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()

```

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 = [

{"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"}]}