Class: Rumale::ModelSelection::StratifiedShuffleSplit

Inherits:
Object
  • Object
show all
Includes:
Base::Splitter
Defined in:
rumale-model_selection/lib/rumale/model_selection/stratified_shuffle_split.rb

Overview

StratifiedShuffleSplit is a class that generates the set of data indices for random permutation cross-validation. The proportion of the number of samples in each class will be almost equal for each fold.

Examples:

require 'rumale/model_selection/stratified_shuffle_split'

ss = Rumale::ModelSelection::StratifiedShuffleSplit.new(n_splits: 3, test_size: 0.2, random_seed: 1)
ss.split(samples, labels).each do |train_ids, test_ids|
  train_samples = samples[train_ids, true]
  test_samples = samples[test_ids, true]
  ...
end

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(n_splits: 3, test_size: 0.1, train_size: nil, random_seed: nil) ⇒ StratifiedShuffleSplit

Create a new data splitter for random permutation cross validation.

Parameters:

  • n_splits (Integer) (defaults to: 3) —

    The number of folds.

  • test_size (Float) (defaults to: 0.1) —

    The ratio of number of samples for test data.

  • train_size (Float) (defaults to: nil) —

    The ratio of number of samples for train data.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator.



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# File 'rumale-model_selection/lib/rumale/model_selection/stratified_shuffle_split.rb', line 37

def initialize(n_splits: 3, test_size: 0.1, train_size: nil, random_seed: nil)
  @n_splits = n_splits
  @test_size = test_size
  @train_size = train_size
  @random_seed = random_seed
  @random_seed ||= srand
  @rng = Random.new(@random_seed)
end

Instance Attribute Details

#n_splits ⇒ Integer (readonly)

Return the number of folds.

Returns:

  • (Integer) 


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# File 'rumale-model_selection/lib/rumale/model_selection/stratified_shuffle_split.rb', line 25

def n_splits
  @n_splits
end

#rng ⇒ Random (readonly)

Return the random generator for shuffling the dataset.

Returns:

  • (Random) 


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# File 'rumale-model_selection/lib/rumale/model_selection/stratified_shuffle_split.rb', line 29

def rng
  @rng
end

Instance Method Details

#split(x, y) ⇒ Array

Generate data indices for stratified random permutation cross validation.

Returns The set of data indices for constructing the training and testing dataset in each fold.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The dataset to be used to generate data indices for stratified random permutation cross validation. This argument exists to unify the interface between the K-fold methods, it is not used in the method.

  • y (Numo::Int32) —

    (shape: [n_samples]) The labels to be used to generate data indices for stratified random permutation cross validation.

Returns:

  • (Array) —

    The set of data indices for constructing the training and testing dataset in each fold.



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# File 'rumale-model_selection/lib/rumale/model_selection/stratified_shuffle_split.rb', line 55

def split(_x, y)
  # Initialize and check some variables.
  train_sz = @train_size.nil? ? 1.0 - @test_size : @train_size
  sub_rng = @rng.dup
  # Check the number of samples in each class.
  unless valid_n_splits?(y)
    raise ArgumentError,
          'The value of n_splits must be not less than 1 and not more than the number of samples in each class.'
  end
  # rubocop:disable Layout/LineLength
  unless enough_data_size_each_class?(y, @test_size, 'test')
    raise RangeError,
          'The number of samples in test split must be not less than 1 and not more than the number of samples in each class.'
  end
  unless enough_data_size_each_class?(y, train_sz, 'train')
    raise RangeError,
          'The number of samples in train split must be not less than 1 and not more than the number of samples in each class.'
  end
  unless enough_data_size_each_class?(y, train_sz + @test_size, 'train')
    raise RangeError,
          'The total number of samples in test split and train split must be not more than the number of samples in each class.'
  end
  # rubocop:enable Layout/LineLength
  # Returns array consisting of the training and testing ids for each fold.
  sample_ids_each_class = y.to_a.uniq.map { |label| y.eq(label).where.to_a }
  Array.new(@n_splits) do
    train_ids = []
    test_ids = []
    sample_ids_each_class.each do |sample_ids|
      n_samples = sample_ids.size
      n_test_samples = (@test_size * n_samples).ceil.to_i
      test_ids += sample_ids.sample(n_test_samples, random: sub_rng)
      train_ids += if @train_size.nil?
                     sample_ids - test_ids
                   else
                     n_train_samples = (train_sz * n_samples).floor.to_i
                     (sample_ids - test_ids).sample(n_train_samples, random: sub_rng)
                   end
    end
    [train_ids, test_ids]
  end
end