def main(args):
"""
Compute predictions and scores for inputs using specified BERT model and LaserTagger model.
Read input sentences from input_file_path, convert the sentences to predicted summaries using pretrained
models whose names are specified in the list_of_models, and compute exact score and SARI score if whether_score is
true. The predictions are stored in an output file pred.tsv. If scores are computed, the scores are stored in
an output file score.tsv.
Args:
args: command line arguments.
"""
whether_score = args.score
input_file_path = args.path_to_input_file
list_of_models = args.models
whether_grammar = args.grammar
__download_models(list_of_models)
__validate_scripts(args)
__clean_up()
subprocess.call(['mkdir', TEMP_FOLDER_NAME], cwd=os.path.expanduser('~'))
spaced_sentences, spaced_summaries = __preprocess_input(
input_file_path, whether_score)
with open(os.path.expanduser(TEMP_FOLDER_PATH + "/cleaned_data.tsv"),
'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
for i, sentence in enumerate(spaced_sentences):
tsv_writer.writerow([sentence, spaced_summaries[i]])
print("-------Number of input is", len(spaced_sentences), "-------")
# calculate and print predictions to output file
for model in list_of_models:
print("------Running on model", model, "-------")
prediction_command = [
'python',
os.path.expanduser(args.abs_path_to_lasertagger) +
'/predict_main.py', "--input_format=wikisplit",
"--label_map_file=./" + model + "/label_map.txt",
"--input_file=" + "./" + TEMP_FOLDER_NAME + "/cleaned_data.tsv",
"--saved_model=./" + model + "/export_model", "--vocab_file=" +
os.path.expanduser(args.abs_path_to_bert) + "/vocab.txt",
"--output_file=" + "./" + TEMP_FOLDER_NAME + "/output_" + model +
".tsv", "--embedding_type=" + args.embedding_type,
"--batch_size=" + str(args.batch_size)
]
if args.masking:
prediction_command.append("--enable_masking=true")
subprocess.call(prediction_command, cwd=os.path.expanduser("~"))
print("------Completed running on model", model, "-------")
output_row_list = []
model = list_of_models[0]
output_row = ["original"]
tsv_file = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/output_" + model + ".tsv"))
read_tsv = csv.reader(tsv_file, delimiter="\t")
for row in read_tsv:
output_row.append(row[0])
output_row_list.append(output_row)
for model in list_of_models:
output_row = [model]
tsv_file = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/output_" + model + ".tsv"))
read_tsv = csv.reader(tsv_file, delimiter="\t")
for row in read_tsv:
output_row.append(post_processing(row[1]))
output_row_list.append(output_row)
if whether_grammar:
tool = language_tool_python.LanguageTool('en-US')
for model in list_of_models:
output_row = [model + "_corrected"]
tsv_file = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/output_" + model +
".tsv"))
read_tsv = csv.reader(tsv_file, delimiter="\t")
for row in read_tsv:
output_row.append(tool.correct(post_processing(row[1])))
output_row_list.append(output_row)
if whether_score:
model = list_of_models[0]
output_row = ["target"]
tsv_file = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/output_" + model + ".tsv"))
read_tsv = csv.reader(tsv_file, delimiter="\t")
for row in read_tsv:
output_row.append(row[2])
output_row_list.append(output_row)
with open(os.path.expanduser("~/pred.tsv"), 'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
for i in range(len(output_row)):
this_row = []
for row in output_row_list:
this_row.append(row[i])
tsv_writer.writerow(this_row)
print("------Predictions written out to pred.tsv------")
# calculate and print scores to output file if whether_score is True
if whether_score:
for model in list_of_models:
print("------Calculating score for model", model, "-------")
f = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/score_" + model +
".txt"), "w")
subprocess.call([
'python',
os.path.expanduser(args.abs_path_to_lasertagger) +
'/score_main.py', "--prediction_file=" + "./" +
TEMP_FOLDER_NAME + "/output_" + model + ".tsv"
],
cwd=os.path.expanduser('~'),
stdout=f)
output_row_list = []
output_row = [
"score", "Exact score", "SARI score", "KEEP score",
"ADDITION score", "DELETION score"
]
output_row_list.append(output_row)
for model in list_of_models:
output_row = [model]
f = open(
os.path.expanduser(TEMP_FOLDER_PATH + "/score_" + model +
".txt"))
lines = f.readlines()
for line in lines:
output_row.append(line.split()[2])
output_row_list.append(output_row)
with open(os.path.expanduser("~/score.tsv"), 'wt') as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
for i in range(len(output_row)):
this_row = []
for row in output_row_list:
this_row.append(row[i])
tsv_writer.writerow(this_row)
print("------Scores written out to score.tsv------")
__clean_up()
def test_with_redundant_marks(self):
input_text = "Test . ? Test"
output = post_processing(input_text)
self.assertEqual(output, 'Test . Test')
def test_with_leading_paired_marks_and_paired_marks_in_middle(self):
input_text = "[ ] . . Test [ ] Test"
output = post_processing(input_text)
self.assertEqual(output, 'Test [] Test')
def test_with_paired_marks(self):
input_text = "[ Test ]"
output = post_processing(input_text)
self.assertEqual(output, '[ Test ]')
def test_with_leading_unpaired_punctuation_marks_and_other_marks(self):
input_text = "' \" . . Test"
output = post_processing(input_text)
self.assertEqual(output, 'Test')