`TerminatedWorkerError` when working with `n_jobs=-1` in `GridSearchCV`

### Describe the bug

`TerminatedWorkerError` when working with `n_jobs=-1` or even `n_jobs=4` in `GridSearchCV`.

I just migrated to a Macbook Pro M3 from an Ubuntu. This info is probably relevant since it seems to be related to how the OS terminate a process? I'm able to run this on my older Ubuntu laptop with older version of Python (v3.8) and Sklearn (v1.0.2). With my Ubuntu laptop, I never encountered this issue before. 

I noticed that this issue happens randomly on my Macbook, which means if the `TerminatedWorkerError` raised, I can re-run the fitting code and it might finish without issue. Sometimes it also happens early in the fitting or a bit later. 

My code is very similar to the attached code below. But, it seems I couldn't reproduce the same error with that code on a fresh jupyter notebook!

It would be great if someone could advise how to debug further, or advise if there is any settings I could change to reduce the 'likelihood' of `TerminatedWorkerError`.

### Steps/Code to Reproduce

```python
# Note: I couldn't reproduce this error with the code below, could be differences in data?
# But this is very similar to what my actual code looks like

import numpy as np
from imblearn.pipeline import Pipeline
from sklearn import tree, datasets
from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
from sklearn.feature_selection import SequentialFeatureSelector, SelectFromModel
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import RandomOverSampler

class CustomGridSearchCV(GridSearchCV):
    @property
    def feature_importances_(self):
        return self.best_estimator_.feature_importances_

rs=42

breast_cancer = datasets.load_breast_cancer()
grid_search_cv = RepeatedStratifiedKFold(random_state=rs, n_repeats=100, n_splits=5)

tree_params = {'criterion': ['gini', 'entropy'],
              'max_depth': range(2, 6),
              'min_samples_leaf': range(3, 5, 1)}

tree_pipe = Pipeline([
    ("scaling", StandardScaler()), 
    ("oversampling", RandomOverSampler(random_state=rs)),
    ("feature_selection", "passthrough"),
    ("clf", tree.DecisionTreeClassifier()),
])

parameters = {
    'feature_selection': [
        SelectFromModel(
            CustomGridSearchCV(
                param_grid=tree_params,
                estimator=DecisionTreeClassifier(random_state=rs), 
                cv=5,
                scoring='balanced_accuracy',
            )
        ),
    ],
    'oversampling__shrinkage': np.arange(0, 1, 0.3),
    'clf__criterion': ['gini', 'entropy'],
    'clf__max_depth': range(2, 10),
    'clf__min_samples_leaf': range(3, 10, 1)
}
clf = GridSearchCV(tree_pipe, parameters, n_jobs=-1, cv=grid_search_cv, verbose=1, error_score='raise')
clf.fit(breast_cancer.data, breast_cancer.target)
```

### Expected Results

No error is thrown.

### Actual Results

```python-traceback
---------------------------------------------------------------------------
TerminatedWorkerError                     Traceback (most recent call last)
File <timed exec>:158

File ~/miniconda3/lib/python3.12/site-packages/sklearn/base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1466     estimator._validate_params()
   1468 with config_context(
   1469     skip_parameter_validation=(
   1470         prefer_skip_nested_validation or global_skip_validation
   1471     )
   1472 ):
-> 1473     return fit_method(estimator, *args, **kwargs)

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1018, in BaseSearchCV.fit(self, X, y, **params)
   1012     results = self._format_results(
   1013         all_candidate_params, n_splits, all_out, all_more_results
   1014     )
   1016     return results
-> 1018 self._run_search(evaluate_candidates)
   1020 # multimetric is determined here because in the case of a callable
   1021 # self.scoring the return type is only known after calling
   1022 first_test_score = all_out[0]["test_scores"]

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1572, in GridSearchCV._run_search(self, evaluate_candidates)
   1570 def _run_search(self, evaluate_candidates):
   1571     """Search all candidates in param_grid"""
-> 1572     evaluate_candidates(ParameterGrid(self.param_grid))

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:964, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    956 if self.verbose > 0:
    957     print(
    958         "Fitting {0} folds for each of {1} candidates,"
    959         " totalling {2} fits".format(
    960             n_splits, n_candidates, n_candidates * n_splits
    961         )
    962     )
--> 964 out = parallel(
    965     delayed(_fit_and_score)(
    966         clone(base_estimator),
    967         X,
    968         y,
    969         train=train,
    970         test=test,
    971         parameters=parameters,
    972         split_progress=(split_idx, n_splits),
    973         candidate_progress=(cand_idx, n_candidates),
    974         **fit_and_score_kwargs,
    975     )
    976     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    977         enumerate(candidate_params),
    978         enumerate(cv.split(X, y, **routed_params.splitter.split)),
    979     )
    980 )
    982 if len(out) < 1:
    983     raise ValueError(
    984         "No fits were performed. "
    985         "Was the CV iterator empty? "
    986         "Were there no candidates?"
    987     )

File ~/miniconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:74, in Parallel.__call__(self, iterable)
     69 config = get_config()
     70 iterable_with_config = (
     71     (_with_config(delayed_func, config), args, kwargs)
     72     for delayed_func, args, kwargs in iterable
     73 )
---> 74 return super().__call__(iterable_with_config)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:2007, in Parallel.__call__(self, iterable)
   2001 # The first item from the output is blank, but it makes the interpreter
   2002 # progress until it enters the Try/Except block of the generator and
   2003 # reaches the first `yield` statement. This starts the asynchronous
   2004 # dispatch of the tasks to the workers.
   2005 next(output)
-> 2007 return output if self.return_generator else list(output)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1650, in Parallel._get_outputs(self, iterator, pre_dispatch)
   1647     yield
   1649     with self._backend.retrieval_context():
-> 1650         yield from self._retrieve()
   1652 except GeneratorExit:
   1653     # The generator has been garbage collected before being fully
   1654     # consumed. This aborts the remaining tasks if possible and warn
   1655     # the user if necessary.
   1656     self._exception = True

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1754, in Parallel._retrieve(self)
   1747 while self._wait_retrieval():
   1748 
   1749     # If the callback thread of a worker has signaled that its task
   1750     # triggered an exception, or if the retrieval loop has raised an
   1751     # exception (e.g. `GeneratorExit`), exit the loop and surface the
   1752     # worker traceback.
   1753     if self._aborting:
-> 1754         self._raise_error_fast()
   1755         break
   1757     # If the next job is not ready for retrieval yet, we just wait for
   1758     # async callbacks to progress.

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1789, in Parallel._raise_error_fast(self)
   1785 # If this error job exists, immediately raise the error by
   1786 # calling get_result. This job might not exists if abort has been
   1787 # called directly or if the generator is gc'ed.
   1788 if error_job is not None:
-> 1789     error_job.get_result(self.timeout)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:745, in BatchCompletionCallBack.get_result(self, timeout)
    739 backend = self.parallel._backend
    741 if backend.supports_retrieve_callback:
    742     # We assume that the result has already been retrieved by the
    743     # callback thread, and is stored internally. It's just waiting to
    744     # be returned.
--> 745     return self._return_or_raise()
    747 # For other backends, the main thread needs to run the retrieval step.
    748 try:

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:763, in BatchCompletionCallBack._return_or_raise(self)
    761 try:
    762     if self.status == TASK_ERROR:
--> 763         raise self._result
    764     return self._result
    765 finally:

TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.

The exit codes of the workers are {SIGBUS(-10)}
```

### Versions

```shell
System:
    python: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 10:07:17) [Clang 14.0.6 ]
executable: /Users/darren/miniconda3/bin/python
   machine: macOS-14.5-arm64-arm-64bit

Python dependencies:
      sklearn: 1.5.1
          pip: 24.1.2
   setuptools: 69.5.1
        numpy: 2.0.1
        scipy: 1.14.0
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.9.1
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: libomp
       filepath: /Users/darren/miniconda3/lib/python3.12/site-packages/sklearn/.dylibs/libomp.dylib
        version: None
```

COMMENT:
A process is terminated by receiving a SIGBUS signal when it tries to access an "undefined function of a memory object".

This is not necessarily happening in scikit-learn / imbalanced learn code. If there was a bug in scikit-learn's Cython code, I would expect to get a segfault instead (SIGSEV) of a bus error (SIGBUS). See: https://stackoverflow.com/questions/212466/what-is-a-bus-error-is-it-different-from-a-segmentation-fault

It could be related to code in joblib it-self, or in loky (used internally by joblib) or even in the Python interpreter or its standard library. Based on the typical code snipppets shown in the answers to the SO question linked above, I suspect it could be related to our use of memory mapping in joblib, however, it should not be triggered for such small numpy arrays (only with array larger than 1 MB which is not the case in your use case).

I checked by adding the following lines in your reproducer:

```python
from joblib import parallel_config
import multiprocessing as mp
import logging

# parallel_config(max_nbytes=None, verbose=100)  # disable memory mapping
# parallel_config(max_nbytes=0.0, verbose=100)  # force enable memory mapping

log = mp.util.log_to_stderr(logging.DEBUG)
log.handlers[0].setFormatter(
    logging.Formatter("[%(levelname)s:%(processName)s:%(threadName)s] %(message)s")
)
```

and I confirm that no memory mapping happens by default for the breast cancer dataset.

Maybe you could try to launch a new run with the `mp.util.log_to_stderr` config. The output can be very large so better dump it to disk, for instance by launching the repro script with:

```
$ python ~/tmp/repro_loky_sigbus.py  &>/tmp/repro_loky_sigbus.log
```

Then you can a attach the resulting (maybe compressed) file to a comment on this issue after checking that the logs do not contain sensitive info.

Labels: Bug, Needs Reproducible Code