Scikit Learn: Randomized Logistic Regression gives ValueError: Output array is read-only
I am trying to match the randomized logistic regression with my data and I cannot proceed. Here is the code:
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)
This gives an error:
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
Can anyone point out what should I do to proceed?
Many thanks.
Hendra
End tracking on request:
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
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3 answers
I got the same error when running a feature on a 32 CPU Ubuntu server. Even though the problem persisted in n_jobs values above 1, it went away when setting n_jobs to the default, i.e. 1. [as described by benbo]
This is a bug in RandomizedLogisticRegression where multiple memory accesses to the same object block access to each other.
Check out the sklearn glyub page, they fix this issue and can be fixed in depth: https://github.com/scikit-learn/scikit-learn/issues/4597
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