class_docstr: Completeness Score.
  
  Completeness [^1] is symmetrical to homogeneity. In order to satisfy the
  completeness criteria, a clustering must assign all of those datapoints
  that are members of a single class to a single cluster. To evaluate completeness,
  we examine the distribution cluster assignments within each class. In a
  perfectly complete clustering solution, each of these distributions will be
  completely skewed to a single cluster.
  
  We can evaluate this degree of skew by calculating the conditional entropy of
  the proposed cluster distribution given the class of the component data points.
  However, in the worst case scenario, each class is represented by every cluster
  with a distribution equal to the distribution of cluster sizes. Therefore,
  symmetric to the claculation above, we define completeness as:
  
  $$
  c = \begin{cases}
  1 if H(K) = 0, \\
  1 - \frac{H(K|C)}{H(K)} otherwise.
  \end{cases}.
  $$
  
  Parameters
  ----------
  cm
      This parameter allows sharing the same confusion
      matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage
      and computation time.
  
  Examples
  --------
  
  >>> from river import metrics
  
  >>> y_true = [1, 1, 2, 2, 3, 3]
  >>> y_pred = [1, 1, 1, 2, 2, 2]
  
  >>> metric = metrics.Completeness()
  >>> for yt, yp in zip(y_true, y_pred):
  ...     metric.update(yt, yp)
  ...     print(metric.get())
  1.0
  1.0
  1.0
  0.3836885465963443
  0.5880325916843805
  0.6666666666666667
  
  >>> metric
  Completeness: 66.67%
  
  References
  ----------
  [^1]: Andrew Rosenberg and Julia Hirschberg (2007).
        V-Measure: A conditional entropy-based external cluster evaluation measure.
        Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language
        Processing and Computational Natural Language Learning, pp. 410 - 420,
        Prague, June 2007.
class_docstr_tok: ["Completeness","Score",".","Completeness","[","^1","]","is","symmetrical","to","homogeneity",".","In","order","to","satisfy","the","completeness","criteria",",","a","clustering","must","assign","all","of","those","datapoints","that","are","members","of","a","single","class","to","a","single","cluster",".","To","evaluate","completeness",",","we","examine","the","distribution","cluster","assignments","within","each","class",".","In","a","perfectly","complete","clustering","solution",",","each","of","these","distributions","will","be","completely","skewed","to","a","single","cluster",".","We","can","evaluate","this","degree","of","skew","by","calculating","the","conditional","entropy","of","the","proposed","cluster","distribution","given","the","class","of","the","component","data","points",".","However",",","in","the","worst","case","scenario",",","each","class","is","represented","by","every","cluster","with","a","distribution","equal","to","the","distribution","of","cluster","sizes",".","Therefore",",","symmetric","to","the","claculation","above",",","we","define","completeness","as",":","$","$","c","=","\\begin","{","cases","}","1","if","H","(","K",")","=","0",",","\\\\","1","-","\\frac","{","H","(","K|C",")","}","{","H","(","K",")","}","otherwise",".","\\end","{","cases","}",".","$","$","Parameters","--","--","--","--","--","cm","This","parameter","allows","sharing","the","same","confusion","matrix","between","multiple","metrics",".","Sharing","a","confusion","matrix","reduces","the","amount","of","storage","and","computation","time",".","Examples","--","--","--","--",">",">",">","from","river","import","metrics",">",">",">","y_true","=","[","1",",","1",",","2",",","2",",","3",",","3","]",">",">",">","y_pred","=","[","1",",","1",",","1",",","2",",","2",",","2","]",">",">",">","metric","=","metrics.Completeness","(",")",">",">",">","for","yt",",","yp","in","zip","(","y_true",",","y_pred",")",":","...","metric.update","(","yt",",","yp",")","...","print","(","metric.get","(",")",")","1.0","1.0","1.0","0.3836885465963443","0.5880325916843805","0.6666666666666667",">",">",">","metric","Completeness",":","66.67","%","References","--","--","--","--","--","[","^1","]",":","Andrew","Rosenberg","and","Julia","Hirschberg","(","2007",")",".","V-Measure",":","A","conditional","entropy-based","external","cluster","evaluation","measure",".","Proceedings","of","the","2007","Joing","Conference","on","Empirical","Methods","in","Natural","Language","Processing","and","Computational","Natural","Language","Learning",",","pp",".","410","-","420",",","Prague",",","June","2007","."]
code: def works_with_weights(self):
      return False
  
file_path: river/river/metrics/vbeta.py
imports: from __future__ import annotations
  import math
  from river import metrics
label_desc: Use image node_id 1 for calling the Completeness obj's underlying member method code with example usage: obj.works_with_weights() and returns: False