class Rumale::SVM::OneClassSVM

OneClassSVM is a class that provides One-class Support Vector Machine in LIBSVM with Rumale interface.

@example

estimator = Rumale::SVM::OneClassSVM.new(nu: 0.5, kernel: 'rbf', gamma: 10.0, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)

Public Class Methods

new(nu: 1.0, kernel: 'rbf', degree: 3, gamma: 1.0, coef0: 0.0, shrinking: true, cache_size: 200.0, tol: 1e-3, verbose: false, random_seed: nil) click to toggle source

Create a new estimator with One-class Support Vector Machine.

@param nu [Float] The regularization parameter. The interval of nu is (0, 1]. @param kernel [String] The type of kernel function ('rbf', 'linear', 'poly', 'sigmoid', and 'precomputed'). @param degree [Integer] The degree parameter in polynomial kernel function. @param gamma [Float] The gamma parameter in rbf/poly/sigmoid kernel function. @param coef0 [Float] The coefficient in poly/sigmoid kernel function. @param shrinking [Boolean] The flag indicating whether to use the shrinking heuristics. @param cache_size [Float] The cache memory size in MB. @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/one_class_svm.rb, line 31
def initialize(nu: 1.0, kernel: 'rbf', degree: 3, gamma: 1.0, coef0: 0.0,
               shrinking: true, cache_size: 200.0, tol: 1e-3, verbose: false, random_seed: nil)
  check_params_numeric(nu: nu, degree: degree, gamma: gamma, coef0: coef0, cache_size: cache_size, tol: tol)
  check_params_string(kernel: kernel)
  check_params_boolean(shrinking: shrinking, verbose: verbose)
  check_params_numeric_or_nil(random_seed: random_seed)
  @params = {}
  @params[:nu] = nu.to_f
  @params[:kernel] = kernel
  @params[:degree] = degree.to_i
  @params[:gamma] = gamma.to_f
  @params[:coef0] = coef0.to_f
  @params[:shrinking] = shrinking
  @params[:cache_size] = cache_size.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

decision_function(x) click to toggle source

Calculate confidence scores for samples.

@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to compute the scores.

If the kernel is 'precomputed', the shape of x must be [n_samples, n_training_samples].

@return [Numo::DFloat] (shape: [n_samples, n_classes]) Confidence score per sample.

# File lib/rumale/svm/one_class_svm.rb, line 68
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)
  Numo::Libsvm.decision_function(x, libsvm_params, @model)
end
duel_coef() click to toggle source

Return the coefficients of the support vector in decision function. @return [Numo::DFloat] (shape: [1, n_support_vectors])

# File lib/rumale/svm/one_class_svm.rb, line 120
def duel_coef
  @model[:sv_coef]
end
fit(x, _y = nil) click to toggle source

Fit the model with given training data.

@overload fit(x) -> OneClassSVM

@param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
  If the kernel is 'precomputed', x must be a square distance matrix (shape: [n_samples, n_samples]).

@return [OneClassSVM] The learned estimator itself.

# File lib/rumale/svm/one_class_svm.rb, line 56
def fit(x, _y = nil)
  x = check_convert_sample_array(x)
  dummy = Numo::DFloat.ones(x.shape[0])
  @model = Numo::Libsvm.train(x, dummy, libsvm_params)
  self
end
intercept() click to toggle source

Return the intercepts in decision function. @return [Numo::DFloat] (shape: [1])

# File lib/rumale/svm/one_class_svm.rb, line 126
def intercept
  @model[:rho]
end
marshal_dump() click to toggle source

Dump marshal data. @return [Hash] The marshal data about SVC.

# File lib/rumale/svm/one_class_svm.rb, line 87
def marshal_dump
  { params: @params,
    model: @model }
end
marshal_load(obj) click to toggle source

Load marshal data. @return [nil]

# File lib/rumale/svm/one_class_svm.rb, line 94
def marshal_load(obj)
  @params = obj[:params]
  @model = obj[:model]
  nil
end
n_support() click to toggle source

Return the number of support vectors. @return [Integer]

# File lib/rumale/svm/one_class_svm.rb, line 114
def n_support
  @model[:sv_indices].size
end
predict(x) click to toggle source

Predict class labels for samples.

@param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the labels.

If the kernel is 'precomputed', the shape of x must be [n_samples, n_training_samples].

@return [Numo::Int32] (shape: [n_samples]) Predicted label per sample.

# File lib/rumale/svm/one_class_svm.rb, line 79
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)
  Numo::Int32.cast(Numo::Libsvm.predict(x, libsvm_params, @model))
end
support() click to toggle source

Return the indices of support vectors. @return [Numo::Int32] (shape: [n_support_vectors])

# File lib/rumale/svm/one_class_svm.rb, line 102
def support
  @model[:sv_indices]
end
support_vectors() click to toggle source

Return the support_vectors. @return [Numo::DFloat] (shape: [n_support_vectors, n_features])

# File lib/rumale/svm/one_class_svm.rb, line 108
def support_vectors
  @model[:SV]
end

Private Instance Methods

libsvm_params() click to toggle source
# File lib/rumale/svm/one_class_svm.rb, line 132
def libsvm_params
  res = @params.merge(svm_type: Numo::Libsvm::SvmType::ONE_CLASS)
  res[:kernel_type] = case res.delete(:kernel)
                      when 'linear'
                        Numo::Libsvm::KernelType::LINEAR
                      when 'poly'
                        Numo::Libsvm::KernelType::POLY
                      when 'sigmoid'
                        Numo::Libsvm::KernelType::SIGMOID
                      when 'precomputed'
                        Numo::Libsvm::KernelType::PRECOMPUTED
                      else
                        Numo::Libsvm::KernelType::RBF
                      end
  res[:eps] = res.delete(:tol)
  res
end
trained?() click to toggle source
# File lib/rumale/svm/one_class_svm.rb, line 150
def trained?
  !@model.nil?
end