title: Scikit Learn: Randomized Logistic Regression gives ValueError: output array is read-only
body: <p>I try to fit Randomized Logistic Regression with my data and I cannot proceed.
  Here is the code:</p>
  
  <pre><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)
  </code></pre>
  
  <p>This gives an error:</p>
  
  <pre><code>    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:
  --&gt; 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
  </code></pre>
  
  <p>Could someone point out what I should do to proceed please?</p>
  
  <p>Thank u very much</p>
  
  <p>Hendra</p>
  
  <p>Complete Traceback as requested:</p>
  
  <pre><code>Traceback (most recent call last):
    File "temp.py", line 88, in &lt;module&gt;
    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 &lt;module&gt;()
       83
       84
       85
       86 if __name__ == '__main__':
       87
  ---&gt; 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
  ---&gt; 82     randomized_LR.fit(X, y)
      randomized_LR.fit = &lt;bound method RandomizedLogisticRegression.fit o...d=0.25,
             tol=0.001, verbose=False)&gt;
      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,
  --&gt; 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=&lt;function _resample_model at 0x7fb5d7d12b18&gt;), *args=(&lt;function _randomized_logistic&gt;, 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):
  --&gt; 281         return self.func(*args, **kwargs)
      self.func = &lt;function _resample_model&gt;
      args = (&lt;function _randomized_logistic&gt;, 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=&lt;function _randomized_logistic&gt;, 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=&lt;mtrand.RandomState object&gt;, 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) &lt; sample_fraction),
       50                 verbose=max(0, verbose - 1),
       51                 **params)
  ---&gt; 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=&lt;itertools.islice object&gt;)
      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
  --&gt; 660             self.retrieve()
      self.retrieve = &lt;bound method Parallel.retrieve of Parallel(n_jobs=32)&gt;
      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:
  --&gt; 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 &lt;module&gt;
      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 &lt;module&gt;()
       83
       84
       85
       86 if __name__ == '__main__':
       87
  ---&gt; 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
  ---&gt; 82     randomized_LR.fit(X, y)
          randomized_LR.fit = &lt;bound method RandomizedLogisticRegression.fit o...d=0.25,
                 tol=0.001, verbose=False)&gt;
          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,
  --&gt; 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=&lt;function _resample_model at 0x7fb5d7d12b18&gt;), *args=(&lt;function _randomized_logistic&gt;, 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):
  --&gt; 281         return self.func(*args, **kwargs)
          self.func = &lt;function _resample_model&gt;
          args = (&lt;function _randomized_logistic&gt;, 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=&lt;function _randomized_logistic&gt;, 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=&lt;mtrand.RandomState object&gt;, 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) &lt; sample_fraction),
       50                 verbose=max(0, verbose - 1),
       51                 **params)
  ---&gt; 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=&lt;itertools.islice object&gt;)
      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
  --&gt; 660             self.retrieve()
          self.retrieve = &lt;bound method Parallel.retrieve of Parallel(n_jobs=32)&gt;
      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:
  --&gt; 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 &lt;module&gt;
      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 &lt;module&gt;()
       83
       84
       85
       86 if __name__ == '__main__':
       87
  ---&gt; 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
  ---&gt; 82     randomized_LR.fit(X, y)
          randomized_LR.fit = &lt;bound method RandomizedLogisticRegression.fit o...d=0.25,
                 tol=0.001, verbose=False)&gt;
          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,
  --&gt; 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=&lt;function _resample_model at 0x7f192c829b18&gt;), *args=(&lt;function _randomized_logistic&gt;, 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):
  --&gt; 281         return self.func(*args, **kwargs)
          self.func = &lt;function _resample_model&gt;
          args = (&lt;function _randomized_logistic&gt;, 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=&lt;function _randomized_logistic&gt;, 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=&lt;mtrand.RandomState object&gt;, 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) &lt; sample_fraction),
       50                 verbose=max(0, verbose - 1),
       51                 **params)
  ---&gt; 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=&lt;itertools.islice object&gt;)
      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
  --&gt; 660             self.retrieve()
          self.retrieve = &lt;bound method Parallel.retrieve of Parallel(n_jobs=32)&gt;
      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:
  --&gt; 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
  ___________________________________________________________________________
  </code></pre>