class Rumale::SVM::LinearSVR
LinearSVR
is a class that provides Support Vector Regressor in LIBLINEAR with Rumale
interface.
@example
estimator = Rumale::SVM::LinearSVR.new(reg_param: 1.0, random_seed: 1) estimator.fit(training_samples, traininig_target_values) results = estimator.predict(testing_samples)
Attributes
Return the bias term (a.k.a. intercept) for LinearSVR
. @return [Numo::DFloat] (shape: [n_classes])
Return the weight vector for LinearSVR
. @return [Numo::DFloat] (shape: [n_classes, n_features])
Public Class Methods
Create a new regressor with Support Vector Regressor.
@param loss [String] The type of loss function ('squared_epsilon_insensitive' or 'epsilon_insensitive'). @param dual [Boolean] The flag indicating whether to solve dual optimization problem.
When n_samples > n_features, dual = false is more preferable. This parameter is ignored if loss = 'epsilon_insensitive'.
@param reg_param [Float] The regularization parameter. @param epsilon [Float] The epsilon parameter in loss function of espsilon-svr. @param fit_bias [Boolean] The flag indicating whether to fit the bias term. @param bias_scale
[Float] The scale of the bias term.
This parameter is ignored if fit_bias = false.
@param tol [Float] The tolerance of termination criterion. @param verbose [Boolean] The flag indicating whether to output learning process message @param random_seed [Integer/Nil] The seed value using to initialize the random generator.
# File lib/rumale/svm/linear_svr.rb, line 41 def initialize(loss: 'squared_epsilon_insensitive', dual: true, reg_param: 1.0, epsilon: 0.1, fit_bias: true, bias_scale: 1.0, tol: 1e-3, verbose: false, random_seed: nil) check_params_string(loss: loss) check_params_numeric(reg_param: reg_param, epsilon: epsilon, bias_scale: bias_scale, tol: tol) check_params_boolean(dual: dual, fit_bias: fit_bias, verbose: verbose) check_params_numeric_or_nil(random_seed: random_seed) @params = {} @params[:loss] = loss == 'epsilon_insensitive' ? 'epsilon_insensitive' : 'squared_epsilon_insensitive' @params[:dual] = dual @params[:reg_param] = reg_param.to_f @params[:epsilon] = epsilon.to_f @params[:fit_bias] = fit_bias @params[:bias_scale] = bias_scale.to_f @params[:tol] = tol.to_f @params[:verbose] = verbose @params[:random_seed] = random_seed.nil? ? nil : random_seed.to_i end
Public Instance Methods
Fit the model with given training data.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model. @param y [Numo::DFloat] (shape: [n_samples]) The target values to be used for fitting the model. @return [LinearSVR] The learned regressor itself.
# File lib/rumale/svm/linear_svr.rb, line 64 def fit(x, y) x = check_convert_sample_array(x) y = check_convert_tvalue_array(y) check_sample_tvalue_size(x, y) xx = fit_bias? ? expand_feature(x) : x @model = Numo::Liblinear.train(xx, y, liblinear_params) @weight_vec, @bias_term = weight_and_bias(@model[:w]) self end
Dump marshal data. @return [Hash] The marshal data about LinearSVR
.
# File lib/rumale/svm/linear_svr.rb, line 87 def marshal_dump { params: @params, model: @model, weight_vec: @weight_vec, bias_term: @bias_term } end
Load marshal data. @return [nil]
# File lib/rumale/svm/linear_svr.rb, line 96 def marshal_load(obj) @params = obj[:params] @model = obj[:model] @weight_vec = obj[:weight_vec] @bias_term = obj[:bias_term] nil end
Predict values for samples.
@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels. @return [Numo::DFloat] (shape: [n_samples]) Predicted value per sample.
# File lib/rumale/svm/linear_svr.rb, line 78 def predict(x) raise "#{self.class.name}\##{__method__} expects to be called after training the model with the fit method." unless trained? x = check_convert_sample_array(x) xx = fit_bias? ? expand_feature(x) : x Numo::Liblinear.predict(xx, liblinear_params, @model) end
Private Instance Methods
# File lib/rumale/svm/linear_svr.rb, line 143 def bias_scale @params[:bias_scale] end
# File lib/rumale/svm/linear_svr.rb, line 106 def expand_feature(x) n_samples = x.shape[0] Numo::NArray.hstack([x, Numo::DFloat.ones([n_samples, 1]) * bias_scale]) end
# File lib/rumale/svm/linear_svr.rb, line 139 def fit_bias? @params[:fit_bias] end
# File lib/rumale/svm/linear_svr.rb, line 121 def liblinear_params res = {} res[:solver_type] = solver_type res[:eps] = @params[:tol] res[:C] = @params[:reg_param] res[:p] = @params[:epsilon] res[:verbose] = @params[:verbose] res[:random_seed] = @params[:random_seed] res end
# File lib/rumale/svm/linear_svr.rb, line 132 def solver_type return Numo::Liblinear::SolverType::L2R_L1LOSS_SVR_DUAL if @params[:loss] == 'epsilon_insensitive' return Numo::Liblinear::SolverType::L2R_L2LOSS_SVR_DUAL if @params[:dual] Numo::Liblinear::SolverType::L2R_L2LOSS_SVR end
# File lib/rumale/svm/linear_svr.rb, line 147 def trained? !@model.nil? end
# File lib/rumale/svm/linear_svr.rb, line 111 def weight_and_bias(base_weight) bias_vec = 0.0 weight_mat = base_weight.dup if fit_bias? bias_vec = weight_mat[-1] weight_mat = weight_mat[0...-1].dup end [weight_mat, bias_vec] end