class_docstr: Container class for a stack of sequentially processed estimators.
  
  The Sequential class stories all layers as an ordered dictionary
  and modifies possesses a ``get_params`` method to appear as an estimator
  in the Scikit-learn API. This allows correct cloning and parameter
  updating.
  
  
  Parameters
  ----------
  stack: list, optional (default = None)
      list of estimators (i.e. layers) to build instance with.
  
  n_jobs : int (default = -1)
      Degree of concurrency. Set ``n_jobs = -1`` for maximal parallelism and
      ``n_jobs=1`` for sequential processing.
  
  backend : str, (default="threading")
      the joblib backend to use (i.e. "multiprocessing" or "threading").
  
  raise_on_exception : bool (default = False)
      raise error on soft exceptions. Otherwise issue warning.
  
  verbose : int or bool (default = False)
      level of verbosity.
  
          - ``verbose = 0`` silent (same as ``verbose = False``)
          - ``verbose = 1`` messages at start and finish
            (same as ``verbose = True``)
          - ``verbose = 2`` messages for each layer
          - etc
  
      If ``verbose >= 10`` prints to ``sys.stderr``, else ``sys.stdout``.
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docstr: Ensemble data
docstr_tok: ["Ensemble","data"]
code: def data(self):
      """Ensemble data"""
      out = list()
      for layer in self.stack:
          d = layer.raw_data
          if not d:
              continue
          out.extend([("%s/%s" % (layer.name, k), v) for k, v in d])
      return Data(out)
  
imports: from __future__ import division, print_function, with_statement
  from abc import ABCMeta, abstractmethod
  import warnings
  from ..None import config
  from ..parallel import Layer, ParallelProcessing, make_group
  from ..parallel.base import BaseStacker
  from ..externals.sklearn.validation import check_random_state
  from ..utils import check_ensemble_build, print_time, safe_print, IdTrain, format_name
  from ..utils.exceptions import LayerSpecificationWarning, NotFittedError, NotInitializedError
  from ..metrics import Data
  from ..externals.sklearn.base import BaseEstimator, clone
label_desc: Use image node_id 8 for calling the Sequential obj's underlying member method code with example usage: obj.data() and returns: Data