Class: Rumale::Ensemble::VRTreesRegressor

Inherits:
RandomForestRegressor show all
Defined in:
rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb

Overview

VRTreesRegressor is a class that implements variable-random (VR) trees for regression

Reference

  • Liu, F. T., Ting, K. M., Yu, Y., and Zhou, Z. H., “Spectrum of Variable-Random Trees,” Journal of Artificial Intelligence Research, vol. 32, pp. 355–384, 2008.

Examples:

require 'rumale/ensemble/vr_trees_regressor'

estimator =
  Rumale::Ensemble::VRTreesRegressor.new(
    n_estimators: 10, criterion: 'mse', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(n_estimators: 10, criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, n_jobs: nil, random_seed: nil) ⇒ VRTreesRegressor

Create a new regressor with variable-random trees.

Parameters:

  • n_estimators (Integer) (defaults to: 10) —

    The numeber of trees for contructing variable-random trees.

  • criterion (String) (defaults to: 'mse') —

    The function to evalue spliting point. Supported criteria are ‘gini’ and ‘entropy’.

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

    The maximum depth of the tree. If nil is given, variable-random tree grows without concern for depth.

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

    The maximum number of leaves on variable-random tree. If nil is given, number of leaves is not limited.

  • min_samples_leaf (Integer) (defaults to: 1) —

    The minimum number of samples at a leaf node.

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

    The number of features to consider when searching optimal split point. If nil is given, split process considers ‘n_features’ features.

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

    The number of jobs for running the fit and predict methods in parallel. If nil is given, the methods do not execute in parallel. If zero or less is given, it becomes equal to the number of processors. This parameter is ignored if the Parallel gem is not loaded.

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

    The seed value using to initialize the random generator. It is used to randomly determine the order of features when deciding spliting point.



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 53

def initialize(n_estimators: 10,
               criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
               max_features: nil, n_jobs: nil, random_seed: nil)
  super
end

Instance Attribute Details

#estimators ⇒ Array<VRTreeRegressor> (readonly)

Return the set of estimators.

Returns:

  • (Array<VRTreeRegressor>) 


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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 26

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 30

def feature_importances
  @feature_importances
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 34

def rng
  @rng
end

Instance Method Details

#apply(x) ⇒ Numo::Int32

Return the index of the leaf that each sample reached.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to assign each leaf.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples, n_estimators]) Leaf index for sample.



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 107

def apply(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)

  super
end

#fit(x, y) ⇒ VRTreesRegressor

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model.

  • y (Numo::DFloat) —

    (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.

Returns:



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 64

def fit(x, y)
  x = ::Rumale::Validation.check_convert_sample_array(x)
  y = ::Rumale::Validation.check_convert_target_value_array(y)
  ::Rumale::Validation.check_sample_size(x, y)

  # Initialize some variables.
  n_features = x.shape[1]
  @params[:max_features] = n_features if @params[:max_features].nil?
  @params[:max_features] = @params[:max_features].clamp(1, n_features)
  sub_rng = @rng.dup
  # Construct forest.
  rng_seeds = Array.new(@params[:n_estimators]) { sub_rng.rand(::Rumale::Ensemble::Value::SEED_BASE) }
  alpha_ratio = 0.5 / @params[:n_estimators]
  alphas = Array.new(@params[:n_estimators]) { |v| v * alpha_ratio }
  @estimators = if enable_parallel?
                  parallel_map(@params[:n_estimators]) { |n| plant_tree(alphas[n], rng_seeds[n]).fit(x, y) }
                else
                  Array.new(@params[:n_estimators]) { |n| plant_tree(alphas[n], rng_seeds[n]).fit(x, y) }
                end
  @feature_importances =
    if enable_parallel?
      parallel_map(@params[:n_estimators]) { |n| @estimators[n].feature_importances }.sum
    else
      @estimators.sum(&:feature_importances)
    end
  @feature_importances /= @feature_importances.sum
  self
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the values.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_outputs]) Predicted value per sample.



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_regressor.rb', line 97

def predict(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)

  super
end