new_contents: #!/usr/bin/env python
  # encoding: utf-8
  
  # The MIT License (MIT)
  
  # Copyright (c) 2014 CNRS
  
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  # AUTHORS
  # Hervé BREDIN - http://herve.niderb.fr
  
  from __future__ import unicode_literals
  
  import numpy as np
  import itertools
  from pyannote.core import Annotation
  from pyannote.core.util import pairwise
  
  
  class SKLearnIOMixin:
  
      def X(self, features):
          return features.data
  
      def Xy(self, annotation, features):
  
          X, y = [], []
          for segment, _, label in annotation.itertracks(label=True):
              _X = features.crop(segment)
              X.append(_X)
              y.extend([label] * _X.shape[0])
  
          return np.vstack(X), y
  
      def Xy_iter(self, annotation_iter, features_iter):
          for ann, features in itertools.izip(annotation_iter, features_iter):
              yield self.Xy(ann, features)
  
      def Xy_stack(self, annotation_iter, features_iter):
          X, y = [], []
          for _X, _y in self.Xy_iter(annotation_iter, features_iter):
              X.append(_X)
              y.extend(_y)
          return np.vstack(X), y
  
      def y2annotation(self, y, sliding_window, labels=None):
  
          if labels is None:
              labels = dict()
  
          annotation = Annotation()
  
          changes_at = [-1] + list(np.where(np.diff(y))[0]) + [len(y)]
  
          for t, T in pairwise(changes_at):
              segment = sliding_window.rangeToSegment(t, T - t)
              label = labels.get(y[t + 1], y[t + 1])
              annotation[segment] = label
  
          return annotation
  
subject: add scikit-learn/pyannote conversion mixin