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from transformers import pipeline
from os import path, listdir, makedirs
from datetime import timedelta
from time import monotonic
from argparse import ArgumentParser
parser = ArgumentParser(prog='classifier.py')
parser.add_argument('-f', '--full', action='store_true', help="use whole dataset")
parser.add_argument('-m', '--multi_label', action='store_true', help="enable multi_label for the classifier")
parser.add_argument('--model', default="facebook/bart-large-mnli", type=str, help="main model to use")
parser.add_argument('--compare', nargs='?', const="MoritzLaurer/deberta-v3-large-zeroshot-v2.0", type=str, help="second model for comparison")
args = parser.parse_args()
positive_categories = ['semantic']
negative_categories = ['other', 'boot', 'network', 'KVM', 'vnc', 'graphic', 'device', 'socket', 'debug', 'files', 'PID', 'permissions', 'performance']
categories = positive_categories + negative_categories
def list_files_recursive(directory):
result = []
for entry in listdir(directory):
full_path = path.join(directory, entry)
if path.isdir(full_path):
result = result + list_files_recursive(full_path)
else:
result.append(full_path)
return result
def output(text : str, category : str, labels : list, scores : list, identifier : str):
file_path = f"output/{category}/{identifier}"
makedirs(path.dirname(file_path), exist_ok = True)
with open(file_path, "w") as file:
for label, score in zip(labels, scores):
if label == "SPLIT":
file.write(f"--------------------\n")
else:
file.write(f"{label}: {score:.3f}\n")
file.write("\n")
file.write(text)
def get_category(classification : dict):
result = classification['labels'][0]
if not args.multi_label:
return result
for label, score in zip(classification["labels"], classification["scores"]):
if label in negative_categories and score >= 0.92:
result = label
break
if classification['labels'][0] == "semantic" and classification['scores'][0] <= 0.91:
result = "other"
if all(i > 0.9 for i in classification["scores"]):
result = "all"
elif all(i < 0.6 for i in classification["scores"]):
result = "none"
return result
def main():
classifier = pipeline("zero-shot-classification", model=args.model)
if args.compare:
compare_classifier = pipeline("zero-shot-classification", model=args.compare)
bugs = list_files_recursive("../results/scraper/mailinglist")
if not args.full:
bugs = bugs + list_files_recursive("../results/scraper/gitlab/semantic_issues")
else:
bugs = bugs + list_files_recursive("../results/scraper/launchpad")
bugs = bugs + list_files_recursive("../results/scraper/gitlab/issues_text")
print(f"{len(bugs)} number of bugs will be processed")
for bug in bugs:
print(f"Processing {bug}")
with open(bug, "r") as file:
text = file.read()
result = classifier(text, categories, multi_label=args.multi_label)
category = get_category(result)
if args.compare:
compare_result = compare_classifier(text, categories, multi_label=args.multi_label)
compare_category = get_category(compare_result)
if category != compare_category:
category = "review"
result['labels'] = result['labels'] + ['SPLIT'] + compare_result['labels']
result['scores'] = result['scores'] + [0] + compare_result['scores']
output(text, category, result['labels'], result['scores'], path.basename(bug))
if __name__ == "__main__":
start_time = monotonic()
main()
end_time = monotonic()
print(timedelta(seconds=end_time - start_time))
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