class_docstr: Benchmark engine without hyper-parameter grid search.
  
  A simplified version of the :class:`Evaluator` that performs a single
  pass over a set of estimators and preprocessing pipelines for
  benchmarking purposes.
  
  .. versionadded:: 0.2.0
  
  Parameters
  ----------
  verbose : bool, int, optional
      Verbosity during estimation.
  
  **kwargs : optional
      Optional keyword argument to :class:`~mlens.parallel.base.BaseBackend`.
docstr: Run benchmarking job on given data with given estimators.
  
  Fit preprocessing if applicable and evaluate estimators if applicable.
  The method automatically determines whether to only run preprocessing,
  only evaluation (possibly on previously fitted preprocessing), or both.
  Calling ``fit`` will overwrite previously stored data where applicable.
  
  Parameters
  ----------
  X : array-like, shape=[n_samples, n_features]
     input data to preprocess and create folds from.
  
  y : array-like, shape=[n_samples, ]
     training labels.
  
  scorer : function
      a scoring function that follows the Scikit-learn API::
  
          score = scorer(estimator, y_true, y_pred)
  
      A user defines scoring function, ``score = f(y_true, y_pred)`` can be
      made into a scorer by calling on the ML-Ensemble implementation of
      Scikit-learn's ``make_scorer``. NOTE: do **not** use Scikit-learn's
      ``make_scorer`` if the Evaluator is to be pickled. ::
  
          from mlens.metrics import make_scorer
          scorer = make_scorer(scoring_function, **kwargs)
  
  error_score : int, optional
      score to assign when fitting an estimator fails. If ``None``, the
      evaluator will raise an error.
  
  cv : int or obj (default = 2)
      cross validation folds to use. Either pass a ``KFold`` class
      that obeys the Scikit-learn API.
  
  estimators : list or dict, optional
      set of estimators to use. If no preprocessing is desired or if
      only on preprocessing pipeline should apply to all, pass a list of
      estimators. The list can contain elements of named tuples
      (i.e. ``('my_name', my_est)``).
  
      If different estimators should be mapped to preprocessing cases,
      a dictionary that maps estimators to each case should
      be passed: ``{'case_a': list_of_est, ...}``.
  
  preprocessing : dict, optional
      preprocessing cases to consider. Pass a dictionary mapping a
      case name to a preprocessing pipeline. ::
  
          preprocessing = {'case_name': transformer_list,}
  
  Returns
  -------
  self : inst
      Fitted Benchmark instance. Results available in the
      ``results`` attribute.
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code: def fit(
      self,
      X,
      y,
      scorer,
      cv,
      estimators,
      preprocessing=None,
      error_score=None,
  ):
      """Run benchmarking job on given data with given estimators.
  
      Fit preprocessing if applicable and evaluate estimators if applicable.
      The method automatically determines whether to only run preprocessing,
      only evaluation (possibly on previously fitted preprocessing), or both.
      Calling ``fit`` will overwrite previously stored data where applicable.
  
      Parameters
      ----------
      X : array-like, shape=[n_samples, n_features]
         input data to preprocess and create folds from.
  
      y : array-like, shape=[n_samples, ]
         training labels.
  
      scorer : function
          a scoring function that follows the Scikit-learn API::
  
              score = scorer(estimator, y_true, y_pred)
  
          A user defines scoring function, ``score = f(y_true, y_pred)`` can be
          made into a scorer by calling on the ML-Ensemble implementation of
          Scikit-learn's ``make_scorer``. NOTE: do **not** use Scikit-learn's
          ``make_scorer`` if the Evaluator is to be pickled. ::
  
              from mlens.metrics import make_scorer
              scorer = make_scorer(scoring_function, **kwargs)
  
      error_score : int, optional
          score to assign when fitting an estimator fails. If ``None``, the
          evaluator will raise an error.
  
      cv : int or obj (default = 2)
          cross validation folds to use. Either pass a ``KFold`` class
          that obeys the Scikit-learn API.
  
      estimators : list or dict, optional
          set of estimators to use. If no preprocessing is desired or if
          only on preprocessing pipeline should apply to all, pass a list of
          estimators. The list can contain elements of named tuples
          (i.e. ``('my_name', my_est)``).
  
          If different estimators should be mapped to preprocessing cases,
          a dictionary that maps estimators to each case should
          be passed: ``{'case_a': list_of_est, ...}``.
  
      preprocessing : dict, optional
          preprocessing cases to consider. Pass a dictionary mapping a
          case name to a preprocessing pipeline. ::
  
              preprocessing = {'case_name': transformer_list,}
  
      Returns
      -------
      self : inst
          Fitted Benchmark instance. Results available in the
          ``results`` attribute.
      """
      self.indexer = FoldIndex(folds=cv)
      assert_correct_format(estimators, preprocessing)
      if preprocessing is not None:
          self._transformers = make_tansformers(
              sorted(check_instances(preprocessing).items()),
              self.indexer,
              verbose=max(0, self.verbose - 14),
          )
  
      generator = [
          (p_name, l_name, est, None, {})
          for p_name, l_name, est in _flatten(
              check_instances(estimators)
          )
      ]
  
      self._learners = make_learners(
          generator,
          self.indexer,
          scorer,
          error_score,
          verbose=max(0, self.verbose - 14),
      )
  
      job = set_job(estimators, preprocessing)
      self._fit(X, y, job)
      self.results = Data(self.raw_data, decimals=3)
      return self
  
imports: from __future__ import division, with_statement
  import warnings
  import numpy
  from ._base_functions import parse_key, set_job, cat, check_scorer, make_learners, make_tansformers, check_instances
  from ..index import FoldIndex
  from ..parallel import ParallelEvaluation
  from ..parallel.base import BaseBackend, IndexMixin
  from ..metrics import Data, assemble_data
  from ..utils.formatting import _flatten, _check_instances
  from ..utils import print_time, safe_print, assert_correct_format
  from ..externals.joblib import delayed
  from ..externals.sklearn.base import clone
label_desc: Use image node_id 2 for calling the Benchmark obj's underlying member method code with example usage: obj.fit(X, y, scorer, cv, estimators, preprocessing, error_score) and returns: self