class_docstr: This pipeline extends the sklearn Pipeline to support transformers that change X, y,
  sample_weight, and the number of samples.
  
  It also adds some new options for setting hyper-parameters with callables and in reference to
  other parameters (see examples).
  
  Parameters
  ----------
  steps : list
      List of (name, transform) tuples (implementing fit/transform) that are chained, in the
      order in which they are chained, with the last object an estimator.
  scorer : sklearn scorer object
  memory : currently not implemented
  
  Attributes
  ----------
  N_train : number of training samples - available after calling fit method
  N_test : number of testing samples - available after calling predict, or score methods
  
  Examples
  --------
  
  >>> from seglearn.transform import FeatureRep, SegmentX
  >>> from seglearn.pipe import Pype
  >>> from seglearn.datasets import load_watch
  >>> from sklearn.ensemble import RandomForestClassifier
  >>> from sklearn.preprocessing import StandardScaler
  >>> data = load_watch()
  >>> X = data['X']
  >>> y = data['y']
  >>> pipe = Pype([('segment', SegmentX()),
  >>>              ('features', FeatureRep()),
  >>>              ('scaler', StandardScaler()),
  >>>              ('rf', RandomForestClassifier())])
  >>> pipe.fit(X, y)
  >>> print(pipe.score(X, y))
docstr: Apply transforms, and decision_function of the final estimator
  
  Parameters
  ----------
  X : iterable
      Data to predict on. Must fulfill input requirements of first step
      of the pipeline.
  
  Returns
  -------
  y_score : array-like, shape = [n_samples, n_classes]
docstr_tok: ["Apply","transforms",",","and","decision_function","of","the","final","estimator","Parameters","--","--","--","--","--","X",":","iterable","Data","to","predict","on",".","Must","fulfill","input","requirements","of","first","step","of","the","pipeline",".","Returns","--","--","--","-","y_score",":","array-like",",","shape","=","[","n_samples",",","n_classes","]"]
code: def decision_function(self, X):
      """
      Apply transforms, and decision_function of the final estimator
  
      Parameters
      ----------
      X : iterable
          Data to predict on. Must fulfill input requirements of first step
          of the pipeline.
  
      Returns
      -------
      y_score : array-like, shape = [n_samples, n_classes]
      """
      Xt, _, _ = self._transform(X)
      return self._final_estimator.decision_function(Xt)
  
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.pipeline import Pipeline
  from .transform import XyTransformerMixin, Segment
  from .util import segmented_prediction_to_series
label_desc: Use image node_id 11 for calling the Pype obj's underlying member method code with example usage: obj.decision_function(X) and returns: self