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: Returns predictions in a list, grouping predictions based on the series they were derived from
  
  Parameters
  ----------
  X : iterable
      Data to predict on. Must fulfill input requirements of first step
      of the pipeline.
  
  Returns
  -------
  yp : list
      Predictions
docstr_tok: ["Returns","predictions","in","a","list",",","grouping","predictions","based","on","the","series","they","were","derived","from","Parameters","--","--","--","--","--","X",":","iterable","Data","to","predict","on",".","Must","fulfill","input","requirements","of","first","step","of","the","pipeline",".","Returns","--","--","--","-","yp",":","list","Predictions"]
code: def predict_as_series(self, X):
      """
      Returns predictions in a list, grouping predictions based on the series they were derived from
  
      Parameters
      ----------
      X : iterable
          Data to predict on. Must fulfill input requirements of first step
          of the pipeline.
  
      Returns
      -------
      yp : list
          Predictions
      """
      ix = np.arange(len(X))  # series index
      ixp, yp = self.transform_predict(X, ix)
      yp = [yp[ixp == i] for i in ix]
      return np.array(yp, dtype=object)
  
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 13 for calling the Pype obj's underlying member method code with example usage: obj.predict_as_series(X) and returns: np