sklearn 错误，而 n_jobs 不是 1 使用 VScode

Question

我正在尝试使用 VScode 用GridSearchCV 做一个支持向量机，代码如下：

from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
parameters = {'C':(1,10,), 'kernel': ("rbf",),'gamma':(1,10,100,1000)}
svc = SVC(probability = True)
svc_cv = GridSearchCV(svc, param_grid = parameters, refit = True, n_jobs= -1)
svc_cv.fit(x_train, y_train)

问题是当我传递不是 1 的 n_jobs（n_jobs = -1 或 2 或任何其他）时，错​​误发生为：

---------------------------------------------------------------------------
UnicodeEncodeError                        Traceback (most recent call last)
 in 
----> 1 svc_cv.fit(x_train, y_train)

C:\Python38\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     71                           FutureWarning)
     72         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73         return f(**kwargs)
     74     return inner_f
     75 

C:\Python38\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
    693                                     verbose=self.verbose)
    694         results = {}
--> 695         with parallel:
    696             all_candidate_params = []
    697             all_out = []

C:\Python38\lib\site-packages\joblib\parallel.py in __enter__(self)
    709     def __enter__(self):
    710         self._managed_backend = True
--> 711         self._initialize_backend()
    712         return self
    713 

C:\Python38\lib\site-packages\joblib\parallel.py in _initialize_backend(self)
    719         """Build a process or thread pool and return the number of workers"""
    720         try:
--> 721             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
    722                                              **self._backend_args)
    723             if self.timeout is not None and not self._backend.supports_timeout:

C:\Python38\lib\site-packages\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
    490                 SequentialBackend(nesting_level=self.nesting_level))
    491 
--> 492         self._workers = get_memmapping_executor(
    493             n_jobs, timeout=idle_worker_timeout,
    494             env=self._prepare_worker_env(n_jobs=n_jobs),

C:\Python38\lib\site-packages\joblib\executor.py in get_memmapping_executor(n_jobs, **kwargs)
     18 
     19 def get_memmapping_executor(n_jobs, **kwargs):
---> 20     return MemmappingExecutor.get_memmapping_executor(n_jobs, **kwargs)
     21 
     22 

C:\Python38\lib\site-packages\joblib\executor.py in get_memmapping_executor(cls, n_jobs, timeout, initializer, initargs, env, temp_folder, context_id, **backend_args)
     40         _executor_args = executor_args
     41 
---> 42         manager = TemporaryResourcesManager(temp_folder)
     43 
     44         # reducers access the temporary folder in which to store temporary

C:\Python38\lib\site-packages\joblib\_memmapping_reducer.py in __init__(self, temp_folder_root, context_id)
    529             # exposes exposes too many low-level details.
    530             context_id = uuid4().hex
--> 531         self.set_current_context(context_id)
    532 
    533     def set_current_context(self, context_id):

C:\Python38\lib\site-packages\joblib\_memmapping_reducer.py in set_current_context(self, context_id)
    533     def set_current_context(self, context_id):
    534         self._current_context_id = context_id
--> 535         self.register_new_context(context_id)
    536 
    537     def register_new_context(self, context_id):

C:\Python38\lib\site-packages\joblib\_memmapping_reducer.py in register_new_context(self, context_id)
    558                 new_folder_name, self._temp_folder_root
    559             )
--> 560             self.register_folder_finalizer(new_folder_path, context_id)
    561             self._cached_temp_folders[context_id] = new_folder_path
    562 

C:\Python38\lib\site-packages\joblib\_memmapping_reducer.py in register_folder_finalizer(self, pool_subfolder, context_id)
    588         # semaphores and pipes
    589         pool_module_name = whichmodule(delete_folder, 'delete_folder')
--> 590         resource_tracker.register(pool_subfolder, "folder")
    591 
    592         def _cleanup():

C:\Python38\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in register(self, name, rtype)
    189         '''Register a named resource, and increment its refcount.'''
    190         self.ensure_running()
--> 191         self._send('REGISTER', name, rtype)
    192 
    193     def unregister(self, name, rtype):

C:\Python38\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py in _send(self, cmd, name, rtype)
    202 
    203     def _send(self, cmd, name, rtype):
--> 204         msg = '{0}:{1}:{2}\n'.format(cmd, name, rtype).encode('ascii')
    205         if len(name) > 512:
    206             # posix guarantees that writes to a pipe of less than PIPE_BUF

UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-19: ordinal not in range(128)

但是，我可以使用基于浏览器的 jupyter notebook 使用n_jobs = -1 完全正常地运行它，不知道出了什么问题。

Answer 1

看起来如果您指定运行多个作业，sklearn 将尝试生成更多进程。当它这样做时，它会触发它通过管道发送到进程的错误，因为它不是 ASCII。我会确保 sklearn 可能需要发送的任何内容都是纯 ASCII（用于制作 msg 的任何内容）。