인프런 커뮤니티 질문&답변

안녕하세요.  수업 잘 듣고 있습니다. 벌써 12월이네요ㅎㅎ

GridSearchCV로 랜덤포레스트 하이퍼 파라미터 튜닝하는 과정에서 에러가 발생해서 문의드립니다.

책으로는 220페이지의 코드입니다.

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from sklearn.model_selection import GridSearchCV

params = {'n_estimators':[100],

         'max_depth':[6,8,10,12],

         'min_samples_leaf':[8,12,18],

         'min_samples_split':[8,16,20]}

rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1) # n_jobs=-1 : 모든 CPU 코어를 이용해 학습

grid_cv = GridSearchCV(rf_clf, param_grid=params, cv=2, n_jobs=-1)

grid_cv.fit(X_train, y_train)

print('최적 하이퍼 파라미터:\n',grid_cv.best_params_)

print('최고 예측 정확도:{0:.4f}'.format(grid_cv.best_score_))

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이렇게 철민님과 동일한 코드를 입력했는데 

UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)

이와 같은 에러가 생깁니다.

보니까 모델에 fit하는 과정에서 발생한 에러인데요

전체 에러 코드는 다음과 같습니다.

---------------------------------------------------------------------------
UnicodeEncodeError                        Traceback (most recent call last)
<ipython-input-15-c470a9641bfc> in <module>
      8 rf_clf = RandomForestClassifier(random_state=0, n_jobs=-1) # n_jobs=-1 : 모든 CPU 코어를 이용해 학습
      9 grid_cv = GridSearchCV(rf_clf, param_grid=params, cv=2, n_jobs=-1)
---> 10 grid_cv.fit(X_train, y_train)
     11 
     12 print('최적 하이퍼 파라미터:\n',grid_cv.best_params_)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

C:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __enter__(self)
    728     def __enter__(self):
    729         self._managed_backend = True
--> 730         self._initialize_backend()
    731         return self
    732 

C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in _initialize_backend(self)
    739         try:
    740             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
--> 741                                              **self._backend_args)
    742             if self.timeout is not None and not self._backend.supports_timeout:
    743                 warnings.warn(

C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, prefer, require, idle_worker_timeout, **memmappingexecutor_args)
    495             n_jobs, timeout=idle_worker_timeout,
    496             env=self._prepare_worker_env(n_jobs=n_jobs),
--> 497             context_id=parallel._id, **memmappingexecutor_args)
    498         self.parallel = parallel
    499         return n_jobs

C:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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:\ProgramData\Anaconda3\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 18-20: ordinal not in range(128)



한글 인코딩이랑은 전혀 관련이 없는 상황인데 왜 이런 에러가 발생했는지 모르겠습니다.

구글링을 해보니(https://stackoverflow.com/questions/64843631/question-about-sklearn-hyperparameter-optimization) 저와 비슷한 에러가 발생하는 사람이 있는데, 답변자는 없어서 혹시 철민님은 아실까 하여 여쭤봅니다.


제 기억에는 아마 예전에 돌렸을 때는 정상 작동을 했었던 갓 같은데, 최근 다시 돌려보니 갑자기 왜 이런 문제가 발생했는지 모르겠습니다.