Class: SVMKit::LinearModel::SVC

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Classifier
Defined in:
lib/svmkit/linear_model/svc.rb

Overview

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

Reference

    1. Shalev-Shwartz and Y. Singer, “Pegasos: Primal Estimated sub-GrAdient SOlver for SVM,” Proc. ICML’07, pp. 807–814, 2007.

Examples:

estimator =
  SVMKit::LinearModel::SVC.new(reg_param: 1.0, max_iter: 100, batch_size: 20, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)

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, fit_bias: false, bias_scale: 1.0, max_iter: 100, batch_size: 50, probability: false, normalize: true, random_seed: nil) ⇒ SVC

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

Parameters:

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

    The regularization parameter.

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

    The flag indicating whether to fit the bias term.

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

    The scale of the bias term.

  • max_iter (Integer) (defaults to: 100) —

    The maximum number of iterations.

  • batch_size (Integer) (defaults to: 50) —

    The size of the mini batches.

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

    The flag indicating whether to perform probability estimation.

  • normalize (Boolean) (defaults to: true) —

    The flag indicating whether to normalize the weight vector.

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

    The seed value using to initialize the random generator.



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# File 'lib/svmkit/linear_model/svc.rb', line 53

def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0,
               max_iter: 100, batch_size: 50, probability: false, normalize: true, random_seed: nil)
  SVMKit::Validation.check_params_float(reg_param: reg_param, bias_scale: bias_scale)
  SVMKit::Validation.check_params_integer(max_iter: max_iter, batch_size: batch_size)
  SVMKit::Validation.check_params_boolean(fit_bias: fit_bias, probability: probability, normalize: normalize)
  SVMKit::Validation.check_params_type_or_nil(Integer, random_seed: random_seed)
  SVMKit::Validation.check_params_positive(reg_param: reg_param, bias_scale: bias_scale, max_iter: max_iter,
                                           batch_size: batch_size)
  @params = {}
  @params[:reg_param] = reg_param
  @params[:fit_bias] = fit_bias
  @params[:bias_scale] = bias_scale
  @params[:max_iter] = max_iter
  @params[:batch_size] = batch_size
  @params[:probability] = probability
  @params[:normalize] = normalize
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @weight_vec = nil
  @bias_term = nil
  @prob_param = nil
  @classes = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

Return the bias term (a.k.a. intercept) for SVC.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes])



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# File 'lib/svmkit/linear_model/svc.rb', line 33

def bias_term
  @bias_term
end

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_classes])



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# File 'lib/svmkit/linear_model/svc.rb', line 37

def classes
  @classes
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling.

Returns:

  • (Random) 


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# File 'lib/svmkit/linear_model/svc.rb', line 41

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for SVC.

Returns:

  • (Numo::DFloat) —

    (shape: [n_classes, n_features])



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# File 'lib/svmkit/linear_model/svc.rb', line 29

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_samples, n_features]) The samples to compute the scores.

Returns:

  • (Numo::DFloat) —

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



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# File 'lib/svmkit/linear_model/svc.rb', line 125

def decision_function(x)
  SVMKit::Validation.check_sample_array(x)

  x.dot(@weight_vec.transpose) + @bias_term
end

#fit(x, y) ⇒ SVC

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model.

  • y (Numo::Int32) —

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

Returns:

  • (SVC) —

    The learned classifier itself.



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# File 'lib/svmkit/linear_model/svc.rb', line 83

def fit(x, y)
  SVMKit::Validation.check_sample_array(x)
  SVMKit::Validation.check_label_array(y)
  SVMKit::Validation.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)
    @bias_term = Numo::DFloat.zeros(n_classes)
    @prob_param = Numo::DFloat.zeros(n_classes, 2)
    n_classes.times do |n|
      bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
      weight, bias = binary_fit(x, bin_y)
      @weight_vec[n, true] = weight
      @bias_term[n] = bias
      @prob_param[n, true] = if @params[:probability]
                               SVMKit::ProbabilisticOutput.fit_sigmoid(x.dot(weight.transpose) + bias, bin_y)
                             else
                               Numo::DFloat[1, 0]
                             end
    end
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
    @weight_vec, @bias_term = binary_fit(x, bin_y)
    @prob_param = if @params[:probability]
                    SVMKit::ProbabilisticOutput.fit_sigmoid(x.dot(@weight_vec.transpose) + @bias_term, bin_y)
                  else
                    Numo::DFloat[1, 0]
                  end
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about SVC.



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# File 'lib/svmkit/linear_model/svc.rb', line 166

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/svmkit/linear_model/svc.rb', line 177

def marshal_load(obj)
  @params = obj[:params]
  @weight_vec = obj[:weight_vec]
  @bias_term = obj[:bias_term]
  @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_samples, n_features]) The samples to predict the labels.

Returns:

  • (Numo::Int32) —

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



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# File 'lib/svmkit/linear_model/svc.rb', line 135

def predict(x)
  SVMKit::Validation.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)
  Numo::Int32.asarray(Array.new(n_samples) { |n| @classes[decision_values[n, true].max_index] })
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the probailities.

Returns:

  • (Numo::DFloat) —

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



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# File 'lib/svmkit/linear_model/svc.rb', line 149

def predict_proba(x)
  SVMKit::Validation.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