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)网上各种方法都试了，还是不行
NameError Traceback (most recent call last)
<ipython-input-30-a31d90ae8313> in <module>
5 import sys
6
----> 7 reload(sys)
8 sys.setdefaultencoding('utf8')
9 from sklearn.model_selection import GridSearchCV
NameError: name 'reload' is not defined
报错信息是什么？
我贴一下
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)
补充一下，我改n_job=1 他就一直运行，不报错，但不出结果
不是太懂，能具体说说嘛？我看网上报错范围有的也不是18-20
可能是训练数据有问题 你检查训练数据看看
什么问题？命名？
Python在安装时，默认的编码是ascii，当程序中出现非ascii编码时，python的处理常常会报这样的错UnicodeDecodeError: 'ascii' codec can't decode byte 0x?? in position 1: ordinal not in range(128)，python没办法处理非ascii编码的，此时需要自己设置将python的默认编码，一般设置为utf8的编码格式。
查询系统默认编码可以在解释器中输入以下命令：
Python代码
设置默认编码时使用：
Python代码
可能会报AttributeError: 'module' object has no attribute 'setdefaultencoding'的错误，执行reload(sys)，在执行以上命令就可以顺利通过。
此时在执行sys.getdefaultencoding()就会发现编码已经被设置为utf8的了，但是在解释器里修改的编码只能保证当次有效，在重启解释器后，会发现，编码又被重置为默认的ascii了，那么有没有办法一次性修改程序或系统的默认编码呢。
有2种方法设置python的默认编码：
一个解决的方案在程序中加入以下代码：
Python代码
另一个方案是在python的Lib\site-packages文件夹下新建一个sitecustomize.py，内容为：
Python代码
此时重启python解释器，执行sys.getdefaultencoding()，发现编码已经被设置为utf8的了，多次重启之后，效果相同，这是因为系统在python启动的时候，自行调用该文件，设置系统的默认编码，而不需要每次都手动的加上解决代码，属于一劳永逸的解决方法。
另外有一种解决方案是在程序中所有涉及到编码的地方，强制编码为utf8，即添加代码encode("utf8")，这种方法并不推荐使用，因为一旦少写一个地方，将会导致大量的错误报告，我曾经遇到这种情况，错误日志压缩之后尚有70多K，全都是这一个问题，让人有很崩溃的感觉。
注意参数
ascii范围必须是1-17，21-128