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# frozen_string_literal: true
require 'rumale/base/evaluator'
require 'rumale/evaluation_measure/precision_recall'
module Rumale
# This module consists of the classes for model evaluation.
module EvaluationMeasure
# FScore is a class that calculates the F1-score of the predicted labels.
#
# @example
# evaluator = Rumale::EvaluationMeasure::FScore.new
# puts evaluator.score(ground_truth, predicted)
class FScore
include Base::Evaluator
include EvaluationMeasure::PrecisionRecall
# Return the average type for calculation of F1-score.
# @return [String] ('binary', 'micro', 'macro')
attr_reader :average
# Create a new evaluation measure calculater for F1-score.
#
# @param average [String] The average type ('binary', 'micro', 'macro')
def initialize(average: 'binary')
check_params_string(average: average)
@average = average
end
# Calculate average F1-score
#
# @param y_true [Numo::Int32] (shape: [n_samples]) Ground truth labels.
# @param y_pred [Numo::Int32] (shape: [n_samples]) Predicted labels.
# @return [Float] Average F1-score
def score(y_true, y_pred)
y_true = check_convert_label_array(y_true)
y_pred = check_convert_label_array(y_pred)
case @average
when 'binary'
f_score_each_class(y_true, y_pred).last
when 'micro'
micro_average_f_score(y_true, y_pred)
when 'macro'
macro_average_f_score(y_true, y_pred)
end
end
end
end
end
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40 entries across 40 versions & 1 rubygems