Class: Rumale::LinearModel::SVR

Inherits:
BaseLinearModel show all
Includes:
Base::Regressor
Defined in:
lib/rumale/linear_model/svr.rb

Overview

SVR is a class that implements Support Vector Regressor with mini-batch stochastic gradient descent optimization.

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::SVR.new(reg_param: 1.0, epsilon: 0.1, max_iter: 1000, batch_size: 20, random_seed: 1)
estimator.fit(training_samples, traininig_target_values)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, epsilon: 0.1, max_iter: 1000, batch_size: 20, optimizer: nil, n_jobs: nil, random_seed: nil) ⇒ SVR

Create a new regressor 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.

  • epsilon (Float) (defaults to: 0.1) —

    The margin of tolerance.

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

    The maximum number of iterations.

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

    The size of the mini batches.

  • 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 method in parallel. If nil is given, the method does 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/svr.rb', line 49

def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, epsilon: 0.1,
               max_iter: 1000, batch_size: 20, optimizer: nil, n_jobs: nil, random_seed: nil)
  check_params_float(reg_param: reg_param, bias_scale: bias_scale, epsilon: epsilon)
  check_params_integer(max_iter: max_iter, batch_size: batch_size)
  check_params_boolean(fit_bias: fit_bias)
  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, epsilon: epsilon,
                        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(:epsilon)
  super(keywd_args)
  @params[:epsilon] = epsilon
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

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

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs])



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# File 'lib/rumale/linear_model/svr.rb', line 28

def bias_term
  @bias_term
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling.

Returns:

  • (Random) 


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# File 'lib/rumale/linear_model/svr.rb', line 32

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector for SVR.

Returns:

  • (Numo::DFloat) —

    (shape: [n_outputs, n_features])



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# File 'lib/rumale/linear_model/svr.rb', line 24

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ SVR

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::DFloat) —

    (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.

Returns:

  • (SVR) —

    The learned regressor itself.



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# File 'lib/rumale/linear_model/svr.rb', line 68

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)

  n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
  n_features = x.shape[1]

  if n_outputs > 1
    @weight_vec = Numo::DFloat.zeros(n_outputs, n_features)
    @bias_term = Numo::DFloat.zeros(n_outputs)
    if enable_parallel?
      models = parallel_map(n_outputs) { |n| partial_fit(x, y[true, n]) }
      n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = models[n] }
    else
      n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = partial_fit(x, y[true, n]) }
    end
  else
    @weight_vec, @bias_term = partial_fit(x, y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about SVR.



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# File 'lib/rumale/linear_model/svr.rb', line 103

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


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# File 'lib/rumale/linear_model/svr.rb', line 112

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

#predict(x) ⇒ Numo::DFloat

Predict values for samples.

Parameters:

  • x (Numo::DFloat) —

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

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_outputs]) Predicted values per sample.



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# File 'lib/rumale/linear_model/svr.rb', line 96

def predict(x)
  check_sample_array(x)
  x.dot(@weight_vec.transpose) + @bias_term
end