmoid(torch.einsum("nj,j->n", [pos_neig_embs, z1])))
loss_total += loss_pku
tmp_pku = 1 if neg_num == 0 else neg_num
C = pos_num // tmp_pku
if C == 0:
C = 1
if neg_num > 0:
neg_neig_embs = nodes_embs[neg_neigs, :]
loss_pku = -1 * torch.sum(F.logsigmoid(-1 * torch.einsum("nj,j->n",[neg_neig_embs, z1])))
loss_total += C * NEG_LOSS_RATIO * loss_pku
return loss_total
def load_data2(filename='', add_public_foe=True):
adj_lists1 = defaultdict(set)
adj_lists1_1 = defaultdict(set)
adj_lists1_2 = defaultdict(set)
adj_lists2 = defaultdict(set)
adj_lists2_1 = defaultdict(set)
adj_lists2_2 = defaultdict(set)
adj_lists3 = defaultdict(set)
with open(filename) as fp:
for i, line in enumerate(fp):
info = line.strip().split()
person1 = int(info[0])
person2 = int(info[1])
v = int(info[2])
adj_lists3[person2].add(person1)
adj_lists3[person1].add(person2)
if v == 1:
adj_lists1[person1].add(person2)
adj_lists1[person2].add(person1)
adj_lists1_1[person1].add(person2)
adj_lists1_2[person2].add(person1)
else:
adj_lists2[person1].add(person2)
adj_lists2[person2].add(person1)
adj_lists2_1[person1].add(person2)
adj_lists2_2[person2].add(person1)
return adj_lists1, adj_lists1_1, adj_lists1_2, adj_lists2, adj_lists2_1, adj_lists2_2, adj_lists3
def read_emb(num_nodes, fpath):
dim = 0
embeddings = 0
with open(fpath) as f:
for i, line in enumerate(f.readlines()):
if i == 0:
dim = int(line.split()[1])
embeddings = np.random.rand(num_nodes, dim)
else:
line_l = line.split()
node = line_l[0]
emb = [float(j) for j in line_l[1:]]
assert len(emb) == dim
embeddings[int(node)] = np.array(emb)
return embeddings
def run( dataset='bitcoin_alpha', k=2):
num_nodes = DATASET_NUM_DIC[dataset] + 3
# adj_lists1, adj_lists2, adj_lists3 = load_data(k, dataset)
filename = './experiment-data/{}-train-{}.edgelist'.format(dataset, k)
adj_lists1, adj_lists1_1, adj_lists1_2, adj_lists2, adj_lists2_1, adj_lists2_2, adj_lists3 = load_data2(filename, add_public_foe=False)
print(k, dataset, 'data load!')
features = nn.Embedding(num_nodes, NODE_FEAT_SIZE)
features.weight.requires_grad = True
features.to(DEVICES)
adj_lists = [adj_lists1, adj_lists1_1, adj_lists1_2, adj_lists2, adj_lists2_1, adj_lists2_2]
#######
fea_model = FeaExtra(dataset=dataset, k=k)
adj_additions1 = [defaultdict(set) for _ in range(16)]
adj_additions2 = [defaultdict(set) for _ in range(16)]
adj_additions0 = [defaultdict(set) for _ in range(16)]
a, b = 0, 0
for i in adj_lists3:
for j in adj_lists3[i]:
v_list = fea_model.feature_part2(i, j)
for index, v in enumerate(v_list):
if v > 0:
adj_additions0[index][i].add(j)
for i in adj_lists1_1:
for j in adj_lists1_1[i]:
v_list = fea_model.feature_part2(i, j)
for index, v in enumerate(v_list):
if v > 0:
adj_additions1[index][i].add(j)
a += 1
for i in adj_lists2_1:
for j in adj_lists2_1[i]:
v_list = fea_model.feature_part2(i, j)
for index, v in enumerate(v_list):
if v > 0:
adj_additions2[index][i].add(j)
b += 1
assert a > 0, 'positive something wrong'
assert b > 0, 'negative something wrong'
# 38
adj_lists = adj_lists + adj_additions1 + adj_additions2
#adj_lists = adj_lists + adj_additions1 + adj_additions2 + [adj_lists3]
########################
# 2
# adj_lists = [adj_lists1, adj_lists2]
# 6
# adj_lists = [adj_lists1, adj_lists1_1, adj_lists1_2, adj_lists2, adj_lists2_1, adj_lists2_2]
# 18
# adj_lists = adj_lists + adj_additions0
print(len(adj_lists), 'motifs')
def func(adj_list):
edges = []
for a in adj_list:
for b in adj_list[a]:
edges.append((a, b))
edges = np.array(edges)
if len(edges) == 0: # fix missing motifs edges
edges = np.array([[0, 0]])
adj = sp.csr_matrix((np.ones(len(edges)), (edges[:,0], edges[:,1])), shape=(num_nodes, num_nodes))
return adj
adj_lists = list(map(func, adj_lists))
features_lists = [features for _ in range(len(adj_lists))]
aggs = [AttentionAggregator(features, NODE_FEAT_SIZE, NODE_FEAT_SIZE, num_nodes) for features, adj in
zip(features_lists, adj_lists)]
enc1 = Encoder(features_lists, NODE_FEAT_SIZE, EMBEDDING_SIZE1, adj_lists, aggs)
model = SiGAT(enc1)
model.to(DEVICES)
print(model.train())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
list(model.parameters()) + list(enc1.parameters()) \
+ list(features.parameters())),
lr=LEARNING_RATE,
weight_decay=WEIGHT_DECAY
)
for epoch in range(EPOCHS + 2):
total_loss = []
if epoch % INTERVAL_PRINT == 0:
model.eval()
all_embedding = np.zeros((NUM_NODE, EMBEDDING_SIZE1))
for i in range(0, NUM_NODE, BATCH_SIZE):
begin_index = i
end_index = i + BATCH_SIZE if i + BATCH_SIZE < NUM_NODE else NUM_NODE
values = np.arange(begin_index, end_index)
embed = model.forward(values.tolist())
embed = embed.data.cpu().numpy()
all_embedding[begin_index: end_index] = embed
fpath = os.path.join(OUTPUT_DIR, 'embedding-{}-{}-{}.npy'.format(dataset, k, str(epoch)) )
np.save(fpath, all_embedding)
model.train()
time1 = time.time()
nodes_pku = np.random.permutation(NUM_NODE).tolist()
for batch in range(NUM_NODE // BATCH_SIZE):
optimizer.zero_grad()
b_index = batch * BATCH_SIZE
e_index = (batch + 1) * BATCH_SIZE
nodes = nodes_pku[b_index:e_index]
loss = model.criterion(
nodes, adj_lists1, adj_lists2
)
total_loss.append(loss.data.cpu().numpy())
loss.backward()
optimizer.step()
print(f'epoch: {epoch}, loss: {np.sum(total_loss)}, time: {time.time()-time1}')
def main():
print('NUM_NODE', NUM_NODE)
print('WEIGHT_DECAY', WEIGHT_DECAY)
print('NODE_FEAT_SIZE', NODE_FEAT_SIZE)
print('EMBEDDING_SIZE1', EMBEDDING_SIZE1)
print('LEARNING_RATE', LEARNING_RATE)
print('BATCH_SIZE', BATCH_SIZE)
print('EPOCHS', EPOCHS)
print('DROUPOUT', DROUPOUT)
run(dataset=args.dataset, k=K)
if __name__ == "__main__":
main()