Class: Rumale::Ensemble::RandomForestRegressor
- Inherits:
-
Object
- Object
- Rumale::Ensemble::RandomForestRegressor
- 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
Direct Known Subclasses
Instance Attribute Summary collapse
-
#estimators ⇒ Array<DecisionTreeRegressor>
readonly
Return the set of estimators.
-
#feature_importances ⇒ Numo::DFloat
readonly
Return the importance for each feature.
-
#rng ⇒ Random
readonly
Return the random generator for random selection of feature index.
Attributes included from Base::BaseEstimator
Instance Method Summary collapse
-
#apply(x) ⇒ Numo::Int32
Return the index of the leaf that each sample reached.
-
#fit(x, y) ⇒ RandomForestRegressor
Fit the model with given training data.
-
#initialize(n_estimators: 10, criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ RandomForestRegressor
constructor
Create a new regressor with random forest.
-
#marshal_dump ⇒ Hash
Dump marshal data.
-
#marshal_load(obj) ⇒ nil
Load marshal data.
-
#predict(x) ⇒ Numo::DFloat
Predict values for samples.
Methods included from Base::Regressor
Constructor Details
#initialize(n_estimators: 10, criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: 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 48 def initialize(n_estimators: 10, 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_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[: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 116 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 77 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 @feature_importances = Numo::DFloat.zeros(n_features) single_target = y.shape[1].nil? # Construct forest. @estimators = Array.new(@params[:n_estimators]) do 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(Rumale::Values.int_max) ) bootstrap_ids = Array.new(n_samples) { @rng.rand(0...n_samples) } tree.fit(x[bootstrap_ids, true], single_target ? y[bootstrap_ids] : y[bootstrap_ids, true]) @feature_importances += tree.feature_importances tree 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 123 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 132 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 107 def predict(x) check_sample_array(x) @estimators.map { |est| est.predict(x) }.reduce(&:+) / @params[:n_estimators] end |