code: def v_measure_score(labels_true, labels_pred):
      """V-measure cluster labeling given a ground truth.
  
      This score is identical to :func:`normalized_mutual_info_score`.
  
      The V-measure is the harmonic mean between homogeneity and completeness::
  
          v = 2 * (homogeneity * completeness) / (homogeneity + completeness)
  
      This metric is independent of the absolute values of the labels:
      a permutation of the class or cluster label values won't change the
      score value in any way.
  
      This metric is furthermore symmetric: switching ``label_true`` with
      ``label_pred`` will return the same score value. This can be useful to
      measure the agreement of two independent label assignments strategies
      on the same dataset when the real ground truth is not known.
  
      Read more in the :ref:`User Guide <homogeneity_completeness>`.
  
      Parameters
      ----------
      labels_true : int array, shape = [n_samples]
          ground truth class labels to be used as a reference
  
      labels_pred : array, shape = [n_samples]
          cluster labels to evaluate
  
      Returns
      -------
      v_measure : float
         score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
  
      References
      ----------
  
      .. [1] `Andrew Rosenberg and Julia Hirschberg, 2007. V-Measure: A
         conditional entropy-based external cluster evaluation measure
         <http://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
  
      See also
      --------
      homogeneity_score
      completeness_score
  
      Examples
      --------
  
      Perfect labelings are both homogeneous and complete, hence have score 1.0::
  
        >>> from sklearn.metrics.cluster import v_measure_score
        >>> v_measure_score([0, 0, 1, 1], [0, 0, 1, 1])
        1.0
        >>> v_measure_score([0, 0, 1, 1], [1, 1, 0, 0])
        1.0
  
      Labelings that assign all classes members to the same clusters
      are complete be not homogeneous, hence penalized::
  
        >>> print("%.6f" % v_measure_score([0, 0, 1, 2], [0, 0, 1, 1]))
        ...                                                  # doctest: +ELLIPSIS
        0.8...
        >>> print("%.6f" % v_measure_score([0, 1, 2, 3], [0, 0, 1, 1]))
        ...                                                  # doctest: +ELLIPSIS
        0.66...
  
      Labelings that have pure clusters with members coming from the same
      classes are homogeneous but un-necessary splits harms completeness
      and thus penalize V-measure as well::
  
        >>> print("%.6f" % v_measure_score([0, 0, 1, 1], [0, 0, 1, 2]))
        ...                                                  # doctest: +ELLIPSIS
        0.8...
        >>> print("%.6f" % v_measure_score([0, 0, 1, 1], [0, 1, 2, 3]))
        ...                                                  # doctest: +ELLIPSIS
        0.66...
  
      If classes members are completely split across different clusters,
      the assignment is totally incomplete, hence the V-Measure is null::
  
        >>> print("%.6f" % v_measure_score([0, 0, 0, 0], [0, 1, 2, 3]))
        ...                                                  # doctest: +ELLIPSIS
        0.0...
  
      Clusters that include samples from totally different classes totally
      destroy the homogeneity of the labeling, hence::
  
        >>> print("%.6f" % v_measure_score([0, 0, 1, 1], [0, 0, 0, 0]))
        ...                                                  # doctest: +ELLIPSIS
        0.0...
  
      """
      return homogeneity_completeness_v_measure(
          labels_true, labels_pred
      )[2]
  
imports: from __future__ import division
  from math import log
  import numpy
  from scipy.misc import comb
  from scipy import sparse
  from .expected_mutual_info_fast import expected_mutual_information
  from ...utils.fixes import bincount
  from ...utils.validation import check_array
label_desc: Use image node_id 8 for calling a global function with example usage: v_measure_score(labels_true, labels_pred) and returns: homogeneity_completeness_v_measure