We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[model:backout] TypeError: All intermediate steps should be transformers and implement fit and transform
Failing on [bugbug integration test](https://github.com/mozilla/bugbug/runs/19338314086):

```
+ bugbug-train backout --limit 30000 --no-download
2023-12-05 17:34:09,725:INFO:scripts.trainer:Skipping download of the databases
2023-12-05 17:34:09,725:INFO:scripts.trainer:Training *backout* model
2023-12-05 17:34:09,781:INFO:bugbug.models.backout:157 commits were backed out
2023-12-05 17:34:09,781:INFO:bugbug.models.backout:299 commits were not backed out
2023-12-05 17:34:09,866:INFO:bugbug.model:X: (456, 3), y: (456,)
Traceback (most recent call last):
  File "/usr/local/bin/bugbug-train", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.10/site-packages/scripts/trainer.py", line 141, in main
    retriever.go(args)
  File "/usr/local/lib/python3.10/site-packages/scripts/trainer.py", line 41, in go
    metrics = model_obj.train(limit=args.limit)
  File "/usr/local/lib/python3.10/site-packages/bugbug/model.py", line 399, in train
    scores = cross_validate(
  File "/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 285, in cross_validate
    _warn_or_raise_about_fit_failures(results, error_score)
  File "/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 367, in _warn_or_raise_about_fit_failures
    raise ValueError(all_fits_failed_message)
ValueError: 
All the 5 fits failed.
It is very likely that your model is misconfigured.
You can try to debug the error by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
5 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py", line 378, in fit
    Xt = self._fit(X, y, **fit_params_steps)
  File "/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py", line 316, in _fit
    self._validate_steps()
  File "/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py", line 207, in _validate_steps
    raise TypeError(
TypeError: All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'RandomUnderSampler(random_state=0)' (type <class 'imblearn.under_sampling._prototype_selection._random_under_sampler.RandomUnderSampler'>) doesn't
```

--- 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/backout.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 logging
7 from datetime import datetime
8 
9 import dateutil.parser
10 import xgboost
11 from dateutil.relativedelta import relativedelta
12 from imblearn.pipeline import Pipeline as ImblearnPipeline
13 from imblearn.under_sampling import RandomUnderSampler
14 from sklearn.compose import ColumnTransformer
15 from sklearn.feature_extraction import DictVectorizer
16 from sklearn.feature_extraction.text import CountVectorizer
17 from sklearn.pipeline import Pipeline
18 
19 from bugbug import bug_features, commit_features, feature_cleanup, repository, utils
20 from bugbug.model import CommitModel
21 
22 logging.basicConfig(level=logging.INFO)
23 logger = logging.getLogger(__name__)
24 
25 
26 class BackoutModel(CommitModel):
27     def __init__(self, lemmatization=False, bug_data=False):
28         CommitModel.__init__(self, lemmatization, bug_data)
29 
30         self.calculate_importance = False
31 
32         feature_extractors = [
33             commit_features.SourceCodeFilesModifiedNum(),
34             commit_features.OtherFilesModifiedNum(),
35             commit_features.TestFilesModifiedNum(),
36             commit_features.SourceCodeFileSize(),
37             commit_features.OtherFileSize(),
38             commit_features.TestFileSize(),
39             commit_features.SourceCodeAdded(),
40             commit_features.OtherAdded(),
41             commit_features.TestAdded(),
42             commit_features.SourceCodeDeleted(),
43             commit_features.OtherDeleted(),
44             commit_features.TestDeleted(),
45             commit_features.AuthorExperience(),
46             commit_features.ReviewerExperience(),
47             commit_features.ReviewersNum(),
48             commit_features.ComponentTouchedPrev(),
49             commit_features.DirectoryTouchedPrev(),
50             commit_features.FileTouchedPrev(),
51             commit_features.Types(),
52             commit_features.Components(),
53             commit_features.Directories(),
54             commit_features.Files(),
55         ]
56 
57         if bug_data:
58             feature_extractors += [
59                 bug_features.Product(),
60                 bug_features.Component(),
61                 bug_features.Severity(),
62                 bug_features.Priority(),
63                 bug_features.HasCrashSignature(),
64                 bug_features.HasRegressionRange(),
65                 bug_features.Whiteboard(),
66                 bug_features.Keywords(),
67                 bug_features.NumberOfBugDependencies(),
68                 bug_features.BlockedBugsNumber(),
69             ]
70 
71         cleanup_functions = [
72             feature_cleanup.fileref(),
73             feature_cleanup.url(),
74             feature_cleanup.synonyms(),
75         ]
76 
77         self.extraction_pipeline = ImblearnPipeline(
78             [
79                 (
80                     "commit_extractor",
81                     commit_features.CommitExtractor(
82                         feature_extractors, cleanup_functions
83                     ),
84                 ),
85             ]
86         )
87 
88         self.clf = Pipeline(
89             [
90                 (
91                     "union",
92                     ColumnTransformer(
93                         [
94                             ("data", DictVectorizer(), "data"),
95                             ("desc", self.text_vectorizer(), "desc"),
96                             (
97                                 "files",
98                                 CountVectorizer(
99                                     analyzer=utils.keep_as_is,
100                                     lowercase=False,
101                                     min_df=0.0014,
102                                 ),
103                                 "files",
104                             ),
105                         ]
106                     ),
107                 ),
108                 ("sampler", RandomUnderSampler(random_state=0)),
109                 (
110                     "estimator",
111                     xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count()),
112                 ),
113             ]
114         )
115 
116     def get_labels(self):
117         classes = {}
118 
119         two_years_and_six_months_ago = datetime.utcnow() - relativedelta(
120             years=2, months=6
121         )
122 
123         for commit_data in repository.get_commits():
124             pushdate = dateutil.parser.parse(commit_data["pushdate"])
125             if pushdate < two_years_and_six_months_ago:
126                 continue
127 
128             classes[commit_data["node"]] = 1 if commit_data["backedoutby"] else 0
129 
130         logger.info(
131             "%d commits were backed out",
132             sum(label == 1 for label in classes.values()),
133         )
134         logger.info(
135             "%d commits were not backed out",
136             sum(label == 0 for label in classes.values()),
137         )
138 
139         return classes, [0, 1]
140 
141     def get_feature_names(self):
142         return self.clf.named_steps["union"].get_feature_names_out()
143
```
--- 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/backout.py b/bugbug/models/backout.py
--- a/bugbug/models/backout.py
+++ b/bugbug/models/backout.py
@@ -74,7 +74,7 @@
             feature_cleanup.synonyms(),
         ]
 
-        self.extraction_pipeline = ImblearnPipeline(
+        self.extraction_pipeline = Pipeline(
             [
                 (
                     "commit_extractor",
@@ -85,7 +85,7 @@
             ]
         )
 
-        self.clf = Pipeline(
+        self.clf = ImblearnPipeline(
             [
                 (
                     "union",

{"golden_diff": "diff --git a/bugbug/models/backout.py b/bugbug/models/backout.py\n--- a/bugbug/models/backout.py\n+++ b/bugbug/models/backout.py\n@@ -74,7 +74,7 @@\n             feature_cleanup.synonyms(),\n         ]\n \n-        self.extraction_pipeline = ImblearnPipeline(\n+        self.extraction_pipeline = Pipeline(\n             [\n                 (\n                     \"commit_extractor\",\n@@ -85,7 +85,7 @@\n             ]\n         )\n \n-        self.clf = Pipeline(\n+        self.clf = ImblearnPipeline(\n             [\n                 (\n                     \"union\",\n", "issue": "[model:backout] TypeError: All intermediate steps should be transformers and implement fit and transform\nFailing on [bugbug integration test](https://github.com/mozilla/bugbug/runs/19338314086):\r\n\r\n```\r\n+ bugbug-train backout --limit 30000 --no-download\r\n2023-12-05 17:34:09,725:INFO:scripts.trainer:Skipping download of the databases\r\n2023-12-05 17:34:09,725:INFO:scripts.trainer:Training *backout* model\r\n2023-12-05 17:34:09,781:INFO:bugbug.models.backout:157 commits were backed out\r\n2023-12-05 17:34:09,781:INFO:bugbug.models.backout:299 commits were not backed out\r\n2023-12-05 17:34:09,866:INFO:bugbug.model:X: (456, 3), y: (456,)\r\nTraceback (most recent call last):\r\n  File \"/usr/local/bin/bugbug-train\", line 8, in <module>\r\n    sys.exit(main())\r\n  File \"/usr/local/lib/python3.10/site-packages/scripts/trainer.py\", line 141, in main\r\n    retriever.go(args)\r\n  File \"/usr/local/lib/python3.10/site-packages/scripts/trainer.py\", line 41, in go\r\n    metrics = model_obj.train(limit=args.limit)\r\n  File \"/usr/local/lib/python3.10/site-packages/bugbug/model.py\", line 399, in train\r\n    scores = cross_validate(\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 285, in cross_validate\r\n    _warn_or_raise_about_fit_failures(results, error_score)\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 367, in _warn_or_raise_about_fit_failures\r\n    raise ValueError(all_fits_failed_message)\r\nValueError: \r\nAll the 5 fits failed.\r\nIt is very likely that your model is misconfigured.\r\nYou can try to debug the error by setting error_score='raise'.\r\n\r\nBelow are more details about the failures:\r\n--------------------------------------------------------------------------------\r\n5 fits failed with the following error:\r\nTraceback (most recent call last):\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\r\n    estimator.fit(X_train, y_train, **fit_params)\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py\", line 378, in fit\r\n    Xt = self._fit(X, y, **fit_params_steps)\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py\", line 316, in _fit\r\n    self._validate_steps()\r\n  File \"/usr/local/lib/python3.10/site-packages/sklearn/pipeline.py\", line 207, in _validate_steps\r\n    raise TypeError(\r\nTypeError: All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'RandomUnderSampler(random_state=0)' (type <class 'imblearn.under_sampling._prototype_selection._random_under_sampler.RandomUnderSampler'>) doesn't\r\n```\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 logging\nfrom datetime import datetime\n\nimport dateutil.parser\nimport xgboost\nfrom dateutil.relativedelta import relativedelta\nfrom imblearn.pipeline import Pipeline as ImblearnPipeline\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.pipeline import Pipeline\n\nfrom bugbug import bug_features, commit_features, feature_cleanup, repository, utils\nfrom bugbug.model import CommitModel\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass BackoutModel(CommitModel):\n    def __init__(self, lemmatization=False, bug_data=False):\n        CommitModel.__init__(self, lemmatization, bug_data)\n\n        self.calculate_importance = False\n\n        feature_extractors = [\n            commit_features.SourceCodeFilesModifiedNum(),\n            commit_features.OtherFilesModifiedNum(),\n            commit_features.TestFilesModifiedNum(),\n            commit_features.SourceCodeFileSize(),\n            commit_features.OtherFileSize(),\n            commit_features.TestFileSize(),\n            commit_features.SourceCodeAdded(),\n            commit_features.OtherAdded(),\n            commit_features.TestAdded(),\n            commit_features.SourceCodeDeleted(),\n            commit_features.OtherDeleted(),\n            commit_features.TestDeleted(),\n            commit_features.AuthorExperience(),\n            commit_features.ReviewerExperience(),\n            commit_features.ReviewersNum(),\n            commit_features.ComponentTouchedPrev(),\n            commit_features.DirectoryTouchedPrev(),\n            commit_features.FileTouchedPrev(),\n            commit_features.Types(),\n            commit_features.Components(),\n            commit_features.Directories(),\n            commit_features.Files(),\n        ]\n\n        if bug_data:\n            feature_extractors += [\n                bug_features.Product(),\n                bug_features.Component(),\n                bug_features.Severity(),\n                bug_features.Priority(),\n                bug_features.HasCrashSignature(),\n                bug_features.HasRegressionRange(),\n                bug_features.Whiteboard(),\n                bug_features.Keywords(),\n                bug_features.NumberOfBugDependencies(),\n                bug_features.BlockedBugsNumber(),\n            ]\n\n        cleanup_functions = [\n            feature_cleanup.fileref(),\n            feature_cleanup.url(),\n            feature_cleanup.synonyms(),\n        ]\n\n        self.extraction_pipeline = ImblearnPipeline(\n            [\n                (\n                    \"commit_extractor\",\n                    commit_features.CommitExtractor(\n                        feature_extractors, cleanup_functions\n                    ),\n                ),\n            ]\n        )\n\n        self.clf = Pipeline(\n            [\n                (\n                    \"union\",\n                    ColumnTransformer(\n                        [\n                            (\"data\", DictVectorizer(), \"data\"),\n                            (\"desc\", self.text_vectorizer(), \"desc\"),\n                            (\n                                \"files\",\n                                CountVectorizer(\n                                    analyzer=utils.keep_as_is,\n                                    lowercase=False,\n                                    min_df=0.0014,\n                                ),\n                                \"files\",\n                            ),\n                        ]\n                    ),\n                ),\n                (\"sampler\", RandomUnderSampler(random_state=0)),\n                (\n                    \"estimator\",\n                    xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count()),\n                ),\n            ]\n        )\n\n    def get_labels(self):\n        classes = {}\n\n        two_years_and_six_months_ago = datetime.utcnow() - relativedelta(\n            years=2, months=6\n        )\n\n        for commit_data in repository.get_commits():\n            pushdate = dateutil.parser.parse(commit_data[\"pushdate\"])\n            if pushdate < two_years_and_six_months_ago:\n                continue\n\n            classes[commit_data[\"node\"]] = 1 if commit_data[\"backedoutby\"] else 0\n\n        logger.info(\n            \"%d commits were backed out\",\n            sum(label == 1 for label in classes.values()),\n        )\n        logger.info(\n            \"%d commits were not backed out\",\n            sum(label == 0 for label in classes.values()),\n        )\n\n        return classes, [0, 1]\n\n    def get_feature_names(self):\n        return self.clf.named_steps[\"union\"].get_feature_names_out()\n", "path": "bugbug/models/backout.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 logging\nfrom datetime import datetime\n\nimport dateutil.parser\nimport xgboost\nfrom dateutil.relativedelta import relativedelta\nfrom imblearn.pipeline import Pipeline as ImblearnPipeline\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.feature_extraction import DictVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.pipeline import Pipeline\n\nfrom bugbug import bug_features, commit_features, feature_cleanup, repository, utils\nfrom bugbug.model import CommitModel\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\nclass BackoutModel(CommitModel):\n    def __init__(self, lemmatization=False, bug_data=False):\n        CommitModel.__init__(self, lemmatization, bug_data)\n\n        self.calculate_importance = False\n\n        feature_extractors = [\n            commit_features.SourceCodeFilesModifiedNum(),\n            commit_features.OtherFilesModifiedNum(),\n            commit_features.TestFilesModifiedNum(),\n            commit_features.SourceCodeFileSize(),\n            commit_features.OtherFileSize(),\n            commit_features.TestFileSize(),\n            commit_features.SourceCodeAdded(),\n            commit_features.OtherAdded(),\n            commit_features.TestAdded(),\n            commit_features.SourceCodeDeleted(),\n            commit_features.OtherDeleted(),\n            commit_features.TestDeleted(),\n            commit_features.AuthorExperience(),\n            commit_features.ReviewerExperience(),\n            commit_features.ReviewersNum(),\n            commit_features.ComponentTouchedPrev(),\n            commit_features.DirectoryTouchedPrev(),\n            commit_features.FileTouchedPrev(),\n            commit_features.Types(),\n            commit_features.Components(),\n            commit_features.Directories(),\n            commit_features.Files(),\n        ]\n\n        if bug_data:\n            feature_extractors += [\n                bug_features.Product(),\n                bug_features.Component(),\n                bug_features.Severity(),\n                bug_features.Priority(),\n                bug_features.HasCrashSignature(),\n                bug_features.HasRegressionRange(),\n                bug_features.Whiteboard(),\n                bug_features.Keywords(),\n                bug_features.NumberOfBugDependencies(),\n                bug_features.BlockedBugsNumber(),\n            ]\n\n        cleanup_functions = [\n            feature_cleanup.fileref(),\n            feature_cleanup.url(),\n            feature_cleanup.synonyms(),\n        ]\n\n        self.extraction_pipeline = Pipeline(\n            [\n                (\n                    \"commit_extractor\",\n                    commit_features.CommitExtractor(\n                        feature_extractors, cleanup_functions\n                    ),\n                ),\n            ]\n        )\n\n        self.clf = ImblearnPipeline(\n            [\n                (\n                    \"union\",\n                    ColumnTransformer(\n                        [\n                            (\"data\", DictVectorizer(), \"data\"),\n                            (\"desc\", self.text_vectorizer(), \"desc\"),\n                            (\n                                \"files\",\n                                CountVectorizer(\n                                    analyzer=utils.keep_as_is,\n                                    lowercase=False,\n                                    min_df=0.0014,\n                                ),\n                                \"files\",\n                            ),\n                        ]\n                    ),\n                ),\n                (\"sampler\", RandomUnderSampler(random_state=0)),\n                (\n                    \"estimator\",\n                    xgboost.XGBClassifier(n_jobs=utils.get_physical_cpu_count()),\n                ),\n            ]\n        )\n\n    def get_labels(self):\n        classes = {}\n\n        two_years_and_six_months_ago = datetime.utcnow() - relativedelta(\n            years=2, months=6\n        )\n\n        for commit_data in repository.get_commits():\n            pushdate = dateutil.parser.parse(commit_data[\"pushdate\"])\n            if pushdate < two_years_and_six_months_ago:\n                continue\n\n            classes[commit_data[\"node\"]] = 1 if commit_data[\"backedoutby\"] else 0\n\n        logger.info(\n            \"%d commits were backed out\",\n            sum(label == 1 for label in classes.values()),\n        )\n        logger.info(\n            \"%d commits were not backed out\",\n            sum(label == 0 for label in classes.values()),\n        )\n\n        return classes, [0, 1]\n\n    def get_feature_names(self):\n        return self.clf.named_steps[\"union\"].get_feature_names_out()\n", "path": "bugbug/models/backout.py"}]}