Class: Rumale::LinearModel::SVC

Inherits:
BaseLinearModel show all
Includes:
Base::Classifier
Defined in:
lib/rumale/linear_model/svc.rb

Overview

SVC is a class that implements Support Vector Classifier with mini-batch stochastic gradient descent 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 =
  Rumale::LinearModel::SVC.new(reg_param: 1.0, max_iter: 1000, 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: 1000, batch_size: 20, probability: false, optimizer: nil, n_jobs: nil, 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: 1000) —

    The maximum number of iterations.

  • batch_size (Integer) (defaults to: 20) —

    The size of the mini batches.

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

    The flag indicating whether to perform probability estimation.

  • optimizer (Optimizer) (defaults to: nil) —

    The optimizer to calculate adaptive learning rate. If nil is given, Nadam is used.

  • 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/linear_model/svc.rb', line 56

def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0,
               max_iter: 1000, batch_size: 20, probability: false, optimizer: nil, n_jobs: nil, random_seed: nil)
  check_params_float(reg_param: reg_param, bias_scale: bias_scale)
  check_params_integer(max_iter: max_iter, batch_size: batch_size)
  check_params_boolean(fit_bias: fit_bias, probability: probability)
  check_params_type_or_nil(Integer, n_jobs: n_jobs, random_seed: random_seed)
  check_params_positive(reg_param: reg_param, bias_scale: bias_scale, max_iter: max_iter, batch_size: batch_size)
  keywd_args = method(:initialize).parameters.map { |_t, arg| [arg, binding.local_variable_get(arg)] }.to_h
  keywd_args.delete(:probability)
  super(keywd_args)
  @params[:probability] = probability
  @prob_param = nil
  @classes = nil
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/rumale/linear_model/svc.rb', line 31

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/rumale/linear_model/svc.rb', line 35

def classes
  @classes
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling.

Returns:

  • (Random) 


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# File 'lib/rumale/linear_model/svc.rb', line 39

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/rumale/linear_model/svc.rb', line 27

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/rumale/linear_model/svc.rb', line 119

def decision_function(x)
  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/rumale/linear_model/svc.rb', line 76

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_features = x.shape[1]

  if n_classes > 2
    # initialize model.
    @weight_vec = Numo::DFloat.zeros(n_classes, n_features)
    @bias_term = Numo::DFloat.zeros(n_classes)
    @prob_param = Numo::DFloat.zeros(n_classes, 2)
    # fit model.
    models = if enable_parallel?
               # :nocov:
               parallel_map(n_classes) do |n|
                 bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
                 partial_fit(x, bin_y)
               end
               # :nocov:
             else
               Array.new(n_classes) do |n|
                 bin_y = Numo::Int32.cast(y.eq(@classes[n])) * 2 - 1
                 partial_fit(x, bin_y)
               end
             end
    # store model.
    models.each_with_index { |model, n| @weight_vec[n, true], @bias_term[n], @prob_param[n, true] = model }
  else
    negative_label = y.to_a.uniq.min
    bin_y = Numo::Int32.cast(y.ne(negative_label)) * 2 - 1
    @weight_vec, @bias_term, @prob_param = partial_fit(x, bin_y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about SVC.



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# File 'lib/rumale/linear_model/svc.rb', line 164

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/rumale/linear_model/svc.rb', line 175

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/rumale/linear_model/svc.rb', line 128

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_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/rumale/linear_model/svc.rb', line 147

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