зеркало из https://github.com/mozilla/bugbug.git
122 строки
3.7 KiB
Python
122 строки
3.7 KiB
Python
# -*- coding: utf-8 -*-
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# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this file,
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# You can obtain one at http://mozilla.org/MPL/2.0/.
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import argparse
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import sys
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import numpy as np
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from bugbug import bugzilla, db, repository
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from bugbug.models import MODELS, get_model_class
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def parse_args(args):
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--lemmatization",
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help="Perform lemmatization (using spaCy)",
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action="store_true",
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)
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parser.add_argument(
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"--training-set-size",
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nargs="?",
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default=14000,
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type=int,
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help="The size of the training set for the duplicate model",
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)
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parser.add_argument(
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"--disable-url-cleanup",
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help="Don't cleanup urls when training the duplicate model",
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dest="cleanup_urls",
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default=True,
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action="store_false",
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)
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parser.add_argument("--train", help="Perform training", action="store_true")
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parser.add_argument(
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"--goal", help="Goal of the classifier", choices=MODELS.keys(), default="defect"
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)
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parser.add_argument(
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"--classifier",
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help="Type of the classifier. Only used for component classification.",
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choices=["default", "nn"],
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default="default",
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)
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parser.add_argument("--classify", help="Perform evaluation", action="store_true")
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parser.add_argument(
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"--historical",
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help="""Analyze historical bugs. Only used for defect, bugtype,
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defectenhancementtask and regression tasks.""",
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action="store_true",
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)
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return parser.parse_args(args)
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def main(args):
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model_file_name = "{}{}model".format(
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args.goal, "" if args.classifier == "default" else args.classifier
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)
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if args.goal == "component":
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if args.classifier == "default":
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model_class_name = "component"
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else:
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model_class_name = "component_nn"
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else:
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model_class_name = args.goal
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model_class = get_model_class(model_class_name)
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if args.train:
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db.download(bugzilla.BUGS_DB)
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db.download(repository.COMMITS_DB)
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historical_supported_tasks = [
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"defect",
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"bugtype",
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"defectenhancementtask",
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"regression",
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]
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if args.goal in historical_supported_tasks:
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model = model_class(args.lemmatization, args.historical)
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elif args.goal == "duplicate":
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model = model_class(
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args.training_set_size, args.lemmatization, args.cleanup_urls
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)
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else:
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model = model_class(args.lemmatization)
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model.train()
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else:
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model = model_class.load(model_file_name)
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if args.classify:
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for bug in bugzilla.get_bugs():
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print(
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f'https://bugzilla.mozilla.org/show_bug.cgi?id={ bug["id"] } - { bug["summary"]} '
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)
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if model.calculate_importance:
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probas, importance = model.classify(
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bug, probabilities=True, importances=True
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)
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feature_names = model.get_human_readable_feature_names()
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model.print_feature_importances(
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importance["importances"], feature_names, class_probabilities=probas
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)
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else:
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probas = model.classify(bug, probabilities=True, importances=False)
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if np.argmax(probas) == 1:
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print(f"Positive! {probas}")
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else:
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print(f"Negative! {probas}")
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input()
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if __name__ == "__main__":
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main(parse_args(sys.argv[1:]))
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