Python 如何将正则化参数传递给模型选择（sklearn）？
我有一个简单的岭回归模型，如下所示：Python 如何将正则化参数传递给模型选择（sklearn）？,python,scikit-learn,Python,Scikit Learn,我有一个简单的岭回归模型，如下所示： import numpy as np import sklearn.linear_model import sklearn.model_selection import matplotlib.pyplot as plt n_samples_train, n_samples_test, n_features = 75, 150, 500 np.random.seed(0) coef = np.random.randn(n_features) coef[50:
import numpy as np
import sklearn.linear_model
import sklearn.model_selection
import matplotlib.pyplot as plt
n_samples_train, n_samples_test, n_features = 75, 150, 500
np.random.seed(0)
coef = np.random.randn(n_features)
coef[50:] = 0.0 # only the top 10 features are impacting the model
X = np.random.randn(n_samples_train + n_samples_test, n_features)
y = np.dot(X, coef)
ridge = linear_model.Ridge(alpha=0.1, fit_intercept=False)
fit_params = {'alpha': 0.1, 'alpha': 1, 'alpha': 10}
ms = model_selection.cross_validate(ridge, X, y, cv=10, verbose=3, scoring='neg_mean_squared_error', n_jobs=-1, return_train_score=True)
f = plt.figure(figsize=(10,7))
ax = f.add_subplot(111)
_ = ax.scatter(np.arange(len(ms['train_score'])), ms['train_score'])
_ = ax.scatter(np.arange(len(ms['train_score'])), ms['test_score'])
ax.set_xlabel('Regularization Parameter')
ax.set_ylabel('Negative '+r'$MSE$')
拟合参数---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 350, in __call__
return self.func(*args, **kwargs)
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 458, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
TypeError: fit() got an unexpected keyword argument 'alpha'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 359, in __call__
raise TransportableException(text, e_type)
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
TypeError Thu Jun 7 19:32:53 2018
PID: 1372 Python 3.6.4: /home/nazariy/anaconda/envs/logging/bin/python
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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>, (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10}), {'return_times': True, 'return_train_score': True})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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 = (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10})
kwargs = {'return_times': True, 'return_train_score': True}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), X=array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), y=array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), scorer={'score': make_scorer(mean_squared_error, greater_is_better=False)}, train=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), test=array([0]), verbose=3, parameters=None, fit_params={'alpha': 10}, return_train_score=True, return_parameters=False, return_n_test_samples=False, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Ridge.fit of Ridge(alpha=0.1, copy...se, random_state=None, solver='auto', tol=0.001)>
X_train = array([[ 2],
[ 3],
[ 4],
[ ... [ 7],
[ 8],
[ 9],
[10]])
y_train = array([ 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1])
fit_params = {'alpha': 10}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time
TypeError: fit() got an unexpected keyword argument 'alpha'
___________________________________________________________________________
"""
The above exception was the direct cause of the following exception:
TransportableException Traceback (most recent call last)
~/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in retrieve(self)
698 if getattr(self._backend, 'supports_timeout', False):
--> 699 self._output.extend(job.get(timeout=self.timeout))
700 else:
~/anaconda/envs/logging/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
643 else:
--> 644 raise self._value
645
TransportableException: TransportableException
___________________________________________________________________________
TypeError Thu Jun 7 19:32:53 2018
PID: 1372 Python 3.6.4: /home/nazariy/anaconda/envs/logging/bin/python
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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>, (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10}), {'return_times': True, 'return_train_score': True})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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 = (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10})
kwargs = {'return_times': True, 'return_train_score': True}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), X=array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), y=array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), scorer={'score': make_scorer(mean_squared_error, greater_is_better=False)}, train=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), test=array([0]), verbose=3, parameters=None, fit_params={'alpha': 10}, return_train_score=True, return_parameters=False, return_n_test_samples=False, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Ridge.fit of Ridge(alpha=0.1, copy...se, random_state=None, solver='auto', tol=0.001)>
X_train = array([[ 2],
[ 3],
[ 4],
[ ... [ 7],
[ 8],
[ 9],
[10]])
y_train = array([ 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1])
fit_params = {'alpha': 10}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time
TypeError: fit() got an unexpected keyword argument 'alpha'
___________________________________________________________________________
During handling of the above exception, another exception occurred:
{'mean_fit_time': array([ 0.03192089, 0.00980701, 0.00800555]),
'mean_score_time': array([ 0.00030019, 0.00010006, 0.00020015]),
'mean_test_score': array([-39.76136733, -39.7700976 , -39.90061844]),
'mean_train_score': array([ -4.55700050e-06, -4.51109497e-04, -4.10175706e-02]),
'param_alpha': masked_array(data = [0.1 1 10],
mask = [False False False],
fill_value = ?),
'params': [{'alpha': 0.1}, {'alpha': 1}, {'alpha': 10}],
'rank_test_score': array([1, 2, 3]),
'split0_test_score': array([-46.32878735, -46.33132325, -46.42467545]),
'split0_train_score': array([ -4.33377675e-06, -4.29368239e-04, -3.93263016e-02]),
'split1_test_score': array([-23.65685521, -23.70826719, -24.23222395]),
'split1_train_score': array([ -4.71698023e-06, -4.66957024e-04, -4.24618411e-02]),
'split2_test_score': array([-25.10691203, -25.11680407, -25.25664803]),
'split2_train_score': array([ -5.07409398e-06, -5.02011049e-04, -4.53910939e-02]),
'split3_test_score': array([-49.02718917, -48.98855648, -48.69939824]),
'split3_train_score': array([ -4.48268791e-06, -4.43818654e-04, -4.04080484e-02]),
'split4_test_score': array([-58.25312869, -58.30869711, -58.89565988]),
'split4_train_score': array([ -4.39368907e-06, -4.35091383e-04, -3.96619155e-02]),
'split5_test_score': array([-34.55649537, -34.61271569, -35.15148114]),
'split5_train_score': array([ -4.79768741e-06, -4.74334047e-04, -4.26642818e-02]),
'split6_test_score': array([-48.89509143, -48.92121206, -49.21661278]),
'split6_train_score': array([ -4.27579707e-06, -4.23581125e-04, -3.87674266e-02]),
'split7_test_score': array([-37.843457 , -37.74098694, -36.80684638]),
'split7_train_score': array([ -4.18314427e-06, -4.14549050e-04, -3.80652817e-02]),
'split8_test_score': array([-49.12264863, -49.14574319, -49.42603306]),
'split8_train_score': array([ -4.42193101e-06, -4.37800204e-04, -3.98496419e-02]),
'split9_test_score': array([-24.66101592, -24.66289001, -24.7145367 ]),
'split9_train_score': array([ -4.89021729e-06, -4.83584192e-04, -4.35798731e-02]),
'std_fit_time': array([ 0.00705221, 0.01158253, 0.00279475]),
'std_score_time': array([ 0.00045855, 0.00030019, 0.0004003 ]),
'std_test_score': array([ 11.77428115, 11.77462622, 11.79882886]),
'std_train_score': array([ 2.79473118e-07, 2.73681039e-05, 2.25174600e-03])}
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 350, in __call__
return self.func(*args, **kwargs)
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 458, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
TypeError: fit() got an unexpected keyword argument 'alpha'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 359, in __call__
raise TransportableException(text, e_type)
sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
TypeError Thu Jun 7 19:32:53 2018
PID: 1372 Python 3.6.4: /home/nazariy/anaconda/envs/logging/bin/python
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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>, (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10}), {'return_times': True, 'return_train_score': True})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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 = (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10})
kwargs = {'return_times': True, 'return_train_score': True}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), X=array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), y=array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), scorer={'score': make_scorer(mean_squared_error, greater_is_better=False)}, train=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), test=array([0]), verbose=3, parameters=None, fit_params={'alpha': 10}, return_train_score=True, return_parameters=False, return_n_test_samples=False, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Ridge.fit of Ridge(alpha=0.1, copy...se, random_state=None, solver='auto', tol=0.001)>
X_train = array([[ 2],
[ 3],
[ 4],
[ ... [ 7],
[ 8],
[ 9],
[10]])
y_train = array([ 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1])
fit_params = {'alpha': 10}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time
TypeError: fit() got an unexpected keyword argument 'alpha'
___________________________________________________________________________
"""
The above exception was the direct cause of the following exception:
TransportableException Traceback (most recent call last)
~/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in retrieve(self)
698 if getattr(self._backend, 'supports_timeout', False):
--> 699 self._output.extend(job.get(timeout=self.timeout))
700 else:
~/anaconda/envs/logging/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
643 else:
--> 644 raise self._value
645
TransportableException: TransportableException
___________________________________________________________________________
TypeError Thu Jun 7 19:32:53 2018
PID: 1372 Python 3.6.4: /home/nazariy/anaconda/envs/logging/bin/python
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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>, (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10}), {'return_times': True, 'return_train_score': True})]
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/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 = (Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), {'score': make_scorer(mean_squared_error, greater_is_better=False)}, array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([0]), 3, None, {'alpha': 10})
kwargs = {'return_times': True, 'return_train_score': True}
132
133 def __len__(self):
134 return self._size
135
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator=Ridge(alpha=0.1, copy_X=True, fit_intercept=Fals...lse, random_state=None, solver='auto', tol=0.001), X=array([[ 1],
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10]]), y=array([ 1. , 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1]), scorer={'score': make_scorer(mean_squared_error, greater_is_better=False)}, train=array([1, 2, 3, 4, 5, 6, 7, 8, 9]), test=array([0]), verbose=3, parameters=None, fit_params={'alpha': 10}, return_train_score=True, return_parameters=False, return_n_test_samples=False, return_times=True, error_score='raise')
453
454 try:
455 if y_train is None:
456 estimator.fit(X_train, **fit_params)
457 else:
--> 458 estimator.fit(X_train, y_train, **fit_params)
estimator.fit = <bound method Ridge.fit of Ridge(alpha=0.1, copy...se, random_state=None, solver='auto', tol=0.001)>
X_train = array([[ 2],
[ 3],
[ 4],
[ ... [ 7],
[ 8],
[ 9],
[10]])
y_train = array([ 3.5, 4. , 4.9, 6.1, 7.2, 8.1, 8.9, 10. , 11.1])
fit_params = {'alpha': 10}
459
460 except Exception as e:
461 # Note fit time as time until error
462 fit_time = time.time() - start_time
TypeError: fit() got an unexpected keyword argument 'alpha'
___________________________________________________________________________
During handling of the above exception, another exception occurred:
{'mean_fit_time': array([ 0.03192089, 0.00980701, 0.00800555]),
'mean_score_time': array([ 0.00030019, 0.00010006, 0.00020015]),
'mean_test_score': array([-39.76136733, -39.7700976 , -39.90061844]),
'mean_train_score': array([ -4.55700050e-06, -4.51109497e-04, -4.10175706e-02]),
'param_alpha': masked_array(data = [0.1 1 10],
mask = [False False False],
fill_value = ?),
'params': [{'alpha': 0.1}, {'alpha': 1}, {'alpha': 10}],
'rank_test_score': array([1, 2, 3]),
'split0_test_score': array([-46.32878735, -46.33132325, -46.42467545]),
'split0_train_score': array([ -4.33377675e-06, -4.29368239e-04, -3.93263016e-02]),
'split1_test_score': array([-23.65685521, -23.70826719, -24.23222395]),
'split1_train_score': array([ -4.71698023e-06, -4.66957024e-04, -4.24618411e-02]),
'split2_test_score': array([-25.10691203, -25.11680407, -25.25664803]),
'split2_train_score': array([ -5.07409398e-06, -5.02011049e-04, -4.53910939e-02]),
'split3_test_score': array([-49.02718917, -48.98855648, -48.69939824]),
'split3_train_score': array([ -4.48268791e-06, -4.43818654e-04, -4.04080484e-02]),
'split4_test_score': array([-58.25312869, -58.30869711, -58.89565988]),
'split4_train_score': array([ -4.39368907e-06, -4.35091383e-04, -3.96619155e-02]),
'split5_test_score': array([-34.55649537, -34.61271569, -35.15148114]),
'split5_train_score': array([ -4.79768741e-06, -4.74334047e-04, -4.26642818e-02]),
'split6_test_score': array([-48.89509143, -48.92121206, -49.21661278]),
'split6_train_score': array([ -4.27579707e-06, -4.23581125e-04, -3.87674266e-02]),
'split7_test_score': array([-37.843457 , -37.74098694, -36.80684638]),
'split7_train_score': array([ -4.18314427e-06, -4.14549050e-04, -3.80652817e-02]),
'split8_test_score': array([-49.12264863, -49.14574319, -49.42603306]),
'split8_train_score': array([ -4.42193101e-06, -4.37800204e-04, -3.98496419e-02]),
'split9_test_score': array([-24.66101592, -24.66289001, -24.7145367 ]),
'split9_train_score': array([ -4.89021729e-06, -4.83584192e-04, -4.35798731e-02]),
'std_fit_time': array([ 0.00705221, 0.01158253, 0.00279475]),
'std_score_time': array([ 0.00045855, 0.00030019, 0.0004003 ]),
'std_test_score': array([ 11.77428115, 11.77462622, 11.79882886]),
'std_train_score': array([ 2.79473118e-07, 2.73681039e-05, 2.25174600e-03])}
---------------------------------------------------------------------------
远程回溯回溯（最近一次呼叫最后一次）
远程回溯：
"""
回溯（最近一次呼叫最后一次）：
文件“/home/nazary/anaconda/envs/logging/lib/python3.6/site packages/sklearn/externals/joblib/_parallel_backends.py”，第350行，在调用中__
返回self.func（*args，**kwargs）
文件“/home/nazary/anaconda/envs/logging/lib/python3.6/site packages/sklearn/externals/joblib/parallel.py”，第131行，in__调用__
返回[func（*args，**kwargs），用于self.items中的func、args、kwargs]
文件“/home/nazary/anaconda/envs/logging/lib/python3.6/site packages/sklearn/externals/joblib/parallel.py”，第131行，在
返回[func（*args，**kwargs），用于self.items中的func、args、kwargs]
文件“/home/nazary/anaconda/envs/logging/lib/python3.6/site packages/sklearn/model_selection/_validation.py”，第458行，in_fit_和_score
估计值拟合（X_序列、y_序列、**拟合参数）
TypeError:fit（）获得意外的关键字参数“alpha”
在处理上述异常期间，发生了另一个异常：
回溯（最近一次呼叫最后一次）：
worker中的文件“/home/nazary/anaconda/envs/logging/lib/python3.6/multiprocessing/pool.py”，第119行
结果=（True，func（*args，**kwds））
文件“/home/nazary/anaconda/envs/logging/lib/python3.6/site packages/sklearn/externals/joblib/_parallel_backends.py”，第359行，在调用中__
引发TransportableException（文本，e_类型）
sklearn.externals.joblib.my_exceptions.TransportableException:TransportableException
___________________________________________________________________________
类型错误Thu Jun 7 19:32:53 2018
PID:1372 Python 3.6.4:/home/nazary/anaconda/envs/logging/bin/Python
...........................................................................
/home/nazariy/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in uuu调用（self=）
126定义初始化（自、迭代器切片）：
127 self.items=列表（迭代器_切片）
128自身尺寸=长度（自身项目）
129
130 def呼叫（自我）：
-->131返回[func（*args，**kwargs），用于self.items中的func、args、kwargs]
self.items=[（，（脊线（alpha=0.1，copy\u X=True，fit\u intercept=Fals…lse，random\u state=None，solver=auto'，tol=0.001），数组（[[1]，
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10] ]），数组（[1,3.5,4,4.9,6.1,7.2,8.1,8.9,10,11.1]），{'score'：make_scorer（均方误差，越大越好=假）}，数组（[1,2,3,4,5,6,7,8,9]），数组（[0]），3，无，{'alpha 10}，{'return times'：True，'return train_score'：True}]
132
133定义长度（自）：
134返回自我。\u尺寸
135
...........................................................................
/home/nazary/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in（.0=）
126定义初始化（自、迭代器切片）：
127 self.items=列表（迭代器_切片）
128自身尺寸=长度（自身项目）
129
130 def呼叫（自我）：
-->131返回[func（*args，**kwargs），用于self.items中的func、args、kwargs]
func=
args=（脊线（alpha=0.1，copy\u X=True，fit\u intercept=Fals…lse，random\u state=None，solver='auto'，tol=0.001），数组（[[1]，
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10] 数组（[1,3.5,4,4.9,6.1,7.2,8.1,8.9,10,11.1]），{'score'：make_scorer（均方误差，越大越好=假）}，数组（[1,2,3,4,5,6,7,8,9]），数组（[0]），3，无，{'alpha:10}）
kwargs={'return\u times'：True，'return\u train\u score'：True}
132
133定义长度（自）：
134返回自我。\u尺寸
135
...........................................................................
/home/nazary/anaconda/envs/logging/lib/python3.6/site-packages/sklearn/model\u selection//u validation.py in\u fit\u and\u score（估计器=Ridge（alpha=0.1，copy\u X=True，fit\u intercept=Fals…lse，random\u state=None，solver='auto'，tol=0.001），X=array（[1]，
[ 2],
[ 3],
[ ... [ 7],
[ 8],
[ 9],
[10] 】），y=array（[1,3.5,4,4.9,6.1,7.2,8.1,8.9,10,11.1]），scorer={'score'：make_scorer（均方误差，越大越好=假）}，train=array（[1,2,3,4,5,6,7,8,9]），test=array（[0]），verbose=3，参数=无，拟合参数={'alpha 10}，return\u train\u score=True，return\u parameters=False，return\u test\u samples=False，return\u times=True，error\u score='raise'）
453
454试试：
455如果y_列车无：
456估算器拟合（X列，**拟合参数）
457其他：
-->458估算器拟合（X_序列、y_序列、**拟合参数）
估计值.fit=
X_列=数组（[[2]，
[ 3],
[ 4],
[ ... [ 7],
[ 8],
[ 9],
[10]])
y_序列=阵列（[3.5,4。