Class: Rumale::Ensemble::RandomForestRegressor

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

Overview

RandomForestRegressor is a class that implements random forest for regression

Examples:

estimator =
  Rumale::Ensemble::RandomForestRegressor.new(
    n_estimators: 10, criterion: 'mse', 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

ExtraTreesRegressor

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: 10, criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, n_jobs: nil, random_seed: nil) ⇒ RandomForestRegressor

Create a new regressor with random forest.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 52

def initialize(n_estimators: 10,
               criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
               max_features: nil, n_jobs: nil, random_seed: nil)
  check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, n_jobs: n_jobs, random_seed: random_seed)
  check_params_integer(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
  check_params_string(criterion: criterion)
  check_params_positive(n_estimators: n_estimators, 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[:criterion] = criterion
  @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[:n_jobs] = n_jobs
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @estimators = nil
  @feature_importances = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#estimators ⇒ Array<DecisionTreeRegressor> (readonly)

Return the set of estimators.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 25

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 29

def feature_importances
  @feature_importances
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 33

def rng
  @rng
end

Instance Method Details

#apply(x) ⇒ Numo::Int32

Return the index of the leaf that each sample reached.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 137

def apply(x)
  check_sample_array(x)
  Numo::Int32[*Array.new(@params[:n_estimators]) { |n| @estimators[n].apply(x) }].transpose
end

#fit(x, y) ⇒ RandomForestRegressor

Fit the model with given training data.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 82

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  # Initialize some variables.
  n_samples, n_features = x.shape
  @params[:max_features] = Math.sqrt(n_features).to_i unless @params[:max_features].is_a?(Integer)
  @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
  single_target = y.shape[1].nil?
  sub_rng = @rng.dup
  rngs = Array.new(@params[:n_estimators]) { Random.new(sub_rng.rand(Rumale::Values.int_max)) }
  # Construct forest.
  @estimators =
    if enable_parallel?
      # :nocov:
      parallel_map(@params[:n_estimators]) do |n|
        bootstrap_ids = Array.new(n_samples) { rngs[n].rand(0...n_samples) }
        tree = plant_tree(rngs[n].rand(Rumale::Values.int_max))
        tree.fit(x[bootstrap_ids, true], single_target ? y[bootstrap_ids] : y[bootstrap_ids, true])
      end
      # :nocov:
    else
      Array.new(@params[:n_estimators]) do |n|
        bootstrap_ids = Array.new(n_samples) { rngs[n].rand(0...n_samples) }
        tree = plant_tree(rngs[n].rand(Rumale::Values.int_max))
        tree.fit(x[bootstrap_ids, true], single_target ? y[bootstrap_ids] : y[bootstrap_ids, true])
      end
    end
  @feature_importances =
    if enable_parallel?
      parallel_map(@params[:n_estimators]) { |n| @estimators[n].feature_importances }.reduce(&:+)
    else
      @estimators.map(&:feature_importances).reduce(&:+)
    end
  @feature_importances /= @feature_importances.sum
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 144

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

#marshal_load(obj) ⇒ nil

Load marshal data.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 153

def marshal_load(obj)
  @params = obj[:params]
  @estimators = obj[:estimators]
  @feature_importances = obj[:feature_importances]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.



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# File 'lib/rumale/ensemble/random_forest_regressor.rb', line 124

def predict(x)
  check_sample_array(x)
  if enable_parallel?
    parallel_map(@params[:n_estimators]) { |n| @estimators[n].predict(x) }.reduce(&:+) / @params[:n_estimators]
  else
    @estimators.map { |tree| tree.predict(x) }.reduce(&:+) / @params[:n_estimators]
  end
end