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run_bcbnoid.py
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run_bcbnoid.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_noid 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=True)
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("numTrainData:")
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
# zzz=0
for data, label in batch:
# zzz+=1
# print(zzz)
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('gmnbcbresultnoid/' + 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('gmnbcbresultnoid/' + 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()'''