def merge_subject_sentiment_value(subject_file_path, sentiment_file_path,
result_file_path):
"""convert_subject_sentiment_value_predict_result"""
subject_file_lines = file_utils.read_all_lines(subject_file_path)
sentiment_file_lines = file_utils.read_all_lines(sentiment_file_path)
result = ['content_id,subject,sentiment_value,sentiment_word']
for i, subject_line in enumerate(subject_file_lines):
subject_line_parts = subject_line.split(',')
sentiment_value = sentiment_file_lines[i].split(',')[1]
result.append(subject_line_parts[0] + ',' + subject_line_parts[2] +
',' + sentiment_value + ',')
file_utils.write_lines(result, result_file_path)
def read_test_labels(file_path):
"""
Args:
file_path: ，
"""
lines = file_utils.read_all_lines(file_path)
labels = [[int(label) for label in line.split(',')[1:]] for line in lines[1:]]
return labels
def read_labels(file_path):
"""
Args:
file_path: ，，label
Returns:
list, label
"""
lines = file_utils.read_all_lines(file_path)
labels = [[int(label) for label in line.split(',')[0].split(' ')] for line in lines]
return labels
def read_subject_train_ids(file_path):
"""
Args:
file_path: ，，id
Returns:
list, id
"""
lines = file_utils.read_all_lines(file_path)
ids = [line.split(',')[2] for line in lines]
return ids
def read_features(file_path):
"""
Args:
file_path: ，，,，
Returns:
list,
"""
lines = file_utils.read_all_lines(file_path)
features = [line.split(',')[1] for line in lines]
return features
def read_subject_of_sentiment_value(file_path):
"""
Args:
file_path: ，，
Returns:
list,
"""
lines = file_utils.read_all_lines(file_path)
features = [ line.split(',')[2] for line in lines]
return features
def read_field(file_path, field_index, separator=',', has_head=True):
"""
Args:
file_path: ，，id
Returns:
list, id
"""
lines = file_utils.read_all_lines(file_path)
if has_head:
lines = lines[1:]
ids = [line.split(separator)[field_index] for line in lines]
return ids
def convert_subject_sentiment_value_predict_result(
subject_subject_sentiment_value_file_path, result_file_path):
"""convert_subject_sentiment_value_predict_result"""
subject_sentiment_value_file_lines = file_utils.read_all_lines(
subject_subject_sentiment_value_file_path)
result = ['content_id,subject,sentiment_value,sentiment_word']
for i, subject_line in enumerate(subject_sentiment_value_file_lines):
id_subjects = subject_line.split(',')
subjects = id_subjects[1].split('|')
for subject in subjects:
result.append(id_subjects[0] + ',' + subject.replace('_', ',') +
',')
file_utils.write_lines(result, result_file_path)
from nlp_tasks.absa.conf import data_path
from nlp_tasks.absa.utils import file_utils
train_data_lines = file_utils.read_all_lines(data_path.train_file_path)[1:]
train_data_content_line_map = {}
for train_data_line in train_data_lines:
parts = train_data_line.split(',')
if parts[1] not in train_data_content_line_map:
train_data_content_line_map[parts[1]] = []
train_data_content_line_map[parts[1]].append(train_data_line)
test_public_for_sentiment_lines = file_utils.read_all_lines(
data_path.test_public_for_sentiment_value_file_path)
result = ['content_id,subject,sentiment_value,sentiment_word']
in_train_data = set()
in_train_data_for_submit = []
for test_public_for_sentiment_line in test_public_for_sentiment_lines:
parts = test_public_for_sentiment_line.split(',')
if parts[1] in train_data_content_line_map:
if parts[1] not in in_train_data:
in_train_data.add(parts[1])
in_train_data_samples = train_data_content_line_map[parts[1]]
for in_train_data_sample in in_train_data_samples:
in_train_data_sample_parts = in_train_data_sample.split(',')
result.append(parts[0] + ',' + in_train_data_sample_parts[2] +
',' + in_train_data_sample_parts[3] + ',')
in_train_data_for_submit.append(parts[0] + ',' +
in_train_data_sample_parts[2] +
',' +
in_train_data_sample_parts[3] +
',')
def preict(data, model):
for i in range(len(data)):
sample = data[i]
parts = sample.split(',')
text = parts[1]
x_train = tokenizer.texts_to_sequences([text])
x_train_len = [len(element) for element in x_train]
subject = parts[2]
subject_repeated = data_utils.repeat_element_in_list([subject],
x_train_len)
x_subject = tokenizer.texts_to_sequences(subject_repeated)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_subject = sequence.pad_sequences(x_subject, maxlen=maxlen)
prob = model.predict([x_train, x_subject])
predict_label = result_utils.convert_sentiment_value_predict(prob)
print(predict_label)
train_data = file_utils.read_all_lines(train_file_path)
test_data = file_utils.read_all_lines(test_file_path)
print('train')
train_sample = preict(train_data, model)
print('test')
test_sample = preict(test_data, model)
from nlp_tasks.absa.conf import data_path
from nlp_tasks.absa.utils import file_utils
in_train_data_for_submit = file_utils.read_all_lines(
data_path.data_base_dir + 'in_train_data_for_submit')
in_train_data_for_submit_id = [
line.split(',')[0] for line in in_train_data_for_submit
]
result_file_name = 'test_public.result_20181028232554_caokong_xingneng.csv'
result = file_utils.read_all_lines(data_path.data_base_dir + result_file_name)
merge_result = [result.pop(0)]
for line in result:
id = line.split(',')[0]
if id in in_train_data_for_submit_id:
continue
else:
merge_result.append(line)
merge_result.extend(in_train_data_for_submit)
file_utils.write_lines(
merge_result,
data_path.data_base_dir + result_file_name + '.merge_result_and_in_train')