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run_bcb.py
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run_bcb.py
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import math
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import time
import sys
import argparse
from tqdm import tqdm, trange
import pycparser
from createclone_bcb import createast,creategmndata,createseparategraph
import models
from torch_geometric.data import Data, DataLoader
parser = argparse.ArgumentParser()
parser.add_argument("--cuda", default=True)
parser.add_argument("--dataset", default='gcj')
parser.add_argument("--graphmode", default='astandnext')
parser.add_argument("--nextsib", default=False)
parser.add_argument("--ifedge", default=False)
parser.add_argument("--whileedge", default=False)
parser.add_argument("--foredge", default=False)
parser.add_argument("--blockedge", default=False)
parser.add_argument("--nexttoken", default=False)
parser.add_argument("--nextuse", default=False)
parser.add_argument("--data_setting", default='11')
parser.add_argument("--batch_size", default=32)
parser.add_argument("--num_layers", default=4)
parser.add_argument("--num_epochs", default=10)
parser.add_argument("--lr", default=0.001)
parser.add_argument("--threshold", default=0)
args = parser.parse_args()
device=torch.device('cuda:0')
#device=torch.device('cpu')
astdict,vocablen,vocabdict=createast()
treedict=createseparategraph(astdict, vocablen, vocabdict,device,mode=args.graphmode,nextsib=args.nextsib,ifedge=args.ifedge,whileedge=args.whileedge,foredge=args.foredge,blockedge=args.blockedge,nexttoken=args.nexttoken,nextuse=args.nextuse)
traindata,validdata,testdata=creategmndata(args.data_setting,treedict,vocablen,vocabdict,device)
print(len(traindata))
#trainloder=DataLoader(traindata,batch_size=1)
num_layers=int(args.num_layers)
model=models.GMNnet(vocablen,embedding_dim=100,num_layers=num_layers,device=device).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion=nn.CosineEmbeddingLoss()
criterion2=nn.MSELoss()
def create_batches(data):
#random.shuffle(data)
batches = [data[graph:graph+args.batch_size] for graph in range(0, len(data), args.batch_size)]
return batches
def test(dataset):
#model.eval()
count=0
correct=0
tp = 0
tn = 0
fp = 0
fn = 0
results=[]
for data,label in dataset:
label=torch.tensor(label, dtype=torch.float, device=device)
x1, x2, edge_index1, edge_index2, edge_attr1, edge_attr2=data
x1=torch.tensor(x1, dtype=torch.long, device=device)
x2=torch.tensor(x2, dtype=torch.long, device=device)
edge_index1=torch.tensor(edge_index1, dtype=torch.long, device=device)
edge_index2=torch.tensor(edge_index2, dtype=torch.long, device=device)
if edge_attr1!=None:
edge_attr1=torch.tensor(edge_attr1, dtype=torch.long, device=device)
edge_attr2=torch.tensor(edge_attr2, dtype=torch.long, device=device)
data=[x1, x2, edge_index1, edge_index2, edge_attr1, edge_attr2]
prediction=model(data)
output=F.cosine_similarity(prediction[0],prediction[1])
results.append(output.item())
prediction = torch.sign(output).item()
if prediction>args.threshold and label.item()==1:
tp+=1
#print('tp')
if prediction<=args.threshold and label.item()==-1:
tn+=1
#print('tn')
if prediction>args.threshold and label.item()==-1:
fp+=1
#print('fp')
if prediction<=args.threshold and label.item()==1:
fn+=1
#print('fn')
print(tp,tn,fp,fn)
p=0.0
r=0.0
f1=0.0
if tp+fp==0:
print('precision is none')
return
p=tp/(tp+fp)
if tp+fn==0:
print('recall is none')
return
r=tp/(tp+fn)
f1=2*p*r/(p+r)
print('precision')
print(p)
print('recall')
print(r)
print('F1')
print(f1)
return results
epochs = trange(args.num_epochs, leave=True, desc = "Epoch")
for epoch in epochs:# without batching
print(epoch)
batches=create_batches(traindata)
totalloss=0.0
main_index=0.0
for index, batch in tqdm(enumerate(batches), total=len(batches), desc = "Batches"):
optimizer.zero_grad()
batchloss= 0
for data,label in batch:
label=torch.tensor(label, dtype=torch.float, device=device)
#print(len(data))
#for i in range(len(data)):
#print(i)
#data[i]=torch.tensor(data[i], dtype=torch.long, device=device)
x1, x2, edge_index1, edge_index2, edge_attr1, edge_attr2=data
x1=torch.tensor(x1, dtype=torch.long, device=device)
x2=torch.tensor(x2, dtype=torch.long, device=device)
edge_index1=torch.tensor(edge_index1, dtype=torch.long, device=device)
edge_index2=torch.tensor(edge_index2, dtype=torch.long, device=device)
if edge_attr1!=None:
edge_attr1=torch.tensor(edge_attr1, dtype=torch.long, device=device)
edge_attr2=torch.tensor(edge_attr2, dtype=torch.long, device=device)
data=[x1, x2, edge_index1, edge_index2, edge_attr1, edge_attr2]
prediction=model(data)
#batchloss=batchloss+criterion(prediction[0],prediction[1],label)
cossim=F.cosine_similarity(prediction[0],prediction[1])
batchloss=batchloss+criterion2(cossim,label)
batchloss.backward(retain_graph=True)
optimizer.step()
loss = batchloss.item()
totalloss+=loss
main_index = main_index + len(batch)
loss=totalloss/main_index
epochs.set_description("Epoch (Loss=%g)" % round(loss,5))
#test(validdata)
devresults=test(validdata)
devfile=open('gmnbcbresult/'+args.graphmode+'_dev_epoch_'+str(epoch+1),mode='w')
for res in devresults:
devfile.write(str(res)+'\n')
devfile.close()
testresults=test(testdata)
resfile=open('gmnbcbresult/'+args.graphmode+'_epoch_'+str(epoch+1),mode='w')
for res in testresults:
resfile.write(str(res)+'\n')
resfile.close()
#torch.save(model,'gmnmodels/gmnbcb'+str(epoch+1))
#for start in range(0, len(traindata), args.batch_size):
#batch = traindata[start:start+args.batch_size]
#epochs.set_description("Epoch (Loss=%g)" % round(loss,5))
'''for batch in trainloder:
batch=batch.to(device)
print(batch)
quit()
time_start=time.time()
model.forward(batch)
time_end=time.time()
print(time_end-time_start)
quit()'''