Class: Rumale::ModelSelection::GroupShuffleSplit

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

Overview

GroupShuffleSplit is a class that generates the set of data indices for random permutation cross-validation by randomly selecting group labels.

Examples:

require 'rumale/model_selection/group_shuffle_split'

cv = Rumale::ModelSelection::GroupShuffleSplit.new(n_splits: 2, test_size: 0.2, random_seed: 1)
x = Numo::DFloat.new(8, 2).rand
groups = Numo::Int32[1, 1, 1, 2, 2, 3, 3, 3]
cv.split(x, nil, groups).each do |train_ids, test_ids|
  puts '---'
  pp train_ids
  pp test_ids
end

# ---
# [0, 1, 2, 5, 6, 7]
# [3, 4]
# ---
# [3, 4, 5, 6, 7]
# [0, 1, 2]

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(n_splits: 5, test_size: 0.2, train_size: nil, random_seed: nil) ⇒ GroupShuffleSplit

Create a new data splitter for random permutation cross validation with given group labels.

Parameters:

  • n_splits (Integer) (defaults to: 5) —

    The number of folds.

  • test_size (Float) (defaults to: 0.2) —

    The ratio of number of groups for test data.

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

    The ratio of number of groups 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/group_shuffle_split.rb', line 46

def initialize(n_splits: 5, test_size: 0.2, 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/group_shuffle_split.rb', line 34

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/group_shuffle_split.rb', line 38

def rng
  @rng
end

Instance Method Details

#split(x, y, groups) ⇒ Array

Generate train and test data indices by randomly selecting group labels.

Parameters:

  • x (Numo::DFloat) —

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

  • y (Numo::Int32) —

    (shape: [n_samples]) This argument exists to unify the interface between the K-fold methods, it is not used in the method.

  • groups (Numo::Int32) —

    (shape: [n_samples]) The group labels to be used to generate data indices for 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/group_shuffle_split.rb', line 65

def split(_x, _y, groups)
  classes = groups.to_a.uniq.sort
  n_groups = classes.size
  n_test_groups = (@test_size * n_groups).ceil.to_i
  n_train_groups = @train_size.nil? ? n_groups - n_test_groups : (@train_size * n_groups).floor.to_i

  unless n_test_groups.between?(1, n_groups)
    raise RangeError,
          'The number of groups in test split must be not less than 1 and not more than the number of groups.'
  end
  unless n_train_groups.between?(1, n_groups)
    raise RangeError,
          'The number of groups in train split must be not less than 1 and not more than the number of groups.'
  end
  if (n_test_groups + n_train_groups) > n_groups
    raise RangeError,
          'The total number of groups in test split and train split must be not more than the number of groups.'
  end

  sub_rng = @rng.dup

  Array.new(@n_splits) do
    test_group_ids = classes.sample(n_test_groups, random: sub_rng)
    train_group_ids = if @train_size.nil?
                        classes - test_group_ids
                      else
                        (classes - test_group_ids).sample(n_train_groups, random: sub_rng)
                      end
    test_ids = in1d(groups, test_group_ids).where.to_a
    train_ids = in1d(groups, train_group_ids).where.to_a
    [train_ids, test_ids]
  end
end