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# frozen_string_literal: true
require 'rumale/base/evaluator'
module Rumale
module EvaluationMeasure
# MutualInformation is a class that calculates the mutual information.
#
# @example
# evaluator = Rumale::EvaluationMeasure::MutualInformation.new
# puts evaluator.score(ground_truth, predicted)
#
# *Reference*
# - N X. Vinh, J. Epps, and J. Bailey, "Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance," J. Machine Learning Research, vol. 11, pp. 2837--1854, 2010.
class MutualInformation
include Base::Evaluator
# Calculate 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] Mutual information.
def score(y_true, y_pred)
y_true = check_convert_label_array(y_true)
y_pred = check_convert_label_array(y_pred)
# initiazlie some variables.
mutual_information = 0.0
n_samples = y_pred.size
class_ids = y_true.to_a.uniq
cluster_ids = y_pred.to_a.uniq
# calculate mutual information.
cluster_ids.map do |k|
pr_sample_ids = y_pred.eq(k).where.to_a
n_pr_samples = pr_sample_ids.size
class_ids.map do |j|
tr_sample_ids = y_true.eq(j).where.to_a
n_tr_samples = tr_sample_ids.size
n_intr_samples = (pr_sample_ids & tr_sample_ids).size
if n_intr_samples.positive?
mutual_information +=
n_intr_samples.fdiv(n_samples) * Math.log((n_samples * n_intr_samples).fdiv(n_pr_samples * n_tr_samples))
end
end
end
mutual_information
end
end
end
end
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19 entries across 19 versions & 1 rubygems