class_docstr: Temporal ensemble class.
  
  The temporal ensemble class uses a time series cross-validation
  strategy to create training and test folds that preserve temporal
  ordering in the data. The cross validation strategy is unrolled
  through time. For instance:
  
  ====  =================  ==========
  fold  train obs          test obs
  ====  =================  ==========
  0     0, 1, 2, 3         4
  1     0, 1, 2, 3, 4      5
  2     0, 1, 2, 3, 4, 5   6
  ====  =================  ==========
  
  Different estimators in the ensemble can operate on different time scales,
  allow efficient combinations of different temporal patterns in one model.
  
  See Also
  --------
  :class:`SuperLearner`, :class:`BlendEnsemble`, :class:`SequentialEnsemble`
  
  
  .. note :: All parameters can be overriden in the :attr:`add` method unless
      otherwise specified. Notably, the ``backend`` and ``n_jobs`` cannot
      be altered in the :attr:`add` method.
  
  Parameters
  ----------
  step_size : int (default=1)
      number of samples to use in each test fold. The final window
      size may be smaller if too few observations remain.
  
  burn_in : int (default=None)
      number of samples to use for first training fold. These observations
      will be dropped from the output. Defaults to ``step_size``.
  
  window: int (default=None)
      number of previous samples to use in each training fold, except first
      which is determined by ``burn_in``. If ``None``, will use all previous
      observations.
  
  lag: int (default=0)
      distance between the most recent training point in the training fold and
      the first test point. For ``lag>0``, the training fold and the test fold
      will not be contiguous.
  
  scorer : object (default = None)
      scoring function. If a function is provided, base estimators will be
      scored on the training set assembled for fitting the meta estimator.
      Since those predictions are out-of-sample, the scores represent valid
      test scores. The scorer should be a function that accepts an array of
      true values and an array of predictions: ``score = f(y_true, y_pred)``.
  
  raise_on_exception : bool (default = True)
      whether to issue warnings on soft exceptions or raise error.
      Examples include lack of layers, bad inputs, and failed fit of an
      estimator in a layer. If set to ``False``, warnings are issued instead
      but estimation continues unless exception is fatal. Note that this
      can result in unexpected behavior unless the exception is anticipated.
  
  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
  
      If ``verbose >= 50`` prints to ``sys.stdout``, else ``sys.stderr``.
      For verbosity in the layers themselves, use ``fit_params``.
  
  n_jobs : int (default = -1)
      Degree of parallel processing. Set to -1 for maximum parallelism and
      1 for sequential processing. Cannot be overriden in the :attr:`add` method.
  
  backend : str or object (default = 'threading')
      backend infrastructure to use during call to
      :class:`mlens.externals.joblib.Parallel`. See Joblib for further
      documentation. To set global backend, set ``mlens.config._BACKEND``.
      Cannot be overriden in the :attr:`add` method.
  
  model_selection: bool (default=False)
      Whether to use the ensemble in model selection mode. If ``True``,
      this will alter the ``transform`` method. When calling ``transform``
      on new data, the ensemble will call ``predict``, while calling
      ``transform`` with the training data reproduces predictions from the
      ``fit`` call. Hence the ensemble can be used as a pure transformer
      in a preprocessing pipeline passed to the :class:`Evaluator`, as
      training folds are faithfully reproduced as during a ``fit``call and
      test folds are transformed with the ``predict`` method.
  
  sample_size: int (default=20)
      size of training set sample
      (``[min(sample_size, X.size[0]), min(X.size[1], sample_size)]``)
  
  Examples
  --------
  >>> from sklearn.linear_model import LinearRegression
  >>> from mlens.ensemble import TemporalEnsemble
  >>> import numpy as np
  >>>
  >>> x = np.linspace(0, 1, 100)
  >>> y = x[1:]
  >>> x = x[:-1]
  >>> x = x.reshape(-1, 1)
  >>>
  >>> ens = TemporalEnsemble(window=1)
  >>> ens.add(LinearRegression())
  >>>
  >>> ens.fit(x, y)
  >>> p = ens.predict(x)
  >>>
  >>>
  >>> print("{:5} | {:5}".format('pred', 'truth'))
  >>> for i in range(5, 10):
  ...     print("{:.3f} | {:.3f}".format(p[i], y[i]))
  >>>
  pred  | truth
  0.061 | 0.061
  0.071 | 0.071
  0.081 | 0.081
  0.091 | 0.091
  0.101 | 0.101
docstr: Add layer to ensemble.
  
  Parameters
  ----------
  estimators: dict of lists or list or instance
      estimators constituting the layer. If preprocessing is none and the
      layer is meant to be the meta estimator, it is permissible to pass
      a single instantiated estimator. If ``preprocessing`` is
      ``None`` or ``list``, ``estimators`` should be a ``list``.
      The list can either contain estimator instances,
      named tuples of estimator instances, or a combination of both. ::
  
          option_1 = [estimator_1, estimator_2]
          option_2 = [("est-1", estimator_1), ("est-2", estimator_2)]
          option_3 = [estimator_1, ("est-2", estimator_2)]
  
      If different preprocessing pipelines are desired, a dictionary
      that maps estimators to preprocessing pipelines must be passed.
      The names of the estimator dictionary must correspond to the
      names of the estimator dictionary. ::
  
          preprocessing_cases = {"case-1": [trans_1, trans_2],
                                 "case-2": [alt_trans_1, alt_trans_2]}
  
          estimators = {"case-1": [est_a, est_b],
                        "case-2": [est_c, est_d]}
  
      The lists for each dictionary entry can be any of ``option_1``,
      ``option_2`` and ``option_3``.
  
  preprocessing: dict of lists or list, optional (default = None)
      preprocessing pipelines for given layer. If
      the same preprocessing applies to all estimators, ``preprocessing``
      should be a list of transformer instances. The list can contain the
      instances directly, named tuples of transformers,
      or a combination of both. ::
  
          option_1 = [transformer_1, transformer_2]
          option_2 = [("trans-1", transformer_1),
                      ("trans-2", transformer_2)]
          option_3 = [transformer_1, ("trans-2", transformer_2)]
  
      If different preprocessing pipelines are desired, a dictionary
      that maps preprocessing pipelines must be passed. The names of the
      preprocessing dictionary must correspond to the names of the
      estimator dictionary. ::
  
          preprocessing_cases = {"case-1": [trans_1, trans_2],
                                 "case-2": [alt_trans_1, alt_trans_2]}
  
          estimators = {"case-1": [est_a, est_b],
                        "case-2": [est_c, est_d]}
  
      The lists for each dictionary entry can be any of ``option_1``,
      ``option_2`` and ``option_3``.
  
  proba : bool
      whether layer should predict class probabilities. Note: setting
      ``proba=True`` will attempt to call an the estimators
      ``predict_proba`` method.
  
  propagate_features : list, optional
      List of column indexes to propagate from the input of
      the layer to the output of the layer. Propagated features are
      concatenated and stored in the leftmost columns of the output
      matrix. The ``propagate_features`` list should define a slice of
      the numpy array containing the input data, e.g. ``[0, 1]`` to
      propagate the first two columns of the input matrix to the output
      matrix.
  
  meta : bool (default = False)
      indicator if the layer added is the final meta estimator. This will
      prevent folded or blended fits of the estimators and only fit them
      once on the full input data.
  
  **kwargs : optional
      optional keyword arguments.
  
  Returns
  -------
  self : instance
      ensemble instance with layer instantiated.
docstr_tok: ["Add","layer","to","ensemble",".","Parameters","--","--","--","--","--","estimators",":","dict","of","lists","or","list","or","instance","estimators","constituting","the","layer",".","If","preprocessing","is","none","and","the","layer","is","meant","to","be","the","meta","estimator",",","it","is","permissible","to","pass","a","single","instantiated","estimator",".","If","``","preprocessing","``","is","``","None","``","or","``","list","``",",","``","estimators","``","should","be","a","``","list","``",".","The","list","can","either","contain","estimator","instances",",","named","tuples","of","estimator","instances",",","or","a","combination","of","both",".",":",":","option_1","=","[","estimator_1",",","estimator_2","]","option_2","=","[","(","``","est-1","''",",","estimator_1",")",",","(","``","est-2","''",",","estimator_2",")","]","option_3","=","[","estimator_1",",","(","``","est-2","''",",","estimator_2",")","]","If","different","preprocessing","pipelines","are","desired",",","a","dictionary","that","maps","estimators","to","preprocessing","pipelines","must","be","passed",".","The","names","of","the","estimator","dictionary","must","correspond","to","the","names","of","the","estimator","dictionary",".",":",":","preprocessing_cases","=","{","``","case-1","''",":","[","trans_1",",","trans_2","]",",","``","case-2","''",":","[","alt_trans_1",",","alt_trans_2","]","}","estimators","=","{","``","case-1","''",":","[","est_a",",","est_b","]",",","``","case-2","''",":","[","est_c",",","est_d","]","}","The","lists","for","each","dictionary","entry","can","be","any","of","``","option_1","``",",","``","option_2","``","and","``","option_3","``",".","preprocessing",":","dict","of","lists","or","list",",","optional","(","default","=","None",")","preprocessing","pipelines","for","given","layer",".","If","the","same","preprocessing","applies","to","all","estimators",",","``","preprocessing","``","should","be","a","list","of","transformer","instances",".","The","list","can","contain","the","instances","directly",",","named","tuples","of","transformers",",","or","a","combination","of","both",".",":",":","option_1","=","[","transformer_1",",","transformer_2","]","option_2","=","[","(","``","trans-1","''",",","transformer_1",")",",","(","``","trans-2","''",",","transformer_2",")","]","option_3","=","[","transformer_1",",","(","``","trans-2","''",",","transformer_2",")","]","If","different","preprocessing","pipelines","are","desired",",","a","dictionary","that","maps","preprocessing","pipelines","must","be","passed",".","The","names","of","the","preprocessing","dictionary","must","correspond","to","the","names","of","the","estimator","dictionary",".",":",":","preprocessing_cases","=","{","``","case-1","''",":","[","trans_1",",","trans_2","]",",","``","case-2","''",":","[","alt_trans_1",",","alt_trans_2","]","}","estimators","=","{","``","case-1","''",":","[","est_a",",","est_b","]",",","``","case-2","''",":","[","est_c",",","est_d","]","}","The","lists","for","each","dictionary","entry","can","be","any","of","``","option_1","``",",","``","option_2","``","and","``","option_3","``",".","proba",":","bool","whether","layer","should","predict","class","probabilities",".","Note",":","setting","``","proba=True","``","will","attempt","to","call","an","the","estimators","``","predict_proba","``","method",".","propagate_features",":","list",",","optional","List","of","column","indexes","to","propagate","from","the","input","of","the","layer","to","the","output","of","the","layer",".","Propagated","features","are","concatenated","and","stored","in","the","leftmost","columns","of","the","output","matrix",".","The","``","propagate_features","``","list","should","define","a","slice","of","the","numpy","array","containing","the","input","data",",","e.g",".","``","[","0",",","1","]","``","to","propagate","the","first","two","columns","of","the","input","matrix","to","the","output","matrix",".","meta",":","bool","(","default","=","False",")","indicator","if","the","layer","added","is","the","final","meta","estimator",".","This","will","prevent","folded","or","blended","fits","of","the","estimators","and","only","fit","them","once","on","the","full","input","data",".","*","*","kwargs",":","optional","optional","keyword","arguments",".","Returns","--","--","--","-","self",":","instance","ensemble","instance","with","layer","instantiated","."]
code: def add(
      self,
      estimators,
      preprocessing=None,
      proba=False,
      meta=False,
      propagate_features=None,
      **kwargs,
  ):
      """Add layer to ensemble.
  
      Parameters
      ----------
      estimators: dict of lists or list or instance
          estimators constituting the layer. If preprocessing is none and the
          layer is meant to be the meta estimator, it is permissible to pass
          a single instantiated estimator. If ``preprocessing`` is
          ``None`` or ``list``, ``estimators`` should be a ``list``.
          The list can either contain estimator instances,
          named tuples of estimator instances, or a combination of both. ::
  
              option_1 = [estimator_1, estimator_2]
              option_2 = [("est-1", estimator_1), ("est-2", estimator_2)]
              option_3 = [estimator_1, ("est-2", estimator_2)]
  
          If different preprocessing pipelines are desired, a dictionary
          that maps estimators to preprocessing pipelines must be passed.
          The names of the estimator dictionary must correspond to the
          names of the estimator dictionary. ::
  
              preprocessing_cases = {"case-1": [trans_1, trans_2],
                                     "case-2": [alt_trans_1, alt_trans_2]}
  
              estimators = {"case-1": [est_a, est_b],
                            "case-2": [est_c, est_d]}
  
          The lists for each dictionary entry can be any of ``option_1``,
          ``option_2`` and ``option_3``.
  
      preprocessing: dict of lists or list, optional (default = None)
          preprocessing pipelines for given layer. If
          the same preprocessing applies to all estimators, ``preprocessing``
          should be a list of transformer instances. The list can contain the
          instances directly, named tuples of transformers,
          or a combination of both. ::
  
              option_1 = [transformer_1, transformer_2]
              option_2 = [("trans-1", transformer_1),
                          ("trans-2", transformer_2)]
              option_3 = [transformer_1, ("trans-2", transformer_2)]
  
          If different preprocessing pipelines are desired, a dictionary
          that maps preprocessing pipelines must be passed. The names of the
          preprocessing dictionary must correspond to the names of the
          estimator dictionary. ::
  
              preprocessing_cases = {"case-1": [trans_1, trans_2],
                                     "case-2": [alt_trans_1, alt_trans_2]}
  
              estimators = {"case-1": [est_a, est_b],
                            "case-2": [est_c, est_d]}
  
          The lists for each dictionary entry can be any of ``option_1``,
          ``option_2`` and ``option_3``.
  
      proba : bool
          whether layer should predict class probabilities. Note: setting
          ``proba=True`` will attempt to call an the estimators
          ``predict_proba`` method.
  
      propagate_features : list, optional
          List of column indexes to propagate from the input of
          the layer to the output of the layer. Propagated features are
          concatenated and stored in the leftmost columns of the output
          matrix. The ``propagate_features`` list should define a slice of
          the numpy array containing the input data, e.g. ``[0, 1]`` to
          propagate the first two columns of the input matrix to the output
          matrix.
  
      meta : bool (default = False)
          indicator if the layer added is the final meta estimator. This will
          prevent folded or blended fits of the estimators and only fit them
          once on the full input data.
  
      **kwargs : optional
          optional keyword arguments.
  
      Returns
      -------
      self : instance
          ensemble instance with layer instantiated.
      """
      s = kwargs.pop("step_size", self.step_size)
      b = kwargs.pop("burn_in", self.burn_in)
      w = kwargs.pop("window", self.window)
      l = kwargs.pop("lag", self.lag)
      if meta:
          idx = FullIndex()
      else:
          idx = TemporalIndex(
              s, b, w, l, raise_on_exception=self.raise_on_exception
          )
  
      return super(TemporalEnsemble, self).add(
          estimators=estimators,
          indexer=idx,
          preprocessing=preprocessing,
          proba=proba,
          propagate_features=propagate_features,
          **kwargs,
      )
  
imports: from __future__ import division
  from .base import BaseEnsemble
  from ..index import TemporalIndex, FullIndex
label_desc: Use image node_id 3 for calling the TemporalEnsemble obj's underlying member method code with example usage: obj.add(estimators, preprocessing, proba, meta, propagate_features) and returns: super