Class: Rumale::Ensemble::AdaBoostRegressor

Inherits:
Base::Estimator show all
Includes:
Base::Regressor
Defined in:
rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb

Overview

AdaBoostRegressor is a class that implements AdaBoost for regression. This class uses decision tree for a weak learner.

Reference

  • Shrestha, D. L., and Solomatine, D. P., “Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression,” Neural Computation 18 (7), pp. 1678–1710, 2006.

Examples:

require 'rumale/ensemble/ada_boost_regressor'

estimator =
  Rumale::Ensemble::AdaBoostRegressor.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)

Instance Attribute Summary collapse

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(n_estimators: 10, threshold: 0.2, exponent: 1.0, criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ AdaBoostRegressor

Create a new regressor with random forest.

Parameters:

  • n_estimators (Integer) (defaults to: 10)

    The numeber of decision trees for contructing AdaBoost regressor.

  • threshold (Float) (defaults to: 0.2)

    The threshold for delimiting correct and incorrect predictions. That is constrained to [0, 1]

  • exponent (Float) (defaults to: 1.0)

    The exponent for the weight of each weak learner.

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

    The function to evalue spliting point. Supported criteria are ‘gini’ and ‘entropy’.

  • 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-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 60

def initialize(n_estimators: 10, threshold: 0.2, exponent: 1.0,
               criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
               max_features: nil, random_seed: nil)
  super()
  @params = {
    n_estimators: n_estimators,
    threshold: threshold,
    exponent: exponent,
    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 Attribute Details

#estimator_weightsNumo::DFloat (readonly)

Return the weight for each weak learner.

Returns:

  • (Numo::DFloat)

    (size: n_estimates)



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# File 'rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 35

def estimator_weights
  @estimator_weights
end

#estimatorsArray<DecisionTreeRegressor> (readonly)

Return the set of estimators.

Returns:

  • (Array<DecisionTreeRegressor>)


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

def estimators
  @estimators
end

#feature_importancesNumo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat)

    (size: n_features)



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# File 'rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 39

def feature_importances
  @feature_importances
end

#rngRandom (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random)


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# File 'rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 43

def rng
  @rng
end

Instance Method Details

#fit(x, y) ⇒ AdaBoostRegressor

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 'rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 83

def fit(x, y) # rubocop:disable Metrics/AbcSize, Metrics/MethodLength
  x = ::Rumale::Validation.check_convert_sample_array(x)
  y = ::Rumale::Validation.check_convert_target_value_array(y)
  ::Rumale::Validation.check_sample_size(x, y)
  unless y.ndim == 1
    raise ArgumentError,
          'AdaBoostRegressor supports only single-target variable regression; ' \
          'the target value array is expected to be 1-D'
  end

  # Initialize some variables.
  n_samples, n_features = x.shape
  @params[:max_features] = n_features unless @params[:max_features].is_a?(Integer)
  @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
  observation_weights = Numo::DFloat.zeros(n_samples) + 1.fdiv(n_samples)
  @estimators = []
  @estimator_weights = []
  @feature_importances = Numo::DFloat.zeros(n_features)
  sub_rng = @rng.dup
  # Construct forest.
  @params[:n_estimators].times do |_t|
    # Fit weak learner.
    ids = ::Rumale::Utils.choice_ids(n_samples, observation_weights, sub_rng)
    tree = ::Rumale::Tree::DecisionTreeRegressor.new(
      criterion: @params[:criterion], max_depth: @params[:max_depth],
      max_leaf_nodes: @params[:max_leaf_nodes], min_samples_leaf: @params[:min_samples_leaf],
      max_features: @params[:max_features], random_seed: sub_rng.rand(::Rumale::Ensemble::Value::SEED_BASE)
    )
    tree.fit(x[ids, true], y[ids])
    pred = tree.predict(x)
    # Calculate errors.
    abs_err = ((pred - y) / y).abs
    sum_target = abs_err.gt(@params[:threshold])
    break if sum_target.count.zero?

    err = observation_weights[sum_target].sum
    break if err <= 0.0

    # Calculate weight.
    beta = err**@params[:exponent]
    weight = Math.log(1.fdiv(beta))
    # Store model.
    @estimators.push(tree)
    @estimator_weights.push(weight)
    @feature_importances += weight * tree.feature_importances
    # Update observation weights.
    update = Numo::DFloat.ones(n_samples)
    update_target = abs_err.le(@params[:threshold])
    break if update_target.count.zero?

    update[update_target] = beta
    observation_weights *= update
    observation_weights = observation_weights.clip(1.0e-15, nil)
    sum_observation_weights = observation_weights.sum
    break if sum_observation_weights.zero?

    observation_weights /= sum_observation_weights
  end
  if @estimators.empty?
    warn('Failed to converge, check hyper-parameters of AdaBoostRegressor.')
    self
  end
  @estimator_weights = Numo::DFloat.asarray(@estimator_weights)
  @feature_importances /= @estimator_weights.sum
  self
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 value per sample.



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# File 'rumale-ensemble/lib/rumale/ensemble/ada_boost_regressor.rb', line 154

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

  n_samples, = x.shape
  predictions = Numo::DFloat.zeros(n_samples)
  @estimators.size.times do |t|
    predictions += @estimator_weights[t] * @estimators[t].predict(x)
  end
  sum_weight = @estimator_weights.sum
  predictions / sum_weight
end