зеркало из https://github.com/mozilla/bugbug.git
132 строки
4.2 KiB
Python
132 строки
4.2 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 csv
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import os
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from datetime import datetime, timedelta
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import numpy as np
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from bugbug import repository # noqa
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from bugbug import bugzilla, db
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from bugbug.models import MODELS, get_model_class
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if __name__ == "__main__":
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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("--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",
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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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"--generate-sheet",
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help="Perform evaluation on bugs from last week and generate a csv file",
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action="store_true",
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)
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parser.add_argument("--token", help="Bugzilla token", action="store")
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parser.add_argument(
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"--historical", help="Analyze historical bugs", action="store_true"
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)
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args = parser.parse_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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model_class_name = args.goal
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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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elif args.classifier == "nn":
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model_class_name = "component_nn"
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else:
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raise ValueError(f"Unknown value {args.classifier}")
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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()
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if args.historical:
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model = model_class(args.lemmatization, args.historical)
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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, importances = model.classify(
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bug, probabilities=True, importances=True
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)
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feature_names = model.get_feature_names()
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for i, (importance, index, is_positive) in enumerate(importances):
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print(
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f'{i + 1}. \'{feature_names[int(index)]}\' ({"+" if (is_positive) else "-"}{importance})'
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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 args.generate_sheet:
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assert (
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args.token is not None
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), "A Bugzilla token should be set in order to download bugs"
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today = datetime.utcnow()
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a_week_ago = today - timedelta(7)
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bugzilla.set_token(args.token)
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bugs = bugzilla.download_bugs_between(a_week_ago, today)
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print(f"Classifying {len(bugs)} bugs...")
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rows = [["Bug", f"{args.goal}(model)", args.goal, "Title"]]
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for bug in bugs:
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p = model.classify(bug, probabilities=True)
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rows.append(
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[
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f'https://bugzilla.mozilla.org/show_bug.cgi?id={bug["id"]}',
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"y" if p[0][1] >= 0.7 else "n",
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"",
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bug["summary"],
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]
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)
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os.makedirs("sheets", exist_ok=True)
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with open(
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os.path.join(
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"sheets",
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f'{args.goal}-{datetime.utcnow().strftime("%Y-%m-%d")}-labels.csv',
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),
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"w",
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) as f:
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writer = csv.writer(f)
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writer.writerows(rows)
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