Class: Rumale::EvaluationMeasure::CalinskiHarabaszScore

Inherits:
Object
  • Object
show all
Includes:
Base::Evaluator
Defined in:
rumale-evaluation_measure/lib/rumale/evaluation_measure/calinski_harabasz_score.rb

Overview

CalinskiHarabaszScore is a class that calculates the Calinski and Harabasz score.

Reference

  • Calinski, T., and Harabsz, J., “A dendrite method for cluster analysis,” Communication in Statistics, Vol. 3 (1), pp. 1–27, 1972.

Examples:

require 'rumale/evaluation_measure/calinski_harabasz_score'

evaluator = Rumale::EvaluationMeasure::CalinskiHarabaszScore.new
puts evaluator.score(x, predicted)

Instance Method Summary collapse

Instance Method Details

#score(x, y) ⇒ Float

Calculates the Calinski and Harabasz score.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to be used for calculating score.

  • y (Numo::Int32) —

    (shape: [n_samples]) The predicted labels for each sample.

Returns:

  • (Float) —

    The Calinski and Harabasz score.



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# File 'rumale-evaluation_measure/lib/rumale/evaluation_measure/calinski_harabasz_score.rb', line 25

def score(x, y)
  labels = y.to_a.uniq.sort
  n_clusters = labels.size
  n_dimensions = x.shape[1]

  centroids = Numo::DFloat.zeros(n_clusters, n_dimensions)

  within_group = 0.0
  n_clusters.times do |n|
    cls_samples = x[y.eq(labels[n]), true]
    cls_centroid = cls_samples.mean(0)
    centroids[n, true] = cls_centroid
    within_group += ((cls_samples - cls_centroid)**2).sum
  end

  return 1.0 if within_group.zero?

  mean_vec = x.mean(0)
  between_group = 0.0
  n_clusters.times do |n|
    sz_cluster = y.eq(labels[n]).count
    between_group += sz_cluster * ((centroids[n, true] - mean_vec)**2).sum
  end

  n_samples = x.shape[0]
  (between_group / (n_clusters - 1)) / (within_group / (n_samples - n_clusters))
end