class_docstr: Estimator mixin
  
  Mixin class to build an estimator from a :mod:`mlens.parallel` backend
  class. The backend class should be set as the ``_backend`` attribute
  of the estimator during a ``fit`` call via a ``_build`` method. E.g::
  
      Foo(EstimatorMixin, Learner):
  
          def __init__(self, ...):
  
              self._backend = None
  
          def _build(self):
              self._backend = Learner(...)
  
  It is recommended to combine :class:`EstimatorMixin` with
  `parallel.base.ParamMixin`.
docstr: Fit
  
  Fit estimator and return cross-validated predictions.
  
  Parameters
  ----------
  X: array of size [n_samples, n_features]
      input data
  
  y: array of size [n_features,]
      targets
  
  proba: bool, optional
      whether to fit for later predict_proba calls. Will register number
      of classes to expect in later predict and transform calls.
  
  refit: bool (default = True)
      Whether to refit already fitted sub-learners.
  
  Returns
  -------
  P: array of size [n_samples, n_prediction_features]
      prediction generated by cross-validation.
docstr_tok: ["Fit","Fit","estimator","and","return","cross-validated","predictions",".","Parameters","--","--","--","--","--","X",":","array","of","size","[","n_samples",",","n_features","]","input","data","y",":","array","of","size","[","n_features",",","]","targets","proba",":","bool",",","optional","whether","to","fit","for","later","predict_proba","calls",".","Will","register","number","of","classes","to","expect","in","later","predict","and","transform","calls",".","refit",":","bool","(","default","=","True",")","Whether","to","refit","already","fitted","sub-learners",".","Returns","--","--","--","-","P",":","array","of","size","[","n_samples",",","n_prediction_features","]","prediction","generated","by","cross-validation","."]
code: def fit_transform(self, X, y, proba=False, refit=True):
      """Fit
  
      Fit estimator and return cross-validated predictions.
  
      Parameters
      ----------
      X: array of size [n_samples, n_features]
          input data
  
      y: array of size [n_features,]
          targets
  
      proba: bool, optional
          whether to fit for later predict_proba calls. Will register number
          of classes to expect in later predict and transform calls.
  
      refit: bool (default = True)
          Whether to refit already fitted sub-learners.
  
      Returns
      -------
      P: array of size [n_samples, n_prediction_features]
          prediction generated by cross-validation.
      """
      if hasattr(self, "_build"):
          self._build()
  
      return run(
          get_backend(self),
          "fit",
          X,
          y,
          proba=proba,
          refit=refit,
          return_preds=True,
      )
  
file_path: mlens/mlens/parallel/wrapper.py
imports: from ..None import config
  from .base import BaseParallel, OutputMixin
  from .backend import ParallelProcessing
  from ..utils.exceptions import ParallelProcessingError, NotFittedError
label_desc: Use image node_id 2 for calling the EstimatorMixin obj's underlying member method code with example usage: obj.fit_transform(X, y, proba, refit) and returns: run