def getDatasetfromDB(vocabDic_path, properties_path):
'''
connect to data base then build dataset
'''
vocabDics = preprocessing.loadVocabDic(["pos", "rel"], vocabDic_path)
pos2idx = vocabDics["pos"]
rel2idx = vocabDics["rel"]
dataset = []
cycle_counter = 0
try:
properties = preprocessing.readDictionary(properties_path)
connection = connect2DB(properties)
with connection.cursor() as cursor:
sentence_query = "SELECT * FROM sentence"
annotation_query = "SELECT relation_root FROM annotation WHERE sid=%s"
cursor.execute(sentence_query)
results = cursor.fetchall()
for result in results:
print("#============building semantic graph [{}]============#".
format(result['sid']))
cursor.execute(annotation_query, (result['sid'], ))
annotations = cursor.fetchall()
if len(annotations) == 0:
annotations = [{'relation_root': 0}]
listLabel = set([
annotation['relation_root'] for annotation in annotations
])
graph = preprocessing.buildSemanticGraph(
DependencyTreeStr=result['dpTreeStr'],
listLabel=listLabel,
use_word=False,
pos2idx=pos2idx,
rel2idx=rel2idx,
sid=result['sid'])
if graph.hasCycle:
cycle_counter += 1
else:
print(graph)
dataset.append(
preprocessing.DataTuple(
indexedWords=graph.indexedWords, graph=graph))
print("There are {} graphs has cycle then drop them from dataset!".
format(cycle_counter))
except Exception as err:
print(err)
except ConnectionError as connerr:
print(connerr)
print("[Error] build semantic graph from db failed!")
else:
return dataset
finally:
connection.close()
options)
print(
"Epoch Eval Metrics: ACC[{:.2f}%] Loss[{:.6f}] \n\t\t\t\t\tP[{:.2f}%], R[{:.2f}%], F1[{:.2f}%]\n"
.format((acc * 100), valid_loss, (p * 100), (r * 100), (F1 * 100)))
saveMetrics(metrics, step_losses, local_time, options)
plotMetrics(metrics, step_losses, local_time, options)
print(" - [Info] Training stage ends")
if __name__ == "__main__":
# load options from properties file
options_dic = preprocessing.readDictionary(
"../src/properties/options.properties")
fpath = preprocessing.readDictionary("../src/properties/fpath.properties")
# set cuda devices
if options_dic['use_cuda']:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = options_dic['cuda_device']
# get vocabulary
vocabDics = preprocessing.loadVocabDic(["pos", "rel", "act"],
fpath['vocabDic_path'])
word2idx = vocabDics["word"]
pos2idx = vocabDics["pos"]
rel2idx = vocabDics["rel"]
label2idx = vocabDics["act"]
def load_angelis_dataset(vocabDic_path, properties_path, test_id_path):
vocabDics = preprocessing.loadVocabDic(["pos", "rel"], vocabDic_path)
pos2idx = vocabDics["pos"]
rel2idx = vocabDics["rel"]
test_set = []
test_set_id = []
cycle_counter = 0
if not os.path.exists(test_id_path):
raise Exception(test_id_path +
" did not exit, please change to full data load mode!")
with open(test_id_path, mode="r", encoding="utf-8") as t_id:
t_id.readline()
for line in t_id.readlines():
if str(line) != "\n":
test_set_id.append(str(line).split("\n")[0])
try:
properties = preprocessing.readDictionary(properties_path)
connection = connect2DB(properties)
with connection.cursor() as cursor:
sentence_query = "SELECT * FROM sentence"
annotation_query = "SELECT relation_root FROM annotation WHERE sid=%s"
pred_query = "SELECT relation_root FROM angelis_preds WHERE sid=%s"
cursor.execute(sentence_query)
results = cursor.fetchall()
for result in results:
print("#============building semantic graph [{}]============#".
format(result['sid']))
cursor.execute(annotation_query, (result['sid'], ))
annotations = cursor.fetchall()
if len(annotations) == 0:
annotations = [{'relation_root': 0}]
listLabel = set([
annotation['relation_root'] for annotation in annotations
])
graph = preprocessing.buildSemanticGraph(
DependencyTreeStr=result['dpTreeStr'],
listLabel=listLabel,
use_word=False,
pos2idx=pos2idx,
rel2idx=rel2idx,
sid=result['sid'])
if graph.hasCycle:
cycle_counter += 1
else:
print(graph)
if result["sid"] in test_set_id:
cursor.execute(pred_query, result['sid'])
preds = cursor.fetchall()
list_pred = set(
[pred['relation_root'] for pred in preds])
for word in graph.indexedWords:
if word.sentence_index in list_pred:
word.pred = 1
test_set.append(
preprocessing.DataTuple(
indexedWords=graph.indexedWords, graph=graph))
print("There are {} graphs has cycle then drop them from dataset!".
format(cycle_counter))
except Exception as err:
print(err)
except ConnectionError as connerr:
print(connerr)
print("[Error] build semantic graph from db failed!")
else:
return test_set
finally:
connection.close()