Class: Rumale::Ensemble::VRTreesClassifier

Inherits:
RandomForestClassifier show all
Defined in:
rumale-ensemble/lib/rumale/ensemble/vr_trees_classifier.rb

Overview

VRTreesClassifier is a class that implements variable-random (VR) trees for classification.

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_classifier'

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

Instance Attribute Summary collapse

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

Constructor Details

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

Create a new classifier with variable-random trees.

Parameters:

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

    The numeber of trees for contructing variable-random trees.

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

    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 method in parallel. If nil is given, the method does 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_classifier.rb', line 57

def initialize(n_estimators: 10,
               criterion: 'gini', 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

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (size: n_classes)



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

def classes
  @classes
end

#estimators ⇒ Array<VRTreeClassifier> (readonly)

Return the set of estimators.

Returns:

  • (Array<VRTreeClassifier>) 


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

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_classifier.rb', line 38

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 predict the labels.

Returns:

  • (Numo::Int32) —

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



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_classifier.rb', line 122

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

  super
end

#fit(x, y) ⇒ VRTreesClassifier

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::Int32) —

    (shape: [n_samples]) The labels to be used for fitting the model.

Returns:



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_classifier.rb', line 68

def fit(x, y)
  x = ::Rumale::Validation.check_convert_sample_array(x)
  y = ::Rumale::Validation.check_convert_label_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)
  @classes = Numo::Int32.asarray(y.to_a.uniq.sort)
  sub_rng = @rng.dup
  # Construct trees.
  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::Int32

Predict class labels for samples.

Parameters:

  • x (Numo::DFloat) —

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

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted class label per sample.



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_classifier.rb', line 102

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

  super
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

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

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_classes]) Predicted probability of each class per sample.



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# File 'rumale-ensemble/lib/rumale/ensemble/vr_trees_classifier.rb', line 112

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

  super
end