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, n_jobs: 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.

  • n_jobs (Integer) (defaults to: nil) —

    The number of jobs for running the fit and predict methods in parallel. If nil is given, the methods do not execute in parallel. If zero or less is given, it becomes equal to the number of processors. This parameter is ignored if the Parallel gem is not loaded.

  • 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.



60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 60

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, 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_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[:n_jobs] = n_jobs
  @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>>]



31
32
33
 
# 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)



36
37
38
 
# 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) 


40
41
42
 
# 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.



140
141
142
143
144
145
146
147
148
149
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 140

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:



93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 93

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)
  # initialize some variables.
  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
  n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
  # train regressor.
  @base_predictions = n_outputs > 1 ? y.mean(0) : y.mean
  @estimators = if n_outputs > 1
                  multivar_estimators(x, y)
                else
                  partial_fit(x, y, @base_predictions)
                end
  # calculate feature importances.
  @feature_importances = if n_outputs > 1
                           multivar_feature_importances
                         else
                           @estimators.map(&:feature_importances).reduce(&:+)
                         end
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about GradientBoostingRegressor.



153
154
155
156
157
158
159
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 153

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) 


163
164
165
166
167
168
169
170
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 163

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.



122
123
124
125
126
127
128
129
130
131
132
133
134
 
# File 'lib/rumale/ensemble/gradient_boosting_regressor.rb', line 122

def predict(x)
  check_sample_array(x)
  n_outputs = @estimators.first.is_a?(Array) ? @estimators.size : 1
  if n_outputs > 1
    multivar_predict(x)
  else
    if enable_parallel?
      parallel_map(@params[:n_estimators]) { |n| @estimators[n].predict(x) }.reduce(&:+) + @base_predictions
    else
      @estimators.map { |tree| tree.predict(x) }.reduce(&:+) + @base_predictions
    end
  end
end