class Rumale::SVM::LinearSVC
LinearSVC
is a class that provides Support Vector Classifier in LIBLINEAR with Rumale
interface.
@example
estimator = Rumale::SVM::LinearSVC.new(penalty: 'l2', loss: 'squared_hinge', reg_param: 1.0, random_seed: 1) estimator.fit(training_samples, traininig_labels) results = estimator.predict(testing_samples)
Attributes
Return the bias term (a.k.a. intercept) for LinearSVC
. @return [Numo::DFloat] (shape: [n_classes])
Return the weight vector for LinearSVC
. @return [Numo::DFloat] (shape: [n_classes, n_features
])
Public Class Methods
Create a new classifier with Support Vector Classifier.
@param penalty [String] The type of norm used in the penalization ('l2' or 'l1'). @param loss [String] The type of loss function ('squared_hinge' or 'hinge').
This parameter is ignored if penalty = 'l1'.
@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 = 'hinge'.
@param reg_param [Float] The regularization parameter. @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 probability [Boolean] The flag indicating whether to train the parameter for probability estimation. @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_svc.rb, line 44 def initialize(penalty: 'l2', loss: 'squared_hinge', dual: true, reg_param: 1.0, fit_bias: true, bias_scale: 1.0, probability: false, tol: 1e-3, verbose: false, random_seed: nil) check_params_string(penalty: penalty, loss: loss) check_params_numeric(reg_param: reg_param, bias_scale: bias_scale, tol: tol) check_params_boolean(dual: dual, fit_bias: fit_bias, probability: probability, verbose: verbose) check_params_numeric_or_nil(random_seed: random_seed) @params = {} @params[:penalty] = penalty == 'l1' ? 'l1' : 'l2' @params[:loss] = loss == 'hinge' ? 'hinge' : 'squared_hinge' @params[:dual] = dual @params[:reg_param] = reg_param.to_f @params[:fit_bias] = fit_bias @params[:bias_scale] = bias_scale.to_f @params[:probability] = probability @params[:tol] = tol.to_f @params[:verbose] = verbose @params[:random_seed] = random_seed.nil? ? nil : random_seed.to_i end
Public Instance Methods
Calculate confidence scores for samples.
@param x [Numo::DFloat] (shape: [n_samples, n_features
]) The samples to compute the scores. @return [Numo::DFloat] (shape: [n_samples, n_classes
]) Confidence score per sample.
# File lib/rumale/svm/linear_svc.rb, line 83 def decision_function(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.decision_function(xx, liblinear_params, @model) end
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::Int32] (shape: [n_samples]) The labels to be used for fitting the model. @return [LinearSVC] The learned classifier itself.
# File lib/rumale/svm/linear_svc.rb, line 68 def fit(x, y) x = check_convert_sample_array(x) y = check_convert_label_array(y) check_sample_label_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]) @prob_param = proba_model(decision_function(x), y) self end
Dump marshal data. @return [Hash] The marshal data about LinearSVC
.
# File lib/rumale/svm/linear_svc.rb, line 122 def marshal_dump { params: @params, model: @model, weight_vec: @weight_vec, bias_term: @bias_term, prob_param: @prob_param } end
Load marshal data. @return [nil]
# File lib/rumale/svm/linear_svc.rb, line 132 def marshal_load(obj) @params = obj[:params] @model = obj[:model] @weight_vec = obj[:weight_vec] @bias_term = obj[:bias_term] @prob_param = obj[:prob_param] nil end
Predict class labels for samples.
@param x [Numo::DFloat] (shape: [n_samples, n_features
]) The samples to predict the labels. @return [Numo::Int32] (shape: [n_samples]) Predicted class label per sample.
# File lib/rumale/svm/linear_svc.rb, line 94 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::Int32.cast(Numo::Liblinear.predict(xx, liblinear_params, @model)) end
Predict class probability for samples. This method works correctly only if the probability parameter is true.
@param x [Numo::DFloat] (shape: [n_samples, n_features
]) The samples to predict the probailities. @return [Numo::DFloat] (shape: [n_samples, n_classes
]) Predicted probability of each class per sample.
# File lib/rumale/svm/linear_svc.rb, line 106 def predict_proba(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) if binary_class? probs = Numo::DFloat.zeros(x.shape[0], 2) probs[true, 0] = 1.0 / (Numo::NMath.exp(@prob_param[0] * decision_function(x) + @prob_param[1]) + 1.0) probs[true, 1] = 1.0 - probs[true, 0] else probs = 1.0 / (Numo::NMath.exp(@prob_param[true, 0] * decision_function(x) + @prob_param[true, 1]) + 1.0) probs = (probs.transpose / probs.sum(axis: 1)).transpose.dup end probs end
Private Instance Methods
# File lib/rumale/svm/linear_svc.rb, line 219 def bias_scale @params[:bias_scale] end
# File lib/rumale/svm/linear_svc.rb, line 207 def binary_class? @model[:nr_class] == 2 end
# File lib/rumale/svm/linear_svc.rb, line 143 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_svc.rb, line 215 def fit_bias? @params[:fit_bias] end
# File lib/rumale/svm/linear_svc.rb, line 211 def fit_probability? @params[:probability] end
# File lib/rumale/svm/linear_svc.rb, line 231 def labels @model[:label] end
# File lib/rumale/svm/linear_svc.rb, line 183 def liblinear_params res = {} res[:solver_type] = solver_type res[:eps] = @params[:tol] res[:C] = @params[:reg_param] res[:verbose] = @params[:verbose] res[:random_seed] = @params[:random_seed] res end
# File lib/rumale/svm/linear_svc.rb, line 223 def n_classes @model[:nr_class] end
# File lib/rumale/svm/linear_svc.rb, line 227 def n_features @model[:nr_feature] end
# File lib/rumale/svm/linear_svc.rb, line 167 def proba_model(df, y) res = binary_class? ? Numo::DFloat[1, 0] : Numo::DFloat.cast([[1, 0]] * n_classes) return res unless fit_probability? if binary_class? bin_y = Numo::Int32.cast(y.eq(labels[0])) * 2 - 1 res = Rumale::ProbabilisticOutput.fit_sigmoid(df, bin_y) else labels.each_with_index do |c, n| bin_y = Numo::Int32.cast(y.eq(c)) * 2 - 1 res[n, true] = Rumale::ProbabilisticOutput.fit_sigmoid(df[true, n], bin_y) end end res end
# File lib/rumale/svm/linear_svc.rb, line 193 def solver_type return Numo::Liblinear::SolverType::L1R_L2LOSS_SVC if @params[:penalty] == 'l1' if @params[:loss] == 'squared_hinge' if @params[:dual] Numo::Liblinear::SolverType::L2R_L2LOSS_SVC_DUAL else Numo::Liblinear::SolverType::L2R_L2LOSS_SVC end else Numo::Liblinear::SolverType::L2R_L1LOSS_SVC_DUAL end end
# File lib/rumale/svm/linear_svc.rb, line 235 def trained? !@model.nil? end
# File lib/rumale/svm/linear_svc.rb, line 148 def weight_and_bias(base_weight) if binary_class? 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 else bias_vec = Numo::DFloat.zeros(n_classes) weight_mat = base_weight.reshape(n_features, n_classes).transpose.dup if fit_bias? bias_vec = weight_mat[true, -1].dup weight_mat = weight_mat[true, 0...-1].dup end end [weight_mat, bias_vec] end