class_docstr: Parallel processing base class.
  
  Base class for parallel processing engines.
  
  Parameters
  ----------
  backend: str, optional
      Type of backend. One of ``'threading'``, ``'multiprocessing'``,
      ``'sequential'``.
  
  n_jobs : int, optional
      Degree of concurrency.
  
  verbose: bool, int, optional
      Level of verbosity of the
      :class:`~mlens.externals.joblib.parallel.Parallel` instance.
class_docstr_tok: ["Parallel","processing","base","class",".","Base","class","for","parallel","processing","engines",".","Parameters","--","--","--","--","--","backend",":","str",",","optional","Type","of","backend",".","One","of","``","'threading","'","``",",","``","'multiprocessing","'","``",",","``","'sequential","'","``",".","n_jobs",":","int",",","optional","Degree","of","concurrency",".","verbose",":","bool",",","int",",","optional","Level","of","verbosity","of","the",":","class",":","`","~mlens.externals.joblib.parallel.Parallel","`","instance","."]
docstr: Initialize processing engine.
  
  Set up the job parameters before an estimation call. Calling
  :func:`~mlens.parallel.backend.BaseProcessor.clear`
  undoes initialization.
  
  Parameters
  ----------
  job : str
      type of job to complete with each task. One of ``'fit'``,
      ``'predict'`` and ``'transform'``.
  
  X : array-like of shape [n_samples, n_features]
      Input data
  
  y : array-like of shape [n_samples,], optional.
      targets. Required for fit, should not be passed to predict or
      transform jobs.
  
  path : str or dict, optional
      Custom estimation cache. Pass a string to force use of persistent
      cache on disk. Pass a ``dict`` for in-memory cache (requires
      ``backend != 'multiprocessing'``.
  
  return_preds : bool or list, optional
      whether to return prediction ouput. If ``True``, final prediction
      is returned. Alternatively, pass a list of task names for which
      output should be returned.
  
  warm_start : bool, optional
      whether to re-use previous input data initialization. Useful if
      repeated jobs are made on the same input arrays.
  
  **kwargs  : optional
      optional keyword arguments to pass onto the task's call method.
  
  Returns
  -------
  out : dict
      An output parameter dictionary to pass to pass to an estimation
      method. Either ``None`` (no output), or
      ``{'final':True}`` for only final prediction, or
      ``{'final': False, 'return_names': return_preds}`` if a list of
      task-specific output was passed.
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code: def initialize(
      self,
      job,
      X,
      y,
      path,
      warm_start=False,
      return_preds=False,
      **kwargs,
  ):
      """Initialize processing engine.
  
      Set up the job parameters before an estimation call. Calling
      :func:`~mlens.parallel.backend.BaseProcessor.clear`
      undoes initialization.
  
      Parameters
      ----------
      job : str
          type of job to complete with each task. One of ``'fit'``,
          ``'predict'`` and ``'transform'``.
  
      X : array-like of shape [n_samples, n_features]
          Input data
  
      y : array-like of shape [n_samples,], optional.
          targets. Required for fit, should not be passed to predict or
          transform jobs.
  
      path : str or dict, optional
          Custom estimation cache. Pass a string to force use of persistent
          cache on disk. Pass a ``dict`` for in-memory cache (requires
          ``backend != 'multiprocessing'``.
  
      return_preds : bool or list, optional
          whether to return prediction ouput. If ``True``, final prediction
          is returned. Alternatively, pass a list of task names for which
          output should be returned.
  
      warm_start : bool, optional
          whether to re-use previous input data initialization. Useful if
          repeated jobs are made on the same input arrays.
  
      **kwargs  : optional
          optional keyword arguments to pass onto the task's call method.
  
      Returns
      -------
      out : dict
          An output parameter dictionary to pass to pass to an estimation
          method. Either ``None`` (no output), or
          ``{'final':True}`` for only final prediction, or
          ``{'final': False, 'return_names': return_preds}`` if a list of
          task-specific output was passed.
      """
      if not warm_start:
          self._initialize(job=job, X=X, y=y, path=path, **kwargs)
  
      if return_preds is True:
          return {"return_final": True}
      if return_preds is False:
          return {}
      return {"return_final": False, "return_names": return_preds}
  
imports: from __future__ import with_statement, division
  import gc
  import os
  import shutil
  import subprocess
  import tempfile
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse, hstack
  from ..None import config
  from ..externals.joblib import Parallel, dump, load
  from ..utils import check_initialized
  from ..utils.exceptions import ParallelProcessingError, ParallelProcessingWarning
  from ..externals.sklearn.validation import check_random_state
label_desc: Use image node_id 3 for calling the BaseProcessor obj's underlying member method code with example usage: obj.initialize(job, X, y, path, warm_start, return_preds) and returns: dict, dict, dict