Sha256: eed7a560970c2dc2964905395cff3b1282eb645fabfd82c51b997359c6e63862
Contents?: true
Size: 1.85 KB
Versions: 7
Compression:
Stored size: 1.85 KB
Contents
# frozen_string_literal: true
require 'rumale/base/evaluator'
require 'rumale/evaluation_measure/mutual_information'
module Rumale
module EvaluationMeasure
# NormalizedMutualInformation is a class that calculates the normalized mutual information.
#
# @example
# require 'rumale/evaluation_measure/normalized_mutual_information'
#
# evaluator = Rumale::EvaluationMeasure::NormalizedMutualInformation.new
# puts evaluator.score(ground_truth, predicted)
#
# *Reference*
# - Manning, C D., Raghavan, P., and Schutze, H., "Introduction to Information Retrieval," Cambridge University Press., 2008.
# - Vinh, N X., Epps, J., and Bailey, J., "Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance," J. Machine Learning Research, vol. 11, pp. 2837--1854, 2010.
class NormalizedMutualInformation
include ::Rumale::Base::Evaluator
# Calculate noramlzied mutual information
#
# @param y_true [Numo::Int32] (shape: [n_samples]) Ground truth labels.
# @param y_pred [Numo::Int32] (shape: [n_samples]) Predicted cluster labels.
# @return [Float] Normalized mutual information
def score(y_true, y_pred)
# calculate entropies.
class_entropy = entropy(y_true)
return 0.0 if class_entropy.zero?
cluster_entropy = entropy(y_pred)
return 0.0 if cluster_entropy.zero?
# calculate mutual information.
mi = MutualInformation.new
mi.score(y_true, y_pred) / Math.sqrt(class_entropy * cluster_entropy)
end
private
def entropy(y)
n_samples = y.size
indices = y.to_a.uniq
sum_log = indices.sum do |k|
ratio = y.eq(k).count.fdiv(n_samples)
ratio * Math.log(ratio)
end
-sum_log
end
end
end
end
Version data entries
7 entries across 7 versions & 1 rubygems