Class: Rumale::KernelMachine::KernelSVC

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Classifier
Defined in:
lib/rumale/kernel_machine/kernel_svc.rb

Overview

KernelSVC is a class that implements (Nonlinear) Kernel Support Vector Classifier with stochastic gradient descent (SGD) optimization. For multiclass classification problem, it uses one-vs-the-rest strategy.

Reference

    1. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, “Pegasos: Primal Estimated sub-GrAdient SOlver for SVM,” Mathematical Programming, vol. 127 (1), pp. 3–30, 2011.

Examples:

training_kernel_matrix = Rumale::PairwiseMetric::rbf_kernel(training_samples)
estimator =
  Rumale::KernelMachine::KernelSVC.new(reg_param: 1.0, max_iter: 1000, random_seed: 1)
estimator.fit(training_kernel_matrix, traininig_labels)
testing_kernel_matrix = Rumale::PairwiseMetric::rbf_kernel(testing_samples, training_samples)
results = estimator.predict(testing_kernel_matrix)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

Constructor Details

#initialize(reg_param: 1.0, max_iter: 1000, probability: false, n_jobs: nil, random_seed: nil) ⇒ KernelSVC

Create a new classifier with Kernel Support Vector Machine by the SGD optimization.

Parameters:

  • reg_param (Float) (defaults to: 1.0) —

    The regularization parameter.

  • max_iter (Integer) (defaults to: 1000) —

    The maximum number of iterations.

  • probability (Boolean) (defaults to: false) —

    The flag indicating whether to perform probability estimation.

  • n_jobs (Integer) (defaults to: nil) —

    The number of jobs for running the fit and predict methods in parallel. If nil is given, the methods do not execute in parallel. If zero or less is given, it becomes equal to the number of processors. This parameter is ignored if the Parallel gem is not loaded.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 50

def initialize(reg_param: 1.0, max_iter: 1000, probability: false, n_jobs: nil, random_seed: nil)
  check_params_float(reg_param: reg_param)
  check_params_integer(max_iter: max_iter)
  check_params_boolean(probability: probability)
  check_params_type_or_nil(Integer, n_jobs: n_jobs, random_seed: random_seed)
  check_params_positive(reg_param: reg_param, max_iter: max_iter)
  @params = {}
  @params[:reg_param] = reg_param
  @params[:max_iter] = max_iter
  @params[:probability] = probability
  @params[:n_jobs] = n_jobs
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @weight_vec = nil
  @prob_param = nil
  @classes = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_classes])



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 34

def classes
  @classes
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling.

Returns:

  • (Random) 


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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 38

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for Kernel SVC.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes, n_trainig_sample])



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 30

def weight_vec
  @weight_vec
end

Instance Method Details

#decision_function(x) ⇒ Numo::DFloat

Calculate confidence scores for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to compute the scores.

Returns:

  • (Numo::DFloat) —

    (shape: [n_testing_samples, n_classes]) Confidence score per sample.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 131

def decision_function(x)
  check_sample_array(x)

  x.dot(@weight_vec.transpose)
end

#fit(x, y) ⇒ KernelSVC

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_training_samples, n_training_samples]) The kernel matrix of the training data to be used for fitting the model.

  • y (Numo::Int32) —

    (shape: [n_training_samples]) The labels to be used for fitting the model.

Returns:

  • (KernelSVC) —

    The learned classifier itself.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 75

def fit(x, y)
  check_sample_array(x)
  check_label_array(y)
  check_sample_label_size(x, y)

  @classes = Numo::Int32[*y.to_a.uniq.sort]
  n_classes = @classes.size
  _n_samples, n_features = x.shape

  if n_classes > 2
    @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
    @prob_param = Numo::DFloat.zeros(n_classes, 2)
    if enable_parallel?
      # :nocov:
      models = parallel_map(n_classes) do |n|
        bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
        w = binary_fit(x, bin_y)
        p = if @params[:probability]
              Rumale::ProbabilisticOutput.fit_sigmoid(x.dot(w), bin_y)
            else
              Numo::DFloat[1, 0]
            end
        [w, p]
      end
      # :nocov:
      n_classes.times { |n| @weight_vec[n, true], @prob_param[n, true] = models[n] }
    else
      n_classes.times do |n|
        bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
        @weight_vec[n, true] = binary_fit(x, bin_y)
        @prob_param[n, true] = if @params[:probability]
                                 Rumale::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec[n, true].transpose), bin_y)
                               else
                                 Numo::DFloat[1, 0]
                               end
      end
    end
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
    @weight_vec = binary_fit(x, bin_y)
    @prob_param = if @params[:probability]
                    Rumale::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec.transpose), bin_y)
                  else
                    Numo::DFloat[1, 0]
                  end
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about KernelSVC.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 179

def marshal_dump
  { params: @params,
    weight_vec: @weight_vec,
    prob_param: @prob_param,
    classes: @classes,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 189

def marshal_load(obj)
  @params = obj[:params]
  @weight_vec = obj[:weight_vec]
  @prob_param = obj[:prob_param]
  @classes = obj[:classes]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::Int32

Predict class labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to predict the labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_testing_samples]) Predicted class label per sample.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 142

def predict(x)
  check_sample_array(x)

  return Numo::Int32.cast(decision_function(x).ge(0.0)) * 2 - 1 if @classes.size <= 2

  n_samples, = x.shape
  decision_values = decision_function(x)
  predicted = if enable_parallel?
                parallel_map(n_samples) { |n| @classes[decision_values[n, true].max_index] }
              else
                Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] }
              end
  Numo::Int32.asarray(predicted)
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_testing_samples, n_training_samples]) The kernel matrix between testing samples and training samples to predict the labels.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_classes]) Predicted probability of each class per sample.



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# File 'lib/rumale/kernel_machine/kernel_svc.rb', line 162

def predict_proba(x)
  check_sample_array(x)

  if @classes.size > 2
    probs = 1.0 / (Numo::NMath.exp(@prob_param[true, 0] * decision_function(x) + @prob_param[true, 1]) + 1.0)
    return (probs.transpose / probs.sum(axis: 1)).transpose
  end

  n_samples, = x.shape
  probs = Numo::DFloat.zeros(n_samples, 2)
  probs[true, 1] = 1.0 / (Numo::NMath.exp(@prob_param[0] * decision_function(x) + @prob_param[1]) + 1.0)
  probs[true, 0] = 1.0 - probs[true, 1]
  probs
end