Class: Rumale::Tree::ExtraTreeRegressor

Inherits:
DecisionTreeRegressor show all
Defined in:
lib/rumale/tree/extra_tree_regressor.rb

Overview

ExtraTreeRegressor is a class that implements extra randomized tree for regression.

Reference

    1. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63 (1), pp. 3–42, 2006.

Examples:

estimator =
  Rumale::Tree::ExtraTreeRegressor.new(
    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 included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Methods inherited from BaseDecisionTree

#apply

Constructor Details

#initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ ExtraTreeRegressor

Create a new regressor with extra randomized tree algorithm.

Parameters:

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

    The function to evaluate spliting point. Supported criteria are ‘mae’ and ‘mse’.

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

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

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

    The maximum number of leaves on extra 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.



47
48
49
50
51
52
53
54
55
56
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 47

def initialize(criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil,
               random_seed: nil)
  check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, random_seed: random_seed)
  check_params_integer(min_samples_leaf: min_samples_leaf)
  check_params_string(criterion: criterion)
  check_params_positive(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                        min_samples_leaf: min_samples_leaf, max_features: max_features)
  super
end

Instance Attribute Details

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



21
22
23
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 21

def feature_importances
  @feature_importances
end

#leaf_values ⇒ Numo::DFloat (readonly)

Return the values assigned each leaf.

Returns:

  • (Numo::DFloat) —

    (shape: [n_leafs, n_outputs])



33
34
35
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 33

def leaf_values
  @leaf_values
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


29
30
31
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 29

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.

Returns:



25
26
27
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 25

def tree
  @tree
end

Instance Method Details

#fit(x, y) ⇒ ExtraTreeRegressor

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 taget values to be used for fitting the model.

Returns:



63
64
65
66
67
68
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 63

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  super
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about ExtraTreeRegressor



81
82
83
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 81

def marshal_dump
  super
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


87
88
89
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 87

def marshal_load(obj)
  super
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 values per sample.



74
75
76
77
 
# File 'lib/rumale/tree/extra_tree_regressor.rb', line 74

def predict(x)
  check_sample_array(x)
  super
end