Scikit 자세히 알아보기 : 무작위 로지스틱 회귀가 ValueError를 제공합니다 : 출력 배열이 읽기 전용

Question

무작위 로지스틱 회귀 분석을 내 데이터에 적합 시키려고 시도 할 수 없습니다. 이 오류를 제공

import numpy as np  
X = np.load("X.npy") 
y = np.load("y.npy") 

randomized_LR = RandomizedLogisticRegression(C=0.1, verbose=True, n_jobs=3) 
randomized_LR.fit(X, y) 

: 여기 코드입니다

344  if issparse(X): 
    345   size = len(weights) 
    346   weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) 
    347   X = X * weight_dia 
    348  else: 
--> 349   X *= (1 - weights) 
    350 
    351  C = np.atleast_1d(np.asarray(C, dtype=np.float)) 
    352  scores = np.zeros((X.shape[1], len(C)), dtype=np.bool) 
    353 

ValueError: output array is read-only 

누군가가 내가하시기 바랍니다 진행해야 할 일 지적 수 있을까요? 요청에 따라

는

헨드라

전체 역 추적 유 대단히 감사 :

Traceback (most recent call last): 
    File "temp.py", line 88, in <module> 
    train_randomized_logistic_regression() 
    File "temp.py", line 82, in train_randomized_logistic_regression 
randomized_LR.fit(X, y) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 110, in fit 
sample_fraction=self.sample_fraction, **params) 
File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py", line 281, in __call__ 
return self.func(*args, **kwargs) 
File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 52, in _resample_model 
for _ in range(n_resampling)): 
File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 660, in __call__ 
self.retrieve() 
File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 543, in retrieve 
raise exception_type(report) 
sklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError 
___________________________________________________________________________ 
Multiprocessing exception: 
........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in <module>() 
    83 
    84 
    85 
    86 if __name__ == '__main__': 
    87 
---> 88  train_randomized_logistic_regression() 
    89 
    90 
    91 
    92 

........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in train_randomized_logistic_regression() 
    77  X = np.load('data/issuemakers/features/new_X.npy') 
    78  y = np.load('data/issuemakers/features/new_y.npy') 
    79 
    80  randomized_LR = RandomizedLogisticRegression(C=0.1, n_jobs=32) 
    81 
---> 82  randomized_LR.fit(X, y) 
    randomized_LR.fit = <bound method RandomizedLogisticRegression.fit o...d=0.25, 
      tol=0.001, verbose=False)> 
    X = array([[ 1.01014900e+06, 7.29970000e+04, 2....460000e+04, 3.11428571e+01, 1.88100000e+03]]) 
    y = array([1, 1, 1, ..., 0, 1, 1]) 
    83 
    84 
    85 
    86 if __name__ == '__main__': 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in fit(self=RandomizedLogisticRegression(C=0.1, fit_intercep...ld=0.25, 
      tol=0.001, verbose=False), X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1])) 
    105  )(
    106    estimator_func, X, y, 
    107    scaling=self.scaling, n_resampling=self.n_resampling, 
    108    n_jobs=self.n_jobs, verbose=self.verbose, 
    109    pre_dispatch=self.pre_dispatch, random_state=self.random_state, 
--> 110    sample_fraction=self.sample_fraction, **params) 
    self.sample_fraction = 0.75 
    params = {'C': 0.1, 'fit_intercept': True, 'tol': 0.001} 
    111 
    112   if scores_.ndim == 1: 
    113    scores_ = scores_[:, np.newaxis] 
    114   self.all_scores_ = scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py in __call__(self=NotMemorizedFunc(func=<function _resample_model at 0x7fb5d7d12b18>), *args=(<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])), **kwargs={'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False}) 
    276  # Should be a light as possible (for speed) 
    277  def __init__(self, func): 
    278   self.func = func 
    279 
    280  def __call__(self, *args, **kwargs): 
--> 281   return self.func(*args, **kwargs) 
    self.func = <function _resample_model> 
    args = (<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])) 
    kwargs = {'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False} 
282 
283  def call_and_shelve(self, *args, **kwargs): 
284   return NotMemorizedResult(self.func(*args, **kwargs)) 
285 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in _resample_model(estimator_func=<function _randomized_logistic>, X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1]), scaling=0.5, n_resampling=200, n_jobs=32, verbose=False, pre_dispatch='3*n_jobs', random_state=<mtrand.RandomState object>, sample_fraction=0.75, **params={'C': 0.1, 'fit_intercept': True, 'tol': 0.001}) 
    47     X, y, weights=scaling * random_state.random_integers(
    48      0, 1, size=(n_features,)), 
    49     mask=(random_state.rand(n_samples) < sample_fraction), 
    50     verbose=max(0, verbose - 1), 
    51     **params) 
---> 52    for _ in range(n_resampling)): 
    n_resampling = 200 
    53   scores_ += active_set 
    54 
    55  scores_ /= n_resampling 
    56  return scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=32), iterable=<itertools.islice object>) 
    655    if pre_dispatch == "all" or n_jobs == 1: 
    656     # The iterable was consumed all at once by the above for loop. 
    657     # No need to wait for async callbacks to trigger to 
    658     # consumption. 
    659     self._iterating = False 
--> 660    self.retrieve() 
    self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=32)> 
    661    # Make sure that we get a last message telling us we are done 
    662    elapsed_time = time.time() - self._start_time 
    663    self._print('Done %3i out of %3i | elapsed: %s finished', 
    664       (len(self._output), 

--------------------------------------------------------------------------- 
Sub-process traceback: 
--------------------------------------------------------------------------- 
ValueError           Fri Jan 2 12:13:54 2015 
PID: 126664    Python 2.7.8: /home/hbunyam1/anaconda/bin/python 
........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.pyc in _randomized_logistic(X=memmap([[ 6.93135506e-04, 8.93676615e-04, -1...234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1]), weights=array([ 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. ,... 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0.5]), mask=array([ True, True, True, ..., True, True, True], dtype=bool), C=0.1, verbose=0, fit_intercept=True, tol=0.001) 
    344  if issparse(X): 
    345   size = len(weights) 
    346   weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) 
    347   X = X * weight_dia 
    348  else: 
--> 349   X *= (1 - weights) 
    350 
    351  C = np.atleast_1d(np.asarray(C, dtype=np.float)) 
    352  scores = np.zeros((X.shape[1], len(C)), dtype=np.bool) 
    353 

ValueError: output array is read-only 
___________________________________________________________________________ 







Traceback (most recent call last): 
    File "temp.py", line 88, in <module> 
    train_randomized_logistic_regression() 
    File "temp.py", line 82, in train_randomized_logistic_regression 
    randomized_LR.fit(X, y) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 110, in fit 
    sample_fraction=self.sample_fraction, **params) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py", line 281, in __call__ 
    return self.func(*args, **kwargs) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 52, in _resample_model 
    for _ in range(n_resampling)): 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 660, in __call__ 
    self.retrieve() 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 543, in retrieve 
    raise exception_type(report) 
sklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError 
___________________________________________________________________________ 
Multiprocessing exception: 
    ........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in <module>() 
    83 
    84 
    85 
    86 if __name__ == '__main__': 
    87 
---> 88  train_randomized_logistic_regression() 
    89 
    90 
    91 
    92 

........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in train_randomized_logistic_regression() 
    77  X = np.load('data/issuemakers/features/new_X.npy') 
    78  y = np.load('data/issuemakers/features/new_y.npy') 
    79 
    80  randomized_LR = RandomizedLogisticRegression(C=0.1, n_jobs=32) 
    81 
---> 82  randomized_LR.fit(X, y) 
     randomized_LR.fit = <bound method RandomizedLogisticRegression.fit o...d=0.25, 
       tol=0.001, verbose=False)> 
     X = array([[ 1.01014900e+06, 7.29970000e+04, 2....460000e+04, 3.11428571e+01, 1.88100000e+03]]) 
     y = array([1, 1, 1, ..., 0, 1, 1]) 
    83 
    84 
    85 
    86 if __name__ == '__main__': 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in fit(self=RandomizedLogisticRegression(C=0.1, fit_intercep...ld=0.25, 
       tol=0.001, verbose=False), X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1])) 
    105  )(
    106    estimator_func, X, y, 
    107    scaling=self.scaling, n_resampling=self.n_resampling, 
    108    n_jobs=self.n_jobs, verbose=self.verbose, 
    109    pre_dispatch=self.pre_dispatch, random_state=self.random_state, 
--> 110    sample_fraction=self.sample_fraction, **params) 
     self.sample_fraction = 0.75 
     params = {'C': 0.1, 'fit_intercept': True, 'tol': 0.001} 
    111 
    112   if scores_.ndim == 1: 
    113    scores_ = scores_[:, np.newaxis] 
    114   self.all_scores_ = scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py in __call__(self=NotMemorizedFunc(func=<function _resample_model at 0x7fb5d7d12b18>), *args=(<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])), **kwargs={'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False}) 
    276  # Should be a light as possible (for speed) 
    277  def __init__(self, func): 
    278   self.func = func 
    279 
    280  def __call__(self, *args, **kwargs): 
--> 281   return self.func(*args, **kwargs) 
     self.func = <function _resample_model> 
     args = (<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])) 
     kwargs = {'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False} 
    282 
    283  def call_and_shelve(self, *args, **kwargs): 
    284   return NotMemorizedResult(self.func(*args, **kwargs)) 
    285 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in _resample_model(estimator_func=<function _randomized_logistic>, X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1]), scaling=0.5, n_resampling=200, n_jobs=32, verbose=False, pre_dispatch='3*n_jobs', random_state=<mtrand.RandomState object>, sample_fraction=0.75, **params={'C': 0.1, 'fit_intercept': True, 'tol': 0.001}) 
    47     X, y, weights=scaling * random_state.random_integers(
    48      0, 1, size=(n_features,)), 
    49     mask=(random_state.rand(n_samples) < sample_fraction), 
    50     verbose=max(0, verbose - 1), 
    51     **params) 
---> 52    for _ in range(n_resampling)): 
     n_resampling = 200 
    53   scores_ += active_set 
    54 
    55  scores_ /= n_resampling 
    56  return scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=32), iterable=<itertools.islice object>) 
    655    if pre_dispatch == "all" or n_jobs == 1: 
    656     # The iterable was consumed all at once by the above for loop. 
    657     # No need to wait for async callbacks to trigger to 
    658     # consumption. 
    659     self._iterating = False 
--> 660    self.retrieve() 
     self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=32)> 
    661    # Make sure that we get a last message telling us we are done 
    662    elapsed_time = time.time() - self._start_time 
    663    self._print('Done %3i out of %3i | elapsed: %s finished', 
    664       (len(self._output), 

    --------------------------------------------------------------------------- 
    Sub-process traceback: 
    --------------------------------------------------------------------------- 
    ValueError           Fri Jan 2 12:13:54 2015 
PID: 126664    Python 2.7.8: /home/hbunyam1/anaconda/bin/python 
........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.pyc in _randomized_logistic(X=memmap([[ 6.93135506e-04, 8.93676615e-04, -1...234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1]), weights=array([ 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. ,... 0. , 0. , 0.5, 0. , 0. , 0. , 0. , 0.5]), mask=array([ True, True, True, ..., True, True, True], dtype=bool), C=0.1, verbose=0, fit_intercept=True, tol=0.001) 
    344  if issparse(X): 
    345   size = len(weights) 
    346   weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) 
    347   X = X * weight_dia 
    348  else: 
--> 349   X *= (1 - weights) 
    350 
    351  C = np.atleast_1d(np.asarray(C, dtype=np.float)) 
    352  scores = np.zeros((X.shape[1], len(C)), dtype=np.bool) 
    353 

ValueError: output array is read-only 
___________________________________________________________________________ 
[hbunyam1@zookst20 social_graph]$ python temp.py 
Traceback (most recent call last): 
    File "temp.py", line 88, in <module> 
    train_randomized_logistic_regression() 
    File "temp.py", line 82, in train_randomized_logistic_regression 
    randomized_LR.fit(X, y) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 110, in fit 
    sample_fraction=self.sample_fraction, **params) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py", line 281, in __call__ 
    return self.func(*args, **kwargs) 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py", line 52, in _resample_model 
    for _ in range(n_resampling)): 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 660, in __call__ 
    self.retrieve() 
    File "/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 543, in retrieve 
    raise exception_type(report) 
sklearn.externals.joblib.my_exceptions.JoblibValueError: JoblibValueError 
___________________________________________________________________________ 
Multiprocessing exception: 
    ........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in <module>() 
    83 
    84 
    85 
    86 if __name__ == '__main__': 
    87 
---> 88  train_randomized_logistic_regression() 
    89 
    90 
    91 
    92 

........................................................................... 
/zfs/ilps-plexest/homedirs/hbunyam1/social_graph/temp.py in train_randomized_logistic_regression() 
    77  X = np.load('data/issuemakers/features/new_X.npy', mmap_mode='r+') 
    78  y = np.load('data/issuemakers/features/new_y.npy', mmap_mode='r+') 
    79 
    80  randomized_LR = RandomizedLogisticRegression(C=0.1, n_jobs=32) 
    81 
---> 82  randomized_LR.fit(X, y) 
     randomized_LR.fit = <bound method RandomizedLogisticRegression.fit o...d=0.25, 
       tol=0.001, verbose=False)> 
     X = memmap([[ 1.01014900e+06, 7.29970000e+04, 2...460000e+04, 3.11428571e+01, 1.88100000e+03]]) 
     y = memmap([1, 1, 1, ..., 0, 1, 1]) 
    83 
    84 
    85 
    86 if __name__ == '__main__': 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in fit(self=RandomizedLogisticRegression(C=0.1, fit_intercep...ld=0.25, 
       tol=0.001, verbose=False), X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1])) 
    105  )(
    106    estimator_func, X, y, 
    107    scaling=self.scaling, n_resampling=self.n_resampling, 
    108    n_jobs=self.n_jobs, verbose=self.verbose, 
    109    pre_dispatch=self.pre_dispatch, random_state=self.random_state, 
--> 110    sample_fraction=self.sample_fraction, **params) 
     self.sample_fraction = 0.75 
     params = {'C': 0.1, 'fit_intercept': True, 'tol': 0.001} 
    111 
    112   if scores_.ndim == 1: 
    113    scores_ = scores_[:, np.newaxis] 
    114   self.all_scores_ = scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/memory.py in __call__(self=NotMemorizedFunc(func=<function _resample_model at 0x7f192c829b18>), *args=(<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])), **kwargs={'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False}) 
    276  # Should be a light as possible (for speed) 
    277  def __init__(self, func): 
    278   self.func = func 
    279 
    280  def __call__(self, *args, **kwargs): 
--> 281   return self.func(*args, **kwargs) 
     self.func = <function _resample_model> 
     args = (<function _randomized_logistic>, array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), array([1, 1, 1, ..., 0, 1, 1])) 
     kwargs = {'C': 0.1, 'fit_intercept': True, 'n_jobs': 32, 'n_resampling': 200, 'pre_dispatch': '3*n_jobs', 'random_state': None, 'sample_fraction': 0.75, 'scaling': 0.5, 'tol': 0.001, 'verbose': False} 
    282 
    283  def call_and_shelve(self, *args, **kwargs): 
    284   return NotMemorizedResult(self.func(*args, **kwargs)) 
    285 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.py in _resample_model(estimator_func=<function _randomized_logistic>, X=array([[ 6.93135506e-04, 8.93676615e-04, -1....234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=array([1, 1, 1, ..., 0, 1, 1]), scaling=0.5, n_resampling=200, n_jobs=32, verbose=False, pre_dispatch='3*n_jobs', random_state=<mtrand.RandomState object>, sample_fraction=0.75, **params={'C': 0.1, 'fit_intercept': True, 'tol': 0.001}) 
    47     X, y, weights=scaling * random_state.random_integers(
    48      0, 1, size=(n_features,)), 
    49     mask=(random_state.rand(n_samples) < sample_fraction), 
    50     verbose=max(0, verbose - 1), 
    51     **params) 
---> 52    for _ in range(n_resampling)): 
     n_resampling = 200 
    53   scores_ += active_set 
    54 
    55  scores_ /= n_resampling 
    56  return scores_ 

........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=32), iterable=<itertools.islice object>) 
    655    if pre_dispatch == "all" or n_jobs == 1: 
    656     # The iterable was consumed all at once by the above for loop. 
    657     # No need to wait for async callbacks to trigger to 
    658     # consumption. 
    659     self._iterating = False 
--> 660    self.retrieve() 
     self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=32)> 
    661    # Make sure that we get a last message telling us we are done 
    662    elapsed_time = time.time() - self._start_time 
    663    self._print('Done %3i out of %3i | elapsed: %s finished', 
    664       (len(self._output), 

    --------------------------------------------------------------------------- 
    Sub-process traceback: 
    --------------------------------------------------------------------------- 
    ValueError           Fri Jan 2 12:57:25 2015 
PID: 127177    Python 2.7.8: /home/hbunyam1/anaconda/bin/python 
........................................................................... 
/home/hbunyam1/anaconda/lib/python2.7/site-packages/sklearn/linear_model/randomized_l1.pyc in _randomized_logistic(X=memmap([[ 6.93135506e-04, 8.93676615e-04, -1...234095e-04, -1.19037488e-04, 4.20921021e-04]]), y=memmap([1, 1, 1, ..., 0, 0, 1]), weights=array([ 0.5, 0.5, 0. , 0.5, 0.5, 0.5, 0.5,... 0. , 0.5, 0. , 0. , 0.5, 0.5, 0.5, 0.5]), mask=array([ True, True, True, ..., False, False, True], dtype=bool), C=0.1, verbose=0, fit_intercept=True, tol=0.001) 
    344  if issparse(X): 
    345   size = len(weights) 
    346   weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) 
    347   X = X * weight_dia 
    348  else: 
--> 349   X *= (1 - weights) 
    350 
    351  C = np.atleast_1d(np.asarray(C, dtype=np.float)) 
    352  scores = np.zeros((X.shape[1], len(C)), dtype=np.bool) 
    353 

ValueError: output array is read-only 
___________________________________________________________________________ 

Answer 1

당신은 numpy.load의 documentation에 따라 np.load('X.npy', mmap_mode='r+')를 사용 할 수 있습니다.

Answer 2

작업 수를 변경하십시오. 처음에는 1로 변경하십시오. n_jobs = 20 (강력한 컴퓨터에서)으로 RandomizedLogisticRegression을 실행할 때도 같은 오류가 발생했습니다. 그러나 n_jobs를 기본 1로 설정하면 코드가 문제없이 실행되었습니다.

Answer 3

32 개의 프로세서 우분투 서버에서이 기능을 실행할 때 동일한 오류가 발생했습니다. 문제가 1보다 큰 n_jobs 값을 유지하는 동안 n_jobs 값을 기본값 인 1로 설정할 때 사라졌습니다. [benbo 설명대로]

이것은 RandomizedLogisticRegression의 버그로, 동일한 메모리에 여러 번 액세스합니다 오브젝트 블록은 서로 액세스하지 못하게합니다.

sklearn의 GitHub의 페이지를 참조하십시오, 그들은 깊이이 문제와 가능한 해결 주소 : https://github.com/scikit-learn/scikit-learn/issues/4597