зеркало из https://github.com/mozilla/bugbug.git
102 строки
3.2 KiB
Python
102 строки
3.2 KiB
Python
# -*- coding: utf-8 -*-
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import argparse
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import os
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from logging import INFO, basicConfig, getLogger
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import numpy as np
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import requests
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from bugbug import bugzilla, db
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from bugbug.models import get_model_class
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from bugbug.utils import download_check_etag, zstd_decompress
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MODELS_WITH_TYPE = ("component",)
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basicConfig(level=INFO)
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logger = getLogger(__name__)
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def classify_bugs(model_name, classifier, bug_id):
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if classifier != "default":
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assert (
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model_name in MODELS_WITH_TYPE
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), f"{classifier} is not a valid classifier type for {model_name}"
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model_file_name = f"{model_name}{classifier}model"
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model_name = f"{model_name}_{classifier}"
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else:
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model_file_name = f"{model_name}model"
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if not os.path.exists(model_file_name):
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logger.info(f"{model_file_name} does not exist. Downloading the model....")
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try:
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download_check_etag(
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f"https://community-tc.services.mozilla.com/api/index/v1/task/project.relman.bugbug.train_{model_name}.latest/artifacts/public/{model_file_name}.zst"
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)
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except requests.HTTPError:
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logger.error(
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f"A pre-trained model is not available, you will need to train it yourself using the trainer script"
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)
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raise SystemExit(1)
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zstd_decompress(model_file_name)
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assert os.path.exists(model_file_name), "Decompressed file doesn't exist"
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model_class = get_model_class(model_name)
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model = model_class.load(model_file_name)
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if bug_id:
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bugs = bugzilla.get(bug_id).values()
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assert bugs, f"A bug with a bug id of {bug_id} was not found"
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else:
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assert db.download(bugzilla.BUGS_DB)
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bugs = bugzilla.get_bugs()
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for bug in 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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model.print_feature_importances(
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importance["importances"], 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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probability = probas[0]
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pred_index = np.argmax(probability)
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if len(probability) > 2:
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pred_class = model.le.inverse_transform([pred_index])[0]
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else:
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pred_class = "Positive" if pred_index == 1 else "Negative"
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print(f"{pred_class} {probability}")
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input()
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def main():
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description = "Perform evaluation on bugs using the specified model"
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parser = argparse.ArgumentParser(description=description)
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parser.add_argument("model", help="Which model to use for evaluation")
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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("--bug-id", help="Classify the given bug id")
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args = parser.parse_args()
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classify_bugs(args.model, args.classifier, args.bug_id)
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if __name__ == "__main__":
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main()
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