Class: Rumale::Ensemble::ExtraTreesRegressor
- Inherits:
-
RandomForestRegressor
- Object
- RandomForestRegressor
- Rumale::Ensemble::ExtraTreesRegressor
- Defined in:
- lib/rumale/ensemble/extra_trees_regressor.rb
Overview
ExtraTreesRegressor is a class that implements extremely randomized trees for regression The algorithm of extremely randomized trees is similar to random forest. The features of the algorithm of extremely randomized trees are not to apply the bagging procedure and to randomly select the threshold for splitting feature space.
Reference
-
Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63 (1), pp. 3–42, 2006.
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Instance Attribute Summary collapse
-
#estimators ⇒ Array<ExtraTreeRegressor>
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) ⇒ ExtraTreesRegressor
Fit the model with given training data.
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#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) ⇒ ExtraTreesRegressor
constructor
Create a new regressor with extremely randomized trees.
-
#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, n_jobs: nil, random_seed: nil) ⇒ ExtraTreesRegressor
Create a new regressor with extremely randomized trees.
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# File 'lib/rumale/ensemble/extra_trees_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) super end |
Instance Attribute Details
#estimators ⇒ Array<ExtraTreeRegressor> (readonly)
Return the set of estimators.
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# File 'lib/rumale/ensemble/extra_trees_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/extra_trees_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/extra_trees_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/extra_trees_regressor.rb', line 109 def apply(x) check_sample_array(x) super end |
#fit(x, y) ⇒ ExtraTreesRegressor
Fit the model with given training data.
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# File 'lib/rumale/ensemble/extra_trees_regressor.rb', line 70 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] = Math.sqrt(n_features).to_i unless @params[:max_features].is_a?(Integer) @params[:max_features] = [[1, @params[:max_features]].max, n_features].min sub_rng = @rng.dup # Construct forest. rng_seeds = Array.new(@params[:n_estimators]) { sub_rng.rand(Rumale::Values.int_max) } @estimators = if enable_parallel? parallel_map(@params[:n_estimators]) { |n| plant_tree(rng_seeds[n]).fit(x, y) } else Array.new(@params[:n_estimators]) { |n| plant_tree(rng_seeds[n]).fit(x, y) } 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/extra_trees_regressor.rb', line 116 def marshal_dump super end |
#marshal_load(obj) ⇒ nil
Load marshal data.
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# File 'lib/rumale/ensemble/extra_trees_regressor.rb', line 122 def marshal_load(obj) super end |
#predict(x) ⇒ Numo::DFloat
Predict values for samples.
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# File 'lib/rumale/ensemble/extra_trees_regressor.rb', line 100 def predict(x) check_sample_array(x) super end |