def train(self, model):
if self.train_params['reload'] > 0:
self.load_model(model, self.train_params['reload'])
valid_every = self.train_params.get('valid_every', None)
save_every = self.train_params.get('save_every', None)
batch_size = self.train_params.get('batch_size', 128)
nb_epoch = self.train_params.get('nb_epoch', 10)
split = self.train_params.get('validation_split', 0)
val_loss = {'loss': 1., 'epoch': 0}
chunk_size = self.train_params.get('chunk_size', 100000)
for i in range(self.train_params['reload'] + 1, nb_epoch):
print('Epoch %d :: \n' % i, end='')
logger.debug('loading data chunk..')
offset = (i - 1) * self.train_params.get('chunk_size', 100000)
names = data_loader.load_hdf5(
self.data_path + self.data_params['train_methname'], offset,
chunk_size)
apis = data_loader.load_hdf5(
self.data_path + self.data_params['train_apiseq'], offset,
chunk_size)
tokens = data_loader.load_hdf5(
self.data_path + self.data_params['train_tokens'], offset,
chunk_size)
descs = data_loader.load_hdf5(
self.data_path + self.data_params['train_desc'], offset,
chunk_size)
logger.debug('padding data..')
methnames = pad(names, self.data_params['methname_len'])
apiseqs = pad(apis, self.data_params['apiseq_len'])
tokens = pad(tokens, self.data_params['tokens_len'])
good_descs = pad(descs, self.data_params['desc_len'])
bad_descs = [desc for desc in descs]
random.shuffle(bad_descs)
bad_descs = pad(bad_descs, self.data_params['desc_len'])
hist = model.fit(
[methnames, apiseqs, tokens, good_descs, bad_descs],
epochs=1,
batch_size=batch_size,
validation_split=split)
if hist.history['val_loss'][0] < val_loss['loss']:
val_loss = {'loss': hist.history['val_loss'][0], 'epoch': i}
print('Best: Loss = {}, Epoch = {}'.format(val_loss['loss'],
val_loss['epoch']))
if save_every is not None and i % save_every == 0:
self.save_model(model, i)
if valid_every is not None and i % valid_every == 0:
acc, mrr, map, ndcg = self.valid(model, 1000, 1)
def repr_code(self, model):
logger.info('Loading the use data ..')
methnames = data_loader.load_hdf5(
self.data_path + self.data_params['use_methname'], 0, -1)
apiseqs = data_loader.load_hdf5(
self.data_path + self.data_params['use_apiseq'], 0, -1)
tokens = data_loader.load_hdf5(
self.data_path + self.data_params['use_tokens'], 0, -1)
methnames = pad(methnames, self.data_params['methname_len'])
apiseqs = pad(apiseqs, self.data_params['apiseq_len'])
tokens = pad(tokens, self.data_params['tokens_len'])
logger.info('Representing code ..')
vecs = model.repr_code([methnames, apiseqs, tokens], batch_size=1000)
vecs = vecs.astype('float32')
vecs = normalize(vecs)
return vecs
def repr_code(self, model):
logger.info('Loading the use data ..')
methnames = data_loader.load_hdf5(
self.data_path + self.data_params['use_methname'], 0, -1)
methnames = pad(methnames, self.data_params['methname_len'])
logger.info('Representing code ..')
vecs = model.repr_code([methnames], batch_size=10000)
vecs = vecs.astype(np.float)
vecs = normalize(vecs)
return vecs
def valid(self, model, poolsize, K):
"""
validate in a code pool.
param: poolsize - size of the code pool, if -1, load the whole test set
"""
def ACC(real, predict):
sum = 0.0
for val in real:
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + 1
return sum / float(len(real))
def MAP(real, predict):
sum = 0.0
for id, val in enumerate(real):
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + (id + 1) / float(index + 1)
return sum / float(len(real))
def MRR(real, predict):
sum = 0.0
for val in real:
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + 1.0 / float(index + 1)
return sum / float(len(real))
def NDCG(real, predict):
dcg = 0.0
idcg = IDCG(len(real))
for i, predictItem in enumerate(predict):
if predictItem in real:
itemRelevance = 1
rank = i + 1
dcg += (math.pow(2, itemRelevance) -
1.0) * (math.log(2) / math.log(rank + 1))
return dcg / float(idcg)
def IDCG(n):
idcg = 0
itemRelevance = 1
for i in range(n):
idcg += (math.pow(2, itemRelevance) - 1.0) * (math.log(2) /
math.log(i + 2))
return idcg
#load valid dataset
if self._eval_sets is None:
methnames = data_loader.load_hdf5(
self.data_path + self.data_params['valid_methname'], 0,
poolsize)
apiseqs = data_loader.load_hdf5(
self.data_path + self.data_params['valid_apiseq'], 0, poolsize)
tokens = data_loader.load_hdf5(
self.data_path + self.data_params['valid_tokens'], 0, poolsize)
descs = data_loader.load_hdf5(
self.data_path + self.data_params['valid_desc'], 0, poolsize)
self._eval_sets = {
'methnames': methnames,
'apiseqs': apiseqs,
'tokens': tokens,
'descs': descs
}
acc, mrr, map, ndcg = 0, 0, 0, 0
data_len = len(self._eval_sets['descs'])
for i in tqdm(range(data_len)):
desc = self._eval_sets['descs'][i] #good desc
descs = pad([desc] * data_len, self.data_params['desc_len'])
methnames = pad(self._eval_sets['methnames'],
self.data_params['methname_len'])
apiseqs = pad(self._eval_sets['apiseqs'],
self.data_params['apiseq_len'])
tokens = pad(self._eval_sets['tokens'],
self.data_params['tokens_len'])
n_results = K
sims = model.predict([methnames, apiseqs, tokens, descs],
batch_size=data_len).flatten()
negsims = np.negative(sims)
predict = np.argsort(
negsims) #predict = np.argpartition(negsims, kth=n_results-1)
predict = predict[:n_results]
predict = [int(k) for k in predict]
real = [i]
acc += ACC(real, predict)
mrr += MRR(real, predict)
map += MAP(real, predict)
ndcg += NDCG(real, predict)
acc = acc / float(data_len)
mrr = mrr / float(data_len)
map = map / float(data_len)
ndcg = ndcg / float(data_len)
logger.info(f'ACC={acc}, MRR={mrr}, MAP={map}, nDCG={ndcg}')
return acc, mrr, map, ndcg
####################################################################################################################################################################
data_param = config.get('data_params', dict())
### Loading training set
data = pd.read_csv('use.rawcode.txt', sep="\n", header=None)
for i in range(len(data[0])):
method_string = data[0][i]
code_embedding_DeepCS.replace_methname(method_string, i)
methname = pd.read_csv('new_methname.txt', sep="\n", header=None)
methnames = code_embedding_DeepCS.text_to_array(methname[0])
descs = data_loader.load_hdf5(data_path + data_param['train_desc'], 0, args.chunk_size)
good_descs = pad(descs, data_param['desc_len'])
bad_descs = [desc for desc in descs]
random.shuffle(bad_descs)
bad_descs = pad(bad_descs, data_param['desc_len'])
#refactoring
refactored_mn = TestSuiteGenerator.generateTestSuite(model, methnames, args.chunk_size, args.gen, args.mutation_rate)
refactored_mn = np.array(refactored_mn).reshape(args.chunk_size, 6)
methnames = np.concatenate((methnames, refactored_mn), axis=0)
good_descs = np.concatenate((good_descs, good_descs), axis=0)
bad_descs = np.concatenate((bad_descs, bad_descs), axis=0)
def valid(self, model, poolsize, K):
"""
validate in a code pool.
param: poolsize - size of the code pool, if -1, load the whole test set
"""
def ACC(real, predict):
sum = 0.0
for val in real:
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + 1
return sum / float(len(real))
def MAP(real, predict):
sum = 0.0
for id, val in enumerate(real):
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + (id + 1) / float(index + 1)
return sum / float(len(real))
def MRR(real, predict):
sum = 0.0
for val in real:
try:
index = predict.index(val)
except ValueError:
index = -1
if index != -1: sum = sum + 1.0 / float(index + 1)
return sum / float(len(real))
def NDCG(real, predict):
dcg = 0.0
idcg = IDCG(len(real))
for i, predictItem in enumerate(predict):
if predictItem in real:
itemRelevance = 1
rank = i + 1
dcg += (math.pow(2, itemRelevance) -
1.0) * (math.log(2) / math.log(rank + 1))
return dcg / float(idcg)
def IDCG(n):
idcg = 0
itemRelevance = 1
for i in range(n):
idcg += (math.pow(2, itemRelevance) - 1.0) * (math.log(2) /
math.log(i + 2))
return idcg
#load valid dataset
print('self._eval_sets,', self._eval_sets)
if self._eval_sets is None:
methnames = data_loader.load_hdf5(
self.data_path + self.data_params['valid_methname'], 0,
poolsize)
descs = data_loader.load_hdf5(
self.data_path + self.data_params['valid_desc'], 0, poolsize)
self._eval_sets = {'methnames': methnames, 'descs': descs}
accs, mrrs, maps, ndcgs = [], [], [], []
data_len = len(self._eval_sets['descs'])
print('data_len', data_len)
for i in tqdm(range(data_len)):
desc = self._eval_sets['descs'][i] #good desc
descs = pad([desc] * data_len, self.data_params['desc_len'])
methnames = pad(self._eval_sets['methnames'],
self.data_params['methname_len'])
n_results = K
sims = model.predict([methnames, descs],
batch_size=data_len).flatten()
negsims = np.negative(sims)
predict = np.argpartition(negsims, kth=n_results - 1)
predict = predict[:n_results]
predict = [int(k) for k in predict]
real = [i]
accs.append(ACC(real, predict))
mrrs.append(MRR(real, predict))
maps.append(MAP(real, predict))
ndcgs.append(NDCG(real, predict))
logger.info(
f'ACC={np.mean(accs)}, MRR={np.mean(mrrs)}, MAP={np.mean(maps)}, nDCG={np.mean(ndcgs)}'
)
return accs, mrrs, maps, ndcgs
mode = 'test'
filename = './log_folder/record.txt'
threshold_CC = 0
threshold_MC = 0.7
symbols_SQ = 2
seq = '[0 ,1]'
seq = re.findall(r"\d+\.?\d*", seq)
TargMetri = None
CoverageStop = 0.9
TestCaseNum = 2000
minimalTest = 0
r = record(filename, time.time())
data_param = config.get('data_params', dict())
### Loading training set
names = data_loader.load_hdf5(data_path + data_param['train_methname'], 0,
data_param.get('chunk_size', 100000))
methnames = pad(names, data_param['methname_len'])
print(methnames[0])
z = methnames[0].reshape(1, 6)
print(z)
z[0][1] = 0
print(z)
apis = data_loader.load_hdf5(
data_path + data_param['train_apiseq'],
0,
data_param.get('chunk_size', 100000),
)
tokens = data_loader.load_hdf5(
data_path + data_param['train_tokens'],
0,
data_param.get('chunk_size', 100000),
