prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Use 'product' and 'component' features in the models
  b7369ea8bf282941ce4b378ad5ad3c832db20668 introduced the features, but we are still not using them.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `bugbug/models/bug.py`
  Content:
  ```
  1 # -*- coding: utf-8 -*-
  2 # This Source Code Form is subject to the terms of the Mozilla Public
  3 # License, v. 2.0. If a copy of the MPL was not distributed with this file,
  4 # You can obtain one at http://mozilla.org/MPL/2.0/.
  5 
  6 import xgboost
  7 from imblearn.over_sampling import BorderlineSMOTE
  8 from sklearn.compose import ColumnTransformer
  9 from sklearn.feature_extraction import DictVectorizer
  10 from sklearn.pipeline import Pipeline
  11 
  12 from bugbug import bug_features
  13 from bugbug import bugzilla
  14 from bugbug import labels
  15 from bugbug.model import Model
  16 
  17 
  18 class BugModel(Model):
  19     def __init__(self, lemmatization=False):
  20         Model.__init__(self, lemmatization)
  21 
  22         self.sampler = BorderlineSMOTE(random_state=0)
  23 
  24         feature_extractors = [
  25             bug_features.has_str(),
  26             bug_features.severity(),
  27             # Ignore keywords that would make the ML completely skewed
  28             # (we are going to use them as 100% rules in the evaluation phase).
  29             bug_features.keywords({'regression', 'talos-regression', 'feature'}),
  30             bug_features.is_coverity_issue(),
  31             bug_features.has_crash_signature(),
  32             bug_features.has_url(),
  33             bug_features.has_w3c_url(),
  34             bug_features.has_github_url(),
  35             bug_features.whiteboard(),
  36             bug_features.patches(),
  37             bug_features.landings(),
  38             bug_features.title(),
  39             bug_features.blocked_bugs_number(),
  40             bug_features.ever_affected(),
  41             bug_features.affected_then_unaffected(),
  42         ]
  43 
  44         cleanup_functions = [
  45             bug_features.cleanup_url,
  46             bug_features.cleanup_fileref,
  47             bug_features.cleanup_synonyms,
  48         ]
  49 
  50         self.extraction_pipeline = Pipeline([
  51             ('bug_extractor', bug_features.BugExtractor(feature_extractors, cleanup_functions)),
  52             ('union', ColumnTransformer([
  53                 ('data', DictVectorizer(), 'data'),
  54 
  55                 ('title', self.text_vectorizer(min_df=0.001), 'title'),
  56 
  57                 ('first_comment', self.text_vectorizer(min_df=0.001), 'first_comment'),
  58 
  59                 ('comments', self.text_vectorizer(min_df=0.001), 'comments'),
  60             ])),
  61         ])
  62 
  63         self.clf = xgboost.XGBClassifier(n_jobs=16)
  64         self.clf.set_params(predictor='cpu_predictor')
  65 
  66     def get_bugbug_labels(self, kind='bug'):
  67         assert kind in ['bug', 'regression', 'defect_feature_task']
  68 
  69         classes = {}
  70 
  71         for bug_id, category in labels.get_labels('bug_nobug'):
  72             assert category in ['True', 'False'], f'unexpected category {category}'
  73             if kind == 'bug':
  74                 classes[int(bug_id)] = 1 if category == 'True' else 0
  75             elif kind == 'regression':
  76                 if category == 'False':
  77                     classes[int(bug_id)] = 0
  78             elif kind == 'defect_feature_task':
  79                 if category == 'True':
  80                     classes[int(bug_id)] = 'd'
  81 
  82         for bug_id, category in labels.get_labels('regression_bug_nobug'):
  83             assert category in ['nobug', 'bug_unknown_regression', 'bug_no_regression', 'regression'], f'unexpected category {category}'
  84             if kind == 'bug':
  85                 classes[int(bug_id)] = 1 if category != 'nobug' else 0
  86             elif kind == 'regression':
  87                 if category == 'bug_unknown_regression':
  88                     continue
  89 
  90                 classes[int(bug_id)] = 1 if category == 'regression' else 0
  91             elif kind == 'defect_feature_task':
  92                 if category != 'nobug':
  93                     classes[int(bug_id)] = 'd'
  94 
  95         for bug_id, category in labels.get_labels('defect_feature_task'):
  96             assert category in ['d', 'f', 't']
  97             if kind == 'bug':
  98                 classes[int(bug_id)] = 1 if category == 'd' else 0
  99             elif kind == 'regression':
  100                 if category in ['f', 't']:
  101                     classes[int(bug_id)] = 0
  102             elif kind == 'defect_feature_task':
  103                 classes[int(bug_id)] = category
  104 
  105         # Augment labes by using bugs marked as 'regression' or 'feature', as they are basically labelled.
  106         bug_ids = set()
  107         for bug in bugzilla.get_bugs():
  108             bug_id = int(bug['id'])
  109 
  110             bug_ids.add(bug_id)
  111 
  112             if bug_id in classes:
  113                 continue
  114 
  115             if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):
  116                 if kind in ['bug', 'regression']:
  117                     classes[bug_id] = 1
  118                 else:
  119                     classes[bug_id] = 'd'
  120             elif any(keyword in bug['keywords'] for keyword in ['feature']):
  121                 if kind in ['bug', 'regression']:
  122                     classes[bug_id] = 0
  123                 else:
  124                     classes[bug_id] = 'f'
  125             elif kind == 'regression':
  126                 for history in bug['history']:
  127                     for change in history['changes']:
  128                         if change['field_name'] == 'keywords' and change['removed'] == 'regression':
  129                             classes[bug_id] = 0
  130 
  131         # Remove labels which belong to bugs for which we have no data.
  132         return {bug_id: label for bug_id, label in classes.items() if bug_id in bug_ids}
  133 
  134     def get_labels(self):
  135         return self.get_bugbug_labels('bug')
  136 
  137     def get_feature_names(self):
  138         return self.extraction_pipeline.named_steps['union'].get_feature_names()
  139 
  140     def overwrite_classes(self, bugs, classes, probabilities):
  141         for i, bug in enumerate(bugs):
  142             if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):
  143                 classes[i] = 1 if not probabilities else [0., 1.]
  144             elif 'feature' in bug['keywords']:
  145                 classes[i] = 0 if not probabilities else [1., 0.]
  146 
  147         return classes
  148
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/bugbug/models/bug.py b/bugbug/models/bug.py
  --- a/bugbug/models/bug.py
  +++ b/bugbug/models/bug.py
  @@ -39,6 +39,8 @@
               bug_features.blocked_bugs_number(),
               bug_features.ever_affected(),
               bug_features.affected_then_unaffected(),
  +            bug_features.product(),
  +            bug_features.component(),
           ]
   
           cleanup_functions = [
  
verification_info: {"golden_diff": "diff --git a/bugbug/models/bug.py b/bugbug/models/bug.py\n--- a/bugbug/models/bug.py\n+++ b/bugbug/models/bug.py\n@@ -39,6 +39,8 @@\n             bug_features.blocked_bugs_number(),\n             bug_features.ever_affected(),\n             bug_features.affected_then_unaffected(),\n+            bug_features.product(),\n+            bug_features.component(),\n         ]\n \n         cleanup_functions = [\n", "issue": "Use 'product' and 'component' features in the models\nb7369ea8bf282941ce4b378ad5ad3c832db20668 introduced the features, but we are still not using them.\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport xgboost\nfrom imblearn.over_sampling import BorderlineSMOTE\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.pipeline import Pipeline\n\nfrom bugbug import bug_features\nfrom bugbug import bugzilla\nfrom bugbug import labels\nfrom bugbug.model import Model\n\n\nclass BugModel(Model):\n    def __init__(self, lemmatization=False):\n        Model.__init__(self, lemmatization)\n\n        self.sampler = BorderlineSMOTE(random_state=0)\n\n        feature_extractors = [\n            bug_features.has_str(),\n            bug_features.severity(),\n            # Ignore keywords that would make the ML completely skewed\n            # (we are going to use them as 100% rules in the evaluation phase).\n            bug_features.keywords({'regression', 'talos-regression', 'feature'}),\n            bug_features.is_coverity_issue(),\n            bug_features.has_crash_signature(),\n            bug_features.has_url(),\n            bug_features.has_w3c_url(),\n            bug_features.has_github_url(),\n            bug_features.whiteboard(),\n            bug_features.patches(),\n            bug_features.landings(),\n            bug_features.title(),\n            bug_features.blocked_bugs_number(),\n            bug_features.ever_affected(),\n            bug_features.affected_then_unaffected(),\n        ]\n\n        cleanup_functions = [\n            bug_features.cleanup_url,\n            bug_features.cleanup_fileref,\n            bug_features.cleanup_synonyms,\n        ]\n\n        self.extraction_pipeline = Pipeline([\n            ('bug_extractor', bug_features.BugExtractor(feature_extractors, cleanup_functions)),\n            ('union', ColumnTransformer([\n                ('data', DictVectorizer(), 'data'),\n\n                ('title', self.text_vectorizer(min_df=0.001), 'title'),\n\n                ('first_comment', self.text_vectorizer(min_df=0.001), 'first_comment'),\n\n                ('comments', self.text_vectorizer(min_df=0.001), 'comments'),\n            ])),\n        ])\n\n        self.clf = xgboost.XGBClassifier(n_jobs=16)\n        self.clf.set_params(predictor='cpu_predictor')\n\n    def get_bugbug_labels(self, kind='bug'):\n        assert kind in ['bug', 'regression', 'defect_feature_task']\n\n        classes = {}\n\n        for bug_id, category in labels.get_labels('bug_nobug'):\n            assert category in ['True', 'False'], f'unexpected category {category}'\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category == 'True' else 0\n            elif kind == 'regression':\n                if category == 'False':\n                    classes[int(bug_id)] = 0\n            elif kind == 'defect_feature_task':\n                if category == 'True':\n                    classes[int(bug_id)] = 'd'\n\n        for bug_id, category in labels.get_labels('regression_bug_nobug'):\n            assert category in ['nobug', 'bug_unknown_regression', 'bug_no_regression', 'regression'], f'unexpected category {category}'\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category != 'nobug' else 0\n            elif kind == 'regression':\n                if category == 'bug_unknown_regression':\n                    continue\n\n                classes[int(bug_id)] = 1 if category == 'regression' else 0\n            elif kind == 'defect_feature_task':\n                if category != 'nobug':\n                    classes[int(bug_id)] = 'd'\n\n        for bug_id, category in labels.get_labels('defect_feature_task'):\n            assert category in ['d', 'f', 't']\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category == 'd' else 0\n            elif kind == 'regression':\n                if category in ['f', 't']:\n                    classes[int(bug_id)] = 0\n            elif kind == 'defect_feature_task':\n                classes[int(bug_id)] = category\n\n        # Augment labes by using bugs marked as 'regression' or 'feature', as they are basically labelled.\n        bug_ids = set()\n        for bug in bugzilla.get_bugs():\n            bug_id = int(bug['id'])\n\n            bug_ids.add(bug_id)\n\n            if bug_id in classes:\n                continue\n\n            if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):\n                if kind in ['bug', 'regression']:\n                    classes[bug_id] = 1\n                else:\n                    classes[bug_id] = 'd'\n            elif any(keyword in bug['keywords'] for keyword in ['feature']):\n                if kind in ['bug', 'regression']:\n                    classes[bug_id] = 0\n                else:\n                    classes[bug_id] = 'f'\n            elif kind == 'regression':\n                for history in bug['history']:\n                    for change in history['changes']:\n                        if change['field_name'] == 'keywords' and change['removed'] == 'regression':\n                            classes[bug_id] = 0\n\n        # Remove labels which belong to bugs for which we have no data.\n        return {bug_id: label for bug_id, label in classes.items() if bug_id in bug_ids}\n\n    def get_labels(self):\n        return self.get_bugbug_labels('bug')\n\n    def get_feature_names(self):\n        return self.extraction_pipeline.named_steps['union'].get_feature_names()\n\n    def overwrite_classes(self, bugs, classes, probabilities):\n        for i, bug in enumerate(bugs):\n            if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):\n                classes[i] = 1 if not probabilities else [0., 1.]\n            elif 'feature' in bug['keywords']:\n                classes[i] = 0 if not probabilities else [1., 0.]\n\n        return classes\n", "path": "bugbug/models/bug.py"}], "after_files": [{"content": "# -*- coding: utf-8 -*-\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport xgboost\nfrom imblearn.over_sampling import BorderlineSMOTE\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.pipeline import Pipeline\n\nfrom bugbug import bug_features\nfrom bugbug import bugzilla\nfrom bugbug import labels\nfrom bugbug.model import Model\n\n\nclass BugModel(Model):\n    def __init__(self, lemmatization=False):\n        Model.__init__(self, lemmatization)\n\n        self.sampler = BorderlineSMOTE(random_state=0)\n\n        feature_extractors = [\n            bug_features.has_str(),\n            bug_features.severity(),\n            # Ignore keywords that would make the ML completely skewed\n            # (we are going to use them as 100% rules in the evaluation phase).\n            bug_features.keywords({'regression', 'talos-regression', 'feature'}),\n            bug_features.is_coverity_issue(),\n            bug_features.has_crash_signature(),\n            bug_features.has_url(),\n            bug_features.has_w3c_url(),\n            bug_features.has_github_url(),\n            bug_features.whiteboard(),\n            bug_features.patches(),\n            bug_features.landings(),\n            bug_features.title(),\n            bug_features.blocked_bugs_number(),\n            bug_features.ever_affected(),\n            bug_features.affected_then_unaffected(),\n            bug_features.product(),\n            bug_features.component(),\n        ]\n\n        cleanup_functions = [\n            bug_features.cleanup_url,\n            bug_features.cleanup_fileref,\n            bug_features.cleanup_synonyms,\n        ]\n\n        self.extraction_pipeline = Pipeline([\n            ('bug_extractor', bug_features.BugExtractor(feature_extractors, cleanup_functions)),\n            ('union', ColumnTransformer([\n                ('data', DictVectorizer(), 'data'),\n\n                ('title', self.text_vectorizer(min_df=0.001), 'title'),\n\n                ('first_comment', self.text_vectorizer(min_df=0.001), 'first_comment'),\n\n                ('comments', self.text_vectorizer(min_df=0.001), 'comments'),\n            ])),\n        ])\n\n        self.clf = xgboost.XGBClassifier(n_jobs=16)\n        self.clf.set_params(predictor='cpu_predictor')\n\n    def get_bugbug_labels(self, kind='bug'):\n        assert kind in ['bug', 'regression', 'defect_feature_task']\n\n        classes = {}\n\n        for bug_id, category in labels.get_labels('bug_nobug'):\n            assert category in ['True', 'False'], f'unexpected category {category}'\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category == 'True' else 0\n            elif kind == 'regression':\n                if category == 'False':\n                    classes[int(bug_id)] = 0\n            elif kind == 'defect_feature_task':\n                if category == 'True':\n                    classes[int(bug_id)] = 'd'\n\n        for bug_id, category in labels.get_labels('regression_bug_nobug'):\n            assert category in ['nobug', 'bug_unknown_regression', 'bug_no_regression', 'regression'], f'unexpected category {category}'\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category != 'nobug' else 0\n            elif kind == 'regression':\n                if category == 'bug_unknown_regression':\n                    continue\n\n                classes[int(bug_id)] = 1 if category == 'regression' else 0\n            elif kind == 'defect_feature_task':\n                if category != 'nobug':\n                    classes[int(bug_id)] = 'd'\n\n        for bug_id, category in labels.get_labels('defect_feature_task'):\n            assert category in ['d', 'f', 't']\n            if kind == 'bug':\n                classes[int(bug_id)] = 1 if category == 'd' else 0\n            elif kind == 'regression':\n                if category in ['f', 't']:\n                    classes[int(bug_id)] = 0\n            elif kind == 'defect_feature_task':\n                classes[int(bug_id)] = category\n\n        # Augment labes by using bugs marked as 'regression' or 'feature', as they are basically labelled.\n        bug_ids = set()\n        for bug in bugzilla.get_bugs():\n            bug_id = int(bug['id'])\n\n            bug_ids.add(bug_id)\n\n            if bug_id in classes:\n                continue\n\n            if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):\n                if kind in ['bug', 'regression']:\n                    classes[bug_id] = 1\n                else:\n                    classes[bug_id] = 'd'\n            elif any(keyword in bug['keywords'] for keyword in ['feature']):\n                if kind in ['bug', 'regression']:\n                    classes[bug_id] = 0\n                else:\n                    classes[bug_id] = 'f'\n            elif kind == 'regression':\n                for history in bug['history']:\n                    for change in history['changes']:\n                        if change['field_name'] == 'keywords' and change['removed'] == 'regression':\n                            classes[bug_id] = 0\n\n        # Remove labels which belong to bugs for which we have no data.\n        return {bug_id: label for bug_id, label in classes.items() if bug_id in bug_ids}\n\n    def get_labels(self):\n        return self.get_bugbug_labels('bug')\n\n    def get_feature_names(self):\n        return self.extraction_pipeline.named_steps['union'].get_feature_names()\n\n    def overwrite_classes(self, bugs, classes, probabilities):\n        for i, bug in enumerate(bugs):\n            if any(keyword in bug['keywords'] for keyword in ['regression', 'talos-regression']) or ('cf_has_regression_range' in bug and bug['cf_has_regression_range'] == 'yes'):\n                classes[i] = 1 if not probabilities else [0., 1.]\n            elif 'feature' in bug['keywords']:\n                classes[i] = 0 if not probabilities else [1., 0.]\n\n        return classes\n", "path": "bugbug/models/bug.py"}]}