Class: Rumale::Ensemble::GradientBoostingRegressor

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Regressor
Defined in:
lib/rumale/ensemble/gradient_boosting_regressor.rb

Overview

GradientBoostingRegressor is a class that implements gradient tree boosting for regression. The class use L2 loss for the loss function.

reference

  • J H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, 29 (5), pp. 1189–1232, 2001.

  • J H. Friedman, “Stochastic Gradient Boosting,” Computational Statistics and Data Analysis, 38 (4), pp. 367–378, 2002.

    1. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proc. KDD’16, pp. 785–794, 2016.

Examples:

estimator =
  Rumale::Ensemble::GradientBoostingRegressor.new(
    n_estimators: 100, learning_rate: 0.3, reg_lambda: 0.001, 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

Constructor Details

#initialize(n_estimators: 100, learning_rate: 0.1, reg_lambda: 0.0, subsample: 1.0, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ GradientBoostingRegressor

Create a new regressor with gradient tree boosting.

Parameters:

  • n_estimators (Integer) (defaults to: 100) —

    The numeber of trees for contructing regressor.

  • learning_rate (Float) (defaults to: 0.1) —

    The boosting learining rate

  • reg_lambda (Float) (defaults to: 0.0) —

    The L2 regularization term on weight.

  • 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/ensemble/gradient_boosting_regressor.rb', line 56

def initialize(n_estimators: 100, learning_rate: 0.1, reg_lambda: 0.0, subsample: 1.0,
               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(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
  check_params_float(learning_rate: learning_rate, reg_lambda: reg_lambda, subsample: subsample)
  check_params_positive(n_estimators: n_estimators,
                        learning_rate: learning_rate, reg_lambda: reg_lambda, subsample: subsample,
                        max_depth: max_depth, max_leaf_nodes: max_leaf_nodes, min_samples_leaf: min_samples_leaf,
                        max_features: max_features)
  @params = {}
  @params[:n_estimators] = n_estimators
  @params[:learning_rate] = learning_rate
  @params[:reg_lambda] = reg_lambda
  @params[:subsample] = subsample
  @params[:max_depth] = max_depth
  @params[:max_leaf_nodes] = max_leaf_nodes
  @params[:min_samples_leaf] = min_samples_leaf
  @params[:max_features] = max_features
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @estimators = nil
  @base_predictions = nil
  @feature_importances = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#estimators ⇒ Array<GradientTreeRegressor> (readonly)

Return the set of estimators.

Returns:

  • (Array<GradientTreeRegressor>) —

    or [Array<Array<GradientTreeRegressor>>]



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 31

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature. The feature importances are calculated based on the numbers of times the feature is used for splitting.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 36

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 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 40

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 values.

Returns:

  • (Numo::Int32) —

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



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 146

def apply(x)
  check_sample_array(x)
  n_outputs = @estimators.first.is_a?(Array) ? @estimators.size : 1
  leaf_ids = if n_outputs > 1
               Array.new(n_outputs) { |n| @estimators[n].map { |tree| tree.apply(x) } }
             else
               @estimators.map { |tree| tree.apply(x) }
             end
  Numo::Int32[*leaf_ids].transpose
end

#fit(x, y) ⇒ GradientBoostingRegressor

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]) The target values to be used for fitting the model.

Returns:



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 89

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

  n_features = x.shape[1]
  @params[:max_features] = n_features if @params[:max_features].nil?
  @params[:max_features] = [[1, @params[:max_features]].max, n_features].min

  # train regressor.
  n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
  @base_predictions = n_outputs > 1 ? y.mean(0) : y.mean
  @estimators = if n_outputs > 1
                  Array.new(n_outputs) do |n|
                    partial_fit(x, y[true, n], @base_predictions[n])
                  end
                else
                  partial_fit(x, y, @base_predictions)
                end

  # calculate feature importances.
  @feature_importances = Numo::DFloat.zeros(n_features)
  if n_outputs > 1
    n_outputs.times do |n|
      @estimators[n].each { |tree| @feature_importances += tree.feature_importances }
    end
  else
    @estimators.each { |tree| @feature_importances += tree.feature_importances }
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about GradientBoostingRegressor.



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 159

def marshal_dump
  { params: @params,
    estimators: @estimators,
    base_predictions: @base_predictions,
    feature_importances: @feature_importances,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 169

def marshal_load(obj)
  @params = obj[:params]
  @estimators = obj[:estimators]
  @base_predictions = obj[:base_predictions]
  @feature_importances = obj[:feature_importances]
  @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]) Predicted values per sample.



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# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 126

def predict(x)
  check_sample_array(x)
  n_samples = x.shape[0]
  n_outputs = @estimators.first.is_a?(Array) ? @estimators.size : 1
  if n_outputs > 1
    predicted = Numo::DFloat.ones(n_samples, n_outputs) * @base_predictions
    n_outputs.times do |n|
      @estimators[n].each { |tree| predicted[true, n] += tree.predict(x) }
    end
  else
    predicted = Numo::DFloat.ones(n_samples) * @base_predictions
    @estimators.each { |tree| predicted += tree.predict(x) }
  end
  predicted
end