Class: Rumale::Tree::DecisionTreeRegressor

Inherits:
BaseDecisionTree show all
Includes:
Base::Regressor
Defined in:
lib/rumale/tree/decision_tree_regressor.rb

Overview

DecisionTreeRegressor is a class that implements decision tree for regression.

Examples:

estimator =
  Rumale::Tree::DecisionTreeRegressor.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)

Direct Known Subclasses

ExtraTreeRegressor

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) ⇒ DecisionTreeRegressor

Create a new regressor with decision 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, 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 'lib/rumale/tree/decision_tree_regressor.rb', line 50

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
  @leaf_values = nil
end

Instance Attribute Details

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 24

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])



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 36

def leaf_values
  @leaf_values
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 32

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.

Returns:



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 28

def tree
  @tree
end

Instance Method Details

#fit(x, y) ⇒ DecisionTreeRegressor

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:



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 67

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  n_samples, n_features = x.shape
  @params[:max_features] = n_features if @params[:max_features].nil?
  @params[:max_features] = [@params[:max_features], n_features].min
  @n_leaves = 0
  @leaf_values = []
  @sub_rng = @rng.dup
  build_tree(x, y)
  eval_importance(n_samples, n_features)
  @leaf_values = Numo::DFloat.cast(@leaf_values)
  @leaf_values = @leaf_values.flatten.dup if @leaf_values.shape[1] == 1
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about DecisionTreeRegressor



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 95

def marshal_dump
  { params: @params,
    tree: @tree,
    feature_importances: @feature_importances,
    leaf_values: @leaf_values,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 105

def marshal_load(obj)
  @params = obj[:params]
  @tree = obj[:tree]
  @feature_importances = obj[:feature_importances]
  @leaf_values = obj[:leaf_values]
  @rng = obj[:rng]
  nil
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.



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# File 'lib/rumale/tree/decision_tree_regressor.rb', line 88

def predict(x)
  check_sample_array(x)
  @leaf_values.shape[1].nil? ? @leaf_values[apply(x)].dup : @leaf_values[apply(x), true].dup
end