Class: SVMKit::LinearModel::Ridge

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

Overview

Ridge is a class that implements Ridge Regression with stochastic gradient descent (SGD) optimization.

Reference

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

    1. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” Proc. ICML’13, pp. 1139–1147, 2013.

    1. Hinton, N. Srivastava, and K. Swersky, “Lecture 6e rmsprop,” Neural Networks for Machine Learning, 2012.

Examples:

estimator =
  SVMKit::LinearModel::Ridge.new(reg_param: 0.1, max_iter: 5000, batch_size: 50, random_seed: 1)
estimator.fit(training_samples, traininig_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, learning_rate: 0.01, decay: 0.9, momentum: 0.9, max_iter: 1000, batch_size: 10, random_seed: nil) ⇒ Ridge

Create a new Ridge regressor.

Parameters:

  • (defaults to: 1.0)

    The regularization parameter.

  • (defaults to: false)

    The flag indicating whether to fit the bias term.

  • (defaults to: 0.01)

    The learning rate for optimization.

  • (defaults to: 0.9)

    The discounting factor for RMS prop optimization.

  • (defaults to: 0.9)

    The Nesterov momentum for optimization.

  • (defaults to: 1000)

    The maximum number of iterations.

  • (defaults to: 10)

    The size of the mini batches.

  • (defaults to: nil)

    The seed value using to initialize the random generator.



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# File 'lib/svmkit/linear_model/ridge.rb', line 49

def initialize(reg_param: 1.0, fit_bias: false, learning_rate: 0.01, decay: 0.9, momentum: 0.9,
               max_iter: 1000, batch_size: 10, random_seed: nil)
  check_params_float(reg_param: reg_param,
                     learning_rate: learning_rate, decay: decay, momentum: momentum)
  check_params_integer(max_iter: max_iter, batch_size: batch_size)
  check_params_boolean(fit_bias: fit_bias)
  check_params_type_or_nil(Integer, random_seed: random_seed)
  check_params_positive(reg_param: reg_param,
                        learning_rate: learning_rate, decay: decay, momentum: momentum,
                        max_iter: max_iter, batch_size: batch_size)
  @params = {}
  @params[:reg_param] = reg_param
  @params[:fit_bias] = fit_bias
  @params[:learning_rate] = learning_rate
  @params[:decay] = decay
  @params[:momentum] = momentum
  @params[:max_iter] = max_iter
  @params[:batch_size] = batch_size
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @weight_vec = nil
  @bias_term = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

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

Returns:

  • (shape: [n_outputs])



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

def bias_term
  @bias_term
end

#rng ⇒ Random (readonly)

Return the random generator for random sampling.

Returns:



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

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector.

Returns:

  • (shape: [n_outputs, n_features])



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

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ Ridge

Fit the model with given training data.

Parameters:

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

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

Returns:

  • The learned regressor itself.



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# File 'lib/svmkit/linear_model/ridge.rb', line 79

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_samples, n_features = x.shape

  if n_outputs > 1
    @weight_vec = Numo::DFloat.zeros(n_outputs, n_features)
    @bias_term = Numo::DFloat.zeros(n_outputs)
    n_outputs.times do |n|
      weight, bias = single_fit(x, y[true, n])
      @weight_vec[n, true] = weight
      @bias_term[n] = bias
    end
  else
    @weight_vec, @bias_term = single_fit(x, y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • The marshal data about Ridge.



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# File 'lib/svmkit/linear_model/ridge.rb', line 113

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:



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# File 'lib/svmkit/linear_model/ridge.rb', line 122

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:

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

Returns:

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



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# File 'lib/svmkit/linear_model/ridge.rb', line 106

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