Class: Rumale::Tree::BaseDecisionTree

Inherits:
Base::Estimator show all
Defined in:
rumale-tree/lib/rumale/tree/base_decision_tree.rb

Overview

BaseDecisionTree is an abstract class for implementation of decision tree-based estimator. This class is used internally.

Instance Attribute Summary

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Constructor Details

#initialize(criterion: nil, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ BaseDecisionTree

Initialize a decision tree-based estimator.

Parameters:

  • criterion (String) (defaults to: nil) —

    The function to evalue spliting point.

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

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

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

    The maximum number of leaves on decision 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 all features.

  • 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-tree/lib/rumale/tree/base_decision_tree.rb', line 25

def initialize(criterion: nil, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil,
               random_seed: nil)
  super()
  @params = {
    criterion: criterion,
    max_depth: max_depth,
    max_leaf_nodes: max_leaf_nodes,
    min_samples_leaf: min_samples_leaf,
    max_features: max_features,
    random_seed: random_seed || srand
  }
  @rng = Random.new(@params[:random_seed])
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]) Leaf index for sample.



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# File 'rumale-tree/lib/rumale/tree/base_decision_tree.rb', line 43

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

  Numo::Int32[*(Array.new(x.shape[0]) { |n| partial_apply(@tree, x[n, true]) })]
end