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: Generates predictions for each time series on the same sampling as the original series, by resampling
  a prediction performed with sliding window segmentation
  
  Requires that one of the Segment transforms be part of the pipeline
  
  See plot_feature_rep.py example
  
  Parameters
  ----------
  X : iterable
      Data to predict on. Must fulfill input requirements of first step
      of the pipeline.
  
  categorical_target : boolean
      Set to True for classification problems, and false for regression problems
  
  Returns
  -------
  yp : iterable
      Time series predictions on the same sampling as X
docstr_tok: ["Generates","predictions","for","each","time","series","on","the","same","sampling","as","the","original","series",",","by","resampling","a","prediction","performed","with","sliding","window","segmentation","Requires","that","one","of","the","Segment","transforms","be","part","of","the","pipeline","See","plot_feature_rep.py","example","Parameters","--","--","--","--","--","X",":","iterable","Data","to","predict","on",".","Must","fulfill","input","requirements","of","first","step","of","the","pipeline",".","categorical_target",":","boolean","Set","to","True","for","classification","problems",",","and","false","for","regression","problems","Returns","--","--","--","-","yp",":","iterable","Time","series","predictions","on","the","same","sampling","as","X"]
code: def predict_unsegmented(self, X, categorical_target=False):
      """
      Generates predictions for each time series on the same sampling as the original series, by resampling
      a prediction performed with sliding window segmentation
  
      Requires that one of the Segment transforms be part of the pipeline
  
      See plot_feature_rep.py example
  
      Parameters
      ----------
      X : iterable
          Data to predict on. Must fulfill input requirements of first step
          of the pipeline.
  
      categorical_target : boolean
          Set to True for classification problems, and false for regression problems
  
      Returns
      -------
      yp : iterable
          Time series predictions on the same sampling as X
  
      """
      segmenter = self._get_segmenter()
  
      if not segmenter:
          raise Exception(
              "Pype does not contain valid segmenter transform"
          )
  
      width = segmenter.width
      step = segmenter._step
  
      yp = self.predict_as_series(X)
      yu = []
  
      for i, yi in enumerate(yp):
          yui = segmented_prediction_to_series(
              yi, step, width, categorical_target
          )
          d = len(X[i]) - len(yui)
          if d > 0:
              yui = np.concatenate(
                  [yui, np.repeat(yui[-1:], d)], axis=0
              )
          yu.append(yui)
  
      return np.array(yu, 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 14 for calling the Pype obj's underlying member method code with example usage: obj.predict_unsegmented(X, categorical_target) and returns: np