出错代码段：
from sklearn.model_selection import GridSearchCV
# Now that we know standard scaling is best for our features, we'll use those for our training and test sets
X_train, X_test, y_train, y_test = train_test_split(
features_scaled,
emotions,
test_size=0.2,
random_state=69
)
# Initialize the MLP Classifier and choose parameters we want to keep constant
model = MLPClassifier(
# tune batch size later
batch_size=10,
# keep random state constant to accurately compare subsequent models
random_state=69
)
# Choose the grid of hyperparameters we want to use for Grid Search to build our candidate models
parameter_space = {
# A single hidden layer of size between 8 (output classes) and 180 (input features) neurons is most probable
# It's a bad idea at guessing the number of hidden layers to have
# ...but we'll give 2 and 3 hidden layers a shot to reaffirm our suspicions that 1 is best
'hidden_layer_sizes': [(8,), (180,), (300,),(100,50,),(10,10,10)],
'activation': ['tanh','relu', 'logistic'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.001, 0.01],
'epsilon': [1e-08, 0.1 ],
'learning_rate': ['adaptive', 'constant']
}
# Create a grid search object which will store the scores and hyperparameters of all candidate models
grid = GridSearchCV(
model,
parameter_space,
cv=10,
n_jobs=4)
# Fit the models specified by the parameter grid
grid.fit(X_train, y_train)
# get the best hyperparameters from grid search object with its best_params_ attribute
print('Best parameters found:\n', grid.best_params_)
报错如下：
UnicodeEncodeError Traceback (most recent call last)
<ipython-input-32-90e0439e78b9> in <module>
41 # Fit the models specified by the parameter grid
42
---> 43 grid.fit(X_train, y_train)
44
45 # get the best hyperparameters from grid search object with its best_params_ attribute
d:\miniconda3\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
d:\miniconda3\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 = []
d:\miniconda3\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
d:\miniconda3\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(
d:\miniconda3\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
d:\miniconda3\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
d:\miniconda3\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
d:\miniconda3\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):
d:\miniconda3\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):
d:\miniconda3\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
d:\miniconda3\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():
d:\miniconda3\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):
d:\miniconda3\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('utf-8')
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)
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