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import tensorflow
from dataProcess import *
import os
from tqdm import tqdm
os.environ["CUDA_VISIBLE_DEVICES"]="1"
def train():
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
ds = DataSet(args)
vds = DataSet(args, "val")
args.Vocsize = len(ds.Char_Voc)
args.Nl_Vocsize = len(ds.Nl_Voc)
args.Code_Vocsize = len(ds.Code_Voc)
Model = ModelWrapper(config)
Model.load_checkpoint()
MaxMrr = 0
writer = tf.summary.FileWriter('logs', graph=Model.model.graph)
global_step = 0
for i in range(10000000):
num = 0
#eval
if i % 10 == 0:
print("eval")
mrr = eval(Model, vds)
print("print current mrr " + str(mrr))
if mrr > MaxMrr:
print("find better mrr " + str(mrr))
MaxMrr = mrr
Model.save_checkpoint()
#eval(Model, vds)
for dBatch in tqdm(ds.Get_Train(args.batch_size)):
num += 1
global_step += 1
feed_dic = {
Model.model.inputNl:dBatch[0],
Model.model.inputNlChar:dBatch[1],
Model.model.inputCode:dBatch[2],
Model.model.inputCodeChar:dBatch[3],
Model.model.inputCodeNeg:dBatch[6],
#Model.model.inputCodeCharNeg:dBatch[5],
Model.model.inputNl_Overlap:dBatch[4],
#Model.model.inputNl_Overlap_Neg:getOverlap(dBatch[0], dBatch[4]),
Model.model.inputCode_Overlap:dBatch[5],
#Model.model.inputCode_Overlap_Neg:getOverlap(dBatch[4], dBatch[0]),
Model.model.keep_prob:0.8
}
Model.model.optim.run(session=Model.sess, feed_dict = feed_dic)
if num % 10 == 0:
s = Model.model.merge.eval(session=Model.sess, feed_dict=feed_dic)
writer.add_summary(s, global_step)
writer.flush()
writer.close()
def getOverlap(Aseq, Bseq):
Aseq = np.array(Aseq, np.int32)
Bseq = np.array(Bseq, np.int32)
mask = np.not_equal(Aseq, 0).astype(np.int32)
mask_N = np.equal(Aseq, 0).astype(np.int32)
Astack = np.stack([Aseq] * len(Bseq[0]), axis=-1)
Bstack = np.stack([Bseq] * len(Aseq[0]), axis=1)
subStack = Astack - Bstack
equalResult = np.equal(subStack, 0)
equalResult = equalResult.astype(np.int32)
equalResult = np.max(equalResult, axis=-1)
equalResult *= mask
equalResult += 2 * mask_N
return equalResult
def eval(Model, vds):
restotal = []
index = 0
f = open("resD.txt", "a")
for dBatch in tqdm(vds.Get_Train(args.poolsize, "val")):
tmpa = []
for i in range(7):
tmpa.append([])
for j in tqdm(range(len(dBatch[0]))):
tmpBatch = np.array([dBatch[0][j]] * len(dBatch[0]))
tmpBatchChar = np.array([dBatch[1][j]] * len(dBatch[0]))
tmpa[0].append(tmpBatch)
tmpa[1].append(tmpBatchChar)
tmpa[2].append(dBatch[2])
tmpa[3].append(dBatch[3])
tmp1 = []
tmp2 = []
for k in range(len(dBatch[0])):
a, b = vds.get_overlap_indices(vds.Nls[index*50+j], vds.Codes[index*50 + k])
tmp1.append(np.array(a, np.int32))
tmp2.append(np.array(b, np.int32))
tmpa[4].append(tmp1)
tmpa[5].append(tmp2)
tmpa[6].append(dBatch[6])
if j % 10 == 9:
for i in range(len(tmpa)):
tmpa[i] = np.concatenate(tmpa[i], axis = 0)
feed_dic = {
Model.model.inputNl: tmpa[0],
Model.model.inputNlChar: tmpa[1],
Model.model.inputCode: tmpa[2],
Model.model.inputCodeChar: tmpa[3],
Model.model.inputCodeNeg: tmpa[6],
#Model.model.inputCodeCharNeg: dBatch[5],
Model.model.inputNl_Overlap: tmpa[4],
#Model.model.inputNl_Overlap_Neg: getOverlap(tmpBatch, dBatch[4]),
Model.model.inputCode_Overlap: tmpa[5],
#Model.model.inputCode_Overlap_Neg: getOverlap(dBatch[4], tmpBatch),
Model.model.keep_prob: 1.0
}
res = Model.model.result.eval(session=Model.sess, feed_dict=feed_dic)
tmpans = []
for x in res:
f.write(str(x[1]) + "\n")
f.flush()
for i in range(10):
tmpans.append(res[50 * i: 50 * (i + 1),1:])
for k, res in enumerate(tmpans):
res = np.max(res, axis=-1)
negres = np.negative(res)
predict = np.argsort(negres)
for i, t in enumerate(predict):
if t == j - (9 - k):
restotal.append(1 / (i + 1))
tmpa = []
for i in range(7):
tmpa.append([])
index += 1
return np.mean(restotal)
def evalMrr(Model, ds):
f = open("res.txt", "w")
wf = open("resP.txt", "a")
resTotal = []
ins = 0
for dBatch in tqdm(ds.Get_Train(50, "dev")):
feed_dic = {
Model.model.inputNl:dBatch[0],
Model.model.inputNlChar:dBatch[1],
Model.model.inputCode:dBatch[2],
Model.model.inputCodeChar:dBatch[3],
Model.model.inputCodeNeg:dBatch[6],
#Model.model.inputCodeCharNeg:dBatch[5],
Model.model.inputNl_Overlap:dBatch[4],
#Model.model.inputNl_Overlap_Neg:getOverlap(dBatch[0], dBatch[4]),
Model.model.inputCode_Overlap:dBatch[5],
#Model.model.inputCode_Overlap_Neg:getOverlap(dBatch[4], dBatch[0]),
Model.model.keep_prob:1.0
}
res = Model.model.result.eval(session=Model.sess, feed_dict=feed_dic)
#print(res)
res = np.max(res[:,1:], axis=-1)
negres = np.negative(res)
predict = np.argsort(negres)
for x in res:
wf.write(str(x))
wf.write("\n")
wf.flush()
for i, t in enumerate(predict):
if t == 0:
f.write(str(i) + "\n")
f.flush()
resTotal.append(1 / (i + 1))
return np.mean(resTotal)
def test():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
ds = DataSet(args, "val")
args.Vocsize = len(ds.Char_Voc)
args.Nl_Vocsize = len(ds.Nl_Voc)
args.Code_Vocsize = len(ds.Code_Voc)
Model = ModelWrapper(config)
Model.load_checkpoint()
res = eval(Model, ds)
print("mrr is " + str(res))
def evals():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
ds = DataSet(args, "eval")
args.Vocsize = len(ds.Char_Voc)
args.Nl_Vocsize = len(ds.Nl_Voc)
args.Code_Vocsize = len(ds.Code_Voc)
Model = ModelWrapper(config)
Model.load_checkpoint()
res = evalMrr(Model, ds)
print("mrr is " + str(res))
def dev():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
ds = DataSet(args, "dev")
args.Vocsize = len(ds.Char_Voc)
args.Nl_Vocsize = len(ds.Nl_Voc)
args.Code_Vocsize = len(ds.Code_Voc)
Model = ModelWrapper(config)
Model.load_checkpoint()
res = evalMrr(Model, ds)
print("mrr is " + str(res))
if __name__ == '__main__':
mode = input("train or test: \n")
if mode == "train":
train()
elif mode == "test":
test()
elif mode == "eval":
evals()
elif mode == "dev":
dev()