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# frozen_string_literal: true
require 'svmkit/validation'
require 'svmkit/base/evaluator'
require 'svmkit/preprocessing/one_hot_encoder'
module SVMKit
module EvaluationMeasure
# LogLoss is a class that calculates the logarithmic loss of predicted class probability.
#
# @example
# evaluator = SVMKit::EvaluationMeasure::LogLoss.new
# puts evaluator.score(ground_truth, predicted)
class LogLoss
include Base::Evaluator
# Calculate mean logarithmic loss.
# If both y_true and y_pred are array (both shapes are [n_samples]), this method calculates
# mean logarithmic loss for binary classification.
#
# @param y_true [Numo::Int32] (shape: [n_samples]) Ground truth labels.
# @param y_pred [Numo::DFloat] (shape: [n_samples, n_classes]) Predicted class probability.
# @param eps [Float] A small value close to zero to avoid outputting infinity in logarithmic calcuation.
# @return [Float] mean logarithmic loss
def score(y_true, y_pred, eps = 1e-15)
SVMKit::Validation.check_params_type(Numo::Int32, y_true: y_true)
SVMKit::Validation.check_params_type(Numo::DFloat, y_pred: y_pred)
n_samples, n_classes = y_pred.shape
clipped_p = y_pred.clip(eps, 1 - eps)
log_loss = if n_classes.nil?
negative_label = y_true.to_a.uniq.min
bin_y_true = Numo::DFloat.cast(y_true.ne(negative_label))
-(bin_y_true * Numo::NMath.log(clipped_p) + (1 - bin_y_true) * Numo::NMath.log(1 - clipped_p))
else
encoder = SVMKit::Preprocessing::OneHotEncoder.new
encoded_y_true = encoder.fit_transform(y_true)
clipped_p /= clipped_p.sum(1).expand_dims(1)
-(encoded_y_true * Numo::NMath.log(clipped_p)).sum(1)
end
log_loss.sum / n_samples
end
end
end
end
Version data entries
17 entries across 17 versions & 1 rubygems