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hera_Twitter_gat.py
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import sys, os
import torch
# sys.path.append('/home/ubuntu/PyProjects_gsuhyl/PyProjects/BiGCN-source')
from Process.process import *
import torch as th
from torch_scatter import scatter_mean
import torch.nn.functional as F
import numpy as np
from tools.earlystopping import EarlyStopping
from torch_geometric.data import DataLoader
from tqdm import tqdm
from Process.rand5fold import *
from tools.evaluate import *
from torch_geometric.nn import GCNConv, Linear
import copy
from torch import nn
from cnn_model import textCNN, LSTMModel
from lstm_config import Config
from PropagationNet import PropagationNet
from KnowlegeNet import KnowledgeNet
from EgoNet import EgoNet
class Net(th.nn.Module):
def __init__(self,textCNN_param,w2vec):
super(Net, self).__init__()
self.KNET = KnowledgeNet(300, 64, 64, textCNN_param, w2vec).to(device)
self.PNET = PropagationNet(5000, 64, 64).to(device)
self.ENET = EgoNet(10, 64, 64)
self.WK = th.nn.Linear(64, 64, bias=None)
self.WE = th.nn.Linear(64, 64, bias=None)
self.WKE = th.nn.Linear(64*2 , 64)
self.WK1 = th.nn.Linear(64, 64, bias=None)
self.WE1 = th.nn.Linear(64, 64, bias=None)
self.WP = th.nn.Linear(64*2,64)
self.WKP = th.nn.Linear(64*2,64)
self.WEP = th.nn.Linear(64*2,64)
self.WKEP = th.nn.Linear(64*2,64)
# self.fc = th.nn.Linear(64*7, 64*4)
self.fc1 = th.nn.Linear(64*4, 4)
def forward(self, pdata, kdata, edata):
kres = self.KNET(kdata)
eres = self.ENET(edata)
# wkres = self.WK(kres)
# weres = self.WE(eres)
wkeres = th.cat((kres, eres), dim=1)
# wkeres = eres
wkeres = self.WKE(wkeres)
# keres = th.relu(keres)
# wkeres = th.relu(wkeres)
pres = self.PNET(pdata, wkeres)
# print(pres.shape)
# print(kres.shape)
# print(e)c
# print(kres)
# print(pres)
# x = th.cat((pres, kres, eres), 1)
# pres = self.WP(pres)
x = th.cat((kres,pres,eres), 1)
# x = th.cat((kres,TD_pres,BU_pres, eres), 1)
# wkres1 = self.WK1(wkres)
# kpres = th.cat((wkres1, pres), 1)
# kpres = self.WKP(kpres)
#
# weres1 = self.WE1(weres)
# epres = th.cat((weres1, pres), 1)
# epres = self.WEP(epres)
#
# kepres = th.cat((kpres,epres),1)
# kepres = self.WKEP(kepres)
# x = self.fc(x)
x = self.fc1(x)
x = F.log_softmax(x, dim=1)
return x
def train_GCN( treeDic,x_test, x_train,PTDdroprate,PBUdroprate,kdroprate,lr, weight_decay,patience,n_epochs,batchsize,dataname,iter):
# raw_word_vec_file = '/home/ubuntu/PyProjects_gsuhyl/PyProjects/Twitter_knowledge_data/model/w2vlstm_model/Process/word2vec_data/raw_word2vec.npy'
raw_word_vec_file = '/home/ubuntu/PyProjects_gsuhyl/PyProjects/Twitter_knowledge_data/model/hera_model_gat/Process/word2vec_data/'+dataname+'/raw_word2vec.npy'
w2vec = np.load(raw_word_vec_file)
# 将词向量转化为Tensor
w2vec = torch.from_numpy(w2vec)
# CUDA接受float32，不接受float64
w2vec = w2vec.float()
textCNN_param = {
# 'vocab_size': len(word2ind),
'embed_dim': 300,
'class_num': 4,
"kernel_num": 16,
"kernel_size": [3, 4, 5],
"dropout": 0.5,
}
model = Net(textCNN_param,w2vec).to(device)
# print(w2vec.shape)
# print(e)
# PTD_params = list(map(id, model.PNET.TDrumorGCN.conv1.parameters()))
# PTD_params += list(map(id, model.PNET.TDrumorGCN.conv2.parameters()))
# KTD_params=list(map(id,model.KNET.TDrumorGCN.conv1.parameters()))
# KTD_params += list(map(id, model.KNET.TDrumorGCN.conv2.parameters()))
#
# base_params=filter(lambda p:id(p) not in TD_params,model.parameters())
base_params=model.parameters()
optimizer = th.optim.Adam([
{'params':base_params},
# {'params':model.TDrumorGCN.conv1.parameters(),'lr':lr/5},
# {'params': model.TDrumorGCN.conv2.parameters(), 'lr': lr/5}
], lr=lr, weight_decay=weight_decay)
model.train()
train_losses = []
val_losses = []
train_accs = []
val_accs = []
early_stopping = EarlyStopping(patience=patience, verbose=True)
for epoch in range(n_epochs):
# traindata_list, testdata_list = loadBiData(dataname, x_train, x_test, TDdroprate,BUdroprate)
#def loadData(dataname,treeDic,fold_x_train,fold_x_test,knowledge_droprate,propagation_droprate):
traindata_list, testdata_list = loadData(dataname,treeDic, x_train, x_test, propagation_droprate=PTDdroprate,\
knowledge_droprate=kdroprate)
train_loader = DataLoader(traindata_list, batch_size=batchsize, shuffle=True, num_workers=5)
test_loader = DataLoader(testdata_list, batch_size=batchsize, shuffle=True, num_workers=5)
avg_loss = []
avg_acc = []
batch_idx = 0
tqdm_train_loader = tqdm(train_loader)
for Batch_pdata,Batch_kdata,Batch_edata in tqdm_train_loader:
Batch_pdata.to(device)
Batch_kdata.to(device)
Batch_edata.to(device)
out_labels= model(Batch_pdata,Batch_kdata,Batch_edata)
finalloss=F.nll_loss(out_labels,Batch_pdata.y)
loss=finalloss
optimizer.zero_grad()
loss.backward()
avg_loss.append(loss.item())
optimizer.step()
_, pred = out_labels.max(dim=-1)
correct = pred.eq(Batch_pdata.y).sum().item()
train_acc = correct / len(Batch_pdata.y)
avg_acc.append(train_acc)
print("Iter {:03d} | Epoch {:05d} | Batch{:02d} | Train_Loss {:.4f}| Train_Accuracy {:.4f}".format(iter,epoch, batch_idx,
loss.item(),
train_acc))
batch_idx = batch_idx + 1
train_losses.append(np.mean(avg_loss))
train_accs.append(np.mean(avg_acc))
temp_val_losses = []
temp_val_accs = []
temp_val_Acc_all, temp_val_Acc1, temp_val_Prec1, temp_val_Recll1, temp_val_F1, \
temp_val_Acc2, temp_val_Prec2, temp_val_Recll2, temp_val_F2, \
temp_val_Acc3, temp_val_Prec3, temp_val_Recll3, temp_val_F3, \
temp_val_Acc4, temp_val_Prec4, temp_val_Recll4, temp_val_F4 = [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []
model.eval()
tqdm_test_loader = tqdm(test_loader)
for Batch_pdata,Batch_kdata,Batch_edata in tqdm_test_loader:
Batch_pdata.to(device)
Batch_kdata.to(device)
Batch_edata.to(device)
val_out = model(Batch_pdata,Batch_kdata,Batch_edata)
val_loss = F.nll_loss(val_out, Batch_pdata.y)
temp_val_losses.append(val_loss.item())
_, val_pred = val_out.max(dim=1)
correct = val_pred.eq(Batch_pdata.y).sum().item()
val_acc = correct / len(Batch_pdata.y)
Acc_all, Acc1, Prec1, Recll1, F1, Acc2, Prec2, Recll2, F2, Acc3, Prec3, Recll3, F3, Acc4, Prec4, Recll4, F4 = evaluation4class(
val_pred, Batch_pdata.y)
temp_val_Acc_all.append(Acc_all), temp_val_Acc1.append(Acc1), temp_val_Prec1.append(
Prec1), temp_val_Recll1.append(Recll1), temp_val_F1.append(F1), \
temp_val_Acc2.append(Acc2), temp_val_Prec2.append(Prec2), temp_val_Recll2.append(
Recll2), temp_val_F2.append(F2), \
temp_val_Acc3.append(Acc3), temp_val_Prec3.append(Prec3), temp_val_Recll3.append(
Recll3), temp_val_F3.append(F3), \
temp_val_Acc4.append(Acc4), temp_val_Prec4.append(Prec4), temp_val_Recll4.append(
Recll4), temp_val_F4.append(F4)
temp_val_accs.append(val_acc)
val_losses.append(np.mean(temp_val_losses))
val_accs.append(np.mean(temp_val_accs))
print("Epoch {:05d} | Val_Loss {:.4f}| Val_Accuracy {:.4f}".format(epoch, np.mean(temp_val_losses),
np.mean(temp_val_accs)))
res = ['acc:{:.4f}'.format(np.mean(temp_val_Acc_all)),
'C1:{:.4f},{:.4f},{:.4f},{:.4f}'.format(np.mean(temp_val_Acc1), np.mean(temp_val_Prec1),
np.mean(temp_val_Recll1), np.mean(temp_val_F1)),
'C2:{:.4f},{:.4f},{:.4f},{:.4f}'.format(np.mean(temp_val_Acc2), np.mean(temp_val_Prec2),
np.mean(temp_val_Recll2), np.mean(temp_val_F2)),
'C3:{:.4f},{:.4f},{:.4f},{:.4f}'.format(np.mean(temp_val_Acc3), np.mean(temp_val_Prec3),
np.mean(temp_val_Recll3), np.mean(temp_val_F3)),
'C4:{:.4f},{:.4f},{:.4f},{:.4f}'.format(np.mean(temp_val_Acc4), np.mean(temp_val_Prec4),
np.mean(temp_val_Recll4), np.mean(temp_val_F4))]
print('results:', res)
early_stopping(np.mean(temp_val_losses), np.mean(temp_val_accs), np.mean(temp_val_F1), np.mean(temp_val_F2),
np.mean(temp_val_F3), np.mean(temp_val_F4), model, 'BiGCN', dataname)
accs =np.mean(temp_val_accs)
F1 = np.mean(temp_val_F1)
F2 = np.mean(temp_val_F2)
F3 = np.mean(temp_val_F3)
F4 = np.mean(temp_val_F4)
if early_stopping.early_stop:
print("Early stopping")
accs=early_stopping.accs
F1=early_stopping.F1
F2 = early_stopping.F2
F3 = early_stopping.F3
F4 = early_stopping.F4
break
return train_losses , val_losses ,train_accs, val_accs,accs,F1,F2,F3,F4
lr=0.0005
weight_decay=1e-4
patience=10
n_epochs=200
batchsize=128
PTDdroprate=0.2
PBUdroprate=0.2
Kdroprate = 0
datasetname=sys.argv[1] #"Twitter15"、"Twitter16"
iterations=int(sys.argv[2])
model="GCN"
device = th.device('cuda:1' if th.cuda.is_available() else 'cpu')
test_accs = []
NR_F1 = []
FR_F1 = []
TR_F1 = []
UR_F1 = []
def set_seed(seed=1):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
th.cuda.manual_seed(seed)
th.backends.cudnn.deterministic = True
print("seed:", seed)
set_seed(123)
for iter in range(iterations):
res_file = cwd+'/'+datasetname+'gatres.txt'
f = open(res_file,'a+')
f.write('-----------------------\n')
fold0_x_test, fold0_x_train, \
fold1_x_test, fold1_x_train, \
fold2_x_test, fold2_x_train, \
fold3_x_test, fold3_x_train, \
fold4_x_test,fold4_x_train = load5foldData(datasetname)
treeDic=loadTree(datasetname)
train_losses, val_losses, train_accs, val_accs0, accs0, F1_0, F2_0, F3_0, F4_0 = train_GCN(treeDic,
fold0_x_test,
fold0_x_train,
PTDdroprate,PBUdroprate,
Kdroprate,lr, weight_decay,
patience,
n_epochs,
batchsize,
datasetname,
iter)
train_losses, val_losses, train_accs, val_accs1, accs1, F1_1, F2_1, F3_1, F4_1 = train_GCN(treeDic,
fold1_x_test,
fold1_x_train,
PTDdroprate,PBUdroprate,
Kdroprate,
lr,
weight_decay,
patience,
n_epochs,
batchsize,
datasetname,
iter)
train_losses, val_losses, train_accs, val_accs2, accs2, F1_2, F2_2, F3_2, F4_2 = train_GCN(treeDic,
fold2_x_test,
fold2_x_train,
PTDdroprate,PBUdroprate,
Kdroprate, lr,
weight_decay,
patience,
n_epochs,
batchsize,
datasetname,
iter)
train_losses, val_losses, train_accs, val_accs3, accs3, F1_3, F2_3, F3_3, F4_3 = train_GCN(treeDic,
fold3_x_test,
fold3_x_train,
PTDdroprate,PBUdroprate,
Kdroprate,lr,
weight_decay,
patience,
n_epochs,
batchsize,
datasetname,
iter)
train_losses, val_losses, train_accs, val_accs4, accs4, F1_4, F2_4, F3_4, F4_4 = train_GCN(treeDic,
fold4_x_test,
fold4_x_train,
PTDdroprate,PBUdroprate,
Kdroprate,lr,
weight_decay,
patience,
n_epochs,
batchsize,
datasetname,
iter)
test_accs.append((accs0+accs1+accs2+accs3+accs4)/5)
NR_F1.append((F1_0+F1_1+F1_2+F1_3+F1_4)/5)
FR_F1.append((F2_0 + F2_1 + F2_2 + F2_3 + F2_4) / 5)
TR_F1.append((F3_0 + F3_1 + F3_2 + F3_3 + F3_4) / 5)
UR_F1.append((F4_0 + F4_1 + F4_2 + F4_3 + F4_4) / 5)
res = "Total_Test_Accuracy: {:.4f}|NR F1: {:.4f}|FR F1: {:.4f}|TR F1: {:.4f}|UR F1: {:.4f}".format(
sum(test_accs) / iterations, sum(NR_F1) / iterations, sum(FR_F1) / iterations, sum(TR_F1) / iterations,
sum(UR_F1) / iterations)
print(str(res))
f.write(str(res))
f.close()