尝试使用 GridSearchCV 拟合神经网络分类器的值错误
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【中文标题】尝试使用 GridSearchCV 拟合神经网络分类器的值错误【英文标题】：Value Error trying to fit a neural network classifier using GridSearchCV 【发布时间】：2018-01-24 14:05:02 【问题描述】：我正在尝试将 GridSearchCV 与 MLPClassifier 一起使用，以便使用最佳参数拟合一些训练数据：
parameters=
'learning_rate': ["constant", "invscaling", "adaptive"],
'hidden_layer_sizes': [x for x in itertools.product((10,20,30,40,50,100),repeat=3)],
'alpha': [10.0 **-np.arange(1, 7)],
'activation': ["logistic", "relu", "Tanh"]
ord_pred = MLPClassifier(hidden_layer_sizes = (100,1))
clf = GridSearchCV(estimator=ord_pred,param_grid=parameters,n_jobs=-1,verbose = 10)
orders_prior1 = orders_prior.groupby('product_id').filter(lambda x: len(x) >= 3).fillna(0)
clf.fit(orders_prior1[['user_id','order_number','order_dow','order_hour_of_day','days_since_prior_order']]\
,orders_prior1['product_id'], orders_prior1['user_order'])
但是我得到了以下错误/异常：
if self.alpha < 0.0:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
The above exception was the direct cause of the following exception:
TransportableException Traceback (most recent call last)
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
681 if 'timeout' in getfullargspec(job.get).args:
--> 682 self._output.extend(job.get(timeout=self.timeout))
683 else:
C:\Anaconda3\lib\multiprocessing\pool.py in get(self, timeout)
643 else:
--> 644 raise self._value
645
TransportableException: TransportableException
___________________________________________________________________________
ValueError Wed Aug 16 19:23:55 2017
PID: 18804 Python 3.6.2: C:\Anaconda3\python.exe
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
126 def __init__(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(<function _fit_and_score>, (MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], 0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, <function _passthrough_scorer>, memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), 10, 'activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant'), 'error_score': 'raise', 'fit_params': , 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True)]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0=<list_iterator object>)
126 def __init__(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
func = <function _fit_and_score>
args = (MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], 0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, <function _passthrough_scorer>, memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), 10, 'activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant')
kwargs = 'error_score': 'raise', 'fit_params': , 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], y=0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, scorer=<function _passthrough_scorer>, train=memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), test=memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), verbose=10, parameters='activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant', fit_params=, return_train_score=True, return_parameters=True, return_n_test_samples=True, return_times=True, error_score='raise')
233
234 try:
235 if y_train is None:
236 estimator.fit(X_train, **fit_params)
237 else:
--> 238 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method BaseMultilayerPerceptron.fit of ML...n_fraction=0.1, verbose=False, warm_start=False)>
X_train = user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns]
y_train = 1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64
fit_params =
239
240 except Exception as e:
241 # Note fit time as time until error
242 fit_time = time.time() - start_time
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in fit(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns], y=1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64)
613
614 Returns
615 -------
616 self : returns a trained MLP model.
617 """
--> 618 return self._fit(X, y, incremental=False)
self._fit = <bound method BaseMultilayerPerceptron._fit of M...n_fraction=0.1, verbose=False, warm_start=False)>
X = user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns]
y = 1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64
619
620 @property
621 def partial_fit(self):
622 """Fit the model to data matrix X and target y.
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in _fit(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns], y=1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64, incremental=False)
320 if not hasattr(hidden_layer_sizes, "__iter__"):
321 hidden_layer_sizes = [hidden_layer_sizes]
322 hidden_layer_sizes = list(hidden_layer_sizes)
323
324 # Validate input parameters.
--> 325 self._validate_hyperparameters()
self._validate_hyperparameters = <bound method BaseMultilayerPerceptron._validate...n_fraction=0.1, verbose=False, warm_start=False)>
326 if np.any(np.array(hidden_layer_sizes) <= 0):
327 raise ValueError("hidden_layer_sizes must be > 0, got %s." %
328 hidden_layer_sizes)
329
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in _validate_hyperparameters(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False))
386 if not isinstance(self.shuffle, bool):
387 raise ValueError("shuffle must be either True or False, got %s." %
388 self.shuffle)
389 if self.max_iter <= 0:
390 raise ValueError("max_iter must be > 0, got %s." % self.max_iter)
--> 391 if self.alpha < 0.0:
self.alpha = array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06])
392 raise ValueError("alpha must be >= 0, got %s." % self.alpha)
393 if (self.learning_rate in ["constant", "invscaling", "adaptive"] and
394 self.learning_rate_init <= 0.0):
395 raise ValueError("learning_rate_init must be > 0, got %s." %
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
___________________________________________________________________________
During handling of the above exception, another exception occurred:
JoblibValueError Traceback (most recent call last)
<ipython-input-20-7c1268d1d451> in <module>()
9 orders_prior1 = orders_prior.groupby('product_id').filter(lambda x: len(x) >= 3).fillna(0)
10 # up = orders_prior['product_id'].unique()
---> 11 clf.fit(orders_prior1 [['user_id','order_number','order_dow','order_hour_of_day','days_since_prior_order']] ,orders_prior1['product_id'], orders_prior1['user_order'])
12
13 # ord_pred.partial_fit(orders_prior.fillna(0).iloc[0:894]\
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups)
943 train/test set.
944 """
--> 945 return self._fit(X, y, groups, ParameterGrid(self.param_grid))
946
947
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _fit(self, X, y, groups, parameter_iterable)
562 return_times=True, return_parameters=True,
563 error_score=self.error_score)
--> 564 for parameters in parameter_iterable
565 for train, test in cv_iter)
566
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
766 # consumption.
767 self._iterating = False
--> 768 self.retrieve()
769 # Make sure that we get a last message telling us we are done
770 elapsed_time = time.time() - self._start_time
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in retrieve(self)
717 ensure_ready = self._managed_backend
718 backend.abort_everything(ensure_ready=ensure_ready)
--> 719 raise exception
720
721 def __call__(self, iterable):
JoblibValueError: JoblibValueError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
C:\Anaconda3\lib\runpy.py in _run_module_as_main(mod_name='ipykernel_launcher', alter_argv=1)
188 sys.exit(msg)
189 main_globals = sys.modules["__main__"].__dict__
190 if alter_argv:
191 sys.argv[0] = mod_spec.origin
192 return _run_code(code, main_globals, None,
--> 193 "__main__", mod_spec)
mod_spec = ModuleSpec(name='ipykernel_launcher', loader=<_f...nda3\\lib\\site-packages\\ipykernel_launcher.py')
194
195 def run_module(mod_name, init_globals=None,
196 run_name=None, alter_sys=False):
197 """Execute a module's code without importing it
F:\thecads_vm-master\eds\Final Project\Instacart\<ipython-input-20-7c1268d1d451> in <module>()
6
7 ord_pred = MLPClassifier(hidden_layer_sizes = (100,1))
8 clf = GridSearchCV(estimator=ord_pred,param_grid=parameters,n_jobs=-1,verbose = 10)
9 orders_prior1 = orders_prior.groupby('product_id').filter(lambda x: len(x) >= 3).fillna(0)
10 # up = orders_prior['product_id'].unique()
---> 11 clf.fit(orders_prior1 [['user_id','order_number','order_dow','order_hour_of_day','days_since_prior_order']] ,orders_prior1['product_id'], orders_prior1['user_order'])
12
13 # ord_pred.partial_fit(orders_prior.fillna(0).iloc[0:894]\
14 # [['user_id','order_number','order_dow','order_hour_of_day','days_since_prior_order']]\
15 # ,orders_prior.iloc[0:894]['product_id'], up)
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self=GridSearchCV(cv=None, error_score='raise',
...rain_score=True,
scoring=None, verbose=10), X= user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], y=0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, groups=0 11
1 11
2 ...Name: user_order, Length: 32433710, dtype: object)
940
941 groups : array-like, with shape (n_samples,), optional
942 Group labels for the samples used while splitting the dataset into
943 train/test set.
944 """
--> 945 return self._fit(X, y, groups, ParameterGrid(self.param_grid))
self._fit = <bound method BaseSearchCV._fit of GridSearchCV(...ain_score=True,
scoring=None, verbose=10)>
X = user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns]
y = 0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64
groups = 0 11
1 11
2 ...Name: user_order, Length: 32433710, dtype: object
self.param_grid = 'activation': ['logistic', 'relu', 'Tanh'], 'alpha': [array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06])], 'hidden_layer_sizes': [(10, 10, 10), (10, 10, 20), (10, 10, 30), (10, 10, 40), (10, 10, 50), (10, 10, 100), (10, 20, 10), (10, 20, 20), (10, 20, 30), (10, 20, 40), (10, 20, 50), (10, 20, 100), (10, 30, 10), (10, 30, 20), (10, 30, 30), (10, 30, 40), (10, 30, 50), (10, 30, 100), (10, 40, 10), (10, 40, 20), ...], 'learning_rate': ['constant', 'invscaling', 'adaptive']
946
947
948 class RandomizedSearchCV(BaseSearchCV):
949 """Randomized search on hyper parameters.
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in _fit(self=GridSearchCV(cv=None, error_score='raise',
...rain_score=True,
scoring=None, verbose=10), X= user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], y=0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, groups=0 11
1 11
2 ...Name: user_order, Length: 32433710, dtype: object, parameter_iterable=<sklearn.model_selection._search.ParameterGrid object>)
559 fit_params=self.fit_params,
560 return_train_score=self.return_train_score,
561 return_n_test_samples=True,
562 return_times=True, return_parameters=True,
563 error_score=self.error_score)
--> 564 for parameters in parameter_iterable
parameters = undefined
parameter_iterable = <sklearn.model_selection._search.ParameterGrid object>
565 for train, test in cv_iter)
566
567 # if one choose to see train score, "out" will contain train score info
568 if self.return_train_score:
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=Parallel(n_jobs=-1), iterable=<generator object BaseSearchCV._fit.<locals>.<genexpr>>)
763 if pre_dispatch == "all" or n_jobs == 1:
764 # The iterable was consumed all at once by the above for loop.
765 # No need to wait for async callbacks to trigger to
766 # consumption.
767 self._iterating = False
--> 768 self.retrieve()
self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-1)>
769 # Make sure that we get a last message telling us we are done
770 elapsed_time = time.time() - self._start_time
771 self._print('Done %3i out of %3i | elapsed: %s finished',
772 (len(self._output), len(self._output),
---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
ValueError Wed Aug 16 19:23:55 2017
PID: 18804 Python 3.6.2: C:\Anaconda3\python.exe
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self=<sklearn.externals.joblib.parallel.BatchedCalls object>)
126 def __init__(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
self.items = [(<function _fit_and_score>, (MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], 0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, <function _passthrough_scorer>, memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), 10, 'activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant'), 'error_score': 'raise', 'fit_params': , 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True)]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0=<list_iterator object>)
126 def __init__(self, iterator_slice):
127 self.items = list(iterator_slice)
128 self._size = len(self.items)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
func = <function _fit_and_score>
args = (MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], 0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, <function _passthrough_scorer>, memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), 10, 'activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant')
kwargs = 'error_score': 'raise', 'fit_params': , 'return_n_test_samples': True, 'return_parameters': True, 'return_times': True, 'return_train_score': True
132
133 def __len__(self):
134 return self._size
135
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[32433710 rows x 5 columns], y=0 196
1 14084
2 ...
Name: product_id, Length: 32433710, dtype: int64, scorer=<function _passthrough_scorer>, train=memmap([ 1606, 1610, 1618, ..., 32433707, 32433708, 32433709]), test=memmap([ 0, 1, 2, ..., 32190332, 32190334, 32190356]), verbose=10, parameters='activation': 'logistic', 'alpha': array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06]), 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'constant', fit_params=, return_train_score=True, return_parameters=True, return_n_test_samples=True, return_times=True, error_score='raise')
233
234 try:
235 if y_train is None:
236 estimator.fit(X_train, **fit_params)
237 else:
--> 238 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method BaseMultilayerPerceptron.fit of ML...n_fraction=0.1, verbose=False, warm_start=False)>
X_train = user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns]
y_train = 1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64
fit_params =
239
240 except Exception as e:
241 # Note fit time as time until error
242 fit_time = time.time() - start_time
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in fit(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns], y=1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64)
613
614 Returns
615 -------
616 self : returns a trained MLP model.
617 """
--> 618 return self._fit(X, y, incremental=False)
self._fit = <bound method BaseMultilayerPerceptron._fit of M...n_fraction=0.1, verbose=False, warm_start=False)>
X = user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns]
y = 1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64
619
620 @property
621 def partial_fit(self):
622 """Fit the model to data matrix X and target y.
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in _fit(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False), X= user_id order_number order_dow orde... 7.0
[21606079 rows x 5 columns], y=1606 17762
1610 17762
1618 ...
Name: product_id, Length: 21606079, dtype: int64, incremental=False)
320 if not hasattr(hidden_layer_sizes, "__iter__"):
321 hidden_layer_sizes = [hidden_layer_sizes]
322 hidden_layer_sizes = list(hidden_layer_sizes)
323
324 # Validate input parameters.
--> 325 self._validate_hyperparameters()
self._validate_hyperparameters = <bound method BaseMultilayerPerceptron._validate...n_fraction=0.1, verbose=False, warm_start=False)>
326 if np.any(np.array(hidden_layer_sizes) <= 0):
327 raise ValueError("hidden_layer_sizes must be > 0, got %s." %
328 hidden_layer_sizes)
329
...........................................................................
C:\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py in _validate_hyperparameters(self=MLPClassifier(activation='logistic',
alph...on_fraction=0.1, verbose=False, warm_start=False))
386 if not isinstance(self.shuffle, bool):
387 raise ValueError("shuffle must be either True or False, got %s." %
388 self.shuffle)
389 if self.max_iter <= 0:
390 raise ValueError("max_iter must be > 0, got %s." % self.max_iter)
--> 391 if self.alpha < 0.0:
self.alpha = array([ 1.00000000e-01, 1.00000000e-02, 1.0...0000000e-04, 1.00000000e-05, 1.00000000e-06])
392 raise ValueError("alpha must be >= 0, got %s." % self.alpha)
393 if (self.learning_rate in ["constant", "invscaling", "adaptive"] and
394 self.learning_rate_init <= 0.0):
395 raise ValueError("learning_rate_init must be > 0, got %s." %
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
很难检测出真正的原因，而且这种情况会持续很长时间。也许这与 alpha 参数搜索的范围或其他有关。怎么修？谢谢。
【问题讨论】：
当您执行 clf.fit 时，您需要有两个输入：X 和 y（分别为特征和目标）。在您的代码中，您有 3 个输入。 第三个是组：类数组，形状为 (n_samples,)，可选组标签，用于将数据集拆分为训练/测试集时使用的样本。 orders_prior1['product_id'] 不应该是组吗？ 这是目标参数。我想预测用户将在他的下一个订单中购买什么，同时希望每个用户和他的订单号的拆分不会有来自同一用户的不同产品和不同拆分中的订单号。 我遇到了和你一样的错误。我让它为我工作的是改变 alpha 值 【参考方案1】：将'alpha': [10.0 **-np.arange(1, 7)] 更改为'alpha': 10.0 **-np.arange(1, 7)
正如您在np.arrange() 的文档中看到的那样，函数本身已经返回了一个数组。用括号将其关闭（使其成为数组中的数组）会导致 GridSearch() 引发异常。
【讨论】：
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