original_docstring: 
    Takes model and trains it on 0.zip...4.zip, then predicts on 5.zip.
    Writes predictions out as a valid submission identified by submit_id.
    
    Args:
      model - the model to train and test.
      infile_base - bare input data name without path or extension
      pases - number of passes over data in training
      bits - the feature space shoul be of dimension 2**bits
      submit_id - the result is written as submissions/submission_<submit_id>.csv
  
    Writes:
      A submission at paths.SUBMIT/submisssion_<submit_id>.csv
    
docstring: 
  
  the model to train and test.
  infile_base - bare input data name without path or extension
  pases - number of passes over data in training
  bits - the feature space shoul be of dimension 2**bits
  submit_id - the result is written as submissions/submission_.csv
  
  
docstring_tokens: the model to train and test . infile_base - bare input data name without path or extension pases - number of passes over data in training bits - the feature space shoul be of dimension 2 ** bits submit_id - the result is written as submissions / submission_ . csv
code: def run_all(model, infile_base, passes, bits, submit_id):
    '''
    Takes model and trains it on 0.zip...4.zip, then predicts on 5.zip.
    Writes predictions out as a valid submission identified by submit_id.
    
    Args:
      model - the model to train and test.
      infile_base - bare input data name without path or extension
      pases - number of passes over data in training
      bits - the feature space shoul be of dimension 2**bits
      submit_id - the result is written as submissions/submission_<submit_id>.csv
  
    Writes:
      A submission at paths.SUBMIT/submisssion_<submit_id>.csv
    '''
    train(model, infile_base, passes, bits)
    pred = test(model, infile_base, bits)
    util.write_submission(pred, submit_id)
code_tokens: def run_all ( model , infile_base , passes , bits , submit_id ) : train ( model , infile_base , passes , bits ) pred = test ( model , infile_base , bits ) util . write_submission ( pred , submit_id )
short_docstring: Takes model and trains it on 0.zip...4.zip, then predicts on 5.zip.
short_docstring_tokens: Takes model and trains it on 0 . zip ... 4 . zip then predicts on 5 . zip .
comment: '''
    Takes model and trains it on 0.zip...4.zip, then predicts on 5.zip.
    Writes predictions out as a valid submission identified by submit_id.
    
    Args:
      model - the model to train and test.
      infile_base - bare input data name without path or extension
      pases - number of passes over data in training
      bits - the feature space shoul be of dimension 2**bits
      submit_id - the result is written as submissions/submission_<submit_id>.csv
  
    Writes:
      A submission at paths.SUBMIT/submisssion_<submit_id>.csv
    '''