Class: SVMKit::Ensemble::AdaBoostRegressor

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

Overview

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

Reference

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

Examples:

estimator =
  SVMKit::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 included from Base::BaseEstimator

#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.



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# File 'lib/svmkit/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)
  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(threshold: threshold, exponent: exponent)
  check_params_string(criterion: criterion)
  check_params_positive(n_estimators: n_estimators, threshold: threshold, exponent: exponent,
                        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[:threshold] = threshold
  @params[:exponent] = exponent
  @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[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @estimators = nil
  @feature_importances = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#estimator_weights ⇒ Numo::DFloat (readonly)

Return the weight for each weak learner.



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

def estimator_weights
  @estimator_weights
end

#estimators ⇒ Array<DecisionTreeRegressor> (readonly)

Return the set of estimators.



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

def estimators
  @estimators
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.



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

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/svmkit/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.

Raises:

  • (ArgumentError) 


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# File 'lib/svmkit/ensemble/ada_boost_regressor.rb', line 93

def fit(x, y) # rubocop:disable Metrics/AbcSize
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  # Check target values
  raise ArgumentError, 'Expect target value vector to be 1-D arrray' unless y.shape.size == 1
  # 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)
  # Construct forest.
  @params[:n_estimators].times do |_t|
    # Fit weak learner.
    ids = SVMKit::Utils.choice_ids(n_samples, observation_weights, @rng)
    tree = 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: @rng.rand(SVMKit::Values::int_max)
    )
    tree.fit(x[ids, true], y[ids])
    p = tree.predict(x)
    # Calculate errors.
    abs_err = ((p - y) / y).abs
    err = observation_weights[abs_err.gt(@params[:threshold])].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[abs_err.le(@params[:threshold])] = 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
  @estimator_weights = Numo::DFloat.asarray(@estimator_weights)
  @feature_importances /= @estimator_weights.sum
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.



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# File 'lib/svmkit/ensemble/ada_boost_regressor.rb', line 160

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

#marshal_load(obj) ⇒ nil

Load marshal data.



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# File 'lib/svmkit/ensemble/ada_boost_regressor.rb', line 170

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

#predict(x) ⇒ Numo::DFloat

Predict values for samples.



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# File 'lib/svmkit/ensemble/ada_boost_regressor.rb', line 147

def predict(x)
  check_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